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    University of Potsdam Reports Findings in Esophagectomy (Enhancing Preoperative Outcome Prediction: A Comparative Retrospective Case-Control Study on Machine Le arning versus the International Esodata Study Group Risk Model for Predicting 90 -Day ...)

    21-22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 Surgery - Esophagectom y is the subject of a report. According to news originating from Potsdam, German y, by NewsRx correspondents, research stated, “Risk prediction prior to oncologi c esophagectomy is crucial for assisting surgeons and patients in their joint in formed decision making. Recently, a new risk prediction model for 90-day mortali ty after esophagectomy using the International Esodata Study Group (IESG) databa se was proposed, allowing for the preoperative assignment of patients into diffe rent risk categories.”Financial supporters for this research include BIH clinician scientist program, CASSANDRA, Einstein Center for Neurosciences.

    Jackson Laboratory for Genomic Medicine Reports Findings in Robotics (Developmen t of an automated 3D high content cell screening platform for organoid phenotypi ng)

    22-23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 Robotics is the subjec t of a report. According to news originating from Farmington, Connecticut, by Ne wsRx correspondents, research stated, “The use of organoid models in biomedical research has grown substantially since their inception. As they gain popularity among scientists seeking more complex and biologically relevant systems, there i s a direct need to expand and clarify potential uses of such systems in diverse experimental contexts.” Our news journalists obtained a quote from the research from Jackson Laboratory for Genomic Medicine, “Herein we outline a high-content screening (HCS) platform that allows researchers to screen drugs or other compounds against three-dimens ional (3D) cell culture systems in a multi-well format (384-well). Furthermore, we compare the quality of robotic liquid handling with manual pipetting and char acterize and contrast the phenotypic effects detected by confocal imaging and bi ochemical assays in response to drug treatment. We show that robotic liquid hand ling is more consistent and amendable to high throughput experimental designs wh en compared to manual pipetting due to improved precision and automated randomiz ation capabilities. We also show that image-based techniques are more sensitive to detecting phenotypic changes within organoid cultures than traditional bioche mical assays that evaluate cell viability, supporting their integration into org anoid screening workflows. Finally, we highlight the enhanced capabilities of co nfocal imaging in this organoid screening platform as they relate to discerning organoid drug responses in single-well co-cultures of organoids derived from pri mary human biopsies and patient-derived xenograft (PDX) models.”

    Study Findings on Machine Learning Reported by a Researcher at Helwan University (Selective Laser Sintering of Polymers: Process Parameters, Machine Learning Ap proaches, and Future Directions)

    23-24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 ar tificial intelligence. According to news reporting out of Cairo, Egypt, by NewsR x editors, research stated, “Selective laser sintering (SLS) is a bed fusion add itive manufacturing technology that facilitates rapid, versatile, intricate, and cost-effective prototype production across various applications.” The news editors obtained a quote from the research from Helwan University: “It supports a wide array of thermoplastics, such as polyamides, ABS, polycarbonates , and nylons. However, manufacturing plastic components using SLS poses signific ant challenges due to issues like low strength, dimensional inaccuracies, and ro ugh surface finishes. The operational principle of SLS involves utilizing a high -power-density laser to fuse polymer or metallic powder surfaces. This paper pre sents a comprehensive analysis of the SLS process, emphasizing the impact of dif ferent processing variables on material properties and the quality of fabricated parts. Additionally, the study explores the application of machine learning (ML ) techniques-supervised, unsupervised, and reinforcement learning-in optimizing processes, detecting defects, and ensuring quality control within SLS. The revie w addresses key challenges associated with integrating ML in SLS, including data availability, model interpretability, and leveraging domain knowledge.”

    Findings from Huazhong University of Science and Technology in Robotics Reported (Flocking Fragmentation Formulation for a Multi-robot System Under Multi-hop an d Lossy Ad Hoc Networks)

    24-25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 Wuhan, People’s Republic of China, by N ewsRx journalists, research stated, “We investigate the impact of network topolo gy characteristics on flocking fragmentation for a multi-robot system under a mu ltihop and lossy ad hoc network, including the network’s hop count features and information’s successful transmission probability (STP). Specifically, we first propose a distributed communication-calculationexecution protocol to describe the practical interaction and control process in the ad hoc network based multi- robot system, where flocking control is realized by a discrete-time Olfati-Saber model incorporating STP-related variables.” The news correspondents obtained a quote from the research from the Huazhong Uni versity of Science and Technology, “Then, we develop a fragmentation prediction model (FPM) to formulate the impact of hop count features on fragmentation for s pecific flocking scenarios. This model identifies the critical system and networ k features that are associated with fragmentation. Further considering general f locking scenarios affected by both hop count features and STP, we formulate the flocking fragmentation probability (FFP) by a data fitting model based on the ba ck propagation neural network, whose input is extracted from the FPM. The FFP fo rmulation quantifies the impact of key network topology characteristics on fragm entation phenomena.”

    Study Data from State University of New York (SUNY) Buffalo Provide New Insights into Machine Learning (Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern . ..)

    25-26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 New York City , United States, by NewsRx journalists, research stated, “Knowledge of the facto rs influencing nutrient-limited subtropical maize yield and subsequent predictio n is crucial for effective nutrient management, maximizing profitability, ensuri ng food security, and promoting environmental sustainability. We analyzed data f rom nutrient omission plot trials (NOPTs) conducted in 324 farmers’ fields acros s ten agroecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields.” Our news editors obtained a quote from the research from State University of New York (SUNY) Buffalo: “An additive main effect and multiplicative interaction (A MMI) model was used to explain maize yield variability with nutrient addition. I nterpretable machine learning (ML) algorithms in automatic machine learning (Aut oML) frameworks were subsequently used to predict attainable yield relative nutr ient-limited yield (RY) and to rank variables that control RY. The stack-ensembl e model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model’s square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature import ance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn.”

    Royal Stoke University Hospital Researcher Provides New Insights into Artificial Intelligence (Assessing the quality and readability of online patient informati on: ENT UK patient information e-leaflets vs responses by a Generative Artificia l ...)

    26-27页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting from Royal Stoke University Hospi tal by NewsRx journalists, research stated, “The evolution of artificial intelli gence has introduced new ways to disseminate health information, including natur al language processing models like ChatGPT. However, the quality and readability of such digitally-generated information remains understudied.” Our news correspondents obtained a quote from the research from Royal Stoke Univ ersity Hospital: “This study is the first to compare the quality and readability of digitally-generated health information against leaflets produced by professi onals. Patient information leaflets for five ENT UK leaflets and their correspon ding ChatGPT responses were extracted from the Internet. Assessors with various degree of medical knowledge evaluated the content using the Ensuring Quality Inf ormation for Patients (EQIP) tool and readability tools including the Flesch-Kin caid Grade Level (FKGL). Statistical analysis was performed to identify differen ces between leaflets, assessors, and sources of information. ENT UK leaflets wer e of moderate quality, scoring a median EQIP of 23. Statistically significant di fferences in overall EQIP score were identified between ENT UK leaflets but Chat GPT responses were of uniform quality. Non-specialist doctors rated the highest EQIP scores while medical students scored the lowest. The mean readability of EN T UK leaflets was higher than ChatGPT responses. The information metrics of ENT UK leaflets were moderate and varied between topics. Equivalent ChatGPT informat ion provided comparable content quality, but with reduced readability.”

    Shanghai Jiao Tong University School of Medicine Reports Findings in Artificial Intelligence (Experimental and clinical validation of an artificial intelligence metal artifact correction algorithm for low-dose following up CT of percutaneou s ...)

    27-28页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Artificial Intelligence is the su bject of a report. According to news reporting originating from Shanghai, People ’s Republic of China, by NewsRx correspondents, research stated, “Lowdose follo wing up computed tomography (CT) of percutaneous vertebroplasty (PVP) that invol ves the use of bone cement usually suffers from lightweight metal artifacts, whe re conventional techniques for CT metal artifact reduction are often not suffici ently effective. This study aimed to validate an artificial intelligence (AI)-ba sed metal artifact correction (MAC) algorithm for use in low-dose following up C T for PVP.” Our news editors obtained a quote from the research from the Shanghai Jiao Tong University School of Medicine, “In experimental validation, an ovine vertebra ph antom was designed to simulate the clinical scenario of PVP. With routine-dose i mages acquired prior to the cement introduction as the reference, low-dose CT sc ans were taken on the cemented phantom and processed with conventional MAC and A I-MAC. The resulting image quality was compared in CT attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR), followed by a qu antitative evaluation of the artifact correction accuracy based on adaptive segm entation of the paraspinal muscle. In clinical validation, ten cases of low-dose following up CT after PVP were enrolled to test the performance of diagnosing s arcopenia with measured CT attenuation per cemented vertebral segment, via recei ver operating characteristic (ROC) analysis. With respect to the reference image , no significant difference was found for AI-MAC in CT attenuation, image noise, SNRs, and CNR (all P>0.05). The paraspinal muscle segme nted on the AIMAC image was 18.6% and 8.3% more com plete to uncorrected and MAC images. Higher area under the curve (AUC) of the RO C analysis was found for AI-MAC (AUC =0.92) compared to the uncorrected (AUC =0. 61) and MAC images (AUC =0.70).”

    Nanjing University Medical School Reports Findings in Machine Learning [FT-NIR combined with machine learning was used to rapidly detect the adulteratio n of pericarpium citri reticulatae (chenpi) and predict the adulteration concent ration]

    28-29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 Nanjing, Peo ple’s Republic of China, by NewsRx correspondents, research stated, “Pericarpium citri reticulatae (PCR) has been used as a food and spice for many years and is known for its rich nutritional content and unique aroma. However, price increas es are often accompanied by adulteration.” Our news editors obtained a quote from the research from Nanjing University Medi cal School, “In this study, two kinds of adulterants (Orange peel-OP and Mandari n Rind-MR) were identified by chromaticity analysis, FT-NIR and machine learning algorithm, and the doping concentration was predicted quantitatively. The resul ts show that colorimetric analysis cannot completely differentiate between PCR a nd adulterants. Using spectral preprocessing combined with machine learning algo rithms, PCR and two adulterants were successfully distinguished, with classifica tion accuracy reaching 99.30 % and 98.64 % respectiv ely. After selecting characteristic wavelengths, the R of the adulterated quanti tative model is greater than 0.99.”

    Study Findings on Machine Learning Detailed by Researchers atFederal University of Juiz de Fora (Long-term natural streamflowforecasting under drought scenari os using data-intelligence modeling)

    29-30页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on artificial intelligenc e is the subject of a new report. According to newsreporting originating from J uiz de Fora, Brazil, by NewsRx correspondents, research stated, “Long-termriver streamflow prediction and modeling are essential for water resource management and decision-makingrelated to water resources.”The news journalists obtained a quote from the research from Federal University of Juiz de Fora: “Thisresearch paper considers the importance of these predicti ons and proposes a model to address scarcityscenarios to support decision-makin g in water allocation, flood management, and drought prediction scenarios.Machi ne learning (ML) techniques offer promising alternatives for improving long-term streamflowprediction. However, most existing studies on ML models for streamfl ow prediction have focused onshorter time horizons, limiting their broader appl icability. Consequently, there is a need for dedicatedresearch that addresses t he use of ML models in long-term streamflow prediction. Considering this research gap, this paper presents an ML-based approach that learns and replicates the n atural flow dynamics ofa river, allowing for the simulation of reduced flow sce narios (25 % and 50 % reduction). This capabilityal lows for simulating drought scenarios of varying severity, providing valuable in sights for water servicemanagers.”

    Data on Colon Cancer Reported by Yuankun Liu and Colleagues (Utilizing machine l earning algorithms for predicting risk factors for bone metastasis from right-si ded colon carcinoma after complete mesocolic excision: a 10-year retrospective . ..)

    30-31页
    查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Oncology - Colon Cancer is the su bject of a report. According to news reporting originating in Wuxi, People’s Rep ublic of China, by NewsRx journalists, research stated, “Bone metastasis (BM) oc curs when colon cancer cells disseminate from the primary tumor site to the skel etal system via the bloodstream or lymphatic system. The emergence of such bone metastases typically heralds a significantly poor prognosis for the patient.” The news reporters obtained a quote from the research, “This study’s primary aim is to develop a machine learning model to identify patients at elevated risk of bone metastasis among those with rightsided colon cancer undergoing complete m esocolonectomy (CME). The study cohort comprised 1,151 individuals diagnosed wit h right-sided colon cancer, with a subset of 73 patients presenting with bone me tastases originating from the colon. We used univariate and multivariate regress ion analyses as well as four machine learning algorithms to screen variables for 38 characteristic variables such as patient demographic characteristics and sur gical information. The study employed four distinct machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and knearest neighbor algorithm (KNN), to develop the predictiv e model. Additionally, the model was assessed using receiver operating character istic (ROC) curves, calibration curves, and decision curve analysis (DCA), while Shapley additive explanation (SHAP) was utilized to visualize and analyze the m odel. The XGBoost algorithm performed the best performance among the four predic tion models. In the training set, the XGBoost algorithm had an area under curve (AUC) value of 0.973 (0.953-0.994), an accuracy of 0.925 (0.913-0.936), a sensit ivity of 0.921 (0.902-0.940), and a specificity of 0.908 (0.894-0.922). In the v alidation set, the XGBoost algorithm had an AUC value of 0.922 (0.833-0.995), an accuracy of 0.908 (0.889-0.926), a sensitivity of 0.924 (0.873-0.975), and a sp ecificity of 0.883 (0.810-0.956). Furthermore, the AUC value of 0.83 for the ext ernal validation set suggests that the XGBoost prediction model possesses strong extrapolation capabilities. The results of SHAP analysis identified alkaline ph osphatase (ALP) levels, tumor size, invasion depth, lymph node metastasis, lung metastasis, and postoperative neutrophilto- lymphocyte ratio (NLR) levels as sig nificant risk factors for BM from right-sided colon cancer subsequent to CME.”