3,381 results match your criteria: "University of A Coruña. Faculty of Computer Science[Affiliation]"

Lateral gene transfer (LGT), also known as horizontal gene transfer, facilitates genomic diversification in microbial populations. While previous work has surveyed LGT in human-associated microbial isolate genomes, the landscape of LGT arising in personal microbiomes is not well understood, as there are no widely adopted methods to characterize LGT from complex communities. Here we developed, benchmarked and validated a computational algorithm (WAAFLE or Workflow to Annotate Assemblies and Find LGT Events) to profile LGT from assembled metagenomes.

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With an increasing number of studies delving into the impact of dietary supplements on combat sports performance, researchers are actively seeking a more efficient dietary supplement for use in these sports. Nonetheless, controversies persist. Hence, we undertook a systematic review and Bayesian network meta-analysis to discern the most effective dietary supplements in combat sports by synthesizing the available evidence.

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Objective: While myoelectric control has been commercialized in prosthetics for decades, its adoption for more general human-machine interaction has been slow. Although high accuracies can be achieved across many gestures, current control approaches are prone to false activations in real-world conditions. This is because the same electromyogram (EMG) signals generated during the elicitation of gestures are also naturally activated when performing activities of daily living (ADLs), such as when driving to work or while typing on a keyboard.

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Behavioral copying is a key process in group actions, but it is challenging for individuals with autism spectrum disorder (ASD). We investigated behavioral contagion, or instinctual replication of behaviors, in Krushinky-Molodkina (KM) rats ( = 16), a new potential rodent model for ASD, compared to control Wistar rats ( = 15). A randomly chosen healthy Wistar male ("demonstrator rat") was introduced to the homecage of experimental rats ("observers") 10-14 days before the experiments to become a member of the group.

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Visual analysis has applications in diverse fields, including urban planning and environmental management. This study explores viewshed generation using two distinct datasets: Digital Surface Model (DSM) and LiDAR (Light Detection and Ranging) point cloud data. We assess the differences in viewsheds derived from these sources, evaluating their respective strengths and weaknesses.

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Despite seemingly inexorable imminent risks of food insecurity that hang over the world, especially in developing countries like Pakistan where traditional agricultural methods are being followed, there still are opportunities created by technology that can help us steer clear of food crisis threats in upcoming years. At present, the agricultural sector worldwide is rapidly pacing towards technology-driven Precision Agriculture (PA) approaches for enhancing crop protection and boosting productivity. Literature highlights the limitations of traditional approaches such as chances of human error in recognizing and counting pests, and require trained labor.

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Vehicle-to-everything (V2X) communication has many benefits. It improves fuel efficiency, road safety, and traffic management. But it raises privacy and security concerns.

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Identifying and understanding the nonlinear behavior of memristive devices.

Sci Rep

December 2024

Chair of Applied Electrodynamics and Plasma Technology, Ruhr University Bochum, Universitätsstraße 150, 44780, Bochum, Germany.

Nonlinearity is a crucial characteristic for implementing hardware security primitives or neuromorphic computing systems. The main feature of all memristive devices is this nonlinear behavior observed in their current-voltage characteristics. To comprehend the nonlinear behavior, we have to understand the coexistence of resistive, capacitive, and inertia (virtual inductive) effects in these devices.

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Leukemia, a life-threatening form of cancer, poses a significant global health challenge affecting individuals of all age groups, including both children and adults. Currently, the diagnostic process relies on manual analysis of microscopic images of blood samples. In recent years, machine learning employing deep learning approaches has emerged as cutting-edge solutions for image classification problems.

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 - a large-scale dataset of 3D medical shapes for computer vision.

Biomed Tech (Berl)

December 2024

Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

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Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.

Sci Rep

December 2024

Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Ha'il, 81481, Saudi Arabia.

Alzheimer's disease (AD) is a brain disorder that causes memory loss and behavioral and thinking problems. The symptoms of Alzheimer's are similar throughout its development stages, which makes it difficult to diagnose manually. Therefore, artificial intelligence (AI) techniques address the limitations of manual diagnosis.

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Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions.

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Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD.

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Photo- and video-based reidentification of green sea turtles using their natural markers is far less invasive than artificial tagging. An RGB camera mounted on a man-portable rig, was used to collect video data on Greater Talang Island (1 °54'45″N 109 °46'33″E) from September to October 2022, and September 2023. This islet is located 30 minutes offshore from the Sematan district in Southwest Sarawak, Malaysia.

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Early periodontitis diagnosis is challenging due to varying staging and grading systems. While clinical parameters like bleeding on probing (BoP) and pocket depth (PD) are commonly used, periapical radiographs provide valuable information about bone loss and periodontal ligament changes. However, a clear definition of early periodontitis, particularly regarding alveolar bone crest changes, remains elusive.

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The agriculture sector is confronted with numerous challenges in the quest for accurate crop yield estimation, which is essential for efficient resource management and mitigating food scarcity in a rapidly growing global population. This research paper delves into the application of advanced Artificial Intelligence (AI) techniques to enhance crop yield estimation in the context of diverse agricultural challenges. Through a systematic literature review and analysis of relevant studies, this paper explores the role of AI methods, such as Machine Learning (ML) and Deep Learning (DL), in addressing the complexities posed by geographical variations, crop diversity, and cultivation areas.

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Mapping of high-resolution daily particulate matter (PM) concentration at the city level through a machine learning-based downscaling approach.

Environ Monit Assess

December 2024

Faculty of Information Technology, University of Engineering and Technology, Vietnam National University Hanoi, E3 Building, 144 Xuan Thuy Street, Dich Vong Hau Ward, Cau Giay District, Ha Noi, 100000, Vietnam.

PM pollution is a major global concern, especially in Vietnam, due to its harmful effects on health and the environment. Monitoring local PM levels is crucial for assessing air quality. However, Vietnam's state-of-the-art (SOTA) dataset with a 3 km resolution needs to be revised to depict spatial variation in smaller regions accurately.

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Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)-based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods.

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The incorporation of sequencing technologies in frontline and public health healthcare settings was vital in developing virus surveillance programs during the Coronavirus Disease 2019 (COVID-19) pandemic caused by transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, increased data acquisition poses challenges for both rapid and accurate analyses. To overcome these hurdles, we developed the SARS-CoV-2 Illumina GeNome Assembly Line (SIGNAL) for quick bulk analyses of Illumina short-read sequencing data.

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The rapid development of Digital Twin (DT) technology has underlined challenges in resource-constrained mobile devices, especially in the application of extended realities (XR), which includes Augmented Reality (AR) and Virtual Reality (VR). These challenges lead to computational inefficiencies that negatively impact user experience when dealing with sizeable 3D model assets. This article applies multiple lossless compression algorithms to improve the efficiency of digital twin asset delivery in Unity's AssetBundle and Addressable asset management frameworks.

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Purpose: Computer-based medical training scenarios, derived from patient's records, often lack variability, modifiability, and availability. Furthermore, generating image datasets and creating scenarios is resource-intensive. Therefore, patient authoring tools for rapid dataset-independent creation of virtual patients (VPs) is a pressing need.

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Introduction: Recognizing human actions is crucial for allowing machines to understand and recognize human behavior, with applications spanning video based surveillance systems, human-robot collaboration, sports analysis systems, and entertainment. The immense diversity in human movement and appearance poses a significant challenge in this field, especially when dealing with drone-recorded (RGB) videos. Factors such as dynamic backgrounds, motion blur, occlusions, varying video capture angles, and exposure issues greatly complicate recognition tasks.

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Prior studies indicate that emotions, particularly high-arousal emotions, may elicit rapid intuitive thinking, thereby decreasing the ability to recognize misinformation. Yet, few studies have distinguished prior affective states from emotional reactions to false news, which could influence belief in falsehoods in different ways. Extending a study by Martel et al.

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[Birth cohorts and their current status and prospects in China].

Zhonghua Liu Xing Bing Xue Za Zhi

December 2024

School of Public Health, Zhejiang University, Hangzhou310000, China Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou310000, China Binjiang Institute of Zhejiang University, Hangzhou310000, China.

In the context of delayed marriage and parenthood, decreased willingness in having children, and population aging in China, maternal and child health has become an important and urgent issue. Being essential platforms for research in maternal and child health, the importance of birth cohorts has been widely recognized. In the past 20 years, tens of birth cohorts have been established in major cities and regions of China, with cohorts ranging from thousands to hundreds of thousands.

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