Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of High-Definition Transcranial Direct-Current Stimulation on Resting-State Functional Connectivity in Patients With Disorders of Consciousness

Recently a positive treatment effect on disorders of consciousness (DOCs) with high-definition transcranial direct-current stimulation (HD-tDCS) has been reported; however, the neural modulation mechanisms of this treatment’s efficacy need further investigation. To better understand the processing of HD-tDCS interventions, a long-lasting HD-tDCS protocol was applied to 15 unresponsive wakefulness syndrome (UWS) patients and 20 minimally conscious states (MCS) patients in this study. Ten minutes of resting-state electroencephalograms (EEGs) were recorded from the patients, and the coma recovery scale-revised scores (CRS-Rs) were assessed for each patient from four time-points (T0, T1, T2, and T3). Brain networks were constructed by calculating the EEG spectral connectivity using the debiased weighted phase lag index (dwPLI) and then quantified the network information transmission efficiency by graph theory. We found that there was an increasing trend in local and global information processing of beta and gamma bands in resting-state functional brain networks during the 14 days of HD-tDCS modulation for MCS patients. Furthermore, the increased functional connectivity not only occurred in the local brain area surrounding the stimulation position but was also present across more global brain areas. Our results suggest that long-lasting HD-tDCS on the precuneus may facilitate information processing among neural populations in MCS patients.
What’s New? Gestures Accompany Inferable Rather Than Brand-New Referents in Discourse

The literature on bimodal discourse reference has shown that gestures are sensitive to referents’ information status in discourse. Gestures occur more often with new referents/first mentions than with given referents/subsequent mentions. However, because not all new entities at first mention occur with gestures, the current study examines whether gestures are sensitive to a difference in information status between brand-new and inferable entities and variation in nominal definiteness. Unexpectedly, the results show that gestures are more frequent with inferable referents (hearer new but discourse old) than with brand-new referents (hearer new and discourse new). The findings reveal new aspects of the relationship between gestures and speech in discourse, specifically suggesting a complementary (disambiguating) function for gestures in the context of first mentioned discourse entities. The results thus highlight the multi-functionality of gestures in relation to speech.
Foot-and-Mouth Disease: Optimization, Reproducibility, and Scalability of High-Yield Production of Virus-Like Particles for a Next-Generation Vaccine

Inactivated Foot-and-Mouth Disease (FMD) vaccine has proven to be effective in the control of the disease. However, its production has some disadvantages, including the costly biosafety facilities required for the production of huge amounts of growing live virus, the need of an exhaustive purification process to eliminate non-structural proteins of the virus in the final formulations in order to differentiate infected from vaccinated animals and variable local regulatory restrictions to produce and commercialize the vaccine. Thus, a novel vaccine against FMD that overcome these restrictions is desirable. Although many developments have been made in this regard, most of them failed in terms of efficacy or when considering their transferability to the industry. We have previously reported the use of transient gene expression in mammalian cells to produce FMD virus-like particles (VLPs) as a novel vaccine for FMD and demonstrated the immunogenicity of the recombinant structures in animal models. Here, we report the optimization of the production system by assaying different DNA:polyethylenimine concentrations, cell densities, and direct and indirect protocols of transfection. Also, we evaluated the reproducibility and scalability of the technology to produce high yields of recombinant VLPs in a cost-effective and scalable system compatible with industrial tech-transfer of an effective and safe vaccine.
Informal accountability. Street-level bureaucrats’ tactics to defy bad reputation in agencies of the Argentinian justice system

ABSTRACT This article illustrates how employees of the Argentinian Territorial Agency for Access to Justice fear being perceived as lazy bureaucrats involved in clientelist networks. In order to improve their image, they fulfil requirements for formal accountability, but use additional, informal modes of accountability to underline the agency’s performance, impact and relevance. I illustrate that employees identify with the agency they are working for and are inspired in their accountability by methods that have proven to be effective in human rights movements and NGOs. Thus, my ethnographic example invites discussions in the overlapping fields of accountability and political activism.
Preliminary Application of ISSR Markers in the Genetic Relationship Analysis of Tea Plants

Inter-simple sequence repeat (ISSR) is a kind of effective molecular marker developed on the basis of microsatellite, or simple sequence repeat (SSR). ISSR markers were applied to detect the relationship of the thirty-nine cultivars of tea plants. Fifteen primers, selected from fourty primers, were used to amplify the tea samples one hundred and fourty-three DNA bands were amplified, of which one hundred and thirty-one bands were polymorphic.The percentage of polymorphic bands was 91.6%. By cluster analysis based on ISSR markers using UPGMA,the genetic similarity was from 0.21 to 0.95. The GS was the greatest between Manqilan and Zhuyeqilan, and they had the highest genetic similarity and the smallest genetic distance.The GS between Chongqinpipacha and Yinghong 1 was the smallest and they had the lowest genetic similarity and the greatest genetic difference. Cluster analysis classified the 39 cultivars into 3 major groups (GS=0.20). Chongqinpipacha and Yinghong 1 fell into one group, belong to primitive form Jiulongzhu and Huanglong also fell into one group. The rest formed the third group. The results suggest that ISSR is a useful tool for molecular genetic diversity and relationship analysis of tea cultivars.
A FAMILY OF EXACT BOUNDED PML FOR THE HELMHOLTZ PROBLEM

Abstract. We study the Helmholtz equation with a Sommerfeld radiation condition in an unbounded domain. We introduce an exact bounded perfectly matched layer (PML) for this problem, in the sense that we recover the exact solution in the physical domain by choosing a singular PML function in a bounded domain. We compute the solution for the PML problem using a standard finite element method and assess its performance through numerical tests.
Moderation Effect of Physical Activity on the Relationship Between Fear of COVID-19 and General Distress: A Pilot Case Study in Arabic Countries

Aims This study aimed to investigate the effects of the fear of COVID-19, level of physical activity, and gender on negative stress (distress) in an Arab population by means of structural equations based on partial least squares. Materials and Methods The sample population comprised of 459 participants from four Arab countries (age M = 33.02, SD = 8.46; n = 237 women and n = 222 men). The level of education was basic (<9 years of study; n = 144), secondary/vocational (between 9 and 12; n = 178), and university (n = 137). The “Fear of COVID-19” Scale, the short form of the “International Physical Activity Questionnaire,” and the “Perceived Stress Scale” questionnaires were disseminated by emails and social networks via Google Forms. SMARTPLS software version 3.2. 9 was used to model the relationships between the variables under study. Results Results confirmed the links between level of physical activity, fear of COVID-19, and gender, showing a significant mediating effect of the fear of COVID-19 on the relationship between gender and general distress. The level of physical activity was also found to influence the fear of COVID-19, varying depending on gender. In addition, the model highlighted the presence of a moderation effect of the level of physical activity. Conclusion Based on the model presented in the present study, we can conclude that the COVID-19 pandemic has a profound impact on psychological distress in the target populations. The impact of the level of physical activity on psychological distress is shown to be very important during the pandemic phase.
Group Cognitive Behavioral Analysis System of Psychotherapy (CBASP): A Pilot Study for Bipolar Depression

Objectives Cognitive Behavioral Analysis System of Psychotherapy (CBASP) is an individually administered treatment model designed specifically for Persistent Depression however bipolar patients have traditionally been excluded from CBASP studies. There is a perception that bipolar depression will be harder to treat and requires a unique psychological approach. This pilot study reports on the feasibility of administering the same 20-week manualized group CBASP therapy with bipolar patients currently in a depressive episode. Methods This non-randomized, single-arm prospective pilot study, reports on an a posteriori exploration of benefits to bipolar depressed patients (n=26) of the same 20-week group CBASP intervention administered to unipolar depressed patients (n=81). The clinical trial for the initial phase examining benefits of the manualized 20-week group CBASP intervention with unipolar patients was registered with the ISRCTN registry, study ID: ISRCTN95149444. Results reported here include mixed ANOVA analyses, across group treatment models and diagnostic categories. Changes over time in self-reported depressive symptoms (Inventory of Depressive Symptoms -IDS-SR), self-reported social functioning, interpersonal problems and interpersonal dispositions are documented for all patients. An exploratory longitudinal latent class analysis was used to examine patients’ trajectories of improvement in depressive symptoms. Finally, the best predictors of change in reported depressive symptoms were explored with a logistic regression for all patients. Results Improvements in depressive symptoms and in social functioning over time were significant for all patients with bipolar patients trending towards a greater improvement in depressive symptoms after controlling for baseline differences. An exploratory Latent Class Analysis identified two different treatment trajectories for the entire sample: 1) moderate to severely depressed patients who improved significantly (49%) and 2) severely depressed patients who did not improve (51%). The best predictors of non-response to group therapy include high baseline problems in social functioning and low rates of self-reported Perceived Improvements in overall health. Conclusion Bipolar patients in a depressive episode appear to benefit from the same 20-week group CBASP model designed originally for the treatment of Persistent Depressive Disorder. Bipolar patients seem more easily mobilized both during and outside of group therapy sessions and report more interpersonal confidence and more agency than unipolar depressed patients.
Computational Study of the Ion and Water Permeation and Transport Mechanisms of the SARS-CoV-2 Pentameric E Protein Channel

Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus (SARS-CoV-2) and represents the causative agent of a potentially fatal disease that is a public health emergency of international concern. Coronaviruses, including SARS-CoV-2, encode an envelope (E) protein, which is a small, hydrophobic membrane protein; the E protein of SARS-CoV-2 shares a high level of homology with severe acute respiratory syndrome coronavirus (SARS-CoV). In this study, we provide insights into the function of the SARS-CoV-2 E protein channel and the ion and water permeation mechanisms using a combination of in silico methods. Based on our results, the pentameric E protein promotes the penetration of cation ions through the channel. An analysis of the potential mean force (PMF), pore radius and diffusion coefficient reveals that Leu10 and Phe19 are the hydrophobic gates of the channel. In addition, the pore exhibits a clear wetting/dewetting transition with cation selectivity under transmembrane voltage, indicating that it is a hydrophobic voltage-dependent channel. Overall, these results provide structure-based insights and molecular dynamic information that are needed to understand the regulatory mechanisms of ion permeability in the pentameric SARS-CoV-2 E protein channel.
Measurement Of The Front Back Asymmetry In Top-Antitop Quark Pairs In 1

A method of reconstructing tt̄ events in the lepton plus jets mode is applied to a measurement of the forward backward asymmetry in t-tbar pair production at CDF. The measurement is a test of discrete symmetries in t-tbar production and strong interactions at large Q. In the present data set it is potentially sensitive to the the presence of parity-violating production channels such as a massive Z′-like boson or new physics within Strong Interactions. Larger data sets will have sensitivity for an interesting charge asymmetry arising from pure QCD calculated at next-to-leading order. We measure the top quark forward-backward asymmetry in 1.9 fb−1 of CDF collision data, using 484 candidate tt̄ lepton plus jets events in the high Pt lepton trigger streams. We measure Afb = 0.17 ± (0.07) ± (0.04) which can be compared to the Standard Model prediction Afb = 0.04± 0.01 .
Increased Internet Searches for Insomnia as an Indicator of Global Mental Health During the COVID-19 Pandemic: Multinational Longitudinal Study

Background Real-time global mental health surveillance is urgently needed for tracking the long-term impact of the COVID-19 pandemic. Objective This study aimed to use Google Trends data to investigate the impact of the pandemic on global mental health by analyzing three keywords indicative of mental distress: “insomnia,” “depression,” and “suicide. ” Methods We examined increases in search queries for 19 countries. Significant increases were defined as the actual daily search value (from March 20 to April 19, 2020) being higher than the 95% CIs of the forecast from the 3-month baseline via ARIMA (autoregressive integrated moving average) modeling. We examined the correlation between increases in COVID-19–related deaths and the number of days with significant increases in search volumes for insomnia, depression, and suicide across multiple nations. Results The countries with the greatest increases in searches for insomnia were Iran, Spain, the United States, and Italy; these countries exhibited a significant increase in insomnia searches on more than 10 of the 31 days observed. The number of COVID-19–related deaths was positively correlated to the number of days with an increase in searches for insomnia in the 19 countries (ρ=0.64, P=.003). By contrast, there was no significant correlation between the number of deaths and increases in searches for depression (ρ=–0.12, P=.63) or suicide (ρ=–0.07, P=.79). Conclusions Our analysis suggests that insomnia could be a part of routine mental health screening during the COVID-19 pandemic.
Towards Formally Verified Key Management for Industrial Control Systems

Adoption of new digital technologies is impacting all aspects of society. While these new technologies are accepted rapidly within the consumer segment, in the area of industrial control systems the pace of change in computing is slower. This is often due to the criticality and security constraints of such systems, since degraded or hijacked control could lead to injuries or competitive disadvantages. Nowadays a critical component of control systems is the key management protocol for protecting communication. This is specifically important as more and more devices become part of industrial control networks. The key management system must be reliable and robust in order to ensure stable operation of the system with minimum downtime. This often means that the system needs to be autonomous and dynamic, capable of periodically changing the keys automatically and authenticating the system components. Different techniques have been used to examine the reliability and robustness of the key management systems, one promising approach is by using formal methods. In this paper we present a formally verified key management system for use within distributed industrial control systems. We demonstrate that the key management system can reliably handle authentication/communication operations in real-time as well as joining/leaving of control units within the system. We use UPPAAL to analyse several security properties, showing that our models satisfy a collection of requirements defined by our industrial partner and are viable for dynamic key management applications.CCS CONCEPTS• Security and privacy $\rightarrow$ Key management; Formal security models; Authorization; Security protocols;
How Much Do I Argue Like You? Towards a Metric on Weighted Argumentation Graphs

When exchanging arguments with other people, it is interesting to know who of the others has the most similar opinion to oneself. In this paper, we suggest using weighted argumentation graphs that can model the relative importance of arguments and certainty of statements. We present a pseudometric to calculate the distance between two weighted argumentation graphs, which is useful for applications like recommender systems, consensus building, and ﬁnding representatives. We propose a list of desiderata which should be fulﬁlled by a metric for those applications and prove that our pseudometric fulﬁlls these desiderata.
Scalable selection of EEG features for compression

Telemedicine using information technology (IT) and communication networks is becoming common. Often, the medical doctor and the patient can discuss the problem by video teleconference and, if necessary, the patient's physiological data can be sent to the doctor. As part of this trend, we believe that brain waves can be used for telemedicine in the future. We expect that the diagnosis of remote patients will be realized by transferring electroencephalogram (EEG) data to a server or cloud. However, if EEG data are sent as they are, the data size will be significantly large. Thus, the compression of EEG data is desirable. Furthermore, should not affect the accuracy of diagnosis if data compression is performed. In this study, the relationship between the selected EEG signal features and the accuracy is investigated.
Multi-lag Stacking for Blood Glucose Level Prediction

This work investigates blood glucose level prediction for type 1 diabetes in two horizons of 30 and 60 minutes. Initially, three conventional regression tools—partial least square regression (PLSR), multilayer perceptron, and long short-term memory—are deployed to create predictive models. They are trained once on 30 minutes and once on 60 minutes of historical data resulting in six basic models for each prediction horizon. A collection of these models are then set as base-learners to develop three stacking systems; two uni-lag and one multi-lag. One of the uni-lag systems uses the three basic models trained on 30 minutes of lag data; the other uses those trained on 60 minutes. The multi-lag system, on the other hand, leverages the basic models trained on both lags. All three stacking systems deploy a PLSR as meta-learner. The results obtained show: i) the stacking systems outperform the basic models, ii) among the stacking systems, the multi-lag shows the best predictive performance with a root mean square error of 19.01 mg/dl and 33.37 mg/dl for the prediction horizon of 30 and 60 minutes, respectively.
Fastbot: A Multi-Agent Model-Based Test Generation System : Beijing Bytedance Network Technology Co., Ltd.

Model-based test (MBT) generation techniques for automated GUI testing are of great value for app testing. Existing GUI model-based testing tools may fall into cyclic operations and run out of resources, when applied to apps with industrial complexity and scalability. In this work, we present a multi-agent GUI MBT system named Fastbot. Fastbot performs model construction on the server end. It applies multi-agent collaboration mechanism to speed up the model construction procedure. The proposed approach was applied on more than 20 applications from Bytedance with more than 1500 million monthly active users. Higher code coverage in less testing time is achieved with comparison of three other automated testing tools including Droidbot, Humanoid and Android Monkey.
A Personalized and Interpretable Deep Learning Based Approach to Predict Blood Glucose Concentration in Type 1 Diabetes

The management of type 1 diabetes mellitus (T1DM) is a burden-some life-long task. In fact, T1DM individuals are request to perform every day tens of actions to adapt the insulin therapy, aimed at maintaining the blood glucose (BG) concentration as much as possible into a safe range coping with the day-to-day variability of their life style. The recent availability of continuous glucose monitoring (CGM) devices and other low-cost wearable sensors to track important vital and activity signals, is stimulating the development of decision support systems to lower this burden. Modern deep learning models, trained using rich amount of information, are a suitable and effective instrument for such purpose, especially if used to predict future BG values. However, the high accuracy of deep learning approaches is often obtained at the expense of less interpretability. To surpass this limit, in this work we propose a new deep learning method for BG prediction based on a personalized bidirectional long short-term memory (LSTM) equipped with a tool that enables its interpretability. The OhioT1DM Dataset was used to develop a model targeting future BG at 30 and 60 minute prediction horizons (PH). The accuracy of model predictions was evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and the time gained (TG) to anticipate the actual glucose concentration. The obtained results show fairly good prediction accuracy (for PH = 30/60 min): RMSE = 20.20/34.19 mg/dl, MAE = 14.74/25.98 mg/dl, and TG = 9.17/18.33 min. Moreover, we showed, in a representative case, that our algorithm is able to preserve the physiological meaning of the considered inputs.