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Abstract Background Disorders of Consciousness (DoC) caused by severe brain injuries represent a challenging clinical entity, which is easy to misdiagnosis and lacks effective treatment options. Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive neuroelectric stimulation method that shows promise in improving consciousness for DoC, especially in minimally conscious state (MCS). However, there is little evidence of its effectiveness, especially in RCT studies. Methods Twenty MCS patients participated in a double-blind, randomized, crossover, sham-controlled clinical study to evaluate the safety and efficacy of rTMS for MCS. Subjects were randomized into two groups: one group received rTMS-active for 10 consecutive days (n = 10), and the other group received rTMS-sham for 10 consecutive days (n = 10). After a 10-day washout period, the two groups were crossed over and received the opposite treatment. the rTMS protocol consisted of 2,000 pulses per day in the left dorsolateral prefrontal cortex (L-DLPFC), sent at 10 Hz. The stimulation intensity was 90% of the resting motor threshold. Coma Recovery Scale Revised (CRS-R), the main evaluation index, was evaluated before and after each phase in a double-blind manner. Meanwhile RS-EEG and TMS-EEG data were acquired and relative alpha power (RAP), and perturbational complexity index based on state transitions (PCIst) were caculated. Results One-way ANOVA revealed significantly higher scores in rTMS-active treatment compared to rTMS-sham across various measures, including CRS-R total score, RAP, PCIst (all P

ObjectiveThis study aimed to determine whether patients with disorders of consciousness (DoC) could experience neural entrainment to individualized music, which explored the cross-modal influences of music on patients with DoC through phase-amplitude coupling (PAC). Furthermore, the study assessed the efficacy of individualized music or preferred music (PM) versus relaxing music (RM) in impacting patient outcomes, and examined the role of cross-modal influences in determining these outcomes.MethodsThirty-two patients with DoC [17 with vegetative state/unresponsive wakefulness syndrome (VS/UWS) and 15 with minimally conscious state (MCS)], alongside 16 healthy controls (HCs), were recruited for this study. Neural activities in the frontal–parietal network were recorded using scalp electroencephalography (EEG) during baseline (BL), RM and PM. Cerebral-acoustic coherence (CACoh) was explored to investigate participants’ abilitiy to track music, meanwhile, the phase-amplitude coupling (PAC) was utilized to evaluate the cross-modal influences of music. Three months post-intervention, the outcomes of patients with DoC were followed up using the Coma Recovery Scale-Revised (CRS-R).ResultsHCs and patients with MCS showed higher CACoh compared to VS/UWS patients within musical pulse frequency (p = 0.016, p = 0.045; p

The text-to-SQL task aims to convert natural language questions into corresponding SQL queries based on a given database schema. Previous models that rely on graph neural networks often struggle to accurately capture the complex grammatical relationships present in these questions, leading to poor performance when generating queries for longer requests. To address these challenges, we propose RGISQL, which integrates refined grammatical information extracted from the question and employs segmentation processing to effectively manage long queries. Additionally, RGISQL minimizes the complexity of edge embeddings by reducing the coupling within graph neural networks. By utilizing grammatical dependency trees, RGISQL is better equipped to capture the inherent structure and grammatical rules of questions. This refined grammatical information offers additional contextual and semantic cues for the model, thereby enhancing both its generalizability and interpretability. Furthermore, we dynamically assess the importance of different edges based on the graph structure, which helps reduce the coupling of edge embeddings and further improves the model’s performance. Multiple sets of experiments conducted on the Spider and Spider-Syn datasets demonstrate that RGISQL outperforms other baselines, achieving the best results in both datasets.

ObjectiveTo explore the effects of inspiratory muscle training (IMT) on hypertension and provide guidance for its clinical application as an auxiliary approach.MethodsArticles published prior to July 2022 were searched in Cochrane Library, Web of Science, PubMed, Embase, CNKI, and Wanfang databases. Included were randomized controlled studies that used IMT to treat individuals with hypertension. The mean difference (MD) was computed using the Revman 5.4 software. In individuals with hypertension, the effects of IMT on systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), and pulse pressure (PP) were compared and studied.ResultsThere were found to be eight randomized controlled trials totaling 215 patients. According to a meta-analysis, the IMT reduced the SBP (MD: −12.55 mmHg, 95% CI: −15.78, −9.33), DBP (MD: −4.77 mmHg, 95% CI: −6.00, −3.54), HR (MD: −5.92 bpm, 95% CI: −8.72, −3.12), and PP (MD: −8.92 mmHg, 95% CI: −12.08, −5.76) in patients with hypertension. In subgroup analyses, low-intensity IMT showed a better reduction in SBP (MD: −14.47 mmHg, 95% CI: −17.60, −11.34), DBP (MD: −7.70 mmHg, 95% CI: −10.21, −5.18).ConclusionIMT may become an auxiliary means to improve the four hemodynamic indexes (SBP, DBP, HR and PP) in patients with hypertension. In subgroup analyses, low-intensity IMT was more effective in regulating blood pressure than medium-high-intensity IMT.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42022300908.

Compared with English named entity recognition (NER), Chinese NER faces significant challenges due to the flexible, non-standard word formation and vague word boundaries, which cause a lot of boundary ambiguity and reduce the accuracy of entity identification. To address this issue, we propose a boundary enhancement with multi-class information model (BEMCI). The model integrates multiple types of information into text embedding while enhancing the subsequent syntax-structure information. A syntactic information analysis module is designed to highlight important syntax information from three aspects, namely part-of-speech tags, syntactic constituents, and dependency relations, to analyze sentence structures. Meanwhile, an improved contextual attention mechanism, which combines contextual and syntactic information using a gate mechanism to control the weight fusion, is proposed to further enhance the model’s boundary determination. Multiple sets of experiments conducted on six general datasets show that BEMCI outperforms other baselines, achieving the best results in four of these six datasets.

BackgroundOne of the main objectives of stroke rehabilitation is to alleviate post-stroke spasticity. Over the recent years, many studies have explored the potential benefits of whole-body vibration (WBV) treatment for post-stroke spasticity, but it is still controversial.ObjectiveThe current study aims to assess the efficacy and safety of WBV for post-stroke spasticity and determine the appropriate application situation.MethodsFrom their establishment until August 2022, the following databases were searched: PubMed, Web of Science, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Medline, China National Knowledge Infrastructure (CNKI), and Wanfang. Only randomized controlled trials (RCTs) that were published in either English or Chinese were taken into consideration. We independently filtered the research, gathered the data from the studies, and evaluated the research quality (Cochrane RoB tool) and the overall evidence quality (GRADE). Rev Man 5.4 software was utilized to conduct statistical analysis.ResultsIn this analysis, 11 RCTs with 475 patients that reported on the effectiveness of WBV therapy for post-stroke spasticity were taken into account. Compared to the control groups, the results revealed that WBV combined with conventional rehabilitation at a vibration frequency lower than 20 Hz (SMD = −0.58, 95% CI: −0.98 to −0.19, P = 0.004) was more effective in relieving upper (SMD = −0.53, 95% CI: −1.04 to 0.03, P = 0.03) and lower limb spasticity (SMD = −0.21, 95% CI: −0.40 to −0.01, P = 0.04); similarly, it was superior for patients aged under 60 years (SMD = −0.41, 95% CI: −0.66 to −0.17, P = 0.0008) with acute and subacute stroke (SMD = −0.39, 95% CI: −0.68 to −0.09, P = 0.01). The valid vibration for reducing spasticity was found to last for 10 min (SMD = −0.41, 95% CI: −0.75 to −0.07, P = 0.02). None of the included studies revealed any serious adverse impact.ConclusionModerate-quality evidence demonstrated when WBV was used as an adjuvant, vibration

ObjectiveIn this study, we aimed to investigate the effects of non-invasive brain stimulation (NIBS) on cognitive and motor functions in patients with multiple sclerosis (pwMS).MethodsA literature search was performed in the Cochrane Library, Embase, PubMed, Web of Science, Medline, CNKI, and Wan fang. The time interval used for database construction was up to December 2022, and the language was not limited. The collected trials were subsequently screened, the data were extracted, the quality was evaluated, and the effect sizes were computed using STATA/MP Version 13 for outcome analysis. Standard mean difference (SMD) and 95% confidence interval (CI) were calculated for domain of interest.ResultsIn total, 17 articles that examined 364 patients with multiple sclerosis were included in this analysis. Non-invasive brain stimulation did not improve the overall cognitive function [SMD = 0.18, 95% CI (−0.32, 0.69), P = 0.475] but helped improve motor function in patients [SMD = 0.52, 95% CI (0.19, 0.85), P = 0.002]. Moreover, this study specifically indicated that non-invasive brain stimulation improved alerting [SMD = 0.68, 95% CI (0.09, 1.26), P = 0.02], whereas non-invasive brain stimulation intervention improved motor function in patients aged

BackgroundWith the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.MethodsQEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.ResultsA total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.ConclusionThe present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

Nematode communities are meaningful biological indicators of soil health and soil processes across different grassland types and management practices and analyses of nematode communities provide insight into structure, function, and sensitivity or resilience across multiple ecosystems. In three model grasslands: meadow steppe (MS), typical steppe (TS), and alpine meadow (AM), this current research examined responses of soil nematode communities and related edaphic characteristics to grazing, mowing, and crop cultivation at two soil depths. The research fills a critical knowledge gap by resolving multidirectional influences between local conditions, grassland management practices, and nematode communities. Across grassland types, nematode abundance in AM was greater than MS and TS grasslands, and nematodes were more abundant near the soil surface. Cultivation resulted in greater nematode abundance compared to all other management practices, and generally, bacterivores were the most dominant nematode trophic group. The TS and MS grasslands had relatively more bacterivores, exhibiting substantial influences on soil mineralization and organic matter decomposition pathways. The AM grassland showed relatively more plant feeding nematodes, driving soil mineralization pathways. Among the three management practices, crop cultivation had the greatest impact on nematode community structure and the soil environment, especially in relatively sensitive AM grasslands. In fact, AM soil environments responded most dramatically to cultivation, with nematode abundance, soil quality, and food web complexity increasing. However, soil ecosystem stability, food web reliance, and food web response to resources decreased in cultivated AM soils. Results indicate that unique environmental characteristics in the Qinghai-Tibet plateau drive substantially different AM grassland nematode community structure and soil conditions compared to TS or MS grasslands. As anthropogenic pressures on these ecosystems mount, it is critical to understand how different management practices influence grassland nematode communities, with cascading effects through soil environments.

Objectives: Disorders of consciousness (DoC) is a dynamic and challenging discipline, presenting intriguing challenges to clinicians and neurorehabilitation specialists for the lack of reliable assessment methods and interventions. Understanding DoC keeps pace with scientific research is urgent to need. We quantitively analyzed publications on DoC over the recent 10 years via bibliometrics analysis, to summarize the intellectual structure, current research hotspots, and future research trends in the field of DoC. Methods: Literature was obtained from the Science Citation Index Expanded of Web of Science Core Collection (WoSCC). To illustrate the knowledge structure of DoC, CiteSpace 5.8.R3 was used to conduct a co-occurrence analysis of countries, institutions, and keywords, and a co-citation analysis of references and journals. Also, Gephi 0.9.2 contributed to the author and co-cited author analysis. We found the most influential journals, authors, and countries and the most talked about keywords in the last decade of research. Results: A total of 1919 publications were collected. Over the past 10 years, the total number of annual publications has continued to increase, with the largest circulation in 2018. We found most DoC research and close cooperation originated from developed countries, e.g., the USA, Canada, and Italy. Academics from Belgium appear to have a strong presence in the field of DoC. The most influential journals were also mainly distributed in the USA and some European countries. Conclusions: This bibliometric study sheds light on the knowledge architecture of DoC research over the past decade, reflecting current hotspots and emerging trends, and providing new insights for clinicians and academics interested in DoC. The hot issues in DoC were diagnosing and differentiating the level of consciousness, and detecting covert awareness in early severe brain-injured patients. New trends focus on exploring the recovery mechanism of DoC and neuromodulation techniques.

Abstract Background Forage nutritive value plays an important role in livestock nutrition and maintaining sustainable grassland ecosystems, and grazing management can affect the quality of forage. In this study, we investigated the effects of different grazing intensities on the nutritive values of Leymus chinensis (Trin.) Tzvelev, Artemisia spp. and Carex duriuscula C. A. Mey in the steppes of China during the growing seasons from 2011 to 2013. Five grazing management treatments were implemented: (1) rest grazing in spring, heavy grazing in summer and moderate grazing in autumn (RHM), (2) rest grazing in spring, moderate grazing in summer and heavy grazing in autumn (RMH), (3) heavy grazing though all seasons (HHH), (4) heavy grazing in spring and summer and moderate grazing in autumn (HHM) and (5) continuous moderate grazing in all seasons (MMM). Results There were significant effects of year, season, treatment, and year × season and year × treatment interactions only on the crude protein of L. chinensis (P 0.05) for ether extract, neutral detergent fiber, acid detergent fiber and Ca concentration for any of the grasses in spring and summer from 2011 to 2013 under the different grazing management treatments. Conclusions The nutritive value of L. chinensis was more responsive to grazing disturbance than Artemisia spp. and C. duriuscula, and heavy grazing maintained a relatively high crude protein content in all species. Seasonal and interannual seasonal differences in grazing management combinations were two of the most important factors in determining the variability of forage nutritive value, including crude protein, ether extract, neutral detergent fiber, acid detergent fiber and calcium, for L. chinensis, Artemisia spp. and C. duriuscula. We suggest that moderate grazing should be adopted to ensure the quality and yield of forage and promote the sustainable development of animal husbandry.

Background: Male and female gametes factors might contribute to the total fertilization failure (TFF). In first in vitro fertilization (IVF) cycles, decision-making of insemination protocol was mainly based on semen quality for the contribution of female clinical characteristics to TFF remained obscure. The purpose of the study was to evaluate the role of semen quality in predicting unexpected TFF.Methods: A single-center retrospective cohort analysis was performed on 19539 cycles between 2013 and 2021. Two algorithms, a Least Absolute Shrinkage and Selection Operator (LASSO) and an Extreme Gradient Boosting (Xgboost) were used to create models with cycle characteristics parameters. By including semen parameters or not, the contribution of semen parameters to the performance of the models was evaluated. The area under the curve (AUC), the calibration, and the net reclassification index (NRI) were used to evaluate the performance of the models.Results: The prevalence of TFF were .07 (95%CI:0.07-0.08), and .08 (95%CI:0.07-0.09) respectively in the development and validation group. Including all characteristics, with the models of LASSO and Xgboost, TFF was predicted with the AUCs of .74 (95%CI:0.72-0.77) and .75 (95%CI:0.72-0.77) in the validation group. The AUCs with models of LASSO and Xgboost without semen parameters were .72 (95%CI:0.69-0.74) and .73 (95%CI:0.7-0.75). The models of LASSO and Xgboost with semen parameters only gave the AUCs of .58 (95%CI:0.55-0.61) and .57 (95%CI:0.55-0.6). For the overall validation cohort, the event NRI values were −5.20 for the LASSO model and −.71 for the Xgboost while the non-event NRI values were 10.40 for LASSO model and 0.64 for Xgboost. In the subgroup of poor responders, the prevalence was .21 (95%CI:0.18-0.24). With refitted models of LASSO and Xgboost, the AUCs were .72 (95%CI:0.67-0.77) and .69 (95%CI:0.65-0.74) respectively.Conclusion: In unselected patients, semen parameters contribute to limited value in predicting TFF. However, oocyte yield is an important predictor for TFF and the prevalence of TFF in poor responders was high. Because reasonable predicting power for TFF could be achieved in poor responders, it may warrant further study to prevent TFF in these patients.

Elastic strain sensor nanocomposites are emerging materials of high scientific and commercial interest. This study analyzes the major factors influencing the electrical behavior of elastic strain sensor nanocomposites. The sensor mechanisms were described for nanocomposites with conductive nanofillers, either dispersed inside the polymer matrix or coated onto the polymer surface. The purely geometrical contributions to the change in resistance were also assessed. The theoretical predictions indicated that maximum Gauge values are achieved for mixture composites with filler fractions slightly above the electrical percolation threshold, especially for nanocomposites with a very rapid conductivity increase around the threshold. PDMS/CB and PDMS/CNT mixture nanocomposites with 0–5.5 vol.% fillers were therefore manufactured and analyzed with resistivity measurements. In agreement with the predictions, the PDMS/CB with 2.0 vol.% CB gave very high Gauge values of around 20,000. The findings in this study will thus facilitate the development of highly optimized conductive polymer composites for strain sensor applications.