Cannabis Grow

Participants’ responses to the ASSIST during the SBIRT intervention procedure were not used to determine this outcome.Baseline demographic characteristics and SSIS were summarized with descriptive statistics.Major advantages of multilevel regression over traditional repeated measures analysis include the fact that cases are not dropped due to missing observations on the dependent variable at any assessment, numeric as well as categorical predictors can be used, and methods for non-normal outcomes are available. Estimation was carried out in Stata/SE Release 14.1 using maximum likelihood and the Expectation–Maximization algorithm. Models were estimated to examine unconditional change over time for unit-increases over the assessment months; differences in the change trajectories as a function of baseline risk of use for each substance, and differences in the change trajectories due to the intervention by initial risk interaction. Due to strong right-skewness in many of the substance use scores , estimation for the multilevel regression models was carried out with a non-parametric bootstrap with 5000 repetitions to obtain bias-corrected confidence intervals, unaffected by either extreme values or skewness. Te study sample size for the overall study was calculated based on the results of a prior clinic waiting room survey that measured current substance use in 33% of clinic patients . Te sample size estimates were large enough to detect a 15% difference in alcohol use between the Computer and Clinician groups,dry racks with 80% power and 95% confidence.A total of 225 people living with HIV were assessed for eligibility to participate in the study . Of these, seven were excluded because they did not meet eligibility criteria, and 10 were excluded because they did not complete the baseline survey.

Te remaining 208 individuals were enrolled in the study and randomized. These 208 participants were primarily male and largely African American , with a mean age of 45.4 years . Te majority had a high school education or less , were unemployed , and reported substance use in the past 3 months . Te mean time since HIV diagnosis was 12.4 years. Mean Specific Substance Involvement Scores were in the moderate risk range for all substances except inhalants, hallucinogens, and other substances. SSIS were highest for tobacco, alcohol, cannabis, cocaine, and amphetamine. Of the 208 individuals enrolled in the study, 134 individuals completed the baseline assessment visit and also received an SBIRT intervention. Of the 134, follow-up assessment rates were: 123 at 1-month, 106 at 3-month and 109 at 6-month; 92 completed all four study assessments and the intervention. Ninety-five participants with high SSIS accepted referrals to the clinic social worker, but only four met with the social worker. There were no significant baseline differences in socio-demographic characteristics or mean SSIS between those who received the intervention and those who did not . Similarly, we found no differences between SBIRT treatment modality in our outcome measures of interest over time . For all substances, mean SSIS increased over time among those initially in the lower risk groups. Te increase was statistically significant for all substances except amphetamines and sedatives . However, among those individuals with moderate–high risk at baseline, mean SSIS for all substances decreased at 6 months. Te decrease was statistically significant for all substances except tobacco and cannabis. For all substances, the decrease in mean SSIS for the moderate– high risk group differed significantly from the increase in mean SSIS for the lower risk group.We conducted a screening, brief intervention, and referral to treatment intervention in an urban safety-net HIV primary care clinic and detected a high prevalence of self-reported alcohol, tobacco, cannabis, cocaine, amphetamine, sedatives, and opioid use at enrollment. For all substances, the mean SSIS score for participants whose baseline substance use risk was moderate–high and who received the SBIRT intervention declined over the 6 months following the intervention, and this decrease was significant when compared to those at baseline lower risk.

While active substance use was not one of the inclusion criteria, 92% of study participants reported any substanceuse in the prior 3 months. This finding is consistent with the known higher prevalence of substance use for PLHIV compared to the general U.S. national population. Our results also show that, in an HIV primary care population, while mean SSISs were in the moderate range for most substances, a number of individuals were in the high risk range, as indicated by the large standard deviations for each substance. These indicators of the severity of self-reported substance use, underscore the opportunity for detection and intervention in HIV primary care settings. We measured a significant reduction over time in the mean SSIS for alcohol − 1.59 among participants who scored in the medium high risk categories. Several other studies that measured self-report of substance use before and following SBIRT implementation in clinical settings have been conducted and allow for a comparison with the findings of our analysis. One of the first studies to determine the effect of SBIRT in diverse clinic populations found SBI to be associated with a decrease in self-reported alcohol use at follow-up. Other studies evaluating measures of alcohol use severity before and after participating in SBIRT show similar results. A more recent study among PLHIV, however, found that although alcohol use declined over time, the decline was not associated with receipt of a brief intervention. We also measured moderate but statistically significant decreases in mean SSISs for illicit drugs, including reductions in cocaine − 0.82 , amphetamines − 0.69 , sedatives − 1.58 and opioids − 1.31 . Other studies have shown mixed results of the impact of SBIRT on illicit drug use following participation in SBIRT. Te ASPIRE study , a 3-group randomized controlled trial for unhealthy drug use among adults from an urban primary care setting, did not demonstrate a decrease in unhealthy drug use following receipt of a primary care based SBIRT intervention. Other studies have shown similar negative results of the effects of SBIRT on illicit substance use. In contrast, Humeniuk and colleagues found significantly reduced SSISs among participants receiving a brief intervention compared to control participants, for all substances except opioids. And Bernstein and colleagues found reductions in cocaine and heroin use among individuals receiving SBIRT. In our study, we saw a reduction in mean SSIS for tobacco use among participants at moderate–high risk at baseline.

Cropsey and colleagues also found that PLHIV who smoked at least five cigarettes per day significantly reduced their smoking over time following an SBIRT intervention that included a counseling session, nicotine replacement therapy, and follow-up visits, compared to those in usual care. In a pilot study of 30 women living with HIV, those who received a motivational interviewing session reported significant reductions in the mean number of cigarettes smoked, compared to those who did not receive the MI intervention. Surprisingly, we found that mean SSIS scores for participants who scored in the lower risk range at baseline increased over the 6 months for all substances at the same time that use dropped for those in the moderate–high risk group . It is possible that the Brief Intervention that was given to those in the moderate–high risk groups had an important effect on reducing SSIS scores, but that the minimal intervention given to those in the lower risk group was not fully effective at keeping risk levels low. This was particularly the case for tobacco and cannabis; for both of these substances, mean SSISs in the lower risk group increased more than the scores decreased in the moderate–high risk group . In some studies, ASSIST scores were reported as lower,drying racks moderate or high risk. Reporting and analyzing SSIS by risk categories is important because individuals who fall into the lower and moderate risk group may derive different benefits, and because brief interventions have previously been shown to be more effective among people with less severe substance use problems. In contrast to other studies, our outcome observation is based on a mean change score and on dichotomized risk groups, which may or may not be clinically useful distinctions. This area needs further study and exploring more effective interventions for people at lower risk for substance use related problems is an important area for future research.While the levels of substance use self-reported among this cohort of PLHIV patients is higher than in the general U.S. national population, it can be difficult to make comparisons between studies because of the variability of substance use measures. For example, in a study with a safety-net primary care population, participants received an intervention based on reports of problem drug use via the Addiction Severity Index-Lite measure.

Because of differences between the Addiction Severity Index and the ASSIST, similar participants in each study may have been assessed at different levels of risk, and therefore may have differed in whether they qualified to receive an SBIRT intervention or not. Such differences make comparisons difficult. Our study has several limitations. First, the data reported here were all self-reported, which could be biased due to participant recall or social desirability. Second, while these findings suggest that SBIRT delivery in HIV care settings may be associated with a decrease in the mean SSIS scores for moderate–high risk substance use, we do not have a good understanding of the clinical significance of these changes in mean scores. A decrease in the SSIS for any substance is a change in the right direction when our goal is to address substance use in HIV clinical settings. However, in the absence of a non-treatment control group it is possible that the decrease in SSIS scores across both arms of the study could be due to regression to the mean and not the intervention. Third, for this analysis, which examined only those participants who received the intervention, our analytic sample may have been under powered, despite the fact that we enrolled a sufficient number of participants into the study based on our a priori sample size calculations for a randomized trial. Notwithstanding the smaller analytic sample size, we did detect a statistically significant decrease on the moderate–high risk mean SSIS of those who received the SBIRT intervention when compared to those with lower risk scores. Fourth, while the ASSIST measure includes many drugs and does not solely capture the level of use for the substance of concern to the participant, the BI that was delivered by each modality was based on the substance of most concern to the patient. Nonetheless, our use of the ASSIST allowed us to gain a more expansive understanding of the number and types of substances used by this HIV primary care sample and this may be one benefit for using the ASSIST. Use of the ASSIST in clinical settings could have the advantage of giving providers screening and assessment information for multiple substances. Fifth, as part of our study procedures we did not adequately document the number of brief intervention visits either with a clinician or through the computer portal so we were unable to capture meaningful information about the dose of the exposure to the intervention to allow for dose–response analyses. Sixth, our findings may not be generalizable to PLHIV who are not engaged in primary care or to patients of HIV clinics that do not serve an urban safety-net population. Further, while we collected baseline substance use and follow up data on 208 participants, and all participants were assigned to one of two SBIRT modalities, we observed a significant drop of between assessment and participation in the intervention; only 64% of participants who completed the baseline study visit actually received the intervention by either modality, leading to our decision to present an “as treated” analysis. This was particularly the case for those assigned to the computer group, which may indicate difficulties or discomfort with accessing computers and the Internet, or concern about the privacy of data entered into a computer linked to the Internet . In addition, very few of those referred to treatment actually met with the social worker as indicated, possibly indicating that they were not ready to take the initiative to seek out treatment for themselves, and that a more immediate and supported linkage might be needed.

Side stream smoke, which is emitted directly from the source between puffs and not exhaled by the smoker, has been shown to produce greater emissions than mainstream smoke . Based on observations of the participant, the inhalation time of the puff was about 2 s, and the exhalation time was ~2–4 s, making the total puff time about 6 s. We applied the same 3-puff protocol to the Absolute Xtracts vaping pen, which carried out an internal 15-s pre-heat mode prior to the start of puffing and produced little side stream emissions between puffs. An important advantage of the 3-puff protocol in our experiments is that it avoided the extremely high PM2.5 concentrations expected to occur in the 43 m3 room if a marijuana joint had been smoked completely in the room, thus allowing the participant to avoid exposure to unacceptably high concentrations. Based on our interviews with experienced cannabis smokers and information available on the Internet, we concluded that marijuana smoking often differed from tobacco cigarette smoking in several respects. Smoking a marijuana joint often takes place in a group setting, where more than one person smokes, following the rule, “take two puffs and pass it to the left.” We also learned that a marijuana smoker, when smoking alone, often takes just 2 or 3 puffs, then puts the joint out so it can be smoked later in the day. The 3-puff protocol in Fig. 1 includes nearly a minute between puffs for the burning joint to emit side stream smoke, thus producing both mainstream and side stream smoke in a realistic manner. This protocol also has a mathematical advantage for calculating emission source strengths,cannabis vertical farming since the 3-min emission time is much shorter than the residence time of the room, which averaged 115 min for the 60 pre-rolled joints, bongs, glass pipes, vaping pens, and cigarettes.

An objective of this study was to compare emission rates from different sources smoked in the same manner by a human participant. Although a smoking machine may reduce experimental variability, we focused on determining whether the differences between the mean emission rate of the prerolled marijuana joints and the mean emission rates of the other sources, including the tobacco cigarettes, were statistically significant. The statistical methods used in this study are designed to test whether there is a statistically significant difference between the means of two unpaired groups. The unpaired t-test is a parametric test based on estimates of the mean and standard deviation of normally distributed populations from which the samples were drawn. It tests whether the difference between two groups is greater than that caused by random sampling variation. The p value is the probability of being wrong in concluding that there is a true difference between the two groups. The smaller the p value, the greater the probability that the samples are drawn from different populations. We chose the probability p < 0.05 as our criterion for statistical significance. The statistical analyses were performed using Sigma Plot 11 , which employs the Kolmogorov-Smirnov test for a normally distributed population. This program also tests for equal variances. If these conditions are met, it performs the unpaired t-test. If either of these conditions is not met, it informs the user that the data are unsuitable for the unpaired t-test, and it recommends using the non-parametric Mann Whitney Rank Sum Test instead, which performs comparisons based on the ranks of the observations. Table 1 provides summary statistics for the 60 experiments in this study with the various cannabis and tobacco sources, based on the 3-puff protocol. The background concentrations were subtracted from the measured PM2.5 concentrations in Table 1, and the last two columns show the background PM2.5 concentrations were much smaller than the background-corrected maximum PM2.5 concentrations measured in the room for all five sources.

The background-corrected ymax concentrations of PM2.5 observed in the 24 experiments with pre-rolled joints had a mean of 540 μg/m3 and ranged from 143 to 809 μg/m3 . By comparison, the PM2.5 ymax concentrations in the 9 Marlboro tobacco cigarette experiments had a mean of 154 μg/m3 and ranged from 22 to 209 μg/m3 . Each marijuana source produced a larger mean maximum concentration ymax than the tobacco cigarettes. Table 2 shows the summary statistics for the 60 experiments with five different sources. The 24 joints had a mean PM2.5 emission rate of 7.8 mg/min, which was greater than all the other cannabis emission rates and was 3.5 times the mean PM2.5 emission rate of the Marlboro cigarettes of 2.2 mg/min. The mean emission rates of the bong and the glass pipe were 67% and 54% of the joint’s mean emission rate, respectively, and the mean emission rate of the vaping pen was 44% that of the mean emission rate of the joints. The box plots shown in Fig. 3 illustrate the frequency distributions of the PM2.5 emission rates, allowing them to be compared graphically. Only the pre-rolled cannabis joints had enough observations to show the 5th and 95th percentiles of the emission rates , while all the box plots showed the 10th and 90th percentiles . The box boundaries themselves represent the 25th and 75th percentiles, and the bong had the largest spread between these two percentiles. This result was consistent with Table 2, which shows the bong also had the greatest coefficient of variation of 0.71 for the five sources. The mean emission rate in Fig. 3 was higher for the joint than for the cigarette, which also is evident in the emission rate column of Table 2. The median in Fig. 3 showed a pattern similar to that of the mean. Table 3 shows the results of applying standard statistical tests to 10 comparisons of the different methods of smoking marijuana, vaping marijuana, and smoking tobacco cigarettes. In five of the comparisons, the t-test met the requirement that the data were normally distributed but did not meet the requirement of equal variances. In these five cases, Sigma-Plot substituted the non-parametric Mann-Whitney Rank Sum Test for the t-test. With both tests, the criterion for statistical significance was the probability p < 0.05.

The difference between the mean emission rate of the joint and the mean emission rate of the bong was statistically significant , and the differences between the mean emission rate of the joint and the mean emission rates of the glass pipe, vaping pen, and cigarette were highly statistically significant . The probabilities listed above the box plots in Fig. 3 show the statistical significance of the differences between the groups. Although there were n = 24 experiments with joints, there were only n = 9 experiments each with bongs, glass pipes, vaping pens, and Marlboro cigarettes. Comparisons of the bong vs. the glass pipe, the bong vs. vaping, the bong vs. the cigarette, the glass pipe vs. vaping, and the cigarette vs. vaping did not show a statistically significant difference in mean emission rates, which is partly due to the small sample sizes. An exception was the mean emission rate of the glass pipe compared to the mean emission rate of the cigarette, which was statistically significant . In general, groups that did not include the joint were less likely to show a statistically significant difference when compared to groups that included the joint with its high emission rate and larger sample size. The difference between the mean emission rate of the marijuana joints and the mean emission rate of the tobacco cigarettes was highly statistically significant . The largest mean decay rate in Table 2 of 0.690 h− 1 occurred with the grow cannabis in containers vaping pen, while the other four mean decay rates were fairly close together, averaging 0.509 h− 1 . When we compared the differences between the five mean decay rates, we found that only one difference was statistically significant: comparison of the mean decay rate of the 24 marijuana joints with the mean decay rate of the 9 vaping pen experiments. Based on the Mann-Whitney Rank Sum Test, the difference between the mean decay rate of the marijuana joints and the mean decay rate of the vaping pens was highly statistically significant .The measured decay rate φ for the SidePak monitor is the sum of the air exchange rate a and the deposition rate k, as well as the other possible particle losses or gains due to evaporation, condensation, and coagulation. That is, the decay rate φ = a + k + other. If we subtract the observed air exchange rate from the observed decay rate, we are left with a term called the “removal rate” due to aerosol dynamics, which is the sum of the deposition rate k and all the other gain or loss mechanisms, excluding the effect of air exchange. For the 24 cannabis joints, the mean removal rate was 0.085 h− 1 . For the bong, the glass pipe, and the cigarette, the mean removal rates were 0.111 h− 1 , 0.096 h− 1 , and 0.103 h− 1 , respectively. The average removal rate of the four marijuana combustion sources was 0.10 h− 1 , which was smaller than deposition rates listed by Thatcher et al. for a furnished room with a small fan or no fan. In contrast, the mean removal rate of the 9 vaping pen experiments was 0.321 h− 1 , which was the largest removal rate of the five sources and was 3.2 times the average removal rate of the four combustion sources . It is likely that this larger removal rate of the vaping pen was due to volatility of the vaping aerosol and its greater evaporative losses. Evaporation of particles from cannabis vaping is not expected to be as great as evaporation from e-cigarette vaping .

We believe this is an important topic for future research. Since each new marijuana joint included a factory label showing the joint’s percent THC content, we also compared the THC listed for each joint with our measurements of the joint’s PM2.5 source strength. Applying the t-test, we found the relationship between the THC percentage and the source strength was statistically significant . However, this result may occur mainly because the larger joints in our study happen to have higher THC percentages, and their larger size may cause their greater source strength. A more detailed study that controls for the size of the joint would be useful. Our measurements of ultra fine particles > 10 nm used a pair of TSI 3007 condensation particle counters that were collocated with the other monitors in the room during these experiments. The UFP results are summarized in Table S2. Of the five sources, the pre-rolled marijuana joints had the greatest average UFP source strength , while the Marlboro cigarettes had an almost equal UFP source strength . The mean UFP source strengths of the three other methods of consuming marijuana were 1.3 x 1012 particles for bongs, 6.4 x. 1011 particles for glass pipes, and 3.3 x 1011 particles for the vaping pens. Overall, the UFP source strengths of bongs, glass pipes, and vaping pens were smaller than the UFP source strengths of either the pre-rolled marijuana joints or the Marlboro cigarettes. McClure et al. studied 20 heavy users of marijuana, reporting that heavy users smoked an average of 11–12 marijuana cigarettes per day, averaging 13–14 puffs per joint. Since our study compared the PM2.5 emission rates based on 3.0 min of smoking or vaping, we also attempted to estimate the emissions produced by a fully-smoked marijuana joint. We used a precision laboratory scale to measure the weights of the 24 marijuana joints before they were smoked, which ranged from 0.56 to 1.35 g with a mean of 1.024 g . By comparison, the pre-smoking weights of the 9 Marlboro cigarettes ranged from 0.83 to 0.89 g with a mean of 0.863 g . We found that measuring the difference in the weight of a joint before and after it was smoked was challenging, because the water used to put out the joint affected its tightly rolled cannabis leaves, causing the post-smoking weight sometimes to be larger than the original weight. In addition, it was difficult to account for the smoking ashes lost in the water. Therefore, we concluded that comparing the weights before and after smoking a joint would need to use a different method of putting out the joint.

The levels of substance use in our results, similar to those found in a primary care sample with depression and an emergency services sample , indicate that a substantial number of patients were at risk. The results suggest that providers in psychiatric settings should conduct screening and offer treatment as needed. For example, brief motivational interventions could effectively supplement other psychiatric services and prevent escalation of alcohol and drug problems. The recent Screening, Brief Intervention, Referral, and Treatment initiative launched by the Substance Abuse and Mental Health Services Administration is actively promoting early intervention with non-dependent patients in primary care, mental health care, and other settings; online resources and multiple training opportunities are available for providers. Effective January 2008, codes approved by the U.S. Centers for Medicare and Medicaid Services allow reimbursement for screening and brief intervention. These policy initiatives recognize that early alcohol and drug use identification and treatment are important medical services that can improve multiple health outcomes. As a mechanism to facilitate identification, we found that the computerized system was acceptable to most patients, consistent with a prior study of computerized depression assessment . Alcohol and drug use questions also were acceptable , although we note that patients with serious alcohol and drug problems had previously been screened out. One limitation was that older adults appeared less willing or able to use the computer,vertical grow system but this may have been due in part to disabilities of patients seeking geropsychiatric services or to assumptions of reception staff regarding disabilities. Service issues for further investigation include staff training, optimal procedures to ensure confidentiality , software options for alcohol and drug screening, and computer-based interventions .

Potential benefits of computerized systems include the ability to collect more detailed information than is easily obtained by paper forms, greater validity for sensitive questions, and more time-efficient assessment. The records obtained from patients are useful for treatment planning at a program level and as a resource for clinical and services research. These findings need to be interpreted with the limitations of the study. Because patients completing the electronic intake were younger on average than those who did not complete it, our substance use findings are less representative of older adult patients.Our results may not generalize to psychiatry clinics that do not prescreen patients with serious alcohol and drug problems before intake. It would have been preferable to use a lower heavy-drinking cutoff for women than men because women are more sensitive to alcohol. Our use of the higher cutoff indicates that our findings regarding heavy drinking by women may be conservative. Although computerized measures are considered valid, under reporting of alcohol and drug use by patients would also make our prevalence rates conservative. Thus substance use in the sample may be even greater than our results indicate.An estimated 15.3 million adults in the United States met criteria for an alcohol use disorder in the past 12 months. Of those with alcohol use disorders, 2.3 million adults also met criteria for a drug use disorder with odds ratios estimated to be 7.4 for any drug use disorder, but 3.4 to 19.2 for specific drug use disorders . Both alcohol and drug use disorders are heritable, with approximately 50% of the variance attributable to heritable factors , although this estimate varies dramatically by substance , age and other characteristics, including co-morbid psychopathology . This heritable variation can be parsed into those genetic influences that are specific to each drug and importantly, those genetic factors that confer a general predisposition to alcohol and/or substance use disorders, and even other dis-inhibited behaviors . Two large twin studies have convincingly shown that a preponderance of the genetic factors influencing illicit drug use disorders overlap . Noticeably, when these models were extended to include alcohol use disorders, there was evidence for highly correlated genetic factors that individually influenced the covariation in alcohol and nicotine dependence as well as cannabis and cocaine dependence . The extent of genetic overlap was strong for some substances—for instance, 55% and 24% of the genetic variance in alcohol dependence was due to the licit and illicit drug factors, respectively, with the remainder being substance specific. In contrast, for nicotine dependence, 63% of the genetic variance was drug specific . Similar to the individual heritability of each substance, there is growing evidence that the heritable covariation across substances changes across development .

Irrespective of development and substance-specific variation, there is broad consensus that gene discovery efforts targeting aggregate genetic variation that indexes a shared liability to a variety of substance use disorders, as well as dis-inhibition, can be c , with one study showing evidence for genome wide pleiotropic effects across substance use disorders . There are multiple approaches, both phenotypic and genetic, to capture the commonality underlying alcohol and substance use disorders and the present study utilizes two straightforward phenotypic approaches. We opted for simple dependence-based phenotypic traits as they lend themselves to replication and future meta-analysis. First, we utilized a binary phenotype, with affection status defined as meeting dependence criteria for at least one substance , termed ANYDEP. Second, we used factor analysis to combine dependence criteria across substances into a continuous quantitative trait representing vulnerability to multiple substance dependence, termed QUANTDEP. This quantitative measure is heritable and has previously been used in genomic studies , the most recent of which utilized a similar expanded factorial measure of behavioral dis-inhibition to conduct genome-wide association and rare nonsynonymous variant analyses . These studies did not identify any single common or rare variant at a genome-wide significant level; however, the authors reported that 84% of the heritability in illicit drug use was explained by both common and rare variants. While the work of McGue and colleagues included multiple measures of nicotine use and dependence, we elected to exclude nicotine from these measures of general liability based on the work by Kendler and colleagues , which showed significant drug-specific genetic influences on nicotine dependence. In this study, we utilized data from 2322 subjects from 118 families of European-American descent ascertained for alcohol dependence liability to conduct genome-wide association analysis of a binary and a continuous index of general substance dependence liability. While some prior genome-wide efforts have utilized similar phenotypes in population samples of related individuals, the ascertainment strategy and extended family-based design in our study should increase our ability to detect genetic variation in this phenotype. First, there is substantial evidence that alcohol use disorders that co-aggregate with other substance use disorders may represent a more heritable form of addiction . Secondly, by modeling the strength of the phenotypic correlation across different degrees of genetic relatedness , we utilize data on all related individuals, even those not meeting criteria for diagnoses, allowing us to better explore the extent of co-aggregation of genetic risk across alcohol, cannabis, cocaine and opioid dependence.

Six sites participating in the Collaborative Study on the Genetics of Alcoholism recruited alcohol-dependent probands from in-patient and outpatient facilities. The probands and their family members were administered a poly-diagnostic interview, the Semi-Structured Assessment for the Genetics of Alcoholism . Individuals 17 years of age or younger were administered an adolescent version of the SSAGA. Institutional review boards at all sites approved the study. A subset of the most genetically informative families was selected for a family-based GWAS. This sample has been described in detail elsewhere but salient characteristics are presented here. Families were prioritized based on the number of family members with: available DNA who were also alcohol dependent; available DNA who also had electrophysiology data; and available DNA, regardless of other phenotypes. To reduce heterogeneity,only families consisting primarily of self-reported European-American ethnicity were included in the sample. The final sample was comprised of 118 large European-American families consisting of 2322 individuals with available DNA.Phenotype data for four substances were obtained from the SSAGA. Some individuals were assessed more than once,cannabis grow equipment in which case data from the SSAGA interview at which an individual reported the maximum number of DSM-IV criteria endorsements for the particular substance was used. Two phenotypes were used in the genetic analyses: ANYDEP, a binary aggregate substance dependence phenotype, and QUANTDEP, a quantitative substance dependence phenotype developed using factor analysis. For ANYDEP, individuals were considered affected if they met DSM-IV lifetime dependence criteria for any of the four substances, and unaffected if they did not meet DSM-IV dependence criteria for all four drugs. Individuals younger than 23 years old at their most recent interview who did not meet criteria for dependence on any of the four drugs were recoded to missing/unknown because they had not passed through the primary age of risk. Selection of this age cutoff was based on the median age of onset of alcohol, cannabis, cocaine and opioid/ heroin dependence in the White sub-sample of the US population-based National Epidemiologic Survey of Alcohol and Related Conditions . The median ages ranged from 18 to 22 years, supporting a cut-off of 23 years. In addition, those individuals with insufficient SSAGA to determine whether they were or were not dependent were also coded as unknown . QUANTDEP, the quantitative factor score, was constructed by conducting a confirmatory factor analysis of the seven DSM-IV lifetime dependence criteria for each of the four substances . As we were interested in those genetic underpinnings that were common to all dependence criteria across the four substances, we elected to use a single factor confirmatory model and did not conduct exploratory analyses, in addition to limiting the factor analysis to the dependence criteria to exclude abuse. All individuals with DSM-IV criteria data were utilized, regardless of age or substance use. The factor score from the resulting confirmatory analyses was utilized as the quantitative phenotype. Heritability was estimated for the two phenotypes using the polygenic option in SOLAR .

The correlation between the total number of DSM-IV criteria endorsed and QUANTDEP was estimated using the Pearson correlation coefficient. ANOVA was used to test if QUANTDEP differed according to the number of substance dependence diagnoses met. We also tested if the average QUANTDEP value differed across alcohol, cannabis, cocaine and opioid dependence diagnoses. Post hoc pairwise comparisons employed a Tukey correction for multiple testing.Genotyping for 2105 subjects in these 118 families was performed at the Genome Technology Access Center at Washington University School of Medicine in St. Louis using the Illumina Human OmniExpress array 12.VI . In addition, genotypes previously generated on the Illumina Human 1M-Duo BeadChip by the Center for Inherited Disease Research were included for 224 subjects from these families . Further details describing data cleaning can be found in Wetherill et al. . The final analytic sample included 2322 genotyped individuals. This yielded an average of 19.6 genotyped members per family. The Genome-Wide Association Analysis with Family Data package was utilized to analyze ANYDEP, implemented as a logistic regression model. Relatedness between family members was accounted for via generalized estimating equations. QUANTDEP was analyzed using a linear mixed effects model as implemented in the kinship library in R . This model in the kinship function allows for the covariance matrix to be completely specified for the random effects. The result is that each family has a different covariance pattern based on the kinship coefficients, to model the familial genetic random effects. Gender and birth cohort defined by year of birth were included as covariates in all analyses described above, including statistical models of association, to account for secular trends . As needed, genomic control was applied to correct for inflation. To reduce the scope of multiple testing, only genotyped single-nucleotide polymorphism were included in the initial analyses. After correcting for the final number of autosomal SNPs , the genome-wide significance threshold was P = 8.45 × 10−8 . In regions with significant association results, we analyzed imputed SNPs to further evaluate the evidence for association. SNPs were imputed to 1000 genomes using BEAGLE 3.3.1 as described in Wang et al. . Secondary analyses were performed for significant SNPs to test whether the observed genetic association could be attributed to dependence on a specific substance.

According to personal accounts, High Sierra volunteers dedicate their time because they feel a sense of land stewardship and an obligation to deter growers from returning to cultivated areas. Without the regular coordination of cleanup efforts by non-profit organizations such as the High Sierra Volunteer Trail Crew, site reclamation would be much less feasible. Volunteer groups rotate from site to site for efficient cleanup, but liability issues extend the time frame before they are allowed to enter any site, if they are allowed entry at all. Even though it has become common for volunteer groups to clean up cultivated landscapes, there remain major bureaucratic barriers that prevent the full utilization of volunteers. Even with sites secured by law enforcement officers, people face an array of hazards on the rugged hikes and potentially dangerous sites. Government agencies therefore require waivers of liability and extensive precautions against injury such as an armed security escort, proper clothing, hard hats, gloves, and use of established trails among other precautions. The reality of remote DTO cultivation negates the romanticized visions of hippies, young experimenters, or mom and pop cultivators growing weed in their backyards. Almost 160,000 plants were eradicated from the national forests in 1983.83 In 2006, after the firm establishment of DTOs in the US, 6,305,202 marijuana plants were eradicated from national forests throughout the United States, over half of which were in California. In the words of rap artist Immortal Technique, “this is big business, this is the American way.” The scale of marijuana production in the United States has boomed in the past thirty years, causing proportional changes in the scale of the market,cannabis grow lights environmental destruction, and safety hazards. The spread of information concerning the problems caused by industrial scale marijuana production has significantly increased, but much more must still be done.

The prevalence of cooperative counter-cultivation efforts shows that the response to DTO cultivation is no longer the burden of a handful of agencies, but of every institution and person with vested interests in public lands. By building working relationships, agencies and people can combine money, labor and strategic resources to make these cooperative efforts a more powerful force. However, until they incorporate holistic approaches for prevention, reclamation, and investigative follow through, their potential impact on remote Cannabis cultivation will never be realized. It will require a combination of new law enforcement strategies, long term investment of the necessary resources, and drastic changes in public policy to change the current trends in marijuana production on public lands. “This issue has been intensifying for several years. The risk to those profiting has been minimal. The incentives to continue have been enormous. To be effective, we must commit to a well designed, long term collaborative strategy.”84 The changes that must take place can only occur over time: investigations need to produce results; central agencies need to conduct regular assessments of their effectiveness and adapt their methods; resources need to be allocated for site processing; citizens need to be educated about the issue; public officials need to reconsider current policies. Preventative law enforcement efforts have proven more effective than previous methods, but commitment to them requires immense funding and strategic planning. They must occur on a daily basis over a widespread geographic area and are therefore difficult to implement comprehensively. Sustained cultivation prevention would require a significant increase of year round staffing within government organizations that are currently operating under budget cuts. If top officials choose to continue on the path laid out in the strategic plan, it is necessary to assign more staff so that marijuana specific employees can maintain manageable workloads, fully complete investigations, process and investigate sites, and continue preventative monitoring. However, continuing the trends of ever increasing demand for law enforcement, or more broadly, the growth of the law enforcement industry, may not be the solution.

If current marijuana control policies remain, increased reliance on tools such as remote sensing, Geographic Information Systems , and centralized information analysis centers can make site detection, information gathering, and remote operations more effective. “Cannabis interdiction operations have involved extensive use of aerial observation to locate actual cultivated plots or potential growth sites. This approach is both time-consuming and expensive, and is also frequently hampered by thick forest vegetation cover [and forest fires]. Therefore, a more efficient method for identifying potential target areas is required to facilitate the interdiction operations.”85 The use of remote sensing can allow law enforcers to detect remote cultivator operations more efficiently using an array of technologies that create less strain on human resources. Specific applications include the use of electronic sensors placed at eradicated sites to detect cultivator return, infrared heat imaging to detect the presence of humans in remote areas, radio transmission interception to record DTO communications, and satellite imagery to detect campsites and tree canopy thinning without alerting cultivators to the presence of helicopters. Land managers can integrate expert knowledge with GIS data input and analysis in order to map eradicated sites, compile statistics for official reports, and to facilitate preventative monitoring. These systems can be used to compile significant data from investigations in order to recognize trends, modify strategies and monitor remote areas in the future. Data compilation and spatial analysis can enable law enforcers to identify potential cultivation sites in an effort to prevent the creation of new ones. The use of these systems at centralized intelligence analysis centers would allow agents to instantly access significant information across agencies, and would foster the development of regular assessments that create awareness in government officials, land management personnel, law enforcement officers, and public citizens. The information gathered at intelligence centers can support “prosecutor-led, intelligence-based task forces that bring together the Department of Justice, the Department of Homeland Security and other agencies to dismantle drug cartels through investigation, extradition and the seizure and forfeiture of assets.”

The reason for this is that unless they are repeat offenders, or provide crucial information that most cultivators don’t have, the individuals are deported regardless of their crimes. The formation of specific task forces within agencies that possess jurisdiction for international operations, such as the DEA and the Department of Homeland Security, can create effective prosecution of cartel members, and impede DTO operation through international relations and governmental partnerships. Proper resource allocation as well as strategic networking is necessary to encourage cooperative efforts at an international level. With a developed understanding of the problems created by DTO marijuana cultivation, officials can collaborate and use more effective methods to oppose DTO operations. The marijuana control struggle no longer revolves around removing plants from the market, but centers on removing the powerful organizations that control the market. “As we’ve found with other large criminal groups, if you take their money and lock up their leaders, you can loosen their grip on the vast organizations that are used to carry out their criminal activities.”However, if DTOs are removed from the market the government’s task will still not be completed. Land management agencies should conduct site reclamation at all damaged landscapes to ensure that all clean up and remediation needs are met. “The mission of the US Forest Service is to sustain the health, diversity, and productivity of the Nation’s forests and grasslands to meet the needs of present and future generations,” but through the evolution of law enforcement responsibilities within land management agencies, the essence of their missions have been lost.Only a small portion of site reclamation needs are being met to the detriment of the health, diversity and productivity of the nation’s public lands. Environmental reclamation should be an inherent step included in post-eradication site processing. The California Conservation Corps has conducted the majority of cleanup efforts, but more is still to be done. Non-profit organizations and environmental groups desire to assist more in the reclamation of natural landscapes,cannabis grow tent and this process can provide a good medium for public education. Utilizing the help of volunteers lowers reclamation costs and informs people first-hand about the realities of remote cultivation. Due to liability issues, however, what is saved in money is sometimes lost in time and restrictions. Volunteers cannot viably clean up the majority of sites because of safety concerns and the sheer scale of labor that is necessary. The result is a widespread neglect of the very areas that land management institutions were created to protect. Public education is a crucial element of preserving public lands. There is a major gap in knowledge between the public, politicians and people who deal with this issue on a day to day basis. The US Forest Service has 192 million visitors every year, most of whom are ignorant to the issues surrounding the valuable public lands that they are visiting.An increased awareness of what is occurring, what the effects are, and what individuals can do to help would foster safe public practices as well as increase reports of suspicious activities. The Strategic Plan sites public education as a major contributor to the long term marijuana control strategy, but experts in the field are stretched too thin to conduct the public education campaign that is necessary to make a difference.

Twenty to thirty percent of cultivation sites are discovered by members of the public who run into a cultivator or spot irrigation lines.Given a widespread understanding of marijuana related activities on national forests, the number of sites safely reported by civilians could increase drastically. Many land management and law enforcement employees are unaware of DTO operations until they are forced to deal with cultivation sites first hand. Individuals such as forest service employees and highway patrolmen need to understand DTO operations because they have a high probability of encountering DTO related activity. A widespread, sustained program is the best way to transmit the breadth of fragmented information on this topic to the public using reliable sources. Such a project could provide land managers and law enforcement with the support they need to adequately monitor areas and respond accordingly. It could also encourage individuals to write to governmental officials and create pressure for policy makers to act. Mexican DTOs are the foremost cultivator group and have the single largest impact on the marijuana industry. The same organizations responsible for the majority of marijuana production on California public lands are the heart of the bloody Mexican drug war. President Obama met with officials in Mexico City and augmented “ongoing US aid to Mexico under the Merida initiative: a three-year, $1.4 billion package aimed at helping Mexico fight the drug cartels with law enforcement training, military equipment and improved intelligence cooperation.”However, this money is yet to incur any noticeable effect on drug cartels.In order to disrupt DTOs, the United States needs to halt the flow of money and weapons from the US to Mexico. By upholding current regulations, we empower cartels to continue their destructive, violent practices. Marijuana cultivation on public lands is a significant problem with viable solutions, but without essential changes in law enforcement strategies and nationwide public policy, it is a problem we can expect to continue, putting the future of our lands and our people at risk. The US war against marijuana has increasingly escalated since its conception because it is not a war that can be won. Drug production has become increasingly destructive and dangerous despite an estimated $7.7 billion spent annually by the US Government to enforce marijuana laws.Such regulation inflates the steady revenue flowing to criminal organizations that in turn generate widespread crime and violence. Regardless of the legal status of marijuana, as long as it remains in high demand there will be a market to supply it, regulated or unregulated. Government-imposed prohibition gives rise to black market systems that are dominated by major criminal organizations that control production and distribution. This system of perpetual crime and punishment is sustained at the cost of all parties involved, and requires a fundamental change in the system itself. Public policy plays the most crucial role in dictating the status of marijuana markets and their effects on governance and fiscal resources. The most powerful mechanism for opposing cultivation trends is to change the role of marijuana in California and the United States through legalization.

Marijuana production trends began to shift from people’s personal property onto public lands around the early 1980s. At that time, California residents began to encroach on the National Parks and successfully grow and harvest marijuana crops.The local Sheriffs could not deter this activity as it began because their time was limited by other casework, their funds were dedicated to addressing other criminal activity, their training and equipment was inadequate for remote operations, and their methods did not effectively identify, much less prevent the continuation of marijuana cultivation on private or public lands. In March 1982, the House Committee on Interior and Insular Affairs and the Subcommittee on Public Lands and National Parks began to recognize the potential threat posed by marijuana cultivation on federal land. The report Illegal and Unauthorized Activities on Public Lands – A Problem with Serious Implications evaluated how “crimes against persons and property, marijuana cultivation, timber thefts, and trespassing – were limiting the ability of the public to use and enjoy natural resources and recreational facilities on federal lands in California and Oregon… The Chairman was especially concerned about the danger marijuana growers posed to federal employees and land users.”Governmental awareness of the issues facing public lands led to action that transformed the roles that various land management agencies would play in the future. In the 1983 report, Additional Actions Taken to Control Marijuana Cultivation and Other Crimes on Federal Lands, major governmental landholders were granted jurisdiction to regulate cannabis grow tray cultivation on public lands. The three principal federal land management agencies, the US Forest Service, the Bureau of Land Management, and the National Park Service were all cited along with the DEA as the primary agencies responsible for addressing threats to preserve the integrity of federal lands.

This meant increased law enforcement responsibilities on the part of all three agencies. The National Park Service would utilize park police and park rangers while the US Forest Service and the Bureau of Land Management would employ special agents and enlist assistance from local and state law enforcement agencies. Cooperative efforts between the DEA, state and local law enforcement were often used to suppress domestic cultivation because they could “promote information sharing and contribute training, equipment, investigative and aircraft resources, and funding to support the efforts of state and local law enforcement agencies.”One significant result of cooperation between federal, state, and local agencies was the development of the Campaign Against Marijuana Planting. What these cooperative efforts show is that despite the increased responsibilities of federal land management agencies, outside sources were still relied upon for enforcement efforts. This is represented in Forest Service budgets that allocate a negligible amount of funds specifically for controlling marijuana cultivation. The funding allocated for marijuana control efforts by the USDA Forest Service in 1982 was $206, only increased to $1,072 in 1983.During that same period of time, the “Forest Service reimbursed state and local law enforcement agencies $5.3 million under cooperative law enforcement agreements.”This shows that while land management agencies were responsible for the preservation and protection of land, they still relied mainly on state and local officers to conduct eradication operations. This may explain why in 1982, the US Forest Service’s Pacific Southwest Region located only 114,911 plants out of an estimated 387,000 plants, and only eradicated 55,561 of those located. Initial law enforcement efforts on public lands focused exclusively on locating and eradicating marijuana plots. To accomplish this goal, cooperative law enforcement agencies assigned agents to marijuana task forces during the late summer months because the harvest season was the only time of year that marijuana plantations reached adequate maturity to be visibly spotted by helicopter reconnaissance.

Helicopter searches served as the primary means of locating plantations, so officers only conducted operations during times of the year when plantations were visible from an aerial perspective. Another reason that agencies only conducted periodic drug enforcement in remote areas was that traditionally, the success of enforcement operations was measured by the number of plants and sites eradicated, illustrated by the focus of law enforcement statistics on the plant eradication count. “Generally, this approach applies the rationale, ‘the more plants eradicated, the greater the success.’ However, a more holistic view recognizes that increased plant numbers may actually reflect a failed strategy.”Short term annual operations maximized the plants eradicated for the resources dedicated, but did little to prevent the proliferation of marijuana cultivation on federal lands. When Sheriffs raided sites for eradication, they apprehended individuals at the scene of the crime, but did not collect evidence, follow leads or conduct investigations. Eradication sometimes meant that plants were carried out in helicopter nets, but more often plants were slashed and burned or left to rot. However, officers did not enter every plantation that was visually identified due to limited budget allocation for remote operations. At the end of the eradication season, officers were transferred back to their regular assignments until eradication efforts began again in the following year. As law enforcement agencies identified the increasing occurrence of cultivation on public lands, they also began to recognize the recurrence of grower operations in areas that had been eradicated in past years. It became evident that cultivators came back to sites year after year because the site infrastructure still remained. Eradicated sites could successfully produce marijuana harvests in years following a bust because federal agencies were so limited in time, staffing and resources that re-visitation of every previously eradicated site was impossible. The implication of grower recurrence on eradicated sites was that undiscovered sites were likely utilized on a yearly basis. The establishment of DTO operations on California lands transformed the nature of law enforcement efforts to combat commercial marijuana production. While eradication efforts increased, “statistics show this approach has been less than effective. Most people that have been involved with this issue for any time agree that we cannot just eradicate our way to success… Another common approach has been to use the number of arrests as a measure of success. Much like the plant count, using an increased number of arrests may also reflect a less than effective program.”When small resident groups dominated marijuana cultivation in California, the eradication of a sole plantation had major economic implications for the individuals involved.

Eradication statistics were directly correlated with the success of law enforcement, with some measure of legitimacy. When larger organizations entered into domestic production, they were able to compensate for losses by creating widespread and diversified operations. Eradicated sites only accounted for a small percentage of the organizations yearly production. In the 1970’s, large eradications were responsible for cutting off the supply to an area for a period of time. Today, eradications serve to increase prices on remaining supplies. While the law enforcement community understood that their longstanding methodology was ineffective at preventing remote cultivation, the organizational structures limited their ability to revolutionize their approach. Therefore, the National Marijuana Initiative was established in 2001 by the Office of National Drug Control Policy to coordinate federal, state, and local agencies.Since its conception, the NMI has played a significant role in facilitating the spread of marijuana related information between law enforcement agencies and political entities. The NMI is responsible for developing the National Drug Intelligence Center’s 2003 Marijuana Threat Assessment and the 2007 Domestic Cannabis Cultivation Assessment which compiled statistics from law enforcement agencies across the nation. Through coordination and cooperation with the widely dispersed agencies involved in marijuana control, the “NMI funded investigations have identified drug trafficking organizations that operate marijuana grows in several western states” and helped focus enforcement efforts toward key growing areas. 45 The NMI is a product of the long-term efforts of governmental agencies in conjunction with the short-term task forces of the 1980s. Interagency task forces such as CAMP have proven to be effective at mobilizing resources and conducting remote operations. Interagency cooperation has enabled task forces to pool funds,cannabis grow tray resources and agents to optimize strategic planning and meet government set objectives. Previously, law enforcement agencies were territorial when it came to marijuana raids because marijuana eradications increased funding and improved departmental image. Now, interagency connections and the personal relationships that result, encourage cooperative efforts instead of creating barriers to them. The NMI played a crucial role in developing a political understanding of the transformation in marijuana production, cultivation by large criminal organizations, and in doing so shifted the primary focus away from eradication. In 2005, Senator Dianne Feinstein became aware of how DTO operations were adversely affecting federal lands through congressional hearings and media coverage on the issue.In response, she held a meeting with the leaders of the DEA, US Forest Service, National Park Service, Bureau of Land Management, CAMP and other major law enforcement authorities in order to establish new objectives to increase the effectiveness of law enforcement. Through Feinstein’s efforts and renewed support in Congress, state and individual organizations allocated more funding for a comprehensive approach to marijuana law enforcement. Marijuana cultivation on state and federal lands became a major law enforcement priority and the US Forest Service, National Guard, CAMP and other cooperative task forces have taken on primary roles in conducting counter-cultivation efforts.

Previous measures of short-term success persisted, including site location, plant eradication and cultivator arrests. However, law enforcement agencies created divisions strictly dedicated to opposing DTOs by assigning patrol officers exclusively to the issue, reassigning alternative workloads, and removing extraneous administrative duties of those in charge in order to focus the necessary resources to impact DTOs. In addition, increased funding allowed agencies around the state to begin a training and recruitment process to significantly increase staffing. In the Pacific Southwest Region of the Forest Service, the additional staff would include 1 supervisory special agent, 3 patrol captains, 18 special agents, 50 law enforcement officers, and 6 administrative assistants, all of whom will be dedicated almost exclusively to marijuana control.This will allow for long-term engagement in year round counter-cultivation efforts that utilize preventative measures as an advantage. Instead of waiting for a site to be found in July or August, agents will be able to look for and follow up on leads throughout the year. Equally important, they can identify priority cases for full investigation so they may be completed to a “reasonable conclusion.”Investigations will be prioritized based on existing intelligence, site logistics, available resources, and special public interest. The process of site review involves a methodical documentation of evidence such as cell phone contacts and the origin of supplies, which is entered into records and scrutinized for pursuable leads. Previously, useful evidence would have remained untouched at the site, or on rare occasions, kept in police storage. The potentially useful information left at sites was lost to neglect. Now, a significant portion of the evidence left behind is subjected to intelligence analysis. Increased utilization of intelligence analysis centers has made this process much more efficient and effective, which enables preventative tactics and helps governmental agencies learn about and infiltrate tight drug trafficking institutions. Governmental agencies have also changed investigation and detection strategies. While some authorities claim that there is nothing better than a helicopter and a well-trained eye, enforcement agencies are developing the use of more sophisticated techniques. These include, but are not limited to, ultraviolet, infrared, and electronic detection systems. Other techniques include night time patrols in high risk areas when cultivators may be less attentive, year-round patrols, and new detection methods such as monitoring for irrigation and cultivation supplies, comparing watershed precipitation with surveys of water flow quantities, and testing for chemical nutrient imbalances in bodies of water. The more time that is dedicated to research and detect sites early, the less time is required to raid and eradicate each site. Raids are carefully planned efforts, designed to reach set goals while minimizing the risk to agents. First, team leaders develop a raid plan and develop logistics such as funding sources, equipment requirements and invasion methods. Agents in charge then gather a team that they brief, supply and prepare. New agents and officers are required to complete a thorough training program to learn remote raid techniques.

A third and final coding guide was applied to the full list of apps. Of note, the coding guide was clarified so that apps referencing heat-not-burn were coded as vaping-related. While tobacco heat-not-burn devices are considered distinct from vaping devices, heat-not-burn of cannabis flower is often considered vaping, and neither involves combustion. Additionally, the group could not determine the purpose of 6 apps from the Google Play Store descriptions. These apps were downloaded and evaluated using additional information from the downloaded app itself. The final 8 purpose categories were do-it-yourself coils, DIY e-liquids, shopping, entertainment, social, device, quitting smoking, and quitting vaping. Summary percentages and means for app metadata and purposes were calculated. Next, apps within each of the 8 purpose categories were ranked by total number of installs, and the top 2 to 5 apps per category were downloaded for review. Instead of ranking the most popular apps, we ranked apps by popularity within categories, so that apps with less overall popularity but potentially important purposes would be included. The number of apps selected per category varied due to ties in the reported number of installs. Because apps could have multiple categories, this procedure resulted in a list of 18 apps with 10 to 1 million downloads each. Three of these apps disappeared from the store before they could be downloaded for review; one could be replaced with a premium version of the same app. A total of 16 apps were downloaded onto 2 Samsung Galaxy Tab A tablets and a Google Pixel 2 smartphone. A random selection was reviewed by all 3 investigators. Discrepancies were discussed, and the coding guide was updated.

The final coding guide included evaluations of whether the content of the downloaded app matched the purpose category and whether it had the following types of information : information about harms of vaping ,mobile grow system information about safer vaping or DIY device use , and information about harms of smoking . For apps coded as being intended for quitting smoking or quitting vaping, the presence of a tracking feature in the downloaded app was noted . For apps coded as pairing with devices, the presence of features for tracking temperature, dosage, or device locking was noted.Finally, the Mobile App Rating Scale was applied to all 16 downloaded apps. The MARS is 23-item multidimensional measure for rating the quality of mobile health apps, with 5 sub-scales in the areas of engagement , functionality , aesthetics , information , and subjective rating. Subjective rating was not applied as these items involve hypothetical personal use. Each item was rated on a scale of 1 to 5, and ratings that differed by more than 2 points between investigators were discussed. Investigators could adjust their ratings after discussion before averaging scores and did so 7 times across the 5 discussed apps. Some information items were rated as not applicable and were excluded from average score calculations. For example, the item about meeting goals would be rated as N/A if the app purpose was not related to quitting smoking or vaping, and the visual information item would be rated as N/A if the app only contained text. Other smaller discrepancies were averaged without further discussion. Average ratings were calculated for each of the 4 sub-scales and then averaged across the 3 investigator ratings. A final score was averaged for all downloaded apps and within each category.The majority of downloaded apps matched the descriptions in the app store .

The exceptions were that 1 app coded as having quitting smoking features did not have any such features, and 1 app that was coded as not having shopping features did, in fact, have links to shopping through the app. Few apps had information about harms of vaping , safer vaping , or harms of smoking . When they did have such information, it was often difficult to find. Of note, 2 of the entertainment apps had a feature where an avatar would cough when vaping too much, which may normalize moderation in vaping; 5 apps had tracking features, which mainly recorded days passed since a user-provided quit-smoking date; and 2 of the apps with tracking features also displayed money saved and health benefits. Both device apps appeared to have temperature controls, but did not have dosage or locking settings visible, though these may have become apparent once paired with a device. Overall, the 16 downloaded apps had a mean MARS score of 3.63, with a highest mean sub-score for functionality and lowest mean sub-score for engagement . Within the sub-types of purposes, the highest mean MARS scores were for social, shopping, and device apps, and the lowest mean scores were for the quitting smoking and DIY coils apps. This study examined the content of the first 100 mobile apps on the Google Play Store using vaping and vape as search terms 1 month after Apple’s ban on vaping-related apps, which was enacted in response to concerns about youth nicotine vaping and EVALI . Of 79 apps determined to be related to vaping, over half were related to nicotine, while only a few were for cannabis, and the rest were unclear in intended substance. The most popular app content, with respect to both number of installations and percentage of these 79 vaping apps, was creating DIY liquids and DIY coils , with 20% in both categories . This may reflect a strong interest in DIY hobbies in vaping culture. DIY may allow users to control the customization process, play with novelty, save money, and achieve higher nicotine concentrations. Overall, the main purposes of the majority of these vaping-related apps on the Google Play Store were to help people continue to vape nicotine. Apps that had features to support quitting smoking or vaping were relatively rare .

Most apps that supported smoking cessation encouraged users to quit smoking by switching to vaping. These apps also contained other features to promote or facilitate vaping, such as e-liquid recipes. The 2 apps for quitting vaping that were downloaded had above average MARS scores but relatively few installations, while the 3 apps for quitting smoking that were downloaded had below average MARS scores and low sub-scores on aesthetics and information. This points to the need for apps to promote vaping cessation using evidence-based behavior-change information and strategies, along with engaging usable interfaces. There were also few apps that paired with devices , and these device apps were mainly for cannabis. Devices that pair with apps using Bluetooth technology are more expensive, which may explain their lower popularity in terms of installs. Features available in the downloaded device apps indicated that the user could control temperature, which may limit throat and lung damage, but not dosage, and there were no locking controls for users with children or those wishing to moderate their use. Given current concerns about youth vaping, the findings that over half the apps had no age controls and that a large proportion of apps without age controls was in the DIY categories are especially concerning. A smaller proportion of entertainment apps had no age controls , though many of the apps with age controls were set at Teen. Age controls may allow parents who utilize family controls to restrict their children’s access to these apps. Of the popular apps that were downloaded and reviewed in-depth, few apps presented information about the harms of vaping or smoking or included information about safer vaping. Information about harms of smoking consisted of articles comparing the harms of smoking to vaping. One app had a widget for tracking “days without smoking” that included “gained days of life” and “avoided radiographs” calculations. Information presented about harms of vaping included acknowledgments of the importance of moderating nicotine intake, the addictiveness of nicotine, or harms of vaping in front of children. The 2 downloaded apps that were intended to help people quit vaping both included links to news stories about young adults with EVALI and articles about concerns with youth vaping and the intentions of vaping companies. Safer vaping information included recommendations about coil and battery materials, causes of e-cigarette explosions, and cautions against mixing e-liquids incorrectly. It should be noted that in 2 entertainment apps with simulated vaping games, the vaping avatar would cough audibly when they “inhaled” a large amount of vapor,mobile vertical rack which could be seen as encouraging moderation in use. Several of these apps included a tracking feature that displayed the number of days since quitting smoking and the number of cigarettes avoided. Self-monitoring is an important component of a smoking cessation plan but is likely insufficient by itself. In addition to a need for apps on the Google Play store that assist people with quitting vaping, study results indicate a need for informational apps to better describe the pros and cons of vaping.

Assuming that these Google Play Store apps were similar in content to the Apple App Store apps that were removed, it appears that Apple’s ban would have had a minimal effect on people who vape with the intention of quitting smoking or who are seeking information about safer vaping. Nevertheless, the decision to remove the vaping-related apps appears to have been taken by Apple in response to rising EVALI cases, which were primarily attributed to cannabis oil additives, rather than nicotine liquids. There appeared to be little publicly available information detailing how apps were determined to be removed, echoing other calls for increased transparency and additional research regarding allowed app platform content and other issues like privacy. Future research should explore other cases touching on the who and how of regulation of apps related to controversial health-behavior for which there is not yet a consensus among health experts. Future research should also examine more explicitly the relationship between vaping app use and vaping behaviors. There were several limitations to this study. First, not all apps that came up in the initial search were reviewed, though most app users would likely not browse more than 100 apps without refining their search. Additionally, the app store gets updated continuously, and a search on a different date may present different results. Indeed, several apps were no longer available a few weeks after the initial search. While the number of installations was recorded for each as a signal of popularity, people may download an app and not use it at all or only use it a limited number of times. Only 1 of the reviewed apps was also available on the iPhone App Store, but it is unclear which of the apps we reviewed were removed from or denied approval in the Apple App Store. Although we only coded Google Play Store apps, a search for vaping in the mobile Apple App Store in December 2019 confirmed that the remaining apps were related to quitting smoking or quitting vaping or were unrelated to vaping behavior. Based on this review of vaping-related apps in the Google Play Store, it appears that the Apple vaping app ban would have had a minimal effect on adults seeking to switch away from smoking or seeking to vape more safely. Most vaping-related apps in the Google Play Store were for purposes related to continuing vaping and had limited age-based access restrictions. Few apps were for controlling device settings, assisting with quitting smoking or vaping, or disseminating information about safer vaping. In the US, as of March 2018, medical use of marijuana is legal in 28 states and the District of Columbia and recreational use is legal in 8 states and the District of Columbia. The liberalization of marijuana laws raises public health concerns, particularly about possible effects on adolescents’ marijuana use and problems. Despite potential risks, the 2016 Monitoring the Future survey shows that 36% of 12th graders and 24% of 10th graders reported past year marijuana use and 23% and 14%, respectively, reported past 30 day use. About 81% of 12th graders and 64% of 10th graders reported that marijuana is “fairly easy” or “very easy” to get. Only 31% of 12th graders and 44% of 10th graders perceived “great risk” in regular marijuana use. Commercialization of cannabis, including marijuana, concentrates, and edibles, may affect adolescents’ use directly by increasing availability or indirectly by promoting beliefs that its use is safe and normative. Although legal sales of recreational marijuana are restricted to adults, enforcement compliance checks indicate that between 11%-23% of recreational outlets may sell to minors.

Key study information extracted included study objective, study design, study location, eligibility criteria, the instrument by which participants were screened, presence of drug use treatment, if any, referral to specialized drug use treatment, if any, and main outcomes relevant to this study’s objective. Extensive heterogeneity of the final selected studies precluded a meta-analysis. All study information was entered in tabular format in Microsoft Excel version 16 . A flow chart of the study selection results can be seen in the Figure PRISMA diagram. The literature search resulted in 2,177 studies imported for screening. We identified 101 studies as duplicates and removed them, leaving 2,076 titles and abstracts. Of those abstracts, 1,984 studies were excluded after a title and abstract screening leaving 92 studies for full-text review. Of the 92 full-text studies, 66 studies were excluded because of wrong study design, no full-text was available , wrong patient population , wrong study setting, wrong study outcomes, or were additional duplicates. Twenty-six studies remained for the final analysis. Together, the 26 study populations spanned all ages. Fourteen studies focused on both adults and adolescents, nine on adults, and three on adolescents. The mean age of the participants ranged from 14.5-38.6 years. Thirteen studies were secondary analyses of prospective studies, which were included. None of the studies were of multiple sites.All studies screened for self-reported drug use among assault-injured participants either by computerized/written survey or in-person interview. Of the 26 studies, five studies screened for recent drug use by either survey or in-person interview without a formal screening instrument.Of the remaining 21 studies, 14 used a combination of the NIDA Quick Screen Question and Modified Alcohol, Smoking and Substance Involvement Screening Test , three used the Substance Abuse Outcomes Module ,mobile grow system two used questions from the Monitoring the Future study to detect prior-year cannabis use,one used questions from the Supporting Adolescents with Guidance and Employment survey to detect past 12-month substance use,42 and one used the Texas Christian University Drug Screen to determine past 30-day substance use.

Among all studies, drug use was found to be closely linked to assault-injury. Study results reported of this relationship were heterogenous. Four of 26 studies found a range of 25-61% of assault-injured individuals who reported drug use within the preceding 12 months.Three studies reported that previous drug use of any type was significantly associated with 1.43-7.41 greater odds of either previous or acute assault-injury.Two studies reported that assault injury was significantly associated with 1.55-1.84 greater odds of previous drug use.Overall, cannabis was the most common drug identified among assault-injured individuals. Eight studies reported cannabis use among assault-injured individuals ranged from 32.1-96.7%.Three studies found that cannabis use was significantly associated with 2.1-7.41 greater odds of assault-injury.Two studies found that cocaine use was also significantly associated with 2.7-3.1 greater odds of assault-injury.One study found prescription drug misuse was significantly associated with a 1.43 greater odds of assault-injury.In this systematic review, we identified ED-based studies that screen, treat, and/or directly refer to specialized treatment services for drug use among assault-injured individuals. Our comprehensive literature search determined that there were 26 studies that met criteria for inclusion. The studies in this review used various screening modalities to identify drug use including an in-person interview as well as computerized and written versions of validated screening instruments for drug use. None of these studies were interventional nor did they provide a direct referral to specialized treatment services. The vast majority of studies found a high prevalence of drug use within this population, with cannabis being the most common drug detected. Although study results were fairly heterogenous, the majority of them found high rates of drug use among assault injured individuals, especially when compared to those injured by other mechanisms. Previous literature demonstrates a close link between assault-injury and drug use.Several pre-existing theories have explained this relationship including the shared risk factors between assault-injury and drug use, the pharmacologic effects of drug use, and the association between assault-injury and the illegal drug trade.Evidence shows that substances such as alcohol, cocaine, amphetamine type stimulants, phencyclidine, and barbiturates cause increased aggression and impaired judgment.However, cannabis was among the most common drugs detected in our review. The evidence to support its role in causing aggressive behavior is mixed.It is more likely that the relationship between cannabis use and assault-injury is associated with the effects of withdrawal, shared risk factors of problem behavior, and facets of the illegal drug trade.

Additionally, cannabis use may also allow assault-injured individuals to mitigate aggression and cope with its negative effects.Future studies are needed to better elucidate this relationship. The practice of SBIRT to facilitate future treatment engagement for drug use in the ED setting has become increasingly common.SBIRT has shown some promise in identifying and managing unhealthy alcohol use and opioid use disorder , particularly when paired with pharmacotherapy .Studies in this review used various screening methods to identify drug use among assault-injured individuals. Several validated screening instruments for drug use exist, yet very few have been evaluated in the ED setting. Nineteen studies used one of the following formal screening instruments: the SAGE, SAOM, Texas Christian University Drug Screen, and the NIDA Quick Screen Question, and Modified ASSIST. The NIDA Quick Screen Question, “How many times in the past year have you used an illegal drug or used a prescription medication for non-medical reasons?”, is likely best suited for the ED clinical care context.1 This single question was found to be 100% sensitive for detecting drug use in the primary care setting.Among high-risk populations such as assault-injured individuals, this instrument has the potential to be the most effective in identifying drug use in the busy ED setting. Despite the ACS mandating the practice of SBIRT at all trauma centers for over two decades,our review demonstrates a marked paucity of literature that examines all aspects of SBIRT for drug use among assault-injured individuals in the ED setting. This includes the practices of brief intervention and/ or referral to specialized treatment services for drug use. This is particularly concerning because the literature supports a strong association between non-partner assault-injury and drug use. Moreover, the COVID-19 pandemic, its associated prevention efforts, and accompanying financial stress have exacerbated both substance use and assault-injury.Yet substance use is a potentially modifiable risk factor, as evidence-based behavioral and pharmacological interventions exist.This gap in literature may be explained by the challenges of engaging the intersection of two exceptionally vulnerable populations that do not often seek healthcare with regularity.Both assault-injury and drug use are sensitive topics to research likely due to a combination of stigmatization, fear of law enforcement involvement, their shared emotional impact, and a host of other shared socioeconomic factors including poverty and racism.

Furthermore, obtaining funding for assault-injury research is notoriously challenging, particularly for firearm-inflicted injuries.This may serve as an additional barrier in performing research in this vulnerable population. Other notable challenges in conducting research in this population include participant loss to follow-up by attrition , undocumented immigrant status and fear of deportation, and a lack of viable and sustained community resources where patients can be referred for counseling and treatment services.Additionally, our review highlights several knowledge gaps in the existing literature surrounding drug use in the context of non-partner assault-injury. Little is known about the mutual risk factors, notably socioecological and psychological, that may contribute to the co-occurrence of assault-injury and drug use, both considered to be problem behaviors.Further, in our review, no study evaluated the potential impact of an intervention, such as a brief behavioral intervention, to reduce drug use and subsequent injury. This is particularly compelling because previous literature has shown that a brief behavioral intervention, delivered in the ED setting, demonstrates considerable promise in reducing cannabis use and its related harm as well.Future studies may use existing theory such as the social-ecological model to inform the development of an intervention that reduces the burden of drug use and injury. 78HEAVY DRINKING AND ALCOHOL PROBLEMS are highly prevalent, chronic, mobile vertical rack and serious conditions that usually begin in the teens and persist over time, with 25% of individuals in their 50s drinking daily and/or ever consuming five or more drinks per occasion . Alcohol use disorders can decrease life spans by a decade , and two thirds of alcohol-related deaths occur between ages 45 and 60 . AUDs typically follow a course of exacerbations and remissions with complex interrelationships among risk factors that are best studied by identifying individuals before the condition develops and evaluating them repeatedly over decades. This can be challenging because research funding is usually short term, and longer follow-ups are expensive. Most longitudinal studies are 1–5 years and some cover 10–15 years , but prospective studies that began at age 20 and continued into the sixth decade of life are rare . For decades, our group has focused on predictors of AUDs and related outcomes that included demography; earlier substance use onsets, frequencies, quantities, and problems; prior treatment experiences; and genetically influenced characteristics . Although limitations in the time subjects were willing to spend during baseline evaluations for these studies and the specific interests of Prior studies indicate that remissions are likely to increase with age, European American background, having been married, and, for non-abstinent outcomes, previous lower alcohol quantities, frequencies, and alcohol problems . Abstinent outcomes are more likely in individuals with greater alcohol problems and those with experience with formal treatment or self-help groups , programs that usually emphasize non-drinking outcomes.

However, with some important exceptions , most longitudinal studies of the course of AUDs were generated from treatment samples that are often of lower socioeconomic status, and less is known about the clinical course of individuals with AUDs from higher functioning groups. Several genetically related characteristics also relate to the course of drinking and AUDs, including an endophenotype of special interest to our group. A low level of response to alcohol increases the AUD risk and might also predict higher remission rates among individuals with AUDs . The low LR is not closely linked to externalizing or internalizing characteristics, and, thus, is not related to dependence on illicit drugs or psychiatric disorders other than alcohol induced conditions . This study extracted data from the San Diego Prospective Study in which male probands entered the protocol at about age 20 as drinking, non–alcohol-dependent college students and nonacademic staff, with more than 90% of these subjects followed at age 30 and every 5 years thereafter. Half of the men had an alcohol-dependent father, and half reported no close relative with an AUD, with the two groups selected to be similar on demography and substance use histories. The selection of a sample, half of whom had an alcohol-dependent relative, resulted in a high proportion who developed an AUD. Thus, the results presented here are relatively unique among long-term studies of individuals with AUDs. Our current interest is in whether clinicians or researchers who had followed the men with AUDs from ages 20 to 50 could predict their alcohol-related outcomes. Four hypotheses guided the analyses. In Hypothesis 1, reflecting the past high education and life achievement for SDPS probands, we predicted that in their sixth decade many of these men would have developed abstinence or low-risk drinking in the absence of multiple alcohol-related problems . As a corollary, few participants will meet the criteria for AUDs between ages 50 and 55. Based on the existing literature, Hypothesis 2 predicted that low-risk drinking would be most likely for men with higher LRs to alcohol, and lower past drinking quantities, frequencies, and problems . Hypothesis 3 predicted that the more problematic outcome of high-risk drinking ages 50–55 would relate to LRs, alcohol intake patterns, and alcohol problems between the low-risk drinkers and probands who maintained abstinence during the most recent follow-up. Hypothesis 4 predicted that the probands who maintained abstinence at ages 50–55 would have the lowest LR and highest drinking quantities and report the highest rates of exposure to formal treatment and/or self-help group participation .Following approval from the University of California, San Diego , Human Subject’s Protections Committee, the SDPS began in 1978 with 18- to 25-year-old male European American and White Hispanic students and nonacademic staff selected among respondents to a randomly mailed questionnaire .

Since OU quantifies the deviation of average velocity of each tray from the designed velocity, a higher OU value indicates that the crops will have better and more uniform photosynthesis. It can be observed from Fig. 5 that the maximum OU obtained for all conditions is case BC at a flow rate of 0.3 kg s−1. To develop a better understanding, the two-dimensional velocity and vorticity distributions in the x-y plane along the middle of the z-direction for all eight cases at a mass flow rate of 0.3 kg s−1 are plotted in Figs. 6 and 7. As can be observed from Figs. 6 to 7, the OU is highest for case BC due to its uniform velocity and vorticity distributions between trays.For case BC, the inlet flow is parallel to the longitudinal direction of the tray and the exit is along the transverse direction . This design allows the flow to travel through the long side of the tray uninterrupted and then form a helical flow orientation near the end of the tray. This spiral formation of flow induces a more uniform and regular flow in the room. This also explains why case AD has very high OU. Similar spiral formation can also be observed when the inlet flow is parallel to the transverse direction of the tray and the exit is along the longitudinal direction , like case DA. However, since the inlet flow is along the short side of the tray, the benefit is not as great and requires much higher inlet mass flow rate. On the other hand, for cases where the inlet and exit are located on the same wall, such as AB or CD, the air flow only has strong mixing effect along the inlet/exit direction which, in turn,cannabis grow supply store reduces the overall flow uniformity. Besides the velocity distribution, the effect of temperature is also a critical parameter for determining convective flow. Fig. 8 shows the two-dimensional temperature distributions in the x-y plane along the middle of the z-direction for all eight cases at a mass flow rate of 0.3 kg s−1.

In our analysis, the temperature of the inlet flow is lower than that of the exit flow due to the heat generated from the LED light. For case BC, the inlet is located near the bottom and the exit is near the top. Due to the density difference, the exit warm stream tends to flow up. This allows the flow to reach the topmost tray more easily and, therefore, achieves more uniform temperature distribution among all trays. Combining the inlet flow along the long side of the tray, the helical flow effect, and the buoyancy, case BC is able to reach the maximum OU of 91.7%. Fig. 9 summarized the velocity and temperature contours for case BC at an inlet mass flow rate of 0.3 kg s−1. The velocity pro- files in Fig. 9a clearly show the spiral effect above each cultivation tray and the local velocity is close to the optimal speed of 0.4 m s−1. In addition, the temperature shows an increasing trend from bottom to top as the flow helically passing through the crops and moving towards the outlet.The distributions of temperature and gas species, such as water vapor and CO2, play an integral role in photosynthesis which, in turn, influences the quality of plant and its growth. Therefore, maintaining these critical parameters in a reasonable range to ensure reliable and efficient production is essential to environmental control of an IVFS. Evaluating the distribution of these parameters can also provide the effectiveness of inlet/exit location. It should be noted that the parameter OU provides an overall assessment of the air flow velocity over planting trays. An optimal design is to achieve desired local temperature and species distribution while maintaining high OU values in an IVFS. In the following discussion, the four cases with highest values of OU at their corresponding mass flow rates are studied and compared to the baseline case AB.Since CO2 is a reactant of photosynthesis, increasing CO2 concentration usually leads to enhancement of crop production. Reports show that increasing the CO2 concentration from the atmospheric average of 400 ppm to 1500 ppm can increase the yield by as much as 30%. In this IVFS analysis, the CO2 level of the inlet mass flow rate is increased by a CO2 generator to be 1000 ppm . Since the consumption rate of CO2 through the exchange zones is fixed, higher overall average CO2 concentration through the system is desirable. Fig. 10 shows the comparison of the average CO2 concentration between the highest OU cases and the baseline case AB at different inlet mass flow rate.

A few general trends of CO2 concentration can be observed from Fig. 10. First, the CO2 concentration increases with inlet flow rate due to increasing supply of CO2 molecules. In addition, tray 1 has the highest CO2 concentration because most of the cold fresh inlet air dwells near the bottom of the IVFS due to the buoyancy effect. In contrast, tray 3 has the lowest CO2 concentration because the fresh inlet air has the highest flow resistance to reach tray 3due to the combination of sharp turns and buoyancy effect. This is particularly true at low inlet flow rates and when the inlet is located on the top, which lead to low flow circulation as cold inlet air flows downward directly. As a result, BC, BA, and DA at 0.3, 0.4, and 0.5 kg s−1, respectively, have relatively high CO2 concentrations. Even though the baseline case AB at 0.5 kg s−1 has the highest CO2 concentration, its OU is too low to be considered a good design.According to Eq. , the power required can be calculated as the product of volume flow rate and pressure drop between the inlet and exit. Even though the inlet/exit locations can change the overall system pressure drop slightly, mass flow rate has a dominating effect on the required power, as shown in Fig. 13. It can be observed that cases AB and BA incur the most pressure drop, which is more obvious at high flow rates. As discussed earlier, placing the inlet and exit on the same wall located at the short side disrupts the helical flow formation, which is known to benefit flow circulation. Under this condition , additional vertices, which is the main source of the increased pressure drop, are observed near the exit region from the flow streamlines. To minimize energy consumption, the inlet/exit location should be placed on opposite walls and the IVFS system should operate at the minimum flow rate that meets other requirements, such as temperature, RH, CO2 concentration, and flow velocity above the exchange region. Since there are multiple variables that can affect the overall design of the inlet/exit location and mass flow rate, an overall efficiency factor is introduced in Eq. to holistically assess the uniformity of all monitored parameters. Fig. 14 shows the final comparison of the overall design efficiency between the four best OU cases and the baseline case. It can be observed that OU has a dominating effect on the overall efficiency since the trends show some resemblance to the overall efficiency.

In terms of overall efficiency and power consumption, case BC operating at 0.3 kg s−1 is the optimal design for this IVFS.A comprehensive understanding of the biological networks at multiple levels is crucial to harness the full potential of ENMs for sustainable food production . In recent years, probing of ENM-plant interaction have evolved from traditional, single endpoint assays to discovery oriented, high-throughput system biology approaches, referred as “omics”. This is supported by advancements in the sensitivity and accuracy of analytical techniques and bio-informatic tools . The suffix “omics” refers to unbiased screening of bio-molecules in an organism, specifically genes , mRNA , proteins, or metabolites . Systems biology approach has been implemented to decode the molecular mechanisms in plants and elucidate the behavior of genes, proteins and metabolites in response to biotic or abiotic stressors . With the emerging need for mechanistic understanding of complex agronomic traits and crops’ response to ENM exposure, omic technologies have gained momentum in precision agriculture and nanotoxicity studies. This paradigm helps in generating hypotheses by monitoring response of biomolecules upon systematically perturbing biological processes with ENMs, followed by integration of global datasets onto pathways using advanced bioinformatics algorithms . Realization of the underlying molecular mechanisms in plants will provide cues for designing ENMs for specific applications like increasing resilience to pests or environmental conditions,cannabis drainage system targeted delivery of nutrients or pesticides, stimuli-responsive agrochemical release, or ENM-enabled biosensing. The sensitivity of omic techniques allows to capture, quantitate and distinguish the cellular and molecular level changes in an organism when exposed to ionic, nano- or bulk- form of any particle of interest at considerably lower and environmentally realistic doses; these deductions are not obvious from phenotypic responses or less sensitive biochemical assays . These techniques also allow to compare responses at multiple hierarchical levels across different plant species, age, growth/environmental conditions, and ENM exposures. In plants, transcriptomics has been the most applied omic technique, used to identify the transcription factors as predictive biomarkers of ENM toxicity , which are correlated to phenotypic responses. However, this bottom-up approach based on upward chain of causality has several constraints that result in inconclusive nature of such approach . These constraints emerge from the uncertainty resulting from post transcriptional processes, post translational protein modifications, and stimulus-induced metabolite level changes. The higher level of organization representing the metabolome or proteome in an organism are not fully determined by the properties of the lower levels ; instead, they regulate the functionality of lower levels in a downward causation chain in response to stimuli . Metabolomic analyses allow functional annotation of uncharacterized genes or proteins, thereby filling knowledge gaps in plant metabolic machinery.

In addition, metabolomics does not depend on the data generated from model plant species, hence could be easily applied to model as well as non-model species . Thus, due to the exploratory nature of ENM-plant interaction studies, it is recommended to follow the downward causation approach that correlates phenotypic expression with the metabolome of plants, which can complement proteomic and transcriptomic profiles in a pathway analysis network. This review consolidates the pioneering studies in plant metabolomics and proteomics, intended to gain insights into ENM-plant interactions. Studies employing other omic tools have been discussed briefly. We discuss novel analytical platforms employed in metabolomics and proteomics in plants in response to ENMs and address the important factors in such analyses. Finally, we postulate the vulnerable biological pathways in plants in response to ENMs and how integration of multiomic datasets can be exploited to address major mechanistic concerns and enable realization of wider application of nanotechnology in agriculture.Metabolites are the end-product of cellular regulatory processes that reflect the ultimate response of an organism to any external stimulus . The plant metabolic pathway databases, curated from experimental literature by the Plant Metabolic Network , report 4,544 compounds involved in 1,123 pathways across 350 plant species . Plants collectively produce a diverse array of 200,000 metabolites, which are broadly divided into two major categories, primary and secondary metabolites . Primary metabolites, which include carbohydrates, amino acids, vitamins, organic acids, and fatty acids, are required for plant growth and development . Secondary metabolites are synthesized from the primary metabolites for adaptation and defense response in plants . The major classes of secondary metabolites are polyketides, terpenoids, steroids, phenylpropanoids, alkaloids, and glucosinolates, which have their own biogenetic pathways and thousands of products and pathway intermediates . Primary metabolites are universal and conserved in their structures throughout the plant kingdom, whereas the secondary metabolites are species-specific and differ in chemical complexity. Any alteration in plant physiology in response to a xenobiotic, such as ENMs, is regulated by molecular events and is reflected at the level of metabolites that participate in interconnected biological pathways such as glycolysis, citric acid cycle, gluconeogenesis, biosynthesis of amino acids, biosynthesis of secondary metabolites, nitrogen metabolism, and fatty acid metabolism. Plant roots also exude metabolites as signaling molecules to defend or adapt to stressors as well as modulate soil chemistry and/or biochemical pathways to influence nutrient bio-availability .

Coca plantations in the 20th century accounted for approximately 7 million hectares of deforestation in the Peruvian Amazon. Trends going into the 21st century reveal that this destruction is still rampant; from 2001-2013 over 290,000 hectares of forest were lost due to processes of cocaine manufacturing. A specific concern of this destruction is that much of the habitat destroyed for drug crops lies inside biodiversity hot spots like the northern-Andean ecosystem, which is singly “the most species-rich region on Earth.”These remote areas are chosen since they happen to be ideal spots for illegal plantations, due to their locations far from urban areas and potential detection. In the aftermath of deforestation, there follows increased levels of erosion and the loss of nutrient-rich top soils, as well as an elevated exposure of species to predation risks and climatic stressors. The deforestation that occurs for coca and marijuana plantations is frequently correlated with “slash and burn” agriculture, making the already destructive practices exponentially more problematic. When trees are felled during a forest clear-cut, not only are they unable to continue sequestering carbon, but the carbon that they have accumulated for decades is then also released into the atmosphere when the trees are incinerated. The production of illicit drugs therefore has an effect beyond the ecosystem level, as plantation efforts further complicate the impacts of greenhouse gasses and climate change. While an in-depth and detailed description is not given here, it is obvious that the loss of old growth forests is a serious risk to the biodiversity and climatic conditions of the world. Efforts by federal agencies, like the Drug Enforcement Administration,cannabis hydroponic set up up to this point, have been focused mainly on stopping the importation, smuggling, sale, and consumption of illicit drugs. This reactive approach of enforcement occurs post drug production, after significant environmental damage has been incurred.

It is necessary for a strategic switch to a more preventive, environmentally-focused approach, that is directed at the public, consumer bases, and law-makers, and focuses on stopping production in its initial stages. By engaging these focal groups, enforcement efforts can rally support from environmental agencies, non-government organizations, and nature advocates. Seeing the strong influence of the widespread “green” and environmental movements, it seems reasonable that an appeal to the ethos of nature may be a valid alternative to the outdated approach of drugs as a “detriment to society”. With the economic support and coordination of intellectual and technological resources, enforcement and environmental agencies, as well as nature advocates, can work in tandem to streamline their preventive efforts aimed at stopping environmentally destructive production processes.The primary solution required for successfully resolving the global and multifaceted issue of drug production, is to improve upon international and intrastate agency cooperation. The concept presented here is not intricate. Simply stated, while limited cooperation does exist between countries, agencies, and via international organizations, like the United Nations, the amount of integration required to accurately address this issue is currently insufficient. Permanent integration is required between these organizations. The current cooperative efforts focused on specific temporal operations are not enough to stem this profound issue that has continually persisted for decades. Intrastate agencies such as the United States Forest Service, Federal Bureau of Investigation, Bureau of Alcohol, Tobacco, Firearms, and Explosives, Central Intelligence Agency, and Drug Enforcement Administration need to fully incorporate their efforts in regards to domestic and international drug control. Internationally, these domestic agencies, led by the initiative of the Executive Branch and the United States Senate, need to establish close links with their counterparts in major cocaine trafficking and growth countries such as Bolivia, Colombia, and Peru. An example of the success that can stem from intimate cooperation between nations is highlighted by the combined efforts of the United States and Colombia in “Plan Colombia”.

A highlight of the cooperation occurred from 2009-2010, when the operation, which closely intertwined multiple agencies and resources of both nations, was able to remove 16,000 hectares of coca plantations, the equivalent of 14% of total Colombian cultivation.While federal outreach programs, such as the Office of National Drug Control Policy’s “Above the Influence” campaign, have addressed marijuana and drug usage in the past through commercial advertisements, the approach used has long been outdated and in need of revision. While the Office of National Drug Control Policy is no longer in oversight of “Above the Influence”, future attempts by the U.S. government or non-government organizations will require an adjustment of focus.Additionally, a 2013 study of marijuana consumption revealed there to be approximately20 million frequent users of cannabis in the U.S.Anti-drug advertisements need to redress their approach by combining traditional health issues with the impacts of environmental destruction that results from cannabis and cocaine production. These campaigns also must make an overt appeal to drug consumers, indicating how they are personally contributing to ecosystem degradation by electing to use these recreational drugs, thus propelling the drug-trade. A new advertising approach focused on enlightening voting constituents and consumers about the environmental damages of marijuana and cocaine production may help reveal issues to the public that they were formerly unaware of, but have vested interests in. Public issue campaigns revealing the determinants that cannabis and cocaine bring to species and ecosystems may prove to be a more substantial deterrent to consumers than the traditional appeals advocating that one should avoid drugs because they are “bad, illegal, and dangerous for your health.” Even if these campaigns are not guaranteed to be effective at declining the consumer base, by exposing to the public and nature advocates the severe impacts brought about by cannabis and cocaine production, an avenue is opened for voters to channel their concerns and appeal to their legislators. State and Federal Congressional members not only have an incentive to follow their constituents’ will, but will also have the power to enact meaningful legal change. Petitions and appeals to legislative bodies such as the Energy and Natural Resource Committee, Caucus on International Narcotics Control, Foreign Affairs Committee, Committee on Homeland Security and Governmental Affairs, Environmental and Public Works Committee, Agriculture, Nutrition, and Forestry Committee will help address issues of cooperation, enforcement, prevention, and regulation.

By appealing to the environmental issues of drug production, enforcement agencies can expand their targeted audience and accrue a wider base of support, thus improving their ability to resolve the multifaceted concerns of cannabis and cocaine production. The main purpose of this paper is to enlighten readers about the non-transparent issues of environmental damage resulting from the drug trade of cocaine and cannabis, and to encourage the integration and cooperation of concerned groups. Provided below are some feasible strategies that could possibly be invoked in future efforts. One of the most practical solutions available would involve the implementation of stricter regulations and enforcement methods for existing and proposed legalized marijuana plantations and facilities. Specifically, there needs to be a detailed review and inspection of greenhouse gas emissions, as well as, the use and disposal of fertilizers and pesticides by certified growers. As the United States continues to expand the number of states that accept the usage of medicinal and recreational marijuana, there needs to be an adaptation of “environmentally friendly” methods of growing, especially in regards to pesticide usage. Whether through solar energy or direct sunlight and natural fertilizers, if legislators are willing to accept the legalized consumption of marijuana in their states they need to also enforce its environmental impacts as well. In regards to the illegal cultivation of cocaine and cannabis, it is imperative that plantation detection and removal methods continue to improve via the implementation of the most advanced technology available. Through the aforementioned incorporation of environmental and enforcement objectives, the overall amount of funding allocated towards preventative enforcement measures will increase. This increase in funds, whether from legislatures expanding budgets, or from private donors and interest groups, will expand the array of options available for developing more economically efficient, and environmentally sound, detection and removal methods. The development of more numerous and effective aerial detection devices, whether in the form of manned or unmanned aircraft, provides a rational solution geared towards monitoring remote regions and identifying where clear-cutting and plantation is occurring, allowing for termination during the initial stages of production. A notable success of aerial detection, and spraying, of coca crops occurred in the combined U.S. and Colombian operation “Plan Colombia”, hydroponic system for cannabis where sustained aerial operations were “credited” with the operation’s successful removal of 16,000 hectares of Colombian coca plantations.However, it is also imperative that there continues to be an evolution of the chemical compounds and pesticides designed to thoroughly exterminate illicit drug crops. Compounds used in the eradication of plantations will continue to have an antithetical effect if they do not simultaneously leave surrounding wildlife, humans, and vegetation unharmed. Finally, by integrating interest groups and concerned citizens into removal processes, governmental organizations can acquire the man-power required to properly dispose of the materials and waste accumulated on cleared plantations; a task typically undermanned and poorly executed. Regarding the societies of nations affected by illicit drug trading, there exists a necessity for the rebuilding of society and reintegration of civilians. Even if crop production is significantly curtailed, without a successful rebuilding process, societies will face issues of adjustment towards legal agricultural, and possibly risk a reversion to the now normalized practices of illicit drug cultivation.

A prime example of what this process entails is provided by Colombia’s “National Consolidation Plan,” which is working to involve and reintegrate Colombian citizens who have been forced into the drug trade, whether out of necessity or violence. With the assistance of the United States, the Colombian government has started eradicating drug crops and subsequently loosening the grips of rebel groups and narcotic organizations, like the Revolutionary Armed Forces of Colombia People’s Army. As these organizations lose their drug supplies, which compose the majority of their incomes, they also lose their ability to exact a stranglehold over local populations. However, since these citizens have been adjusted to violence and forced into illegal methods of raising revenues, such as cultivating coca crops, they require assistance to be reintegrated into society and in reverting back to traditional forms of agriculture.Without demilitarization and reintegration, not only will citizens be unable to confirm to, and thrive in, a legalized society, but many of the former large cartel operations will likely end up splintering into smaller local operation, continuing environmental and societal degradation. To help prevent this type of situation from occurring, the United States Agency for International Development and the Colombian government have worked to implement “livelihood projects” that go beyond illicit crop eradication and include “enterprise development, natural resource protection, institutional strengthening, and promoting access to markets.”Both nations have also worked to introduce drug prevention programs throughout the nation and to reform and improve the legal and judicial systems.Despite the health risks and societal costs of cigarette smoking, the prevalence of smoking in the USA remains high at ∼19 % . Roughly 44 % of cigarettes are used by smokers with substance abuse/dependence and/or mental illness , and people with almost all substance abuse and mental illness diagnoses have elevated rates of cigarette smoking . Cigarette smokers have elevated rates of both caffeine and marijuana use. Roughly half of smokers drink coffee and report drinking almost twice as much coffee per day as nonsmokers . Similarly, among smokers, 57.9 % have ever used marijuana, and smokers are about 8 times more likely than non-smokers to have a marijuana use disorder , with cigarette smoking and marijuana use being associated even after controlling for potential confounding variables, such as depression, alcohol use, and stressful life events . Given the high comorbidity of smoking and both caffeine and marijuana use, it is important to better understand biological factors that may be associated with these co-occurrences. One of the most well-established effects of chronic cigarette smoking on the human brain is widespread upregulation of α4β2* nicotinic acetylcholine receptors . Recent studies using single-photon emission computed tomography and positron emission tomography have consistently demonstrated significant upregulation of these receptors in smokers compared to nonsmokers.

If the dispensary had any online activity within the past month , it would be considered active1 . After removing inactive businesses, businesses not selling marijuana, and businesses without storefronts during the verification procedure, the 2,121 unique records were reduced to 826 businesses . These 826 dispensaries constituted the call-verified, combined database of active brick-and-mortar dispensaries in California. Validity statistics, including sensitivity, specificity, positive predictive value , and negative predictive value were computed for each of the four secondary data sources when applicable. Definitions and calculations were described in Technical Note S1. To compute validity statistics, a gold standard must be defined that can identify the “true positive” and the “true negative”. Field census is typically considered the gold standard in retail outlet research. However, it is infeasible in this study due to budget and time constraints for a statewide census. Two gold standards were adopted alternatively to answer the two research questions. To answer the first question regarding the validity of online crowd sourcing platforms in enumerating licensed brick-and-mortar marijuana dispensaries, the first gold standard was whether a record was listed in the BCC state licensing directory . To answer the second question regarding the validity of state licensing directory and online crowd sourcing platforms in enumerating active brick-and-mortar marijuana dispensaries, the second gold standard was whether a record was included in the call-verified, combined database of active dispensaries . We must also define a test that can identify the “positive test” and the “negative test” in validity statistics calculations. Two tests were conducted. The first test was whether a record was present in a given data source after online data cleaning . We used this test to examine the validity of using a single data source with simple online data cleaning for dispensary identification,vertical farming supplies an approach requiring moderate resources.

The second test was whether a record passed call verification; in other words, whether the record was verified to be an active brick-and-mortar dispensary . We used this test to examine the validity of using a single data source with simple online data cleaning plus call verification for dispensary identification, an approach requiring much more resources. To illustrate these validity statistics in the context of this study, we provide an example below . In this example, the data source of interest is Weed maps, the gold standard is whether a record on Weed maps was present in the BCC state licensing directory, and the test is whether a record was present on Weed maps after online data cleaning. Sensitivity measures the probability of a record present on Weed maps conditional on the record being included in the BCC directory, calculated as the number of records that were present on both Weed maps and the BCC directory divided by the number of records present on the BCC directory. Specificity measures the probability of a record absent on Weed maps conditional on the record being excluded from the BCC directory, calculated as the number of records that were neither present on Weed maps nor present on the BCC directory divided by the number of records excluded from the BCC directory. PPV measures the probability of a record included in the BCC directory conditional on the record being present on Weed maps, calculated as the number of records that were present on both Weed maps and the BCC directory divided by the number of records present on Weed maps. NPV measures the probability of a record excluded from the BCC directory conditional on the record being absent on Weed maps, calculated as the number of records that were neither present on Weed maps nor present on the BCC directory divided by the number of records being absent on Weed maps. You will notice that specificity and NPV cannot be calculated in this example, because we were not able to identify a “true negative”, a record that was excluded from Weed maps and also absent in the BCC directory. In fact, not all validity statistics were applicable to a combination of a gold standard and a test with the current study design . Following tobacco outlet research , we considered validity statistics 0-0.2 to be poor, 0.21-0.4 to be fair, 0.41-0.6 to be moderate, 0.61-0.8 to be good, and 0.81-1.0 to be very good. R Version 3.5.3 was used to calculate 95% confidence intervals for all the validity statistics. We computed overall statistics as well as the statistics by dispensary category and county population size . Locations of call-verified active brick-and-mortar dispensaries in California were mapped with ArcGIS Version 10.5.

A total of 2,121 business records were combined from BCC and the three online crowd sourcing platforms after online data cleaning. BCC, Weed maps, Leafly, and Yelp had 630, 811, 535, and 1,468 records included in the combined database, respectively. The overlaps across the data sources were presented in Figure S1. Only 240 records were present in all four data sources. Following call verification, the 2,121 records were reduced to 826, which were confirmed to be active brick-and-mortar dispensaries. Among the 1,295 records removed during call verification, 56.0% were closed, 4.2% were not open yet, 38.0% were not selling marijuana, and 1.8% had no storefronts . BCC, Weed maps, Leafly, and Yelp had 486, 659, 459, and 471 records included in these 826 verified dispensaries, respectively. The overlaps across the data sources were presented in Figure S2. The 826 records included 77 recreational-only, 65 medical-only, and 684 recreational & medical dispensaries.Table 1 reports validity statistics using the BCC licensing directory as the gold standard. When the test was whether being present on each online crowd sourcing platform after online data cleaning, Leafly had good sensitivity and Weed maps and Yelp had moderate sensitivity . It indicated that 70% of the BCC licensing directory could be found on Leafly. Leafly also had very good PPV , yet Yelp’s PPV was only fair . It indicated that 83% of Leafly records were included in the BCC licensing directory. When the test was whether passing call verification, Leafly still had the highest sensitivity and PPV , and Yelp had the highest specificity and NPV . It indicated that, call-verified Leafly records performed the best for identifying truly licensed dispensaries and call-verified Yelp records performed the best for identifying truly unlicensed dispensaries in this scenario. Table 2 reports validity statistics using the call-verified, combined database as the gold standard. When the test was whether being present in each data source after online data cleaning, Weed maps had the highest sensitivity and BCC, Leafly, and Yelp all had moderate level of sensitivity ranging from .56 to .59. It indicated that 80% of the call-verified, combined database of active dispensaries could be found on Weed maps. Leafly and Weed maps had very good PPV , and Yelp’s PPV was only fair . It indicated that 86% of Leafly records were included in the call-verified, combined database of active dispensaries. When the test was whether passing call verification, sensitivity statistics remained the same as when the test was whether being present in each data source. This was because call-verified businesses in each data source were a subset of the businesses included in each data source before call verification, such that the numerators and denominators for sensitivity calculation remained the same. Yelp had the highest NPV and Leafly had the lowest NPV . It indicated that call-verified Yelp records performed the best for identifying truly not active brick-and-mortar dispensaries.Table 3 reports the agreement between BCC, online crowd sourcing platforms, and call verification in terms of the category of the 630 licensed dispensaries.

Approximately 25% of the licensed dispensaries on Weed maps and 29% of the licensed dispensaries on Leafly posted their category that disagreed with what was approved in the BCC license. Approximately 12% of the call-verified, licensed dispensaries stated their category in call verification that disagreed with what was approved in the BCC license. Most of the businesses that stated an unapproved category on online crowd sourcing platforms and/or in call verification claimed themselves to be recreational & medical when they were only licensed for recreational-only or medical-only. Table S3 quantifies category-specific validity statistics when the gold standard was whether being present in the BCC licensing directory. Leafly had the highest sensitivity in recreational-only and recreational & medical categories and Weed maps had the highest sensitivity in medical-only category,cannabis indoor greenhouse regardless of the definition of a test. Table S4 quantifies category-specific validity statistics when the gold standard was whether being present in the call verified, combined database. When the test was whether being present in each data source after online data cleaning, Weed maps had the highest sensitivity in identifying recreational-only and medical-only dispensaries, yet BCC had the highest sensitivity in identifying recreational & medical dispensaries. When the test was whether passing call verification, Weed maps overall had the highest sensitivity in all three categories. In 2019, California had 16 counties with a population size above one million and 42 counties with a population size below one million. Table S5 reports validity statistics by county population size when the gold standard was whether being present in the BCC licensing directory. Leafly had the highest sensitivity regardless of test definition and county population size. Table S6 reports validity statistics by county population size when the gold standard was whether being present in the call-verified, combined database. Regardless of test definition, Weed maps had the highest sensitivity in more populated counties and BCC had the highest sensitivity in less populated counties. This study is the first to assess the validity of secondary data sources in identifying brick and-mortar marijuana dispensaries across a large state. We reported the validity of online crowd sourcing platforms in enumerating licensed dispensaries and the validity of state licensing directory and online crowd sourcing platforms in enumerating active dispensaries. Regarding the validity of using online crowd sourcing platforms in identifying the BCC licensing directory, all three online crowd sourcing platforms were able to include over 50% records in the BCC directory, with Leafly containing the largest number of licensed dispensaries . These findings suggested that the online crowd sourcing platforms could serve as a reasonable proxy for the licensing directory. It evidences the validity for many existing and future studies to utilize online crowd sourcing platforms for dispensary identification, especially if a licensing system is not open to the public or is updated infrequently.

It should be noted, however, that the dispensary category registered in the BCC directory may be mismatched with the “de facto” category in which dispensaries operated. Over 25% licensed dispensaries on online crowd sourcing platforms posted their category that disagreed with the BCC license and over 10% call-verified, licensed dispensaries stated their category in call verification that disagreed with the BCC license. Particularly, most of such dispensaries claimed themselves to be recreational & medical while they were only licensed for recreational only or medical only. Such disagreement might be intentionally used as a means of attracting customers or be reflective of how dispensaries operate in practice. Regarding the validity of using the state licensing directory in identifying active brick and-mortar dispensaries, over 20% licensed dispensaries did not pass call verification. This indicated that business licenses may not accurately represent businesses’ operation status in reality. For instance, a business may have been closed before its license is expired and a business may not be open yet even though its license has been approved. In the final 826 call-verified dispensaries, 58.8% were included in the BCC licensing directory. This indicated that the BCC directory failed to capture unlicensed dispensaries, which accounted for over 40% of the total active dispensaries in California. Solely relying on a state licensing directory would overestimate active, licensed dispensaries whereby overlook active, unlicensed dispensaries. Regarding the validity of using online crowd sourcing platforms in identifying active brick-and-mortar dispensaries, Weed maps had a nearly very good sensitivity; it contributed 80% of the records in the final call-verified, combined database. It had the highest sensitivity in identifying recreational-only and medical-only dispensaries. It was also the most sensitive database in identifying dispensaries in more populated counties, which were mostly urban areas. The high concentration of dispensaries and intense competition in urban areas may motivate more businesses to promote themselves on this highly visible and popular platform . Leafly had the lowest sensitivity in identifying active dispensaries. It also had the lowest sensitivity in identifying all three dispensary categories. It is likely because the costs of advertising on Leafly were substantially higher than other online crowd sourcing platforms specialized in marijuana .