ased approach is useful to rank the phenolics that are associated with CSC genes. Our results suggested some phenolics are potential molecules for CSC-related cancer treatment. Our PR ranking based approach is useful to rank the phenolics that are associated with CSC genes. Our results suggested some phenolics are potential molecules for CSC-related cancer treatment. The re-introduction of medical students into healthcare systems struggling with the COVID-19 pandemic raises concerns as to whether they will be supported when confronted with death and dying patients in resource-limited settings and with reduced support from senior clinicians. Better understanding of how medical students respond to death and dying will inform educationalists and clinicians on how to best support them. We adopt Krishna's Systematic Evidence Based Approach to carry out a Systematic Scoping Review (SSR in SEBA) on the impact of death and dying on medical students. https://www.selleckchem.com/products/idasanutlin-rg-7388.html This structured search process and concurrent use of thematic and directed content analysis of data from six databases (Split Approach) enhances the transparency and reproducibility of this review. Seven thousand six hundred nineteen were identified, 149 articles reviewed and 52 articles included. The Split Approach revealed similar themes and categories that correspond to the Innate, Individual, Relational and Societal domains in the Ring Theory of Personhood. Facing death and dying amongst their patients affect how medical students envisage their personhood. This underlines the need for timely, holistic and longitudinal support systems to ensure that problems faced are addressed early. To do so, there must be effective training and a structured support mechanism. Facing death and dying amongst their patients affect how medical students envisage their personhood. This underlines the need for timely, holistic and longitudinal support systems to ensure that problems faced are addressed early. To do so, there must be effective training and a structured support mechanism.In 2019, a conference in Israel showcased new frontiers in technology in healthcare, highlighting research conducted in Israel as well as across the globe. At the time, no one realized how critical-and ubiquitous-some of these technologies would become. In the wake of a global pandemic, the ability to provide healthcare remotely has become ever more important. We explore some Israeli innovations and consider how healthcare may be permanently changed. Fingerprint biometrics play an essential role in authentication. It remains a challenge to match fingerprints with the minutiae or ridges missing. Many fingerprints failed to match their targets due to the incompleteness. In this work, we modeled the fingerprints with Bezier curves and proposed a novel algorithm to detect and restore fragmented ridges in incomplete fingerprints. In the proposed model, the Bezier curves' control points represent the fingerprint fragments, reducing the data size by 89% compared to image representations. The representation is lossless as the restoration from the control points fully recovering the image. Our algorithm can effectively restore incomplete fingerprints. In the SFinGe synthetic dataset, the fingerprint image matching score increased by an average of 39.54%, the ERR (equal error rate) is 4.59%, and the FMR1000 (false match rate) is 2.83%, these are lower than 6.56% (ERR) and 5.93% (FMR1000) before restoration. In FVC2004 DB1 real fingerprint dataset, the average matching score increased by 13.22%. The ERR reduced from 8.46% before restoration to 7.23%, and the FMR1000 reduced from 20.58 to 18.01%. Moreover, We assessed the proposed algorithm against FDP-M-net and U-finger in SFinGe synthetic dataset, where FDP-M-net and U-finger are both convolutional neural network models. The results show that the average match score improvement ratio of FDP-M-net is 1.39%, U-finger is 14.62%, both of which are lower than 39.54%, yielded by our algorithm. Experimental results show that the proposed algorithm can successfully repair and reconstruct ridges in single or multiple damaged regions of incomplete fingerprint images, and hence improve the accuracy of fingerprint matching. Experimental results show that the proposed algorithm can successfully repair and reconstruct ridges in single or multiple damaged regions of incomplete fingerprint images, and hence improve the accuracy of fingerprint matching. Genome assembly is fundamental for de novo genome analysis. Hybrid assembly, utilizing various sequencing technologies increases both contiguity and accuracy. While such approaches require extra costly sequencing efforts, the information provided millions of existed whole-genome sequencing data have not been fully utilized to resolve the task of scaffolding. Genetic recombination patterns in population data indicate non-random association among alleles at different loci, can provide physical distance signals to guide scaffolding. In this paper, we propose LDscaff for draft genome assembly incorporating linkage disequilibrium information in population data. We evaluated the performance of our method with both simulated data and real data. We simulated scaffolds by splitting the pig reference genome and reassembled them. Gaps between scaffolds were introduced ranging from 0 to 100KB. The genome misassembly rate is 2.43% when there is no gap. Then we implemented our method to refine the Giant Panda genome and the donkey genome, which are purely assembled by NGS data. After LDscaff treatment, the resulting Panda assembly has scaffold N50 of 3.6MB, 2.5 times larger than the original N50 (1.3MB). The re-assembled donkey assembly has an improved N50 length of 32.1MB from 23.8MB. Our method effectively improves the assemblies with existed re-sequencing data, and is an potential alternative to the existing assemblers required for the collection of new data. Our method effectively improves the assemblies with existed re-sequencing data, and is an potential alternative to the existing assemblers required for the collection of new data. Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans. The 3D convolutional neural network (CNN)-based classification model of AD from cognitively normal (CN) yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform.