Explore the vast range of titles available.

Recent long-read assemblies often exceed the quality and completeness of available reference genomes, making validation challenging. Here we present Merqury, a novel tool for reference-free assembly evaluation based on efficient k-mer set operations. By comparing k-mers in a de novo assembly to those found in unassembled high-accuracy reads, Merqury estimates base-level accuracy and completeness. For trios, Merqury can also evaluate haplotype-specific accuracy, completeness, phase block continuity, and switch errors. Multiple visualizations, such as k-mer spectrum plots, can be generated for evaluation. We demonstrate on both human and plant genomes that Merqury is a fast and robust method for assembly validation.

Assembly validation is a key part of product design. Current methods, such as physical prototyping are time-consuming and do not offer immediate validation results. Assembly motion simulation systems have been proposed as a solution to this problem. However, widespread adoption of such systems is hindered due to their ties to proprietary computer aided design (CAD) software or expensive and often cumbersome hardware. Recently, virtual/augmented reality (VR/AR) technologies and simulation have been heralded as two of the key enabling factors of Industry 4.0. Collective interests in these technologies by industry and community have brought many low-cost software and hardware tools to the market, which opens a gateway to achieving assembly validation at a much lower cost. This paper presents an assembly validation system that is independent of CAD packages, interoperable and implemented using relatively low-cost and commercially available hardware and software tools. The system features intuitive bare-hand manipulation of part models through a virtual hand model that tracks the hands. Collision detection and physics modelling allow for hand-part and part-part interactions to be natural, thus validating assembly interactions. An assembly feature extraction algorithm has also been implemented to analyse the planar face features of the part models to detect possible mating assembly features between parts concerned. A constraint management system considers identified mating features and determines the allowable motion of parts once constraints are applied and removed. Pulling force is used to facilitate the removal of constraints.

With the advent of short-reads-based genome sequencing approaches, large number of organisms are being sequenced all over the world. Most of these assemblies are done using some de novo short read assemblers and other related approaches. However, the contigs produced this way are prone to wrong assembly. So far, there is a conspicuous dearth of reliable tools to identify mis-assembled contigs. Mis-assemblies could result from incorrectly deleted or wrongly arranged genomic sequences. In the present work various factors related to sequence, sequencing and assembling have been assessed for their role in causing mis-assembly by using different genome sequencing data. Finally, some mis-assembly detecting tools have been evaluated for their ability to detect the wrongly assembled primary contigs, suggesting a lot of scope for improvement in this area. The present work also proposes a simple unsupervised learning-based novel approach to identify mis-assemblies in the contigs which was found performing reasonably well when compared to the already existing tools to report mis-assembled contigs. It was observed that the proposed methodology may work as a complementary system to the existing tools to enhance their accuracy.

The recent advances in Industry 4.0 have promoted manufacturing industries towards the use of augmented reality (AR), virtual reality (VR), and mixed reality (MR) for visualization and training applications. AR assistance is extremely helpful in assembly task visualization during the stages of product assembly and in disassembly plan visualization during the repair and maintenance of a product/system. Generating such assembly and disassembly task animations consume a lot of time and demands skilled user intervention. In assembly or disassembly processes, each operation must be validated for geometric feasibility regarding its practical implementation in the real-time product. In this manuscript, a novel method for automated assembly task simulation with improved geometric feasibility testing is proposed and verified. The proposed framework considers the assembly sequence plan as input in the form of textual instructions and generates a virtual assembly task plan for the product; furthermore, these instructions are used to ensure there are no collisions using a combination of multiple linear directions. Once the textual instructions achieve geometric feasibility for the entire assembly operation, the visual animations of the assembly operations are successively produced in a game engine and are integrated with the AR platform in order to visualize them in the physical environment. The framework is implemented on various products and validated for its correctness and completeness.

A large array of species distribution model (SDM) approaches has been developed for explaining and predicting the occurrences of individual species or species assemblages. Given the wealth of existing models, it is unclear which models perform best for interpolation or extrapolation of existing data sets, particularly when one is concerned with species assemblages. We compared the predictive performance of 33 variants of 15 widely applied and recently emerged SDMs in the context of multispecies data, including both joint SDMs that model multiple species together, and stacked SDMs that model each species individually combining the predictions afterward. We offer a comprehensive evaluation of these SDM approaches by examining their performance in predicting withheld empirical validation data of different sizes representing five different taxonomic groups, and for prediction tasks related to both interpolation and extrapolation. We measure predictive performance by 12 measures of accuracy, discrimination power, calibration, and precision of predictions, for the biological levels of species occurrence, species richness, and community composition. Our results show large variation among the models in their predictive performance, especially for communities comprising many species that are rare. The results do not reveal any major trade-offs among measures of model performance; the same models performed generally well in terms of accuracy, discrimination, and calibration, and for the biological levels of individual species, species richness, and community composition. In contrast, the models that gave the most precise predictions were not well calibrated, suggesting that poorly performing models can make overconfident predictions. However, none of the models performed well for all prediction tasks. As a general strategy, we therefore propose that researchers fit a small set of models showing complementary performance, and then apply a cross-validation procedure involving separate data to establish which of these models performs best for the goal of the study.

Suet Leung, Jiangchun Xu and colleagues report exome sequencing of 22 gastric cancers. They found that genes involved in chromatin modification were commonly mutated, including ARID1A encoding an SWI/SNF chromatin-remodeling complex component that had a high rate of mutation. Gastric cancer is a heterogeneous disease with multiple environmental etiologies and alternative pathways of carcinogenesis 1 , 2 . Beyond mutations in TP53 , alterations in other genes or pathways account for only small subsets of the disease. We performed exome sequencing of 22 gastric cancer samples and identified previously unreported mutated genes and pathway alterations; in particular, we found genes involved in chromatin modification to be commonly mutated. A downstream validation study confirmed frequent inactivating mutations or protein deficiency of ARID1A , which encodes a member of the SWI-SNF chromatin remodeling family, in 83% of gastric cancers with microsatellite instability (MSI), 73% of those with Epstein-Barr virus (EBV) infection and 11% of those that were not infected with EBV and microsatellite stable (MSS). The mutation spectrum for ARID1A differs between molecular subtypes of gastric cancer, and mutation prevalence is negatively associated with mutations in TP53 . Clinically, ARID1A alterations were associated with better prognosis in a stage-independent manner. These results reveal the genomic landscape, and highlight the importance of chromatin remodeling, in the molecular taxonomy of gastric cancer.

Background Ergonomic behaviors play a crucial role in preventing work-related musculoskeletal disorders (WMSDs). To measure these behaviors, this research aimed to develop and evaluate an ergonomic behaviors tool (EBET) based on the Social Cognitive Theory (SCT) among women workers on assembly lines (WwAL). Methods The study was conducted from December 2022 to January 2023 with a focus on the psychometric assessment of EBET. Initially, a literature review and interviews were carried out to identify crucial concepts and primary items. The questionnaire’s validity was evaluated using the Content Validity Ratio (CVR) and the Content Validity Index (CVI). To determine the domains of the tool, construct validity was examined by administering the items to 270 eligible women. The reliability of the tool was assessed using McDonald’s Omega coefficient. Results From a total of 67 primary items, 50 were confirmed. The study demonstrated good validity with CVR = 0.92 and CVI = 0.97, along with reliable results indicated by McDonald’s Omega coefficient of 0.74. The exploratory factor analysis (EFA) revealed ten distinct dimensions: outcome expectations, outcome expectancies, normative beliefs, perceived barriers, social support, observational learning, reinforcement, behavioral skills, self-efficacy, and intention. Together, these dimensions accounted for 66.25% of the variance in the data. Additionally, the confirmatory factor analysis results supported the presence of these ten constructs and demonstrated a satisfactory fit. Conclusions EBET is a dependable and valid instrument for evaluating the ergonomic behaviors of workers, utilizing the principles of SCT. Researchers can employ EBET to gather data and implement suitable training interventions to enhance ergonomic behavior among WwAL. However, it is crucial to recognize that EBET may not encompass all facets of ergonomic behaviors. Therefore, it is imperative for future research to prioritize the evaluation of EBET’s suitability among diverse worker populations and to consider additional dimensions of ergonomics to ensure its wider applicability and effectiveness.

This study investigates the effectiveness of the Xsens motion capture system in performing ergonomic analysis compared to traditional manual assessments by experts in the specific environment of assembly lines. A comprehensive literature review emphasizes the need to investigate the reliability of new, promising high-tech systems. The main objective was therefore to compare the Ergonomic Assessment Worksheet (EAWS) assessment approach performed with Xsens motion capture technology and Process Simulate V16 software with the manual method using EAWS digital prepared by experts in the controlled workflow. The greatest value of the research conducted lies in the novel integration of the state-of-the-art Xsens motion capture technology with the Process Simulate V16 software environment and the use of the licensed EAWS ergonomic method and Methods-Time Measurement Universal Analyzing System (MTM-UAS). The results are presented in the form of a case study. The results show a large similarity between the whole-body results and a large difference in the upper limb results, confirming the initial benefits of the Xsens equipment but also pointing to the need to verify its reliability on larger samples. The study highlights the potential of integrating Xsens motion capture data into ergonomic assessments and tuning of the assembly line to increase productivity and worker safety.

Variant benchmarking is often performed by comparing a test callset to a gold standard set of variants. In repetitive regions of the genome, it may be difficult to establish what is the truth for a call, for example, when different alignment scoring metrics provide equally supported but different variant calls on the same data. Here, we provide an alternative approach, TT-Mars, that takes advantage of the recent production of high-quality haplotype-resolved genome assemblies by providing false discovery rates for variant calls based on how well their call reflects the content of the assembly, rather than comparing calls themselves.