Miracle-dz/UniFAR-FTUs huggingface dataset:
Teacher Questions: Mediated-Learning Behaviors Involved in Teacher-Student Interaction During Whole-Class Instruction in Chinese English Classrooms

Teacher questions have long been considered important in mediating students’ learning in language classrooms. This paper examined the mediated-learning behaviors involved in teacher questions during whole-class instruction in high schools of China. Five lessons of different topics were observed. Conversation analytic approach was applied to analyze teachers’ verbal interactions with students during whole-class teaching. Teachers’ questions and students’ responses were transcribed and categorized as display questions or referential questions. The mediated-learning behaviors involved in the two types of questions were discussed by presenting six sessions of interaction. The study investigated which question type initiated the interaction involving more variety of mediated-learning behaviors and what pedagogical implications this may have for teacher questioning techniques that enhance student learning. The study found the interactions initiated by referential questions contain more varieties of mediated-learning behaviors. This study suggests that teachers need to be encouraged to use referential questions more frequently whether in display interactions or in referential interactions.
School Climate, Moral Disengagement and, Empathy as Predictors of Bullying in Adolescents

Our work aimed to study the relationships between different dimensions of school climate, moral disengagement, empathy, and bullying behaviors (perpetration and victimization). The study sample consisted of 629 students (304 boys and 325 girls) aged 12–14 years (M = 12.55, SD = 0.67). Results showed how different dimensions of school climate predicted moral disengagement, empathy, and victimization, and these, in turn, predicted bullying perpetration. The results show the need to generate favorable educational environments to reduce the levels of moral disengagement and victimization and to increase empathy in students as a strategy to prevent negative consequences related to bullying.
Vasopressin Loading for Refractory Septic Shock: A Preliminary Analysis of a Case Series

Background: Vasopressin is one of the strong vasopressor agents associated with ischemic events. Responses to the administration of vasopressin differ among patients with septic shock. Although the administration of a high dose of vasopressin needs to be avoided, the effects of bolus loading have not yet been examined. Since the half-life of vasopressin is longer than that of catecholamines, we hypothesized that vasopressin loading may be effective for predicting responses to its continuous administration. Methods: We retrospectively analyzed consecutive cases of septic shock for which vasopressin was introduced with loading under noradrenaline at >0.2 μg/kg/min during the study period. Vasopressin was administered in a 1 U bolus followed by its continuous administration at 1 U/h. The proportion of patients with a negative catecholamine index (CAI) change 6 h after the introduction of vasopressin was set as the primary outcome. We defined non-responders for exploration as those with a mean arterial pressure change <18 mmHg 1 min after vasopressin loading, among whom none had a change in CAI <0. Results: Twenty-one consecutive cases were examined in the present study, and included 14 responders and 7 non-responders. The primary outcome accounted for 71.4% of responders and 0% of non-responders, with a significant difference (p = 0.0039). Median CAI changes 2, 4, and 6 h after the administration of vasopressin were 0, −5, and −10 in responders and +20, +10, and +10 in non-responders, respectively. CAI was not reduced in any non-responder. Outcomes including mortality were not significantly different between responders and non-responders. Digital ischemia (1/21) and mesenteric ischemia (1/21) were observed. Conclusions: Vasopressin loading may predict responses to its continuous administration in septic shock patients. Further investigations involving a safety analysis are needed.
Motor Abnormalities in Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Are Associated With Regional Grey Matter Volumes

Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are associated with motor impairments, with some children holding a comorbid diagnosis of Developmental Coordination Disorder (DCD). However, DCD is underdiagnosed in these populations and the volume abnormalities that contribute to explaining these motor impairments are poorly understood. In this study, motor abilities as measured by the Developmental Coordination Disorder Questionnaire (DCDQ) were compared between children with ADHD, children with ASD and typically developing (TD) children, aged 8–12 years old. Additionally, the association between the DCDQ scores (general coordination, fine motor/handwriting, control during movement, total) and regional volume abnormalities were explored in 6 regions of interest (pre-central gyrus, post-central gyrus, inferior parietal cortex, superior frontal gyrus, middle frontal gyrus, medial frontal gyrus), within each group and across all participants. Children with ASD and children with ADHD showed impaired motor abilities in all the DCDQ-derived scores compared to TD children. Additionally, most children with ASD or ADHD had an indication or suspicion of DCD. Within the ASD group, coordination abilities were associated with the volume of the right medial frontal gyrus, and within the ADHD group, the total DCDQ score was associated with the volume of the right superior frontal gyrus. This study underlines the importance of routinely checking motor abilities in populations with ASD or ADHD in clinical practise and contributes to the understanding of structural abnormalities subtending motor impairments in these disorders.
Percutaneous Coronary Intervention in Transcatheter Aortic Valve Implantation Patients: Overview and Practical Management

Coronary artery disease (CAD) is present in 40–75% of patients undergoing transcatheter aortic valve implantation (TAVI) for severe symptomatic aortic stenosis. Currently, the indication for TAVI is expanding toward younger patients at lower surgical risk. Given the progressive nature of CAD, the necessity for coronary angiography (CA), including percutaneous coronary intervention (PCI), will subsequently increase as in the future TAVI patients will be younger and have a longer life expectancy. Data on the impact of PCI in patients with severe CAD scheduled for TAVI are controversial, and although European and US guidelines recommend PCI before TAVI, the optimal timing for PCI remains unclear due to a lack of evidence. Depending on the valve type, position, and axial alignment of the implanted device, CA and/or PCI after TAVI can be challenging. Hence, every interventionalist should be familiar with the different types of transcatheter heart valves and their characteristics and technical issues that can arise during invasive coronary procedures. This review provides an overview of current data regarding the prevalence and clinical implications of CAD and PCI in TAVI patients and includes useful guidance for practical management in the clinical routine.
Molecular Docking and Dynamics Simulation Revealed the Potential Inhibitory Activity of ACEIs Against SARS-CoV-2 Targeting the hACE2 Receptor

The rapid and global spread of a new human coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has produced an immediate urgency to discover promising targets for the treatment of COVID-19. Here, we consider drug repurposing as an attractive approach that can facilitate the drug discovery process by repurposing existing pharmaceuticals to treat illnesses other than their primary indications. We review current information concerning the global health issue of COVID-19 including promising approved drugs, e.g., human angiotensin-converting enzyme inhibitors (hACEIs). Besides, we describe computational approaches to be used in drug repurposing and highlight examples of in-silico studies of drug development efforts against SARS-CoV-2. Alacepril and lisinopril were found to interact with human angiotensin-converting enzyme 2 (hACE2), the host entranceway for SARS-CoV-2 spike protein, through exhibiting the most acceptable rmsd_refine values and the best binding affinity through forming a strong hydrogen bond with Asn90, which is assumed to be essential for the activity, as well as significant extra interactions with other receptor-binding residues. Furthermore, molecular dynamics (MD) simulations followed by calculation of the binding free energy were also carried out for the most promising two ligand-pocket complexes from docking studies (alacepril and lisinopril) to clarify some information on their thermodynamic and dynamic properties and confirm the docking results as well. These results we obtained probably provided an excellent lead candidate for the development of therapeutic drugs against COVID-19. Eventually, animal experiments and accurate clinical trials are needed to confirm the potential preventive and treatment effect of these compounds.
Dynamic Functional Connectivity Change-Point Detection With Random Matrix Theory Inference

To study the dynamic nature of brain activity, functional magnetic resonance imaging (fMRI) data is useful including some temporal dependencies between the corresponding neural activity estimates. Recent studies have shown that the functional connectivity (FC) varies according to time and location which should be incorporated into the model. Modeling this dynamic FC (DFC) requires time-varying measures of spatial region of interest (ROI) sets. To know about the DFC, change-point detection in FC is of particular interest. In this paper, we propose a method of detecting a change-point based on the maximum of eigenvalues via random matrix theory (RMT). From covariance matrices for FC of all ROI's, the temporal change-point of FC is decided by an RMT approach. Simulation results show that our proposed method can detect meaningful FC change-points. We also illustrate the effectiveness of our FC detection approach by applying our method to epilepsy data where change-points detected are explained by the changes in memory capacity. Our study shows the possibility of RMT based approach in DFC change-point problem and in studying the complex dynamic pattern of functional brain interactions.
Clinical Impact of PET With 18F-FDG and 11C-PIB in Patients With Dementia in a Developing Country

Introduction: The objective of this study was to evaluate the clinical impact PET with 18F-FDG and 11C-PIB in patients with dementia in a developing country. Methodology: Retrospective study of the patients referred for the evaluation of dementia to the only PET center in Uruguay. A total of 248 patients were identified, from which 70 patients were included based on the availability of medical history and clinical follow-up. Main outcomes included change in diagnosis, diagnostic dilemma and AD treatment. We evaluated the association of clinical outcomes with PET concordance with baseline diagnosis, diagnostic dilemma, level of education, AD pathology/Non-AD pathology (AD/Non-AD), baseline diagnosis and 11C-PIB PET result. Results: Baseline clinical diagnosis was concordant with 18F-FDG and 11C-PIB PET results in 64.7 and 77.1% of the patients, respectively. Change in diagnosis after PET was identified in 30.0% of the patients and was associated with discordant 18F-FDG (p = 0.002) and 11C-PIB (p < 0.001) PET results, previous diagnostic dilemma (p = 0.005), low education (p = 0.027), Non-AD baseline diagnosis (p = 0.027), and negative 11C-PIB PET result (p < 0.001). Only the last variable remained significant in the multivariate analysis (adjusted p = 0.038). Diagnostic dilemma decreased after PET from 15.7 to 7.1% (p = 0.11) and was associated with Non-AD diagnosis (p = 0.002) and negative 11C-PIB PET result (p = 0.003). Change in AD treatment after PET occurred in 45.7% of the patients. Conclusion: 18F-FDG and 11C-PIB PET had a significant clinical impact in terms of change in diagnosis and treatment in patients with dementia in a developing country, similar to that reported in high-income countries.
Efficacy and Safety of Pyrotinib Versus T-DM1 in HER2+ Metastatic Breast Cancer Patients Pre-Treated With Trastuzumab and a Taxane: A Bayesian Network Meta-Analysis

Purpose To compare the efficacy and safety between pyrotinib (Pyr) and trastuzumab emtansine (T-DM1) in pre-treated human epidermal growth factor receptor 2-positive (HER2+) metastatic breast cancer (MBC) patients. Methods A comprehensive literature search of the PubMed, EMBASE, and Web of Science was performed in August 2020. Randomized clinical trials comparing the efficacy and safety between different anti-HER2 regimens in patients pre-treated with trastuzumab (Tra) and a taxane in metastatic settings (≤second-line treatment) were included. A fixed effects network meta-analysis based on the Bayesian inferential framework was conducted for progression-free survival (PFS), overall survival (OS), overall response rate (ORR), and grade ≥3 adverse events (AEs). Values of surface under cumulative ranking probability curve (SUCRA) were calculated to offer a ranking of all regimens. Results Twelve studies with 4,353 subjects were identified. Nine regimens were included into the network: T-DM1, lapatinib-capecitabine (Lap-Cap), Tra-Cap, Cap, neratinib (Ner), pertuzumab (Per)-Tra-Cap, Pyr-Cap, atezolizumab (Ate)-T-DM1, and Ner-Cap. For PFS, Pyr-Cap was more favorable than T-DM1 (hazard ratio, 95% confidence interval: 0.77, 0.70–0.86), Lap-Cap (0.64, 0.59–0.69), Tra-Cap (0.63, 0.56–0.70), Cap (0.50, 0.45–0.56), Ner (0.59, 0.51–0.69), Per-Tra-Cap (0.68, 0.59–0.79), and Ner-Cap (0.72, 0.64–0.81). For OS, Pyr-Cap showed further improvement than Lap-Cap (hazard ratio, 95% confidence interval: 0.71, 0.52–0.99), Cap (0.68, 0.49–0.96), and Ner (0.65, 0.45–0.94). For ORR, Pyr-Cap was significantly superior than Cap (odds ratio, 95% confidence interval: 7.87, 1.22–56.51). No significant difference was observed in grade ≥3 AEs among all the regimens. Pyr-Cap ranked in the highest in PFS, OS, ORR, and grade ≥3 AEs (SUCRA = 99.4, 89.7, 86.4, and 89.3%). Conclusions These results indicate that Pyr may be more effective than T-DM1 in HER2+ MBC patients pre-treated with Tra and a taxane. However, it may be associated with more grade ≥3 AEs.
Polysomnographic Predictors of Treatment Response to Cognitive Behavioral Therapy for Insomnia in Participants With Co-morbid Insomnia and Sleep Apnea: Secondary Analysis of a Randomized Controlled Trial

Objective Co-morbid insomnia and sleep apnea (COMISA) is a common and debilitating condition that is more difficult to treat compared to insomnia or sleep apnea-alone. Emerging evidence suggests that cognitive behavioral therapy for insomnia (CBTi) is effective in patients with COMISA, however, those with more severe sleep apnea and evidence of greater objective sleep disturbance may be less responsive to CBTi. Polysomnographic sleep study data has been used to predict treatment response to CBTi in patients with insomnia-alone, but not in patients with COMISA. We used randomized controlled trial data to investigate polysomnographic predictors of insomnia improvement following CBTi, versus control in participants with COMISA. Methods One hundred and forty five participants with insomnia (ICSD-3) and sleep apnea [apnea-hypopnea index (AHI) ≥ 15] were randomized to CBTi (n = 72) or no-treatment control (n = 73). Mixed models were used to investigate the effect of pre-treatment AHI, sleep duration, and other traditional (AASM sleep macrostructure), and novel [quantitative electroencephalography (qEEG)] polysomnographic predictors of between-group changes in Insomnia Severity Index (ISI) scores from pre-treatment to post-treatment. Results Compared to control, CBTi was associated with greater ISI improvement among participants with; higher AHI (interaction p = 0.011), less wake after sleep onset (interaction p = 0.045), and less N3 sleep (interaction p = 0.005). No quantitative electroencephalographic, or other traditional polysomnographic variables predicted between-group ISI change (all p > 0.09). Discussion Among participants with COMISA, higher OSA severity predicted a greater treatment-response to CBTi, versus control. People with COMISA should be treated with CBTi, which is effective even in the presence of severe OSA and objective sleep disturbance.
Imaging the Subsurface Structure of Mount Agung in Bali (Indonesia) Using Volcano-Tectonic (VT) Earthquake Tomography

Local seismic tomography is a well-known and commonly used method for obtaining detailed information about the internal structure of volcanoes. The eruption of Mt. Agung in 2017 was a vital opportunity scientifically because it is the first eruption that had sufficient seismic observation networks to carry out local seismic tomography at this volcano. In this study, we investigate the subsurface structure of Mt. Agung in Bali, which is one of the highest risk volcanoes in Indonesia. We conducted travel-time tomography using P- and S-wave arrival times of volcano-tectonic (VT) events to determine the three-dimensional (3D) Vp, Vs, and Vp/Vs ratio structure beneath Mt. Agung. We used 1,926 VT events, with corresponding 9482-P and 8683-S wave arrival times recorded by eight seismic stations over an observation time spanning from October 18 to December 31, 2017. We obtain the hypocenter solution for VT events using the maximum likelihood estimation algorithm and use an optimum 1D velocity model as input for the Joint 3-D seismic tomographic inversion. Local earthquake tomography revealed five anomalous regions that are useful to describe the overall seismic activity around Mt. Agung. We interpret these anomalous regions qualitatively due to limited data resolution in this study. We have successfully localized a high Vp/Vs ratio (∼1.82), low Vs (−1.9%) and high Vp (+3.8%), within a low seismicity zone at depths between 2 and 5 km below the Mt. Agung summit, which may be related to a shallow magma reservoir. There is also an anomalous region between Mt. Agung and Batur with moderate to high Vp/Vs ratios (1.76–1.79) where most of the earthquakes recorded before the 2017 eruption originated. We interpret this anomaly to be related to the existence of sub-vertical dyke complex at depths between 8 and 14 km. The results of our study provide new insights into the subsurface structure of the magma plumbing system beneath Mt. Agung, which can be used to improve the quality of determining the location of the hypocenter and source modeling for future eruption forecasting.
Fostering Awareness Content Creation by Self-Determined Regulation

Prior studies have shown that without explicit instruction participants do not create complete awareness contents. Group awareness tools work implicitly. In this study it is investigated whether the construction of awareness contents can also be encouraged implicitly. Based on the self-determination theory it is expected that implicit regulation is more effective than explicit instruction. First results suggest that this is indeed the case.
An In-Depth Study of Correlated Failures in Production SSD-Based Data Centers

Flash-based solid-state drives (SSDs) are increasingly adopted as the mainstream storage media in modern data centers. However, little is known about how SSD failures in the field are correlated, both spatially and temporally. We argue that characterizing correlated failures of SSDs is critical, especially for guiding the design of redundancy protection for high storage reliability. We present an in-depth data-driven analysis on the correlated failures in the SSD-based data centers at Alibaba. We study nearly one million SSDs of 11 drive models based on a dataset of SMART logs, trouble tickets, physical locations, and applications. We show that correlated failures in the same node or rack are common, and study the possible impacting factors on those correlated failures. We also evaluate via trace-driven simulation how various redundancy schemes affect the storage reliability under correlated failures. To this end, we report 15 findings. Our dataset and source code are now released for public use.
Moving Beyond Case Studies: Using Social Network Analysis to Study Learning as Participation in Communities of Practice

We argue that social network analysis is a useful methodology to study learning under the participation metaphor and to extend scholarly knowledge on the process of legitimate peripheral participation in communities of practice. We emphasize that it is necessary to extend current research about the epistemic aspects of participative learning and to take also the social aspects into account; we first review work on legitimate peripheral participation and how research on this topic is currently conducted. Then we describe the theoretical and methodological foundations of social network analysis. Exemplary studies that use social network analysis including studies on legitimate peripheral participation are presented. We conclude that social network analysis can enable us to make inferences about participative learning across specific setting and individual communities. For the future, the challenge is to relate dynamically changing patterns of legitimate peripheral participation to emerging and changing epistemic practices of a community.
Predicting Next Touch Point In A Customer Journey: A Use Case In Telecommunication

Customer journey analysis is rapidly increasing in popularity, as it is essential for companies to understand how their customers think and behave. Recent studies investigate how customers traverse their journeys and how they can be improved for the future. However, those researches only focus on improving the process for future customers by analyzing the historical data. This research focuses on helping the current customer immediately, by analyzing if it is possible to predict what the customer will do next and accordingly take proactive steps. We propose a model to predict the customer's next contact type (touch point). At first we will analyze the customer journey data by applying process mining techniques. We will use these insights then together with the historical data of accumulated customer journeys to train several classifiers. The winning of those classifiers, namely XGBoost, is used to perform a prediction on a customer's journey while the journey is still active. We show on three different real datasets coming from interactions between a telecommunication company and its customers that we always beat a baseline classifier thanks to our thorough pre-processing of the data.