Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyper-parameter tuning and feature extraction for asynchronous action detection from sub-thalamic nucleus local field potentials

Introduction Decoding brain states from subcortical local field potentials (LFPs) indicative of activities such as voluntary movement, tremor, or sleep stages, holds significant potential in treating neurodegenerative disorders and offers new paradigms in brain-computer interface (BCI). Identified states can serve as control signals in coupled human-machine systems, e.g., to regulate deep brain stimulation (DBS) therapy or control prosthetic limbs. However, the behavior, performance, and efficiency of LFP decoders depend on an array of design and calibration settings encapsulated into a single set of hyper-parameters. Although methods exist to tune hyper-parameters automatically, decoders are typically found through exhaustive trial-and-error, manual search, and intuitive experience. Methods This study introduces a Bayesian optimization (BO) approach to hyper-parameter tuning, applicable through feature extraction, channel selection, classification, and stage transition stages of the entire decoding pipeline. The optimization method is compared with five real-time feature extraction methods paired with four classifiers to decode voluntary movement asynchronously based on LFPs recorded with DBS electrodes implanted in the subthalamic nucleus of Parkinson’s disease patients. Results Detection performance, measured as the geometric mean between classifier specificity and sensitivity, is automatically optimized. BO demonstrates improved decoding performance from initial parameter setting across all methods. The best decoders achieve a maximum performance of 0.74 ± 0.06 (mean ± SD across all participants) sensitivity-specificity geometric mean. In addition, parameter relevance is determined using the BO surrogate models. Discussion Hyper-parameters tend to be sub-optimally fixed across different users rather than individually adjusted or even specifically set for a decoding task. The relevance of each parameter to the optimization problem and comparisons between algorithms can also be difficult to track with the evolution of the decoding problem. We believe that the proposed decoding pipeline and BO approach is a promising solution to such challenges surrounding hyper-parameter tuning and that the study’s findings can inform future design iterations of neural decoders for adaptive DBS and BCI.
Implementing biofeedback treatment in a psychosomatic-psychotherapeutic inpatient unit: a mixed methods evaluation of acceptance, satisfaction, and feasibility

Introduction Feedback-based therapies such as biofeedback have a benefit in patients with mental health disorders. While biofeedback is heavily researched in outpatient settings, it has been rarely investigated in psychosomatic inpatient settings. The implementation of an additional treatment option in inpatient settings holds special requirements. The aim of this pilot study is the evaluation of additional biofeedback treatment in an inpatient psychosomatic-psychotherapeutic unit to derive clinical implications and recommendations for the future implementation of biofeedback offers. Methods The evaluation of the implementation process was investigated using a convergent parallel mixed methods approach (following MMARS guidelines). Quantitative questionnaires measured patients’ acceptance and satisfaction with biofeedback treatment after receiving 10 sessions in addition to treatment as usual. After 6 months during implementation, qualitative interviews were conducted with biofeedback practitioners, i.e., staff nurses, examining acceptance and feasibility. Data analysis was conducted using either descriptive statistics or Mayring’s qualitative content analysis. Results In total, 40 patients and 10 biofeedback practitioners were included. Quantitative questionnaires revealed high satisfaction and acceptance in patients regarding biofeedback treatment. Qualitative interviews showed high acceptance in biofeedback practitioners but revealed several challenges that were encountered during the implementation process, e.g., increased workload due to additional tasks, organizational and structural difficulties. However, biofeedback practitioners were enabled to expand their own competencies and take over a therapeutic part of the inpatient treatment. Discussion Even though patient satisfaction and staff motivation are high, the implementation of biofeedback in an inpatient unit requires special actions to be taken. Not only should personnel resources be planned and available in advance of implementation but also be the workflow for biofeedback practitioners as easy and quality of biofeedback treatment as high as possible. Consequently, the implementation of a manualized biofeedback treatment should be considered. Nevertheless, more research needs to be done about suitable biofeedback protocols for this patient clientele.
Change energy image for gait recognition: an approach based on symbolic representation

Gait can be identified by observing static and dynamic parts of human body. In this paper a variant of gait energy image called change energy images (CEI) are generated to capture detailed static and dynamic information of human gait. Radon transform is applied to CEI in four different directions (vertical, horizontal and two opposite cross sections) considering four different angles to compute discriminative feature values. The extracted features are represented in the form of interval -valued type symbolic data. The proposed method is capable of recognizing an individual when he/she have variations in their gait due to different clothes they wear, in different normal conditions and carrying a bag. A similarity measure suitable for the proposed gait representation is explored for the purpose of establishing similarity match for gait recognition. Experiments are conducted on CASIA database B and the results have shown better recognition performance compared to some of the existing methods.
Employing Kronecker Canonical Form for H 1 Synthesis Problems

The Kronecker canonical form (KCF) can be employed when solving H∞ synthesis problem. The KCF structure reveals variables that can be eliminated in the semidefinite program that defines the controller. The structure can also be used to remove states in the controller without sacrificing performance. In order to find the KCF structure, we can transform the relevant matrices to a Guptri (Generalized upper triangular) form using orthogonal transformations. Thus we can avoid finding the KCF structure explicitly, which is a badly conditioned problem.
Nanoparticles as drug delivery systems in the treatment of oral squamous cell carcinoma: current status and recent progression

Oral squamous cell carcinoma (OSCC) is a common human malignancy with an estimated incidence of around 377,713 new cases worldwide in 2020. Despite the advance in clinical management, some of OSCC patients still miss the opportunity of completable resection of tumor, and have to accept medical therapies, e.g., chemotherapy, radiotherapy, or immunotherapy when the disease develops into the advanced stage. However, these therapies have been reported to be far from ideal due to the low efficiency of conventional delivery approaches. To obtain a better therapeutic effect, considerable attempts have been made toward to develop an effective drug delivery system (DDS). Nanoparticles (NPs) including inorganic NPs, polymer NPs, lipid NP, extracellular vesicles and cell membrane-based NPs have been evaluated as the better DDS candidates that can specifically accumulate in the tumor microenvironment along with a large amount of blood vessels. Emerging evidence suggested that NPs formulated with anticancer drugs including chemotherapeutic drugs, radiotherapy and immunotarget antibodies could remarkably improve the release and increase concentration of these drugs at the tumor site and show a better therapeutic efficacy, suggesting that NPs might serve as promising DDSs in the treatment of OSCC. Therefore, we have conducted this review to summarize recent progression and current status of diverse NPs as DDSs in this research field.
Gut microbiota decreased inflammation induced by chronic unpredictable mild stress through affecting NLRP3 inflammasome

Dysbiosis of the gut microbiota is associated with the development of depression, but the underlying mechanism remains unclear. The aim of this study was to determine the relationship between microbiota and NLRP3 inflammasome induced by chronic unpredictable mild stress (CUMS). Fecal transplantation (FMT) experiment was conducted to elucidate the potential mechanism. Levels of NLRP3 inflammasome, microbiota, inflammatory factors and tight junction proteins were measured. CUMS stimulation significantly increased the levels of NLRP3, Caspase-1 and ASC in brain and colon(p<0.05), decreased the levels of tight junction proteins Occludin and ZO-1 (p<0.05). Interestingly, increased NLRP3 inflammasome and inflammatory cytokines and decreased tight junction proteins were found in antibiotic-treated (Abx) rats received CUMS rat fecal microbiota transplantation. Furthermore, fecal microbiota transplantation altered the microbiota in Abx rats, which partially overlapped with that of the donor rats. Importantly, probiotic administration amended the alteration of microbiota induced by CUMS treatment, then reduced the levels of NLRP3 inflammasome and inflammatory factors. In conclusion, these findings suggested that depression-like behaviors induced by CUMS stimulation were related to altered gut microbiota, broke the intestinal barrier, promoted the expression of NLRP3 inflammasome and elevated inflammation. Therefore, improving the composition of microbiota via probiotic can attenuate inflammation by amending the microbiota and suppressing the activation of NLRP3 inflammasome, which is considered as a novel therapeutic strategy for depression.
The effects of cardiac structure, valvular regurgitation, and left ventricular diastolic dysfunction on the diagnostic accuracy of Murray law–based quantitative flow ratio

Objective The study aimed to investigate the diagnostic accuracy of Murray law–based quantitative flow ratio (μQFR) from a single angiographic view in patients with abnormal cardiac structure, left ventricular diastolic dysfunction, and valvular regurgitation. Background μQFR is a novel fluid dynamics method for deriving fractional flow reserve (FFR). In addition, current studies of μQFR mainly analyzed patients with normal cardiac structure and function. The accuracy of μQFR when patients had abnormal cardiac structure, left ventricular diastolic dysfunction, and valvular regurgitation has not been clear. Methods This study retrospectively analyzed 261 patients with 286 vessels that underwent both FFR and μQFR prior to intervention. The cardiac structure and function were measured using echocardiography. Pressure wire–derived FFR ≤0.80 was defined as hemodynamically significant coronary stenosis. Results μQFR had a moderate correlation with FFR (r = 0.73, p < 0.001), and the Bland–Altman plot presented no difference between the μQFR and FFR (0.006 ± 0.075, p = 0.192). With FFR as the standard, the diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of μQFR were 94.06% (90.65–96.50), 82.56% (72.87–89.90), 99.00% (96.44–99.88), 97.26 (89.91–99.30), and 92.96% (89.29–95.44), respectively. The concordance of μQFR/FFR was not associated with abnormal cardiac structure, valvular regurgitation (aortic valve, mitral valve, and tricuspid valve), and left ventricular diastolic function. Coronary hemodynamics showed no difference between normality and abnormality of cardiac structure and left ventricular diastolic function. Coronary hemodynamics demonstrated no difference among valvular regurgitation (none, mild, moderate, or severe). Conclusion μQFR showed an excellent agreement with FFR. The effect of abnormal cardiac structure, valvular regurgitation, and left ventricular diastolic function did not correlate with the diagnostic accuracy of μQFR. Coronary hemodynamics showed no difference in patients with abnormal cardiac structure, valvular regurgitation, and left ventricular diastolic function.
Perceived use and value of reproductive, problem-solving, and athlete-initiated teaching by coaches and athletes

In the sports coaching environment, it is recognized that developing athletes’ autonomy and problem-solving skills are crucial to support holistic development and ensure optimal performance. However, there needs to be more information on how coaches use and value different teaching methods in training and how athletes perceive and value these methods. This study aimed to examine coaches’ and athletes’ perceptions of the use and value of reproductive, productive problem-solving, and productive athlete-initiated teaching methods. To this end, the Coaches’ Use of Teaching Methods Scale which is validated for the use of coaches and athletes, was applied to 70 coaches and their 294 athletes of youth sports teams purposefully selected from four cities in Türkiye. Data were analyzed by nonparametric methods, including Friedman’s and Mann–Whitney tests (p < 0.05). Although there were statistically significant differences between the responses of coaches and athletes regarding the use of different teaching methods in their training and the value they gave to these methods, both groups marked the frequent use of reproductive, occasional use of productive problem-solving and rare use of productive athlete-initiated teaching methods during training. The value given to productive athlete-initiated teaching methods in terms of enjoyment, learning, and motivation by the athletes was higher than the value given to them by the coaches. The study’s findings strongly indicate the coaches’ professional needs in their pedagogical knowledge, specifically on their value perceptions of productive problem-solving and productive athlete-initiated teaching methods and the capacity to apply them.
Tracing the evolutionary history of hepatitis B virus genotype H endemic to Mexico

Hepatitis B virus (HBV) spreads efficiently among all human populations worldwide. HBV is classified into ten genotypes (A to J) with their geographic distribution and clinical features. In Mexico, HBV genotype H is the leading cause of hepatitis B and has been detected in indigenous populations, suggesting that HBV genotype H may be native to Mexico. However, little is known about the evolutionary history of HBV genotype H. Thus, we aimed to determine the age of HBV genotype H in Mexico using molecular dating techniques. Ninety-two HBV sequences of the reverse transcriptase (RT) domain of the polymerase gene (~1,251 bp) were analyzed; 48 were genotype H, 43 were genotype F, and the oldest HBV sequence from America was included as the root. All sequences were aligned, and the most recent common ancestor (TMRCA) time was calculated using the Bayesian Skyline Evolutionary Analysis. Our results estimate a TMRCA for the genotype H in Mexico of 2070.9 (667.5–4489.2) years before the present (YBP). We identified four major diversification events in genotype H, named H1, H2, H3, and H4. The TMRCA of H1 was 1213.0 (253.3–2638.3) YBP, followed by H2 1175.5 (557.5–2424.2) YBP, H3 949.6 (279.3–2105.0) YBP, and H4 1230.5 (336.3, 2756.7) YBP. We estimated that genotype H diverged from its sister genotype F around 8140.8 (1867.5–18012.8) YBP. In conclusion, this study found that genotype H in Mexico has an estimated age of 2070.9 (667.5–4489.2) YBP and has experienced at least four major diversification events since then.
The emergence of phonological dispersion through interaction: an exploratory secondary analysis of a communicative game

Introduction Why is it that phonologies exhibit greater dispersion than we might expect by chance? In earlier work we investigated this using a non-linguistic communication game in which pairs of participants sent each other series of colors to communicate a set of animal silhouettes. They found that above-chance levels of dispersion, similar to that seen in vowel systems, emerged as a result of the production and perception demands acting on the participants. However, they did not investigate the process by which this dispersion came about. Method To investigate this we conducted a secondary statistical analysis of the data, looking in particular at how participants approached the communication task, how dispersion emerged, and what convergence looked like. Results We found that dispersion was not planned from the start but emerged as a large-scale consequence of smaller-scale choices and adjustments. In particular, participants learned to reproduce colors more reliably over time, paid attention to signaling success, and shifted towards more extreme areas of the space over time. Conclusion This study sheds light on the role of interactive processes in mediating between human minds and the emergence or larger-scale structure, as well as the distribution of features across the world's languages.
Identification of cavitation state of centrifugal pump based on current signal

Centrifugal pump, which is widely used in water conservancy, electric power, petrochemical, ship, aerospace, and other technical fields, is the core equipment used to ensure all kinds of energy transfer. Cavitation not only affects the service life of the centrifugal pump but also seriously impacts the reliability of the process flow or device system. Due to the influence of the life, position and number of vibration sensors, the existing cavitation fault feature identification accuracy is not enough. The state analysis and characteristic recognition of the current signal under the cavitation state of the centrifugal pump are conducted in this paper based on soft sensing technology, and the signal component and judgment threshold representing the cavitation state are obtained. The following results are presented. Under different critical cavitation numbers, a small number of bubbles appeared near the suction surface of the inlet edge of the impeller, which verified the reliability of criterion for the critical cavitation number when the head coefficient decreased by 3%. The overall accuracy of binary classification cavitation recognition based on the current signal is 12.9% higher than that of three classification cavitation recognition. The recognition rate of VMD decomposition under different working conditions is higher than that of EMD, in design conditions, for example, overall accuracy improved by 7.3%, which also indicates that the obtained cavitation information of each component by VMD decomposition is richer than that obtained by EMD decomposition. Comparing different working conditions, a large flow rate easily leads to cavitation and high recognition current rate, compared with the flow of 0.75 Q and 1.25 Q, the overall accuracy is improved by 9.6%.
SSR and 18S rDNA based molecular profiling of Neopyropia yezoensis (Rhodophyta) in China: insight into genetic impact of laver aquaculture on wild resource

Neopyropia yezoensis is a typical intertidal seaweed and an indispensable species for conservation of seaweed resources. As one of the most important marine vegetables, the cultivation area of N. yezoensis has been largely increasing in the past decade and ranked the second highest among all the farmed seaweeds in China. It remains unknown whether large-scale cultivation of N. yezoensis has a genetic impact on wild populations. In this study, SSR markers and 18S rDNA were applied for diversity and genetic structure analysis of 22 N. yezoensis populations from North China. Twenty-two haplotypes were generated from 352 18S rDNA sequences and only three haplotypes were shared by both cultivated and wild populations. Based on 11 polymorphic SSR markers developed, the average polymorphism of the cultivated N. yezoensis populations was higher than that of the wild populations, with the percentage of polymorphic loci being 90.91% in most cultivated populations. The cultivated populations were clustered separately from the wild ones based on the population phylogenetic tree. This indicates that cultivated N. yezoensis populations are diverse and divergent from wild populations in China. However, there were cultivated individuals mixed with the wild ones based on the individual phylogenetic tree and STRUCTURE analysis. The genetic differentiation between cultivated and wild populations decreased with increasing cultivation time, suggesting a possible long-term and slow process of genetic introgression between cultivated N. yezoensis and the wild resource. The wild populations were grouped into two distinct clades by SSRs, with one distributed around the intersection between the Yellow Sea and the Bohai Sea, where the populations were characterized by a specific 18S rDNA haplotype. These findings provide useful insights into germplasm conservation, genetic breeding and improvement of N. yezoensis farming practices.
On temporal scale-free non-periodic stimulation and its mechanisms as an infinite improbability drive of the brain’s functional connectogram

Rationalized development of electrical stimulation (ES) therapy is of paramount importance. Not only it will foster new techniques and technologies with increased levels of safety, efficacy, and efficiency, but it will also facilitate the translation from basic research to clinical practice. For such endeavor, design of new technologies must dialogue with state-of-the-art neuroscientific knowledge. By its turn, neuroscience is transitioning—a movement started a couple of decades earlier—into adopting a new conceptual framework for brain architecture, in which time and thus temporal patterns plays a central role in the neuronal representation of sampled data from the world. This article discusses how neuroscience has evolved to understand the importance of brain rhythms in the overall functional architecture of the nervous system and, consequently, that neuromodulation research should embrace this new conceptual framework. Based on such support, we revisit the literature on standard (fixed-frequency pulsatile stimuli) and mostly non-standard patterns of ES to put forward our own rationale on how temporally complex stimulation schemes may impact neuromodulation strategies. We then proceed to present a low frequency, on average (thus low energy), scale-free temporally randomized ES pattern for the treatment of experimental epilepsy, devised by our group and termed NPS (Non-periodic Stimulation). The approach has been shown to have robust anticonvulsant effects in different animal models of acute and chronic seizures (displaying dysfunctional hyperexcitable tissue), while also preserving neural function. In our understanding, accumulated mechanistic evidence suggests such a beneficial mechanism of action may be due to the natural-like characteristic of a scale-free temporal pattern that may robustly compete with aberrant epileptiform activity for the recruitment of neural circuits. Delivering temporally patterned or random stimuli within specific phases of the underlying oscillations (i.e., those involved in the communication within and across brain regions) could both potentiate and disrupt the formation of neuronal assemblies with random probability. The usage of infinite improbability drive here is obviously a reference to the “The Hitchhiker’s Guide to the Galaxy” comedy science fiction classic, written by Douglas Adams. The parallel is that dynamically driving brain functional connectogram, through neuromodulation, in a manner that would not favor any specific neuronal assembly and/or circuit, could re-stabilize a system that is transitioning to fall under the control of a single attractor. We conclude by discussing future avenues of investigation and their potentially disruptive impact on neurotechnology, with a particular interest in NPS implications in neural plasticity, motor rehabilitation, and its potential for clinical translation.