Project description:Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. This survey summarizes the current usage of machine learning methods in human movement biomechanics and highlights best practices that will enable critical evaluation of the literature. We carried out a PubMed/Medline database search for original research articles that used machine learning to study movement biomechanics in patients with musculoskeletal and neuromuscular diseases. Most studies that met our inclusion criteria focused on classifying pathological movement, predicting risk of developing a disease, estimating the effect of an intervention, or automatically recognizing activities to facilitate out-of-clinic patient monitoring. We found that research studies build and evaluate models inconsistently, which motivated our discussion of best practices. We provide recommendations for training and evaluating machine learning models and discuss the potential of several underutilized approaches, such as deep learning, to generate new knowledge about human movement. We believe that cross-training biomechanists in data science and a cultural shift toward sharing of data and tools are essential to maximize the impact of biomechanics research.
Project description:The academia-industry interface is important, and, despite challenges that inevitably occur, bears the potential for positive synergies to emerge. Perceived barriers to wider collaboration in academia-industry oncology research in Europe need to be addressed, current academic cooperative group and industry models for collaboration need to be discussed, and a common terminology to facilitate understanding of both sectors' concerns needs to be established with an eye towards improving academia-industry partnerships on clinical trials for the benefit of patients with cancer. CAREFOR (Clinical Academic Cancer Research Forum), a multi-stakeholder platform formed to improve the direction for academic clinical trials in the field of oncology in Europe, formed the CAREFOR-Industry Working Group comprised of experienced professionals from European academic cooperative groups joined by industry representatives selected based on their activities in the area of medical oncology. They jointly discussed academic cooperative groups, clinical trials conducted between academic cooperative groups and industry, examples of successful collaborative models, common legal negotiation points in clinical trial contracts, data access, and principles of interaction. Four principles of interaction between the academia and industry are proposed: (1) clarify the roles and responsibilities of all partners involved in the study, (2) involve legal teams from an early stage; (3) acknowledge that data is an important output of the study, (4) agree on the intent of the trial prior to its start. The CAREFOR-Industry Working Group describes current models, challenges, and effective strategies for academia-industry research in Europe with an eye towards improving academia-industry partnerships on clinical trials for patients with cancer. Current perceived challenges are explained, and future opportunities/recommendations for improvement are described for the areas of most significant impact. Challenges are addressed from both the academic and industry perspectives, and principles of interaction for the optimal alignment between academia and industry in selected areas are proposed.
Project description:Retention among academic medicine faculty is problematic, and there has been a decline in the number of physicians pursuing careers in academia. The education of future physicians relies upon physicians who pursue careers in academic medicine. Therefore, efforts must be taken to increase the percentage of physicians who conduct research and/or teach medical trainees. Recognizing this need, the New York Institute of Technology College of Osteopathic Medicine (NYITCOM) established the Academic Medicine Scholars Program ("Scholars Program"), which was designed to prepare outstanding osteopathic medical students for a career in academic medicine. Here we aim to determine the extent to which participants in NYITCOM's Scholars Program go on to pursue research and teaching endeavors during their residency and/or fellowship programs. An anonymous survey was administered to participants in the Scholars Program from 2012 through 2018 and asked about the participants' research and teaching experiences at the following time points: during the Scholars Program, residency, and fellowship, if applicable. Participation in the program led to a significant increase in survey respondents' teaching and research skills and an increased participation in scholarly activity as compared with the national average. The results also demonstrated that the program assisted alumni in securing positions in competitive residency and fellowship programs. As residents and fellows, alumni continued to pursue scholarly endeavors, primarily by publishing abstracts and posters, attending both regional and national conferences, and delivering lectures. We are hopeful that other medical schools will take part in producing capable academic medicine physicians by incorporating a similar program into their curriculum.
Project description:Academic Core Facilities are optimally situated to improve the quality of preclinical research by implementing quality control measures and offering these to their users.
Project description:Enhanced cross-linking immunoprecipitation (eCLIP) featuring a size-matched input control has been recently applied to profile the binding sites of more than one hundred RNA binding proteins (RBPs). However computational pipelines and quality control metrics needed to process CLIP data at scale have yet to be well defined. Here, we describe our ENCODE eCLIP processing pipeline (https://github.com/YeoLab/eclip), enabling users to go from raw reads to processed peaks that are enriched above paired input, reproducible across biological replicates, and can be directly compared against the public ENCODE eCLIP resource. In particular, we discuss processing steps designed to address common artifacts, including properly quantifying unique RNA fragments bound by both unique genomic- and repetitive element-mapped reads. Using manual quality annotation of 350 ENCODE eCLIP experiments, we develop metrics for quality assessment of eCLIP experiments prior to and after sequencing, including library yield, number of unique fragments in the library, total binding relative information, and biological reproducibility. In particular, we quantify the commonly believed linkage between depth of sequencing and peak discovery, and derive methods for estimating required sequencing depth based on pre-sequencing metrics. Finally we provide recommendations for the common question of integrating RBP binding information with RNA-seq to generate splicing maps representing the positional effect of binding on alternative splicing. These pipelines and QC metrics enable large-scale processing and analysis of eCLIP data, and will help to standardize rigorous analysis of RBP binding data.
Project description:Enhanced cross-linking immunoprecipitation (eCLIP) featuring a size-matched input control has been recently applied to profile the binding sites of more than one hundred RNA binding proteins (RBPs). However computational pipelines and quality control metrics needed to process CLIP data at scale have yet to be well defined. Here, we describe our ENCODE eCLIP processing pipeline (https://github.com/YeoLab/eclip), enabling users to go from raw reads to processed peaks that are enriched above paired input, reproducible across biological replicates, and can be directly compared against the public ENCODE eCLIP resource. In particular, we discuss processing steps designed to address common artifacts, including properly quantifying unique RNA fragments bound by both unique genomic- and repetitive element-mapped reads. Using manual quality annotation of 350 ENCODE eCLIP experiments, we develop metrics for quality assessment of eCLIP experiments prior to and after sequencing, including library yield, number of unique fragments in the library, total binding relative information, and biological reproducibility. In particular, we quantify the commonly believed linkage between depth of sequencing and peak discovery, and derive methods for estimating required sequencing depth based on pre-sequencing metrics. Finally we provide recommendations for the common question of integrating RBP binding information with RNA-seq to generate splicing maps representing the positional effect of binding on alternative splicing. These pipelines and QC metrics enable large-scale processing and analysis of eCLIP data, and will help to standardize rigorous analysis of RBP binding data.
Project description:This submission is a dataset of single-nucleus multi-omics of uninjured and injured spinal cords of mice harvested and profiled using 10x Multiome ATAC + Gene Expression kit.
Project description:This article presents new teaching methodologies implemented in subjects in the Ground Engineering Area. Specifically, it focuses on a series of activities carried out due to the appearance of the COVID-19 pandemic that resulted in restrictions on class attendance. The new teaching methodologies brought about substantial changes in the way students learn and are assessed. For the practice sessions, a series of videos were prepared so students could attend and take part in the laboratory practices remotely. As regards the final theory exam, a comprehensive multiple-choice question bank was made available to students prior to the exam to consolidate the concepts seen in the master classes, which we call training and learning. We evaluated the impact of these new methodologies, implemented during two academic years, through the analysis of voluntary and anonymous student surveys and a series of indicators related to the results of the final exams. After analyzing the impact of the new teaching methodologies, we conclude that students are positive about the video experience for laboratory practices, but only as a complementary activity to in-person laboratory sessions. The students also stated that they would like the multiple-choice question bank to continue to be available in successive academic years. Improvements in the final grades of the theory exams demonstrate the success of this new teaching methodology.
Project description:R-loops represent an abundant class of large non-B DNA structures in genomes. Even though they form transiently and at modest frequencies, interfering with R-loop formation or dissolution significantly impacts genome stability. Addressing the mechanism(s) of R-loop-mediated genome destabilization requires a precise characterization of their distribution in genomes. A number of independent methods have been developed to visualize and map R-loops, but their results are at times discordant, leading to confusion. Here we review the main existing methodologies underlying R-loop mapping and assess their limitations and the robustness of existing datasets. We offer a set of best practices to improve the reproducibility of maps, hoping that such guidelines could be useful for authors and referees alike. Finally, we offer a possible resolution to the apparent contradictions in R-loop mapping outcomes between antibody-based and RNase H1-based mapping approaches.