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Motor actions adapt dynamically to external changes through the brain's ability to predict sensory outcomes and adjust for discrepancies between anticipated and actual sensory inputs. In this study, we investigated how changes in target speed (v) and direction influenced visuomotor responses, focusing on gaze and manual joystick control during an interception task. Participants tracked a moving target with sinusoidal variations in v and directional changes, generating sensory prediction errors and requiring real-time adjustments. Our results demonstrate slow variations in v entrained gaze and joystick metrics, with participants synchronizing their responses to the cycles of target motion. While target directional changes alone had limited impact, combining them with sinusoidal variations in v led to robust behavioral entrainment. Participants also exhibited rapid within-trial adjustments, with peak gaze and joystick gains increasing linearly with v frequency, highlighting the critical role of manual control in matching or exceeding v for successful interception. Additionally, responses to sudden phase changes in the v sinusoid revealed the continuous monitoring of prediction errors driven by the magnitude of phase shifts. These findings illustrate the brain's predictive system's ability to integrate continuous visual feedback and sensory prediction errors to fine-tune motor responses and anticipate future target speeds.
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http://dx.doi.org/10.1016/j.neuroscience.2025.01.047 | DOI Listing |
Immunol Res
September 2025
Department of Immunology and Allergy, Faculty of Medicine, Necmettin Erbakan University, Konya, Türkiye.
Background: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results, particularly in the context of inborn errors of immunity such as severe combined immunodeficiency (SCID). The inconsistency among computational prediction tools often necessitates expensive and time-consuming wet-lab analyses.
Objective: This study aimed to develop disease-specific, multi-class machine learning models using in silico scores to classify SCID-associated genetic variants and improve the interpretation of VUS.
Neotrop Entomol
September 2025
Dept of Entomology, Federal Univ of Viçosa, Viçosa, MG, Brazil.
The fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae) is one of the main pests in apple orchards. Artificial neural networks (ANNs) are tools with good ability to predict phenomena such as the seasonal dynamics of pest populations. Thus, the objective of this work was to determine a prediction model for the seasonal dynamics of A.
View Article and Find Full Text PDFPharm Res
September 2025
Axcelead Tokyo West Partners, Inc. Translational Science, Discovery DMPK, Hino-Shi, Tokyo, 191-0065, Japan.
Purpose: Accurate prediction of human clearance (CL) is essential in early drug development. Single Species Scaling (SSS) using rat pharmacokinetic (PK) data, particularly with unbound plasma fraction (f), is widely used. However, its accuracy declines for compounds with extremely low f, and no systematic method has addressed this limitation.
View Article and Find Full Text PDFAnal Bioanal Chem
September 2025
School of Artificial Intelligence, Hangzhou Dianzi University, Hangzhou, 310018, China.
The prompt and accurate identification of pathogenic bacteria is crucial for mitigating the transmission of infections. Conventional detection methods face limitations, including lengthy processing, complex sample pretreatment, high instrumentation costs, and insufficient sensitivity for rapid on-site screening. To address these challenges, an aptamer (Apt)-sensor based on functionalized magnetic nanoparticles (MNPs) was developed for detecting Escherichia coli.
View Article and Find Full Text PDFTrends Plant Sci
September 2025
Crop and Soils Sciences, University of Georgia, Athens, GA 30602, USA; Institute of Plant Breeding and Genetics and Genomics, University of Georgia, Athens, GA 30602, USA.
Synthetic biology holds great potential to transform agriculture, yet its progress is constrained by the complexity of multigenomic, multitrait, and multi-environment data. Desirable traits often arise from complex gene networks acting across diverse conditions, making them difficult to predict and optimize manually. In the past decade, artificial intelligence (AI) has supported this process, but its large data needs and poor integration limit its role to pattern recognition rather than explanatory trait design.
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