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    Machine learning promises to accelerate metabolism research

    Michael B. McCarthy
    1-2页
    查看更多>>摘要：A new study shows that it is possible to use machine learning and statistics to address a problem that has long hindered the field of metabolomics: large variations in the data collected at different sites. “We don’t always know the source of the variation,” said Daniel Raftery, professor of anesthesiology and pain medicine at the University of Washington School of Medicine in Seattle. “It could be because the subjects are different with different genetics, diets and environmental exposures. Or it could be the way samples were collected and processed.” Raftery and his research colleagues wanted to see if machine learning - a form of artificial intelligence that uses computer algorithms to process large volumes of historical data and to identify data patterns - could reduce this variation between data from different sites without obscuring important differences.
      原文链接:
    • NETL
    • NSTL

    New Findings from East China University of Science and Technology in the Area of Support Vector Machines Described (Mechanomyography Signals Pattern Recognition In Hand Movements Using Swarm Intelligence Algorithm Optimized Support Vector ...)

    1-2页
    查看更多>>摘要：Fresh data on Support Vector Machines are presented in a new report. According to news reporting originating in Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human -machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements based on MMG signal is studied with swarm intelligence algorithms introduced to optimize support vector machine (SVM).” Financial support for this research came from Shanghai University Youth Teacher Training Assistance Scheme. The news reporters obtained a quote from the research from the East China University of Science and Technology, “Time domain (TD) features, wavelet packet node energy (WPNE) features, frequency domain (FD) features, convolution neural network (CNN) features were extracted from each channel to constitute different feature sets. Three novel swarm intelligence algorithms (i.e., bald eagle search (BES), sparrow search algorithm (SSA), grey wolf optimization (GWO)) optimized SVM is proposed to train the models and recognition of hand movements are tested for each MMG feature extraction method. Using GWO as the optimization algorithm, time consumption is less than using the other two swarm algorithms. Using GWO with TD+FD features can obtain the classification accuracy of 93.55 %, which is higher than other methods while using CNN to extract features can be independent of domain knowledge.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    Findings from University of Science and Technology Beijing Yields New Findings on Intelligent Vehicles (A Trustworthy Internet of Vehicles: the Dao To Safe, Secure, and Collaborative Autonomous Driving)

    2-3页
    查看更多>>摘要：Investigators discuss new findings in Transportation - Intelligent Vehicles. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “The potential of the Internet of Vehicles (IoV) to reduce on-board system costs in autonomous vehicles through shared intelligence is considerable. However, it still faces significant challenges, including concerns over data breaches and privacy, inefficiencies and limited fault tolerance in centralized management, and the challenge of ensuring data accuracy.” Financial support for this research came from National Key Research and Development Program of China. Our news journalists obtained a quote from the research from the University of Science and Technology Beijing, “This letter marks the inaugural report from a series of IEEE Transactions on Intelligent Vehicles (TIV) Trustworthy IoV Workshops, which aim to address these issues. In these workshops, we explore the concept of a decentralized IoV (DeIoV), underpinned by decentralized autonomous organizations and operations (DAOs), to facilitate trustworthy interactions between vehicles and other entities. The proposed DeIoV is structured in two layers: the local DAO layer and the global DAO layer. This dual-layer architecture distinguishes between real-time and non-real-time decision-making tasks, aiding in their efficient completion. To ensure data security, integrity, and accuracy, we employ blockchain technology and smart contracts, which allow for mutual verification among adjacent members and utilize encryption algorithms. A reputation value-based voting mechanism for decision-making is also introduced, which helps prevent the monopolization of power through token-based systems, a common issue in traditional DAOs.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    Findings on Androids Reported by Investigators at University of Aveiro (Enhancement of Humanoid Robot Locomotion On Slippery Floors Using an Adaptive Controller)

    3-4页
    查看更多>>摘要：New research on Robotics - Androids is the subject of a report. According to news reporting out of Aveiro, Portugal, by NewsRx editors, research stated, “This paper presents a comprehensive strategy to improve the locomotion performance of humanoid robots on various slippery floors. The strategy involves the implementation and adaptation of a divergent component of motion (DCM) based control architecture for the humanoid NAO, and the introduction of an embedded yaw controller (EYC), which is based on a proportional-integral-derivative (PID) control algorithm.” Financial support for this research came from Fundacao para a Ciencia e a Tecnologia (FCT). Our news journalists obtained a quote from the research from the University of Aveiro, “The EYC is designed not only to address the slip behavior of the robot on low-friction floors but also to tackle the issue of non-straight walking patterns that we observed in this humanoid, even on non-slippery floors. To fine-tune the PID gains for the EYC, a systematic trial-and-error approach is employed. We iteratively adjusted the P (Proportional), I (Integral), and D (Derivative) parameters while keeping the others fixed. This process allowed us to optimize the PID controller’s response to different walking conditions and floor types. A series of locomotion experiments are conducted in a simulated environment, where the humanoid step frequency and PID gains are varied for each type of floor. The effectiveness of the strategy is evaluated using metrics such as robot stability, energy consumption, and task duration. The results of the study demonstrate that the proposed approach significantly improves humanoid locomotion on different slippery floors, by enhancing stability and reducing energy consumption.”
      原文链接:
    • NETL
    • NSTL
    • Cambridge Univ Press

    Shanghai Jiao Tong University Reports Findings in Support Vector Machines (Seismic landslide susceptibility assessment using principal component analysis and support vector machine)

    4-5页
    查看更多>>摘要：New research on Support Vector Machines is the subject of a report. According to news reporting from Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “Seismic landslides are dangerous natural hazards that can cause immense damage to human lives and property. Susceptibility assessment of earthquake-triggered landslides provides the scientific basis and theoretical foundation for disaster emergency management in engineering projects.” The news correspondents obtained a quote from the research from Shanghai Jiao Tong University, “However, landslide susceptibility assessment requires a massive amount of historical landslide data. Evidence of past landslide activities may be lost due to changes in geographical conditions and human factors over time. The lack of landslide data poses difficulties in assessing landslide susceptibility. The aim of this study is to establish a generalized seismic landslide susceptibility assessment model for applying it to the Dayong highway in the Chenghai area, where earthquakes occur frequently but with a lack of landslide data. The landslide data used comes from the 2014 Ludian Ms (Surface wave magnitude) 6.5 earthquake in a region with geographical conditions similar to those in the Chenghai area. The influencing factors considered include elevation, slope, slope aspect, distance to streams, distance to faults, geology, terrain wetness index, normalized difference vegetation index, epicenter distance and peak ground acceleration. The frequency ratio method is used to eliminate influencing factors with poor statistical dispersion of landslides. Principal component analysis (PCA) is utilized to reduce the dimensionality of landslide conditioning factors and to improve the transferability of the assessment model to different regions. A support vector machine model is used to establish the susceptibility assessment model. The results show that the accuracy of the PCA-SVM model reaches 93.6%. The landslide susceptibility of the Chenghai area is classified into 5 classes, with the ‘Very high’ landslide susceptibility class accounting for 0.63%. The 13-km section in the middle of the Dayong highway, which accounts for 8.9%, is identified as the high-risk area most obviously impacted by seismic landslides.”
      原文链接:
    • NETL
    • NSTL

    Ningbo Medical Center Lihuili Hospital Reports Findings in Machine Learning (Development of a novel lncRNA-derived immune gene score using machine learning-based ensembles for predicting the survival of HCC)

    5-6页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting originating in Ningbo, People’s Republic of China, by NewsRx journalists, research stated, “Long noncoding RNAs (lncRNAs) are implicated in the tumor immunology of hepatocellular carcinoma (HCC). HCC mRNA and lncRNA expression profiles were used to extract immune-related genes with the ImmPort database, and immune-related lncRNAs with the ImmLnc algorithm.” Financial support for this research came from Ningbo special support program for high-level personnel recruitment. The news reporters obtained a quote from the research from Ningbo Medical Center Lihuili Hospital, “The MOVICS package was used to cluster immune-related mRNA, immune-related lncRNA, gene mutation and methylation data on HCC from the TCGA. GEO and ICGC datasets were used to validate the model. Data from single-cell sequencing was used to determine the expression of genes from the model in various immune cell types. With this model, the area under the curve (AUC) for 1-, 3- and 5-year survival of HCC patients was 0.862, 0.869 and 0.912, respectively. Single-cell sequencing showed EREG was significantly expressed in a variety of immune cell types. Knockdown of the EREG target gene resulted in significant anti-apoptosis, pro-proliferation and pro-migration effects in HepG2 and HUH7 cells. Moreover, serum and liver tissue EREG levels in HCC patients were significantly higher than those of healthy control patients. We built a prognostic model with good accuracy for predicting HCC patient survival.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Study Results from University of the West of England (UWE) Provide New Insights into Artificial Intelligence (Optimisation of Small- Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities)

    6-7页
    查看更多>>摘要：New research on artificial intelligence is the subject of a new report. According to news originating from Bristol, United Kingdom, by NewsRx correspondents, research stated, “Environment changes, water scarcity, soil depletion, and urbanisation are making it harder to produce food using traditional methods in various regions and countries. Aquaponics is emerging as a sustainable food production system that produces fish and plants in a closed-loop system.” Funders for this research include University of The West of England in Collaboration With The Industry Partner Sciflair Ltd.. Our news journalists obtained a quote from the research from University of the West of England (UWE): “Aquaponics is not dependent on soil or external environmental factors. It uses fish waste to fertilise plants and can save up to 90-95% water. Aquaponics is an innovative system for growing food and is expected to be very promising, but it has its challenges. It is a complex ecosystem that requires multidisciplinary knowledge, proper monitoring of all crucial parameters, and high maintenance and initial investment costs to build the system. Artificial intelligence (AI) and the Internet of Things (IoT) are key technologies that can overcome these challenges. Numerous recent studies focus on the use of AI and the IoT to automate the process, improve efficiency and reliability, provide better management, and reduce operating costs. However, these studies often focus on limited aspects of the system, each considering different domains and parameters of the aquaponics system.”
      原文链接:
    • NETL
    • NSTL

    New Machine Learning Data Have Been Reported by Researchers at Harbin Institute of Technology (An Integrated Approach of Machine Learning and Bayesian Spatial Poisson Model for Large-scale Realtime Traffic Conflict Prediction)

    7-8页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news reporting out of Harbin, People’s Republic of China, by NewsRx editors, research stated, “The use of traffic conflicts in road safety evaluation is gaining considerable popularity as it plays a vital role in developing a proactive safety management strategy and allowing for real-time safety analysis. This study proposes an integrated approach that combines a machine learning (ML) algorithm and a Bayesian spatial Poisson (BSP) model to conduct large-scale real-time traffic conflict prediction by considering traffic states as the explanatory variables.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Heilongjiang Province, Opening Project of Intelligent Policing Key Laboratory of Sichuan Province, China, Fundamental Research Funds for the Central Universities, China Scholarship Council. Our news journalists obtained a quote from the research from the Harbin Institute of Technology, “Traffic conflicts are measured by two indicators, the Time to Collision (TTC) and the Post-Encroachment Time (PET). Based on both TTC and PET, traffic conflict severity is classified into five categories. For each conflict severity category, a binary variable (conflict occurrence) and a count variable (conflict frequency) are developed, respectively. In addition to conflict variables, traffic state parameters are extracted from a large-scale high-resolution trajectory dataset. The traffic parameters include volume, density, speed, and the corresponding space-based and space-time-based measures within a 30-second interval. Eight ML-based classifiers are applied to predict conflict occurrence, and the best classifier is selected. A binary logistic regression is developed to explore the potential linkages between traffic states and conflict occurrence. As well, a resampling technique Borderline-SMOTE is used to mitigate the sparsity caused by the predefined short interval. The BSP model is utilized to predict the specific number of conflicts. Further, the BSP model can also explain the relationship between traffic states and conflict frequency, and thus the significant influencing traffic states are identified. The results show that random forest outperforms the other MLs in terms of conflict occurrence prediction accuracy. Further, the proposed integrated approach achieves a high performance of conflict frequency prediction with RMSE values of 0.1384 similar to 0.1699, MAPE values of 9.25% similar to 36.99%, and MAE values of 0.0087 similar to 0.6398.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    First Affiliated Hospital of Nanjing Medical University Reports Findings in Ankylosing Spondylitis (Clinical outcome analysis of robotassisted pedicle screw insertion in the treatment of ankylosing spondylitis complicated with spinal fractures)

    8-9页
    查看更多>>摘要：New research on Musculoskeletal Diseases and Conditions - Ankylosing Spondylitis is the subject of a report. According to news originating from Jiangsu, People’s Republic of China, by NewsRx correspondents, research stated, “Vague spinal anatomical landmarks in patients with ankylosing spondylitis (AS) make intraoperative insertion of pedicle screws difficult under direct vision. Currently, the clinical outcome is significantly improved with robot guidance.” Our news journalists obtained a quote from the research from the First Affiliated Hospital of Nanjing Medical University, “This study aims to explore the efficacy of robot-assisted pedicle screw insertion in treating AS combined with spinal fractures. 40 patients (341 screws) who underwent pedicle screw insertion with AS complicated with spinal fractures were included. According to different surgical methods, 16 patients (135 screws) were classified into the robot group and 24 (206 screws) into the free-hand group. Intraoperative blood loss, operative duration, and adverse events were compared between the two groups. Gertzbein and Robbins classification was used to classify the accuracy of screw position. Clinical outcomes were evaluated by Visual Analog Scale, Japanese Orthopedic Association, and Oswestry Disability Index. No statistically significant differences between baseline data of the groups. The difference in the blood loss between groups wasn’t significant, nor was the operative duration. No severe adverse events related to pedicle screw insertion were reported in either group. Notably, the accuracy of screw insertion was significantly higher in the robot group (129/135) than in the free-hand group (182/206). The lateral perforation prevalence didn’t differ among groups. VAS in the third month postoperatively was lower in the robot group than in the free-hand group, with a significant difference.”
      原文链接:
    • NETL
    • NSTL

    Texas A&M University Reports Findings in Personalized Medicine (Mining the Metabolic Capacity of Clostridium sporogenes Aided by Machine Learning)

    9-10页
    查看更多>>摘要：New research on Drugs and Therapies - Personalized Medicine is the subject of a report. According to news reporting originating in College Station, United States, by NewsRx journalists, research stated, “Anaerobes dominate the microbiota of the gastrointestinal (GI) tract, where a significant portion of small molecules can be degraded or modified. However, the enormous metabolic capacity of gut anaerobes remains largely elusive in contrast to aerobic bacteria, mainly due to the requirement of sophisticated laboratory settings.” The news reporters obtained a quote from the research from Texas A&M University, “In this study, we employed an in silico machine learning platform, MoleculeX, to predict the metabolic capacity of a gut anaerobe, Clostridium sporogenes, against small molecules. Experiments revealed that among the top seven candidates predicted as unstable, six indeed exhibited instability in C. sporogenes culture. We further identified several metabolites resulting from the supplementation of everolimus in the bacterial culture for the first time. By utilizing bioinformatics and in vitro biochemical assays, we successfully identified an enzyme encoded in the genome of C. sporogenes responsible for everolimus transformation.”
      原文链接:
    • NETL
    • NSTL
    • Wiley