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.     

Reduction of pollutants in pulp paper mill effluent treated by PCP-degrading bacterial strains

Two PCP-degrading bacterial strains, Bacillus cereus (ITRC-S6) and Serratia marcescens (ITRC-S7) were used for the treatment of pulp and paper mill effluent at conditions; 1.0% glucose and 0.5% peptone at 30 +/- 1 degrees C at 120 rpm for 168 h of incubation. These two bacterial strains effectively reduced colour (45-52%), lignin (30-42%), BOD (40-70%), COD (50-60%), total phenol (32-40%) and PCP (85-90%) within 168 h of incubation. However, the highest reduction in colour (62%), lignin (54%), BOD (70%), COD (90%), total phenol (90%) and PCP (100%) was recorded by mixed culture treatment. The bacterial mechanism for the degradation of pulp and paper mill effluent may be explained by an increase in the cells biomass using added co-substrates resulting liberation of significant amount of chloride due to bacterial dechlorination of chlorolignins and chlorophenols this showed reduction in colour, lignin and toxicity in the effluent. Further, GC-MS analysis of ethyl acetate-extractable compounds from treated pulp paper mill effluent reinforces the bacterium capability for the degradation of lignin and pentachlorophenol, as many aromatic compounds such as 2-chlorophenol, 2, 4, 6-trichlorophenol and tetrachlorohydroquinone, 6-chlorohydroxyquinol and tetrachlorohydroquinone detected which were not present in the untreated effluent.     

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.