Salt tolerance of cowpea genotypes during seed germination and seedling growth

Cowpea (Vigna unguiculata (L.) Walp.) is one of the most important grain legumes worldwide and its production is affected by increasing soil salinity due to global climate change. An experiment was conducted at the Plant Physiology Laboratory of the Department of Crop Botany, Bangladesh Agricultural University, Mymensingh to evaluate the germination capability of seven cowpea genotypes under salt stress. The germination test was carried out in Petri dishes following two factorial CRD with three replications. Seven cowpea genotypes viz, Red Pine, Green Super, Hai Jiang San Hao, Kegornatki, Kegornatki Green, Kegornatki HYV & Kegornatki Red; and three salt levels viz, 0, 6 & 12 dSm-1, were used as experimental treatments. The germination percentage (GP), mean germination time (MGT), radicle and plumule length, radicle and plumule fresh and dry weight and different stress tolerance indices were recorded to screen the genotypes for salt tolerance. The study highlighted that salt concentrations drastically reduced seed germination and significantly delayed the process in all genotypes. The GP, length and biomass of radicle and plumule and salt tolerance indices were significantly decreased while the MGT was significantly increased with increasing salt stress in all cowpea genotypes. A significant variation among the genotypes in response to salt stress was also observed. Among the seven cowpea genotypes, Hai Jiang San Hao and Green Super showed higher salt tolerance in comparison to the other genotypes based on the measured traits; these genotypes can be used for further breeding program and/or cultivation in coastal saline prone areas with further investigation

Breast density classification for cancer detection using DCT-PCA feature extraction and classifier ensemble

It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN

An adaptive medical cyber-physical system for post diagnosis patient care using cloud computing and machine learning approach

Medical care is one of the most basic human needs. Due to the global shortage of doctors, nurses, and other healthcare personnel, medical cyber-physical systems are quickly becoming a viable option. Post-diagnosis surveillance is an essential application of these systems, which can be performed more successfully using various monitoring devices rather than active observation by nurses in their physical presence. However, most existing solutions for this application are rigid and do not consider current difficulties. Intelligent and adaptive systems can overcome the challenges because of the advances in relevant technology, especially healthcare 4.0. Therefore, this work presents an adaptive system based on cloud and edge computing architecture and machine learning approaches to perform post-diagnosis medical tasks on patients, thus reducing the need for nurses, especially in the post-diagnosis phase

Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts

Medical Named Entity Recognition (MedNER) is an indispensable task in biomedical text mining. NER aims to recognize and categorize named entities in scientific literature, such as genes, proteins, diseases, and medications. This work is difficult due to the complexity of scientific language and the abundance of available material in the biomedical sector. Using domain-specific embedding and Bi-LSTM, we propose a novel NER model that employs deep learning approaches to improve the performance of NER on scientific publications. Our model gets 98% F1-score on a curated data-set of Covid-related scientific publications published in multiple web of science and pubmed indexed journals, significantly outperforming previous approaches deployed on the same data-set. Our findings illustrate the efficacy of our approach in reliably recognizing and classifying named entities (drug and disease) in scientific literature, opening the way for future developments in biomedical text mining

Enrichment, in vitro, and quantification study of antidiabetic compounds from neglected weed Mimosa pudica using supercritical CO2 and CO2-Soxhlet

Supercritical fluid extraction (SFE) using carbon dioxide (CO2) and liquid CO2 using Soxhlet (CO2-Soxhlet) extraction were employed to extract three (3) antidiabetic compounds viz. stigmasterol, quercetin, and avicularin from Mimosa pudica. Various extraction parameters were studied. Extracts were analyzed pharmacologically, qualitatively and quantitatively to ascertain enrichment levels. All three antidiabetic compounds were effectively enriched under optimized conditions of temperature 60°C, pressure 40 MPa, co-solvent ratio 30%, and CO2 flow rate of 5 ml min−1. SFE was found to be the better method for enrichment of the antidiabetic compounds than the CO2-Soxhlet method. Extraction conditions were seen to affect the enrichment of desired compounds

Population Enumeration and Household Utilization Survey Methods in the Enterics for Global Health (EFGH): Shigella Surveillance Study

Background: Accurate estimation of diarrhea incidence from facility-based surveillance requires estimating the population at risk and accounting for case patients who do not seek care. The Enterics for Global Health (EFGH) Shigella surveillance study will characterize population denominators and healthcare-seeking behavior proportions to calculate incidence rates of Shigella diarrhea in children aged 6–35 months across 7 sites in Africa, Asia, and Latin America. Methods: The Enterics for Global Health (EFGH) Shigella surveillance study will use a hybrid surveillance design, supplementing facility-based surveillance with population-based surveys to estimate population size and the proportion of children with diarrhea brought for care at EFGH health facilities. Continuous data collection over a 24 month period captures seasonality and ensures representative sampling of the population at risk during the period of facility-based enrollments. Study catchment areas are broken into randomized clusters, each sized to be feasibly enumerated by individual field teams. Conclusions: The methods presented herein aim to minimize the challenges associated with hybrid surveillance, such as poor parity between survey area coverage and facility coverage, population fluctuations, seasonal variability, and adjustments to care-seeking behavior

Agro-morphological characterization of flax (Linum usitatissimum L.) accessions at north-western part of Bangladesh

Flax (Linum usitatissimum L.), grown throughout the world for millennia. It is a multipurpose agricultural crop that can provide food, fuel and fibre. An agro-morphological characterization based on 13 traits of 26 flax accessions was carried out during the Rabi season 2017-2018 at the Agricultural Research Station, Bangladesh Agricultural Research Institute, Rangpur. The field experiment was laid out in a randomized completed block design having four replications. Flax seeds were sown in 3.0 m × 0.6 m plot with continuous line sowing (two lines). The seed germination (%) and vigour indices of all flax accessions varied from 44.1 – 77.7 and 44.1 – 119.4, respectively. A significant variation in all growth and yield attributing descriptors was observed except 1000-seed weight of flax. Among the accessions, BD-10708 possessed the highest seed yield (182.9 g plant–1) and yield attributing descriptors viz., number of capsules plant–1 (142) and seeds plant–1 (513) of flax. The performance of the local accesson Ulipur was observed poor compared to some of the test accessions of flax. Some of these flax accessions could be used as breeding materials in varietal developmental and improvement programmes with higher yield potentials of flax in Bangladesh

Enhancing branch predictors using genetic algorithm

Dynamic branch prediction is a hardware technique used to speculate the direction of control branches. Inaccurate prediction will make all speculative works useless while accurate prediction will significantly improve microprocessors performance. In this work, we have shown that Genetic Algorithm (GA) can be used to select (near) optimal parameters for branch predictors in most cases. The GA-enhanced predictors take time to find suitable parameters, but once the values of these parameters are determined, the GA-enhanced predictors take the same time to execute as the basic predictors with increased accuracy. © 2019 IEEE.E

Shrimp Culture Impact on the Surface and Ground Water of Bangladesh

Abstract A case study was carried out to see the impacts of shrimp culture on the surface (pond) and ground water (tube-well) quality in three coastal sub-districts of Bagherhat Sadar, Rampal and Morrelganj of Bangladesh. The people of Rampal (100%), Morrelgonj (87.5%) and Bagherhat (75.5%) expressed that salinity of both surface and ground water increased after shrimp culture, and water becomes more turbid, odorous and less tasty compared to pre-shrimp culture scenario. The ground water pH was foo be slightly acidic (6.07-6.71) but the surface water was mildly alkaline in nature (7.00-7.46). Ground water was more saline (1893-2673ppm) than surface water (513-2253ppm). Potassium level of surface water was very high (97-242ppm) compared to the ground water (11.73-27.37 ppm). This exceeds the WHO Guideline Value (10ppm) and the Bangladesh Standard for Drinking Water (12ppm). The pollution levels of phosphorous and iron were found to be a little higher but other pollutants like nitrate, boron and zinc were found to be very low in surface and ground water in the shrimp culture area of Bangladesh

Immediate Impact of Uni-nephrectomy among Bangladeshi Healthy Live Kidney Donors: BIRDEM General Hospital Experience

Background: Kidney transplantation is the preferred treatment option for end stage kidney disease. Live kidney donation is an established form of organ donation; but it carries the risk of an unnecessary surgery in a normal individual. These donors remain at an increased risk of multiple medical problems for the rest of their life. Objective: In this study, we evaluated the immediate impact of uninephrectomy among kidney donors during the period of post-transplant hospital stay. Materials and Methods: This cross-sectional study was done at BIRDEM General Hospital from January 2006 to June 2014. All kidney donors who had undergone graft nephrectomy during the study period were the study population. All the donors underwent Tc-99m diethylene triamine pentaacetic acid (Tc-99m DTPA) renogram for measurement of glomerular filtration rate (GFR). GFR was also estimated by different equations in both pre-transplant and post-transplant periods. Pre-uninephrectomy GFR and post-uninephrectomy GFR of donors were compared. Results: Total number of subjects was 81, male 48 and female 33. Mean age was 36.3 ± 9.9 years. Mean postoperative hospital stay was 8.2 ± 2.0 days. The mean pre-operative measured glomerular filtration rate (mGFRDTPA) was 99.54 ± 19.06 mL/min/1.73 m2 and mean estimated glomerular filtration rate (eGFRCKD-EPI) was 99.0 ± 18.55 mL/min/1.73 m2 (p=0.855). In post-nephrectomy period mean urine output decreased from 2708.1 ± 842.8 to 2228.4 ± 702.4 mL/day (p=0.000). Mean SBP lowered from 120.3 ± 12.5 to 115.6 ± 9.2 mm of Hg (p=0.000) after nephrectomy. There was significant increase in blood urea (from 19.7 ± 5.7 to 30.4 ± 9.5 mg/dL, p=0.000) and serum creatinine (from 0.90 ± 0.16 to 1.26 ± 0.24 mg/dL, p=0.000) in post-uninephrectomy period. Mean mGFRDTPA of the subjects of non-nephrectomized kidney of the donors was 49.18 ± 9.50 mL/min/1.73 m2 and mean eGFRCKD-EPI of same kidneys was 69.09 ± 16.79 mL/min/1.73m2 (p=0.000) after uni-nephrectomy. Conclusion: Uninephrectomy in healthy adult kidney donors has immediate impacts on urine output, blood pressure, blood urea and serum creatinine levels