Miracle-dz/UniFAR-FTUs huggingface dataset:
Vitamin D Ameliorates Angiotensin II-Induced Human Endothelial Progenitor Cell Injury via the PPAR-γ/HO-1 Pathway

Vitamin D has an important protective effect on chronic inflammatory disease. Angiotensin II (AngII) triggers vascular damage and plays a key role in vascular diseases via several mechanisms, including inflammation. Conversely, vitamin D has been shown to have an important protective effect on chronic inflammation. There is evidence showing that vitamin D can reverse the effects of AngII, but the molecular mechanisms by which this occurs are not known. Our results demonstrate that vitamin D improved the viability, migration ability, and tube formation of AngII-pretreated endothelial progenitor cells (EPCs) and inhibited the apoptosis of EPCs induced by AngII. Vitamin D also reversed reactive oxygen species production, vascular inflammatory cytokine generation, and nuclear factor kappa-B activation in EPCs induced by AngII. Furthermore, EPC pretreatment with GW9662 (the antagonist for PPAR-γ) or siHO-1 decreased the protective effect of vitamin D on AngII-induced EPC injury. Overall, our data indicate that vitamin D ameliorated AngII-induced abnormal EPC injury by decreasing oxidative stress and inflammatory cytokine levels. These findings also suggest that vitamin D protected EPCs from AngII-induced vascular injury via the activation of the PPAR-γ/HO-1 signaling pathway.
Wireless sensor nodes localiser based on sine-cosine algorithm

Wireless sensor networks are an emerging technology deployed in many fields. Knowing the positions of sensor nodes is critical for many applications to perform their tasks. Hence, accurate node localisation in wireless sensor networks is an essential issue. The node localisation problem can be formulated as a multidimensional optimisation problem. In this study, the authors propose a node localisation scheme based on a sine–cosine algorithm. The proposed scheme addresses the flip ambiguity problem, which can badly affect the localisation accuracy of the entire network. The efficacy of the proposed scheme is evaluated through intensive experimentation under different scenarios. The obtained results show the superiority of the proposed scheme against other optimisation algorithms in terms of the localisation accuracy, the number of localised nodes, and the computation time.
The Effect of Nutrition Knowledge on Nutritional Status in Sport Science Students

The knowledge may vary to eating behavior which contributes to nutritional status and performance. Therefore, this study aims to know the relationship between nutrition knowledge and the nutritional status of students in the Sport Science Faculty of Universitas Negeri Medan. The study used a cross-sectional research design. Samples were 27 students in Sports Science. Nutritional knowledge was collected by using the Sport Nutrition Knowledge Questionnaire (SNKQ). Body mass index (BMI) was measured by anthropometry. Data analysis was descriptive and regression analysis. On average, nutrition knowledge score was in poor category whereas the knowledge of a balanced diet was in the good category. Meanwhile, the knowledge of carbohydrates, proteins, fats, vitamins and minerals was in poor category. Whereas water knowledge was in sufficient category. The result shows that 81.5% students have got normal BMI, while 14.5% and 3.7% are in overweight and underweight. The result of regression test shows r = 0.580 with p 0.004<0.005, which means that nutrition knowledge influences nutritional status.
Neuropathology of Dystonia

Background Dystonia is characterized by sustained or intermittent muscle contractions resulting in abnormal, often repetitive, movements, postures, or both. Neuropathologic research has been essential in understanding the etiology and disease progression of other movement disorders, including Parkinson’s disease and cerebellar ataxias. In the field of dystonia, however, research is stymied by the paucity of post-mortem tissue available and the phenotypic heterogeneity found in those with dystonia. Methods A PubMed search was conducted using the term “neuropathology of dystonia”. The resulting list of references was limited to English-language human neuropathology articles. A total of 20 publications were retrieved and reviewed. Results Historically, based on study of acquired forms of dystonia, lesions of the putamen and globus pallidus have been identified as causing dystonia. After the identification of genetic causes of dystonia and the study of limited tissue available from those cases, as well as findings from cases of isolated focal and segmental dystonia, there is evidence that brainstem cholinergic neurons and specific cell populations within the cerebellum also play a role in the pathophysiology of dystonia. Discussion Based on limited available brain tissue, there is evidence that the pathophysiology of dystonia may involve a combination of dysfunction within neurons of the brainstem, cerebellum, putamen, and globus pallidus. In order to gain a better understanding of the pathophysiology of dystonia, a prospective, quantitative study in well-phenotyped subjects with different types of genetic and isolated dystonia is required.
Executing Entity Matching End to End: A Case Study

Entity matching (EM) identifies data instances that refer to the same real-world entity. Numerous EM works have covered a wide spectrum, from developing new EM algorithms to scaling them to building EM systems. But there has been very little if any published work on how EM is carried out in practice, end to end . In this paper we describe in detail a case study of applying EM to a particular domain end to end (i.e., going from the raw data all the way to the matches). Specifically, we describe a real-world application for EM in the science policy research community. We describe how our team (the EM team) interact with the science policy team to carry out the EM process, using PyMatcher , a state-of-the-art EM system developed in the Magellan project at UW-Madison. We highlight the communication between the two teams and the zig-zag nature of the EM process. We identify a set of challenges that we believe arise in many real-world EM projects but that current EM systems have either ignored or are not even aware of. Finally, we provide all data underlying this case study, including labeled tuple pairs and documentation supplied by the science policy team, to serve as a good challenge problem for EM researchers.
Finding Meaningful Contrast Patterns for Quantitative Data

Contrast set mining identifies patterns that can best distinguish between two groups of data. While many machine learning models share the same goal, contrast set mining focuses on data understanding and interpretability. Most existing work in contrast set mining focuses on categorical data. In this work, we propose an algorithm that discovers contrast patterns on mixed data (datasets that contain both categorical and continuous attributes). Our algorithm is able to discover multivariate interactions using a supervised adaptive binning strategy. The binning strategy identifies meaningful bin boundaries in continuous attributes based on their relationships with other attributes. This in turn allows us to form better and more meaningful contrast patterns than traditional techniques that use global, pre-binning approaches. We propose various pruning strategies to reduce the search space, and show the utility of our algorithm on simulated data, several datasets from the UCI repository, as well as real manufacturing data. ACM Reference Format: Rohan Khade, Jessica Lin, and Nital Patel. 2019. Finding Meaningful Contrast Patterns for Quantitative Data. In Proceedings of 22nd International Conference on Extending Database Technology (EDBT), Lisbon, Portugal, March 26-29, 2019 (EDBT 2019), 12 pages.
STUDY OF RECOMBINANT PROTEIN FRACTIONS FROM THE BOMBYX MORI AND PICHIA PASTORIS USING "NATIVE PAGE" METHOD

We have studied the recombinant protein fractions of ̴ 24-34 kDa obtained from Bombyx mori - silkworm larvae and yeast Pichia pastoris. Fractions, containing the recombinant proteins by gel-electrophoresis using the “Native PAGE” method, which allows for differentiation of proteins according to their natural (surface) charge and conformational properties were studied. The results showed that the recombinant protein fraction from Bombyx mori (̴ 34 kDa) consisted of four components and fraction from Pichia pastoris (̴ 24 kDa) consisted one component. The most optimal conditions for the “Native PAGE”- the main gel: Tris HCl (pH-8.8) 244 mM, Bis-Acrylamide 10%, TEMED 0.08%, APS 0.08%; concentration gel: Tris HCl (pH-6.8) 125 mM, Bis-Acrylamide 4%, TEMED 0.05%, APS 0.05% were found.
Voting models on graphs

This thesis deals with voting processes. A Voting process models the exchange of opinions in a population of agents, commonly represented by vertices of a graph. Usually, an opinion represents the current state of the agent, and by interacting with neighbouring agents, such an opinion may change over time. Depending on the context, the set of valid opinions can be, for instance, {0, 1}, {+,−}, {agree, disagree}, and {healthy, infected} among others. The main research questions about a voting process are: i) Does the system reach consensus? , that is, if the system reaches a stable configuration where all vertices share the same opinion. ii) Subject to reaching consensus, what is the final opinion of the system? iii) How long does it take to reach consensus? Throughout this thesis, we study three stochastic models of voting. Firstly, we introduce and study the Linear Voting model. The main motivation to introduce this model is to make a step toward unifying certain models of voting on graphs in a common framework. In this regard, our model is proven to be flexible enough to cover several other models as particular instances without compromising tractability. As a particular case, our model subsumes well-known models as the voter model (pull voting) and push voting. Moreover, due to its tractability, we are able to extend several of the well-known techniques used to study pull voting, to properties of this much richer model. Among the studied properties, we include consensus time, winning probabilities, and the construction of a dual process. Secondly, we analyse the Coalescing and Branching random walks (COBRA) pro-
Effects of Heat‐Producing Elements on the Stability of Deep Mantle Thermochemical Piles

Geochemical observations of ocean island and mid‐ocean ridge basalts suggest that abundances of heat‐producing elements (HPEs: U, Th, and K) vary within the mantle. Combined with bulk silicate Earth models and constraints on the Earth's heat budget, these observations suggest the presence of a more enriched (potentially deep and undepleted) reservoir in the mantle. Such a reservoir may be related to seismically observed deep mantle structures known as large low shear velocity provinces (LLSVPs). LLSVPs might represent thermochemical piles of an intrinsically denser composition, and many studies have shown such piles to remain stable over hundreds of Myr or longer. However, few studies have examined if thermochemical piles can remain stable if they are enriched in HPEs, a necessary condition for them to constitute an enriched HPE reservoir. We conduct a suite of mantle convection simulations to examine the effect of HPE enrichment up to 25× the ambient mantle on pile stability. Model results are evaluated against present‐day pile morphology and tested for resulting seismic signatures using self‐consistent potential pile compositions. We find that stable piles can form from an initial basal layer of dense material even if the layer is enriched in HPEs, depending on the density of the layer and degree of HPE enrichment, with denser basal layers requiring increased HPE enrichment to form pile‐like morphology instead of a stable layer. Thermochemical piles or LLSVPs may therefore constitute an enriched reservoir in the deep mantle.
On the Physical Layer Security of a Decode and Forward Based Mixed FSO/RF Co-Operative System

In this letter, the secrecy performance of a decode-and-forward (DF) principle based mixed free space optics (FSO) and radio frequency (RF) system is analyzed. The analysis is done from physical layer (PHY) security perspective. Herein, two different eavesdroppers are considered while signal is transmitting from source (S) to destination (D) through a relay (R) point. The FSO based and RF based eavesdroppers are trying to intercept the confidential signal from S-R link and R-D links respectively. The faded FSO links are modeled by Málaga (<inline-formula> <tex-math notation="LaTeX">$\mathcal {M} $ </tex-math></inline-formula>) distribution and RF links are characterized by Nakagami-<inline-formula> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> distribution. Exact closed form expression for secrecy performance metric such as secrecy outage probability is derived and analyzed for the aforementioned system in terms of Meijer’s <inline-formula> <tex-math notation="LaTeX">${G}$ </tex-math></inline-formula>-function and bivariate Fox’s <inline-formula> <tex-math notation="LaTeX">${H}$ </tex-math></inline-formula>-function. Furthermore, the asymptotic expressions for these performance metrics are derived by employing high signal to noise ratio (SNR) approximation. Afterwards the results are verified by Monte-Carlo simulations. Finally, the secrecy threshold for the system is derived to get insight of the system reliability and secrecy.
No-reference remote sensing image quality assessment based on gradient-weighted natural scene statistics in spatial domain

Abstract. Considering the relatively poor real-time performance when extracting transform-domain image features and the insufficiency of spatial domain features extraction, a no-reference remote sensing image quality assessment method based on gradient-weighted spatial natural scene statistics is proposed. A 36-dimensional image feature vector is constructed by extracting the local normalized luminance features and the gradient-weighted local binary pattern features of local normalized luminance map in three scales. First, a support vector machine classifier is obtained by learning the relationship between image features and distortion types. Then based on the support vector machine classifier, the support vector regression scorer is obtained by learning the relationship between image features and image quality scores. A series of comparative experiments were carried out in the optics remote sensing image database, the LIVE database, the LIVEMD database, and the TID2013 database, respectively. Experimental results show the high accuracy of distinguishing distortion types, the high consistency with subjective scores, and the high robustness of the method for remote sensing images. In addition, experiments also show the independence for the database and the relatively high operation efficiency of this method.
Enzymes in a golden cage†

We describe a general method for the entrapment of enzymes within bulk metallic gold. This is a new approach for the immobilization of enzymes on metals, which is commonly carried out by 2D adsorption or covalent biding, that is, the enzyme is in contact with the metal at a specific contact zone of the enzyme, while most of the rest of it remains exposed to the environment. The 3D metallic encaging of the enzymes is quite different: the enzyme is in contact with the metallic cage walls all around it and is well protected inside. The porous nature of the metallic matrix enables substrate molecules to diffuse inside, reach the active site, and let product molecules diffuse out. The generality of the approach was proven by the successful entrapment of five enzymes representing different classes and different bio- and medical applications: l-asparaginase (Asp), collagenase, horseradish peroxidase (HRP), laccase and glucose oxidase (GOx). GOx–gold conjugates have been of particular interest in the literature. The main challenge we had to solve was how to keep the enzyme active in the process of gold-synthesis from its cation – this required careful tailoring of reaction conditions, which are detailed in the paper. The gold entrapped enzymes gain thermal stability and protectability against harsh conditions. For instance, we could keep Asp alive at the extreme pH of 13, which normally kills the enzyme instantly. The entrapped enzymes obey the Michaelis–Menten kinetics, and activation energies were determined. Good recyclability for eight cycles was found. Multi-enzymatic reactions by combinations of the off-the-shelf bioactive enzyme@gold powders are possible, as demonstrated for the classical detection of GOx activity with HRP. Detailed material characterization and proposed mechanisms for the 3D protectability of the enzymes are provided. The new enzyme immobilization method is of wide potential uses in medicine, biotechnology, bio-fuel cells and enzymatic (electro)sensing applications.
Prostaglandins and its Types

Prostaglandins are potent bioactive lipid messengers synthesized from arachidonic acid mediated by enzyme COX. Prostaglandins (PGs) play a key role in the initiation of the inflammatory response. Their biosynthesis is significantly increased in inflamed tissue, and they contribute to the development of the cardinal signs of acute inflammation. Although the proinflammatory properties of individual PGs during the acute inflammatory response are well established, their role in the resolution of inflammation is more controversial.
Independent Components of Neural Activation Associated with 100 Days of Cognitive Training

Some cognitive training studies have reported working memory benefits that generalize beyond the trained tasks, whereas others have only found task-specific training effects. What brain networks are associated with general training effects, rather than task-specific effects? We investigated this question in the context of working memory training using the COGITO data set, a longitudinal project including behavioral assessments before and after 100 days of cognitive training in 101 younger (20–31 years) and 103 older (65–80 years) adults. Pre- and postassessments included verbal, numerical, and spatial measures of working memory. It was therefore possible to assess training effects on working memory at a general latent ability level. Previous analyses of these data found training-related improvements on this latent working memory factor in both young and old participants. fMRI data were collected from a subsample of participants (24 young and 15 old) during pre- and post-training sessions. We used independent component analysis to identify networks involved in a perceptual decision-making task performed in the scanner. We identified five task-positive components that were task-related: two frontal networks, a ventral visual network, a motor network, and a cerebellar network. Pre-training activity of the motor network predicted latent working memory performance before training. Additionally, activity in the motor network predicted training-related changes in working memory ability. These findings suggest activity in the motor network plays a role in task-independent working memory improvements and have implications for our understanding of working memory training and for the design and implementation of future training interventions.
Advances in green synthesis of nanoparticles

Abstract Nanotechnology is a developing branch of pharmaceutical sciences wherein the particles extend in nanosizes and turn out to be more responsive when contrasted with their unique counter parts. In the past numerous years, the utilization of synthetic concoctions and physical strategies were in mould; however, the acknowledgment of their toxic impacts on human well-being and condition influenced serious world view for the researchers. Presently, green synthesis is the watch word for the combination of nanoparticles (NPs) by plants or their metabolites. This innovation is particularly compensating as far as decreasing the poisonous quality caused by the conventionally integrated NPs. In this review, we cover the perspectives by which metal particles can be integrated from green methods in the perspective of green methods utilized in the NPs combination. In the green strategies, plant metabolites and natural substances are utilized to orchestrate the NPs for the pharmaceutical and other applications. Some characterization methods are also reviewed along with applications of NPs. Graphical Abstract
Reliable local dynamics in the brain across sessions are revealed by whole‐brain modeling of resting state activity

Resting state fMRI is a tool for studying the functional organization of the human brain. Ongoing brain activity at “rest” is highly dynamic, but procedures such as correlation or independent component analysis treat functional connectivity (FC) as if, theoretically, it is stationary and therefore the fluctuations observed in FC are thought as noise. Consequently, FC is not usually used as a single‐subject level marker and it is limited to group studies. Here we develop an imaging‐based technique capable of reliably portraying information of local dynamics at a single‐subject level by using a whole‐brain model of ongoing dynamics that estimates a local parameter, which reflects if each brain region presents stable, asynchronous or transitory oscillations. Using 50 longitudinal resting‐state sessions of one single subject and single resting‐state sessions from a group of 50 participants we demonstrate that brain dynamics can be quantified consistently with respect to group dynamics using a scanning time of 20 min. We show that brain hubs are closer to a transition point between synchronous and asynchronous oscillatory dynamics and that dynamics in frontal areas have larger heterogeneity in its values compared to other lobules. Nevertheless, frontal regions and hubs showed higher consistency within the same subject while the inter‐session variability found in primary visual and motor areas was only as high as the one found across subjects. The framework presented here can be used to study functional brain dynamics at group and, more importantly, at individual level, opening new avenues for possible clinical applications.
Overload Capability of Multiphase Machines Under Normal and Open-Phase Fault Conditions: A Thermal Analysis Approach

Multiphase drives are convenient for high-power/high-current applications as they allow the reduction of the phase current for given rated power and phase voltage. Due to their redundant structure, the multiphase drives have intrinsic open-phase fault-tolerant operation capability. This situation may happen when one or more power electronic units are turned off after a fault event, and the drive configuration allows phase disconnection. In this case, the healthy machine phases can be overloaded to keep the torque constant and without any pulsations. The goal of the work is the evaluation of the thermal parameters of the stator windings of multiphase machines to be used in the analysis of both short thermal transients and steady-state operation, during normal and open-phase faults. The approach has general validity and can be applied to any ac multiphase machine having a distributed winding configuration. The prototype used for the experimental tests is an asymmetrical 12-phase induction machine, having four 3-phase stator sets with isolated neutral points. The stator windings thermal model is obtained experimentally, by considering the mutual heat exchange phenomena among the windings when one or more winding sets are disconnected. This thermal model allows at evaluating the proper machine overloading for short thermal transients and steady-state operation in case of open-phase faults.