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    Researchers use machine learning to predict how ingested drugs will interact with transport proteins

    1-1页
    查看更多>>摘要：Before orally administered drugs can make their way throughout the body, they must first bind to membrane proteins called drug transporters, which carry compounds across the intestinal tract and help them reach their intended targets. But because one drug can bind to several different drug transporters, they may struggle to get past this gut barrier, potentially leading to decreased drug absorption and efficacy. If another drug is added to the mix, interactions between the two compounds and their transporters can cause dangerous side effects. Researchers from Brigham and Women’s Hospital, a founding member of the Mass General Brigham healthcare system, and MIT have designed a model that analyzes the flow of drugs through tissues and uses machine learning to predict how specific compounds will interact with different transporters. When they used pig tissue to test their machine learning model on 50 approved and investigational drugs, they identified 58 previously unknown drug-transporter interactions and 1,810,270 unknown potential interactions between different drugs.
      原文链接:
    • NETL
    • NSTL

    Ministry of Education MOE Key Laboratory of Bioinformatics Reports Findings in Artificial Intelligence (Artificial intelligence in liver imaging: methods and applications)

    1-2页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Liver disease is regarded as one of the major health threats to humans. Radiographic assessments hold promise in terms of addressing the current demands for precisely diagnosing and treating liver diseases, and artificial intelligence (AI), which excels at automatically making quantitative assessments of complex medical image characteristics, has made great strides regarding the qualitative interpretation of medical imaging by clinicians.” Our news editors obtained a quote from the research from the Ministry of Education MOE Key Laboratory of Bioinformatics, “Here, we review the current state of medical-imaging-based AI methodologies and their applications concerning the management of liver diseases. We summarize the representative AI methodologies in liver imaging with focusing on deep learning, and illustrate their promising clinical applications across the spectrum of precise liver disease detection, diagnosis and treatment. We also address the current challenges and future perspectives of AI in liver imaging, with an emphasis on feature interpretability, multimodal data integration and multicenter study.”
      原文链接:
    • NETL
    • NSTL

    New Findings from University of Electronic Science and Technology of China Describe Advances in Machine Learning (Hydrogen Diffusion In Zirconium Hydrides From On-the-fly Machine Learning Molecular Dynamics)

    2-3页
    查看更多>>摘要：Fresh data on Machine Learning are presented in a new report. According to news reporting from Chengdu, People’s Republic of China, by NewsRx journalists, research stated, “Reactor pressure vessels and fuel cladding tubes have repeatedly failed due to zirconium hydrides. Zirconium hydride precipitation and growth are directly affected by hydrogen atom transport properties, which would make nuclear fuel storage less safe over long periods of time.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Key Project of National Natural Science Foundation of China -China Academy of Engineering Physics joint Foundation (NSAF). The news correspondents obtained a quote from the research from the University of Electronic Science and Technology of China, “Herein, we employ first-principles calculations to investigate the hydrogen diffusion mechanism in zirconium hydrides, utilizing on-the-fly machine learning force field molecular dynamics. It is verified that the machine learning force field can accurately describe the hydrogen atomic diffusion properties in zirconium hydrides at several temperatures and compositions. The atomic migration paths of hydrogen in zirconium hydrides as well as their barriers and pre-factors are also calculated.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Dalhousie University Researchers Update Understanding of Machine Learning (Seafloor morphology and substrate mapping in the Gulf of St Lawrence, Canada, using machine learning approaches)

    3-4页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting originating from Dalhousie University by NewsRx correspondents, research stated, “Detailed maps of seafloor substrata and morphology can act as valuable proxies for predicting and understanding the distributions of benthic communities and are important for guiding conservation initiatives.” Funders for this research include Mitacs. Our news journalists obtained a quote from the research from Dalhousie University: “High resolution acoustic remote sensing data can facilitate the production of detailed seafloor maps, but are cost-prohibitive to collect and not widely available. In the absence of targeted high resolution data, global bathymetric data of a lower resolution, combined with legacy seafloor sampling data, can provide an alternative for generating maps of seafloor substrate and morphology. Here we apply regression random forest to legacy data in the Gulf of St Lawrence, Canada, to generate a map of seabed sediment distribution. We further apply kmeans clustering to a principal component analysis output to identify seafloor morphology classes from the GEBCO bathymetric grid. The morphology classification identified most morphological features but could not discriminate valleys and canyons. The random forest results were in line with previous sediment mapping work done in the area, but a large proportion of zero values skewed the explained variance.”
      原文链接:
    • NETL
    • NSTL
    • Frontiers Media Sa

    Data from Mapua University Broaden Understanding of Machine Learning [Prediction of Hydrogen Adsorption and Moduli of Metal-Organic Frameworks (MOFs) Using Machine Learning Strategies]

    4-5页
    查看更多>>摘要：A new study on artificial intelligence is now available. According to news reporting originating from Manila, Philippines, by NewsRx correspondents, research stated, “For hydrogen-powered vehicles, the efficiency cost brought about by the current industry choices of hydrogen storage methods greatly reduces the system’s overall efficiency.” Funders for this research include Office of Directed Research For Innovation And Value Enhancement (Drive) of Mapua University. Our news correspondents obtained a quote from the research from Mapua University: “The physisorption of hydrogen fuel onto metal-organic frameworks (MOFs) is a promising alternative storage method due to their large surface areas and exceptional tunability. However, the massive selection of MOFs poses a challenge for the efficient screening of top-performing MOF structures that are capable of meeting target hydrogen uptakes. This study examined the performance of 13 machine learning (ML) models in the prediction of the gravimetric and volumetric hydrogen uptakes of real MOF structures for comparison with simulated and experimental results. Among the 13 models studied, 12 models gave an R2 greater than 0.95 in the prediction of both the gravimetric and the volumetric uptakes in MOFs.”
      原文链接:
    • NETL
    • NSTL
    • Mdpi

    Researcher at Chongqing Technology and Business University Has Published New Study Findings on Artificial Intelligence (Artificial Intelligence and Digital Museum VR Environment Design Based on Embedded Image Processing)

    5-5页
    查看更多>>摘要：Research findings on artificial intelligence are discussed in a new report. According to news reporting from Chongqing, People’s Republic of China, by NewsRx journalists, research stated, “As a carrier of human cultural heritage, it is of great practical significance to apply VR technology to the construction of modern digital museums.” The news journalists obtained a quote from the research from Chongqing Technology and Business University: “This paper combines artificial intelligence and SNN algorithms for image segmentation and proposes the overall framework of ‘embedded image acquisition and SNN segmentation processing’. This system is utilized to improve the picture clarity and delay efficiency of VR technology and reduce the vertigo feeling of the user experience. The VR environment of the digital museum is designed according to the user’s intentions. Finally, the effectiveness of the designed museum VR environment is verified by evaluating the comfort, interaction, and emotional experience of the VR museum. The experimental results show that in terms of comfort experience, the SSQ scale score of the VR museum experience based on the embedded image processing is 7.1, which is nearly double the score of the original VR museum experience. In terms of interaction and emotional experience, the overall satisfaction score of the VR environment design of the M museum was 9.4.”
      原文链接:
    • NETL
    • NSTL

    New Findings from Institute of Information Technology Update Understanding of Machine Learning (Ma-cat: Misclassification-aware Contrastive Adversarial Training)

    6-6页
    查看更多>>摘要：Investigators publish new report on Machine Learning. According to news reporting out of Zhengzhou, People’s Republic of China, by NewsRx editors, research stated, “Vulnerability to adversarial examples poses a significant challenge to the secure application of deep neural networks. Adversarial training and its variants have shown great potential in addressing this problem.” Financial supporters for this research include Song Shan Laboratory, Program of Song Shan Laboratory (Included in the management of the Major Science and Technology Program of Henan Province). Our news journalists obtained a quote from the research from the Institute of Information Technology, “However, such approaches, which directly optimize the decision boundary, often result in overly complex adversarial decision boundaries that are detrimental to generalization. To deal with this issue, a novel plug-and-play method known as Misclassification-Aware Contrastive Adversarial Training (MA-CAT) from the perspective of data distribution optimization is proposed. MA-CAT leverages supervised decoupled contrastive learning to cluster nature examples within the same class in the logit space, indirectly increasing the margins of examples. Moreover, by taking into account the varying difficulty levels of adversarial training for different examples, MA-CAT adaptively customizes the strength of adversarial training for each example using an instance-wise misclassification-aware adaptive temperature coefficient.”
      原文链接:
    • NETL
    • NSTL
    • Wiley

    Amsterdam University Medical Center Reports Findings in Machine Learning (An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study)

    6-7页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Amsterdam, Netherlands, by NewsRx correspondents, research stated, “People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value.” Financial support for this research came from IMI-RHAPSODY. Our news editors obtained a quote from the research from Amsterdam University Medical Center, “In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0-11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3-11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Investigators from School of Mechanical & Electrical Engineering Have Reported New Data on Robotics (Novel Potential Guided Bidirectional Rrt* With Direct Connection Strategy for Path Planning of Redundant Robot Manipulators In Joint Space)

    8-8页
    查看更多>>摘要：Investigators publish new report on Robotics. According to news reporting out of Changsha, People’s Republic of China, by NewsRx editors, research stated, “To avoid the cumbersome calculation of inverse kinematics and improve the efficiency of obstacle avoidance, a novel potential guided bidirectional rapidly-exploring random tree star with the direct connection strategy for redundant robot manipulators in the joint space is proposed. First, an expansion strategy based on the artificial potential field is designed in the joint space.” Financial support for this research came from National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from the School of Mechanical & Electrical Engineering, “Then, this expansion strategy is combined with the goal-biased bidirectional rapidly-exploring random tree star (GB-RRT*) to improve the ability of obstacle avoidance. Second, a direct connection strategy is designed to improve expansion efficiency. Finally, the effectiveness and superiority of the proposed algorithm are verified by simulations and experiments. The results show that, compared with bidirectional RRT and GB-RRT*, the proposed algorithm can plan a shorter path with a wider clearance between the redundant robot manipulator and obstacles, generate fewer invalid nodes that collide with obstacles, and spend less time.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Chongqing University Reports Findings in Machine Learning (Analysis of factors influencing the energy efficiency in Chinese wastewater treatment plants through machine learning and SHapley Additive exPlanations)

    9-9页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating from Chongqing, People’s Republic of China, by NewsRx correspondents, research stated, “Wastewater treatment plants (WWTPs) contribute significantly to the control of pollution in water. However, they are significant energy consumers.” Our news editors obtained a quote from the research from Chongqing University, “Identifying the factors influencing energy consumption is crucial for enhancing the energy efficiency of WWTPs. To address this, the unit energy consumption (UEC) of WWTPs was predicted using machine learning models. In order to accurately evaluate WWTPs’ energy utilization efficiency, a comprehensive energy evaluation indicator, UEC (kWh/kg TOD) was utilized in this study. Among the prediction models, the eXtreme Gradient Boosting (XGBoost) achieves the highest prediction accuracy. SHapley Additive exPlanations (SHAP) was adopted as the model explanation system, and the results revealed that UEC was negatively affected by TN concentration, which was the most influential factor. The stoichiometry-based model calculation result indicates that the nitrification consumes average 77 % of the overall oxygen demand. SHAP analysis illustrated that the UEC of main technologies decreases with increasing influential factors. Partial dependence plot (PDP) compared average UEC of these technologies and SBR consumed the least amount of energy. The research also indicated that low influent TN concentration is the main problem in China. Consequently, it is imperative to exert efforts in ensuring the influent TN concentration while simultaneously making appropriate adjustments to the treatment process.”
      原文链接:
    • NETL
    • NSTL