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Lung cancer is the most common fatal malignancy in adults worldwide, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer diagnoses. Computed tomography (CT) is routinely used in clinical practice to determine lung cancer treatment and assess prognosis. Here, we developed LungNet, a shallow convolutional neural network for predicting outcomes of NSCLC patients. We trained and evaluated LungNet on four independent cohorts of NSCLC patients from four medical centers: Stanford Hospital (n = 129), H. Lee Moffitt Cancer Center and Research Institute (n = 185), MAASTRO Clinic (n = 311) and Charité - Universitätsmedizin (n=84). We show that outcomes from LungNet are predictive of overall survival in all four independent survival cohorts as measured by concordance indices of 0.62, 0.62, 0.62 and 0.58 on cohorts 1, 2, 3, and 4, respectively. Further, the survival model can be used, via transfer learning, for classifying benign vs malignant nodules on the Lung Image Database Consortium (n = 1010), with improved performance (AUC=0.85) versus training from scratch (AUC=0.82). LungNet can be used as a noninvasive predictor for prognosis in NSCLC patients and can facilitate interpretation of CT images for lung cancer stratification and prognostication., Competing Interests: Competing interests: All authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Background: In well-responding patients to chemoradiotherapy for locally advanced rectal cancer (LARC), a watch-and-wait strategy can be considered. To implement organ-sparing strategies, accurate patient selection is needed. We investigate the use of MRI-based radiomics models to predict tumor response to improve patient selection., Materials and Methods: Models were developed in a cohort of 70 patients and validated in an external cohort of 55 patients. Patients received chemoradiation followed by surgery and underwent T2-weighted and diffusion-weighted MRI (DW-MRI) before and after chemoradiation. The outcome measure was (near-)complete pathological tumor response (ypT0-1N0). Tumor segmentation was done on T2-images and transferred to b800-images and ADC maps, after which quantitative and four semantic features were extracted. We combined features using principal component analysis and built models using LASSO regression analysis. The best models based on precision and performance were selected for validation., Results: 21/70 patients (30%) achieved ypT0-1N0 in the development cohort versus 13/55 patients (24%) in the validation cohort. Three models (t2_dwi_pre_post, semantic_dwi_adc_pre, semantic_dwi_post) were identified with an area-under-the-curve (AUC) of 0.83 (95% CI 0.70-0.95), 0.86 (95% CI 0.75-0.98) and 0.84 (95% CI 0.75-0.94) respectively. Two models (t2_dwi_pre_post, semantic_dwi_post) validated well in the external cohort with AUCs of 0.83 (95% CI 0.70-0.95) and 0.86 (95% CI 0.76-0.97). These models however did not outperform a previously established four-feature semantic model., Conclusion: Prediction models based on MRI radiomics non-invasively predict tumor response after chemoradiation for rectal cancer and can be used as an additional tool to identify patients eligible for an organ-preserving treatment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Fly ash (FA), the major by-product of coal-fired thermal power plants, causes significant environmental degradation owing to its injurious heavy metal contents. Leaching of arsenic (As) from ash ponds is especially significant as As released from FA can increase As concentration of drinking water above maximum contaminant level of 10 ppb. The aim of this paper was demonstration of As bioremediation potential of indigenous As resistant bacteria present in the weathered pond ash sample. Ten isolates belonging to Bacillus, Micrococcus, Kytococcus and Staphylococcus genera were characterized. Biochemical tests showed reduction of relatively non toxic arsenate to more toxic arsenite by two strains while four strains showed oxidation of arsenite to arsenate. Two exoplolysaccharide producing strains were shown to absorb As within their biomass. Total heterotrophs versus As resistant heterotrophs counting performed showed that FA was enriched with As resistant heterotrophs. Column leaching based microcosm study revealed overall As detoxification potential of the isolated microbes.

Eight chromium resistant bacteria were isolated from a dry fly ash sample of DVC-MTPS thermal power plant located in Bankura, West Bengal, India. These isolates displayed different degrees of chromate reduction under aerobic conditions. According to 16S rDNA gene analysis, five of them were Staphylococcus, two were Bacillus and one was Micrococcus. The minimum inhibitory concentration towards chromium and the ability to reduce hexavalent chromium to trivalent chromium was highest in Staphylococcus haemolyticus strain HMR17. All the strains were resistant to multiple heavy metals (As, Cu, Cd, Co, Zn, Mn, Pb and Fe) and reduced toxic hexavalent chromium to relatively non toxic trivalent chromium even in the presence of these multiple heavy metals. All of them showed resistance to different antibiotics. In a soil microcosm study, S. haemolyticus strain HMR17 completely reduced 4 mM hexavalent chromium within 7 days of incubation.

Arsenic (As), cited as the most hazardous substance by the U.S. Agency for Toxic Substance and Disease Registry (ATSDR, 2005), is an ubiquitous metalloid which when ingested for prolonged periods cause extensive health effects leading to ultimate untimely death. Plants and microbes can help mitigate soil and groundwater As problem since they have evolved elaborate detoxification machineries against this toxic metalloid as a result of their coexistence with this since the origin of life on earth. Utilization of the phytoremediation and bioremediation potential of the plants and microbes, respectively, is now regarded as two innovative tools that encompass biology, geology, biotechnology and allied sciences with cutting edge applications for sustainable mitigation of As epidemic. Discovery of As hyperaccumulating plants that uptake and concentrate large amounts of this toxic metalloid in their shoots or roots offered new hope to As phytoremediation, solar power based nature's own green remediation. This review focuses on how phytoremediation and bioremediation can be merged together to form an integrated phytobial remediation which could synergistically achieve the goal of large scale removal of As from soil, sediment and groundwater and overcome the drawbacks of the either processes alone. The review also points to the feasibility of the introduction of transgenic plants and microbes that bring new hope for more efficient treatment of As. The review identifies one critical research gap on the importance of remediation of As contaminated groundwater not only for drinking purpose but also for irrigation purpose and stresses that more research should be conducted on the use of constructed wetland, one of the most suitable areas of application of phytobial remediation. Finally the review has narrowed down on different phytoinvestigation and phytodisposal methods, which constitute the most essential and the most difficult part of pilot scale and field scale applications of phytoremediation programs., (Copyright © 2014 Elsevier Ltd. All rights reserved.)