Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavioral Outcomes Following Brain–Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial

Stroke is a leading cause of persistent upper extremity (UE) motor disability in adults. Brain–computer interface (BCI) intervention has demonstrated potential as a motor rehabilitation strategy for stroke survivors. This sub-analysis of ongoing clinical trial (NCT02098265) examines rehabilitative efficacy of this BCI design and seeks to identify stroke participant characteristics associated with behavioral improvement. Stroke participants (n = 21) with UE impairment were assessed using Action Research Arm Test (ARAT) and measures of function. Nine participants completed three assessments during the experimental BCI intervention period and at 1-month follow-up. Twelve other participants first completed three assessments over a parallel time-matched control period and then crossed over into the BCI intervention condition 1-month later. Participants who realized positive change (≥1 point) in total ARAT performance of the stroke affected UE between the first and third assessments of the intervention period were dichotomized as “responders” (<1 = “non-responders”) and similarly analyzed. Of the 14 participants with room for ARAT improvement, 64% (9/14) showed some positive change at completion and approximately 43% (6/14) of the participants had changes of minimal detectable change (MDC = 3 pts) or minimally clinical important difference (MCID = 5.7 points). Participants with room for improvement in the primary outcome measure made significant mean gains in ARATtotal score at completion (ΔARATtotal = 2, p = 0.028) and 1-month follow-up (ΔARATtotal = 3.4, p = 0.0010), controlling for severity, gender, chronicity, and concordance. Secondary outcome measures, SISmobility, SISadl, SISstrength, and 9HPTaffected, also showed significant improvement over time during intervention. Participants in intervention through follow-up showed a significantly increased improvement rate in SISstrength compared to controls (p = 0.0117), controlling for severity, chronicity, gender, as well as the individual effects of time and intervention type. Participants who best responded to BCI intervention, as evaluated by ARAT score improvement, showed significantly increased outcome values through completion and follow-up for SISmobility (p = 0.0002, p = 0.002) and SISstrength (p = 0.04995, p = 0.0483). These findings may suggest possible secondary outcome measure patterns indicative of increased improvement resulting from this BCI intervention regimen as well as demonstrating primary efficacy of this BCI design for treatment of UE impairment in stroke survivors. Clinical Trial Registration: ClinicalTrials.gov, NCT02098265.
A Virtual Conversational Agent for Teens with Autism: Experimental Results and Design Lessons

We present the design of an online social skills development interface for teenagers with autism spectrum disorder (ASD), who often lack access to social skills training. The interface is intended to enable private conversation practice anywhere, anytime using a browser. Users converse informally with an on-screen persona, receiving feedback on nonverbal cues in real-time, and summary feedback. The prototype was developed in consultation with an expert UX designer, two psychologists, and a pediatrician. Using the data from 47 individuals, feedback and dialogue generation were automated using the hidden Markov model and a schema-driven dialogue manager capable of handling multi-topic conversations. We conducted a study with nine high-functioning ASD teenagers, and through thematic analysis of post-experiment interviews, identified several key design considerations, notably: 1) Users should be fully briefed at the outset about the purpose and limitations of the system, to avoid unrealistic expectations. 2) An interface should incorporate positive acknowledgment of behavior change. 3) Realistic appearance of a virtual agent and responsiveness are important in engaging users. 4) Conversation personalization, for instance in prompting laconic users for more input and reciprocal questions, would help the teenagers engage for longer terms and increase the system's utility.
A Translational Invariant Sar-Atr Method Based on Convolutional Neural Networks

A SAR-ATR method with favorable performance on translational invariance is proposed in this paper. Nowadays, supervised learning is the main way to realize image processing and target detection, but performance of most trained models will be greatly influenced by distribution of targets in training samples. In some recent researches of SAR-ATR, models trained on MSTAR dataset apply methods of data augmentation to increase randomness of target position in training samples, so that the models could have a certain capability of translational invariance. The method that we proposed based on convolutional neural network (CNN) doesn't need operation of data augmentation at all, and the comparison experiments based on manually shifted target slices proved that our model performed better in detecting and recognizing shifted targets. Experiments show that the proposed method can accurately locate position of different kinds of shifted targets and realize detection and recognition correctly.
Transcriptional Landscape of PARs in Epithelial Malignancies

G protein-coupled receptors (GPCRs), the largest family of cell receptors, act as important regulators of diverse signaling pathways. Our understanding of the impact of GPCRs in tumors is emerging, yet there is no therapeutic platform based on GPCR driver genes. As cancer progresses, it disrupts normal epithelial organization and maintains the cells outside their normal niche. The dynamic and flexible microenvironment of a tumor contains both soluble and matrix-immobilized proteases that contribute to the process of cancer advancement. An example is the activation of cell surface protease-activated receptors (PARs). Mammalian PARs are a subgroup of GPCRs that form a family of four members, PAR1–4, which are uniquely activated by proteases found in the microenvironment. PAR1 and PAR2 play central roles in tumor biology, and PAR3 acts as a coreceptor. The significance of PAR4 in neoplasia is just beginning to emerge. PAR1 has been shown to be overexpressed in malignant epithelia, in direct correlation with tumor aggressiveness, but there is no expression in normal epithelium. In this review, the involvement of key transcription factors such as Egr1, p53, Twist, AP2, and Sp1 that control PAR1 expression levels specifically, as well as hormone transcriptional regulation by both estrogen receptors (ER) and androgen receptors (AR) are discussed. The cloning of the human protease-activated receptor 2; Par2 (hPar2) promoter region and transcriptional regulation of estrogen (E2) via binding of the E2–ER complex to estrogen response elements (ERE) are shown. In addition, evidence that TEA domain 4 (TEAD4) motifs are present within the hPar2 promoter is presented since the YAP oncogene, which plays a central part in tumor etiology, acts via the TEAD4 transcription factor. As of now, no information is available on regulation of the hPar3 promoter. With regard to hPar4, only data showing CpG methylation promoter regulation is available. Characterization of the PAR transcriptional landscape may identify powerful targets for cancer therapies.
Knowledge pricing structures on MOOC platform - A use case analysis on edX

University courses provided online in form of MOOCs (Massive Open Online Courses) are gaining increased attention, yet their pricing structure is rarely studied. MOOCs can be treated as knowledge products, and MOOC platforms, therefore, become the marketplace for market-participants to trade those products. A functional knowledge market cannot be established without an appropriate and reliable pricing model, but so far, there have only been a very limited number of studies focusing on the pricing strategies in MOOCs. This study fills this gap by providing a systematic price analysis on one of the largest non-for-profit MOOC platforms, edx.org. In doing so, we establish a model to explain the price differences among different courses. This study can act as a well-grounded starting-point for future MOOC-pricing studies and knowledge products' valuation research.
Gene and Protein Expression Profile of Selected Molecular Targets Mediating Electrophysiological Function in Pgc-1α Deficient Murine Atria

Increases in the prevalence of obesity, insulin resistance, and metabolic syndrome has led to the increase of atrial fibrillation (AF) cases in the developed world. These AF risk factors are associated with mitochondrial dysfunction, previously modelled using peroxisome proliferator activated receptor-γ (PPARγ) coactivator-1 (Pgc-1)-deficient murine cardiac models. We explored gene and protein expression profiles of selected molecular targets related to electrophysiological function in murine Pgc-1α−/− atria. qPCR analysis surveyed genes related to Na+-K+-ATPase, K+ conductance, hyperpolarisation-activated cyclic nucleotide-gated (Hcn), Na+ channels, Ca2+ channels, and indicators for adrenergic and cholinergic receptor modulation. Western blot analysis for molecular targets specific to conduction velocity (Nav1.5 channel and gap junctions) was performed. Transcription profiles revealed downregulation of molecules related to Na+-K+-ATPase transport, Hcn-dependent pacemaker function, Na+ channel-dependent action potential activation and propagation, Ca2+ current generation, calsequestrin-2 dependent Ca2+ homeostasis, and adrenergic α1D dependent protection from hypertrophic change. Nav1.5 channel protein expression but not gap junction expression was reduced in Pgc-1α−/− atria compared to WT. Nav1.5 reduction reflects corresponding reduction in its gene expression profile. These changes, as well as the underlying Pgc-1α−/− alteration, suggest potential pharmacological targets directed towards either upstream PGC-1 signalling mechanisms or downstream ion channel changes.
Semantic segmentation-based traffic sign detection and recognition using deep learning techniques

We present a method for detecting and classifying traffic signs based on two deep neural network architectures. A Fully Convolutional Network (FCN) - based semantic segmentation model is modified to extract traffic sign regions of interest. These regions are further passed to a Convolutional Neural Network (CNN) for traffic sign classification. We propose a novel CNN architecture for the classification step. In evaluating our approach, we contrast the efficiency and the robustness of the deep learning image segmentation approach with classical image processing filters traditionally applied for traffic sign detection. We also show the effectiveness of our CNN-based recognition method by integrating it in our system.
The Relationship between Generalized and Abdominal Obesity with Diabetic Kidney Disease in Type 2 Diabetes: A Multiethnic Asian Study and Meta-Analysis

This study examined the associations of body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR) and waist-height ratio (WHtR) with diabetic kidney disease (DKD) in a clinical sample of Asian patients with type 2 diabetes (T2DM); substantiated with a meta-analysis of the above associations. We recruited 405 patients with T2DM (mean (standard deviation (SD)) age: 58 (7.5) years; 277 (68.4%) male; 203 (50.1%) with DKD) from a tertiary care centre in Singapore. DKD was defined as urinary albumin-creatinine ratio >3.3 mg/mmoL and/or estimated glomerular filtration rate <60 mL/min/1.73 m2. All exposures were analysed continuously and categorically (World Health Organization cut-points for BMI and WC; median for WHR and WHtR) with DKD using stepwise logistic regression models adjusted for traditional risk factors. Additionally, we synthesized the pooled odds ratio of 18 studies (N = 19,755) in a meta-analysis of the above relationships in T2DM. We found that overweight and obese persons (categorized using BMI) were more likely to have DKD compared to under/normal weight individuals, while no associations were found for abdominal obesity exposures. In meta-analyses however, all obesity parameters were associated with increased odds of DKD. The discordance in our abdominal obesity findings compared to the pooled analyses warrants further validation via longitudinal cohorts.
Long-read sequencing technology indicates genome-wide effects of non-B DNA on polymerization speed and error rate

DNA conformation may deviate from the classical B-form in ~13% of the human genome. Non-B DNA regulates many cellular processes; however, its effects on DNA polymerization speed and accuracy have not been investigated genome-wide. Such an inquiry is critical for understanding neurological diseases and cancer genome instability. Here we present the first simultaneous examination of DNA polymerization kinetics and errors in the human genome sequenced with Single-Molecule-Real-Time technology. We show that polymerization speed differs between non-B and B-DNA: it decelerates at G-quadruplexes and fluctuates periodically at disease-causing tandem repeats. Analyzing polymerization kinetics profiles, we predict and validate experimentally non-B DNA formation for a novel motif. We demonstrate that several non-B motifs affect sequencing errors (e.g., G-quadruplexes increase error rates) and that sequencing errors are positively associated with polymerase slowdown. Finally, we show that highly divergent G4 motifs have pronounced polymerization slowdown and high sequencing error rates, suggesting similar mechanisms for sequencing errors and germline mutations.
Water Across Synthetic Aperture Radar Data (WASARD): SAR Water Body Classification for the Open Data Cube

The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australia's WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS.
Visual Attention is Beyond One Single Saliency Map

Of later years, numerous bottom-up attention models have been proposed on different assumptions. However, the produced saliency maps may be different from each other even from the same input image. We also observe that human fixation map varies across time greatly. When people freely view an image, they tend to allocate attention at salient regions of large scale at first, and then search more and more detailed regions. In this paper, we argue that, for one input image visual attention cannot be described by only one single saliency map, and this mechanism should be modeled as a dynamic process. Under the frequency domain paradigm, we proposed a global inhibition model to mimic this process by suppressing the {\it non-saliency} in the input image; we also show that the dynamic process is influenced by one parameter in the frequency domain. Experiments illustrate that the proposed model is capable of predicting human dynamic fixation distribution.
Regulation of Connexin32 by ephrin receptors and T-cell protein-tyrosine phosphatase

Gap junctions are intercellular conduits that permit the passage of ions, small metabolites, and signaling molecules between cells. Connexin32 (Cx32) is a major gap junction protein in the liver and brain. Phosphorylation is integral to regulating connexin assembly, degradation, and electrical and metabolic coupling, as well as to interactions with molecular partners. Cx32 contains two intracellular tyrosine residues, and tyrosine phosphorylation of Cx32 has been detected after activation of the epidermal growth factor receptor; however, the specific tyrosine residue and the functional implication of this phosphorylation remain unknown. To address the limited available information on Cx32 regulation by tyrosine kinases, here we used the Cx32 C-terminal (CT) domain in an in vitro kinase-screening assay, which identified ephrin (Eph) receptor family members as tyrosine kinases that phosphorylate Cx32. We found that EphB1 and EphA1 phosphorylate the Cx32CT domain residue Tyr243. Unlike for Cx43, the tyrosine phosphorylation of the Cx32CT increased gap junction intercellular communication. We also demonstrated that T-cell protein-tyrosine phosphatase dephosphorylates pTyr243. The data presented above along with additional examples throughout the literature of gap junction regulation by kinases, indicate that one cannot extrapolate the effect of a kinase on one connexin to another.
Impact of IS/IT Investments on Firm Performance in the Indian Context: A Plan of Inquiry

Understanding the relationship between investments and firm performance has long been of interest to researchers. Studies regarding impact of information system/information technology (IS/IT) investments on firm performance so far report mostly mixed results which motivates the need for additional exploration on the issue. Similar type of inquiries is anyway under-represented in emerging markets like India. This study attempts to assess the impact of IS/IT investment made by Indian firms over the last one and half decades on their performance. Our proposed examination based on secondary data uses a combination of univariate and multivariate analysis to explore relative performance of investing firms in comparison to comparable firms not making an investment. The final results offer possibilities of both theoretical and practical contributions which are also delineated.
Visualize the Organizational Cultures in a Japanese Manufacturing Company with Multi-Dimensional Scaling-How Do We Change Corporate Culture?

There are many companies which try to change their organizational culture to adjust with their business environment and to win market, but most of them seem struggling with the initiative due to the invisibility of organizational culture. We proposed a method to visualize organizational culture, using multi-dimensional scaling analysis with 39 keywords extracted from Cameron and Quinn (2006). Applying the method to a Japanese manufacturing company, we tried to visualize a corporate-level organizational culture and 10 department-level organizational cultures. The results and the interpretation of them were evaluated as good by three employees in the company since they were described the cultures well. Thus it could be an effective method to visualize organizational culture.
High Throughput Image Codec for High-Resolution Satellite Images

The growth in the use of satellite images has generated the need for their fast compression, processing, and distribution. JPEG2000 is a widespread standard for the compression and transmission of such images once they are in the ground. Despite its advanced features and excellent coding performance, JPEG2000 demands significant computational resources. This paper introduces a wavelet-based codec that uses the JPEG2000 framework, but replaces its most computationally demanding coding stage by a highly parallel engine. When executed in Graphics Processing Units to code high-resolution satellite images, the proposed codec achieves speed-ups of up to $8\times$ when compared to the fastest implementation of JPEG2000 executed in a multi-core platform.
Lysozyme-rich milk mitigates effects of malnutrition in a pig model of malnutrition and infection

Abstract Malnutrition remains a leading contributor to the morbidity and mortality of children under the age of 5 years and can weaken the immune system and increase the severity of concurrent infections. Livestock milk with the protective properties of human milk is a potential therapeutic to modulate intestinal microbiota and improve outcomes. The aim of this study was to develop an infection model of childhood malnutrition in the pig to investigate the clinical, intestinal and microbiota changes associated with malnutrition and enterotoxigenic Escherichia coli (ETEC) infection and to test the ability of goat milk and milk from genetically engineered goats expressing the antimicrobial human lysozyme (hLZ) milk to mitigate these effects. Pigs were weaned onto a protein–energy-restricted diet and after 3 weeks were supplemented daily with goat, hLZ or no milk for a further 2 weeks and then challenged with ETEC. The restricted diet enriched faecal microbiota in Proteobacteria as seen in stunted children. Before infection, hLZ milk supplementation improved barrier function and villous height to a greater extent than goat milk. Both goat and hLZ milk enriched for taxa (Ruminococcaceae) associated with weight gain. Post-ETEC infection, pigs supplemented with hLZ milk weighed more, had improved Z-scores, longer villi and showed more stable bacterial populations during ETEC challenge than both the goat and no milk groups. This model of childhood disease was developed to test the confounding effects of malnutrition and infection and demonstrated the potential use of hLZ goat milk to mitigate the impacts of malnutrition and infection.
Epidemiology of Road Traffic Injuries among Elderly People; A Systematic Review and Meta-Analysis

Objective: To systematically review the epidemiological patterns and interventions for prevention of road traffic injuries (RTIs) among elderly. Methods: Searching keywords including: accident, trauma, road injury, road traffic injuries, aging, old, elder, strategy, intervention, road traffic crash prevention and traffic accident in databases including, Google scholar, SID, IranMedex, PubMed and Scopus. English and non-Persian articles, articles presented in congresses, articles that considered elderly people to have age under than 60 years were excluded. The reporting quality of articles was assessed by two experts using Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) check list. Results: RTIs compromised 23.6% of total injuries among elderly. The most frequent injuries were about car accidents (51.4%). Pedestrian injuries composed 48.1% of the RTIs. Head and neck (32.1%) were most injured body parts. There was a significant difference between elderly and non-elderly people in terms of RTIs associated mortality (Odd=2.57 [1.2-5.4 CI 95%]). Overall 25 main domains of intervention and 73 subordinate domains were extracted in five categories (human, road and environment, tools and cars, medical, legal and political issues). Conclusion: According to the notable prevalence and fatality of RTIs, lack of sufficient studies and valid evidence of the present study can provide an appropriate evidence for better interventions for RTIs prevention among elderly.