Pallister-Killian syndrome diagnosed by chorionic villus sampling

The first prenatal diagnosis of Pallister-Killian syndrome by chorionic villus sampling is presented. Fetal hydrops was noted on ultrasound in early pregnancy, and the karyotype revealed isochromosome 12p mosaicism.     

Adsorptive removal of Cd2+ ions using dolochar at an industrial-scale process optimization by response surface methodology

Environmental Science and Pollution Research - In this work, performance of laboratory-synthesized dolochar has been investigated for adsorption of Cd2+ ions in a large-scale process with the...     

SOIL SALINITY PREDICTION USING ARTIFICIAL NEURAL NETWORKS1

ABSTRACT: This study explores the applicability of Artificial Neural Networks (ANNs) for predicting salt build‐up in the crop root zone. ANN models were developed with salinity data from field lysimeters subirrigated with brackish water. Different ANN architectures were explored by varying the number of processing elements (PEs) (from 1 to 30) for replicate data from a 0.4 m water table, 0.8 m water table, and both 0.4 and 0.8 m water table lysimeters. Different ANN models were developed by using individual replicate treatment values as well as the mean value for each treatment. For replicate data, the models with twenty, seven, and six PEs were found to be the best for the water tables at 0.4 m, 0.8 m and both water tables combined, respectively. The correlation coefficients between observed salinity and ANN predicted salinity of the test data with these models were 0.89, 0.91, and 0.89, respectively. The performance of the ANNs developed using mean salinity values of the replicates was found to be similar to those with replicate data. Not only was there agreement between observed and ANN predicted salinity values, the results clearly indicated the potential use of ANN models for predicting salt build‐up in soil profile at a specific site.     

Why bees are critical for achieving sustainable development

Reductions in global bee populations are threatening the pollination benefits to both the planet and people. Whilst the contribution of bee pollination in promoting sustainable development goals through food security and biodiversity is widely acknowledged, a range of other benefits provided by bees has yet to be fully recognised. We explore the contributions of bees towards achieving the United Nation’s Sustainable Development Goals (SDGs). Our insights suggest that bees potentially contribute towards 15 of the 17 SDGs and a minimum of 30 SDG targets. We identify common themes in which bees play an essential role, and suggest that improved understanding of bee contributions to sustainable development is crucial for ensuring viable bee systems.     

Male exposure mediated adverse reproductive outcomes in carbon disulphide exposed rayon workers

The authors examined 100 carbon disulphide (CS2) exposed male workers who had been employed ten years prior to study were selected for the study. They were virtually obliged to participate in the study by the Medical Labor Inspector and all of them participated voluntary. The aim was to assess the effects of occupational exposure to carbon disulphide concentrations below the threshold limit value (31 mg/m3) on the reproductive functions with special emphasis on miscarriages. Specially, workers history records were build up on number of children, miscarriages and general weakness, mental fatigue etc. It was found that the incidences of number of miscarriages against number of living children correlated well with environmental concentration of CS2. Where the average CS2 levels were 1.695 ppm, the incidences of miscarriages was 5.71% (group 1). Where as in group 2 environmental concentrations were 12.28 ppm and the incidences of miscarriages were 18.91%. It was also found that in the spinning department the exposure exceeds many times the Threshold Limit Values (TL V).