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276; p  less then  .001) and mental (β = -0.551; p  less then  .001) health status 12-months post ICD implantation, adjusting for demographic and clinical variables. CONCLUSIONS The 4-item ANX4 shows to be a valid measure of anxiety symptoms in ICD patients and predicts physical and mental health status up to 12 months follow-up. Further studies are warranted to replicate these findings, determine the cut-off score for clinical relevant symptoms, and whether the ANX4 can be used in other populations. Mitochondria was used to clarify the effects of Coolia malayensis strain UNR-02 crude extract by studying mitochondrial membrane potential (ΔΨm) generation and the fluctuations of ΔΨm associated with the induction of mitochondrial permeability transition (MPT). The cytoxicity of C. malayensis was also determined using both HepG2 and H9c2(2-1) cells. C. malayensis extract significantly depressed the oxidative phosphorylation efficiency, as was inferred from the perturbations in ΔΨm and in the phosphorylative cycle induced by ADP. Increased susceptibility to Ca2+-induced MPT was also observed. At the cellular level, the extract significantly decreased cell mass of both cell lines. Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. learn more Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization approach, it is not a necessity in computer vision applications. Furthermore, Free Convolutional Networks match the performance observed in standard architectures when trained using properly translated data (akin to video). Under the assumption of translationally augmented data, Free Convolutional Networks learn translationally invariant representations that yield an approximate form of weight-sharing. Convolutional neural network (CNN) models have recently demonstrated impressive performance in medical image analysis. However, there is no clear understanding of why they perform so well, or what they have learned. In this paper, a three-dimensional convolutional neural network (3D-CNN) is employed to classify brain MRI scans into two predefined groups. In addition, a genetic algorithm based brain masking (GABM) method is proposed as a visualization technique that provides new insights into the function of the 3D-CNN. The proposed GABM method consists of two main steps. In the first step, a set of brain MRI scans is used to train the 3D-CNN. In the second step, a genetic algorithm (GA) is applied to discover knowledgeable brain regions in the MRI scans. The knowledgeable regions are those areas of the brain which the 3D-CNN has mostly used to extract important and discriminative features from them. For applying GA on the brain MRI scans, a new chromosome encoding approach is proposed. The proposed framework has been evaluated using ADNI (including 140 subjects for Alzheimer’s disease classification) and ABIDE (including 1000 subjects for Autism classification) brain MRI datasets. Experimental results show a 5-fold classification accuracy of 0.85 for the ADNI dataset and 0.70 for the ABIDE dataset. The proposed GABM method has extracted 6 to 65 knowledgeable brain regions in ADNI dataset (and 15 to 75 knowledgeable brain regions in ABIDE dataset). These regions are interpreted as the segments of the brain which are mostly used by the 3D-CNN to extract features for brain disease classification. Experimental results show that besides the model interpretability, the proposed GABM method has increased final performance of the classification model in some cases with respect to model parameters. Rapid industrialization and urbanization have resulted in serious environmental deterioration, especially in terms of heavy metal contamination in soil. Arsenic is one of the primary heavy metal contaminants in the soil and possesses a severe threat to all the plants and animals including humans. The conventional methods for analyzing arsenic contamination in soil have tedious, time-consuming sample preparation steps and require laboratory equipped instruments and skilled personnel. The present work demonstrates a novel method for arsenic As(III) detection in the contaminated soil based on field applicable sample preparation and smartphone-based optical sensing. Soil sample preparation has been simplified and optimized using acid extraction and serial application of different solid phase extraction (SPE) cartridges for the removal of interfering ions with high arsenic yield in one step. The acidic extraction and SPE efficiencies were found to be 35.4% and 54.0%, respectively, for arsenic contaminated field soil samples. The quantification of As(III) was performed by aptamer-AuNPs based colorimetric assay with a smartphone coupled optical unit. This aptasensor integrated detection system (ADS) has shown a detection limit of 14.44 ppb for aqueous samples and 1.97 ppm for field soil samples. In the accuracy comparison with ICP-MS, arsenic contaminated field soils from various sources have been tested and the results depicted a highly significant correlation coefficient of 0.997 with an average difference of 1.67 ppm. By integrating all the required analytical steps into a portable format, the presented setup enables on-site tests of arsenic contamination in soil. Direct Black G (DBG) is a typical toxic azo dye with extensive applications but it poses a serious threat to the aquatic ecosystem and humans. It is necessary to efficiently and safely remove DBG from environments by the application of various treatment technologies. A thermophilic microflora previously isolated from the soil can effectively metabolize DBG. However, the molecular basis of DBG degradation by this thermophilic microflora remains unknown. In this study, metagenomic sequencing technology and qRT-PCR have been used to elucidate the functional potential of genes and their modes of action on DBG. A quantitative metaproteomic method was further utilized to identify the relative functional proteins involved. Subsequently, the possible co-metabolic molecular mechanisms of DBG degradation by candidate genes and functional proteins of the thermophilic microflora were illustrated. The combination of metagenomics and metaproteomics to investigate the degradation of DBG by a microflora was reported for the first time in recent literature; this can further provide a deep insight into the molecular degradation mechanism of dye pollutants by natural microflora.