Cannabis Grow – Page 44 – How To Grow Cannabis

Descriptive statistics for covariates were calculated by AUD/remission status

The bivariate association of each covariate with AUD/remission status was tested using multi-nomial regression analysis with persistent AUD as the reference category. Concordance rates for AUD/remission status in related and unrelated pairs were calculated. Relatives’ AUD/remission status was the dependent variable in a multi-variable multinomial logistic regression, with relatives’ persistent AUD as the reference category and non-abstinent and abstinent remission as the two outcome categories. The primary independent variables were proband non-abstinent and abstinent remission; their association with relatives’ remission status was adjusted for the covariates listed above. The interactions of proband non-abstinent and abstinent remission with the dummy variable representing related pairs were tested one at a time in the fully adjusted regression to determine whether the association of probands’ AUD/remission status with relatives’ AUD/remission status varied in related and unrelated pairs. The final regression was calculated separately in related and unrelated pairs. The Huber–White robust variance estimator was used to adjust for the clustering of family data. Data sets and variables were created using SAS statistical software, version 9.2. Analyses were performed using Stata Statistical Software, version 14.2.This study explicitly modeled abstinent and non-abstinent remission in probands who were recruited from AUD treatment programs and in their first-degree family members with life-time AUDs to test for familial associations of remission in high-risk families and to define a phenotype which can be used to explore associations of remission with potentially heritable characteristics. Results showed that individuals who were related to an abstinent proband were more than three times as likely to be abstinent themselves, compared to individuals related to a proband with persistent AUD; this association was not significant in unrelated pairs. The significant association of probands’ with relatives’ abstinent remission in related but not in unrelated proband-relative pairs suggests there are familial influences on abstinent remission which may be due to genetic or familial environmental factors.

The familial association of abstinent remission in this sample selected for high-risk for AUDs has not been observed previously. The association of abstinence in one family member with abstinence in another stands in contrast to a host of null findings regarding familial influences on remission from other studies in population-based,vertical farming tower high-risk and clinical samples using a variety of definitions of remission. The current analyses used an explicit abstinent and non-abstinent remission phenotype, distinct from AUDs and consistent with the idea that the distribution of risks for development of, and for remission from, AUDs may not lie on the same continuum. Our results suggest that there may be genetic or familial environmental influences on abstinent remission and demonstrate that departing from the more common risk factor-to-remission comparisons within families may indeed prove useful. When remission is the target phenotype, remission in all family members should be measured explicitly, rather than measuring it as an outcome only in target subjects but not in their relatives. This will facilitate the examination of potentially heritable characteristics underpinning abstinent outcomes, such as social responsiveness, that may increase the likelihood of remission, as well as the investigation of family environments associated with remission from AUDs. Much more work will need to be conducted to identify heritable traits that may be related to abstinent remission and to probe for mediators and moderators of their effect. In addition to potentially heritable effects on abstinent remission, another explanation for the current findings might rest with a social contagion model, or the spread of behavior within a family due to social proximity. Analysis of large social networks from a population-based study indicated that both heavy drinking and abstinence clustered in networks, and also that the heavy drinking or abstinence of relatives and friends at one time-point were associated with changes in the subject’s alcohol consumption, to heavier drinking or abstinence, at a subsequent time-point. The same may be true within families affected by severe AUDs, where abstinence in one person may influence another family member with an AUD to try to quit drinking. This possibility is consistent with evidence that abstinence is the most stable form of remission among individuals with severe AUDs. If older family members with life-time AUD are abstinent as younger family members are developing alcohol problems, it is possible that younger members, if they recognize severe problems in themselves, may look to older members for direction or example, or that older members may recognize problems in younger members and intervene.

In fact, analysis of twin data showed that the variance associated with treatment-seeking for alcohol problems was accounted for primarily by familial influences, with 41% of the variance due to genetics, 40% due to shared environment, and just 19% to unique environment. In the current study, all probands had by definition been treated, which precluded examination of familial associations for treatment-seeking; however, abstinent relatives had the highest rates of treatment seeking in the sample, suggesting an association of relatives’ with probands’ treatment-seeking. More than 40% of probands and relatives were remitted in this high-risk sample, with abstinence the most common type of remission in probands and abstinent and non abstinent remission equally common in relatives. An earlier study in the COGA sample found that more than 50% of all subjects with life-time alcohol dependence reported periods of abstinence lasting 3 months or more, with 16.1% reporting abstinence of 5 or more years. Similar to the relatives in the current study, abstainers were older than individuals who never abstained, had a greater number of life-time symptoms and were more likely to have sought formal treatment and to have attended self-help groups. Other sampling frames also show similarities to the current data. Abstinent individuals with life-time AUD from population-based data had more AUD symptoms than remitted non-abstinent individuals. In a national sample of individuals self-identified as ‘in recovery’, abstainers compared to non-abstainers were older, more likely to have received professional treatment and to have attended self-help meetings, and had significantly more life-time alcohol dependence symptoms. These similarities across a range of samples suggest that individuals who become abstinent, regardless of sampling frame, represent a severe end of the AUD continuum. In the current study, abstinence may represent a common end-point for individuals with severe AUD. It is possible that non-abstinent remitters will become abstinent for a period, or periods, of time. Given that nearly half of abstinent relatives in the current study had been remitted for 10 or more years, abstinence may indeed represent an end-point for subjects who remit from severe AUDs.Despite efforts to improve mental health over the last 60 years, suicide remains a critical public health concern worldwide.Suicide was the second leading cause of death globally in 2012 among individuals aged 15–29years,with an estimated 80%–90% of suicide deaths attributable to mental health or substance use disorders.

Significant gaps remain in empirical research examining suicidality among marginalised populations. Marginalised women, such as sex workers who are street involved or use drugs, experience disproportionately high levels of social and health-related risks and harms, including stigma, discrimination and violence as a result of dynamic structural drivers including poverty, criminalisation and racism. While sex workers are a diverse population working from indoor in-call and out-call venues to street-based settings, previous studies high light substantial unmet mental health needs of more marginalised and street-involved sex workers. Studies among street-based sex workers and those who use drugs underscore the associations of social exclusion,vertical indoor farming depression and post-traumatic stress disorder with suicidality.Research demonstrates greater risk for suicidality among those with a history of trauma and among street-involved sex workers who report histor ical experiences of violence and childhood abuse.Furthermore, indigenous women are vastly over-repre sented among street-based sex workers in North America and face devastating and multi-generational effects of trauma and socioeconomic dislocation as a result of colonialism, racialised policies and displacement from land and home communities.Various biological, interpersonal and socio-structural factors contribute to our understanding of suicidal behaviours.While evidence has demonstrated that some forms of cognitive behavioural therapy and pharmacological interventions may reduce suicidality, the literature is hampered by publication bias and significant heterogeneity of strategies and outcome measures.Due to ethical challenges and limitations to studying suicide and its proxies , there remains a paucity of evidence from randomised controlled trials to support the efficacy of prevention interventions.Researchers have largely focused on examining suicidality outcomes , which may not be fully generalisable to understanding suicide or accurately evaluating treatment approaches.Furthermore, stigma continues to hinder research and reporting of suicidality.There remains an urgency to better understand pathways to suicidality, with literature highlighting the need for innovative psycho logical and psychosocial treatments and tailored inter vention approaches for key marginalised populations.Given the complex aetiological pathways to suicide and limited effectiveness of well-established evidence-based interventions to reduce the burden of suicidality, the US National Institute of Mental Health has called for innovative research on suicide prevention and treatment for suicidality.A number of psychedelic drug therapies are being revisited following a 40-year hiatus in research into their potential for the treatment of depression, anxiety, PTSD, eating disorders and addiction.Psychedelic drugs include the classic serotonergic psychedelics or ‘hallucinogens’ lysergic acid diethylamide , psilocybin, dimeth yltryptamine and mescaline, as well as the ‘enactogen’ or ‘empathogen’ methylenedioxymethamphetamine ,all of which are being investigated in clinical/preclinical studies for their neuropharmacological functions and potential as adjuncts to psychotherapy.While renewed interest in psychedelic medicine is challenged by various funding and methodological and legal impediments, the emerging evidence indicating improved outcomes for some individuals suffering from mental health and addiction issues has generated new scientific inquiry and an imposing obligation to advance this research.Recent observational studies in the USA demonstrate significant associations between life time psychedelic use and reduced recidivism and intimate partner violence among populations of prison inmates and reduced psychological distress and suicidality among the general adult population.Despite the multifaceted structural and social inequities that shape poor mental health burden among marginalised and street-involved sex workers, there remains a paucity of data on suicide rates and research that system atically examines factors that potentiate or mitigate suicidality among sex workers, particularly in the global north. Some evidence suggests that psychedelic drug use may be protective with regard to suicidality and is associated with significant improvements in psychological well-being and reductions in depression and anxiety in clinical settings,yet existent research is characterised by large gaps. Given the urgency of addressing and preventing suicide and calls for prioritising innovative interventions, this study aimed to longitudinally investigate whether life time psychedelic drug use is associated with a reduced incidence of suicidality among a community-based prospective cohort of marginalised women. We postulated that psychedelic drug use would have an independent protective effect on suicidality over the study period.Data for this study were drawn from a large, community-based, prospective cohort of women sex workers initiated in 2010, known as An Evaluation of Sex Workers Health Access . Eligibility criteria for study participants included cisgender or transgender women, 14 years of age or older, who exchanged sex for money within the last 30 days. AESHA participants completed interviewer-administered questionnaires and HIV/sexually transmitted infection /hepatitis C virus serology testing at enrolment and biannually. Experiential staff are represented across interview, outreach and nursing teams, including coordinators with substantial community experience. Participants were recruited across Metro Vancouver using time–location sampling and community mapping strategies, with day and late-night outreach to outdoor sex work locations , indoor sex work venues and online. Weekly outreach by experiential staff is conducted to over 100 sex work venues by outreach/nursing teams operating a mobile van, with regular contact as well as encouraging drop-in to women only spaces at the research office, contributing to an annual retention rate of >90% for AESHA participants. The main interview questionnaire elicits responses related to sociodemographics , the work environment , client characteristics , intimate partners , trauma and violence and comprehensive injection and non-injection drug use patterns. The clinical questionnaire relates to overall physical, mental and emotional health, and HIV testing and treatment experiences to support education, referral and linkages with care. The research team works in close partnership with the affected community and a diversity of stakeholders and regularly engages in knowledge exchange efforts. AESHA is monitored by a Community Advisory Board of over 15 sex work, women’s health and HIV service agencies, as well as representatives from the health authority and policy experts, and holds ethical approval through Providence Health Care/University of British Columbia Research Ethics Board.

Youth with worse HIV disease severity are more likely to engage in substance use

A GWAS of opioid response in a Japanese sample found that the C allele of rs2952768, which is in an LD block with rs7591784 , was significantly associated with greater postoperative opioid analgesic requirements, as well as lower reward dependence in healthy volunteers, lower risk of polydrug use in volunteers with methamphetamine dependence, alcohol dependence, and eating disorders, and increased expression of CREB1 in human postmortem brains . The G allele in rs7591784 was associated in our study with lower pretreatment methamphetamine use and better treatment outcomes, both suggestive of less severe methamphetamine use disorder, and as rs7591784 and rs2952768 are strongly linked, our results provide support for the previous association between the C allele of rs2952768 and lower severity of methamphetamine use disorder observed in the Japanese GWAS. Whether rs7591784 directly effects CREB expression or function is not known, but our results and previous studies suggest that variability in CREB signaling and subsequent changes in methamphetamine-induced gene expression may influence clinical severity of methamphetamine use problems and success in quitting methamphetamine and that the CREB signaling pathway may be a target for the development of medications to treat methamphetamine use disorder. Phosphodiesterase inhibitors modulate signaling via the CREB pathway via increases in cAMP and ibudilast, a nonselective phosphodiesterase inhibitor,vertical farming company is in clinical development for methamphetamine use disorder . Previous GWAS found SNPs in CDH13 to be among the most significant SNPs associated with a diagnosis of methamphetamine dependence and with the subjective response to amphetamine among healthy volunteers . None of the SNPs related to CDH13 in our study were significantly associated with methamphetamine use frequency following Bonferroni correction. The lack of significant association in our study may be due to the different phenotypes examined in the previous GWAS compared to the current study that examined methamphetamine use frequency in a treatment-seeking sample or may be due to limited power to detect SNPs with small effect size in our small sample. Methamphetamine use frequency as well as results of our SNP analyses differed greatly between males and females.

None of the three SNPs that were nominally significant in males, including rs7591784, approached significance in females suggesting that although the female sample size was relatively small, the lack of significant associations for these SNPs in females is unlikely to be due to limited power in females alone. Previous studies in rodents have found sex differences in methamphetamine pharmacokinetics , methamphetamine-induced plasma corticosterone levels , methamphetamine-related neurotoxicity , and methamphetamine self-administration with female rats acquiring methamphetamine self-administration faster, self-administering more methamphetamine, and exhibiting higher rates of methamphetamine reinstatement than male rats . In humans, female methamphetamine users have a higher risk of Parkinson’s disease , greater reductions in hippocampal volume and higher prevalence of physiologic dependence symptoms compared to male methamphetamine users and these biological or other psycho social differences may have a greater influence on methamphetamine use frequency in females than the SNPs examined here. Interestingly, amphetamine-induced CREB-mediated transcription differs dramatically between male and female mice in the nucleus accumbens, ventral tegmental area, amygdala, and locus coeruleus with greater CREB-meditated gene transcription following amphetamine in females suggesting that the significant association between rs7591784 and methamphetamine-related phenotypes observed in our study in males but not females may be due to underlying sexual dimorphism in the CREB signaling pathway. The one SNP that was nominally associated with methamphetamine use frequency assuming an additive model in females, rs163030, was associated with caudate volume in a GWAS and rs163030 may influence methamphetamine use frequency in females by altering structure or functioning of the caudate, a brain region implicated in impulsivity and methamphetamine addiction . Additional studies investigating sex differences in the biological and social influences on methamphetamine addiction are warranted. This study has several limitations. The sample size is small and the power to detect an association between a candidate SNP and methamphetamine use frequency with a small effect size is limited.

As a result the study is subject to false negative results. Also, numerous findings from candidate gene studies have failed to replicate and results from this study are preliminary and require replication in an independent sample prior to making any conclusions. To mitigate this risk, we emphasized selection of candidate SNPs that had previously been associated with methamphetamine-relevant phenotypes in GWAS. Our study did not genotype rs2709386, which was most strongly associated with opioid sensitivity in the previous Japanese GWAS, and although rs2709386 and rs7591784 are highly linked, future studies are necessary to determine which SNP is more strongly associated with methamphetamine use and treatment outcomes. Lastly, the sample was drawn participants of several methamphetamine pharmacotherapy clinical trials and results from a treatment-seeking sample may not be generalizable to methamphetamine uses as a whole. In summary, we found an association between rs7591784 near CREB1 and pretreatment methamphetamine use, an important indicator of disease severity and predictor of subsequent treatment outcomes, as well as methamphetamine use during treatment independent of pretreatment methamphetamine use in males but not females. Replication of this result in independent samples is necessary but our results combined with previous research suggest that variability in CREB signaling may influence severity of methamphetamine use disorder as well as success in quitting methamphetamine with outpatient treatment and that medications targeting the CREB pathway such as the non selective phosphodiesterase inhibitor ibudilast may be effective treatments for methamphetamine use disorder. Future studies should examine the role of CREB-related polymorphisms and the associated epigenetic changes on response to treatment for methamphetamine use disorder and whether these biological influences on methamphetamine use differ between males and females.Worldwide, it is estimated that there are over three million youth living with HIV globally, with the majority of youth acquiring HIV perinatally . Youth with perinatally-acquired HIV may show cognitive deficits as well as developmental delay even among those with reconstituted immunologic and virologic status, making PHIV a common infectious cause of perinatally-acquired developmental disability globally . Combination antiretroviral therapy for children with PHIV has resulted in substantial improvements in health with survival beyond childhood and reductions in morbidity and mortality .

Early HIV infection, immune activation, and viral persistence during a critical period of development may be especially detrimental to developing brains in youth with PHIV . Brain development is an extended process that begins prenatally and continues throughout the first two decades of life,indoor vertical farming with increased sensitivity to experience during the first year of life in pathways responsible for sensory, language and higher order cognitive development . Adolescence is also a crucial developmental window marked by a period of rapid brain maturation via synaptic pruning and myelination. White matter volume increases while grey matter volume decreases , with parietal grey matter reduction prominent before adolescence, followed by dorsal, mesial, and orbital frontal grey matter reduction during and after adolescence . Studies including neuroimaging combined with cognitive evaluation allow for an in vivo characterization of how HIV and cART may mediate brain development . In adults, studies of post-mortem tissue and in vivo neuroimaging combined with cognitive testing have revealed atrophy in cortical and subcortical structures that is related to HIV severity and cognitive performance . Still, the effects of early HIV infection on the underlying brain in adolescents with PHIV have not been well-characterized . Adolescent brains are also subject to environmental influences, including substance use. Neuroimaging and neuropsychological studies in youth who use substances have found structural brain abnormalities, including grey matter volume reductions, as well as cognitive dysfunction .Thus, to carefully study effects of PHIV on youth treated with cART, it is important to account for substance use. We present one of the first studies to investigate the impact of HIV severity and coincident substance use on regional and total brain volumes and their association with cognition in PHIV youth. Other studies on grey matter volumes in PHIV do not focus on regional grey matter or substance use or are in PHIV populations with varying clinical characteristics from our cohort . Since PHIV youth often exhibit global cognitive functioning, working memory, and processing speed deficits , we hypothesized that frontal and parietal regions, regions important for higher-order cognitive functioning, would show volume reduction as compared to typically-developing, HIV unexposed and uninfected youth and smaller volumes would be associated with worse cognitive performance. We also hypothesized that HIV disease severity and substance use would be associated with reduced cortical grey matter volumes among adolescents with PHIV. 40 PHIV youth from a single site participating in the Adolescent Master Protocol study of the NIH Pediatric HIV/ AIDS Cohort Study network were recruited. Institutional review board approvals were obtained. Parents, legal guardians, or youth aged 18 years or older provided written informed consent; minors provided written assent. A control group of 334 typically developing, HIV-unexposed and uninfected youth was generated using frequency-matching for sex and age from the Pediatric Imaging, Neurocognition, and Genetics study Magnetic Resonance Imaging database from five sites . Of note, information regarding alcohol and drug use was not collected in the PING cohort. Total grey matter and 10 a priori cortical ROIs volumes were identified as primary outcomes. The remaining 74 ROIs were analyzed as secondary outcomes. Descriptive statistics and graphical methods were used to confirm the normality assumption for volume measures. Volumes were compared between PHIV youth and HIV-unexposed and uninfected youth using linear regression models. Results were reported for models with and without adjusting for age at scan, sex, race, caregiver education attainment, annual household income , and intracranial volume. Caregiver education was classified as high school education and below vs. greater than high school. Annual household income was classified as $30,000 and below vs. greater than $30,000. Percent change in volume as compared to HIV-unexposed and uninfected youth was calculated based on adjusted means.For evaluating associations of volume with cognitive functioning, we considered the 10 primary ROIs as well as total grey matter. Linear regression was used to evaluate associations between ROI volume as well as total grey matter and working memory, processing speed, and cognitive proficiency indices, adjusting for sex and age at scan. Three sets of sensitivity analyses were conducted: 1) including substance use in models of associations of brain volumes with HIV measures for grey matter volumes; 2) adjusting for total grey matter volume in models evaluating associations between brain volumes and substance use for secondary ROIs; 3) including substance use, sex, and age at scan in analyses evaluating associations of brain volumes with cognition. As described previously for other neuroimaging studies in this cohort , for PHIV youth, the mean interval between scanning and assessment of recent disease markers was 1.8 months with 83% of recent VL measures within three months prior to scanning. Mean interval between scanning and cognitive testing was 3.8 months . All but 2 participants completed cognitive assessments within 1 year of neuroimaging, with 31 within 3 months. One fourth of PHIV youth reported tobacco use, 35% alcohol, and 35% marijuana use. 13 youth reported both alcohol and marijuana use, and 8 youth reported use of tobacco, alcohol, and marijuana. Due to small numbers reporting illicit drug use beyond marijuana , this measure was not considered further in statistical analyses . There was no difference in substance use by race. Adjustment for substance use in models evaluating associations between primary ROI volumes and HIV disease severity as well as cognitive function did not alter findings. Similarly, adjustment for total grey matter volume in models evaluating associations of HIV severity and substance use measures with secondary ROI volumes did not alter findings, with no associations identified based on a FDR level of 0.10. Positive associations observed between primary brain volumes and cognitive function persisted after adjustment for substance use. Bilateral superior frontal volumes positively correlated with CPI, WMI, and PSI after adjustment for substance use.The current study is one of the first to examine relationships among PHIV infection, disease severity, and substance use on the brain grey matter and cognitive outcomes in PHIV youth. We found that PHIV youth had reduced total grey matter volume as well as reduced volumes in the rostral middle frontal, postcentral, precentral, and superior parietal gyri, compared to similarly-aged HIV-unexposed and uninfected youth. These patterns persisted after adjusting for sex, age at scan, race, caregiver education attainment, annual household income, and intracranial volume.

How Much Does Vertical Farming Cost

The cost of vertical farming can vary significantly depending on various factors such as the scale of the operation, the technology and equipment used, the location, and the crops being grown. Here are some key cost considerations in vertical farming:

Infrastructure and Facility Costs: The initial investment in setting up a vertical farm includes costs for acquiring or constructing a suitable facility, such as a building or warehouse. This may involve retrofitting the space with necessary systems like lighting, climate control, irrigation, and vertical growing structures. The costs can vary greatly based on the size and complexity of the operation.
Lighting Systems: Vertical farms typically use artificial lighting, such as LED grow lights, to provide the necessary light spectrum for plant growth. The cost of lighting systems depends on the size of the farm, the type and quality of lights used, and the specific lighting requirements of the crops being grown. LED lighting can be energy-efficient but may have a higher upfront cost compared to traditional lighting options.
Climate Control and HVAC: Vertical farms require precise control over temperature, humidity, and ventilation to create optimal growing conditions. The cost of climate control systems, including heating, ventilation, and air conditioning (HVAC), depends on the size of the facility and the level of automation and sophistication required.
Growing Systems and Equipment: Vertical farming often utilizes hydroponic or aeroponic systems to grow plants in a soilless environment. The cost of these systems varies based on the scale, complexity, and type of technology employed. This includes costs for nutrient delivery systems, pumps, sensors, automation equipment, and monitoring systems.
Operational Expenses: Ongoing operational expenses in vertical farming include costs for electricity, water, nutrients, labor, and crop inputs such as seeds or seedlings. These costs will depend on the size of the operation, the efficiency of the systems, and local utility rates.
Research and Development: Depending on the level of innovation and technology used, vertical farming may involve research and development costs for optimizing growing techniques, developing proprietary systems, or customizing technologies.

It is challenging to provide a specific cost estimate for vertical farming as it can vary widely depending on the factors mentioned above. Large-scale commercial vertical farms with advanced technology and automation can require significant upfront investment, while smaller-scale operations or vertical farming at home can be more affordable. Conducting a detailed feasibility study, considering the specific requirements of your operation, and consulting with experts in the field can help provide a more accurate cost estimation for your vertical farming project.

When Did Vertical Farming Start

Vertical farming as a concept has been around for several decades, but its modern form began to take shape in the early 2000s. The idea of growing plants vertically, stacked in layers or on vertical surfaces, emerged as a potential solution to address various challenges in agriculture, such as land scarcity, climate limitations, and food security concerns.

One of the pioneers in vertical farming is Dickson Despommier, a professor of environmental health sciences at Columbia University. Despommier popularized the concept of vertical farming in 1999 with his book “The Vertical Farm: Feeding the World in the 21st Century.” His book laid out the vision and potential benefits of growing crops in multilevel indoor environments using hydroponics or aeroponics.

Following Despommier’s work, vertical farming gained more attention and traction in the early 2000s. Companies and entrepreneurs began exploring and implementing vertical farming systems, experimenting with different technologies and designs to maximize productivity and efficiency. Advancements in LED lighting, hydroponic systems, and automation technologies also contributed to the growth and development of vertical farming.

Around the mid-2000s, the first commercial vertical farming ventures started to emerge. These early pioneers focused on leafy greens and herbs as the primary crops due to their fast growth and suitability for controlled indoor environments. As the industry evolved, vertical farming expanded to include a wider range of crops, including strawberries, tomatoes, peppers, and even vine crops like cucumbers.

Since then, vertical farming has continued to gain momentum globally. Numerous vertical farms have been established in various countries, utilizing innovative techniques and technologies to improve efficiency, sustainability, and crop quality. The industry is constantly evolving, with ongoing research and development driving advancements in vertical farming practices.

Overall, while the concept of growing plants vertically can be traced back further, the modern era of vertical farming began to take shape in the early 2000s with the work of Dickson Despommier and subsequent efforts by researchers, entrepreneurs, and companies to explore and develop this innovative farming approach.

How Long to Dry Cannabis on Drying Rack

The drying process for cannabis after harvest typically takes around 7 to 14 days, depending on various factors such as humidity, temperature, and the desired moisture content. When using a drying rack, here are some general guidelines to follow:

Hang the buds: After harvesting the cannabis plants, trim the excess leaves and branches, leaving only the buds. Hang the buds upside down on the drying rack. This allows for proper airflow around the buds.
Ideal environment: The drying environment should have a humidity level of around 45-55% and a temperature of approximately 20-24°C (68-75°F). These conditions help prevent mold or mildew growth and preserve the quality of the buds.
Check for dryness: During the drying process, regularly monitor the buds for moisture content. Gently squeeze a small bud between your fingers to assess its dryness. It should feel slightly dry and crispy on the outside but still have some moisture inside.
Slow drying: It’s important to dry the cannabis buds slowly to ensure proper curing and preservation of cannabinoids and terpenes. Rapid drying can result in a harsh taste and reduced potency. Avoid using excessive heat or forced air, as this can accelerate the drying process and negatively impact the final product.
Patience: Drying cannabis properly requires patience. It’s better to dry the buds slowly over a slightly longer period than to rush the process. This allows for a more controlled and even drying, resulting in better quality buds.
Final moisture content: The drying process is considered complete when the buds reach a moisture content of around 10-15%. To determine this, use a hygrometer or moisture meter to measure the moisture levels in the buds. Once the desired moisture content is reached, the buds are ready for the curing process.

Remember, these guidelines are general and can vary depending on the specific strain, environmental conditions, and personal preferences. It’s essential to closely monitor the drying process and make adjustments as needed to achieve the desired results. Properly dried and cured cannabis will have improved flavor, potency, and overall quality.

How to Set Up a Commercial Grow Room

Setting up a commercial grow room requires careful planning and consideration of various factors to ensure optimal conditions for plant growth. Here are the basic steps involved in setting up a commercial grow room:

Determine the Purpose: Decide what type of crops you plan to grow in your commercial grow room. Different plants have varying requirements, such as lighting, temperature, humidity, and ventilation. Understanding your specific crop needs will guide your decisions throughout the setup process.
Select a Suitable Location: Choose a location for your grow room that offers enough space for the desired number of plants and equipment. Consider factors such as access to electricity, water supply, and ventilation options. Ensure the space is clean and free from pests or contaminants.
Design the Layout: Plan the layout of your grow room to optimize space utilization and workflow efficiency. Divide the space into distinct areas for different growth stages, including propagation, vegetative growth, and flowering. Consider pathways, workstations, and storage areas for equipment and supplies.
Install Proper Lighting: Lighting is crucial for plant growth. Select the appropriate type of lighting system based on your crop’s requirements and budget. High-intensity discharge (HID) lights, such as metal halide (MH) and high-pressure sodium (HPS) lamps, or light-emitting diodes (LEDs) are commonly used in commercial grow rooms. Position the lights at the right distance and height above the plants to ensure even coverage.
Control Temperature and Humidity: Maintain proper temperature and humidity levels to create an optimal growing environment. Install heating, ventilation, and air conditioning (HVAC) systems to regulate temperature. Consider using dehumidifiers or humidifiers to control humidity levels. Monitor and adjust these parameters regularly to ensure they remain within the ideal range for your crop.
Provide Adequate Ventilation: Adequate airflow and ventilation help maintain fresh air supply and control temperature and humidity. Install exhaust fans to remove stale air and bring in fresh air. Use carbon filters to eliminate odors and air pollutants. Consider implementing a passive intake system or air circulation fans to maintain a consistent airflow within the grow room.
Set up Irrigation and Nutrient Delivery: Determine the most suitable irrigation system for your plants, such as drip irrigation, flood and drain systems, or aeroponics. Install timers and sensors to automate watering and nutrient delivery. Consider using a water filtration system to ensure the water source is clean and free from contaminants.
Ensure Proper Environmental Controls: Install environmental control systems to monitor and adjust various parameters in the grow room, such as temperature, humidity, CO2 levels, and lighting schedules. These systems can be automated and integrated with sensors and controllers to maintain optimal conditions.
Implement Security Measures: Protect your commercial grow room by implementing security measures. Install surveillance cameras, access control systems, and alarms to prevent unauthorized access and monitor the facility.
Test and Adjust: Before introducing plants, thoroughly test all systems, including lighting, irrigation, ventilation, and environmental controls. Monitor and adjust the various parameters as needed to create an ideal environment for your crops. Regularly inspect and maintain equipment to ensure optimal performance.

Remember to consult with experts, such as horticulturists, HVAC professionals, and electricians, to ensure compliance with local regulations and to address any specific requirements for your commercial grow room setup.

Do’s & Don’s for Your Grow Room

When setting up and maintaining a grow room, it’s important to follow certain guidelines to ensure optimal plant growth and avoid potential issues. Here are some do’s and don’ts for your grow room:

Do’s:

Do plan and design your grow room: Consider factors like space, lighting, ventilation, and access to water and electricity when setting up your grow room. Plan the layout efficiently to maximize space and create an optimal environment for your plants.
Do maintain cleanliness and hygiene: Keep your grow room clean and free from pests, pathogens, and debris. Regularly sanitize surfaces, tools, and equipment to prevent the spread of diseases and maintain a healthy growing environment.
Do provide proper ventilation: Install an effective ventilation system to ensure a continuous supply of fresh air and control temperature and humidity levels. Proper air exchange prevents the buildup of stale air, reduces the risk of mold and fungal growth, and promotes healthy plant growth.
Do control temperature and humidity: Monitor and maintain appropriate temperature and humidity levels for your specific plant species. Maintaining the ideal range helps prevent stress, diseases, and pests. Consider using fans, heaters, humidifiers, or dehumidifiers as needed.
Do provide proper lighting: Use suitable grow lights that provide the right spectrum and intensity for your plants’ growth stages. Ensure adequate coverage and adjust the light height as the plants grow to prevent light burn or light deficiency.
Do monitor and adjust nutrient levels: Regularly check the pH and nutrient levels in your hydroponic or soil-based system. Adjust the nutrient solution or soil amendments to maintain optimal levels for healthy plant growth.

Don’ts:

Don’t neglect pest and disease prevention: Implement preventive measures such as quarantining new plants,vertical grow system regularly inspecting plants for pests or signs of disease, and using organic or appropriate pest control methods if necessary. Act promptly to prevent pest and disease outbreaks from spreading.
Don’t overwater or underwater: Follow proper watering practices for your specific plant species and growing medium. Avoid overwatering, which can lead to root rot, or underwatering, which can cause stress and nutrient deficiencies.
Don’t overcrowd your plants: Give your plants enough space to grow and receive adequate light and airflow. Overcrowding can lead to poor air circulation, increased humidity, and the spread of diseases.
Don’t ignore plant training and pruning: Regularly train and prune your plants to encourage proper growth, airflow, and light penetration. Remove dead or damaged foliage to prevent the spread of diseases.
Don’t ignore maintenance and equipment checks: Regularly inspect and maintain your equipment, including lights, fans, timers, and irrigation systems. Replace worn-out parts, clean filters, and ensure everything is functioning properly.
Don’t neglect record-keeping: Keep a record of your plant’s growth, nutrient schedules, and any adjustments or treatments applied. This helps you track progress, identify patterns, and make informed decisions for future crops.

By following these do’s and don’ts, you can create a healthy and productive grow room that promotes optimal plant growth and maximizes your yields.

How Much Does A Vertical Farm Cost

The cost of a vertical farm can vary significantly depending on various factors such as the scale of the operation, the technology used, the location, and the specific requirements of the farm. Vertical farming systems involve the use of stacked layers or racks to maximize growing space and efficiency in an indoor environment. Here are some cost considerations:

Infrastructure: The cost of constructing or retrofitting a building for vertical farming can vary widely depending on the size and condition of the space. This includes expenses such as renovation, insulation, HVAC systems, lighting, plumbing, and electrical installations. Costs can range from tens of thousands to millions of dollars.
Equipment: The cost of equipment will depend on the complexity and scale of the vertical farm. This includes vertical farming systems (racks, shelves, or towers), lighting systems (LED or other artificial lighting), irrigation systems, climate control systems, nutrient delivery systems, automation and monitoring systems, and other specialized equipment. Costs for equipment can range from several thousand dollars to hundreds of thousands of dollars.
Growing medium and plants: The cost of growing media (such as hydroponic substrates) and plants will depend on the size of the farm and the type of crops being grown. Costs can vary based on the quantity and quality of materials and can range from a few hundred to several thousand dollars.
Operational costs: This includes ongoing expenses such as utilities (electricity, water), labor, seeds or seedlings, plant nutrients, pest control, maintenance and repairs, packaging materials, marketing, and administrative costs. Operational costs can vary significantly depending on the scale of the operation and the specific requirements of the crops being grown.
Research and development: Investing in research and development for optimizing production techniques, crop selection, and automation can be an additional cost for vertical farming projects.
Location and real estate: The cost of land or leasing a suitable location can vary significantly depending on the region and proximity to urban areas. Urban locations or repurposing existing structures may have higher real estate costs.

Given the wide range of variables, it is challenging to provide an exact cost for a vertical farm. Small-scale vertical farms can be established with a limited budget, while large-scale commercial operations can require significant investments. It is important to conduct a detailed feasibility study and cost analysis based on your specific project plans and local market conditions to get a more accurate estimation of the costs involved. Consulting with industry experts and professionals can also provide valuable insights into the cost considerations for vertical farming.

The DEH maintains a database of all county retailers that apply for food permits

When reported by adolescents, parental involvement in the adolescent’s school life and having a mother that yells at you were significant. Lack of parental involvement in school has been shown previously to be associated with adolescent behavior problems, and yelling is a well known family stressor and supports the finding reported here . Other significant variables were identical between the two models. That is, having a lot of close friends , and peers that model gateway drug use. The discrepancy between adolescent and parent reports is an innovative finding, and suggests that adolescents may provide a more accurate estimate of parenting practices than do parents. Other studies have shown that discrepancies in perceptions between adolescents and their parents may be negatively related to adolescent adjustment, including increased levels of conflict and stress within the family resulting in problem behaviors . Although this study’s objective was not to test how discrepancies predict problem behaviors in adolescents, adolescent perceptions should be considered with or preferentially over that of parent’s estimates of their parenting practices. To this end, Barnes and Farrell suggest that reliance on one respondent in the family represents a common methodological shortfall in many studies, especially when there is not perfect agreement between adolescent and parental perspectives. Findings reported here suggest that in addition to nonconcordance between parents and adolescents, there is a difference in the predictive value of these reports. In terms of impact on future studies, it should be noted that the true parenting behaviors in this study remain unknown. That is, both parent and adolescent reports are subject to error and may include biases. Pelegrina et al. state that adolescents assess certain family characteristics more negatively than their parents,plant grow trays whereas the parents’ self-reports tend to exaggerate certain dimensions, such as acceptance and discipline.

Future studies of the influence of parenting behaviors should incorporate more state of the art objective observational procedures to advance the current state of the science and determine the true parents’ parenting practices. To the extent that such procedures and innovations are incorporated, scientific understanding of parental influence on children will be clarified. The findings from this study should be interpreted in the context of the sample’s limitations. The study sample, though entirely Latino, was not population-based and only represented data from mothers. Whereas these findings may not be generalizable to the Latino population at large, they do speak to the need for the development of more refined measures of parenting practices. Nevertheless, the use of an entirely Latino sample of parents and adolescent dyads represents an important feature of this study, making it a valuable addition to extant literature. Adolescent gateway drug use continues to be a serious problem, contributing to immediate and long-term health consequences and costs to society . Decades of research on uptake of these substances have identified significant individual and social-level risk factors. However, ecological influences have only recently been explored. Recent advancements in software programs that use geographic information systems technology provide the necessary tools to conduct innovative exploratory analyses of ecological influences on gateway drug use. Recent reports regarding adolescent tobacco acquisition suggest that drug products are usually provided by three common sources: a stranger who buys them, family or friends who buy or give them, or a retailer who sells them . Some studies indicate it is easy for adolescents to buy both alcohol and tobacco . Indeed, the California Department of Public Health and Tobacco Control reports that one of the simplest ways for adolescents to buy tobacco is in their local neighborhood store. Data from the American Lung Association indicate that 33% to 50% of San Diego County retailers sell to adolescents . At least three issues should be considered with respect to retailer impact on substance use. First, adolescents are sensitive to cost, including the cost of time spent traveling to purchase products in other neighborhoods . They may also have unreliable transportation and limited spending money.

Frequent use or use of multiple substances may require more disposable income than would be true for youth who use drugs infrequently or only use one substance, e.g., alcohol. Youth who use drugs frequently or many different drugs may have less disposable income to invest and would be more sensitive to the distance from home to a retail business from which alcohol or tobacco could be purchased. Second, substances for first time use are usually not purchased by the adolescent experimenter, and instead may be provided at partiesor group gatherings. Users under these conditions may be less influenced by retailer proximity to their home. Third, retailer presence may provide a critical link to facilitate social processes in a given neighborhood. For example, modeling is a known risk factor for substance use, and seeing other adolescents purchase and use alcohol and tobacco may prompt consumption . The effects of retailer dispersion may be due to increased availability to substances, but it may also relate to increased opportunities for modeling, imitation by substance use, which can then be reinforced by peers. In their introduction of a community systems approach, Treno & Holder identify a limitation with group/individual-level prevention efforts. Mainly, such approaches are effective when the conditions that give rise to undesirable behavior lie solely within the target group, or individual, e.g., lack of knowledge. Indeed, education and awareness prevention efforts have historically been very popular . Nevertheless, efforts that focus entirely at such levels fail to demonstrate long-term results in circumstances where behavioral determinants extend beyond the individual, e.g., policies, alcohol and tobacco access, peer modeling, etc. Uptake and consumption of substances during adolescence is the result of many influences outside the purview of the individual. Despite the rational for environmental approaches, and the compelling evidence of environmental correlates associated with risky substance use practices, proven interventions are still limited in number. Indeed, structural environmental factors rarely makes it past the stage of a theoretical construct to inclusion in analytical models . This is in part due to the relevant infancy-stage of development . Notwithstanding this infancy, various theoretical models have emerged that attempt to address these environmental influences.

The Behavioral Ecological Model is based on the notion that behavioral determinants reside in the environment. Operational measures of Intra personal factors are difficult to validate and therefore excluded from the model that also explicitly assumes that ignoring such individual-level variables does not compromise prediction or control of behavior, thereby placing behavioral causes in the environment . In light of reports describing gender differences with respect to tobacco acquisition,custom grow rooms and the recent reports about the relationship between retailer presence and high risk behaviors, surprisingly little work has been done to highlight differences on this dimension between boys and girls. The purpose of this exploratory study based on the BEM and previous work by Pokorny et al. was to compare gender differences on the relationship between gateway drug use and variables representing both structural and social environments. Alcohol, tobacco and marijuana use variables were selected from a cross-sectional interview of Latino adolescents along the California/Mexico border in San Diego County. Demographic and peer modeling variables were also selected from the interview. A measure of alcohol and tobacco retailer dispersion was calculated from retailer location data obtained through the San Diego County Department of Environmental Health Food & Housing Division . Gateway drug use was regressed using least squares regression on 8 independent variables representing both structural and social environmental influences .The sample of 226 Latino adolescents in this study were students ages 13 to 19, attending high school in south San Diego County, who tested positive for latent tuberculosis infection , volunteered to participate in a medication adherence trial, and planned to receive treatment of their infection in the United States . Data were collected between 2004 and 2005. Participation in this study was limited to adolescents with a residential address in the US. After obtaining informed consent, trained bilingual staff completed a baseline interview in the participant’s home. Age, gender, and acculturation were selected to represent demographic characteristics. Acculturation was measured using the Bidimensional Acculturation Scale for Hispanics . The acculturation scale consists of 24 questions regarding language use , linguistic proficiency , and electronic media use . Each question had four possible responses: very poorly, poorly, well, or very well. The questions were separated into 2 domains, Hispanic and non-Hispanic , with 12 items in each. For each cultural domain, an average of the 12 items was calculated, obtaining a mean range of scores between 1 and 4. Scores on both domains were used to determine the level of acculturation. Acculturation categories were computed using a 2.5 cutoff score to indicate low or high level of adherence to each cultural domain. Individuals scoring higher than 2.5 in both domains were considered bicultural . The participant residential address was geocoded in ArcView 9.2.

Geocoding refers to the process of creating a point along a roadway segment that defines the location of any given address. A quarter-mile street network buffer was then created around each participant’s residential location or point. This buffer was intended to reflect the “walking neighborhood,” or those locations where the participant could easily walk to access various nearby alcohol and tobacco retailers. Currently no standard exists to define a buffer size that appropriately reflects “neighborhood;” however, given the typically limited travel choices of adolescents, the area within a 5-minute walk of his/her home can reasonably be considered a highly accessible area. The quarter mile distance was developed assuming a walking speed of 3.4 miles/hour . One previous study used a circular buffer of 0.5 miles . Buffers created using distances along the street network, such as that employed in the current study, exclude areas of the urban environment that are not accessible via roadways, and are generally considered to more accurately reflect those locations that are truly accessible. US Census Bureau data were obtained from San Diego Geographic Information Source , and used to identify neighborhood characteristics. Items were selected using an adaptation of an approach employed by Sampson, Raudenbush, & Earls and mostly represent indicators of neighborhood poverty . The values used in this study were: 1) percentage of families living below the poverty level, 2) percentage of unemployment, 3) percentage of adults with a high school diploma, 4) percentage of owner occupied homes, 5) percentage of the population under 18 years of age, 6) percentage of homes headed by a single mother, and 7) percentage of Hispanics. The neighborhood characteristic variables from SanGIS were available by Census Block Groups , a census geography that reflects aggregations of several Census Blocks. Since the participant’s neighborhood buffers were irregular and did not fall exactly on the boundaries of the CBGs, it was necessary to estimate Census variable values within each participant’s buffer using a method referred to as “apportioning”. This procedure involves calculating the proportion of each CBG that overlaps with a neighborhood buffer and then using that percentage to factor each respective Census variable. For example, if a participant’s neighborhood buffer included 25% of one CBG, 55% of another, and 20% of a third CBG, then these percentages were used to weight the census values associated with each CBG to develop a unique value more closely aligned with the boundaries of the neighborhood buffer. This is a recognized approach to adjusting Census data so that it more accurately reflects a unique, non-census geography.This study analyzed retailers from the 2004 database, and was limited to convenience stores. Retailers that did not sell alcohol and tobacco were removed. The retailer address was geocoded using ArcView 9.2 and then used to create a measure of retailer dispersion: distance to nearest retailer from each participant’s residential location. Distance to the nearest retailer was calculated using the Network Analyst function in ArcView, which is capable of finding and then measuring the distance of the shortest roadway path between a given participant’s residential point and the nearest retailer point. This variable demonstrated a non-normal distribution and required transformations to reach normality.

How Does A Vertical Farm Work

What is vertical farming?A vertical farm is an innovative type of indoor agriculture that utilizes vertical space to grow crops in stacked layers or shelves. It is designed to maximize production while minimizing the use of land and resources. Here’s a general overview of how a vertical farm works:

Structure and Design: A vertical farm is typically housed in a controlled environment facility such as a warehouse or a specially constructed building. The structure incorporates multiple levels or shelves to create a vertical growing space.
Lighting: Artificial lighting systems, such as LED lights, are used to provide the necessary light spectrum for plant growth. The lights are positioned at optimal distances and angles to ensure uniform light distribution across all levels.
Hydroponics or Aeroponics: Most vertical farms use hydroponic or aeroponic systems to grow plants without soil. In hydroponics, plants are grown in nutrient-rich water solutions, while in aeroponics, plants are grown in mist or air with nutrient-dense solutions. Both methods provide plants with the necessary nutrients directly to their roots.
Climate Control: Vertical farms rely on precise control of temperature, humidity, and airflow to create ideal growing conditions for the plants. This allows year-round cultivation and the ability to grow crops that are not native to the local climate.
Irrigation and Nutrient Delivery: The water and nutrient solutions required for plant growth are delivered directly to the plants through a network of irrigation systems. The solutions are carefully monitored and adjusted to maintain optimal nutrient levels for each crop.
Monitoring and Automation: Vertical farms often employ advanced monitoring systems to track various environmental factors such as light levels, temperature, humidity, and nutrient concentration. Automation systems control and adjust these factors based on predetermined parameters, ensuring the optimal conditions for plant growth.
Plant Growth and Harvesting: Seeds or seedlings are planted in trays or containers and placed in the growing system. As the plants grow, they are periodically moved to higher shelves to utilize the available vertical space efficiently. Harvesting is done manually, typically on a rotating schedule, as different crops reach their maturity.

Benefits of Vertical Farming:

Increased crop yields: Vertical farming allows for more efficient use of space, enabling higher crop yields per square foot compared to traditional farming.
Reduced resource usage: Vertical farms typically use less water and fertilizers compared to conventional agriculture.
Year-round production: The controlled environment in vertical farms allows for continuous cultivation regardless of external weather conditions.
Locally grown produce: Vertical farms can be located in urban areas, bringing fresh produce closer to consumers, reducing transportation costs, and lowering carbon emissions.

Overall, vertical farming companies presents an innovative approach to sustainable agriculture, offering the potential to address food security, reduce environmental impact, and create more resilient and efficient food production systems.