Miracle-dz/UniFAR-FTUs huggingface dataset:
Protein kinase Cα mediates recovery of renal and mitochondrial functions following acute injury

Previously, we have shown that active protein kinase Cα (PKCα) promotes recovery of mitochondrial function after injury in vitro [Nowak G & Bakajsova D (2012) Am J Physiol Renal Physiol 303, F515‐F526]. This study examined whether PKCα regulates recovery of mitochondrial and kidney functions after ischemia‐induced acute injury (AKI) in vivo. Markers of kidney injury were increased after bilateral ischemia and returned to normal levels in wild‐type (WT) mice. Maximum mitochondrial respiration and activities of respiratory complexes and FoF1‐ATPase decreased after ischemia and recovered in WT mice. Reperfusion after ischemia was accompanied by translocation of active PKCα to mitochondria. PKCα deletion reduced mitochondrial respiration and activities of respiratory complex I and FoF1‐ATPase in noninjured kidneys, indicating that PKCα is essential in developing fully functional renal mitochondria. These changes in PKCα‐deficient mice were accompanied by lower levels of complex I subunits (NDUFA9 and NDUFS3) and the γ‐subunit of FoF1‐ATPase. Also, lack of PKCα exacerbated ischemia‐induced decreases in respiration, complex I and FoF1‐ATPase activities, and blocked their recovery after injury, indicating a crucial role of PKCα in promoting mitochondrial recovery after AKI. Further, PKCα deletion exacerbated acetylation and succinylation of key mitochondrial proteins of energy metabolism after ischemia due to decreases in deacetylase and desuccinylase (sirtuin3 and sirtuin5) levels in renal mitochondria. Thus, our data show a novel role for PKCα in regulating levels of mitochondrial sirtuins and acetylation and succinylation of key mitochondrial proteins. We conclude that PKCα deletion: (a) affects renal physiology by decreasing mitochondrial capacity for maximum respiration; (b) blocks recovery of mitochondrial functions, renal morphology, and functions after AKI; and (c) decreases survival after AKI.
The Importance of Health Information on the Internet: How It Saved My Life and How it Can Save Yours

The internet holds the potential promise of improved patient outcomes, especially when one is faced with a critical or life-threatening disease or condition. Appropriate and timely access to health information can support informed negotiation of optimal treatments, optimal management, and expedited recovery, and to an improved outcome for a patient. However, there are many human and technical barriers that may prevent the application of the best possible information for both patient and provider alike, making the patient journey complex and potentially dangerous. In this viewpoint paper, the author (who is also a JMIR editor) reflects on a personal patient journey, where use of the internet facilitated a means of reaching a good patient outcome in the face of a variety of informational and organizational limitations and gaps. This journey illustrates the importance of human-related factors affecting access to health information. The application of a range of internet information resources at critical points can result in a positive patient outcome, as this case illustrates. This paper reflects on how the experience highlights several information needs and concerns. It also highlights the need for improved access to appropriate health information along the patient journey that can support patient and provider joint decision-making. This access to information can make the difference between positive clinical outcomes and death, illustrating how health information on the internet can be both critical and life saving.
Looking beyond the usual suspects: sulfide stress in schizophrenia pathophysiology

Schizophrenia is a complex, multifactorial disease that displays heterogeneous behavioral and cognitive syndrome (Lieberman & First, 2018). The origin of schizophrenia appears to lie in genetic and/or environmental disruption of brain development (Owen et al, 2016). In spite of current treatment that largely consists in antipsychotic drugs combined with psychological therapies, social support, and rehabilitation, developing more effective therapeutic interventions is an essential issue.
Visual Analytic Tools and Techniques in Population Health and Health Services Research: Protocol for a Scoping Review

Background Visual analytics (VA) promotes the understanding of data using visual, interactive techniques and using analytic and visual engines. The analytic engine includes machine learning and other automated techniques, whereas common visual outputs include flow maps and spatiotemporal hotspots for studying service gaps and disease distribution. The principal objective of this scoping review is to examine the state of science on VA and the various tools, strategies, and frameworks used in population health and health services research (HSR). Objective The purpose of this scoping review is to develop an overarching global view of established techniques, frameworks, and methods of VA in population health and HSR. The main objectives are to explore, map, and synthesize the literature related to VA in its application to the two main focus areas of health care. Methods We will use established scoping review methods to meet the study objective. As the use of the term visual analytics is inconsistent, one of the major challenges was operationalizing the concepts for developing the search strategy, based on the three main concepts of population health, HSR, and VA. We included peer reviewed and grey literature sources from 2005 till March 2019 in the search. Independent teams of researchers will screen the titles, abstracts and full text articles, whereas an independent researcher will arbiter conflicts. Data will be abstracted and presented using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews checklist and explanation by two independent researchers. Results As of late August 2019, the scoping review is in the full-text screening stage. Data synthesis will follow and the first results are expected to be submitted for publication in December 2019. In this protocol, the methods for undertaking this scoping review are detailed. We present how we operationalized the varied concepts of population health, health services, and VA. The main results of the scoping review will synthesize peer reviewed and grey literature sources on the main methods of VA in the interrelated fields of population health and health services research from January 2005 till March 2019. Conclusions VA is being increasingly used and integrated with emerging technologies to support decision making using large data sets. This scoping review of the VA tools, strategies, and frameworks applied to population health and health services aims to increase awareness of this approach for uptake by decision makers working within and toward developing learning health systems globally. International Registered Report Identifier (IRRID) DERR1-10.2196/14019
Biologie chirurgicale de la cicatrisation.

Wound healing is a dynamic, interactive process involving soluble mediators, blood cells, extracellular matrix, and parenchymal cells. It has three phases-inflammation, tissue formation, and tissue remodelling-that overlap in time. Tissue remodelling of an acute wound is closely controlled by regulatory mechanisms which maintain a delicate balance between synthesis and degradation, leading to normal healing. In chronic wounds (diabetic foot, venous ulcer, pressure sore), the healing process is disturbed by various factors which prolong the inflammatory phase, creating a cascade of tissue responses which perpetuate a "non-healing" state. Among those factors we recognize tissue necrosis, infection, tissue hypoxia, exsudates with excess of inflammatory cytokines. The local treatment of chronic wounds with modern dressings, although efficacious, is too slow and too conservative. By transforming a chronic wound into an acute, the surgeon not only remove those disturbing factors but re-establishes the dynamic process of a normal wound healing.
Advancing non-convex and constrained learning: challenges and opportunities

As data gets more complex and applications of machine learning (ML) algorithms for decision-making broaden and diversify, traditional ML methods by minimizing an unconstrained or simply constrained convex objective are becoming increasingly unsatisfactory. To address this new challenge, recent ML research has sparked a paradigm shift in learning predictive models into non-convex learning and heavily constrained learning. Non-Convex Learning (NCL) refers to a family of learning methods that involve optimizing non-convex objectives. Heavily Constrained Learning (HCL) refers to a family of learning methods that involve constraints that are much more complicated than a simple norm constraint (e.g., data-dependent functional constraints, non-convex constraints), as in conventional learning. This paradigm shift has already created many promising outcomes: (i) non-convex deep learning has brought breakthroughs for learning representations from large-scale structured data (e.g., images, speech) (LeCun, Bengio, & Hinton, 2015; Krizhevsky, Sutskever, & Hinton, 2012; Amodei et al., 2016; Deng & Liu, 2018); (ii) non-convex regularizers (e.g., for enforcing sparsity or low-rank) could be more effective than their convex counterparts for learning high-dimensional structured models (C.-H. Zhang & Zhang, 2012; J. Fan & Li, 2001; C.-H. Zhang, 2010; T. Zhang, 2010); (iii) constrained learning is being used to learn predictive models that satisfy various constraints to respect social norms (e.g., fairness) (B. E. Woodworth, Gunasekar, Ohannessian, & Srebro, 2017; Hardt, Price, Srebro, et al., 2016; Zafar, Valera, Gomez Rodriguez, & Gummadi, 2017; A. Agarwal, Beygelzimer, Dudík, Langford, & Wallach, 2018), to improve the interpretability (Gupta et al., 2016; Canini, Cotter, Gupta, Fard, & Pfeifer, 2016; You, Ding, Canini, Pfeifer, & Gupta, 2017), to enhance the robustness (Globerson & Roweis, 2006a; Sra, Nowozin, & Wright, 2011; T. Yang, Mahdavi, Jin, Zhang, & Zhou, 2012), etc. In spite of great promises brought by these new learning paradigms, they also bring emerging challenges to the design of computationally efficient algorithms for big data and the analysis of their statistical properties.
A Case Study: Exploring Student Academic Performance Data for Actionable Knowledge

For decades, university and college administrators have been concerned with student academic performance regarding time-to-degree and time-to-attrition as well as determining the reasons why students take too long to graduate or leave school before receiving their degree. Although this problem has been researched extensively, the concerns remain to this day. With the advent of big data, data science, and sophisticated visualization approaches, there are more tools at our disposal to analyze and explore student data in the hopes of better identifying or predicting which students are at-risk. However, determining which features to mine remain a challenge for this type of analysis. In this case study, we analyzed 10 years of student data in the College of Computing and Informatics (CCI) of a large state university. Using EventFlow, a tool that analyzes event sequences, we developed a novel knowledge generation approach that can be used to test hypotheses about reasons for student attrition.
2.6 Managing for target species

Forest conservation management often focuses on maintaining general habitat features such as natural tree species composition, uneven age structure, and perhaps most importantly, the presence of old and dead trees. The most widely used strategy for maintaining biodiversity is to emulate natural forest dynamics at the stand and landscape levels. For example, selective cutting or partial cutting can be used as alternatives to traditional clear-cutting, and untreated habitat patches, individual retention trees, and deadwood can be retained in the cutting area. The basic assumption is that by mimicking natural disturbances it should be possible to maintain the structural features and processes that are important for species. The general justification for focusing on structural features instead of species is that there are simply too many species to take into account the habitat requirements of each individual species. Therefore, it is often necessary to use structural features as surrogates for species (Figure 38).
Imitation Learning Across Embodiments via Adversarial Structured Prediction

Imitation learning via inverse optimal control typically assumes that a demonstrator and a learner operate within the same state-action space of a common decision process. However, in many settings the capabilities of the demonstrator and the learner may differ substantially: robots’ joint and torque limits differ from those of a human demonstrator or another robot; transferring demonstrations of driving from one vehicle to another may be desired; or when, in general, automated devices are less capable and more expensive than manually-controlled devices. This paper introduces the imitation learning across different embodiment problem where assessing the learner’s behavior relative to demonstrated behavior is easy. The learner’s goal then is to behave in a way that is compatible with the demonstrator’s behavior (unknown to the learner at test time), despite differences in capabilities. We develop an adversarial prediction formulation for learning to imitate across embodiments in this paper.
Serum Uric Acid – Risk Factor for Acute Ischemic Stroke and Poor Outcomes

Over the last decades several studies among the adult population have attempted to establish a correlation between the risk of stroke incidence and serum uric acid (SUA) concentration, and how these levels influence the patient’s neurological outcome after a stroke. But, to date, the results are conflicting. In this review, an extensive literature search was performed through PubMed for articles published until May 2019 to review the association. The study selection was narrowed by searching PubMed database using the Medical Subject Headings (MesH) and associated keywords. Only articles conducted in English and on human subjects were included. We considered an article for this review if it had statistics on either the incidence, stroke mortality or post-stroke functional outcomes along with serum uric acid levels in adults. This review includes 21 articles with data of 33,580 cases of stroke and 1,100,888 participants. We can divide the articles reviewed into two separate cohorts of studies. One relates serum uric acid levels to stroke frequency and mortality, while the other is associated with serum uric acid and outcomes for stroke survivors. Based on our review, no significant relationship is observed with uric acid exhibiting protective effects on stroke outcome. Large clinical trials are advised to provide well-defined solutions to further assess the benefits of uric acid level lowering treatment in patients of vascular events, such as a stroke. However, we confidently report that increasing uric acid levels poses a higher risk for incidence of stroke.
Too Much Time, Not Enough Space: Multi-Scale Event Sequence Visual Analytics with m-SVEN

Many techniques exist to visually represent temporal event sequences. However, few techniques are purposely designed for direct visual comparison of many (e.g., tens or hundreds) event sequences. This task is important for understanding recurring patterns, trends, and detecting anomalies, but visual comparison across event sequences is challenging when sequences are not trivially short. We propose a general framework for visualization of event sequence collections with storylines designed specifically for this comparison task. Storylines, just like parallel coordinate plots, suffer from usability issues when the number of time windows is large, so our framework includes a multi-scale technique to transform high temporal resolution event sequence data into a more appropriate format for storylines. We also built a user interface that supports zooming on the storyline visualization to leverage the multi-scale representation of the temporal event sequences. We evaluated our tool with a case study on the IEEE VAST Challenge 2014 data which included eliciting feedback from an expert user on our interface.
CHARACTERISTICS OF KCC DRAINAGE WATER OF EXISTING OUTLETS OVER MAYUR RIVER AND ITS TREATMENT FOR SAFE DISPOSAL

Water quality focuses on the various aspects of the physico-chemical parameters of raw water which plays the key role in detecting the status of pollution and suitability of a particular water body for various aquatic organisms. Urban residents in Khulna city use Mayur river, which is situated at the back swamp of Rupsha and Bhairab rivers, as a dumping site for discharging waste. The present study was conducted to measure the various water quality parameters of the drainage outlets existing over Mayur river and to develop a simple lowcost treatment unit for safe disposal of the drainage water into the river. To fulfill the purpose of the research, a simple treatment unit consisting of mainly roughing filter and sand filter was developed. The raw and treated water quality parameters were monitored to determine the performance of the proposed unit. From the initial testing of raw water, it can be seen that pH, hardness, chloride, nitrate and sulphate values were within the standard limit. However, the collected samples were heavily polluted by organic matter and microorganisms. The BOD ranges from 124.8 mg/l to 205.8 mg/l and total coliform ranges from 12000 N/100ml to 22600 N/100ml of sample. Color and turbidity values were of high range. For reducing the quality parameters, the simple drainage treatment process were used and the removal efficiency of color, turbidity, total suspended solid, BOD and total coliform were found to be around 91%, 88%, 79%, 91% and 98% respectively.
Towards a new Semantic Social Navigation Paradigm for Autonomous Robots using CORTEX

Navigation is one of the most basic and widely studied problems in the field of autonomous robots. The scientific community assumes the navigation task as a combination of three fundamental robotics skills, which can be summarized in i) self-localization; ii) path-planning; and iii) map building. Traditionally, these problems have been addressed using a geometric world model, that is, a 2D or 3D map representation of the environment. This tendency is now changing, and the scientific community is experiencing an increasing interest in socalled semantic solutions, which integrate semantic knowledge and geometrical information. In addition, new generation of robots should be able to work also taking into account social conventions, which is commonly named social navigation. This paper describes the ongoing work of a new proposal for a navigation paradigm where the semantic knowledge of the robot’s surroundings and different social rules are used in conjunction with the geometric representation of the environment. The proposal uses CORTEX, an agent-based Robotics Cognitive Architecture which provides a set of different agents in the deliberative-reactive spectrum. This paper introduces three cases of use that will be tested in two different social robots within the NAVLOC project.
A problem of detecting dynamical irregular response of a cam-follower system

The identification of dynamical irregular response, due to mechanical problems (e.g., wear or positioning), is the objective of the present paper. The dynamical response of a mechanical system, represented by a cam-follower, was evaluated by means the application of the Discrete Wavelet Transform to the signals captured during the rotation of a cam, on different conditions of rotation (e.g., clockwise and anticlockwise, off-centred or not). In terms of quality assurance, since it is fundamental to prevent mechanical damages, it is important the identification of such anomalies. In fact, one of the most important objective, to day, is the quality maintenance with the minimum cost. Therefore, the aim of present paper is the proposal of a simple and reliable method for the improvement of a test run for detecting the presence of dynamical irregularities during the rotation of the cam due to its superficial wear or mechanical positioning. Key-Words: cam-follower system, signal processing, wavelet transform.
The German Socio-Economic Panel as Reference Data Set

This paper discusses how household panels in general - and the German Socio-Economic Panel (SOEP) in particular - can serve as reference data for researchers collecting datasets that do not represent the full universe of the population of interest (e.g., through clinical trials, intervention studies, laboratory and behavioural experiments, and cohort studies). We first discuss potential benefits of using questions similar to those in the SOEP for studies where researchers are interested in using the SOEP as reference data. We present a comprehensive list of SOEP core questions that we recommend researchers to consider when collecting their own data. We focus on seven topics: (1) demographic and parental characteristics; (2) labour market; (3) health; (4) personality, preferences, and subjective orientations; (5) subjective wellbeing; (6) political involvement and participation, and finally, a set of core questions for young children before they enter school. Of course the selection of a minimum set of questions depends on the research question. In this paper, we offer general advice for the selection of variables to researchers interested in comparing their own data with the SOEP.
Wikilite > 25-Guidelines for multiple myeloma and related disorders

These guidelines discuss published data that provides evidence for the utility and application of sFLC assays for most plasma cell disorders, including symptomatic MM, nonsecretory MM (NSMM), light chain MM (LCMM), SMM, MGUS, solitary plasmacytoma and AL amyloidosis. Furthermore, the guidelines highlight key recommendations for the use of sFLC assays in screening, prognosis and in the assessment of patient response to treatment. Specific emphasis is placed on distinguishing between proven utility and those potential utilities that remain under investigation.
NETWORK TOOLS FOR THE ANALYSIS OF BRAND IMAGE

In Text Mining, large corpora are explored, in order to discover and synthesize their content, in an automatic, time-saving way. If the aim is to understand the similarity of the documents in the corpus, it is interesting to represent them as a network, measuring their proximity in relation to the words they use. Therefore, statistical tools, developed for the analysis of Social Networks, can be applied. In Social Network Analysis, it is often important to introduce information related to the actors (nodes), such as characteristics identifying their belonging to a specific group. In literature, among other proposals, the homophily index E-I has been introduced by Krackhardt and Stern in order to measure the tendency of actors to have relations with actors similar to themselves. Analogously, in the analysis of textual data, better results can be achieved by introducing information related to each document (e.g. characteristics of the author, or when the document has been written). Here we focus our attention on advertisements, and their textual component. In competitive markets there is a complex relation between the “image” of a brand, and the messages of the advertising campaigns over time. In the paper we propose a statistical procedure for analyzing the evolution of the brand image through the different campaigns of a famous brand, namely Coca Cola, on the basis of the E-I index. The validity of our results has been confirmed by means of simulations.