Frontostriatal networks play critical roles in grounding action semantics and syntactic skills. Indeed, their atrophy distinctively disrupts both domains, as observed in patients with Huntington's disease (HD) and Parkinson's disease, even during early disease stages. However, frontostriatal degeneration in these conditions may begin up to 15 years before the onset of clinical symptoms, opening avenues for pre-clinical detection via sensitive tasks. Such a mission is particularly critical in HD, given that patients' children have 50% chances of inheriting the disease. Against this background, we assessed whether deficits in the above-mentioned domains emerge in subjects at risk to develop HD. We administered tasks tapping action semantics, object semantics, and two forms of syntactic processing to 18 patients with HD, 19 asymptomatic first-degree relatives, and sociodemographically matched controls for each group. The patients evinced significant deficits in all tasks, but only those in the two target domains were independent of overall cognitive state. More crucially, relative to controls, the asymptomatic relatives were selectively impaired in action semantics and in the more complex syntactic task, with both patterns emerging irrespective of the subjects' overall cognitive state. Our findings highlight the relevance of these dysfunctions as potential prodromal biomarkers of HD. Moreover, they offer theoretical insights into the differential contributions of frontostriatal hubs to both domains while paving the way for innovations in diagnostic procedures.
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Int J Neural Syst
January 2025
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, P. R. China.
Visual semantic decoding aims to extract perceived semantic information from the visual responses of the human brain and convert it into interpretable semantic labels. Although significant progress has been made in semantic decoding across individual visual cortices, studies on the semantic decoding of the ventral and dorsal cortical visual pathways remain limited. This study proposed a graph neural network (GNN)-based semantic decoding model on a natural scene dataset (NSD) to investigate the decoding differences between the dorsal and ventral pathways in process various parts of speech, including verbs, nouns, and adjectives.
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December 2024
School of Computer and Electronic Information, Guangxi University, University Road, Nanning, 530004, Guangxi, China. Electronic address:
Vision-language navigation (VLN) is a challenging task that requires agents to capture the correlation between different modalities from redundant information according to instructions, and then make sequential decisions on visual scenes and text instructions in the action space. Recent research has focused on extracting visual features and enhancing text knowledge, ignoring the potential bias in multi-modal data and the problem of spurious correlations between vision and text. Therefore, this paper studies the relationship structure between multi-modal data from the perspective of causality and weakens the potential correlation between different modalities through cross-modal causality reasoning.
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January 2025
Consiglio Nazionale delle Ricerche, Istituto di Geoscienze e Georisorse, Pisa, Italy.
The Nature Restoration Law adopted by the European Union in 2024 aims to implement measures to restore at least 20% of its land and sea by 2030 and all ecosystems in need of restoration by 2050, focusing on among others agricultural land, forests, urban, marine, freshwater, and wetlands areas. The goal is to enhance the natural and semi-natural habitats' role in achieving climate targets and preserving biodiversity. Member States must submit detailed national restoration plans, outlining specific actions and mechanisms for monitoring progress.
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December 2024
CAS Key Laboratory of GIPAS, University of Science and Technology of China, Hefei, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China. Electronic address:
In MARL (Multi-Agent Reinforcement Learning), the trial-and-error learning paradigm based on multiple agents requires massive interactions to produce training samples, significantly increasing both the training cost and difficulty. Therefore, enhancing data efficiency is a core issue in MARL. However, in the context of MARL, agent partially observed information leads to a lack of consideration for agent interactions and coordination from an ego perspective under the world model, which becomes the main obstacle to improving the data efficiency of current proposed MARL methods.
View Article and Find Full Text PDFSci Rep
January 2025
Institute of Visual Informatics, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including high costs and substantial side effects. Recently the high selectivity of peptides has garnered significant attention from scientists due to their reliable targeted actions and minimal adverse effects.
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