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    Shandong Provincial Hospital Reports Findings in Neuroendocrine Cancer (Identifi cation of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analys is Based on Multiparameter MRI)

    19-19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 Oncology - Neuroendocr ine Cancer is the subject of a report. According to news originating from Jinan, People's Republic of China, by NewsRx correspondents, research stated, "This st udy aims to investigate the feasibility of preoperatively predicting histologica l subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning a nd radiomics based on multiparameter MRI. Patients with PitNETs from January 201 6 to May 2022 were retrospectively enrolled from four medical centers." Our news journalists obtained a quote from the research from Shandong Provincial Hospital, "A cfVBNet network was used to automatically segment PitNET multipar ameter MRI. Radiomics features were extracted from the MRI, and the radiomics sc ore (Radscore) of each patient was calculated. To predict histological subtypes, the Gaussian process (GP) machine learning classifier based on radiomics featur es was performed. Multi-classification (six-class histological subtype) and bina ry classification (PRL vs. non-PRL) GP model was constructed. Then, a clinical-r adiomics nomogram combining clinical factors and Radscores was constructed using the multivariate logistic regression analysis. The performance of the models wa s evaluated using receiver operating characteristic (ROC) curves. The PitNET aut osegmentation model eventually achieved the mean Dice similarity coefficient of 0.888 in 1206 patients (mean age 49.3 ± SD years, 52% female). In the multi-classification model, the GP of T2WI got the best area under the ROC curve (AUC), with 0.791, 0.801, and 0.711 in the training, validation, and exter nal testing set, respectively. In the binary classification model, the GP of T2W I combined with CE T1WI demonstrated good performance, with AUC of 0.936, 0.882, and 0.791 in training, validation, and external testing sets, respectively. In the clinical-radiomics nomogram, Radscores and Hardy' grade were identified as p redictors for PRL expression."
      原文链接:
    • NETL
    • NSTL

    Investigators from University of Lisbon Release New Data on Robotics ('i Want To Send a Message To My Friend'Exploring the Shift of Agency To Older Adults In Hr i)

    20-20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 originating from Lisbon, Portugal, by NewsRx correspon dents, research stated, "Communication among some older adults is affected by co gnitive and mobility impairments. This increases isolation, particularly for tho se residing in care homes, and leads to accelerated cognitive decline." Financial supporters for this research include ShiftHRI project, a CMU Portugal Exploratory Project - Fundaco para a Ciencia e Tecnologia (FCT), LASIGE Research Unit, Fundacao para a Ciencia e a Tecnologia (FCT), FCT|FCCN (b-on). Our news journalists obtained a quote from the research from the University of L isbon, "Previous research has leveraged assistive robots to promote recreational routines and communication among older adults, with the robot leading the inter action. However, older adults could have more agency in the interaction, as robo ts could extend elders' intentions and needs. Therefore, we explored an approach whereby the robot's agency is shifted to the older adults who lead the interact ion by commanding a robot's actions using interactive physical blocks (tangible blocks). We conducted sessions with 22 care home dwellers where they could excha nge messages and objects using the robot. Based on older adults' observed behavi ors during the sessions and perspectives gathered from interviews with geriatric professionals, we reflect on the opportunities and challenges for increased use r agency and the asymmetries that emerged from differing abilities and personali ty traits."
      原文链接:
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    Studies from Army Engineering University of PLA Provide New Data on Machine Lear ning (Buckling critical load prediction of pultruded fiber-reinforced polymer co lumns and feature analysis by machine learning)

    21-21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 out of Nanjing, People's Republic of China, by NewsRx editors, research stated, "For slender FRP columns, predict ing the global buckling critical loads is crucial in structural design." Funders for this research include National Natural Science Foundation of China. Our news journalists obtained a quote from the research from Army Engineering Un iversity of PLA: "However, there is a lack of a consensus prediction method base d on specialized domain knowledge. To address this issue, this study created a c omprehensive database by collecting 365 experimental data related to global buck ling of axially loaded pultruded FRP columns to predict buckling critical loads using such machine learning methods as extreme gradient boosting, artificial neu ral network, and support vector regression. The prediction accuracy and stabilit y of the machine learning prediction methods were evaluated, and the interpretab ility of the features was analyzed in depth. The results show that the predictio n accuracy of the traditional theoretical methods is low, while that of the mach ine learning methods is high. The contribution of geometric parameters to the bu ckling critical load is more than 80 %."
      原文链接:
    • NETL
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    Data on Machine Learning Described by a Researcher at Graduate School of Enginee ring (Identification of Respiratory Pauses during Swallowing by Unconstrained Me asuring Using Millimeter Wave Radar)

    22-22页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on artificial intelligence are presented in a new report. According to news reporting out of Osaka, Japan, by NewsRx editors, research stated, "Breathing temporarily pauses during swallow ing, and the occurrence of inspiration before and after these pauses may increas e the likelihood of aspiration, a serious health problem in older adults." Funders for this research include Japan Society For The Promotion of Science. Our news correspondents obtained a quote from the research from Graduate School of Engineering: "Therefore, the automatic detection of these pauses without cons traints is important. We propose methods for measuring respiratory movements dur ing swallowing using millimeter wave radar to detect these pauses. The experimen t involved 20 healthy adult participants. The results showed a correlation of 0. 71 with the measurement data obtained from a band-type sensor used as a referenc e, demonstrating the potential to measure chest movements associated with respir ation using a non-contact method. Additionally, temporary respiratory pauses cau sed by swallowing were confirmed by the measured data. Furthermore, using machin e learning, the presence of respiring alone was detected with an accuracy of 88. 5%, which is higher than that reported in previous studies."
      原文链接:
    • NETL
    • NSTL

    University of Bristol Reports Findings in Deep Vein Thrombosis (Evaluating the b enefits of machine learning for diagnosing deep vein thrombosis compared to gold standard ultrasound- a feasibility study)

    23-23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 Cardiovascular Disease s and Conditions - Deep Vein Thrombosis is the subject of a report. According to news originating from Bristol, United Kingdom, by NewsRx correspondents, resear ch stated, "This study evaluates the feasibility of remote deep venous thrombosi s (DVT) diagnosis via ultrasound sequences facilitated by ThinkSono Guidance, an artificial intelligence (AI)-app, for point-of-care ultrasound (POCUS). The aim is to assess the effectiveness of AI-guided POCUS conducted by non-specialists in capturing valid ultrasound images for remote diagnosis of DVT." Our news journalists obtained a quote from the research from the University of B ristol, "Over a 3.5- month period, patients with suspected DVT underwent AI-guide d POCUS conducted by non-specialists using a handheld ultrasound probe connected to the app. These ultrasound sequences were uploaded to a cloud-dashboard for r emote specialist review. Additionally, participants received a formal DVT scans. Patients underwent AI-guided POCUS using handheld probes connected to the AI-ap p, followed by formal DVT scans. Ultrasound sequences acquired during the AI-gui ded scan were uploaded to a cloud-dashboard for remote specialist review, where image quality was assessed, and diagnoses were provided. Among 91 predominantly elderly female participants, 18% of scans were incomplete. Of the rest, 91% had sufficient quality, with 64% categoris ed by remote clinicians as 'compressible' or 'incompressible.' Sensitivity and s pecificity for adequately imaged scans were 100% and 91% , respectively. Notably, 53% were low risk, potentially obviating formal scans. ThinkSono Guidance effectively directed non-specialists, streamlin ing DVT diagnosis and treatment. It may reduce the need for formal scans, partic ularly with negative findings, and extend diagnostic capabilities to primary car e."
      原文链接:
    • NETL
    • NSTL

    Studies from University of the Philippines Have Provided New Data on Chemical En gineering (Teaching classical machine learning as a graduate-level course in che mical engineering: An algorithmic approach)

    24-24页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on chemical engi neering. According to news originating from Quezon City, Philippines, by NewsRx editors, the research stated, "The demand for engineering graduates with technic al skills in data science, machine learning (ML), and artificial intelligence (A I) is now growing." Financial supporters for this research include University of The Philippines Dil iman. Our news journalists obtained a quote from the research from University of the P hilippines: "Chemical engineering (ChemE) departments around the world are curre ntly addressing this skills gap by instituting AI or ML elective courses in thei r program. However, designing such a course is difficult since the issue of whic h ML models to teach and the depth of theory to be discussed remains unclear. In this paper, we present a graduate-level ML course particularly designed such th at students will be able to apply ML for research in ChemE. To achieve this, the course intends to cover a wide selection of ML models with emphasis on their mo tivations, derivations, and training algorithms, followed by their applications to ChemE-related data sets. We argue that this algorithmic approach to teaching ML can help broaden the capabilities of students since they can judge for themse lves which tool to use when, even for problems outside the process industries, o r they can modify the methods to test novel ideas. We found that students remain engaged in the mathematical details as long as every topic is properly motivate d and the gaps in the required statistical and computer science concepts are fil led."
      原文链接:
    • NETL
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    Researchers at Zhengzhou University of Light Industry Have Reported New Data on Robotics (Extrinsic Calibration Method for Integrating Infrared Thermal Imaging Camera and 3d Lidar)

    25-25页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Robotics. According to news reporting originating in Zhengzhou, People's Republi c of China, by NewsRx editors, the research stated, "PurposeIn the context of fi re incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenge s." Funders for this research include Science and Technology Department of Henan Pro vince, Henan Provincial Science and Technology Research Project.
      原文链接:
    • NETL
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    Medical University of Innsbruck Reports Findings in Prostate Cancer (Prediction of Clinically Significant Prostate Cancer by a Specific Collagen-related Transcr iptome, Proteome, and Urinome Signature)

    26-27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 Oncology - Prostate Ca ncer is the subject of a report. According to news reporting out of Innsbruck, A ustria, by NewsRx editors, research stated, "While collagen density has been ass ociated with poor outcomes in various cancers, its role in prostate cancer (PCa) remains elusive. Our aim was to analyze collagen-related transcriptomic, proteo mic, and urinome alterations in the context of detection of clinically significa nt PCa (csPCa, International Society of Urological Pathology [ISUP] grade group 2)." Our news journalists obtained a quote from the research from the Medical Univers ity of Innsbruck, "Comprehensive analyses for PCa transcriptome (n = 1393), prot eome (n = 104), and urinome (n = 923) data sets focused on 55 collagen-related g enes. Investigation of the cellular source of collagen-related transcripts via s ingle-cell RNA sequencing was conducted. Statistical evaluations, clustering, an d machine learning models were used for data analysis to identify csPCa signatur es. Differential expression of 30 of 55 collagen-related genes and 34 proteins w as confirmed in csPCa in comparison to benign prostate tissue or ISUP 1 cancer. A collagen-high cancer cluster exhibited distinct cellular and molecular charact eristics, including fibroblast and endothelial cell infiltration, intense extrac ellular matrix turnover, and enhanced growth factor and inflammatory signaling. Robust collagen-based machine learning models were established to identify csPCa . The models outcompeted prostate-specific antigen (PSA) and age, showing compar able performance to multiparametric magnetic resonance imaging (mpMRI) in predic ting csPCa. Of note, the urinome-based collagen model identified four of five cs PCa cases among patients with Prostate Imaging- Reporting and Data System (PI-IRA DS) 3 lesions, for which the presence of csPCa is considered equivocal. The retr ospective character of the study is a limitation. Collagen-related transcriptome , proteome, and urinome signatures exhibited superior accuracy in detecting csPC a in comparison to PSA and age. The collagen signatures, especially in cases of ambiguous lesions on mpMRI, successfully identified csPCa and could potentially reduce unnecessary biopsies. The urinome-based collagen signature represents a p romising liquid biopsy tool that requires prospective evaluation to improve the potential of this collagenbased approach to enhance diagnostic precision in PCa for risk stratification and guiding personalized interventions."
      原文链接:
    • NETL
    • NSTL

    King Saud University Researchers Advance Knowledge in Machine Translation [Error Analysis of Pretrained Language Models (PLMs) in English-to-Arabic Machine Translation]

    27-27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 ma chine translation. According to news reporting originating from King Saud Univer sity by NewsRx correspondents, research stated, "Advances in neural machine tran slation utilizing pretrained language models (PLMs) have shown promise in improv ing the translation quality between diverse languages." Our news editors obtained a quote from the research from King Saud University: " However, translation from English to languages with complex morphology, such as Arabic, remains challenging. This study investigated the prevailing error patter ns of state-of-the-art PLMs when translating from English to Arabic across diffe rent text domains. Through empirical analysis using automatic metrics (chrF, BER TScore, COMET) and manual evaluation with the Multidimensional Quality Metrics ( MQM) framework, we compared Google Translate and five PLMs (Helsinki, Marefa, Fa cebook, GPT-3.5-turbo, and GPT-4)." According to the news editors, the research concluded: "Key findings provide val uable insights into current PLM limitations in handling aspects of Arabic gramma r and vocabulary while also informing future improvements for advancing English- Arabic machine translation capabilities and accessibility."
      原文链接:
    • NETL
    • NSTL

    Fuzhou University Reports Findings in Machine Learning (Vancomycin trough concen tration in adult patients with periprosthetic joint infection: A machine learnin g-based covariate model)

    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 Fuzhou, Peop le's Republic of China, by NewsRx correspondents, research stated, "Although the re are various model-based approaches to individualized vancomycin (VCM) adminis tration, few have been reported for adult patients with periprosthetic joint inf ection (PJI). This work attempted to develop a machine learning (ML)-based model for predicting VCM trough concentration in adult PJI patients." Our news editors obtained a quote from the research from Fuzhou University, "The dataset of 287 VCM trough concentrations from 130 adult PJI patients was split into a training set (229) and a testing set (58) at a ratio of 8:2, and an indep endent external 32 concentrations were collected as a validation set. A total of 13 covariates and the target variable (VCM trough concentration) were included in the dataset. A covariate model was respectively constructed by support vector regression, random forest regression and gradient boosted regression trees and interpreted by SHapley Additive exPlanation (SHAP). The SHAP plots visualized th e weight of the covariates in the models, with estimated glomerular filtration r ate and VCM daily dose as the 2 most important factors, which were adopted for t he model construction. Random forest regression was the optimal ML algorithm wit h a relative accuracy of 82.8% and absolute accuracy of 67.2% (R =.61, mean absolute error = 2.4, mean square error = 10.1), and its predictio n performance was verified in the validation set. The proposed ML-based model ca n satisfactorily predict the VCM trough concentration in adult PJI patients."
      原文链接:
    • NETL
    • NSTL