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 new report. According to news originating from Tokyo, Japan, by NewsRx co rrespondents, research stated, "Cable robots have been used as haptic interfaces for several decades now, with the most notable examples being the SPIDAR and it s numerous iterations throughout the years, as well as the more recent IPAnema 3 Mini manufactured by Fraunhofer IPA." Funders for this research include Japan Society For The Promotion of Science. The news editors obtained a quote from the research from Tokyo Denki University: "However, these robots still have drawbacks, particularly their high number of cables required to maintain a high workspaceto- installation-space ratio. Using a hybrid structure cable robot (HSCR) could prevent some collisions that occur b etween the cables and the user's body. More specifically, some applications requ iring multimodal feedback could benefit from the flexibility that a reduced numb er of cables offers. Therefore, this paper presents a novel SPIDAR-like HSCR and its sensor-less force control method based on motor current. The purpose of thi s work is to clarify the advantages that a variable-structure can provide for ha ptic interaction. In this regard, experimental results regarding the device's wo rkspace and its force feedback capabilities are presented."
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 out of Ifrane, Morocco , by NewsRx editors, research stated, "Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dir e outcomes. The challenge of treating glioblastoma is exacerbated by the converg ence of genetic mutations and disruptions in gene expression, driven by alterati ons in epigenetic mechanisms." Our news journalists obtained a quote from the research from Al Akhawayn Univers ity, "The integration of artificial intelligence, inclusive of machine learning algorithms, has emerged as an indispensable asset in medical analyses. AI is bec oming a necessary tool in medicine and beyond. Current research on Glioblastoma predominantly revolves around non-omics data modalities, prominently including m agnetic resonance imaging, computed tomography, and positron emission tomography . Nonetheless, the assimilation of omic data-encompassing gene expression throug h transcriptomics and epigenomics-offers pivotal insights into patients' conditi ons. These insights, reciprocally, hold significant value in refining diagnoses, guiding decision- making processes, and devising efficacious treatment strategi es. This survey's core objective encompasses a comprehensive exploration of note worthy applications of machine learning methodologies in the domain of glioblast oma, alongside closely associated research pursuits. The study accentuates the d eployment of artificial intelligence techniques for both non-omics and omics dat a, encompassing a range of tasks."
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 Artificial Intelligence. According to news originating from Nanchang, People's R epublic of China, by NewsRx correspondents, research stated, "In the manufacturi ng process of 3D Concrete Printing (3DCP), defects and anomalies have a signific ant impact on both the success rate and the quality of the final products, under scoring the need for real-time monitoring. Currently, monitoring is primarily ba sed on manual observation and existing automated methods are limited in real-tim e performance and accuracy." Funders for this research include National Key Research and Development Program of China, Australian Research Council.
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 originating from West Bengal, India, by NewsRx correspondents, research stated, "The study assesses t he optimal aeration efficiency of a stepwise cascade aeration system through exp erimental trials in a lab scale model setup, aimed at determining the geometric and flow characteristics of the cascade system. Subsequently, the collected data sets are employed to evaluate the efficacy of four advanced machine learning alg orithms, namely K-nearest neighbour (KNN), gradient boosting regressor (GBR), de cision tree regressor (DTR), and random forest regressor (RFR), in predicting th e aeration efficiency at 20 degrees C (E20) of the cascade aeration system." Financial support for this research came from School of Water Resources Engineer ing of Jadavpur University in India.
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 from San Francisco, Californi a, by NewsRx journalists, research stated, "The increasing development of sequen ce-based machine learning models has raised the demand for manipulating sequence s for this application. However, existing approaches to edit and evaluate genome sequences using models have limitations, such as incompatibility with structura l variants, challenges in identifying responsible sequence perturbations, and th e need for vcf file inputs and phased data." Funders for this research include National Institutes of Health, Additional Vent ures, and Gladstone Institutes. The news correspondents obtained a quote from the research from Gladstone Instit utes, "To address these bottlenecks, we present Sequence Mutator for Predictive Models (SuPreMo), a scalable and comprehensive tool for performing and supportin g in silico mutagenesis experiments. We then demonstrate how pairs of reference and perturbed sequences can be used with machine learning models to prioritize p athogenic variants or discover new functional sequences. SuPreMo was written in Python, and can be run using only one line of code to generate both sequences an d 3D genome disruption scores."
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 out of Dublin, Ireland , by NewsRx editors, research stated, "Colorectal cancer remains a major cause o f cancer death and morbidity worldwide. Surgery is a major treatment modality fo r primary and, increasingly, secondary curative therapy." Financial support for this research came from University College Dublin. Our news journalists obtained a quote from the research from University College Dublin, "However, with more patients being diagnosed with early stage and premal ignant disease manifesting as large polyps, greater accuracy in diagnostic and t herapeutic precision is needed right from the time of first endoscopic encounter . Rapid advancements in the field of artificial intelligence (AI), coupled with widespread availability of near infrared imaging (currently based around indocya nine green (ICG)) can enable colonoscopic tissue classification and prognostic s tratification for significant polyps, in a similar manner to contemporary dynami c radiological perfusion imaging but with the advantage of being able to do so d irectly within interventional procedural time frames. It can provide an explaina ble method for immediate digital biopsies that could guide or even replace tradi tional forceps biopsies and provide guidance re margins (both areas where curren t practice is only approximately 80% accurate prior to definitive excision). Here, we discuss the concept and practice of AI enhanced ICG perfusio n analysis for rectal cancer surgery while highlighting recent and essential nea r-future advancements. These include breakthrough developments in computer visio n and time series analysis that allow for real-time quantification and classific ation of fluorescent perfusion signals of rectal cancer tissue intraoperatively that accurately distinguish between normal, benign, and malignant tissues in sit u endoscopically, which are now undergoing international prospective validation (the Horizon Europe CLASSICA study). Next stage advancements may include detaile d digital characterisation of small rectal malignancy based on intraoperative as sessment of specific intratumoral fluorescent signal pattern. This could include T staging and intratumoral molecular process profiling (e.g. regarding angiogen esis, differentiation, inflammatory component, and tumour to stroma ratio) with the potential to accurately predict the microscopic local response to nonsurgica l treatment enabling personalised therapy via decision support tools. Such advan cements are also applicable to the next generation fluorophores and imaging agen ts currently emerging from clinical trials."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Current study results on Robotics have been publi shed. According to news originating from Manchester, United Kingdom, by NewsRx c orrespondents, research stated, "This article is concerned with the problem of p lanning optimal maneuver trajectories and guiding the mobile robot toward target positions in uncertain environments for exploration purposes. A hierarchical de ep learning-based control framework is proposed which consists of an upper level motion planning layer and a lower level waypoint tracking layer." Financial support for this research came from Engineering & Physic al Sciences Research Council (EPSRC).
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 - Psychosis is the subject of a report. According to news report ing originating from Lisbon, Portugal, by NewsRx correspondents, research stated , "Despite years of research, we are still not able to reliably predict who migh t benefit from electroconvulsive therapy (ECT) treatment. As we exhaust what is possible using traditional statistical analysis, ECT remains a good candidate fo r machine learning approaches due to the large data sets with data captured thro ugh electroencephalography (EEG) and other objective measures." Our news editors obtained a quote from the research from Hospital Beatriz Angelo , "A systematic review of 6 databases led to the full-text examination of 26 art icles using machine learning approaches in examining data predicting response to ECT treatment. The identified articles used a wide variety of data types coveri ng structural and functional imaging data (n = 15), clinical data (n = 5), a com bination of clinical and imaging data (n = 2), EEG (n = 3), and social media pos ts (n = 1). The clinical indications in which response prediction was assessed w ere depression (n = 21) and psychosis (n = 4). Changes in multiple anatomical re gions in the brain were identified as holding a predictive value for response to ECT. These primarily centered on the limbic system and associated networks. Cli nical features predicting good response to ECT in depression included shorter du ration, lower severity, higher medication dose, psychotic features, low cortisol levels, and positive family history. It has also been possible to predict the l ikelihood of relapse of readmission with psychosis after ECT treatment, includin g a better response if higher transfer entropy was calculated from EEG signals."
查看更多>>摘要：By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating in Tamil Nadu, India, by News Rx journalists, research stated, "Recent improvements in Selective Laser Sinteri ng (SLS) technology have prompted researchers to examine the fabrication method as a solution for patient-specific orthopedic issues. Although SLS remains a des irable method for sintering polymer materials, poor selection of process paramet er ranges can reduce the porosity of manufactured components and decrease the me chanical performance."
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 originating from Guangzhou, People's Re public of China, by NewsRx correspondents, research stated, "Accumulating eviden ce suggests that a wide variety of cell deaths are deeply involved in cancer imm unity. However, their roles in glioma have not been explored." Financial supporters for this research include National Natural Science Foundati on of China, China Postdoctoral Science Foundation, Natural Science Foundation o f Hunan Province. Our news journalists obtained a quote from the research from Southern Medical Un iversity, "We employed a logistic regression model with the shrinkage regulariza tion operator (LASSO) Cox combined with seven machine learning algorithms to ana lyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performan ce of the nomogram was assessed through the use of receiver operating characteri stic (ROC) curves and calibration curves. Cell-type identification was estimated by using the cell-type identification by estimating relative subsets of known R NA transcripts (CIBERSORT) and single sample gene set enrichment analysis method s. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 w ere investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram p erformed well in predicting outcomes according to time-dependent ROC and calibra tion plots. In addition, a high-risk score was significantly related to high exp ression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death-based prognostic model. Upregu lated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associate d with poor clinical outcomes and was positively related to CD163, PD-L1 and vim entin expression in the in-horse cohort. The cell death score provides a precise stratification and immune status for glioma."
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