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In recent years, surface electromyography (sEMG)-based human–computer interaction has been developed to improve the quality of life for people. Gesture recognition based on the instantaneous values of sEMG has the advantages of accurate prediction and low latency. However, the low generalization ability of the hand gesture recognition method limits its application to new subjects and new hand gestures, and brings a heavy training burden. For this reason, based on a convolutional neural network, a transfer learning (TL) strategy for instantaneous gesture recognition is proposed to improve the generalization performance of the target network. CapgMyo and NinaPro DB1 are used to evaluate the validity of our proposed strategy. Compared with the non-transfer learning (non-TL) strategy, our proposed strategy improves the average accuracy of new subject and new gesture recognition by 18.7% and 8.74%, respectively, when up to three repeated gestures are employed. The TL strategy reduces the training time by a factor of three. Experiments verify the transferability of spatial features and the validity of the proposed strategy in improving the recognition accuracy of new subjects and new gestures, and reducing the training burden. The proposed TL strategy provides an effective way of improving the generalization ability of the gesture recognition system.

•We propose a novel clustering method for partitioning large-scale brain networks based on specific cognitive functions, focusing on semantic representation as the target function.•Our method reveals distinct brain network organizations during cognitive tasks, identifying seven unique semantic networks, highlighting differences from resting-state networks.•A strong correlation is observed between the stability of the identified semantic networks and their semantic representation capabilities, providing new insights into the functional organization of the brain.•Our findings reveal that certain semantic networks integrate information from both linguistic and visual sources, while others predominantly rely on visual inputs. Constructing task-state large-scale brain networks can enhance our understanding of the organization of brain functions during cognitive tasks. The primary goal of brain network partitioning is to cluster functionally homogeneous brain regions. However, a brain region often serves multiple cognitive functions, complicating the partitioning process. This study proposes a novel clustering method for partitioning large-scale brain networks based on specific cognitive functions, selecting semantic representation as the target cognitive function to evaluate the validity of the proposed method. Specifically, we analyzed functional magnetic resonance imaging (fMRI) data from 11 subjects, each exposed to 672 concepts, and correlated this with semantic rating data related to these concepts. We identified distinct semantic networks based on the concept comprehension task and validated the robustness of our network partitioning through multiple methods. We found that the semantic networks derived from multidimensional semantic activation clustering exhibit high reliability and cross-semantic model consistency (semantic ratings and word embeddings extracted from GPT-2), particularly in networks associated with high semantic functions. Moreover, these semantic networks exhibits significant differences from the resting-state and task-based brain networks obtained using traditional methods. Further analysis revealed functional differences between semantic networks, including disparities in their multidimensional semantic representation capabilities, differences in the information modalities they rely on to acquire semantic information, and varying associations with general cognitive domains. This study introduces a novel approach for analyzing brain networks tailored to specific cognitive functions, establishing a standard semantic parcellation with seven networks for future research, potentially enriching our understanding of complex cognitive processes and their neural bases.

We present a synchronized multimodal neuroimaging dataset for studying brain language processing (SMN4Lang) that contains functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data on the same 12 healthy volunteers while the volunteers listened to 6 hours of naturalistic stories, as well as high-resolution structural (T1, T2), diffusion MRI and resting-state fMRI data for each participant. We also provide rich linguistic annotations for the stimuli, including word frequencies, syntactic tree structures, time-aligned characters and words, and various types of word and character embeddings. Quality assessment indicators verify that this is a high-quality neuroimaging dataset. Such synchronized data is separately collected by the same group of participants first listening to story materials in fMRI and then in MEG which are well suited to studying the dynamic processing of language comprehension, such as the time and location of different linguistic features encoded in the brain. In addition, this dataset, comprising a large vocabulary from stories with various topics, can serve as a brain benchmark to evaluate and improve computational language models.Measurement(s)functional brain measurement • MagnetoencephalographyTechnology Type(s)Functional Magnetic Resonance Imaging • MagnetoencephalographyFactor Type(s)naturalistic stimuli listeningSample Characteristic - Organismhumanbeings

Objective Sleep disturbance is common in alcohol dependence (AD). Far-infrared ray (FIR) therapy has anti-inflammatory properties and may improve sleep, offering a potential non-pharmacological treatment during withdrawal. The primary objective of this study was to compare sleep quality and serum pro-inflammatory cytokine levels between AD patients who received FIR therapy and those who received standard care only. Methods A total of 114 male AD patients who completed a two-week withdrawal program were randomized to a control group ( n  = 61) receiving supportive care, or a treatment group ( n  = 53) receiving supportive care plus five FIR sessions. Sleep quality was assessed using cardiopulmonary coupling (CPC), and serum levels of 10 cytokines were measured via Luminex assay before and after the intervention. Analysis of covariance (ANCOVA) compared post-treatment outcomes between groups, controlling for baseline values. Pearson correlation analysis examined relationships between changes in sleep parameters and cytokine levels. Results Compared to controls, the treatment group showed significantly increased total sleep time and sleep efficiency. Moreover, serum levels of IL-1β, IL-6, and TNF-α were significantly reduced in the treatment group. Addtionally, improvements in total sleep time and sleep efficiency were negatively correlated with changes in IL-1β and TNF-α levels. Conclusions FIR therapy may improve sleep quality and reduce pro-inflammatory cytokines in AD patients during withdrawal, suggesting its potential as an alternative to hypnotic medications for sleep management.

Evidence from psychology and cognitive neuroscience indicates that the human brain’s semantic system contains several specific subsystems, each representing a particular dimension of semantic information. Word ratings on these different semantic dimensions can help investigate the behavioral and neural impacts of semantic dimensions on language processes and build computational representations of language meaning according to the semantic space of the human cognitive system. Existing semantic rating databases provide ratings for hundreds to thousands of words, which can hardly support a comprehensive semantic analysis of natural texts or speech. This article reports a large database, the Six Semantic Dimension Database (SSDD), which contains subjective ratings for 17,940 commonly used Chinese words on six major semantic dimensions: vision, motor, socialness, emotion, time, and space. Furthermore, using computational models to learn the mapping relations between subjective ratings and word embeddings, we include the estimated semantic ratings for 1,427,992 Chinese and 1,515,633 English words in the SSDD. The SSDD will aid studies on natural language processing, text analysis, and semantic representation in the brain.

The neural representation of concepts is a focus of many cognitive neuroscience studies. Prior works studying concept representation with neural imaging data have been largely limited to concrete concepts. The use of relatively small and constrained sets of stimuli leaves open the question of whether the findings can generalize other concepts. We share an fMRI dataset in which 11 participants thought of 672 individual concepts, including both concrete and abstract concepts. The concepts were probed using words paired with images in which the words were selected to cover a wide range of semantic categories. Furthermore, according to the componential theories of concept representation, we collected the 54 semantic features of the 672 concepts comprising sensory, motor, spatial, temporal, affective, social, and cognitive experiences by crowdsourcing annotations. The quality assessment results verify this as a high-quality neuroimaging dataset. Such a dataset is well suited to study how the brain represents different semantic features and concepts, creating the essential condition to investigate the neural representation of individual concepts.Measurement(s)Blood Oxygen Level-Dependent Functional MRITechnology Type(s)Functional Magnetic Resonance ImagingFactor Type(s)noneSample Characteristic - OrganismHomo sapiens

Signals of flight flutter testing exhibit non-stationary characteristics, closely spaced modes, and low signal-to-noise ratio, presenting challenges in data processing. In recent years, the variational mode decomposition (VMD) method has emerged as a promising approach to mitigate mode mixing and exhibit robust noise resistance. Therefore, a novel time-frequency domain modal parameter identification method based on VMD is proposed to process impulse response signals in flight flutter testing. The modal frequency and damping ratio are determined through a three-step process: VMD analysis, Hilbert transform, and least square fitting. The efficacy of the proposed method in identifying closely spaced modes and resisting noise is validated through a numerical example. Furthermore, this method is applied to analyze two types of pulse excitation signals in actual flight flutter testing: one induced by the pilot’s shaking stick and the other induced by small rocket excitation. The obtained modal parameters are compared with those from ground vibration tests and specialized software, respectively, to showcase the effectiveness and superiority of the proposed method.

Studies in rodent models of epilepsy suggest that multidrug efflux transporters at the blood–brain barrier, such as P-glycoprotein, might contribute to pharmacoresistance by reducing target-site concentrations of antiepileptic drugs. We assessed P-glycoprotein activity in vivo in patients with temporal lobe epilepsy. We selected 16 patients with pharmacoresistant temporal lobe epilepsy who had seizures despite treatment with at least two antiepileptic drugs, eight patients who had been seizure-free on antiepileptic drugs for at least a year after 3 or more years of active temporal lobe epilepsy, and 17 healthy controls. All participants had a baseline PET scan with the P-glycoprotein substrate (R)-[11C]verapamil. Pharmacoresistant patients and healthy controls then received a 30-min infusion of the P-glycoprotein-inhibitor tariquidar followed by another (R)-[11C]verapamil PET scan 60 min later. Seizure-free patients had a second scan on the same day, but without tariquidar infusion. Voxel-by-voxel, we calculated the (R)-[11C]verapamil plasma-to-brain transport rate constant, K1 (mL/min/cm3). Low baseline K1 and attenuated K1 increases after tariquidar correspond to high P-glycoprotein activity. Between October, 2008, and November, 2011, we completed (R)-[11C]verapamil PET studies in 14 pharmacoresistant patients, eight seizure-free patients, and 13 healthy controls. Voxel-based analysis revealed that pharmacoresistant patients had lower baseline K1, corresponding to higher baseline P-glycoprotein activity, than seizure-free patients in ipsilateral amygdala (0·031 vs 0·036 mL/min/cm3; p=0·014), bilateral parahippocampus (0·032 vs 0·037; p<0·0001), fusiform gyrus (0·036 vs 0·041; p<0·0001), inferior temporal gyrus (0·035 vs 0·041; p<0·0001), and middle temporal gyrus (0·038 vs 0·044; p<0·0001). Higher P-glycoprotein activity was associated with higher seizure frequency in whole-brain grey matter (p=0·016) and the hippocampus (p=0·029). In healthy controls, we noted a 56·8% increase of whole-brain K1 after 2 mg/kg tariquidar, and 57·9% for 3 mg/kg; in patients with pharmacoresistant temporal lobe epilepsy, whole-brain K1 increased by only 21·9% for 2 mg/kg and 42·6% after 3 mg/kg. This difference in tariquidar response was most pronounced in the sclerotic hippocampus (mean 24·5% increase in patients vs mean 65% increase in healthy controls, p<0·0001). Our results support the hypothesis that there is an association between P-glycoprotein overactivity in some regions of the brain and pharmacoresistance in temporal lobe epilepsy. If this relation is confirmed, and P-glycoprotein can be identified as a contributor to pharmacoresistance, overcoming P-glycoprotein overactivity could be investigated as a potential treatment strategy. EU-FP7 programme (EURIPIDES number 201380).

Background and Objectives Cebranopadol is a novel first-in-class analgesic acting as a nociceptin/orphanin FQ peptide and opioid peptide receptor agonist with central analgesic activity. It is currently in clinical development for the treatment of chronic pain conditions. This trial focuses on the clinical pharmacokinetic (PK) properties of cebranopadol after oral single- and multiple-dose administration. Methods The basic PK properties of cebranopadol were assessed by means of noncompartmental methods in six phase I clinical trials in healthy subjects and patients. A population PK analysis included two further phase I and six phase II clinical trials. Results After oral administration of the immediate-release (IR) formulation, cebranopadol is characterized by a late time to reach maximum plasma concentration [ C max ] (4–6 h), a long half-value duration [HVD] (14–15 h), and a terminal phase half-life in the range of 62–96 h. After multiple once-daily dosing in patients, an operational half-life (the dosing interval resulting in an accumulation factor [AF] of 2) of 24 h was found to be the relevant factor to describe the multiple-dose PKs of cebranopadol. The time to reach steady state was approximately 2 weeks, the AF was approximately 2, and peak-trough fluctuation (PTF) was low (70–80%). Dose proportionality at steady state was shown for a broad dose range of cebranopadol 200–1600 µg. A two-compartment disposition model with two lagged transition compartments and a first-order elimination process best describes cebranopadol data in healthy subjects and patients after single- and multiple-dose administration. Conclusions Cebranopadol formulated as an IR product can be used as a once-daily formulation; it reaches C max after only 4–6 h, and has a long HVD and a low PTF. Therefore, from a PK perspective, cebranopadol is an attractive treatment option for patients with chronic pain.