These systems also offer a unique opportunity to tailor crop characteristics to changing consumer preferences by altering environmental conditions such as light quality for example , where blue light has been used to increase the glucosinolate content of several Brassica species including, pak choi and watercress . Here we investigate differences in yield, morphology, and glucosinolate content of watercress grown under three different cultivation systems.This research provides foundation information to suggest that high yield watercress crop production is possible in vertical farming systems and that watercress quality may be further enhanced for improved anti-cancer characteristics. We have shown that the quality and yield of the leafy green salad crop watercress can be significantly improved by growth in an indoor vertical hydroponic system, enriched in blue light. The CDC ranked watercress as the most nutrient dense crop based on the content of 17 nutrients that are associated with reducing chronic disease risk . Our results show the yield and nutrient content of watercress can be enhanced even further by utilizing a novel vertical indoor growing environment other than the current commercial system used in the UK. Yield increases may be explained by the ability to tightly control environmental conditions in the VF that generate a consistent optimal nutrient and temperature environment. The increase in glucosinolate content from UK to CA is probably explained by heat stress in CA,cannabis drying system with the maximum temperature recorded at the CA site at 43.8 ◦C compared to 30.9 ◦C for the UK. Glucosinolate accumulation is associated with improved heat and drought stress tolerance in Arabidopsis and increases in GLSs are observed in heat-stressed Brassica rapa .

Increases observed in GLS content in VF can be explained by prolonged blue light exposure and a longer growth period . The mechanism of different LEDs on GLS biosynthesis regulations still remain unclear, but a short-duration blue light photoperiod increased the total aliphatic GLSs in broccoli . A similar result from a genome wide association mapping of Arabidopsis also revealed that blue light controlled GLS accumulation by altering the PHOT1/PHOT2 blue light receptors . Increasing blue light in the VF increased total GLSs content and although not statistically significant, it confirms the study by Chen et al. that showed increased GLSs content with increased blue light. Rosa et al. showed that GLS concentrations are more sensitive to the effect of temperature than of photoperiod and this is consistent with our results in total GLSs between the UK and CA sites. Our results support the idea that indoor farm cultivation is effective in promoting health-beneficial chemical properties. Watercress produced PBGLS in both the VF treatments, but this compound was not detected in either the UK or CA trials. PBGLS strengthens the nutrient profile of watercress. PEITC derived from PEGLS has already been proven to be an extremely effective naturally-occurring dietary isothiocyanates against cancer . Inhibitory potency increases several-fold when the glucosinolate alkyl chain gets longer , suggesting that PBITC, with its elongated alkyl chain compared to PEITC, may contribute an additional health benefit to this super food, although this remains to be proven. It is evident that watercress is particularly well-suited for indoor hydroponic growing systems, where plants exhibited the highest yielding leafy growth with improved nutritional profiles, ideal for consumer preferences. Altering the blue:red light ratio may further enhance the anti-cancer properties of this highly nutritious salad crop, but further studies are required to hone the light recipe for indoor cultivation. Tuberculosis is a major infectious disease that causes illness and death worldwide . In 2006, there were about 9.2 million new TB cases and 1.7 million TB-related deaths [World Health Organization 2008].

Most new cases and deaths occurred in Asia and Africa. In Nepal, a South Asian country, TB is a major public health problem , with an overall annual incidence of all forms of TB estimated at 176 per 100,000 persons . A range of social, environmental, and behavioral factors influence exposure and susceptibility to Mycobacterium tuberculosis infection. Identifying TB risk factors and minimizing exposure to them could reduce the TB burden in Nepal and other developing countries. Active tobacco smoking, for example, has been shown to be a risk factor for TB, presumably by damaging immune and other protective mechanisms, allowing TB infection to prosper . The composition of tobacco smoke has many similarities to that of indoor cooking smoke from biomass fuel , exposure to which is common in the developing world, including Nepal. Therefore, an association of TB with indoor cooking smoke is plausible. Six previous epidemiologic studies have investigated whether an association exists between TB and exposure to cooking-fuel smoke . Although four of these studies found some evidence of an association, all the studies had limitations. The first study to find an association between exposure to cooking-fuel smoke and TB presented limited data on potential confounding factors, and the risk model was adjusted only for age, which left open the possibility of confounding by socioeconomic factors or smoking . Mishra et al. also reported evidence of an association; however, they used data from the 1992–1993 Indian National Family Survey, which was based on self-reported TB status. This leaves the possibility of outcome mis-classification. A third study found an association between cooking smoke exposure and TB but included no validation of key components of the questionnaire . In a study conducted in Malawi, Crampin et al. found no association between cooking smoke exposure and TB, but the study participants varied little in the type of fuel they used, and the risk model was adjusted only for age, sex, area of residence, and HIV status, leaving open the possibility of confounding by other socioeconomic factors or smoking. The fifth study, conducted in South India by Shetty et al. , also found no association of cooking-fuel smoke with TB, but they did find an association between TB and not having a separate kitchen. The sixth study was conducted by Kolappan and Subramani in Chennai, India; they found a marginal association between biomass fuel and pulmonary TB in their study population [adjusted OR = 1.7; 95% confidence interval , 1.0–2.9]. The study participants in this study were primarily men but because women do most of the cooking, they are more likely to be exposed to smoke from cooking fuel. We conducted a TB case–control study in the Pokhara municipality of Nepal where cooking with biomass fuels in unvented indoor stoves is a common practice.

Our main objectives were to confirm results of earlier studies using clinically confirmed TB cases and to investigate possible confounding of the relationship using a validated questionnaire and exposure assessment in the kitchens of a subset of participants’ houses.old, who visited TB clinics in RTC and MTH and who had been newly diagnosed with active pulmonary TB by chest X-ray and positive active sputum smears , which are routinely conducted at the hospital using methods recommended by the WHO . Women who were pregnant, who were on chemotherapy for cancer, who had HIV/ AIDS or diabetes, or who had a history of TB were excluded from the study. Controls were recruited from outpatient and inpatient departments at the MTH, in the same months when cases were identified. For each case,rolling greenhouse benches the control subjects were the first eligible female patients without pulmonary TB, matched to cases on age , who presented at MTH between 0900 and 1000 hours after case enrollment. Controls were excluded from the study for the same reasons as for the cases. Control subjects were interviewed only after medical screening confirmed that they did not have TB. The ratio of cases to controls was 1:2. After obtaining an informed oral consent to participate, all cases and controls were interviewed face-to-face by trained interviewers shortly after diagnosis while they were still at the hospital. The three interviewers were unavoidably aware of the case or control status of the interviewees but were not aware of the main exposure of interest or hypothesis of the study. All interviewers interviewed both cases and controls. The questionnaire collected data on education level, area of residence , history of use of cooking fuels and stoves that included present and previous cooking fuels and stoves, present kitchen type and location, kitchen ventilation, house type, participant’s smoking history and smoking status of family members, alcohol consumption, vitamin supplement consumption, use of mosquito coils and incense, household crowding, vehicle ownership, and annual income level.Liquefied petroleum gas and biogas were designated “gaseousfuel stoves” , which was used as the reference category for most analyses compared with kerosene-fuel stoves and biomass-fuel stoves . Very few participants reported burning biomass in stoves with flues or chimneys venting to the outside, and no one reported using an electric cooker. For this reason, no separate category was created for vented BFS, and these subjects were included in the BFS category. We examined the extent of agreement of responses on the exposure information obtained during face-to-face interviews at the hospital with data obtained from actual inspection of these features in the houses of the first 28 study participants . The effect of mis-classification was calculated in terms of sensitivity and specificity. We combined information on kitchen location and windows in the kitchen to create a composite dichotomous variable for ventilation. “Fully and partially ventilated kitchens” included open-air kitchens, separate kitchens outside the house, and partitioned kitchens with windows inside the house. This was used as the reference category for ventilation. Unventilated kitchens included partitioned and non-partitioned kitchens without windows inside the house. We were unable to clearly interpret questionnaire data on closing doors in a way that could be used to characterize ventilation. To calculate the number of pack-years of smoking, we combined the information on the average number of tobacco products smoked every day multiplied by the duration of smoking in years divided by 20, assuming that a pack of cigarette contains twenty cigarettes/bidis. One participant who reported she smoked a hukka was excluded from this analysis. We calculated crude odds ratios between exposure and outcome. We decided a priori to include all statistically significant variables in the model, as well as any other recognized risk factors for TB. Then we applied a stepwise backward elimination model, with a variable selection criterion of p = 0.2, to all the variables to identify any others that should be included in the final model. Using the selected covariates, we constructed a multivariate unconditional logistic regression model for risk of TB. We calculated adjusted female population-attributable fractions and associated CIs using the aflogit command in Stata statistical software . This procedure assumes that the proportion of controls exposed is a good estimate of the proportion exposed in the target population.Four potential interviewees did not meet the inclusion criteria: two were diabetic and two were HIV positive. During recruitment, one potential control was found to have pulmonary TB and was transferred to the case group. Except for one control, all potential interviewees agreed to participate in this study. In total, we recruited and interviewed 125 cases and 250 controls. Cases were more likely to be referred by a health care professional than were controls . This might reasonably be expected because TB causes serious illness, but many of the controls would have had much less severe conditions. Table 1 lists descriptive data for the cases and controls, with unadjusted ORs and CIs. With the exception of the income variable, few data were missing. Confirming the success of the matching process, distributions of cases and controls were similar in terms of age. Most cases and controls were from the Kaski district. Cases were more likely than controls to be Buddhist, to live in urban and periurban areas, to reside in poorer quality houses , to be illiterate, to have non-partitioned and unventilated kitchens indoors, and to use kerosene wick lamps as their main source of light. Cases were also more likely than controls to regularly consume alcohol, to be tobacco smokers, to have more smokers in the family than controls, and to have not always lived in their present house. We think that, to some extent, the latter variable probably captures the likelihood of previously having used other cooking fuels.