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    University of Toulouse Reports Findings in Machine Learning (Augmenting the avai lability of historical GDP per capita estimates through machine learning)

    39-40页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Toulouse, Fr ance, by NewsRx correspondents, research stated, "Can we use data on the biograp hies of historical figures to estimate the GDP per capita of countries and regio ns? Here, we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past seven centuries starting from data on the places of birth, death, and oc cupations of hundreds of thousands of historical figures. We build an elastic ne t regression model to perform feature selection and generate out-of-sample estim ates that explain 90 % of the variance in known historical income l evels." Our news editors obtained a quote from the research from the University of Toulo use, "We use this model to generate GDP per capita estimates for countries, regi ons, and time periods for which these data are not available and externAlly vali date our estimates by comparing them with four proxies of economic output: urban ization rates in the past 500 y, body height in the 18 century, well-being in 18 50, and church building activity in the 14 and 15 century. AdditionAlly, we show our estimates reproduce the well-known reversal of fortune between southwestern and northwestern Europe between 1300 and 1800 and find this is largely driven b y countries and regions engaged in Atlantic trade. These findings validate the u se of fine-grained biographical data as a method to augment historical GDP per c apita estimates."
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    Dunedin Hospital Reports Findings in Artificial Intelligence (A comparative eval uation of deep learning approaches for ophthalmology)

    40-40页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news originating from Dunedin, New Ze aland, by NewsRx correspondents, research stated, "There is a growing number of publicly available ophthalmic imaging datasets and open-source code for Machine Learning algorithms. This Allows ophthalmic researchers and practitioners to ind ependently perform various deeplearning tasks." Our news journalists obtained a quote from the research from Dunedin Hospital, " With the advancement in artificial intelligence (AI) and in the field of imaging , the choice of the most appropriate AI architecture for different tasks will va ry greatly. The best-performing AI-dataset combination will depend on the specif ic problem that needs to be solved and the type of data available. The article d iscusses different machine learning models and deep learning architectures curre ntly used for various ophthalmic imaging modalities and for different machine le arning tasks. It also proposes the most appropriate models based on accuracy and other important factors such as training time, the ability to deploy the model on clinical devices/smartphones, heatmaps that enhance the self-explanatory natu re of classification decisions, and the ability to train/adapt on smAll image da tasets to determine if further data collection is worthwhile. The article extens ively reviews the existing state-of-the-art AI methods focused on useful machine -learning applications for ophthalmology. It estimates their performance and via bility through training and evaluating architectures with different public and p rivate image datasets of different modalities, such as full-color retinal images , OCT images, and 3D OCT scans."
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    University of Catania Reports Findings in Klebsiella pneumoniae (Prediction of a ntimicrobial resistance of Klebsiella pneumoniae from genomic data through machi ne learning)

    41-41页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Gram-Negative Bacteria - Klebsiella pneumoniae is the subject of a report. According to news reporting from Catania, Italy, by NewsRx journalists, research stated, "Antimicrobials, s uch as antibiotics or antivirals are medications employed to prevent and treat i nfectious diseases in humans, animals, and plants. Antimicrobial Resistance occu rs when bacteria, viruses, and parasites no longer respond to these medicines." Financial support for this research came from MUR PNRR Extended Partnership Init iative on 558 Emerging Infectious Diseases. The news correspondents obtained a quote from the research from the University o f Catania, "This resistance renders antibiotics and other antimicrobial drugs in effective, making infections chAllenging or impossible to treat. This escalation in drug resistance heightens the risk of disease spread, severe illness, disabi lity, and mortality. With datasets now containing hundreds or even thousands of pathogen genomes, machine learning techniques are on the rise for predicting ant ibiotic resistance in pathogens, prediction based on gene content and genome com position."
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    Hospital General Universitario Gregorio Maranon Reports Findings in Venous Throm boembolism (Prediction model for major bleeding in anticoagulated patients with cancer-associated venous thromboembolism using machine learning and natural lang uage ...)

    42-43页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Cardiovascular Disease s and Conditions - Venous Thromboembolism is the subject of a report. According to news reporting out of Madrid, Spain, by NewsRx editors, research stated, "We developed a predictive model to assess the risk of major bleeding (MB) within 6 months of primary venous thromboembolism (VTE) in cancer patients receiving anti coagulant treatment. We also sought to describe the prevalence and incidence of VTE in cancer patients, and to describe clinical characteristics at baseline and bleeding events during follow-up in patients receiving anticoagulants." Financial support for this research came from BMS-Pfizer Alliance. Our news journalists obtained a quote from the research from Hospital General Un iversitario Gregorio Maranon, "This observational, retrospective, and multicente r study used natural language processing and machine learning (ML), to analyze u nstructured clinical data from electronic health records from nine Spanish hospi tals between 2014 and 2018. All adult cancer patients with VTE receiving anticoa gulants were included. Both clinicAlly- and ML-driven feature selection was perf ormed to identify MB predictors. Logistic regression (LR), decision tree (DT), a nd random forest (RF) algorithms were used to train predictive models, which wer e validated in a hold-out dataset and compared to the previously developed CAT-B LEED score. Of the 2,893,108 cancer patients screened, in-hospital VTE prevalenc e was 5.8% and the annual incidence ranged from 2.7 to 3.9% . We identified 21,227 patients with active cancer and VTE receiving anticoagula nts (53.9% men, median age of 70 years). MB events after VTE diagn osis occurred in 10.9% of patients within the first six months. MB predictors included: hemoglobin, metastasis, age, platelets, leukocytes, and se rum creatinine. The LR, DT, and RF models had AUC-ROC (95% confide nce interval) values of 0.60 (0.55, 0.65), 0.60 (0.55, 0.65), and 0.61 (0.56, 0. 66), respectively. These models outperformed the CAT-BLEED score with values of 0.53 (0.48, 0.59)."
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    Studies from Beijing Institute of Technology Further Understanding of Robotics ( W-VSLAM: A Visual Mapping Algorithm for Indoor Inspection Robots)

    43-43页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on robotics have been presented. Ac cording to news reporting from Beijing, People's Republic of China, by NewsRx jo urnalists, research stated, "In recent years, with the widespread application of indoor inspection robots, high-precision, robust environmental perception has b ecome essential for robotic mapping." The news editors obtained a quote from the research from Beijing Institute of Te chnology: "Addressing the issues of visual-inertial estimation inaccuracies due to redundant pose degrees of freedom and accelerometer drift during the planar m otion of mobile robots in indoor environments, we propose a visual SLAM percepti on method that integrates wheel odometry information. First, the robot's body po se is parameterized in SE(2) and the corresponding camera pose is parameterized in SE(3). On this basis, we derive the visual constraint residuals and their Jac obian matrices for reprojection observations using the camera projection model. We employ the concept of pre-integration to derive pose-constraint residuals and their Jacobian matrices and utilize marginalization theory to derive the relati ve pose residuals and their Jacobians for loop closure constraints. This approac h solves the nonlinear optimization problem to obtain the optimal pose and landm ark points of the ground-moving robot. A comparison with the ORBSLAM3 algorithm reveals that, in the recorded indoor environment datasets, the proposed algorith m demonstrates significantly higher perception accuracy, with root mean square e rror (RMSE) improvements of 89.2% in translation and 98.5% in rotation for absolute trajectory error (ATE)."
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    Report Summarizes Machine Learning Study Findings from Xinjiang University (Weig hted Variable Optimization-Based Method for Estimating Soil Salinity Using Multi -Source Remote Sensing Data: A Case Study in the Weiku Oasis, Xinjiang, China)

    44-44页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting originating from Urumqi, Pe ople's Republic of China, by NewsRx correspondents, research stated, "Soil salin ization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinizati on. Remote sensing technology enables large-scale inversion of soil salinity, fa cilitating the monitoring and assessment of soil salinization levels, thus suppo rting the prevention and management of soil salinization." Funders for this research include Esearch Project on Spatial And Temporal Evolut ion of Soil Salinization in The Aksu River Basin; Technology Innovation Team (Ti anshan Innovation Team), Innovative Team For Efficient Utilization of Water Reso urces in Arid Regions; Key Project of Natural Science Foundation of Xinjiang Uyg ur Autonomous Region; National Natural Science Foundation of China.
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    University of British Columbia Reports Findings in Machine Learning (Predictive modelling of Immunogenicity to Botulinumtoxin A Treatments for Glabellar Lines)

    45-46页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Vancouver, C anada, by NewsRx correspondents, research stated, "Botulinum toxin A (BoNT-A), d erived from Clostridium botulinum, is widely used in medical and aesthetic treat ments. Its clinical application extends from managing chronic conditions like ce rvical dystonia and migraine to reducing facial wrinkles." Our news editors obtained a quote from the research from the University of Briti sh Columbia, "Despite its efficacy, a significant chAllenge associated with BoNT -A therapy is immunogenicity, where the immune system produces neutralising anti bodies (NAbs) against BoNT-A, reducing its effectiveness over time. This issue i s critical for patients requiring repeated treatments. The study aims to compare FDA-approved BoNT-A products, examining the factors influencing NAbs developmen t using advanced machine learning techniques. This research analysed data from r andomised controlled trials (RCTs) involving five main BoNT-A products. The stud y selected trials based on detailed immunogenic responses to these treatments, p articularly for glabellar lines. Machine learning models, including logistic reg ression, random forest classifiers, and Bayesian logistic regression, were emplo yed to assess how treatment specifics and BoNT-A product types affect the develo pment of NAbs. Analysis of 14 studies with 8,190 participants revealed that dosa ge and treatment frequency are key factors influencing the risk of NAbs developm ent. Among BoNT-A products, IncobotulinumtoxinA shows the lowest, and Abobotulin umtoxinA the highest likelihood of inducing NAbs. The study's machine learning a nd logistic regression findings indicated that treatment planning must consider these variables to minimise immunogenicity. The study underscores the importance of understanding BoNT-A immunogenicity in clinical practice. By identifying the main predictors of NAbs development and differentiating the immunogenic potenti al of BoNT-A products, the research provides insights for clinicians in optimisi ng treatment strategies."
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    Research from Yale University Reveals New Findings on Machine Learning (Site-spe cific template generative approach for retrosynthetic planning)

    46-46页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news reporting from Yale Universi ty by NewsRx journalists, research stated, "Retrosynthesis, the strategy of devi sing laboratory pathways by working backwards from the target compound, is cruci al yet chAllenging." Our news editors obtained a quote from the research from Yale University: "Enhan cing retrosynthetic efficiency requires overcoming the vast complexity of chemic al space, the limited known interconversions between molecules, and the chAlleng es posed by limited experimental datasets. This study introduces generative mach ine learning methods for retrosynthetic planning. The approach features three in novations: generating reaction templates instead of reactants or synthons to cre ate novel chemical transformations, Allowing user selection of specific bonds to change for human-influenced synthesis, and employing a conditional kernel-elast ic autoencoder (CKAE) to measure the similarity between generated and known reac tions for chemical viability insights. These features form a coherent retrosynth etic framework, validated experimentAlly by designing a 3-step synthetic pathway for a chAllenging smAll molecule, demonstrating a significant improvement over previous 5-9 step approaches."
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    Reports Outline Robotics Research from Ben-Gurion University of the Negev (Desig n, Experiments, and Path Planning for a Lightweight 3D MinimAlly Actuated Serial Robot with a Mobile Actuator)

    46-47页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ro botics. According to news reporting from Beer-Sheva, Israel, by NewsRx journalis ts, research stated, "This paper presents a novel three-dimensional (3D)minimal ly actuated serial robot (MASR) and its unique kinematic analysis."Financial supporters for this research include Israel Science Foundation; Pearls tone Center For Aeronautical Studies; Helmsley Charitable Trust.
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    CAS Reports Findings in Machine Learning (In Silico Insights: QSAR Modeling of T BK1 Kinase Inhibitors for Enhanced Drug Discovery)

    47-48页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Columbus, Oh io, by NewsRx correspondents, research stated, "TBK1, or TANKbinding kinase 1, is an enzyme that functions as a serine/threonine protein kinase. It plays a cru cial role in various cellular processes, including the innate immune response to viruses, cell proliferation, apoptosis, autophagy, and antitumor immunity." Our news editors obtained a quote from the research from CAS, "Dysregulation of TBK1 activity can lead to autoimmune diseases, neurodegenerative disorders, and cancer. Due to its central role in these critical pathways, TBK1 is a significan t focus of research for therapeutic drug development. In this paper, we explore data from the CAS Content Collection regarding TBK1 and its implication in a lar ge assortment of diseases and disorders. With the demand for developing efficien t TBK1 inhibitors being outlined, we focus on utilizing a machine learning appro ach for developing predictive models for TBK1 inhibition, derived from the fragm ent-functional analysis descriptors. Using the extensive CAS Content Collection, we assembled a training set of TBK1 inhibitors with experimentAlly measured IC5 0 values. We explored several machine learning techniques combined with various molecular descriptors to derive and select the best TBK1 inhibitor QSAR models. Certain significant structural alerts that potentiAlly contribute to inhibition of TBK1 are outlined and discussed."
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