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 Nanjing, Peopl e's Republic of China, by NewsRx journalists, research stated, "Dento-maxillofac ial deformities are common problems. Orthodontic-orthognathic surgery is the pri mary treatment but accurate diagnosis and careful surgical planning are essentia l for optimum outcomes." Financial supporters for this research include Key Research and Development Prog ram of Science and Technology Department of Sichuan Province, Research and Devel op Program West China Hospital of Stomatology Sichuan University. The news reporters obtained a quote from the research from the Nanjing Institute of Technology, "This study aimed to establish and verify a machine learning-bas ed decision support system for treatment of dento-maxillofacial malformations. P atients (n = 574) with dento-maxillofacial deformities undergoing spiral CT duri ng January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were co mpared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm w as employed to formulate the orthognathic surgical plan, and subsequently evalua ted by maxillofacial surgeons in a cohort of 50 patients. The objective evaluati on included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepanci es in postoperative cephalometric analysis outcomes. The binary relevance extrem e gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; t he exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and w as improved after human-computer interaction. There was no statistically signifi cant difference between the actual and AI- groups."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New research on Oncology-Pancreatic Cancer is t he subject of a report. According to news reporting from Zhejiang, People's Repu blic of China, by NewsRx journalists, research stated, "This study aimed to cons truct a blood diagnostic model for pancreatic cancer (PC) using miRNA signatures by a combination of machine learning and biological experimental verification. Gene expression profiles of patients with PC and transcriptome normalization dat a were obtained from the Gene Expression Omnibus (GEO) database." The news correspondents obtained a quote from the research from the Affiliated P eople's Hospital of Ningbo University, "Using random forest algorithm, lasso reg ression algorithm, and multivariate cox regression analyses, the classifier of d ifferentially expressed miRNAs was identified based on algorithms and functional properties. Next, the ROC curve analysis was used to evaluate the predictive pe rformance of the diagnostic model. Finally, we analyzed the expression of two sp ecific miRNAs in Capan-1, PANC-1, and MIA PaCa-2 pancreatic cells using qRT-PCR. Integrated microarray analysis revealed that 33 common miRNAs exhibited signifi cant differences in expression profiles between tumor and normal groups (P value <0.05 and |logFC| > 0.3). Pathway analysis showed that differentially expressed miRNAs were related to P00059 p53 pathway, hsa04062 chemokine signaling pathway, and cancer-related pathways incl uding PC. In ENCORI database, the hsa-miR-4486 and hsa-miR-6075 were identified by random forest algorithm and lasso regression algorithm and introduced as majo r miRNA markers in PC diagnosis. Further, the receiver operating characteristic curve analysis achieved the area under curve score > 80%, showing good sensitivity and specificity of the two-miRNA signature model in P C diagnosis. Additionally, hsa-miR-4486 and hsa-miR-6075 genes expressions in th ree pancreatic cells were all up-regulated by qRT-PCR."
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-Lung Cancer is the subject of a report. According to news reporting out of Beijing, People' s Republic of China, by NewsRx editors, research stated, "Lung cancer ranks amon g the cancers with the highest global incidence rates and mortality. Swift and e xtensive screening is crucial for the early-stage diagnosis of lung cancer." Our news journalists obtained a quote from the research from Beijing National La boratory for Molecular Sciences (BNLMS), "Laser desorption/ionization mass spect rometry (LDI-MS) possesses clear advantages over traditional analytical methods for large-scale analysis due to its unique features, such as simple sample proce ssing, rapid speed, and high-throughput performance. As n-type semiconductors, t itanate-based perovskite materials can generate charge carriers under ultraviole t light irradiation, providing the capability for use as an LDI-MS substrate. In this study, we employ Rh-doped SrTiO (STO/Rh)-assisted LDI-MS combined with mac hine learning to establish a method for urine-based lung cancer screening. We di rectly analyzed urine metabolites from lung cancer patients (LCs), pneumonia pat ients (PNs), and healthy controls (HCs) without employing any pretreatment. Thro ugh the integration of machine learning, LCs are successfully distinguished from HCs and PNs, achieving impressive area under the curve (AUC) values of 0.940 fo r LCs vs HCs and 0.864 for LCs vs PNs. Furthermore, we identified 10 metabolites with significantly altered levels in LCs, leading to the discovery of related p athways through metabolic enrichment analysis." According to the news editors, the research concluded: "These results suggest th e potential of this method for rapidly distinguishing LCs in clinical applicatio ns and promoting precision medicine." This research has been peer-reviewed.
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news originating from Umm Al-Qura University by Ne wsRx editors, the research stated, "Growth and expansion in construction has inc reased recently and especially in coastal areas. In Alexandria, Egypt, mega proj ects such as El-Max Port Project (Middle Port), Port of ABU QIR (EG AKI), hotels, and restaurants were spread along the coastal lines, thus, it will need a high electrical energy." The news correspondents obtained a quote from the research from Umm Al-Qura Univ ersity: "Although, the great economic benefits of such projects, it will have so me negative impacts, such as overloading on the present grid. According to recom mendations of COP 27, Egypt is one of the countries targeting to increase the de pendency on green energy to minimize the production of greenhouse gases. This st udy is interested in wave energy as a renewable source of energy. Using a machin e learning model that predicts wave height and wave period through the year 2030 in three separate places (Alamein, Alexandria, and Mersa-Matruh), this study wi ll try to estimate the future amount of wave energy along Egypt's coast. Hourly measurements of the significant height and the mean wave period for the period 1 979-2023 have been utilized for this. An extractor for wave energy can also be b uilt on the Overtopping Breakwater for Energy Conversion (OBREC) in order to use this energy to fill the hole in the electric grid. The machine learning model w as developed using hourly wave height and period data from three buoys, and as a result, the results have a root mean square error (RMSE) of 0.52. The amount of energy taken, wave power, and system efficiency at each place were then fully d etermined using a mathematical model for each of the three locations."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-New study results on artificial intelligence have been published. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "The constitutive model refers to the mapping relationship between the stress and deformation conditions (such as strain, strain rate, and temperature) after being loaded." Funders for this research include Basic Scientific Research Project of Education Department of Liaoning Province For Colleges And Universities. The news editors obtained a quote from the research from Central Iron and Steel Research Institute: "In this work, the hot deformation behavior of a Ni-Cr-Mo st eel was investigated by conducting isothermal compression tests using a Gleeble- 3800 thermal simulator with deformation temperatures ranging from 800 °C to 1200 °C, strain rates ranging from 0.01 s-1 to 10 s-1, and deformations of 55% . To analyze the constitutive relation of the Ni-Cr-Mo steel at high temperature s, five machine learning algorithms were employed to predict the flow stress, na mely, back-propagation artificial neural network (BP-ANN), Random Committee, Bag ging, k-nearest neighbor (k-NN), and a library for support vector machines (libS VM). A comparative study between the experimental and the predicted results was performed."
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 originating from Dalian, People's Republic of China, by NewsRx correspondents, research stated, "This paper considers the pro blem of exploring an unknown rectangular cellular environment with a rectangular hole using a mobile robot. The robot's task is to visit each cell at least once and return to the start." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news journalists obtained a quote from the research from Liaoning Normal Uni versity, "The robot has limited visibility that can only detect four cells adjac ent to it. And it has large amount of memory that can store a map of discovered cells. The goal of this work is to find the shortest exploration tour, that is, minimizing the total number of multiple visited cells. We consider the environme nt in four possible scenarios: rectangular, L-shaped, C -shaped or O -shaped gri d polygon. An on-line strategy has been proposed for these scenarios." According to the news editors, the research concluded: "We prove that it is opti mal for rectangular, L-shaped, C -shaped grid polygon, and is 4/3 -competitive f or O -shaped grid polygon." This research has been peer-reviewed.
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 Columbia, Missouri, by NewsR x correspondents, research stated, "Vaccines are the main pharmaceutical interve ntion used against the global public health threat posed by influenza viruses. T imely selection of optimal seed viruses with matched antigenicity between vaccin e antigen and circulating viruses and with high yield underscore vaccine efficac y and supply, respectively." Financial support for this research came from U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases. Our news journalists obtained a quote from the research from the University of M issouri, "Current methods for selecting influenza seed vaccines are labor intens ive and time-consuming. Here, we report the Machine-learning Assisted Influenza VaccinE Strain Selection framework, MAIVeSS, that enables streamlined selection of naturally circulating, antigenically matched, and high-yield influenza vaccin e strains directly from clinical samples by using molecular signatures of antige nicity and yield to support optimal candidate vaccine virus selection. We apply our framework on publicly available sequences to select A(H1N1)pdm09 vaccine can didates and experimentally confirm that these candidates have optimal antigenici ty and growth in cells and eggs. Our framework can potentially reduce the optima l vaccine candidate selection time from months to days and thus facilitate timel y supply of seasonal vaccines. Vaccines combat global influenza threats, relying on timely selection of optimal seed viruses."
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 originating from Busan, South Kor ea, by NewsRx correspondents, research stated, "Mg alloys possess an inherent pl astic anisotropy owing to the selective activation of deformation mechanisms dep ending on the loading condition." Our news journalists obtained a quote from the research from Pusan National Univ ersity: "This characteristic results in a diverse range of flow curves that vary with a deformation condition. This study proposes a novel approach for accurate ly predicting an anisotropic deformation behavior of wrought Mg alloys using mac hine learning (ML) with data augmentation. The developed model combines four key strategies from data science: learning the entire flow curves, generative adver sarial networks (GAN), algorithm-driven hyperparameter tuning, and gated recurre nt unit (GRU) architecture. The proposed model, namely GANaided GRU, was extens ively evaluated for various predictive scenarios, such as interpolation, extrapo lation, and a limited dataset size. The model exhibited significant predictabili ty and improved generalizability for estimating the anisotropic compressive beha vior of ZK60 Mg alloys under 11 annealing conditions and for three loading direc tions. The GAN-aided GRU results were superior to those of previous ML models an d constitutive equations."
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 reporting out of Debrece n, Hungary, by NewsRx editors, research stated, "The efficiency of natural langu age processing has improved dramatically with the advent of machine learning mod els, particularly neural network-based solutions." Our news reporters obtained a quote from the research from University of Debrece n: "However, some tasks are still challenging, especially when considering speci fic domains. This paper presents a model that can extract insights from customer reviews using machine learning methods integrated into a pipeline. For topic mo deling, our composite model uses transformer-based neural networks designed for natural language processing, vector-embedding-based keyword extraction, and clus tering. The elements of our model have been integrated and tailored to better me et the requirements of efficient information extraction and topic modeling of th e extracted information for opinion mining."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Gram-Negative Bacteria-Cyanobacteria is the subject of a report. According to news reporting out of Uppsala, Sweden, by NewsRx editors, research stated, "Metabolic fluxes and their control mechanisms are fundamental in cellular metabolism, offering insights fo r the study of biological systems and biotechnological applications. However, qu antitative and predictive understanding of controlling biochemical reactions in microbial cell factories, especially at the system level, is limited." Our news journalists obtained a quote from the research from Uppsala University, "In this work, we present ARCTICA, a computational framework that integrates co nstraint-based modelling with machine learning tools to address this challenge. Using the model cyanobacterium Synechocystis sp. PCC 6803 as chassis, we demonst rate that ARCTICA effectively simulates global-scale metabolic flux control. Key findings are that (i) the photosynthetic bioproduction is mainly governed by en zymes within the Calvin- Benson-Bassham (CBB) cycle, rather than by those involve in the biosynthesis of the end-product, (ii) the catalytic capacity of the CBB cycle limits the photosynthetic activity and downstream pathways and (iii) ribul ose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a major, but not the mos t, limiting step within the CBB cycle. Predicted metabolic reactions qualitative ly align with prior experimental observations, validating our modelling approach ."
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