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    'Artificial intelligence will play an increasing role in scientific publications'

    1-1页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – (Boston)-Artificial intelligence (AI), in various forms, has burst onto the scene in both society and medicine. Its ro le in medicine is still evolving, but undoubtedly, it will assist in the evaluat ion of images (radiographs, pathological reports, videos of colonoscopy,) as wel l as in preparing discharge summaries, consultative evaluations and diagnosis. I t may also help in the long-awaited goal of precision medicine. In addition, it has and will play an increasing role in scientific publication in at least two a reas, peer-review and drafting manuscripts. According to former editor-in-chief of the Journal of the American Medical Assoc iation Howard Bauchner, MD, in the coming years, AI will transform the writing o f scientific manuscripts, assist in reviewing them, and help editors select the most impactful papers. “Potentially it may help editors increase the influence o f their journals,” says Bauchner, professor of pediatrics at Boston University C hobanian & Avedisian School of Medicine. In a guest editorial in the European Journal of Emergency Medicine, Bauchner exa mines how AI could be used by editors. “Given that identifying enough peer-revie wers is getting increasingly difficult, editors could use AI to provide an initi al “score.” An article with what is determined to have a good score could then b e sent for external peer-review (with simply a cursory review by the editors). F or articles with an inadequate score, the editors could still consider it for pu blication after reviewing it or even possibly depending upon the report, ask aut hors to revise the manuscript,” he explains.
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    Findings from Research Center for Astronomy Broaden Understanding of Machine Lea rning (The New herschel/pacs Point Source Catalogue)

    2-3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 in Budapest, Hung ary, by NewsRx journalists, research stated, “Herschel operated as an observator y, and therefore it did not cover the whole sky, but still observed similar to 8 % of it. The first version of an overall Herschel/PACS Point Sourc e Catalogue (PSC) was released in 2017.” Funders for this research include European Union (EU), International Space Scien ce Institute (ISSI) in Bern, through ISSI International Team project, Hungarian Academy of Sciences, European Space Agency. The news reporters obtained a quote from the research from Research Center for A stronomy, “The data are still unique and are very important for research using f ar-infrared information, especially because no new far-infrared mission is fores een for at least the next decade. In the framework of the NEMESIS project, we re visited all the photometric observations obtained by the PACS instrument on-boar d the Herschel space observatory, using more advanced techniques than before, in cluding machine learning techniques. Our aim was to build the most complete and most accurate Herschel/PACS catalogue to date. Our primary goal was to increase the number of real sources, and decrease the number of spurious sources identifi ed on a strongly variable background, which is due to the thermal emission of th e interstellar dust, mostly located in star-forming regions. Our goal was to bui ld a blind catalogue, meaning that source extraction is conducted without relyin g on prior detections at various wavelengths, allowing us to detect sources neve r catalogued before. The methods for data analysis have evolved continuously sin ce the first release of a uniform Herschel/PACS catalogue. We define a hybrid st rategy that includes classical and machine learning source identification and ch aracterisation methods that optimise faint-source detection, providing catalogue s at much higher completeness levels than before. Quality assessment also involv es machine learning techniques. Our source extraction methodology facilitates a systematic and impartial comparison of sensitivity levels across various Hersche l fields, a task that was typically beyond the scope of individual programmes. W e created a high-reliability and a rejected source catalogue for each PACS passb and: 70, 100, and 160 mu m. With the high-reliability catalogue, we managed to s ignificantly increase the completeness in all bands, especially at 70 mu m. At t he same time, while the number of high-reliability detections decreased, the num ber of sources matching with existing catalogues increased, suggesting that the purity is also higher than before.”
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    Findings from University of Copenhagen in Quantum Dots Reported (Direct observat ion of a few-photon phase shift induced by a single quantum emitter in a wavegui de)

    3-3页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in quantum dots. Acco rding to news originating from the University of Copenhagen by NewsRx correspond ents, research stated, “Realizing a sensitive photon-number-dependent phase shif t on a light beam is required both in classical and quantum photonics.” Financial supporters for this research include Danmarks Grundforskningsfond; Ec | Horizon 2020 Framework Programme; Bundesministerium Fur Bildung Und Forschung. Our news journalists obtained a quote from the research from University of Copen hagen: “It may lead to new applications for classical and quantum photonics mach ine learning or pave the way for realizing photon-photon gate operations. Nonlin ear phase-shifts require efficient light-matter interaction, and recently quantu m dots coupled to nanophotonic devices have enabled near-deterministic single-ph oton coupling. We experimentally realize an optical phase shift of 0.19p ± 0.03 radians ( 34 degrees) using a weak coherent state interacting with a single quan tum dot in a planar nanophotonic waveguide. The phase shift is probed by interfe rometric measurements of the light scattered from the quantum dot in the wavegui de. The process is nonlinear in power, the saturation at the single-photon level and compatible with scalable photonic integrated circuitry.”
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    Yichang Central People’s Hospital Reports Findings in Artificial Intelligence [Integrating artificial intelligence (S-Detect software) and contrast-enhanced ul trasound for enhanced diagnosis of thyroid nodules: A comprehensive evaluation s tudy]

    4-4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 report. According to news reporting originating from Hubei , People’s Republic of China, by NewsRx correspondents, research stated, “This s tudy aims to assess the diagnostic efficacy of Korean Thyroid imaging reporting and data system (K-TIRADS), S-Detect software and contrast-enhanced ultrasound ( CEUS) when employed individually, as well as their combined application, for the evaluation of thyroid nodules, with the objective of identifying the optimal me thod for diagnosing thyroid nodules. Two hundred and sixty eight cases pathologi cally proven of thyroid nodules were retrospectively enrolled.” Our news editors obtained a quote from the research from Yichang Central People’ s Hospital, “Each nodule was classified according to K-TIRADS. S-Detect software was utilized for intelligent analysis. CEUS was employed to acquire contrast-en hanced features. The area under curve (AUC) values for diagnosing benign and mal ignant thyroid nodules using K-TIRADS alone, S-Detect software alone, CEUS alone , the combined application of K-TIRADS and CEUS, the combined application of S-D etect software and CEUS were 0.668, 0.668, 0.719, 0.741, and 0.759, respectively (p <0.001). The sensitivity rate of S-Detect software was 89.9% (p <0.001). It was the highest of the five diagnostic methods above. The utilization of S-Detect software can be serv ed as a powerful tool for early screening.”
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    Newcastle University Reports Findings in Mental Health Diseases and Conditions ( Machine Learning Model Reveals Determinators for Admission to Acute Mental Healt h Wards From Emergency Department Presentations)

    5-5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 Mental Health Diseases and Conditions is the subject of a report. According to news reporting originat ing in Callaghan, Australia, by NewsRx journalists, research stated, “This resea rch addresses the critical issue of identifying factors contributing to admissio ns to acute mental health (MH) wards for individuals presenting to the emergency department (ED) with MH concerns as their primary issue, notably suicidality. T his study aims to leverage machine learning (ML) models to assess the likelihood of admission to acute MH wards for this vulnerable population.” The news reporters obtained a quote from the research from Newcastle University, “Data collection for this study used existing ED data from 1 January 2016 to 31 December 2021. Data selection was based on specific criteria related to the pre senting problem. Analysis was conducted using Python and the Interpretable Machi ne Learning (InterpretML) machine learning library. InterpretML calculates overa ll importance based on the mean absolute score, which was used to measure the im pact of each feature on admission. A person’s ‘Age’ and ‘Triage category’ are ra nked significantly higher than ‘Facility identifier’, ‘Presenting problem’ and ‘ Active Client’. The contribution of other presentation features on admission sho ws a minimal effect. Aligning the models closely with service delivery will help services understand their service users and provide insight into financial and clinical variations. Suicidal ideation negatively correlates to admission yet re presents the largest number of presentations. The nurse’s role at triage is a cr itical factor in assessing the needs of the presenting individual. The gap that emerges in this context is significant; MH triage requires a complex understandi ng of MH and presents a significant challenge in the ED.”
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    New Machine Learning Study Results from Inner Mongolia University of Technology Described (Prediction of Coal Gangue Volcanic Ash Activity Based On Machine Lear ning)

    6-6页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on Machine Learning have been presented. According to news originating from Hohhot, People’s Republic of China, by NewsRx correspondents, research stated, “Coal gangue (CG) as an suppl ementary material with cementitious properties to substitute for cement, not onl y reduces the energy consumed and carbon dioxide emitted in cement production, b ut also facilitates the resourceful use of CG waste. However, the raw CG’s low v olcanic ash reactivity and the instability of its potential pozzolanic substance content, the activation test process becomes both cumbersome and time-consuming .” Financial supporters for this research include Central Guidance Fund for Local S cience and Technology Development, Doctoral Fund Project of Inner Mongolia Unive rsity of Technology, Ordos Key RD Pro-gram.
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    Studies from Chinese Academy of Sciences Have Provided New Information about Rob otics (Configuration Optimization of a Dualarm Reconfigurable Space Robot Based On Closed-chain Inertia Matching)

    7-8页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Robotics have been published. According to news reporting from Shenyang, People’s Republic of China, by NewsRx journalists, research stated, “The dual-arm space robot usuall y forms a closed-chain constraint system with a target through collaborative ope rations when performing tasks. Most previous related research has focused on the performance of open-chain robots themselves.” Funders for this research include National Key Research & Developm ent Program of China, Chinese Academy of Sciences Inter disciplinary Innovation Team, National Natural Science Foundation of China (NSFC).
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    University of Colorado Boulder Reports Findings in Machine Learning (SeqImprove: Machine-Learning-Assisted Curation of Genetic Circuit Sequence Information)

    8-8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 Boulder, Col orado, by NewsRx correspondents, research stated, “The progress and utility of s ynthetic biology is currently hindered by the lengthy process of studying litera ture and replicating poorly documented work. Reconstruction of crucial design in formation through post hoc curation is highly noisy and error-prone.” Our news editors obtained a quote from the research from the University of Color ado Boulder, “To combat this, author participation during the curation process i s crucial. To encourage author participation without overburdening them, an ML-a ssisted curation tool called SeqImprove has been developed. Using named entity r ecognition, called entity normalization, and sequence matching, SeqImprove creat es machine-accessible sequence data and metadata annotations, which authors can then review and edit before submitting a final sequence file.” According to the news editors, the research concluded: “SeqImprove makes it easi er for authors to submit sequence data that is FAIR (findable, accessible, inter operable, and reusable).” This research has been peer-reviewed.
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    Department of Oncology Reports Findings in Pancreatic Cancer (A prognostic bioma rker of disulfidptosis constructed by machine learning framework model as potent ial reporters of pancreatic adenocarcinoma)

    9-10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 - Pancreatic Cancer is the subject of a report. According to news reporting out of Jiangsu, P eople’s Republic of China, by NewsRx editors, research stated, “Pancreatic adeno carcinoma (PAAD), known for its high lethality, has not been thoroughly explored in terms of its mechanisms and patterns of immune infiltration. Disulfidptosis, a newly identified mode of cell death, is likely associated with tumorigenesis and progression but remains poorly understood in PAAD at the genetic and mechani stic levels.” Our news journalists obtained a quote from the research from the Department of O ncology, “Sixteen PAAD samples from the GSE154778 scRNA-seq dataset were subject ed to single-cell analysis. Disulfidptosis grouping and scores were established across various immune cell populations. Using the TCGA-PAAD database, LASSO regr ession was employed to construct prognostic markers linked to disulfidptosis. Th e performance of this model was assessed in both training and independent valida tion cohorts. Subsequent analyses explored the correlations between disulfidptos is scores, immune infiltration, and drug sensitivity. Cellular experiments furth er confirmed the significant positive relationship of the gene MET with disulfid ptosis and its role in influencing the invasion and metastasis of PAAD. WGCNA id entified Disulf-High and Disulf-Low as modules strongly correlated with disulfid ptosis. Five prognostically significant genes were selected to construct prognos tic models. Survival analysis demonstrated that the disulfidptosis-related biolo gical model successfully achieved prognostic stratification in PAAD patients. Ad ditionally, the disulfidptosis score was significantly correlated with both immu ne infiltration and drug sensitivity. Knockdown of the MET gene substantially in hibited cell multiplication and cell cycle progression in two PAAD cell lines, e ffects potentially induced by the activation of the PI3K/AKT signalling pathway in the tumour. Key genes associated with disulfidptosis significantly correlate with immune infiltration and the development of PAAD.”
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    Researchers from Case Western Reserve University Discuss Findings in Artificial Intelligence (Artificial Intelligence In Manufacturing: State of the Art, Perspe ctives, and Future Directions)

    9-9页
    查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Artificial In telligence have been published. According to news reporting originating from Cle veland, Ohio, by NewsRx correspondents, research stated, “Inspired by the natura l intelligence of humans and bio-evolution, Artificial Intelligence (AI) has see n accelerated growth since the beginning of the 21st century.” Financial supporters for this research include National Science Foundation (NSF) , National Research, Development & Innovation Office (NRDIO) - Hun gary. Our news editors obtained a quote from the research from Case Western Reserve Un iversity, “Successful AI applications have been broadly reported, with Industry 4.0 providing a thematic platform for AI-related research and development in man ufacturing. This paper highlights applications of AI in manufacturing, ranging f rom production system design and planning to process modeling, optimization, qua lity assurance, maintenance, automated assembly and disassembly.”
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