For elite performers, psychomotor behavior's success or failure can be traced to differences in brain dynamics. The psychomotor efficiency hypothesis suggests refined cortical activity through 1) selective activat...
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For elite performers, psychomotor behavior's success or failure can be traced to differences in brain dynamics. The psychomotor efficiency hypothesis suggests refined cortical activity through 1) selective activation of task-relevant processes and 2) inhibition of non-essential processes. The use of electroencephalography (eeG) has been applied to investigate psychomotor performance's neural processes. TheeeG markers that reflect an elevation of psychomotor efficiency include left temporal alpha (T3 alpha), frontal midline theta (Fm theta), sensorimotor rhythm (SMR), and the coherence between frontal and left temporal regions. However, the relationship between elite performers' task-relevant and non-essential neural processes is still not well understood. Therefore, this study aimed to explore how each task-relevant and inhibition of non-essential processes contribute to superior psychomotor behavior. Thirty-five highly skilled marksmen were recruited to perform 30 shots in the shooting task while theeeG was recorded. The marksmen were divided into two groups (superior & inferior) based on a median split of shooting performance. The superior group exhibited higher accuracy and precision, with a reduction in movement jerk. eeG measures revealed that the superior group exhibited higher SMR before the trigger pull than the inferior group. In addition, the superior group demonstrated reduced Fz-T3 coherence in their bull's eye shots than the missed shots. These results suggest that the superior group exhibited less effortful engagement of task-relevant processes and lower interference from non-essential cortical regions than the inferior group. The study's overall findings support the psychomotor efficiency hypothesis. When comparing highly skilled performers, the slight differences in brain dynamics ultimately contribute to the success or failure of psychomotor performance.
It is essential to examine the physical or chemical properties of molecular gas in starburst galaxies to reveal the underlying mechanisms characterizing starbursts. We used non-negative matrix factorization (NMF) to e...
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The Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) is an instrument to screen substance-use-related health risks. However, little is known whether the ASSIST could be further shortened while remain...
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We present the multi-wavelength counterparts of 850-μm selected submillimetre sources over a 2-deg2field centred on the North ecliptic Pole. In order to overcome the large beam size (15 arcsec) of the 850-μm images,...
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Objectives: This study aimed to examine the psychometric properties of the P4 suicide screener in a multinational sample. The primary goal was to evaluate the reliability and validity of the scale and investigate its ...
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This study developed two questionnaires, named Technological Pedagogical Content Knowledge-Robot (TPACK-R) and Teaching Beliefs about Robotics education (RTBS), to investigate 94 teachers' TPACK-R as well as to as...
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One of the most poorly understood aspects of low-mass star formation is how multiple-star systems are formed. Here we present the results of Atacama Large Millimeter/submillimeter Array (ALMA) Band 6 observations towa...
Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is ...
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Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in english, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in english, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is r...
BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provideestimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.
As part of the B-fields In Star-forming Region Observations survey, we present James Clerk Maxwell Telescope (JCMT) 850 μm polarimetric observations toward the Orion integral-shaped filament (ISF) that covers three p...
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