查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Digestive System Disea ses and Conditions - Cholangitis is the subject of a report. According to news o riginating from Beijing, People's Republic of China, by NewsRx correspondents, r esearch stated, "Primary biliary cholangitis (PBC) is associated closely with th e gut microbiota. This study aimed to explore the characteristics of the gut mic robiota after the progress of PBC to cirrhosis." Our news journalists obtained a quote from the research from Capital Medical Uni versity, "This study focuses on utilizing the 16S rRNA gene sequencing method to screen for differences in gut microbiota in PBC patients who progress to cirrho sis. Then, we divided the data into training and verification sets and used seve n different machine learning (ML) models to validate them respectively, calculat ing and comparing the accuracy, F1 score, precision, and recall, and screening t he dominant intestinal flora affecting PBC cirrhosis. PBC cirrhosis patients sho wed decreased diversity and richness of gut microbiota. Additionally, there are alterations in the composition of gut microbiota in PBC cirrhosis patients. The abundance of Faecalibacterium and Gemmiger bacteria significantly decreases, whi le the abundance of Veillonella and Streptococcus significantly increases. Furth ermore, machine learning methods identify Streptococcus and Gemmiger as the pred ominant gut microbiota in PBC patients with cirrhosis, serving as non-invasive b iomarkers (AUC = 0.902). Our study revealed that PBC cirrhosis patients gut micr obiota composition and function have significantly changed."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Artificial Intelligen ce have been presented. According to news originating from Guangzhou, People's R epublic of China, by NewsRx correspondents, research stated, "With the continuou s development of human-centric, resilient, and sustainable manufacturing towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportuni ties for additional functionalities, new features, and tendencies in the industr ial landscape. On the other hand, the technology-driven Industry 4.0 paradigm is still in full swing." Funders for this research include National Key Research and Development Program of China, National Natural Science Foundation of China (NSFC), National Natural Science Foundation of Guangdong Province.
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 - Gliomas is the subject of a report. According to news reporting out of Turku, Finland, by N ewsRx editors, research stated, "Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and st ored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the background coming from embedding media. Spontaneous Rama n spectroscopy was used for molecular fingerprinting of FFPE tissue from 46 pati ent samples with known methylation subtypes." Our news journalists obtained a quote from the research from the University of T urku, "Spectra were used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methy lation-subtype classifiers. Support vector machine and random forest were used t o identify the most discriminatory Raman frequencies. Stimulated Raman spectrosc opy was used to validate the frequencies identified. Mass spectrometry of glioma cell lines and TCGA were used to validate the biological findings. Here we deve lop APOLLO (rAmanbased PathOLogy of maLignant glioma) - a computational workflo w that predicts different subtypes of glioma from spontaneous Raman spectra of F FPE tissue slides. Our novel APOLLO platform distinguishes tumors from nontumor tissue and identifies novel Raman peaks corresponding to DNA and proteins that a re more intense in the tumor. APOLLO differentiates isocitrate dehydrogenase 1 m utant (IDH1mut) from wildtype (IDH1WT) tumors and identifies cholesterol ester l evels to be highly abundant in IDHmut glioma. Moreover, APOLLO achieves high dis criminative power between finer, clinically relevant glioma methylation subtypes , distinguishing between the CpG island hypermethylated phenotype (G-CIMP)-high and G-CIMP-low molecular phenotypes within the IDH1mut types."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on robotics is the subject of a new report. According to news originating from Karnataka, India, by NewsRx editors, the research stated, "In this paper, a robust online Multi-robot Simultaneous ex ploration and coverage path planning problem is presented." The news editors obtained a quote from the research from Manipal Academy of High er Education: "The entire workspace is initially partitioned using a variant of Voronoi partitioning, Manhattan Voronoi, and the robots execute simultaneous exp loration and coverage using Spanning Tree Coverage algorithm and cover the works pace. Once the robot(s) failure is detected the uncovered portions of the Vorono i cell of the failed robot will be shared between other eligible robots or a rep lacement strategy,if available, is performed. Simulation experiments within the V-rep environment is used to demonstrate and validate the performance of the pro posed algorithm."
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 from Rotterdam, Netherlands, by NewsRx journalists, research stated, "Malignant peripheral nerve sheath tumor s (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Curre nt magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasi ve biopsies." The news journalists obtained a quote from the research from Erasmus University Medical Center Cancer Institute: "This study aims to develop a radiomics model u sing quantitative imaging features and machine learning to distinguish MPNSTs fr om BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000-2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi -automatically segmented on MRI scans, and radiomics features were extracted usi ng the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100 x random-s plit cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1- weighted (T1w) MRI radiomics model outperformed others with an area under the cu rve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance per formance."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from Mansour a, Egypt, by NewsRx correspondents, research stated, "ABSTRACT: Transitions in a n open channel refer to the change in flow behavior due to changes in the channe l geometry." Our news editors obtained a quote from the research from Civil Engineering Depar tment: "Determining flow characteristics through transitions is an important top ic as it is necessary to guarantee the ideal hydraulic performance of water stru ctures with low costs. This research focuses on the flow characteristics through vertical and horizontal transitions through experimental study and then utilizi ng machine learning to predict the flow characteristics. The proposed framework aims to develop both the cascade-forward artificial neural network (CFANN) model and the regression model to enhance the prediction of flow characteristics. The first model developed modifies the CFANN using dandelion optimizer (DO) to dete rmine the ideal CFANN configuration. The second model used gene expression progr amming to develop statistical equations. The obtained CFANN-DO model has proven high accuracy in predicting the flow rates at various water loads and speeds ach ieving a coefficient of determination of approximately 100% for tr aining data and 99.5% for testing data."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Technology - Imaging T echnology is the subject of a report. According to news reporting out of Tai'an, People's Republic of China, by NewsRx editors, research stated, "Growth period determination and color coordinates prediction are essential for comparing posth arvest fruit quality. This paper proposes a tomato growth period judgment and co lor coordinates prediction model based on hyperspectral imaging technology." Our news journalists obtained a quote from the research from Shandong Agricultur al University, "It utilizes the most effective color coordinates prediction mode l to obtain a color visual image. Firstly, hyperspectral images were taken of to matoes at different growth periods (green-ripe, color-changing, half-ripe, and f ull-ripe), and color coordinates (L*, a*, b*, c, h) were obtained using a colori meter. The sample set was divided by the sample set partitioning based on joint X-Y distances (SPXY). The support vector machine (SVM), K-nearest neighbors (KNN ), and linear discriminant analysis (LDA) were used to discriminate growth perio d. Results show that the LDA model has the best prediction effect with a predict ion set accuracy of 93.1%. In addition, effective wavelengths were selected using competitive adaptive reweighted sampling (CARS) and successive pr ojections algorithm (SPA), and chromaticity prediction models were established u sing partial least squares regression (PLSR), multiple linear regression (MLR), principal component regression (PCR) and support vector machine regression (SVR) Finally, the color of each pixel of the tomato is calculated using the optimal model, generating a visual distribution image of the color coordinate."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on Fungal Diseases and Cond itions - Candida are presented in a new report. According to news reporting from Genoa, Italy, by NewsRx journalists, research stated, "In this narrative review , we discuss studies assessing the use of machine learning (ML) models for the e arly diagnosis of candidemia, focusing on employed models and the related implic ations. There are currently few studies evaluating ML techniques for the early d iagnosis of candidemia as a prediction task based on clinical and laboratory fea tures."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting out of Pune, India, by N ewsRx editors, research stated, "Increasing urbanisation and industrialisation p roduce major environmental challenges such as air pollution, which endangers hum an, animal, and vegetation life." Our news correspondents obtained a quote from the research from Bharti Vidyapeet h Deemed University Medical College: "Reliable measurement, monitoring, and pred iction of Air Quality (AQ) have emerged as key global concerns. The State Govern ment-Municipal Corporation is working on policy reforms to fight the deteriorati on of air quality in Pune and other Indian cities. In this paper, Real Time Air Quality Surveillance & Forecasting System (RTAQSFS) has been devel oped, which work in the cascaded model incorporating electronics hardware as wel l as machine learning algorithms. The presence of air pollutants is measured usi ng sensors like MQ135, MQ7, MQ131 etc. The performance of machine learning algor ithms viz. Linear regression, Ridge regression, Lasso regression, Decision tree and Random Forest has been evaluated wrt."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting from Richland, Washin gton, by NewsRx journalists, research stated, "Aqueous organic redox flow batter ies (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness, and scalability. The rapid discovery of aqueo us soluble organic (ASO) redoxactive materials necessitates efficient machine l earning surrogates for predicting battery performance." Funders for this research include United States Department of Energy (DOE), Ener gy Storage Materials Initiative (ESMI) under the Laboratory Directed Research an d Development (LDRD) program at Pacific Northwest National Laboratory (PNNL), Un ited States Department of Energy (DOE). The news correspondents obtained a quote from the research from Pacific Northwes t National Laboratory, "The physics-guided continual learning (PGCL) method prop osed in this study can incrementally learn data from new ASO electrolytes while addressing catastrophic forgetting issues in conventional machine learning. Usin g an AORFB database with a thousand potential materials generated by a 780 cm(2) interdigitated cell model, PGCL incorporates AORFB physics to optimize the cont inual learning task formation and training strategies to retain previously learn ed battery material knowledge."
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