Goal-irrelevant information in working memory (WM) may enter the focus of attention (FOA) during a task and cause proactive interference (PI). In the current study we used fMRI to test several hypotheses concerning the boundary conditions of PI in WM using a modified verbal 2-back task. Temporal distance between item and lure presentation was manipulated to evaluate potential differences among hypothesized states of FOA, short-term memory and long-term memory. PI was present for the most proximal 3-back lures but dissipated with lure distance along with increased activation in brain regions critical for memory recollection, such as right prefrontal cortex, parietal cortex, and hippocampus. Reduced PI and less IFG activation were also observed after repeated item presentation, supporting the notion that a rehearsed encoding of item-context information reduces the need for interference control. Moreover, a trial-by-trial approach revealed activity in ACC, insula, IFG, and parietal cortex with increasing lure trial interference regardless of distance. The current results are first evidence for an observable transition of cognitive control, to include MTL regions involved in recalling task-relevant information from outside the FOA when resolving PI in WM.
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http://dx.doi.org/10.1016/j.neuroimage.2022.119098 | DOI Listing |
Theranostics
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Center of Regenerative Medicine, Department of Stomatology, Renmin Hospital of Wuhan University, Wuhan University, Wuhan, China.
Disrupted hippocampal functions and progressive neuronal loss represent significant challenges in the treatment of Alzheimer's disease (AD). How to achieve the improvement of pathological progression and effective neural regeneration to ameliorate the intracerebral dysfunctional environment and cognitive impairment is the goal of the current AD therapy. We examined the therapeutic potential of clinical-grade human derived dental pulp stem cells (hDPSCs) in cognitive function and neuropathology in AD.
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January 2025
Department of Physics & Hunan Institute of Advanced Sensing and Information Technology, Xiangtan University, Xiangtan, 411105, People's Republic of China.
This study predicts the thermoelectric figure of merit (ZT) for defective gamma-graphyne nanoribbons (γ-GYNRs) using binary coding, convolutional neural networks (CNN), long short-term memory networks (LSTM), and multi-scale feature fusion. The approach accurately predicts ZT values with only 500 initial structures (3% of 16,512 candidates), achieving an R above 0.91 and a mean absolute error (MAE) of 0.
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January 2025
School of Mechanical Engineering, Southwest Petroleum University, Chengdu, Sichuan 610500, China.
Efficient identification of the flocculation state of waste drilling fluid remains a significant challenge. This study proposes an improved You Only Look Once version 8 nano-algorithm (YOLOv8n), specifically optimized for real-time monitoring of drilling fluid flocculation under field conditions. The algorithm employs MobileNetV3 as the backbone network to minimize memory usage, improve detection speed, and reduce computational requirements.
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January 2025
Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China.
Human brucellosis remains a significant public health issue in the Ili Kazak Autonomous Prefecture, Xinjiang, China. To assist local Centers for Disease Control and Prevention (CDC) in promptly formulate effective prevention and control measures, this study leveraged time-series data on brucellosis cases from February 2010 to September 2023 in Ili Kazak Autonomous Prefecture. Three distinct predictive modeling techniques-Seasonal Autoregressive Integrated Moving Average (SARIMA), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks-were employed for long-term forecasting.
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