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A Descriptive Analysis of the Execution of the Expert Field Medical Badge Competition with Mitigation Measures during the COVID-19 Pandemic.

Med J (Ft Sam Houst Tex) 2022 Jul-Sep(Per 22-07/08/09):12-22

Expert Field Medical Badge Test Control Office, United States Army Medical Center of Excellence, Fort Sam Houston, TX.

Objective: Introduction: In September 2020, the 2nd Stryker Brigade Combat Team of the 4th Infantry Division at Fort Carson, CO, executed an Expert Field Medical Badge (EFMB) event, unique in its implementation of Coronavirus Disease 2019 (COVID-19) mitigation measures. We conducted a descriptive analysis of our experience to inform future EFMB events.

Methods: We planned and resourced the EFMB competition in accordance with the Army Medical Department Center and School Pamphlet 350-10. Read More

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ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild.

IEEE Trans Pattern Anal Mach Intell 2022 Aug 8;PP. Epub 2022 Aug 8.

This paper investigates the task of 2D whole-body human pose estimation, which aims to localize dense landmarks on the entire human body including body, feet, face, and hands. We propose a single-network approach, termed ZoomNet, to take into account the hierarchical structure of the full human body and solve the scale variation of different body parts. We further propose a neural architecture search framework, termed ZoomNAS, to promote both the accuracy and efficiency of whole-body pose estimation. Read More

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Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks.

J Biomed Inform 2022 Jul 28;133:104145. Epub 2022 Jul 28.

Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand. Electronic address:

In many countries, mental health issues are among the most serious public health concerns. National mental health statistics are frequently collected from reported patient cases or government-sponsored surveys, which have restricted coverage, frequency, and timeliness. Many domains of study, including public healthcare and biomedical informatics, have recently adopted social media data as a feasible real-time alternative to traditional methods of gathering representative information at the population level in a variety of contexts. Read More

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Detection Of Critical Spinal Epidural Lesions on CT Using Machine Learning.

Spine (Phila Pa 1976) 2022 Jul 29. Epub 2022 Jul 29.

Virtual Radiologic, Eden Prairie, MN, USA.

Background: Critical spinal epidural pathologies can cause paralysis or death if untreated. Although MRI is the preferred modality for visualizing these pathologies, CT occurs far more commonly than MRI in the clinical setting.

Objective: A machine learning model was developed to screen for critical epidural lesions on CT images at a large-scale teleradiology practice. Read More

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Incidents1M: a Large-Scale Dataset of Images With Natural Disasters, Damage, and Incidents.

IEEE Trans Pattern Anal Mach Intell 2022 Jul 29;PP. Epub 2022 Jul 29.

Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the Earth undergoes global warming. It is difficult to predict when and where an incident will occur, so timely emergency response is critical to saving the lives of those endangered by destructive events. Fortunately, technology can play a role in these situations. Read More

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PerAnSel:  A  Novel Deep Neural Network-Based System for Persian Question Answering.

Comput Intell Neurosci 2022 18;2022:3661286. Epub 2022 Jul 18.

Big Data Research Group, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran.

Question answering (QA) systems have attracted considerable attention in recent years. They receive the user's questions in natural language and respond to them with precise answers. Most of the works on QA were initially proposed for the English language, but some research studies have recently been performed on non-English languages. Read More

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A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger-Train Matching Probability.

Entropy (Basel) 2022 Jul 26;24(8). Epub 2022 Jul 26.

Beijing Metro Network Administration Co., Ltd., Beijing 100101, China.

The ever-increasing travel demand has brought great challenges to the organization, operation, and management of the subway system. An accurate estimation of passenger flow distribution can help subway operators design corresponding operation plans and strategies scientifically. Although some literature has studied the problem of passenger flow distribution by analyzing the passengers' path choice behaviors based on AFC (automated fare collection) data, few studies focus on the passenger flow distribution while considering the passenger-train matching probability, which is the key problem of passenger flow distribution. Read More

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Performance of Machine Learning for Tissue Outcome Prediction in Acute Ischemic Stroke: A Systematic Review and Meta-Analysis.

Front Neurol 2022 8;13:910259. Epub 2022 Jul 8.

Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.

Machine learning (ML) has been proposed for lesion segmentation in acute ischemic stroke (AIS). This study aimed to provide a systematic review and meta-analysis of the overall performance of current ML algorithms for final infarct prediction from baseline imaging. We made a comprehensive literature search on eligible studies developing ML models for core infarcted tissue estimation on admission CT or MRI in AIS patients. Read More

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A hyperspectral dataset of precancerous lesions in gastric cancer and benchmarks for pathological diagnosis.

J Biophotonics 2022 Jul 23:e202200163. Epub 2022 Jul 23.

Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China.

Gastric cancer (GC) is one of the most common cancers worldwide. A lot of studies have found that early GC has good prognosis. Unfortunately, the diagnosis rate of early GC is suboptimal due to inadequate disease screening and the insidious nature of early lesions. Read More

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Transfer Learning Aurora Image Classification and Magnetic Disturbance Evaluation.

J Geophys Res Space Phys 2022 Jan 19;127(1):e2021JA029683. Epub 2022 Jan 19.

Philips Research Hamburg Germany.

We develop an open source algorithm to apply ransfer learning to urora image classification and agnetic disturbance valuation (TAME). For this purpose, we evaluate the performance of 80 pretrained neural networks using the Oslo Auroral THEMIS (OATH) data set of all-sky images, both in terms of runtime and their features' predictive capability. From the features extracted by the best network, we retrain the last neural network layer using the Support Vector Machine (SVM) algorithm to distinguish between the labels "arc," "diffuse," "discrete," "cloud," "moon" and "clear sky/ no aurora". Read More

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January 2022

Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate.

IEEE Trans Med Imaging 2022 Jul 21;PP. Epub 2022 Jul 21.

Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data is often hard to interpret visually and in part because the 3D interfaces are awkward to use. In this paper, we introduce a method that explicitly accounts for annotation inaccuracies. Read More

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Personalized Retrogress-Resilient Federated Learning Towards Imbalanced Medical Data.

IEEE Trans Med Imaging 2022 Jul 19;PP. Epub 2022 Jul 19.

Clinically oriented deep learning algorithms, combined with large-scale medical datasets, have significantly promoted computer-aided diagnosis. To address increasing ethical and privacy issues, Federated Learning (FL) adopts a distributed paradigm to collaboratively train models, rather than collecting samples from multiple institutions for centralized training. Despite intensive research on FL, two major challenges are still existing when applying FL in the real-world medical scenarios, including the performance degradation (i. Read More

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An externally validated deep learning model for the accurate segmentation of the lumbar paravertebral muscles.

Eur Spine J 2022 Aug 19;31(8):2156-2164. Epub 2022 Jul 19.

Center for Trauma Research Ulm, Institute of Orthopaedic Research and Biomechanics, Ulm University, Ulm, Germany.

Purpose: Imaging studies about the relevance of muscles in spinal disorders, and sarcopenia in general, require the segmentation of the muscles in the images which is very labour-intensive if performed manually and poses a practical limit to the number of investigated subjects. This study aimed at developing a deep learning-based tool able to fully automatically perform an accurate segmentation of the lumbar muscles in axial MRI scans, and at validating the new tool on an external dataset.

Methods: A set of 60 axial MRI images of the lumbar spine was retrospectively collected from a clinical database. Read More

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Pandora's Box: A spatiotemporal assessment of elephant-train casualties in Assam, India.

PLoS One 2022 13;17(7):e0271416. Epub 2022 Jul 13.

Department of Geography, Gauhati University, Guwahati, India.

Railways are an indispensable component of sustainable transportation systems, but also exact a toll on wildlife. Wild Asian elephants are often killed by trains in Assam, India, where we assess temporal variations in the occurrences of elephant-train collisions (ETCs) and casualties during 1990-2018. This study also assesses spatially varying relationships between elephant-train collision (ETC) rates and elephant and train densities in the adjoining 10 km2 grid cells of 11 prioritized railroad segments using ordinary least squares (OLS) and geographically weighted regression (GWR) models. Read More

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Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data.

IEEE Internet Things J 2022 Jul 29;9(14):12848-12860. Epub 2021 Dec 29.

School of Arts, Media and Engineering and School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85281 USA.

Deep neural networks are parametrized by several thousands or millions of parameters, and have shown tremendous success in many classification problems. However, the large number of parameters makes it difficult to integrate these models into edge devices such as smartphones and wearable devices. To address this problem, knowledge distillation (KD) has been widely employed, that uses a pre-trained high capacity network to train a much smaller network, suitable for edge devices. Read More

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Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room.

Med Image Anal 2022 08 3;80:102525. Epub 2022 Jul 3.

ICube, University of Strasbourg, CNRS, France; IHU Strasbourg, France. Electronic address:

The fine-grained localization of clinicians in the operating room (OR) is a key component to design the new generation of OR support systems. Computer vision models for person pixel-based segmentation and body-keypoints detection are needed to better understand the clinical activities and the spatial layout of the OR. This is challenging, not only because OR images are very different from traditional vision datasets, but also because data and annotations are hard to collect and generate in the OR due to privacy concerns. Read More

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Multi-perspective label based deep learning framework for cerebral vasculature segmentation in whole-brain fluorescence images.

Biomed Opt Express 2022 Jun 1;13(6):3657-3671. Epub 2022 Jun 1.

Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, 430074, China.

The popularity of fluorescent labelling and mesoscopic optical imaging techniques enable the acquisition of whole mammalian brain vasculature images at capillary resolution. Segmentation of the cerebrovascular network is essential for analyzing the cerebrovascular structure and revealing the pathogenesis of brain diseases. Existing deep learning methods use a single type of annotated labels with the same pixel weight to train the neural network and segment vessels. Read More

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CRS-CONT: A Well-Trained General Encoder for Facial Expression Analysis.

IEEE Trans Image Process 2022 12;31:4637-4650. Epub 2022 Jul 12.

Existing facial expression recognition (FER) methods train encoders with different large-scale training data for specific FER applications. In this paper, we propose a new task in this field. This task aims to pre-train a general encoder to extract any facial expression representations without fine-tuning. Read More

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PaCAR: COVID-19 Pandemic Control Decision Making via Large-Scale Agent-Based Modeling and Deep Reinforcement Learning.

Med Decis Making 2022 Jul 1:272989X221107902. Epub 2022 Jul 1.

Department of Automation, Tsinghua University, Beijing, China.

Background: Policy makers are facing more complicated challenges to balance saving lives and economic development in the post-vaccination era during a pandemic. Epidemic simulation models and pandemic control methods are designed to tackle this problem. However, most of the existing approaches cannot be applied to real-world cases due to the lack of adaptability to new scenarios and micro representational ability (especially for system dynamics models), the huge computation demand, and the inefficient use of historical information. Read More

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Uncertainty-based Self-training for Biomedical Keyphrase Extraction.

IEEE EMBS Int Conf Biomed Health Inform 2021 Jul 10;2021. Epub 2021 Aug 10.

Department of Computer Science, Emory University, Atlanta, USA.

To keep pace with the increased generation and digitization of documents, automated methods that can improve search, discovery and mining of the vast body of literature are essential. Keyphrases provide a concise representation by identifying salient concepts in a document. Various supervised approaches model keyphrase extraction using local context to predict the label for each token and perform much better than the unsupervised counterparts. Read More

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MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 - 2015).

Data Brief 2022 Aug 13;43:108369. Epub 2022 Jun 13.

University of Colorado Boulder, Institute of Behavioral Science, 483 UCB, Boulder, CO-80309, USA.

Despite abundant data on the spatial distribution of contemporary human settlements, historical datasets on the long-term evolution of human settlements at fine spatial and temporal granularity are scarce, limiting our quantitative understanding of long-term changes of built-up areas. This is because commonly used large-scale mapping methods (e.g. Read More

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Aglow: A Fluorescence Assay and Machine Learning Model to Identify Inhibitors of Intracellular Infection.

ACS Infect Dis 2022 Jul 24;8(7):1280-1290. Epub 2022 Jun 24.

Department of Medicine, and the Ruy V. Lourenco Center for the Study of Emerging and Reemerging Pathogens, Rutgers University - New Jersey Medical School, Medical Sciences Building, 185 South Orange Avenue, Newark, New Jersey 07103, United States.

is a genus of Gram-negative bacteria that has for centuries caused large-scale morbidity and mortality. In recent years, the resurgence of rickettsial diseases as a major cause of pyrexias of unknown origin, bioterrorism concerns, vector movement, and concerns over drug resistance is driving a need to identify novel treatments for these obligate intracellular bacteria. Utilizing an uvGFP plasmid reporter, we developed a screen for identifying anti-rickettsial small molecule inhibitors using as a model organism. Read More

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Interpreting protein variant effects with computational predictors and deep mutational scanning.

Dis Model Mech 2022 06 23;15(6). Epub 2022 Jun 23.

MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK.

Computational predictors of genetic variant effect have advanced rapidly in recent years. These programs provide clinical and research laboratories with a rapid and scalable method to assess the likely impacts of novel variants. However, it can be difficult to know to what extent we can trust their results. Read More

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Effectiveness and Factors Associated with Improved Life Skill Levels of Participants of a Large-Scale Youth-Focused Life Skills Training and Counselling Services Program (LSTCP): Evidence from India.

Behav Sci (Basel) 2022 Jun 15;12(6). Epub 2022 Jun 15.

Department of Epidemiology, Center for Public Health, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru 560029, India.

To empower and facilitate mental health promotion for nearly 18 million youth, a pioneering state-wide Life Skills Training and Counselling Services Program (LSTCP) was implemented in Karnataka, India. This study assesses the changes in life skills scores, level of life skills and factors associated with increased life skills among participants of the LSTCP. This pre-post study design was conducted on 2669 participants who underwent a six-day structured LSTCP. Read More

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Face to face or blended learning? A case study: Teacher training in the pedagogical use of ICT.

Educ Inf Technol (Dordr) 2022 Jun 17:1-29. Epub 2022 Jun 17.

Department of Training and Certification, Computer Technology Institute & Press - "Diophantus", Patras, Greece.

We are experiencing a transitional period in education: from the traditional, face to face teaching model to new teaching and learning models that apply modern pedagogical approaches, utilize technological achievements and respond to current social needs. For a number of reasons including the recent pandemic covid-19 situation, technology enhanced distance learning, seems to gain ground against traditional face to face teaching and in fact, in a sharp way. Acknowledging that changes in education need time, research and careful steps in order to be successfully applied and established at large scale, in this paper we attempt to compare face to face ("traditional") teacher training with teacher training through a blended learning approach/ model. Read More

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Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision.

Gigascience 2022 Jun;11

Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven, 53757 Sankt Augustin, Germany.

Background: Unmanned aerial vehicle (UAV)-based image retrieval in modern agriculture enables gathering large amounts of spatially referenced crop image data. In large-scale experiments, however, UAV images suffer from containing a multitudinous amount of crops in a complex canopy architecture. Especially for the observation of temporal effects, this complicates the recognition of individual plants over several images and the extraction of relevant information tremendously. Read More

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Feasibility of large-scale eOSCES: the simultaneous evaluation of 500 medical students during a mock examination.

Med Educ Online 2022 Dec;27(1):2084261

UFR de Médecine, Université Paris Cité, Paris, France.

The COVID-19 pandemic has led health schools to cancel many on-site training and exams. Teachers were looking for the best option to carry out online OSCEs, and Zoom was the obvious choice since many schools have used it to pursue education purposes.

Methods: We conducted a feasibility study during the 2020-2021 college year divided into six pilot phases and the large-scale eOSCEs on Zoom on June 30th, 2021. Read More

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December 2022

SAPPHIRE: A stacking-based ensemble learning framework for accurate prediction of thermophilic proteins.

Comput Biol Med 2022 07 7;146:105704. Epub 2022 Jun 7.

Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand. Electronic address:

Thermophilic proteins (TPPs) are important in the field of protein biochemistry and development of new enzymes. Thus, computational methods must be urgently developed to accurately and rapidly identify TPPs. To date, several computational methods have been developed for TPP identification; however, few limitations in terms of performance and utility remain. Read More

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Optical flow estimation of coronary angiography sequences based on semi-supervised learning.

Comput Biol Med 2022 07 26;146:105663. Epub 2022 May 26.

SDIM, Southern University of Science and Technology, No. 1088, Xueyuan Avenue, Nanshan, Shenzhen, 518055, China.

Optical flow is widely used in medical image processing, such as image registration, segmentation, 3D reconstruction, and temporal super-resolution. However, high-precision optical flow training datasets for medical images are challenging to produce. The current optical flow estimation models trained on these non-medical datasets, such as KITTI, Sintel, and FlyingChairs are unsuitable for medical images. Read More

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A 10-hour within-participant magnetoencephalography narrative dataset to test models of language comprehension.

Sci Data 2022 06 8;9(1):278. Epub 2022 Jun 8.

Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.

Recently, cognitive neuroscientists have increasingly studied the brain responses to narratives. At the same time, we are witnessing exciting developments in natural language processing where large-scale neural network models can be used to instantiate cognitive hypotheses in narrative processing. Yet, they learn from text alone and we lack ways of incorporating biological constraints during training. Read More

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