Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of Elevated Fibrinogen in Burn-Induced Mitochondrial Dysfunction: Protective Effects of Glycyrrhizin

Introduction: Skeletal muscle wasting and weakness with mitochondrial dysfunction (MD) are major pathological problems in burn injury (BI) patients. Fibrinogen levels elevated in plasma is an accepted risk factor for poor prognosis in many human diseases, and is also designated one of damage-associated molecular pattern (DAMPs) proteins. The roles of upregulated fibrinogen on muscle changes of critical illness including BI are unknown. The hypothesis tested was that BI-upregulated fibrinogen plays a pivotal role in the inflammatory responses and MD in muscles, and that DAMPs inhibitor, glycyrrhizin mitigates the muscle changes. Methods: After third degree BI to mice, fibrinogen levels in the plasma and at skeletal muscles were compared between BI and sham-burn (SB) mice. Fibrinogen effects on inflammatory responses and mitochondrial membrane potential (MMP) loss were analyzed in C2C12 myotubes. In addition to survival, the anti-inflammatory and mitochondrial protective effects of glycyrrhizin were tested using in vivo microscopy of skeletal muscles of BI and SB mice. Results: Fibrinogen in plasma and its extravasation to muscles significantly increased in BI versus SB mice. Fibrinogen applied to myotubes evoked inflammatory responses (increased MCP-1 and TNF-&agr;; 32.6 and 3.9-fold, respectively) and reduced MMP; these changes were ameliorated by glycyrrhizin treatment. In vivo MMP loss and superoxide production in skeletal muscles of BI mice were significantly attenuated by glycyrrhizin treatment, together with improvement of BI survival rate. Conclusions: Inflammatory responses and MMP loss in myotubes induced by fibrinogen were reversed by glycyrrhizin. Anti-inflammatory and mitochondrial protective effect of glycyrrhizin in vivo leads to amelioration of muscle MD and improvement of BI survival rate.
Does Touch Matter?: The Effects of Haptic Visualization on Human Performance, Behavior and Perception

Chang S. Nam1, Paul Richard2, Takehiko Yamaguchi3, and Sangwoo Bahn4 1Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, North Carolina, USA 2Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d’Angers, Angers, France 3Department of Applied Electronics, Tokyo University of Science, Tokyo, Japan 4Department of Industrial and Management Engineering, Myongji University, Yongin, Korea
Utility Decomposition with Deep Corrections for Scalable Planning under Uncertainty

Decomposition methods have been proposed in the past to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility decomposition can be used where each individual entity is considered independently. The individual utility functions are then combined in real time to solve the global problem. Although these techniques can perform well empirically, they sacrifice optimality. This paper proposes an approach inspired from multi-fidelity optimization to learn a correction term with a neural network representation. Learning this correction can significantly improve performance. We demonstrate this approach on a pedestrian avoidance problem for autonomous driving. By leveraging strategies to avoid a single pedestrian, the decomposition method can scale to avoid multiple pedestrians. We verify empirically that the proposed correction method leads to a significant improvement over the decomposition method alone and outperforms a policy trained on the full scale problem without utility decomposition.
Improving Hypernymy Extraction with Distributional Semantic Classes

In this paper, we show for the first time how distributionally-induced semantic classes can be helpful for extraction of hypernyms. We present a method for (1) inducing sense-aware semantic classes using distributional semantics and (2) using these induced semantic classes for filtering noisy hypernymy relations. Denoising of hypernyms is performed by labeling each semantic class with its hypernyms. On one hand, this allows us to filter out wrong extractions using the global structure of the distributionally similar senses. On the other hand, we infer missing hypernyms via label propagation to cluster terms. We conduct a large-scale crowdsourcing study showing that processing of automatically extracted hypernyms using our approach improves the quality of the hypernymy extraction both in terms of precision and recall. Furthermore, we show the utility of our method in the domain taxonomy induction task, achieving the state-of-the-art results on a benchmarking dataset.
Time collection and storage conditions of lipid profile

The stability of samples is crucial for getting reliable concentrations of many analytes, including lipid profile. Thus, the goal of this study was to analyze lipid profile under different storage and temperature conditions. This was a prospective study with 809 patients of both genders. Total cholesterol, triglycerides, high-density lipoprotein cholesterol, low density lipoprotein cholesterol and non-high-density lipoprotein were measured within 1 h from collection at room temperature, after 2-3 h of refrigeration (8°C) and after 4-5 h at room temperature. The processing time and storage conditions did not affect the analytes measured. These findings are important for multicenter studies, because of the difficulties related to centrifugation and freezing of samples immediately after collection.
A randomized controlled trial on the effect of behavioral strategies for adherence to oral antidiabetic drugs: study protocol

Background: Non-adherence to oral antidiabetics drugs (OADs) has been a common problem and may contribute to poor glycemic control. Aim: To describe an experimental study protocol that aims at implementing and evaluating the effect of the “action planning and coping planning” interventions on medication adherence to OADs in patients with type 2 diabetes mellitus (T2DM) in follow-up at primary care services. Design: A randomized controlled trial. Methods: Two groups (intervention and control) will be followed over a period of 105 days. The intervention group will receive a combination of the “action planning” and “coping planning” intervention strategies. There will be in-person meetings and phone calls to reinforce the intervention. The control group will receive the usual care from the health unit. Conclusions: It is hoped that this study will help health professionals to improve their approach with patients who have T2DM in relation to medication adherence.
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization

We analyze stochastic gradient algorithms for optimizing nonconvex, nonsmooth finite-sum problems. In particular, the objective function is given by the summation of a differentiable (possibly nonconvex) component, together with a possibly non-differentiable but convex component. We propose a proximal stochastic gradient algorithm based on variance reduction, called ProxSVRG+. The algorithm is a slight variant of the ProxSVRG algorithm [Reddi et al., 2016b]. Our main contribution lies in the analysis of ProxSVRG+. It recovers several existing convergence results (in terms of the number of stochastic gradient oracle calls and proximal operations), and improves/generalizes some others. In particular, ProxSVRG+ generalizes the best results given by the SCSG algorithm, recently proposed by [Lei et al., 2017] for the smooth nonconvex case. ProxSVRG+ is more straightforward than SCSG and yields simpler analysis. Moreover, ProxSVRG+ outperforms the deterministic proximal gradient descent (ProxGD) for a wide range of minibatch sizes, which partially solves an open problem proposed in [Reddi et al., 2016b]. Finally, for nonconvex functions satisfied Polyak-{\L}ojasiewicz condition, we show that ProxSVRG+ achieves global linear convergence rate without restart. ProxSVRG+ is always no worse than ProxGD and ProxSVRG/SAGA, and sometimes outperforms them (and generalizes the results of SCSG) in this case.
On the characteristics and origins of internet flow rates

This paper considers the distribution of the rates at which flows transmit data, and the causes of these rates. First, using packet level traces from several Internet links, and summary flow statistics from an ISP backbone, we examine Internet flow rates and the relationship between the rate and other flow characteristics such as size and duration. We find, as have others, that while the distribution of flow rates is skewed, it is not as highly skewed as the distribution of flow sizes. We also find that for large flows the size and rate are highly correlated. Second, we attempt to determine the cause of the rates at which flows transmit data by developing a tool, T-RAT, to analyze packet-level TCP dynamics. In our traces, the most frequent causes appear to be network congestion and receiver window limits.
A simple spectrophotometric method for the direct determination of uric acid in avian excreta.

A procedure was developed for the direct quantitation of uric acid in poultry excreta. It involved the determination of the absorbance of a perchloric acid extract of excreta at 285 nm. Interference from other ultraviolet absorbing material was shown to be negligible. The correlation (r) between this procedure and a recently developed high-performance liquid chromatographic procedure for uric acid content of excreta from birds fed different diets was . 993 (P less than .01). It was concluded that this procedure provides a simple and reliable method for the quantitation of uric acid in avian excreta.
Generating unambiguous URL clusters from web search

This paper reports on the generation of unambiguous clusters of URLs from clickthrough data from the MSN search query log excerpt (the RFP 2006 dataset). Selections (clickthroughs) by a single user from a single query can be assumed to have some mutual semantic relevance, and the URLs coselected in this way can be aggregated to form single-sense clusters. When the graphs for a single term separate into distinct clusters, the semantics of the distinct clusters can be interpreted as disambiguated aggregations of URLs. This principle had been tested on smaller and more constrained datasets previously, and this paper reports on findings from applying a method based on the principle to the RFP 2006 dataset. This paper evaluates the proposed coselection method for generating single-sense clusters against two other methods, with varying parameters. The evaluation is done both with a human evaluation to determine the quality of the clusters generated by the different methods, and by a simple "edit distance" analysis to determine the content difference of the methods. The main questions addressed are i) whether it is feasible to generate single-sense / sense-coherent clusters, and ii) whether, in a closed world, it would be feasible to discover ambiguous terms. The experimentation showed that sense-coherent clusters were found and further indicated that ambiguous terms could be detected from observing small overlap between large clusters.
Regulated phosphorylation of the K-Cl cotransporter KCC3 is a molecular switch of intracellular potassium content and cell volume homeostasis

The defense of cell volume against excessive shrinkage or swelling is a requirement for cell function and organismal survival. Cell swelling triggers a coordinated homeostatic response termed regulatory volume decrease (RVD), resulting in K+ and Cl− efflux via activation of K+ channels, volume-regulated anion channels (VRACs), and the K+-Cl− cotransporters, including KCC3. Here, we show genetic alanine (Ala) substitution at threonines (Thr) 991 and 1048 in the KCC3a isoform carboxyl-terminus, preventing inhibitory phosphorylation at these sites, not only significantly up-regulates KCC3a activity up to 25-fold in normally inhibitory isotonic conditions, but is also accompanied by reversal of activity of the related bumetanide-sensitive Na+-K+-2Cl− cotransporter isoform 1 (NKCC1). This results in a rapid (<10 min) and significant (>90%) reduction in intracellular K+ content (Ki) via both Cl-dependent (KCC3a + NKCC1) and Cl-independent [DCPIB (VRAC inhibitor)-sensitive] pathways, which collectively renders cells less prone to acute swelling in hypotonic osmotic stress. Together, these data demonstrate the phosphorylation state of Thr991/Thr1048 in KCC3a encodes a potent switch of transporter activity, Ki homeostasis, and cell volume regulation, and reveal novel observations into the functional interaction among ion transport molecules involved in RVD.
Comparative testing of anticoccidials in broiler chickens: the role of coccidial lesion scores.

The relationship between oocyst dose and lesion score was evaluated in trials involving five field isolates each of Eimeria acervulina, Eimeria maxima, and Eimeria tenella. Each trial included an uninfected, unmedicated treatment, and at least three treatments of unmedicated birds given different doses of oocysts from a single isolate. In four trials each with E. acervulina and E. tenella, and all five trials with E. maxima, infected, salinomycin-medicated (60 ppm) treatments were included. Each treatment consisted of five cages with eight male broiler birds per cage using a randomized complete block design. The relationship between oocyst dose and lesion score was examined within each coccidial species using the linear model: Y = beta0 + beta1(log(n) oocyst dose + 1). The results demonstrated that in unmedicated birds, low oocyst doses caused mean lesion scores up to 2.0, but the numbers required to cause higher mean scores were many times greater. Second, the estimated oocyst dose in salinomycin-medicated birds for any given mean lesion score was substantially more than the corresponding estimate for unmedicated birds. These results indicated that there could be wide differences in levels of oocyst dose between unmedicated and medicated birds that lesion scores failed to measure. If lesion scores are used in trials comparing anticoccidial drugs, an alternative design may be to include three infected, unmedicated treatments each given a different level of inoculum (e.g., low, medium, and high). Medicated treatments, given the highest oocyst dose only, would then be compared to each of the infected, unmedicated treatments.
Low-Distortion Rankings from Highly Opaque Preferences

Work on implicit utilitarian voting advocates the design of voting rules that maximize utilitarian social welfare with respect to latent utility functions, based only on observed rankings of the alternatives. This approach has been successfully deployed in order to help people choose a single alternative or a subset of alternatives, but it has previously been unclear how to apply the same approach to the case where the desired output is a ranking. We propose to address this problem by assuming that voters’ utilities for rankings are induced by unknown weights and unknown utility functions, which, moreover, have a combinatorial (subadditive) structure. Despite the extreme lack of information about voters’ preferences, we show that it is possible to choose rankings such that the worst-case gap between their social welfare and that of the optimal ranking, called distortion, is no larger (up to polylogarithmic factors) than the distortion associated with much simpler problems. Through experiments, we identify practical methods that achieve near-optimal social welfare on average.
An Improved Collaborative Filtering Based on Item Similarity Modified and Common Ratings

Many of the recent algorithms have been developed to improve the various aspects of collaborative filtering recommender systems, however, most of them do not take the sectional data of users and items information or characteristic into account. This paper, we present a new improved collaborative filtering based on item similarity modified and item common ratings which take full advantage of the sectional data of item-user matrix information to modify the similarity calculation and rating prediction. Extensive experiments have been conducted on two different dataset to analyze our proposal approach. The results show that our approach can improve the prediction accuracy of the item-based collaborative filtering not only on different neighbors, but also on different training ratio data set.
Graffinity: Visualizing Connectivity in Large Graphs

Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node‐link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query‐based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand. We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open‐source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open‐source tool with illustrative examples using flight and connectomics data.
Full-scan LBIST with capture-per-cycle hybrid test points

This paper presents a novel low-area scan-based logic built-in self-test (LBIST) scheme that addresses stringent test requirements of certain application domains such as the fast-growing automotive electronics market. These requirements, largely driven by safety standards, are met by significantly reducing test application time while preserving the high fault coverage of conventional BIST schemes. Alternatively, one may consider applying a much larger number of vectors within the same time interval. Although the new scheme may resemble traditional BIST logic, it is a combination of pseudorandom test patterns delivered in a test-per-clock fashion through conventional scan chains and per-cycle-driven hybrid test points that creates this new synergistic LBIST paradigm. The hybrid observation points, inserted at the most suitable locations, capture faulty effects every shift cycle into dedicated flip-flops that form separate scan chains. Their content is gradually shifted into a compactor, which is shared with the remaining scan chains that still deliver test responses captured once the entire test pattern has been shifted-in. Experimental results obtained for industrial designs illustrate feasibility of the proposed BIST scheme in terms of test time, test coverage, and area overhead, and they are reported herein.
A proangiogenic signaling axis in myeloid cells promotes malignant progression of glioma

Tumors are capable of coopting hematopoietic cells to create a suitable microenvironment to support malignant growth. Here, we have demonstrated that upregulation of kinase insert domain receptor (KDR), also known as VEGFR2, in a myeloid cell sublineage is necessary for malignant progression of gliomas in transgenic murine models and is associated with high-grade tumors in patients. KDR expression increased in myeloid cells as myeloid-derived suppressor cells (MDSCs) accumulated, which was associated with the transformation and progression of low-grade fibrillary astrocytoma to high-grade anaplastic gliomas. KDR deficiency in murine BM-derived cells (BMDCs) suppressed the differentiation of myeloid lineages and reduced granulocytic/monocytic populations. The depletion of myeloid-derived KDR compromised its proangiogenic function, which inhibited the angiogenic switch necessary for malignant progression of low-grade to high-grade tumors. We also identified inhibitor of DNA binding protein 2 (ID2) as a key upstream regulator of KDR activation during myeloid differentiation. Deficiency of ID2 in BMDCs led to downregulation of KDR, suppression of proangiogenic myeloid cells, and prevention of low-grade to high-grade transition. Tumor-secreted TGF-&bgr; and granulocyte-macrophage CSF (GM-CSF) enhanced the KDR/ID2 signaling axis in BMDCs. Our results suggest that modulation of KDR/ID2 signaling may restrict tumor-associated myeloid cells and could potentially be a therapeutic strategy for preventing transformation of premalignant gliomas.
The treatment effects of HET0016 on demyelinating Mice induced by cuprizone

Cytochrome P450s (CYP) ω-hydroxylase and its metabolites 20 - hydroxyeicosatetraenoic acid (20-HETE) play an important role in the central nervous system disorders. The purpose of this study was to determine the effects of the 20-HETE inhibitor, HET0016, on myelin sheath after demylination in mice. Methods: We have studied the impact of HET0016 on remyelination using the toxic cuprizone model. Animals were assigned randomly to various groups and injected intraperitoneally with 10% lecithin or 2.0 and 0.2 mg/kg of HET0016 per day in 2 weeks. The animals were killed and the brains were reserved. We investigated spatial memory of mice using the Y-maze. We observed the myelin of corpus callosum by histochemical staining. Results: there were no significant differences in body weight, food intake and water intake between the model group and HET0016 (2.0mg/kg, 0.2mg/kg) group; spatial memory improved significantly in HET0016 (0.2mg/kg, 2.0mg/kg) group compared with model group; corpus callosum structure was significantly remyelinated after 2 weeks by HET0016 treatment. Conclusion: HET0016, a selective inhibitor of CYP ω-hydroxylase, has potent therapeutic effect on multiple sclerosis mice.
Detecting and Attributing Health Burdens to Climate Change

Background: Detection and attribution of health impacts caused by climate change uses formal methods to determine a) whether the occurrence of adverse health outcomes has changed, and b) the extent to which that change could be attributed to climate change. There have been limited efforts to undertake detection and attribution analyses in health. Objective: Our goal was to show a range of approaches for conducting detection and attribution analyses. Results: Case studies for heatwaves, Lyme disease in Canada, and Vibrio emergence in northern Europe highlight evidence that climate change is adversely affecting human health. Changes in rates and geographic distribution of adverse health outcomes were detected, and, in each instance, a proportion of the observed changes could, in our judgment, be attributed to changes in weather patterns associated with climate change. Conclusions: The results of detection and attribution studies can inform evidence-based risk management to reduce current, and plan for future, changes in health risks associated with climate change. Gaining a better understanding of the size, timing, and distribution of the climate change burden of disease and injury requires reliable long-term data sets, more knowledge about the factors that confound and modify the effects of climate on health, and refinement of analytic techniques for detection and attribution. At the same time, significant advances are possible in the absence of complete data and statistical certainty: there is a place for well-informed judgments, based on understanding of underlying processes and matching of patterns of health, climate, and other determinants of human well-being. https://doi.org/10.1289/EHP1509