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    Reports Outline Machine Learning Findings from Georgia Institute of Technology (Early Prediction of Impending Exertional Heat Stroke With Wearable Multimodal Sensing and Anomaly Detection)

    38-39页
    查看更多>>摘要：New research on Machine Learning is the subject of a report. According to news originating from Atlanta, Georgia, by NewsRx correspondents, research stated, “We employed wearable multimodal sensing (heart rate and triaxial accelerometry) with machine learning to enable early prediction of impending exertional heat stroke (EHS). US Army Rangers and Combat Engineers (N = 2,102) were instrumented while participating in rigorous 7-mile and 12-mile loaded rucksack timed marches.” Financial supporters for this research include Office of Naval Research, Medical Research and Development Command Institutional Review Board. Our news journalists obtained a quote from the research from the Georgia Institute of Technology, “There were three EHS cases, and data from 478 Rangers were analyzed for model building and controls. The data-driven machine learning approach incorporated estimates of physiological strain (heart rate) and physical stress (estimated metabolic rate) trajectories, followed by reconstruction to obtain compressed representations which then fed into anomaly detection for EHS prediction. Impending EHS was predicted from 33 to 69 min before collapse. These findings demonstrate that low dimensional physiological stress to strain patterns with machine learning anomaly detection enables early prediction of impending EHS which will allow interventions that minimize or avoid pathophysiological sequelae.”
      原文链接:
    • NETL
    • NSTL
    • IEEE

    New Artificial Intelligence Findings Reported from Hefei University of Technology (Does Artificial Intelligence Promote Firms’ Innovation Efficiency: Evidence From the Robot Application)

    39-40页
    查看更多>>摘要：Investigators publish new report on Artificial Intelligence. According to news originating from Hefei, People’s Republic of China, by NewsRx correspondents, research stated, “While artificial intelligence (AI) is widely acknowledged as a transformative technology with the potential to boost productivity, there is limited understanding of its specific impact on firm innovation efficiency. This study leverages robot data from the International Federation of Robotics (IFR) and detailed data on Chinese manufacturing firms spanning 2015 to 2019.” 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 Hefei University of Technology, “The analysis utilizes the Data Envelopment Analysis (DEA) method to evaluate firms’ innovation efficiency and employs the Tobit model to examine the influence of AI on innovation efficiency. Furthermore, the study delves into the heterogeneity of this impact by considering variations in firm ownership, industries, and regions and explores the mechanisms through which AI affects innovation efficiency. The findings demonstrate that AI application significantly enhances firms’ innovation efficiency, a result that holds robustly even after employing alternative AI proxies and instrumental variable regression. Moreover, the positive effects of AI adoption are primarily observed in state-owned enterprises, traditional manufacturing industries, and developed cities. Further analyses indicate that AI adoption modifies human capital, innovation patterns, and the market environment, thereby influencing innovation efficiency.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Fourth Military Medical University Reports Findings in Brain Injury (Machine learning prediction models for in-hospital postoperative functional outcome after moderate-to-severe traumatic brain injury)

    40-41页
    查看更多>>摘要：New research on Central Nervous System Diseases and Conditions - Brain Injury is the subject of a report. According to news reporting out of Xi’an, People’s Republic of China, by NewsRx editors, research stated, “This study aims to utilize machine learning (ML) and logistic regression (LR) models to predict surgical outcomes among patients with traumatic brain injury (TBI) based on admission examination, assisting in making optimal surgical treatment decision for these patients. We conducted a retrospective review of patients hospitalized in our department for moderate-to-severe TBI.” Our news journalists obtained a quote from the research from Fourth Military Medical University, “Patients admitted between October 2011 and October 2022 were assigned to the training set, while patients admitted between November 2022 and May 2023 were designated as the external validation set. Five ML algorithms and LR model were employed to predict the postoperative Glasgow Outcome Scale (GOS) status at discharge using clinical and routine blood data collected upon admission. The Shapley (SHAP) plot was utilized for interpreting the models. A total of 416 patients were included in this study, and they were divided into the training set (n = 396) and the external validation set (n = 47). The ML models, using both clinical and routine blood data, were able to predict postoperative GOS outcomes with area under the curve (AUC) values ranging from 0.860 to 0.900 during the internal cross-validation and from 0.801 to 0.890 during the external validation. In contrast, the LR model had the lowest AUC values during the internal and external validation (0.844 and 0.567, respectively). When blood data was not available, the ML models achieved AUCs of 0.849 to 0.870 during the internal cross-validation and 0.714 to 0.861 during the external validation. Similarly, the LR model had the lowest AUC values (0.821 and 0.638, respectively). Through repeated cross-validation analysis, we found that routine blood data had a significant association with higher mean AUC values in all ML and LR models. The SHAP plot was used to visualize the contributions of all predictors and highlighted the significance of blood data in the lightGBM model. The study concluded that ML models could provide rapid and accurate predictions for postoperative GOS outcomes at discharge following moderate-to-severe TBI.”
      原文链接:
    • NETL
    • NSTL

    Icahn School of Medicine at Mount Sinai Reports Findings in Obstructive Sleep Apnea [Heterogeneous Effects of CPAP in Non- Sleepy OSA on CVD Outcomes: Post-hoc Machine Learning Analysis of the ISAACC Trial (ECSACT Study)]

    41-42页
    查看更多>>摘要：New research on Respiratory Tract Diseases and Conditions - Obstructive Sleep Apnea is the subject of a report. According to news reporting from New York City, New York, by NewsRx journalists, research stated, “Randomized controlled trials of continuous positive airway pressure (CPAP) therapy for cardiovascular disease (CVD) prevention among patients with obstructive sleep apnea (OSA) have been largely neutral. However, given OSA is a heterogeneous disease, there may be unidentified subgroups demonstrating differential treatment effects.” The news correspondents obtained a quote from the research from the Icahn School of Medicine at Mount Sinai, “Apply a novel data-drive approach to identify non-sleepy OSA subgroups with heterogeneous effects of CPAP on CVD outcomes within the ISAACC study. Participants were randomly partitioned into two datasets. One for training (70%) our machine learning model and a second (30%) for validation of significant findings. Model-based recursive partitioning was applied to identify subgroups with heterogeneous treatment effects. Survival analysis was conducted to compare treatment (CPAP versus usual care [UC]) outcomes within subgroups. A total of 1,224 non-sleepy OSA participants were included. Of fifty-five features entered into our model only two appeared in the final model (i.e., average OSA event duration and hypercholesterolemia). Among participants at or below the model-derived average event duration threshold (19.5 seconds), CPAP was protective for a composite of CVD events (training Hazard Ratio [HR] 0.46, p=0.002). For those with longer event duration (＞19.5 seconds), an additional split occurred by hypercholesterolemia status. Among participants with longer event duration and hypercholesterolemia, CPAP resulted in more CVD events compared to UC (training HR 2.24, p=0.011). The point estimate for this harmful signal was also replicated in the testing dataset (HR 1.83, p=0.118). We discovered subgroups of non-sleepy OSA participants within the ISAACC study with heterogeneous effects of CPAP.”
      原文链接:
    • NETL
    • NSTL

    Researchers from Lomonosov Moscow State University Describe Findings in Machine Learning (Starkml: Application of Machine Learning To Overcome Lack of Data On Electron-impact Broadening Parameters)

    42-43页
    查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news originating from Moscow, Russia, by NewsRx correspondents, research stated, “Parameters of electron-impact (Stark) broadening and shift of spectral lines are of key importance in various studies of plasma spectroscopy and astrophysics. To overcome the lack of accurately known Stark parameters, we developed a machine learning approach for predicting Stark parameters of neutral atoms’ lines.” Financial support for this research came from Non-commercial Foundation for the Advancement of Science and Education INTELLECT. Our news journalists obtained a quote from the research from Lomonosov Moscow State University, “By implementing a data pre-processing routine and explicitly testing models’ predictive ability and generalizability, we achieve a high level of accuracy in parameters prediction as well as physically meaningful temperature dependence. The applicability of the results is demonstrated by the case of low-temperature plasma diagnostics.”
      原文链接:
    • NETL
    • NSTL
    • Oxford Univ Press

    Data on Artificial Intelligence Reported by Zhihua Ni and Colleagues (Application of artificial intelligence-based dual source CT scanning in the differentiation of lung adenocarcinoma in situ and minimally invasive adenocarcinoma)

    43-44页
    查看更多>>摘要：New research on Artificial Intelligence is the subject of a report. According to news reporting from Shanghai, People’s Republic of China, by NewsRx journalists, research stated, “Lung adenocarcinoma is the most common type of lung cancer with highly incidence and mortality. Due to the overlap of morphological features, it is difficult to distinguish clinically between preinvasive lesions (in situ adenocarcinoma, AIS) and invasive lesions (minimally invasive adenocarcinoma, MIA), which appear as ground glass cloudy nodules.” The news correspondents obtained a quote from the research, “This study was performed to probe the application value of artificial intelligence (AI)-based dual source CT scanning in the differentiation of AIS as well as MIA. The clinical data of 136 patients in Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine from January 2019 to January 2022 were retrospectively analyzed. The accuracy of AI in distinguishing lung AIS (n=76) and MIA (n=60) were analyzed. The effectiveness of AI in detecting nodules and its diagnostic efficacy for AIS and MIA were explored. The proportion of patients with clear and regular lesion boundaries in AIS was higher than that in MIA. The mean lesion diameter of AIS patients was shorter than MIA patients. There was no difference in the CT value between AIS and MIA in the ground glass nodule density area of pure ground glass nodule and mixed ground glass nodule, but the CT value of the solid nodule density area in AIS was lower. The occurrence of pulmonary vascular abnormality, air bronchogram sign, and pleural depression in AIS patients were lower than MIA patients. The detection rate of AI for lung adenocarcinoma with nodule diameter 5 mm, complete solid nodules and ground glass nodules was significantly higher than radiologists. The sensitivity, specificity, positive prediction rate, negative prediction rate and accuracy of AI detection were significantly higher than radiologists.”
      原文链接:
    • NETL
    • NSTL
    • Professional Medical Publications

    Shihezi University Reports Findings in COVID-19 (Optimal resource allocation model for COVID-19: a systematic review and metaanalysis)

    44-45页
    查看更多>>摘要：New research on Coronavirus - COVID-19 is the subject of a report. According to news reporting originating from Shihezi, People’s Republic of China, by NewsRx correspondents, research stated, “A lack of health resources is a common problem after the outbreak of infectious diseases, and resource optimization is an important means to solve the lack of prevention and control capacity caused by resource constraints. This study systematically evaluated the similarities and differences in the application of coronavirus disease (COVID-19) resource allocation models and analyzed the effects of different optimal resource allocations on epidemic control.” Our news editors obtained a quote from the research from Shihezi University, “A systematic literature search was conducted of CNKI, WanFang, VIP, CBD, PubMed, Web of Science, Scopus and Embase for articles published from January 1, 2019, through November 23, 2023. Two reviewers independently evaluated the quality of the included studies, extracted and cross-checked the data. Moreover, publication bias and sensitivity analysis were evaluated. A total of 22 articles were included for systematic review; in the application of optimal allocation models, 59.09% of the studies used propagation dynamics models to simulate the allocation of various resources, and some scholars also used mathematical optimization functions (36.36%) and machine learning algorithms (31.82%) to solve the problem of resource allocation; the results of the systematic review show that differential equation modeling was more considered when testing resources optimization, the optimization function or machine learning algorithm were mostly used to optimize the bed resources; the meta-analysis results showed that the epidemic trend was obviously effectively controlled through the optimal allocation of resources, and the average control efficiency was 0.38(95%CI 0.25-0.51); Subgroup analysis revealed that the average control efficiency from high to low was health specialists 0.48(95%CI 0.37-0.59), vaccines 0.47(95%CI 0.11-0.82), testing 0.38(95%CI 0.19-0.57), personal protective equipment (PPE) 0.38(95%CI 0.06-0.70), beds 0.34(95%CI 0.14-0.53), medicines and equipment for treatment 0.32(95%CI 0.12-0.51); Funnel plots and Egger’s test showed no publication bias, and sensitivity analysis suggested robust results. When the data are insufficient and the simulation time is short, the researchers mostly use the constructor for research; When the data are relatively sufficient and the simulation time is long, researchers choose differential equations or machine learning algorithms for research. In addition, our study showed that control efficiency is an important indicator to evaluate the effectiveness of epidemic prevention and control.”
      原文链接:
    • NETL
    • NSTL
    • Springer Nature

    Mohammed Ⅵ Polytechnic University Researchers Publish New Data on Machine Learning (Machine Learning and Deep Learning Guided Assessment of Groundwater Reservoir Hydrodynamic Parameters: A Case Study of The El Haouz Aquifer)

    45-46页
    查看更多>>摘要：Investigators publish new report on artificial intelligence. According to news reporting from Mohammed Ⅵ Polytechnic University by NewsRx journalists, research stated, “The Plio-Quaternary aquifer in the EL-Haouz-Mejjate region of Morocco is critical for water supply, necessitating accurate characterization for sustainable management. This study pioneers machine learning (ML) and deep learning (DL) techniques to elucidate the aquifer’s properties.” Our news correspondents obtained a quote from the research from Mohammed Ⅵ Polytechnic University: “Supervised algorithms, including random forest, regression, support vector machines, Gaussian process regression and neural networks, are trained on available hydrogeological data. Diverse features capture complex input-output relationships to predict key hydrodynamic factors like hydraulic conductivity and transmissivity fields. Aquifer architecture attributes, including substratum depth, thickness, and height, are also estimated. Model outputs are validated with field measurements, demonstrating promising accuracy. Enhanced hydrodynamic insights improve the conceptual model and groundwater flow modeling confidence. Uncertainties are reduced through this data-driven approach, enabling optimized aquifer management.”
      原文链接:
    • NETL
    • NSTL

    Researchers’ Work from Stanford University Focuses on Machine Learning (Deep Learning and Crispr-cas13d Ortholog Discovery for Optimized Rna Targeting)

    46-47页
    查看更多>>摘要：Investigators discuss new findings in Machine Learning. According to news reporting originating in Stanford, California, by NewsRx journalists, research stated, “Effective and precise mammalian transcriptome engineering technologies are needed to accelerate biolog-ical discovery and RNA therapeutics. Despite the promise of programmable CRISPR-Cas13 ribonucleases, their utility has been hampered by an incomplete understanding of guide RNA design rules and cellular toxicity resulting from off-target or collateral RNA cleavage.” Financial supporters for this research include UCSD Eureka! Scholarship, National Institutes of Health (NIH) - USA, Defense Advanced Research Projects Agency (DARPA), Emergent Ventures, Shurl and Kay Curci Foundation, Rainwater Charitable Foundation, Arc Institute as a Core Investigator. The news reporters obtained a quote from the research from Stanford University, “Here, we quantified the performance of over 127,000 RfxCas13d (CasRx) guide RNAs and systematically evaluated seven machine learning models to build a guide efficiency prediction algorithm orthogonally validated across multiple human cell types. Deep learning model interpretation revealed preferred sequence motifs and secondary features for highly efficient guides. We next identified and screened 46 novel Cas13d orthologs, finding that DjCas13d achieves low cellular toxicity and high specificity-even when targeting abundant transcripts in sensitive cell types, including stem cells and neurons.”
      原文链接:
    • NETL
    • NSTL
    • Elsevier

    New Robotics Study Findings Have Been Reported from Southeast University (A comparative study of three modes for realizing transmedia standing-and-hovering behavior in robotic dolphins)

    47-48页
    查看更多>>摘要：A new study on robotics is now available. According to news reporting originating from Nanjing, People’s Republic of China, by NewsRx correspondents, research stated, “Three different hovering modes, namely, the caudal fin, pectoral fins, and multi fins, were utilized to achieve the standingand- hovering behavior in robotic dolphins.” Financial supporters for this research include National Natural Science Foundation of China; State Key Laboratory of Robotics And System. The news reporters obtained a quote from the research from Southeast University: “A three-dimensional dolphin model, consisting of body, caudal fin, and symmetric pectoral fins, was used as the virtual swimmer to implement three hovering modes. A novel paddling motion was proposed, and a symmetric shape was designed of the pectoral fins. The hovering mechanisms of different modes were revealed, and the mapping relationships between different motion and performance parameters such as hovering height, efficiency, stability, and rapidity were established. The respective advantages of the three hovering modes were compared. The results showed that the caudal fin mode had the best hovering stability, while the pectoral fins mode had the best hovering rapidity. Moreover, it is worth noting that the multi fins mode had both the good hovering stability and rapidity.”
      原文链接:
    • NETL
    • NSTL