Miracle-dz/UniFAR-FTUs huggingface dataset:
Hepatocyte growth factor acts as a mitogen for equine satellite cells via protein kinase C δ directed signaling.

Hepatocyte growth factor (HGF) signals mediate mouse skeletal muscle stem cell, or satellite cell (SC), reentry into the cell cycle and myoblast proliferation. Because the athletic horse experiences exercise-induced muscle damage, the objective of the experiment was to determine the effect of HGF on equine SC (eqSC) bioactivity. Fresh isolates of adult eqSC were incubated with increasing concentrations of HGF and the initial time to DNA synthesis was measured. Media supplementation with HGF did not shorten (P > 0.05) the duration of G0/G1 transition suggesting the growth factor does not affect activation. Treatment with 25 ng/mL HGF increased (P < 0.05) eqSC proliferation that was coincident with phosphorylation of extracellular signal-regulated kinase (ERK)1/2 and AKT serine/threonine kinase 1 (AKT1). Chemical inhibition of the upstream effectors of ERK1/2 or AKT1 elicited no effect (P > 0.05) on HGF-mediated EdU incorporation. By contrast, treatment of eqSC with 2 µm Gö6983, a pan-protein kinase C (PKC) inhibitor, blocked (P < 0.05) HGF-initiated mitotic activity. Gene expression analysis revealed that eqSC express PKCα, -δ and -ε isoforms. Knockdown of PKCδ with a small interfering RNA (siRNA) prevented (P > 0.05) HGF-mediated EdU incorporation. The siPKCδ was specific to the kinase and did not affect (P > 0.05) expression of either PKCα or PKCε. Treatment of confluent eqSCs with 25 ng/mL HGF suppressed (P < 0.05) nuclear myogenin expression during the early stages of differentiation. These results demonstrate that HGF may not affect activation but can act as a mitogen and modest suppressor of differentiation.
Validity and reliability of textile system Sensoria for posturographic measurements.

OBJECTIVES Smart fabrics and interactive textiles are a relatively new area of research, with many potential applications in the field of biomedical engineering. The ability of smart textiles to interact with the body provides a novel means to sense the wearer's physiology and respond to the needs of the wearer. Physiological signals, such as heart rate, breathing rates, and activity levels, are useful indicators of health status. These signals can be measured by means of textile-based sensors integrated into smart clothing which has the ability to keep a digital record of the patient's physiological responses since his or her last clinical visit, allowing doctors to make a more accurate diagnosis. Similarly, in rehabilitation, it is difficult for therapists to ensure that patients are complying with prescribed exercises. Smart garments sensing body movements have the potential to guide wearers through their exercises, while also recording their individual movements and adherence to their prescribed programme. METHODS In this paper, we present the new wireless textile system Sensoria, with pressure sensing capability for static posturography. The gold standard for static posturography is currently the use of a pressure or force plate but, due to their very complexity and expensiveness, the applicability outside laboratories is extremely limited. RESULTS This paper focuses on the agreement between the static computed posturography assessed by means of a traditional stabilometric platform and the Sensoria system, in twenty subjects with Parkinson's Disease (PD). CONCLUSIONS Preliminary results showed a significant agreement between the two methods, suggesting a clinical use of Sensoria for low cost home care based balance impairment assessments.
Virtual Valcamonica: Collaborative Exploration of Prehistoric Petroglyphs and Their Surrounding Environment in Multi-User Virtual Reality

Abstract In this article, we present a novel, multi-user, virtual reality environment for the interactive, collaborative 3D analysis of large 3D scans and the technical advancements that were necessary to build it: a multi-view rendering system for large 3D point clouds, a suitable display infrastructure, and a suite of collaborative 3D interaction techniques. The cultural heritage site of Valcamonica in Italy with its large collection of prehistoric rock-art served as an exemplary use case for evaluation. The results show that our output-sensitive level-of-detail rendering system is capable of visualizing a 3D dataset with an aggregate size of more than 14 billion points at interactive frame rates. The system design in this exemplar application results from close exchange with a small group of potential users: archaeologists with expertise in rockart. The system allows them to explore the prehistoric art and its spatial context with highly realistic appearance. A set of dedicated interaction techniques was developed to facilitate collaborative visual analysis. A multi-display workspace supports the immediate comparison of geographically distributed artifacts. An expert review of the final demonstrator confirmed the potential for added value in rock-art research and the usability of our collaborative interaction techniques.
Poster: Designing Bug Detection Rules for Fewer False Alarms

One of the challenging issues of the existing static analysis tools is the high false alarm rate. To address the false alarm issue, we design bug detection rules by learning from a large number of real bugs from open-source projects from GitHub. Specifically, we build a framework that learns and refines bug detection rules for fewer false positives. Based on the framework, we implemented ten patterns, six of which are new ones to existing tools. To evaluate the framework, we implemented a static analysis tool, FeeFin, based on the framework with the ten bug detection rules and applied the tool for 1,800 open-source projects in GitHub. The 57 detected bugs by FeeFin has been confirmed by developers as true positives and 44 bugs out of the detected bugs were actually fixed.
Pedestrian Motion Learning Based Indoor WLAN Localization via Spatial Clustering

Applications on Location Based Services (LBSs) have driven the increasing demand for indoor localization technology. The conventional location fingerprinting based localization involves heavy time and labor cost for database construction, while the well-known Simultaneous Localization and Mapping (SLAM) technique requires assistant motion sensors as well as complicated data fusion algorithms. To solve the above problems, a new pedestrian motion learning based indoor Wireless Local Area Network (WLAN) localization approach is proposed in this paper to achieve satisfactory LBS without the demand for location calibration or motion sensors. First of all, the concept of pedestrian motion learning is adopted to construct users’ motion paths in the target environment. Second, based on the timestamp relation of the collected Received Signal Strength (RSS) sequences, the RSS segments are constructed to obtain the signal clusters with the newly defined high-dimensional linear distance. Third, the PageRank algorithm is performed to establish the hotspot mapping relations between the physical and signal spaces which are then used to localize the target. Finally, the experimental results show that the proposed approach can effectively estimate the target’s locations and analyze users’ motion preference in indoor environment.
Classification Performance Analysis Regarding Margin of Energy-Based Model

When you train an energy-based model, it is important to set a good margin. However, it is almost impossible to train a good margin using stochastic gradient descent (SDG). Because the margin will saturate to zero while minimizing the cost function. For that reason, we usually set the margin to a non-trainable scalar to penalize offending answers linearly to more apart than a certain distance. A good performance setting relates to the length of margin and dimension to which the feature being mapped. In this paper, we will show that a large margin does not always lead to a better performance and affirm the well-tuned margin can achieve better results.
Detecting Dead Weights and Units in Neural Networks

Deep Neural Networks are highly over-parameterized and the size of the neural networks can be reduced significantly after training without any decrease in performance. One can clearly see this phenomenon in a wide range of architectures trained for various problems. Weight/channel pruning, distillation, quantization, matrix factorization are some of the main methods one can use to remove the redundancy to come up with smaller and faster models. This work starts with a short informative chapter, where we motivate the pruning idea and provide the necessary notation. In the second chapter, we compare various saliency scores in the context of parameter pruning. Using the insights obtained from this comparison and stating the problems it brings we motivate why pruning units instead of the individual parameters might be a better idea. We propose some set of definitions to quantify and analyze units that don't learn and create any useful information. We propose an efficient way for detecting dead units and use it to select which units to prune. We get 5x model size reduction through unit-wise pruning on MNIST.
Neurological impairments in hypoxic neonates and lactate levels

ABSTRACT Introduction: Metabolic acidosis with increasing lactate concentration develops due to the lack of oxygen in the tissues. Objectives: The effect of lactic acidosis on neurological development in the first year of life. Materials and Methods: Our study included 50 newborns with perinatal hypoxia requiring oxygen therapy and 50 healthy newborns. pH, pCO2, pO2, base excess (BE) and lactates from arterialized capillary blood were determined in both groups of newborns, in the first and second hours after birth, and neurological development in the first year of life was estimated. Results: pH, pCO2, pO2, BE and lactates differed significantly between the groups in the first and second hours after birth p < 0.01. Hypotonia was recorded in 20/50 cases and hypertonia was recorded in 10/50 cases in the first year of life. Conclusion: Lactate concentration may be an indicator of neurological damage in neonates with perinatal hypoxia.
On Derivation Languages of a Class of Splicing Systems

Derivation languages are language theoretical tools that describe halting derivation processes of a generating device. We consider two types of derivation languages, namely Szilard and control languages for splicing systems where iterated splicing is done in non-uniform way defined by Mitrana, Petre and Rogojin in 2010. The families of Szilard (rules and labels are mapped in a one to one manner) and control (more than one rule can share the same label) languages generated by splicing systems of this type are then compared with the family of languages in the Chomsky hierarchy. We show that context-free languages can be generated as Szilard and control languages and any non-empty context-free language is a morphic image of the Szilard language of this type of system with finite set of rules and axioms. Moreover, we show that these systems with finite set of axioms and regular set of rules are capable of generating any recursively enumerable language as a control language.
HFMV: Hybridizing Formal Methods and Machine Learning for Verification of Analog and Mixed-Signal Circuits

With increasing design complexity and robustness requirement, analog and mixed-signal (AMS) verification manifests itself as a key bottleneck. While formal methods and machine learning have been proposed for AMS verification, these two techniques suffer from their own limitations, with the former being specifically limited by scalability and the latter by the inherent uncertainty in learning-based models. We present a new direction in AMS verification by proposing a hybrid formal/machine-learning verification technique (HFMV) to combine the best of the two worlds. HFMV adds formalism on the top of a probabilistic learning model while providing a sense of coverage for extremely rare failure detection. HFMV intelligently and iteratively reduces uncertainty of the learning model by a proposed formally-guided active learning strategy and discovers potential rare failure regions in complex high-dimensional parameter spaces. It leads to reliable failure prediction in the case of a failing circuit, or a high-confidence pass decision in the case of a good circuit. We demonstrate that HFMV is able to employ a modest amount of data to identify hard-to-find rare failures which are completely missed by state-of-the-art sampling methods even with high volume sampling data.
Transglutaminase type 2 in the regulation of proteostasis

Abstract The maintenance of protein homeostasis (proteostasis) is a fundamental aspect of cell physiology that is essential for the survival of organisms under a variety of environmental and/or intracellular stress conditions. Acute and/or persistent stress exceeding the capacity of the intracellular homeostatic systems results in protein aggregation and/or damaged organelles that leads to pathological cellular states often resulting in cell death. These events are continuously suppressed by a complex macromolecular machinery that uses different intracellular pathways to maintain the proteome integrity in the various subcellular compartments ensuring a healthy cellular life span. Recent findings have highlighted the role of the multifunctional enzyme type 2 transglutaminase (TG2) as a key player in the regulation of intracellular pathways, such as autophagy/mitophagy, exosomes formation and chaperones function, which form the basis of proteostasis regulation under conditions of cellular stress. Here, we review the role of TG2 in these stress response pathways and how its various enzymatic activities might contributes to the proteostasis control.
Lactobacillus bambusae sp. nov., isolated from traditional fermented ma bamboo shoots in Taiwan.

A Gram-stain-positive strain, BS-W1T, was isolated from a traditional fermented ma bamboo shoots (Dendrocalamus latiflorus Munro) product of Taiwan. It was rod-shaped, non-motile, non-haemolytic, asporogenous, facultatively anaerobic, heterofermentative and did not exhibit catalase or oxidase activities. Comparative analysis of 16S rRNA, pheS, rpoA and gyrB gene sequences demonstrated that the novel strain BS-W1T was a member of the genus Lactobacillus. On the basis of 16S RNA gene sequence similarity, the type strains of Lactobacillus oryzae (94.4 % similarity), Lactobacillus acidifarinae (93.8 %), Lactobacillus namurensis (93.7 %) and Lactobacillus zymae (93.7 %) were the closest neighbours to strain BS-W1T. The pheS, rpoA and gyrB gene sequence similarities of strain BS-W1T to closely related these species were less than 80.2 %. DNA-DNA reassociation values with these type strains were 21.0-33.8 %. The DNA G+C content was 46.6 mol%. The average nucleotide identity values between BS-W1T and the closest relatives were lower than 70 %. Phenotypic and genotypic features demonstrated that the strain represents a novel species of the genus Lactobacillus, for which the name Lactobacillus bambusae sp. nov. is proposed. The type strain is BS-W1T (=BCRC 80970T=NBRC 112377T).
NNSim: Fast Performance Estimation Based on Sampled Simulation of GPGPU Kernels for Neural Networks

Existent GPU simulators are too slow to use for neural networks implemented in GPUs. For fast performance estimation, we propose a novel hybrid method of analytical performance modeling and sampled simulation of GPUs. By taking full advantage of repeated computation of neural networks, three sampling techniques are devised: Inter-Kernel sampling, Intra-Kernel sampling, and Streaming Multiprocessor sampling. The key technique is to estimate the average IPC through sampled simulation, considering the effect of the warp scheduler and memory access contention. Compared with GPGPU-Sim, the proposed technique reduces the simulation time by up to 450 times with less than 5.0% of accuracy loss.
Family outreach and empowerment program: Health promotion model for medical students

INTRODUCTION: Providing health promotion and communication learning opportunities for medical students practising their theoretical information into action, addressing the health problems, and trying to find suitable and feasible interventions is very much neededto learn and experience directly about healthrelated behavior in the community. Health promotion practice of PRECEDE-PROCEED model, is used as the foundation of this Family Outreach and Empowerment Program (FOEP). This study aims to implement and evaluate FOEP as an effective health promotion learning model for undergraduate medical students. METHODS: Two hundred and forty-four medical students joined the program for the whole weeks. The reports were evaluated to assess their activities throughout the process. Evaluation was done using the assessment checklists based on PRECEDE-PROCEED framework. Data collected from the assessment will be processed and analyzed using the IBM SPSS Statistics 22 program. RESULTS: Two hundred and forty-four report's has been evaluated, the report's scores in the category very good is 136, good 64, enough 26, bad 12 and very bad 6. The main problem in FOEP is behavior (37,6%) and the most alternate intervention choosen is education (232). DISCUSSION: The most intervention used by students for FOEP was education because it has been proven that can demonstrate feasibility, effectivity, increase knowledge control, and self-efficacy among disease. CONCLUSION: Student's reports was already satisfied.
Strategy for Continuous Testing in iDevOps

This tutorial presentation describes the state-of-the-art in the emerging area of continuous testing in a DevOps context. It specifies the building blocks of a strategy for continuous testing in industrial-grade DevOps projects (iDevOps) and shares our motivations, achievements, and experiences on our journey to transform testing into the iDevOps world.
A Diverse Lipid Antigen–Specific TCR Repertoire Is Clonally Expanded during Active Tuberculosis

Human T cells that recognize lipid Ags presented by highly conserved CD1 proteins often express semi-invariant TCRs, but the true diversity of lipid Ag–specific TCRs remains unknown. We use CD1b tetramers and high-throughput immunosequencing to analyze thousands of TCRs from ex vivo–sorted or in vitro–expanded T cells specific for the mycobacterial lipid Ag, glucose monomycolate. Our results reveal a surprisingly diverse repertoire resulting from editing of germline-encoded gene rearrangements analogous to MHC-restricted TCRs. We used a distance-based metric (TCRDist) to show how this diverse TCR repertoire builds upon previously reported conserved motifs by including subject-specific TCRs. In a South African cohort, we show that TCRDist can identify clonal expansion of diverse glucose monomycolate–specific TCRs and accurately distinguish patients with active tuberculosis from control subjects. These data suggest that similar mechanisms govern the selection and expansion of peptide and lipid Ag–specific T cells despite the nonpolymorphic nature of CD1.
Poster: Answering the Requirements Traceability Questions

To understand requirements traceability in practice, we present a preliminary study of identifying questions from requirements repositories and examining their answering status. Investigating four open-source projects results in 733 requirements questions, among which 43% were answered successfully, 35% were answered unsuccessfully, and 22% were not answered at all. We evaluate the accuracy of using a state-of-the-art natural language processing tool to identify the requirements questions and illuminate automated ways to classify their answering status.
S2FA: An Accelerator Automation Framework for Heterogeneous Computing in Datacenters

Big data analytics using the JVM-based MapReduce framework has become a popular approach to address the explosive growth of data sizes. Adopting FPGAs in datacenters as accelerators to improve performance and energy efficiency also attracts increasing attention. However, the integration of FPGAs into such JVM-based frameworks raises the challenge of poor programmability. Programmers must not only rewrite Java/Scala programs to C/C++ or OpenCL, but, to achieve high performance, they must also take into consideration the intricacies of FPGAs. To address this challenge, we present S2FA (Spark-to-FPGA-Accelerator), an automation framework that generates FPGA accelerator designs from Apache Spark programs written in Scala. S2FA bridges the semantic gap between object-oriented languages and HLS C while achieving high performance using learning-based design space exploration. Evaluation results show that our generated FPGA designs achieve up to 49.9× performance improvement for several machine learning applications compared to their corresponding implementations on the JVM.
Mind–Body Interactions and Mindfulness Meditation in Diabetes

Abstract Diabetes is associated with significant psychological distress. It is, therefore, important to ensure the physical and emotional as well as psychosocial wellbeing of individuals living with diabetes. Meditation-based strategies have been evaluated for their complementary role in several chronic disorders including depression, anxiety, obesity, hypertension, cardiovascular disease and diabetes. The practice of meditation is associated with reduction in stress and negative emotions and improvements in patient attitude, health-related behaviour and coping skills. There is increased parasympathetic activity with reduction in sympathetic vascular tone, stress hormones and inflammatory markers. Additionally, several studies evaluated the role of mindfulness-based stress reduction in diabetic individuals and demonstrated modest improvements in body weight, glycaemic control and blood pressure. Thus, mindfulness meditation-based intervention can lead to improvements across all domains of holistic care – biological, psychological and social. Though most of these studies have been of short duration and included small numbers of patients, meditation strategies can be useful adjunctive techniques to lifestyle modification and pharmacological management of diabetes and help improve patient wellbeing.