Open-source Methods Research Articles

Comparison of physical activity metrics from two research-grade accelerometers worn on the non-dominant wrist and thigh in children

ABSTRACT This study compared physical activity metrics from the activPAL (AP) worn on the thigh with the ActiGraph worn on the non-dominant wrist using open-source methods. Measures included average acceleration, intensity gradient (IG) and the minimum acceleration value of the most active X mins (MX). Fifty-two children (26 boys; age: 10.4 ± 0.6 years) provided≥1 day (24 h) of concurrent wear time from the activPAL and ActiGraph. Measures tended to be lower from the activPAL versus the ActiGraph. Poor agreement was evident for average acceleration but good for the IG. For the IG, the absolute and relative zones needed to reach equivalence was 4% and 0.4 SDs, respectively and for average acceleration were 10% and 1.2 SDs, respectively. Good agreement was evident for M60, M30, M20, M15 and M10 between devices. Regardless of the reference device used, equivalent estimates for the intensity gradient, M60, M30, M20, M15 and M10 were observed with relative and absolute equivalence zones being≤4% and≤0.5 SDs, respectively. The IG, M60, M30, M20, M15 and M10 appear good candidates for comparing activity data collected from the activPAL and ActiGraph. Future research can use the AP to report on sedentary behaviours as well as PA outcomes.

Lane Transformer: A High-Efficiency Trajectory Prediction Model

Trajectory prediction is a crucial step in the pipeline for autonomous driving because it not only improves the planning of future routes, but also ensures vehicle safety. On the basis of deep neural networks, numerous trajectory prediction models have been proposed and have already achieved high performance on public datasets due to the well-designed model structure and complex optimization procedure. However, the majority of these methods overlook the fact that vehicles’ limited computing resources can be utilized for online real-time inference. We proposed a Lane Transformer to achieve high accuracy and efficiency in trajectory prediction to tackle this problem. On the one hand, inspired by the well-known transformer, we use attention blocks to replace the commonly used Graph Convolution Network (GCN) in trajectory prediction models, thereby drastically reducing the time cost while maintaining the accuracy. In contrast, we construct our prediction model to be compatible with TensorRT, allowing it to be further optimized and easily transformed into a deployment-friendly form of TensorRT. Experiments demonstrate that our model outperforms the baseline LaneGCN model in quantitative prediction accuracy on the Argoverse dataset by a factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25\times $ </tex-math></inline-formula> . Our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7ms$ </tex-math></inline-formula> inference time is the fastest among all open source methods currently available. Our code is publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/mmdzb/Lane-Transformer</uri> .

A general deep learning based framework for 3D reconstruction from multi-view stereo satellite images

In this paper, we propose a general deep learning based framework, named Sat-MVSF, to perform three-dimensional (3D) reconstruction of the Earth’s surface from multi-view optical satellite images. The framework is a complete processing pipeline, including pre-processing, a multi-view stereo (MVS) network for satellite imagery (Sat-MVSNet), and post-processing. The pre-processing handles the geometric and radiometric configuration of the multi-view images and their cropping. The cropped multi-view patches are then fed into Sat-MVSNet, which includes deep feature extraction, rational polynomial camera (RPC) warping, pyramid cost volume construction, regularization, and regression, to obtain the height maps. The error matches are then filtered out and a digital surface model (DSM) is generated in the post-processing. Considering the complexity and diversity of real-world scenes, we also introduce a self-refinement strategy that does not require any ground-truth labels to enhance the performance and robustness of the Sat-MVSF framework. We comprehensively compare the proposed framework with popular commercial software and open-source methods, to demonstrate the potential of the proposed deep learning framework. On the WHU-TLC dataset, where the images are captured with a three-line camera (TLC), the proposed framework outperforms all the other solutions in terms of reconstruction fineness, and also outperforms most of the other methods in terms of efficiency. On the challenging MVS3D dataset, where the images are captured by the WorldView-3 satellite at different times and seasons, the proposed framework also exceeds the existing methods when using the model pretrained on aerial images and the introduced self-refinement strategy, demonstrating a high generalization ability. We also note that the lack of training samples hinders research in this field, and the availability of more high-quality open-source training data will greatly accelerate the research into deep learning based MVS satellite image reconstruction. The code will be available at https://gpcv.whu.edu.cn/data.

Stepping towards More Intuitive Physical Activity Metrics with Wrist-Worn Accelerometry: Validity of an Open-Source Step-Count Algorithm.

Stepping-based targets such as the number of steps per day provide an intuitive and commonly used method of prescribing and self-monitoring physical activity goals. Physical activity surveillance is increasingly being obtained from wrist-worn accelerometers. However, the ability to derive stepping-based metrics from this wear location still lacks validation and open-source methods. This study aimed to assess the concurrent validity of two versions (1. original and 2. optimized) of the Verisense step-count algorithm at estimating step-counts from wrist-worn accelerometry, compared with steps from the thigh-worn activPAL as the comparator. Participants (n = 713), across three datasets, had >24 h continuous concurrent accelerometry wear on the non-dominant wrist and thigh. Compared with activPAL, total daily steps were overestimated by 913 ± 141 (mean bias ± 95% limits of agreement) and 742 ± 150 steps/day with Verisense algorithms 1 and 2, respectively, but moderate-to-vigorous physical activity (MVPA) steps were underestimated by 2207 ± 145 and 1204 ± 103 steps/day in Verisense algorithms 1 and 2, respectively. In summary, the optimized Verisense algorithm was more accurate in detecting total and MVPA steps. Findings highlight the importance of assessing algorithm performance beyond total step count, as not all steps are equal. The optimized Verisense open-source algorithm presents acceptable accuracy for derivation of stepping-based metrics from wrist-worn accelerometry.

Smartphone-Based Gait Cadence to Identify Older Adults with Decreased Functional Capacity

Background: Functional capacity assessment is a critical step in the preoperative evaluation to identify patients at increased risk of cardiac complications and disability after major noncardiac surgery. Smartphones offer the potential to objectively measure functional capacity but are limited by inaccuracy in patients with poor functional capacity. Open-source methods exist to analyze accelerometer data to estimate gait cadence (steps/min), which is directly associated with activity intensity. Here, we used an updated Step Test smartphone application with an open-source method to analyze accelerometer data to estimate gait cadence and functional capacity in older adults. Methods: We performed a prospective observational cohort study within the Frailty, Activity, Body Composition and Energy Expenditure in Aging study at the University of Chicago. Participants completed the Duke Activity Status Index (DASI) and performed an in-clinic 6-min walk test (6MWT) while using the Step Test application on a study smartphone. Gait cadence was measured from the raw accelerometer data using an adaptive empirical pattern transformation method, which has been previously validated. A 6MWT distance of 370 m was used as an objective threshold to identify patients at high risk. We performed multivariable logistic regression to predict walking distance using a priori explanatory variables. Results: Sixty patients were enrolled in the study. Thirty-seven patients completed the protocol and were included in the final data analysis. The median (IQR) age of the overall cohort was 71 (69–74) years, with a body mass index of 31 (27–32). There were no differences in any clinical characteristics or functional measures between participants that were able to walk 370 m during the 6MWT and those that could not walk that distance. Median (IQR) gait cadence for the entire cohort was 110 (102–114) steps/min during the 6MWT. Median (IQR) gait cadence was higher in participants that walked more than 370 m during the 6MWT 112 (108–118) versus 106 (96–114) steps/min; p = 0.0157). The final multivariable model to identify participants that could not walk 370 m included only median gait cadence. The Youden’s index cut-point was 107 steps/min with a sensitivity of 0.81 (95% CI: 0.77, 0.85) and a specificity of 0.57 (95% CI: 0.55, 0.59) and an AUCROC of 0.69 (95% CI: 0.51, 0.87). Conclusions: Our pilot study demonstrates the feasibility of using gait cadence as a measure to estimate functional capacity. Our study was limited by a smaller than expected sample size due to COVID-19, and thus, a prospective study with preoperative patients that measures outcomes is necessary to validate our findings.

A Review of Decision Making Under Deep Uncertainty Applications Using Green Infrastructure for Flood Management

AbstractDecision making under deep uncertainty (DMDU) approaches are well‐suited for making decisions about infrastructure to manage flooding exacerbated by climate change. One important system for climate resilience and flood management is green infrastructure, which refers to a network of natural and semi‐engineered areas that provides ecosystem functions. Green infrastructure is often characterized as a low‐regret strategy with multiple co‐benefits under uncertainty. These attributes enable green infrastructure to be an important adaptation strategy under DMDU frameworks for flood management. However, DMDU analyses that include green infrastructure are still relatively limited, perhaps due to computational or modeling complexity and other barriers. This paper identifies and reviews publications in the flood management literature that use DMDU frameworks and refer to green infrastructure adaptation strategies, in order to identify trends and inform future research. The reviewed publications are categorized according to a variety of performance metrics, climate change scenarios, DMDU metrics, and hydrologic modeling techniques, and represent several adaptation strategies applied to case studies on five continents using a range of data sources and assumptions. This paper highlights a number of solutions that can be employed to facilitate additional research at the intersection of these fields. Primary among these is the transparent documentation and use of open source models, methods, and data. Future research should also focus on communication among different stakeholders, particularly in ensuring definitions, assumptions, and data requirements are clear. These partnerships can facilitate effective application of robust strategies such as green infrastructure for urban adaptation to the effects of climate change.

Abstract 5038: Applying reproducible genomic data science methods for the analysis of a rare tumor type

Abstract Background: The new and emerging discipline of data science demands reproducibility, which is vital in science and presents a significant challenge for high throughput genomics. To further complicate matters, large and complex projects require collaboration by multiple investigators examining and analyzing the massive data of multiple genomic modalities from different perspectives. Today, researchers are rarely able to reproduce published genomic studies for a variety of reasons, for example: i) differences between versions of software used, ii) lack of detail regarding software parameters, iii) lack of data access, and iv) source code not provided. Here we combine our data infrastructure approach with a molecular infrastructure and apply it to the exploration of a multimodality genomic analysis of a patient with a pulmonary pneumocytoma. Methods: Open source methods are utilized that include the SQLite database with R and python packages and custom code. Results: Our approach is able to generate a wide variety of plots and tables for the purposes of exploratory data analysis (EDA) and/or other user-specific analyses, such as finding differentially expressed genes (DEGs). In addition to traditional EDA plots, the R library RCircos is used to visualize multiple NGS studies (eg, 7) in single plot. Differential gene expression (DGE) analysis takes normalized RNA-based read count data and performs a statistical analysis, to find quantitative changes in expression levels between different experimental groups. A DGE analysis report is routinely generated. An abbreviated copy number variation (CNV) report derived from an ultra-low-pass whole genome (tumor/germline) NGS approach is also generated. A Python/Jupyter Notebook utilizing a library from scikit-learn is used to generate a clustergram plot. This approach is used as part of finding the optimal number of clusters for a K-Means analysis. RNA-seq data normalized across three sample types using DESeq2 were used in this example. Finally, advanced pathway analysis is performed for the identification of activated and deactivated molecular pathways. Conclusion: The next evolution in oncology research and cancer care are being driven by data science. In the field of genomic data science, accuracy and reproducibility remains a considerable challenge due to the sheer size, complexity, and dynamic nature of the experimental data plus relative inventiveness of the quantitative biology approaches. The accuracy and reproducibility challenge does not just block the path to new scientific discoveries, more importantly, it may lead to a scenario where critical findings used for medical decision making are found to be incorrect. Our approach has been developed to meet the unmet need of improving accuracy and reproducibility in genomic data science. Specific findings related to the rare pneumocytoma tumor will be presented. Citation Format: Li Ma, Erich Peterson, Mathew Steliga, Jason Muesse, Katy Marino, Konstantinos Arnaoutakis, Ikjae Shin, Donald J. Johann. Applying reproducible genomic data science methods for the analysis of a rare tumor type [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5038.

Orthofaçade-Based Assisted Inspection Method for Buildings

Building façade assessment could be performed in a more efficient way using a multidisciplinary approach and modern technologies. This study proposes the orthofaçade-based assisted inspection method (AIM), universal and applicable to different types of façade cladding and suitable for application in the condition assessment of inaccessible building façades or high-rise and large structures of all kinds. The AIM method offers a multidisciplinary approach by combining unmanned aerial vehicle (UAV) technology, electronic tachymetry, and digital image processing techniques (photogrammetry and open-source computer vision methods). The method was verified in a case study performed on a high-rise building façade. On-site data acquisition of high-resolution images of façade and control points was conducted by UAV and tachymetry. The data were further processed in photogrammetric software in order to generate a georeferenced orthofaçade. Crack detection was performed at pixel level via computer code using the OpenCV library methods. The established diagnostic model, defined by control points, enables precise determination of crack location. Crack length, width, or area could be calculated based on the coordinates of its points, by performing simple mathematical operations. The AIM method provides automation of crack detection and precise determination of location and geometrical parameters of detected crack.

A LiDAR SLAM With PCA-Based Feature Extraction and Two-Stage Matching

Simultaneous localization and mapping (SLAM) has been studied for decades in the field of robotics, in which light detection and ranging (LiDAR) is widely used in various application areas benefiting from its accessibility of direct, accurate, and reliable 3-D measurements. However, the performance of LiDAR SLAM may be degraded when running in degenerate scenario, which makes it still a challenging problem to realize real-time, robust, and accurate state estimation in complex environments. In this article, we propose a keyframe-based 3-D LiDAR SLAM using an accurate principal component analysis (PCA)-based feature extraction method and an efficient two-stage matching strategy, toward a more robust, accurate, and globally consistent estimation performance. The effectiveness and performance are demonstrated and evaluated by comparing our method with the state-of-the-art open-source methods, LOAM and LeGo-LOAM, on KITTI datasets and custom datasets collected by our sensor system. The experimental results show obvious improvement of odometry accuracy and mapping consistency without loss of real-time performance.

A Hybrid Machine Learning Model for Predicting USA NBA All-Stars

Throughout the modern age, sports have been a very important part of human existence. As our documentation of sports has become more advanced, so have the prediction capabilities. Presently, analysts keep track of a massive amount of information about each team, player, coach, and matchup. This collection has led to the development of unparalleled prediction systems with high levels of accuracy. The issue with these prediction systems is that they are proprietary and very costly to maintain. In other words, they are unusable by the average person. Sports, being one of the most heavily analyzed activities on the planet, should be accessible to everyone. In this paper, a preliminary system for using publicly available statistics and open-source methods for predicting NBA All-Stars is introduced and modified to improve the accuracy of the predictions, which reaches values close to 0.9 in raw accuracy, and higher than 0.9 in specificity.

Portable, open-source solutions for estimating wrist position during reaching in people with stroke

Arm movement kinematics may provide a more sensitive way to assess neurorehabilitation outcomes than existing metrics. However, measuring arm kinematics in people with stroke can be challenging for traditional optical tracking systems due to non-ideal environments, expense, and difficulty performing required calibration. Here, we present two open-source methods, one using inertial measurement units (IMUs) and another using virtual reality (Vive) sensors, for accurate measurements of wrist position with respect to the shoulder during reaching movements in people with stroke. We assessed the accuracy of each method during a 3D reaching task. We also demonstrated each method’s ability to track two metrics derived from kinematics-sweep area and smoothness-in people with chronic stroke. We computed correlation coefficients between the kinematics estimated by each method when appropriate. Compared to a traditional optical tracking system, both methods accurately tracked the wrist during reaching, with mean signed errors of 0.09 ± 1.81 cm and 0.48 ± 1.58 cm for the IMUs and Vive, respectively. Furthermore, both methods’ estimated kinematics were highly correlated with each other (p < 0.01). By using relatively inexpensive wearable sensors, these methods may be useful for developing kinematic metrics to evaluate stroke rehabilitation outcomes in both laboratory and clinical environments.

Treatment Planning System for Clinical Translation of Electron FLASH Radiotherapy

Electron ultrahigh dose rate FLASH radiotherapy (UHDR/eFLASH-RT) is currently being extensively tested and modeled, and was studied in several rodent and large animal experiments and one clinical cancer patient. These studies suggest prospective dosimetry assessments are necessary to ensure safe delivery and widespread clinical translation of eFLASH-RT would clearly benefit from prior dose prediction and a treatment planning process that accounts for anatomical heterogeneity and complex geometries. In this study, a GAMOS Monte Carlo beam model was developed and implemented in a clinical treatment planning system for a modified UHDR electron LINAC to demonstrate eFLASH-RT planning of patients.The gantry head was modelled without scattering foils or targets, representative of the LINAC modifications. Centered at the nominal energy of 10 MeV, the energy spectrum (σE) was varied to match the central axis percent depth dose (PDD) profiles of an open field measured with radiographic film. The beam's emittance cone angle (θcone) was determined by matching calculated and film measured lateral profiles, at several depths. The beam model and its configuration in the treatment planning system were validated with film measured lateral profiles and PDD's of the open field and nominal fields for clinical applicators. A GAMOS forward dose calculation was conducted for a mouse whole brain treatment and an eFLASH-RT plan was compared to a conventional electron plan for a human patient with a rib metastasis.Our eFLASH beam model agrees best with measurements at σE = 0.5MeV and θcone = 3.9o ± 0.2o. The model and its treatment planning system configuration were validated to clinically acceptable accuracy (the absolute average error was within 1.5% for in water lateral, 3% for in air lateral, and 2% for PDD's). The forward dose calculation showed dose was delivered to the entire mouse brain with adequate conformality (90% of the tumor volume receiving 90% of the prescribed dose). The comparison plans for the patient undergoing treatment for a rib metastasis demonstrated the capability to achieve an acceptable eFLASH-RT plan with routine accessories (90% of the tumor receiving 95% and 90% of the prescribed dose for eFLASH and conventional, respectively) in relatively complex geometry (right posterior oblique treatment with patient specific applicator cutout).To the best of our knowledge, this study presents the first functional beam model commissioned in a clinical TPS for eFLASH-RT, enabling treatment planning and evaluation with minimal deviation from conventional radiotherapy workflow. By producing plans with the quality comparable to conventional plans, specifically for human patients involving complex geometries and tissue heterogeneity, the clinical translation of eFLASH-RT can be expedited. The open-source methods (described in https://github.com/mr3536/eFLASHBeamModeltoTPS) can model other eFLASH irradiators with different beam characteristics.

Signal-based optical map alignment.

In genomics, optical mapping technology provides long-range contiguity information to improve genome sequence assemblies and detect structural variation. Originally a laborious manual process, Bionano Genomics platforms now offer high-throughput, automated optical mapping based on chips packed with nanochannels through which unwound DNA is guided and the fluorescent DNA backbone and specific restriction sites are recorded. Although the raw image data obtained is of high quality, the processing and assembly software accompanying the platforms is closed source and does not seem to make full use of data, labeling approximately half of the measured signals as unusable. Here we introduce two new software tools, independent of Bionano Genomics software, to extract and process molecules from raw images (OptiScan) and to perform molecule-to-molecule and molecule-to-reference alignments using a novel signal-based approach (OptiMap). We demonstrate that the molecules detected by OptiScan can yield better assemblies, and that the approach taken by OptiMap results in higher use of molecules from the raw data. These tools lay the foundation for a suite of open-source methods to process and analyze high-throughput optical mapping data. The Python implementations of the OptiTools are publicly available through http://www.bif.wur.nl/.

Count- versus MAD-based accelerometry-assessed movement behaviors and associations with child adiposity and fitness.

Estimations of time spent sedentary and in various physical activity intensities may vary according to data reduction methods applied. This study compared associations between children's accelerometer data and adiposity and fitness markers using open source (mean amplitude deviation, MAD) and proprietary (counts) data reduction methods. Complete-case accelerometer, adiposity (Body Mass Index z-score, waist circumference), and fitness (cardiorespiratory, musculoskeletal) data from 118 children (10.4±0.6years, 49% girls) were analyzed. Estimates of sedentary behavior, light-, moderate-, vigorous- (VPA), and moderate- to vigorous-intensity (MVPA) physical activity were calculated using count- and MAD-based data reduction methods. Linear regression models between time in movement behaviours and fitness and adiposity markers were conducted. Significant differences in estimates of time spent in all intensities were observed between MAD-based and count-based methods. Both methods produced evidence to suggest that sedentary behavior was detrimentally, and physical activity (any intensity) was beneficially, associated with waist circumference. MVPA and VPA were beneficially associated with fitness markers using both data reduction measures. Overall, findings suggest that estimates of sedentary time and physical activity were not comparable. However, the strength and direction of the associations obtained between the different data reduction methods and adiposity and fitness outcomes were fairly comparable, with both methods finding stronger associations for VPA compared to MVPA. This suggests that future studies may be able to pool data using different data reduction approaches when examining associations between activity and health risk factors, albeit with caution.

Aninternationalvirtualhackathon tobuildtools for theanalysis ofstructuralvariants withinspeciesranging fromcoronaviruses tovertebrates.

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine &DNAnexushackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2.Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformaticsprovides valuable insights for both participants and the research community.

Research Note: Comparing the Gay and Trans Panic Defenses

During the past fifty years, scholars have identified the following four ways that criminal defendants in the United States have used gay and trans panic defenses in murder cases involving victims who were gay men and transgender women: (1) insanity, (2) diminished capacity, (3) provocation, and (4) self-defense. While scholars have studied the first three defenses for gay men, little scholarship exists about the gay panic cases involving claims of self-defense. Additionally, there is a paucity of research regarding the trans panic defense. To address these empirical gaps, this research note uses open-source methods to study similarities between 99 homicide cases that occurred in the United States where criminal defendants made a gay or trans panic argument.

Open-source methods for estimating health risks of fine particulate matter from coal-fired power plants: A demonstration from Karachi, Pakistan

Environmental impact assessments (EIAs) effectiveness and accessibility by the public is determined by local environmental enforcement and regulation. Massive energy infrastructure projects like China's Belt and Road initiative (BRI) could accentuate social and economic costs, as regulation is deferred to host countries with varying levels of domestic enforcement. To fill major gaps in the public evaluation of EIA practices and results, our objective here is to present an open-source approach for evaluating localized air pollution health impacts for BRI-financed coal-fired power plants. This analysis focuses on one specific problem, additional attributable mortality from stroke, ischemic heart disease, and chronic obstructive pulmonary disease for adults ages 25–84 from fine particulate matter (PM2.5), known to be major risk factor for premature deaths globally. As a concrete example, we examine the case of the Port Qasim power plant located near Karachi, Sindh Province, Pakistan. We present results for two main scenarios: a base case, assuming fully operational pollution control technologies, in particular electrostatic precipitators (ESP); and an alternative case without ESPs. All material and methods for replicating this analysis for the case study and for other projects are available in our institutional open repository. Our approach can be applied immediately to virtually any source location in the world, due to the public and global availability of input data. SynopsisWe present an open-source approach for evaluating localized health impacts due to fine particulate matter emitted from coal-fired power plants.

Comparability of Postural and Physical Activity Metrics from Different Accelerometer Brands Worn on the Thigh: Data Harmonization Possibilities

ABSTRACT The aim was to establish which postural and physical activity outcomes are comparable across different accelerometer brands worn on the thigh when processed using open-source methods. Twenty participants wore four accelerometers (Axivity, ActiGraph, activPAL, GENEActiv) for three free-living days. Postural and physical activity outputs (average acceleration, intensity gradient, intensity of the most active 30 min, 60 min, and 8 h) were generated. Postural outputs: Mean absolute percent errors (MAPEs) were low, reliability excellent, and equivalency within the 5% zone across all monitor pairings for sitting/lying and upright times, but not specific lying postures. Physical activity outputs: MAPEs were higher and reliability lower than for sitting/lying and upright time. However, the majority of the outcomes were within the 10% equivalency zone for Axivity/GENEActiv and Axivity/ActiGraph pairings. Total sitting/lying and upright times show strong potential for harmonization across studies utilizing different thigh-worn accelerometers. The majority of acceleration outcomes compare well for Axivity, GENEActiv, and ActiGraph.

An international virtual hackathon to build tools for the analysis of structural variants within species ranging from coronaviruses to vertebrates

In October 2020, 62 scientists from nine nations worked together remotely in the Second Baylor College of Medicine & DNAnexus hackathon, focusing on different related topics on Structural Variation, Pan-genomes, and SARS-CoV-2 related research. The overarching focus was to assess the current status of the field and identify the remaining challenges. Furthermore, how to combine the strengths of the different interests to drive research and method development forward. Over the four days, eight groups each designed and developed new open-source methods to improve the identification and analysis of variations among species, including humans and SARS-CoV-2. These included improvements in SV calling, genotyping, annotations and filtering. Together with advancements in benchmarking existing methods. Furthermore, groups focused on the diversity of SARS-CoV-2. Daily discussion summary and methods are available publicly at https://github.com/collaborativebioinformatics provides valuable insights for both participants and the research community.

A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data

Pedestrian accessibility is an important factor in urban transport and land use policy and critical for creating healthy, sustainable cities. Developing and evaluating indicators measuring inequalities in pedestrian accessibility can help planners and policymakers benchmark and monitor the progress of city planning interventions. However, measuring and assessing indicators of urban design and transport features at high resolution worldwide to enable city comparisons is challenging due to limited availability of official, high‐quality, and comparable spatial data, as well as spatial analysis tools offering customizable frameworks for indicator construction and analysis. To address these challenges, this study develops an open source software framework to construct pedestrian accessibility indicators for cities using open and consistent data. It presents a generalized method to consistently measure pedestrian accessibility at high resolution and spatially aggregated scale, to allow for both within‐ and between‐city analyses. The open source and open data methods developed in this study can be extended to other cities worldwide to support local planning and policymaking. The software is made publicly available for reuse in an open repository.