Screening of Different Tomato Varieties in Saline Areas of Bangladesh

A field study was conducted to screen out a number of Bangladeshi Tomato (Lycopersicon esculentum L.) varieties for salinity tolerance. Three levels of salinity were 2.0-4.0 dS m-1, 4.1-8.0 dS m-1 and 8.1-12.0 dS m-1. Significant varietal and or salinity treatment effects were registered on plant height, leaf area, plant growth, yield, dry matter plant-1, Na+ and Claccumulation in tomato tissues. Variety BARI Tomato 14, BARI Hybrid Tomato 5 and BARI Tomato 2 consistently showed superior biological activity at moderate salinity (4.1-8.0 dS m-1), based on dry matter biomass production thus displaying relatively greater adaptation to salinity. Under saline condition, all plant parameters of tomato varieties were reduced compared to the control except number of fruits of BARI Tomato 14, BARI Hybrid Tomato 5 and BARI Tomato 2. Thus, BARI Tomato 14, BARI Hybrid Tomato 5 and BARI Tomato 2 can be regarded as a breeding material for development of new tomato varieties for tolerance to salinity in saline areas of Bangladesh. DOI: http://dx.doi.org/10.3329/ijarit.v2i1.13989 Int. J. Agril. Res. Innov. & Tech. 2 (1): 13-18, June, 201

Investigation of heavy metal contents in Cow milk samples from area of Dhaka, Bangladesh

Background: Cow milk is considered as one of the responsible food sources contaminated with heavy metals. The objectives of the study were to assess the content of selected metals in cow milk and its associated human health risks in the food chain of Bangladesh. A total of 90 cow milk samples of Branded, Dairy and Domestically produced milk were collected randomly from different sources of Savar Upazila in Dhaka area. Cadmium (Cd), chromium (Cr), lead (Pb), manganese (Mn), copper (Cu) and iron (Fe) contents in collected milk samples were determined using Flame Atomic Absorption Spectrometry (FAAS). To ensure quality control, one of the best quality control parameters i.e. recovery test; from eight various sample digestion methods were used. The Hazard Quotient (HQ) and Carcinogenic Risk (CR) values were also calculated. Results: From the results, it was found that, the orders of heavy metal content in brand, dairy and domestic cow milk were Cr > Fe > Cu>Mn > Cd > Pb, Cr > Fe > Mn > Cu > Cd > Pb and Fe > Cr > Mn > Cu > Cd > Pb, respectively. Among the six metals, only Cr showed to exceed the highest Estimated Daily Intake (EDI) rate (for brand cow milk: 0.413 mg/day, dairy farm cow milk: 0.243 mg/day, domestic cow milk: 0. 352 mg/day),and the comparison percentages of calculated values per permeable values were as follows; 206.5 % for brand cow milk,121.5 % for dairy farm cow milk and 176.0 % for domestic cow milk. Hazard Quotients (HQ) values and Carcinogenic Risk (CR) values were found within the acceptable level. Conclusion: Although, the metal content in sampled cow milks were within the safe limit, the potential human health risks cannot be neglected for the regular/long time consumption of heavy metal contained cow milk

Toxic metals (Ni2+, Pb2+, Hg2+) binding affinity of dissolved organic matter (DOM) derived from different ages municipal landfill leachate

Abstract Solid waste production is rapidly increasing in Bangladesh and landfill leachate is the consequence of the decomposition of this waste. These leachates contain heavy metals and significant amount of dissolved organic matter (DOM). DOM is known to have considerable role in heavy metals speciation. Hence, it is important to characterize DOM/leachate and evaluate toxic metals binding affinity of DOM. The objectives of this study were to characterize the DOM in landfill leachate through physico-chemical and optical analyses and to investigate the toxic metals (Ni2+, Pb2+ and Hg2+) binding affinity of three different ages (fresh sample L-1, young sample L-2 and mature sample L-3) DOM samples. Results suggested that leachate is a potential pollutant which contained very high organic pollutant load. Conditional stability constant (LogK) and percentages of fluorophores that correspond to metal binding (%f) values indicated that young DOM sample (L-2) had the highest binding affinity to all the three metals ions. In general, DOM samples showed the following order affinity to the metal ions; Ni2+ binding affinity: L-2 > L-3 > L-1, Pb2+ binding affinity: L-2 > L-3 > L-1 and Hg2+ binding affinity: L-2 > L-1 > L-3

SCREENING OF DIFFERENT TOMATO VARIETIES IN SALINE AREAS OF BANGLADESH

A field study was conducted to screen out a number of Bangladeshi Tomato (Lycopersicon esculentum L.) varieties for salinity tolerance. Three levels of salinity were 2.0-4.0 dS m-1 , 4.1-8.0 dS m-1 and 8.1-12.0 dS m-1 . Significant varietal and or salinity treatment effects were registered on plant height, leaf area, plant growth, yield, dry matter plant-1 , Na+ and Claccumulation in tomato tissues. Variety BARI Tomato 14, BARI Hybrid Tomato 5 and BARI Tomato 2 consistently showed superior biological activity at moderate salinity (4.1-8.0 dS m1), based on dry matter biomass production thus displaying relatively greater adaptation to salinity. Under saline condition, all plant parameters of tomato varieties were reduced compared to the control except number of fruits of BARI Tomato 14, BARI Hybrid Tomato 5 and BARI Tomato 2. Thus, BARI Tomato 14, BARI Hybrid Tomato 5 and BARI Tomato 2 can be regarded as a breeding material for development of new tomato varieties for tolerance to salinity in saline areas of Bangladesh

Assessment of the Status of Groundwater Arsenic at Singair Upazila, Manikganj Bangladesh : Exploring the Correlation with Other Metals and Ions

Comparative study was conducted to correlate Arsenic (As), Iron (Fe), Copper (Cu), Manganese (Mn), Calcium (Ca2+), Magnesium (Mg2+), Potassium (K+), Nitrate (NO3¯), Phosphate (PO43¯), and Ammonia (NH3) by determining their concentration at different depth of the tube-wells in the selected study area at Singair, Manikganj Bangladesh. Total 99 tube-well water samples were collected from the study area. In most of the sampling points the present concentrations of As were less than the previous concentrations. The correlation between As and Fe was positively significant. It can be suggested possible adsorption/coprecipication of As with Fe in shallow aquifer. However, the relationship between As and Mn was not significantly observed. On the other hand, relationship between Cu and As showed a positive significant correlation. The correlation between As and PO43¯ was also significant, although the correlation between As and NO3¯ was not significant. PO43¯ may be comes from phosphate fertilizers application and can be a contributer of As in the shallow aquifer. The PCA biplot also indicated the significant relationship between As, Cu, Fe and PO43-. Excessive withdrawal of tubewell water along with aquifer dynamics along with ionic interference might be responsible for the mobilization of As in the study area

Removal of Pollutants from Water by Using Single-Walled Carbon Nanotubes (SWCNTs) and Multi-walled Carbon Nanotubes (MWCNTs)

Water crisis is one of the supreme challenges worldwide as clean water is the ultimate need for human civilization and all other life on earth. In the present study, continuous adsorption experiments were carried out in an adsorption column to survey the efficiency of the carbon nanotubes (CNTs) for removal of pollutants from water/wastewater in terms of physicochemical parameters, such as electrical conductivity, total dissolved solids (TDS), pH, chemical oxygen demand (COD) and total organic carbon (TOC), by using both single-walled carbon nanotubes and multi-walled carbon nanotubes. Sample solutions were allowed to flow in down-flow mode through the fixed-bed of CNTs. The CNTs column showed a reduction efficiency of electrical conductivity 80 % from effluent treatment plant (ETP) treated water sample, 69.23 % from raw effluent sample, and 53.33 % from the synthetic salt water sample. Similarly, the efficiency of TDS reduction was 78.61 % from raw effluent sample, 66.86 % from ETP treated water sample, and 62.02 % from the synthetic salt water sample. COD also reduced 84.71 % from raw effluent sample and 39.58 % from the ETP treated water sample. In case of TOC, the column showed a reduction efficiency of 85.88 % from the ETP treated water sample and 70.79 % from the raw effluent sample. These findings suggested that CNTs present a great potential in removal of pollutants in terms of physicochemical parameters from water/wastewater

Synthesis of substituted and unsubstituted 5-(1,3-diaryl-1-oxopropyl)pyrimidine

122-125Reactions of 1,3-diaryl-2-propene-1-ones 2a-i give the corres¬ponding 5-(1,3-diaryl-1-oxopropyl)pyrimidine-(1H,3H,5H)-2,4,6-triones 3a-i with barbituric acid 1 under refluxing condition without using any catalyst

A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images

Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy countries have made significant progress in detecting and containing this disease, most underdeveloped countries are still struggling to identify COVID-19 cases in large populations. With the rising number of COVID-19 cases, there are often insufficient COVID-19 diagnostic kits and related resources in such countries. However, other basic diagnostic resources often do exist, which motivated us to develop Deep Learning models to assist clinicians and radiologists to provide prompt diagnostic support to the patients. In this study, we have developed a deep learning-based COVID-19 case detection model trained with a dataset consisting of chest CT scans and X-ray images. A modified ResNet50V2 architecture was employed as deep learning architecture in the proposed model. The dataset utilized to train the model was collected from various publicly available sources and included four class labels: confirmed COVID-19, normal controls and confirmed viral and bacterial pneumonia cases. The aggregated dataset was preprocessed through a sharpening filter before feeding the dataset into the proposed model. This model attained an accuracy of 96.452% for four-class cases (COVID-19/Normal/Bacterial pneumonia/Viral pneumonia), 97.242% for three-class cases (COVID-19/Normal/Bacterial pneumonia) and 98.954% for two-class cases (COVID-19/Viral pneumonia) using chest X-ray images. The model acquired a comprehensive accuracy of 99.012% for three-class cases (COVID-19/Normal/Community-acquired pneumonia) and 99.99% for two-class cases (Normal/COVID-19) using CT-scan images of the chest. This high accuracy presents a new and potentially important resource to enable radiologists to identify and rapidly diagnose COVID-19 cases with only basic but widely available equipment