91,385 results match your criteria machine learning


The Role of OCT Criteria and Machine Learning in Multiple Sclerosis and Optic Neuritis Diagnosis.

Neurology 2022 Jun 28. Epub 2022 Jun 28.

- Department of Neurology, Johns Hopkins University, Baltimore, MD, USA.

Background And Objectives: Recent studies have suggested that inter-eye differences (IEDs) in peripapillary retinal nerve fiber layer (pRNFL) or ganglion cell+inner plexiform (GCIPL) thickness by spectral-domain optical coherence tomography (SD-OCT) may identify people with a history of unilateral optic neuritis (ON). However, this requires further validation. Machine learning classification may be useful for validating thresholds for OCT IEDs and for examining added utility for visual function tests, such as low-contrast letter acuity (LCLA), in the diagnosis of people with multiple sclerosis (PwMS) and for unilateral ON history. Read More

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Machine Learning Utility for Optical Coherence Tomography in Multiple Sclerosis: Is the Future Now?

Neurology 2022 Jun 28. Epub 2022 Jun 28.

Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square UCL Institute of Neurology, University College London, UK.

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Magnetic resonance imaging parameter-based machine learning for prognosis prediction of high-intensity focused ultrasound ablation of uterine fibroids.

Int J Hyperthermia 2022 ;39(1):835-846

State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Key Laboratory of Biomedical Engineering, Chongqing Medical University, Chongqing, China.

To develop and apply magnetic resonance imaging (MRI) parameter-based machine learning (ML) models to predict non-perfused volume (NPV) reduction and residual regrowth of uterine fibroids after high-intensity focused ultrasound (HIFU) ablation. MRI data of 573 uterine fibroids in 410 women who underwent HIFU ablation from the Chongqing Haifu Hospital (training set,  = 405) and the First Affiliated Hospital of Chongqing Medical University (testing set,  = 168) were retrospectively analyzed. Fourteen MRI parameters were screened for important predictors using the Boruta algorithm. Read More

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January 2022

Data reconstruction of daily MODIS chlorophyll-a concentration and spatio-temporal variations in the Northwestern Pacific.

Sci Total Environ 2022 Jun 25:156981. Epub 2022 Jun 25.

College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China. Electronic address:

Sea surface chlorophyll-a concentration (Chl-a) is a key proxy for phytoplankton biomass. Spatio-temporal continuous Chl-a data are important to understand the mechanisms of chlorophyll occurrence and development and track phytoplankton changes. However, the greatest challenge in utilizing daily Chl-a data is massive missing pixels due to orbital position and cloud coverage. Read More

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De Novo design of potential inhibitors against SARS-CoV-2 Mpro.

Comput Biol Med 2022 Jun 15;147:105728. Epub 2022 Jun 15.

Shenyang Key Laboratory of Computer Simulating and Information Processing of Bio-macromolecules, Shenyang, 110036, China; Research Center for Computer Simulating and Information Processing of Bio-macromolecules of Liaoning Province, Shenyang, 110036, China; Engineering Laboratory for Molecular Simulation and Designing of Drug Molecules of Liaoning, Shenyang, 110036, China; School of Pharmaceutical Sciences, Liaoning University, Shenyang, 110036, China. Electronic address:

The impact of the ravages of COVID-19 on people's lives is obvious, and the development of novel potential inhibitors against SARS-CoV-2 main protease (Mpro), which has been validated as a potential target for drug design, is urgently needed. This study developed a model named MproI-GEN, which can be used for the de novo design of potential Mpro inhibitors (MproIs) based on deep learning. The model was mainly composed of long-short term memory modules, and the last layer was re-trained with transfer learning. Read More

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Machine learning for prediction of in-hospital mortality in coronavirus disease 2019 patients: results from an Italian multicenter study.

J Cardiovasc Med (Hagerstown) 2022 Jul;23(7):439-446

Heart Failure Unit, G da Saliceto Hospital, AUSL Piacenza, Piacenza.

Background: Several risk factors have been identified to predict worse outcomes in patients affected by SARS-CoV-2 infection. Machine learning algorithms represent a novel approach to identifying a prediction model with a good discriminatory capacity to be easily used in clinical practice. The aim of this study was to obtain a risk score for in-hospital mortality in patients with coronavirus disease infection (COVID-19) based on a limited number of features collected at hospital admission. Read More

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A Comparison of Logistic Regression Against Machine Learning Algorithms for Gastric Cancer Risk Prediction Within Real-World Clinical Data Streams.

JCO Clin Cancer Inform 2022 Jun;6:e2200039

Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, CA.

Purpose: Noncardia gastric cancer (NCGC) is a leading cause of global cancer mortality, and is often diagnosed at advanced stages. Development of NCGC risk models within electronic health records (EHR) may allow for improved cancer prevention. There has been much recent interest in use of machine learning (ML) for cancer prediction, but few studies comparing ML with classical statistical models for NCGC risk prediction. Read More

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The machine learning-powered BirdNET App reduces barriers to global bird research by enabling citizen science participation.

PLoS Biol 2022 Jun 28;20(6):e3001670. Epub 2022 Jun 28.

K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, New York, United States of America.

The BirdNET App, a free bird sound identification app for Android and iOS that includes over 3,000 bird species, reduces barriers to citizen science while generating tens of millions of bird observations globally that can be used to replicate known patterns in avian ecology. Read More

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Hypertension Diagnosis and Management in Africa Using Mobile Phones: A Scoping Review.

IEEE Rev Biomed Eng 2022 Jun 28;PP. Epub 2022 Jun 28.

Target 3.4 of the third Sustainable Development Goal (SDG) of the United Nations (UN) General Assembly proposes to reduce premature mortality from non-communicable diseases (NCDs) by one-third. Epidemiological data presented by the World Health Organization (WHO) in 2016 show that out of a total of 57 million deaths worldwide, approximately 41 million deaths occurred due to NCDs, with 78% of such deaths occurring in low-and-middle-income countries (LMICs). Read More

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Prediction of vedolizumab treatment outcomes by machine learning.

Authors:
Philippe Pinton

J Biopharm Stat 2022 Jun 28:1-3. Epub 2022 Jun 28.

Translational Medicine and Clinical Pharmacology, International PharmaScience Center, Ferring Pharmaceuticals, Copenhagen S, Denmark.

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Assessing the Influence of Zeolite Composition on Oxygen-Bridged Diamino Dicopper(II) Complexes in Cu-CHA DeNO Catalysts by Machine Learning-Assisted X-ray Absorption Spectroscopy.

J Phys Chem Lett 2022 Jun 28:6164-6170. Epub 2022 Jun 28.

Department of Chemistry and NIS Centre, University of Turin, Via Giuria 7, 10125 Turin, Italy.

Cu-exchanged chabazite is the catalyst of choice for NO abatement in diesel vehicles aftertreatment systems via ammonia-assisted selective catalytic reduction (NH-SCR). Herein, we exploit X-ray absorption spectroscopy powered by wavelet transform analysis and machine learning-assisted fitting to assess the impact of the zeolite composition on NH-mobilized Cu-complexes formed during the reduction and oxidation half-cycles in NH-SCR at 200 °C. Comparatively analyzing well-characterized Cu-CHA catalysts, we show that the Si/Al ratio of the zeolite host affects the structure of mobile dicopper(II) complexes formed during the oxidation of the [Cu(NH)] complexes by O. Read More

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Pre- and post-dam river water temperature alteration prediction using advanced machine learning models.

Environ Sci Pollut Res Int 2022 Jun 28. Epub 2022 Jun 28.

CERIS, Instituto Superior Técnico, University of Lisbon, 1649-004, Lisbon, Portugal.

Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water's temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water. Read More

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Factor analysis of error in oxidation potential calculation: A machine learning study.

J Comput Chem 2022 Jun 28. Epub 2022 Jun 28.

Department of Chemistry, Graduate School of Pure and Applied Science, University of Tsukuba, Tsukuba, Japan.

The conductor-like polarizable continuum model (C-PCM), which is a low-cost solvation model, cannot treat characteristic interactions between the solvent and substructure(s) of the solute. Moreover, the error in a charged system is significant. Using machine learning, we clarified that the systematic error of the oxidation potential calculated by the G3B3/C-PCM was correlated with the molecular size of a solute. Read More

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Leveraging Existing 16SrRNA Microbial Data to Define a Composite Biomarker for Autism Spectrum Disorder.

Microbiol Spectr 2022 Jun 28:e0033122. Epub 2022 Jun 28.

Division of Gastroenterology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Cumulative studies have utilized high-throughput sequencing of the 16SrRNA gene to characterize the composition and structure of the microbiota in autism spectrum disorder (ASD). However, they do not always obtain consistent results; thus, conducting cross-study comparisons is necessary. This study sought to analyze the alteration of fecal microbiota and the diagnostic capabilities of gut microbiota biomarkers in individuals with ASD using the existing 16SrRNA microbial data and explore heterogeneity among studies. Read More

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Bending Sensor Based on Controlled Microcracking Regions for Application toward Wearable Electronics and Robotics.

ACS Appl Mater Interfaces 2022 Jun 28. Epub 2022 Jun 28.

Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.

A soft bending sensor based on the inverse pyramid structure is demonstrated, revealing that it can effectively suppress microcrack formation in designated regions, thus allowing the cracks to open gradually with bending in a controlled manner. Such a feature enabled the bending sensor to simultaneously have a wide dynamic range of bending strain (0.025-5. Read More

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Prospective Validation of a Machine Learning Model for Low-Density Lipoprotein Cholesterol Estimation.

Lab Med 2022 Jun 28. Epub 2022 Jun 28.

Faculty of Medicine, Université Saint Joseph, Beirut, Lebanon.

Objective: We aim to prospectively validate a previously developed machine learning algorithm for low-density lipoprotein cholesterol (LDL-C) estimation.

Methods: We retrospectively and prospectively evaluated a machine learning algorithm based on k-nearest neighbors (KNN) according to age, sex, healthcare setting, and triglyceridemia against a direct LDL-C assay. The agreement of low-density lipoprotein-k-nearest neighbors (LDL-KNN) with the direct measurement was assessed using intraclass correlation coefficient (ICC). Read More

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AlphaFold accurately predicts distinct conformations based on the oligomeric state of a de novo designed protein.

Protein Sci 2022 Jul;31(7):e4368

Department of Biochemistry and Biophysics, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.

Using the molecular modeling program Rosetta, we designed a de novo protein, called SEWN0.1, which binds the heterotrimeric G protein Gα The design is helical, well-folded, and primarily monomeric in solution at a concentration of 10 μM. However, when we solved the crystal structure of SEWN0. Read More

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Automated Bonding Analysis with Crystal Orbital Hamilton Populations.

Chempluschem 2022 Jun 7:e202200123. Epub 2022 Jun 7.

Université Catholique de Louvain, Institute of Condensed Matter and Nanosciences, Chemin des Étoiles 8, 1348, Louvain-la-Neuve, Belgium.

Understanding crystalline structures based on their chemical bonding is growing in importance. In this context, chemical bonding can be studied with the Crystal Orbital Hamilton Population (COHP), allowing for quantifying interatomic bond strength. Here we present a new set of tools to automate the calculation of COHP and analyze the results. Read More

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A comprehensive comparative review of protein sequence based computational prediction models of lysine succinylation sites.

Curr Protein Pept Sci 2022 Jun 28. Epub 2022 Jun 28.

Bioinformatics Lab., Dept. of Statistics, Rajshahi University, Rajshahi-6205, Bangladesh.

Lysine succinylation is a post-translational modification (PTM) of protein in which a succinyl group (-CO-CH2-CH2-CO2H) is added to a lysine residue of protein that reverses lysine's positive charge to a negative charge and leads to the significant changes in protein structure and function. It occurs on a wide range of proteins and plays an important role in various cellular and biological processes in both eukaryotes and prokaryotes. Beyond experimentally identified succinylation sites, there have been a lot of studies for developing sequence-based prediction using machine learning approaches, because it has the promise of being extremely time-saving, accurate, robust, and cost-effective. Read More

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Literature Review of Automated Grading Systems utilising MRI for Neuroforaminal Stenosis.

Curr Med Imaging 2022 Jun 28. Epub 2022 Jun 28.

Department of Neurosurgery, Leeds General Infirmary, Leeds, UK.

Background Cervical neural foraminal stenosis is a common and debilitating condition affecting people 40-60 years old. Although it is established that MRI is the best method of scanning the neural foramen, the question remains whether there is a role for three-dimensional MRIs and subsequently if it is possible to develop a computer aided automated grading system to establish the degree of clinically relevant cervical foraminal stenosis. Objective The aim of the study is to review the literature for current or emerging automated grading systems of the cervical neural foramen, also including volumetric assessments of the neural foramen using MRI. Read More

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Profiling of Urine Carbonyl Metabolic Fingerprints in Bladder Cancer Based on Ambient Ionization Mass Spectrometry.

Anal Chem 2022 Jun 28. Epub 2022 Jun 28.

Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, Chinese Academy of Sciences, Beijing 100190, China.

The diagnosis of bladder cancer (BC) is currently based on cystoscopy, which is invasive and expensive. Here, we describe a noninvasive profiling method for carbonyl metabolic fingerprints in BC, which is based on a desorption, separation, and ionization mass spectrometry (DSI-MS) platform with ,-dimethylethylenediamine (DMED) as a differential labeling reagent. The DSI-MS platform avoids the interferences from intra- and/or intersamples. Read More

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Multi-omics to characterize the functional relationships of R-loops with epigenetic modifications, RNAPII transcription and gene expression.

Brief Bioinform 2022 Jun 27. Epub 2022 Jun 27.

Division of Experimental Hematology and Cancer Biology, Brain Tumor Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA.

Abnormal accumulation of R-loops results in replication stress, genome instability, chromatin alterations and gene silencing. Little research has been done to characterize functional relationships among R-loops, histone marks, RNA polymerase II (RNAPII) transcription and gene regulation. We built extremely randomized trees (ETs) models to predict the genome-wide R-loops using RNAPII and multiple histone modifications chromatin immunoprecipitation (ChIP)-seq, DNase-seq, Global Run-On sequencing (GRO-seq) and R-loop profiling data. Read More

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The Future of Causal Inference.

Am J Epidemiol 2022 Jun 28. Epub 2022 Jun 28.

Department of Statistics, University of Pennsylvania, Philadelphia, Pennysylvania, United States.

The past several decades have seen an exponential growth in causal inference approaches and their applications. In this commentary, we provide our top ten list of emerging and exciting areas of research in causal inference. These include methods for high dimensional data and precision medicine, causal machine learning, causal discovery, and others. Read More

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Nurse preferences of caring robots: A conjoint experiment to explore most valued robot features.

Nurs Open 2022 Jun 27. Epub 2022 Jun 27.

Head of Living Lab based Smart Care Center, Faculty of Health, University of Pécs, Pécs, Hungary.

Aim: Due to the COVID pandemic and technological innovation, robots gain increasing role in nursing services. While studies investigated negative attitudes of nurses towards robots, we lack an understanding of nurses' preferences about robot characteristics. Our aim was to explore how key robot features compare when weighed together. Read More

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The opacity myth: A response to Swofford & Champod (2022).

Forensic Sci Int Synerg 2022 19;5:100275. Epub 2022 Jun 19.

Forensic Data Science Laboratory, Aston University, Birmingham, UK.

Swofford & Champod (2022) FSI Synergy article 100220 reports the results of semi-structured interviews that asked interviewees their views on probabilistic evaluation of forensic evidence in general, and probabilistic evaluation of forensic evidence performed using computational algorithms in particular. The interview protocol included a leading question based on the premise that machine-learning methods used in forensic inference are not understandable even to those who develop those methods. We contend that this is a false premise. Read More

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Relating instance hardness to classification performance in a dataset: a visual approach.

Mach Learn 2022 Jun 22:1-39. Epub 2022 Jun 22.

Instituto Tecnológico de Aeronáutica (ITA), São José dos Campos, São Paulo Brazil.

Machine Learning studies often involve a series of computational experiments in which the predictive performance of multiple models are compared across one or more datasets. The results obtained are usually summarized through average statistics, either in numeric tables or simple plots. Such approaches fail to reveal interesting subtleties about algorithmic performance, including which observations an algorithm may find easy or hard to classify, and also which observations within a dataset may present unique challenges. Read More

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Automatic symptoms identification from a massive volume of unstructured medical consultations using deep neural and BERT models.

Heliyon 2022 Jun 10;8(6):e09683. Epub 2022 Jun 10.

Altibbi1https://altibbi.com., Amman, Jordan.

Automatic symptom identification plays a crucial role in assisting doctors during the diagnosis process in Telemedicine. In general, physicians spend considerable time on clinical documentation and symptom identification, which is unfeasible due to their full schedule. With text-based consultation services in telemedicine, the identification of symptoms from a user's consultation is a sophisticated process and time-consuming. Read More

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Vehicle Safety-Assisted Driving Technology Based on Computer Artificial Intelligence Environment.

Authors:
Haibo Yan

Comput Intell Neurosci 2022 18;2022:4390394. Epub 2022 Jun 18.

Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China.

In this paper, we propose an assisted driving system implemented with a Jetson nano-high-performance embedded platform by using machine vision and deep learning technologies. The vehicle dynamics model is established under multiconditional assumptions, the path planner and path tracking controller are designed based on the model predictive control algorithm, and the local desired path is reasonably planned in combination with the behavioral decision system. The behavioral decision algorithm based on finite state machine reasonably transforms the driving state according to the environmental changes, realizes the following of the target vehicle speed, and can take effective emergency braking in time when there is a collision danger. Read More

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What do we really know about the drivers of undeclared work? An evaluation of the current state of affairs using machine learning.

Authors:
Josip Franic

AI Soc 2022 Jun 23:1-20. Epub 2022 Jun 23.

Institute of Public Finance, Smiciklasova 21, 10000 Zagreb, Croatia.

It is nowadays widely understood that undeclared work cannot be efficiently combated without a holistic view on the mechanisms underlying its existence. However, the question remains whether we possess all the pieces of the . To fill the gap, in this paper, we test if the features so far known to affect the behaviour of taxpayers are sufficient to detect noncompliance with outstanding precision. Read More

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Incorporating artificial intelligence in medical diagnosis: A case for an invisible and (un)disruptive approach.

J Eval Clin Pract 2022 Jun 27. Epub 2022 Jun 27.

Department of Medicine, Division of Innovation and Education, McMaster University, Hamilton, ON, Canada.

As big data becomes more publicly accessible, artificial intelligence (AI) is increasingly available and applicable to problems around clinical decision-making. Yet the adoption of AI technology in healthcare lags well behind other industries. The gap between what technology could do, and what technology is actually being used for is rapidly widening. Read More

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