Three patients with visual neglect were tested on their ability to detect target letters at ipsilesional and contralesional locations on a monitor, and at different locations within large shapes on the monitor. When patients were asked to detect targets within the entire monitor, they showed neglect for all the contralesional hemifield. In contrast when they were asked to detect targets within a particular object, they showed object-based neglect. In these two conditions the displays, the targets and the response were identical, with the only difference being the space that is represented for the task. These results show that the reference frame of visual neglect may be altered by task-instructions changing how a structured visual scene is represented, with neglect applying to the contralesional side of this represented space.
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http://dx.doi.org/10.1016/s0010-9452(08)70119-9 | DOI Listing |
Brief Bioinform
November 2024
Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, Hunan 410003, China.
Motivation: Accurately predicting the degradation capabilities of proteolysis-targeting chimeras (PROTACs) for given target proteins and E3 ligases is important for PROTAC design. The distinctive ternary structure of PROTACs presents a challenge to traditional drug-target interaction prediction methods, necessitating more innovative approaches. While current state-of-the-art (SOTA) methods using graph neural networks (GNNs) can discern the molecular structure of PROTACs and proteins, thus enabling the efficient prediction of PROTACs' degradation capabilities, they rely heavily on limited crystal structure data of the POI-PROTAC-E3 ternary complex.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
With the advancement of service robot technology, the demand for higher boundary precision in indoor semantic segmentation has increased. Traditional methods of extracting Euclidean features using point cloud and voxel data often neglect geodesic information, reducing boundary accuracy for adjacent objects and consuming significant computational resources. This study proposes a novel network, the Euclidean-geodesic network (EGNet), which uses point cloud-voxel-mesh data to characterize detail, contour, and geodesic features, respectively.
View Article and Find Full Text PDFBiomedicines
December 2024
Department of Health and Nursing Sciences, Faculty of Health and Sport Sciences, Széchenyi István University, Egyetem tér 1, 9026 Győr, Hungary.
Balance and proprioception are essential elements in postural control and injury prevention. Proprioception, the body's sense of position and movement, is closely tied to balance, which depends on input from the visual, vestibular, and somatosensory systems. This article explores the link between trauma experiences and proprioceptive dysfunction, emphasizing how heightened muscle tone, dissociation, and altered sensory processing contribute to balance issues and the risk of injury.
View Article and Find Full Text PDFCancers (Basel)
December 2024
Neurosurgery, Department of Neuroscience, Psychology, Pharmacology and Child Health, University Hospital of Careggi, University of Florence, 50134 Florence, Italy.
Navigated transcranial magnetic stimulation (nTMS) has seldom been used to study visuospatial (VS) circuits so far. Our work studied (I) VS functions in neurosurgical oncological patients by using repetitive nTMS (rnTMS), (II) the possible subcortical circuits underneath, and (III) the correspondence between nTMS and direct cortical stimulation (DCS) during awake procedures. We designed a monocentric prospective study, adopting a protocol to use rnTMS for preoperative planning, including VS functions for lesions potentially involving the VS network, including neurosurgical awake and asleep procedures.
View Article and Find Full Text PDFCurrent neural network models of primate vision focus on replicating overall levels of behavioral accuracy, often neglecting perceptual decisions' rich, dynamic nature. Here, we introduce a novel computational framework to model the dynamics of human behavioral choices by learning to align the temporal dynamics of a recurrent neural network (RNN) to human reaction times (RTs). We describe an approximation that allows us to constrain the number of time steps an RNN takes to solve a task with human RTs.
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