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    Mount Sinai Health System Reports Findings in Personalized Medicine (Development and Internal Validation of Machine Learning Models for Personalized Survival Predictions in Spinal Cord Glioma Patients)

    39-40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 from New York City, New York, by NewsRx journalists, research stated, "Numerous factors have been associated with the survival outcomes in patients with spinal cord gliomas (SCG). Recognizing these specific determinants is crucial, yet it is also vital to establish a reliable and precise prognostic model for estimating individual survival outcomes." The news correspondents obtained a quote from the research from Mount Sinai Health System, "The objectives of this study are twofold: first, to create an array of interpretable machine learning (ML) models developed for predicting survival outcomes among SCG patients; and second, to integrate these models into an easily navigable online calculator to showcase their prospective clinical applicability. This was a retrospective, population-based cohort study aiming to predict the outcomes of interest, which were binary categorical variables, in SCG patients with ML models. The National Cancer Database (NCDB) was utilized to identify adults aged 18 years or older who were diagnosed with histologically confirmed SCGs between 2010 and 2019. The outcomes of interest were survival outcomes at three specific time points post-diagnosis: 1, 3, and 5 years. These outcomes were formed by combining the 'Vital Status' and 'Last Contact or Death (Months from Diagnosis)' variables. Model performance was evaluated visually and numerically. The visual evaluation utilized receiver operating characteristic (ROC) curves, precisionrecall curves (PRCs) and calibration curves. The numerical evaluation involved metrics such as sensitivity, specificity, accuracy, area under the PRC (AUPRC), area under the ROC curve (AUROC), and Brier Score. We employed five ML algorithms-TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest-along with the Optuna library for hyperparameter optimization. The models that yielded the highest AUROC values were chosen for integration into the online calculator. To enhance the explicability of our models, we utilized SHapley Additive exPlanations (SHAP) for assessing the relative significance of predictor variables and incorporated partial dependence plots (PDPs) to delineate the influence of singular variables on the predictions made by the top performing models. For the 1-year survival analysis, 4,913 patients [5.6% with 1-year mortality]; for the 3-year survival analysis, 4,027 patients (11.5% with 3-year mortality]; and for the 5-year survival analysis, 2,854 patients (20.4% with 5-year mortality) were included. The top models achieved AUROCs of 0.938 for 1-year mortality (TabPFN), 0.907 for 3-year mortality (LightGBM), and 0.902 for 5-year mortality (Random Forest). Global SHAP analyses across survival outcomes at different time points identified histology, tumor grade, age, surgery, radiotherapy, and tumor size as the most significant predictor variables for the top-performing models. This study demonstrates ML techniques can develop highly accurate prognostic models for SCG patients with excellent discriminatory ability. The interactive online calculator provides a tool for assessment by physicians. Local interpretability informs prediction influences for a given individual. External validation across diverse datasets could further substantiate potential utility and generalizability."

    University of Aveiro Reports Findings in Machine Learning (Predicting the efficiency of luminescent solar concentrators for solar energy harvesting using machine learning)

    40-41页
    查看更多>>摘要:New research on Machine Learning is the subject of a report. According to news reporting out of Aveiro, Portugal, by NewsRx editors, research stated, "Building-integrated photovoltaics (BIPV) is an emerging technology in the solar energy field. It involves using luminescent solar concentrators to convert traditional windows into energy generators by utilizing light harvesting and conversion materials." Financial support for this research came from Fundacao para a Ciencia e a Tecnologia. Our news journalists obtained a quote from the research from the University of Aveiro, "This study investigates the application of machine learning (ML) to advance the fundamental understanding of optical material design. By leveraging accessible photoluminescent measurements, ML models estimate optical properties, streamlining the process of developing novel materials, offering a cost-effective and efficient alternative to traditional methods, and facilitating the selection of competitive materials. Regression and clustering methods were used to estimate the optical conversion efficiency and power conversion efficiency. The regression models achieved a Mean Absolute Error (MAE) of 10%, which demonstrates accuracy within a 10% range of possible values." According to the news editors, the research concluded: "Both regression and clustering models showed high agreement, with a minimal MAE of 7%, highlighting the efficacy of ML in predicting optical properties of luminescent materials for BIPV."

    Researchers at Anhui University Release New Data on Robotics (Multi-objective Optimization Design of External Rotor Permanent Magnet Synchronous Motor for Robot Arm)

    41-42页
    查看更多>>摘要:Investigators publish new report on Robotics. According to news reporting originating from Anhui, People's Republic of China, by NewsRx correspondents, research stated, "On the basis of the skewed pole model, this study uses the elite opposition-based golden-sine whale optimization algorithm to carry out the multi-objective optimization design of the frameless external rotor permanent magnet synchronous motor of the robot arm and achieve high torque density and low torque ripple. First, the key parameters affecting the torque ripple and torque density of the motor are analyzed." Financial supporters for this research include Natural Science Foundation of Anhui Province, National Natural Science Foundation of China (NSFC), Key project of National Natural Science funds. Our news editors obtained a quote from the research from Anhui University, "Second, the angle of the oblique pole is studied to reduce torque ripple. On the basis of the oblique pole model of the motor, the sample library is established according to the key parameters, the K-nearest neighbor algorithm is introduced for regression fitting, and the high-precision and fast calculation model of the motor is established. Third, the elite opposition-based golden-sine whale optimization algorithm (EGWOA) is introduced to optimize the key parameters of the fitting model with the objective of reducing torque ripple and increasing torque density, and the non dominated sorting and crowding degree calculation methods are used to improve it." According to the news editors, the research concluded: "Finally, a prototype is made to prove the effectiveness of the optimized design." This research has been peer-reviewed.

    New Machine Learning Study Results Reported from Henan University (Rational Design of Single Transition-metal Atoms Anchored On a Ptse2 Monolayer As Bifunctional Oer/orr Electrocatalysts: a Defect Chemistry and Machine Learning Study)

    42-43页
    查看更多>>摘要:Current study results on Machine Learning have been published. According to news reporting originating from Kaifeng, People's Republic of China, by NewsRx correspondents, research stated, "Searching for highly efficient, economical, and environmentally friendly bifunctional electrocatalysts for the oxygen reduction reaction (OER) and oxygen evolution reaction (ORR) is crucial in developing renewable energy conversion and storage technology. In this study, we systematically investigate the effect of defect charges on the electrocatalytic performance of transition metal (TM = Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag) single atoms anchored on a PtSe2 monolayer (TM@PtSe2) using first-principles calculations." Financial supporters for this research include National Natural Science Foundation of China (NSFC), National Natural Science Foundation of China (NSFC), Guizhou Provincial Basic Research Program (Natural Science), Functional Materials and Devices Technology Innovation Team of Guizhou Province University. Our news editors obtained a quote from the research from Henan University, "Based on our formation energy calculation, we find that Pt-rich conditions can promote the anchoring of TM atoms on PtSe2 and demonstrate that 29 types of TM@PtSe2 in different charge states are stable. Among these materials, Pd-center dot@PtSe2 (eta(OER/ORR) = 0.31/0.43 V) and Pd-x@PtSe2 (eta(OER/ORR) = 0.36/0.74 V) systems not only have low formation energy but also exhibit excellent catalytic performance, due to their ultralow overpotential (eta). Interestingly, our results reveal that adjusting the charge states of TM@PtSe2 is a new effective method for designing low overpotential bifunctional OER/ORR electrocatalysts. This adjustment can tune the interaction strength between the oxygenated intermediates and TM@ PtSe2. Additionally, we employ machine learning (ML) models to investigate the origin of activity in OER/ORR processes. Our results reveal that the first ionization energy (I-m), the electronegativity (N-m), the number of TM-d electrons (N-e), the d-band center (epsilon(d)), the electron affinity (chi(m)), and the charge transfer of TM atoms (Q(e)) of TM@PtSe2 are the primary descriptors characterizing the adsorption behavior."

    New Robotics Study Findings Recently Were Reported by Researchers at Anhui University of Science and Technology (Design, Simulation, Control of a Hybrid Pouring Robot: Enhancing Automation Level In the Foundry Industry)

    43-44页
    查看更多>>摘要:Data detailed on Robotics have been presented. According to news reporting out of Anhui, People's Republic of China, by NewsRx editors, research stated, "Currently, workers in sand casting face harsh environments and the operation safety is poor. Existing pouring robots have insufficient stability and load-bearing capacity and cannot perform intelligent pouring according to the demand of pouring process." Funders for this research include National Innovation Method Work Special Project, Collaborative Innovation Project of Universities in Anhui Province, Science and Technology Major Special Program Project in Anhui Province. Our news journalists obtained a quote from the research from the Anhui University of Science and Technology, "In this paper, a hybrid pouring robot is proposed to solve these limitations, and a visionbased hardware-in-the-loop (HIL) control technology is designed to achieve the real-time control problems of simulated pouring and pouring process. Firstly, based on the pouring mechanism and the motion demand of ladle, a hybrid pouring robot with a 2UPR-2RPU parallel mechanism as the main body is designed. And the equivalent hybrid kinematic model was established by using Eulerian method and differential motion. Subsequently, a motion control strategy based on HIL simulation technique was designed and presented. The working space of the robot was obtained through simulation experiments to meet the usage requirements. And the stability of the robot was tested through the key motion parameters of the robot joints. Based on the analysis of pouring quality and trajectory, optimal dynamic parameters for the experimental prototype are obtained through water simulation experiments, the pouring liquid height area is 35-40 cm, the average flow rate of pouring liquid is 112 cm3/s, and the ladle tilting speed is 0.0182 rad/s. Experimental results validate the reasonableness of the designed pouring robot structure. Its control system realizes the coordinated movement of each branch chain to complete the pouring tasks with different variable parameters."

    Recent Findings from University of Laval Has Provided New Information about Machine Learning (Automatic Estimation of Lipid Content From in Situ Images of Arctic Copepods Using Machine Learning)

    44-45页
    查看更多>>摘要:Current study results on Machine Learning have been published. According to news originating from Quebec City, Canada, by NewsRx correspondents, research stated, "In Arctic marine ecosystems, large planktonic copepods form a crucial hub of matter and energy. Their energy-rich lipid stores play a central role in marine trophic networks and the biological carbon pump." Financial support for this research came from Natural Sciences and Engineering Research Council of Canada (NSERC). Our news journalists obtained a quote from the research from the University of Laval, "Since the past similar to 15 years, in situ imaging devices provide images whose resolution allows us to estimate an individual copepod's lipid sac volume, and this reveals many ecological information inaccessible otherwise. One such device is the Lightframe On-sight Keyspecies Investigation. However, when done manually, weeks of work are needed by trained personnel to obtain such information for only a handful of sampled images. We removed this hurdle by training a machine learning algorithm (a convolutional neural network) to estimate the lipid content of individual Arctic copepods from the in situ images." According to the news editors, the research concluded: "This algorithm obtains such information at a speed (a few minutes) and a resolution (individuals, over half a meter on the vertical), allowing us to revisit historical datasets of in situ images to better understand the dynamics of lipid production and distribution and to develop efficient monitoring protocols at a moment when marine ecosystems are facing rapid upheavals and increasing threats."

    Central South University Reports Findings in Glioblastomas (Machine learning-based investigation of regulated cell death for predicting prognosis and immunotherapy response in glioma patients)

    45-46页
    查看更多>>摘要:New research on Oncology - Glioblastomas is the subject of a report. According to news reporting originating from Changsha, People's Republic of China, by NewsRx correspondents, research stated, "Glioblastoma is a highly aggressive and malignant type of brain cancer that originates from glial cells in the brain, with a median survival time of 15 months and a 5-year survival rate of less than 5%. Regulated cell death (RCD) is the autonomous and orderly cell death under genetic control, controlled by precise signaling pathways and molecularly defined effector mechanisms, modulated by pharmacological or genetic interventions, and plays a key role in maintaining homeostasis of the internal environment." Financial supporters for this research include National Natural Science Foundation of China, Special funds for innovation in Hunan Province, High talent project of Hunan Province. Our news editors obtained a quote from the research from Central South University, "The comprehensive and systemic landscape of the RCD in glioma is not fully investigated and explored. After collecting 18 RCD-related signatures from the opening literature, we comprehensively explored the RCD landscape, integrating the multi-omics data, including large-scale bulk data, single-cell level data, glioma cell lines, and proteome level data. We also provided a machine learning framework for screening the potentially therapeutic candidates. Here, based on bulk and single-cell sequencing samples, we explored RCD-related phenotypes, investigated the profile of the RCD, and developed an RCD gene pair scoring system, named RCD.GP signature, showing a reliable and robust performance in predicting the prognosis of glioblastoma. Using the machine learning framework consisting of Lasso, RSF, XgBoost, Enet, CoxBoost and Boruta, we identified seven RCD genes as potential therapeutic targets in glioma and verified that the SLC43A3 highly expressed in glioma grades and glioma cell lines through qRT-PCR."

    New Robotics Study Findings Have Been Reported by a Researcher at Stephen F. Austin State University (Tools or Fools: Are We Educating Managers or Creating Tool-Dependent Robots?)

    46-47页
    查看更多>>摘要:Fresh data on robotics are presented in a new report. According to news originating from Nacogdoches, Texas, by NewsRx correspondents, research stated, "This essay examines strategies for thoughtfully integrating generative AI (Gen-AI) into management curricula to enhance student learning while mitigating risks like overreliance." Our news correspondents obtained a quote from the research from Stephen F. Austin State University: "We make the case that outright resistance is counterproductive; instead, management educators should embrace Gen-AI's potential to create more engaging, experiential learning aligned with andragogical principles. We provide a conceptual framework mapping nine Gen-AI objectives to the principles of andragogy. A semester-long course example illustrates this framework in action through AI activities fostering autonomy, competence, and real-world application. Student surveys revealed overwhelmingly positive perceptions of Gen-AI integration and improved exam scores. However, dependence risks remain." According to the news reporters, the research concluded: "The essay discusses strategies to enhance critical thinking, personal growth, and academic integrity. Overall, we propose that prudent Gen-AI adoption can enrich management education, but long-term vigilance regarding overreliance is vital." For more information on this research see: Tools or Fools: Are We Educating Managers or Creating Tool- Dependent Robots?. Journal of Management Education, 2024. The publisher for Journal of Management Education is SAGE Publications.

    Imperial College London Reports Findings in Chemical Engineering (Explainable Ai Models for Predicting Drop Coalescence In Microfluidics Device)

    47-47页
    查看更多>>摘要:Researchers detail new data in Engineering - Chemical Engineering. According to news reporting originating in London, United Kingdom, by NewsRx journalists, research stated, "In the field of chemical engineering, understanding the dynamics and probability of drop coalescence is not just an academic pursuit, but a critical requirement for advancing process design by applying energy only where it is needed to build necessary interfacial structures, increasing efficiency towards Net Zero manufacture. This research applies machine learning predictive models to unravel the sophisticated relationships embedded in the experimental data on drop coalescence in a microfluidics device." Funders for this research include PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PRE-MIERE) , United Kingdom, Leverhulme Trust. The news reporters obtained a quote from the research from Imperial College London, "Through the deployment of SHapley Additive exPlanations values, critical features relevant to coalescence processes are consistently identified. Comprehensive feature ablation tests further delineate the robustness and susceptibility of each model. Furthermore, the incorporation of Local Interpretable Model -agnostic Explanations for local interpretability offers an elucidative perspective, clarifying the intricate decision -making mechanisms inherent to each model's predictions. As a result, this research provides the relative importance of the features for the outcome of drop interactions."

    Third Clinical Institute Affiliated to Wenzhou Medical University Reports Findings in Gastric Cancer [A novel single-port robot for total gastrectomy to treat gastric cancer: A case report (with video)]

    48-48页
    查看更多>>摘要:New research on Oncology - Gastric Cancer is the subject of a report. According to news originating from Wenzhou, People's Republic of China, by NewsRx correspondents, research stated, "Multiport robots are now widely used for total gastrectomy for gastric cancer, while there is almost a void of research on whether single-port (SP) robots can be used for total gastrectomy. Here, we report a case of a 75-year-old female patient who was diagnosed with gastric cardia adenocarcinoma by gastroscopy and underwent total gastrectomy assisted by the SHURUI SP robot." Our news journalists obtained a quote from the research from Third Clinical Institute Affiliated to Wenzhou Medical University, "We successfully accomplished total gastrectomy and D2 lymph node dissection using the novel SP robotic platform. The patient was discharged from the hospital successfully with no complications during or after the surgery. Pathologic diagnosis showed adenocarcinoma of the gastric mucosa with partial signet-ring cell carcinoma, and no metastasis was found in the 29 cleared lymph nodes." According to the news editors, the research concluded: "The use of the SHURUI SP robot for total gastrectomy in treating gastric cancer is both technically feasible and safe." This research has been peer-reviewed.