Miracle-dz/UniFAR-FTUs huggingface dataset:
Therapeutic implications of the specific inhibition of causative matrix metalloproteinases in experimental colitis induced by dextran sulphate sodium

Extracellular matrix dynamics, crucial for tissue remodelling, are highly regulated by a cascade of matrix metalloproteinases (MMPs) during inflammation and wound healing processes in inflammatory bowel disease (IBD). Contrary to expectations, there are limited reports to date that MMP inhibitors have some beneficial therapeutic effects in experimental colitis models. Furthermore, clinical trials of MMP inhibitors against certain tumours have failed to show any therapeutic benefit. One major reason for this lack of success may be the apparent uncertainty about the precise spectrum of inhibitory activity required. Since tumour necrosis factor alpha (TNFα), a key mediator in colonic inflammation, promotes MMP production in a dose‐dependent manner, the therapeutic success of anti‐TNFα agents against IBD motivated us to re‐evaluate the therapeutic potential of MMP inhibition. First, using a quantitative polymerase chain reaction (PCR), western blotting, and zymography, we determined which MMPs were relevant to experimental colitis induced in mice by dextran sulphate sodium. Next, we examined a distinct role for MAPK and NFκB signalling pathways in the regulation of the expression of these MMP genes. Finally, we examined whether transcriptional regulation of these MMPs, either indirectly using inhibitors of MAPK and/or NFκB signalling pathways or directly using siRNA directed against these MMPs, contributes to the prevention of colitis. Changes in the expression level of colonic MMP‐3 and MMP‐10 preceded the clinical course of colitis. Colitis improved in mice that received these signal inhibitors, together with suppression of MMP expression. Moreover, siRNA that targeted MMP‐3 and MMP‐10 effectively reduced both the transcription of these MMPs and the severity of colitis. We conclude that MMP‐3 and MMP‐10 play a causal role in excess tissue destruction in colitis. Specific inhibition of these MMPs should provide novel therapeutics against IBD. Copyright © 2006 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
In Silico Screening of Antibacterial Compounds from Herbal Sources Against Vibrio cholerae

Background: The prolonged use of antibiotic viz., tetracycline, quinolones, ampicillin, etc., to reduce the infection of cholera, may failed due to the emergence of new Vibrio cholerae antibiotics resistant strains. Moreover, these antibiotics even restricted for patient suffering from severe dehydration. Hence, there is a call to find an alternative therapeutics against V. cholerae. The natures serve different herbs in its lap which might contain several natural therapeutic compounds almost all diseases. Computer-aided designing is the initial steps for screening the novel inhibitors. Objective: To identify and evaluate natural compounds with low side effects with high efficacy against V. cholerae has been done. Materials and Methods: In silico screening, absorption, digestion, metabolism, and excretion (ADME), and docking of herbal compounds have been performed on to the target ToxT (transcriptional activator of V. cholerae). The compound with good ADME properties and drug-likeness property were subjected to docking. Results: From 70 herbal compounds, some compounds such as aloin, campesterol, lupeol, and ursolic acid showed a violation of the rule of five and compounds such as lupeol and beta carotene showed negative binding energy. Luteolin, catechin, brevifolin, etc., compounds were selected based on ADME, drug-likeness property, and docking studies. Conclusion: Two compounds named catechin and luteolin showed better inhibition properties against ToxT and good ADME and drug-likeness property were selected as a better lead molecule for drug development in future. The Genetic Optimization for Ligand Docking fitness score for catechin is 48.74 kcal/mol and luteolin 38.12 kcal/mol. SUMMARY Vibrio cholerae became antibiotic resistance and associated with several cholera epidemic and pandemic. Hence, there is a need to find an alternative therapeutics against V. cholerae. Many herbal compounds present in nature having high medicinal value. From in-silico study, found two compound Luteolin from Tulsi and Catechin from Green Tea which showed good binding energy and druggish property. Abbreviations used: V. cholerae: Vibrio cholera, ADME: Absorption, digestion, metabolism and excretion, CT: Cholera toxin, TCP: Toxin co-regulated Pilus, GOLD: Genetic Optimization for Ligand Docking, Asp: Aspartic acid, Arg: Arginine, Lys: Lysine, Thr: Threonine, Tyr: Tyrosine, KEGG: Kyoto encyclopedia of Genes and Genomes.
Improving the accessibility of touchscreen-based mobile devices: Integrating Android-based devices and Braille notetakers

The article presents the concept and pilot implementation of wireless (Blutetooth-based) integration of the Braille notetaker environment and the environment of touchscreen-based devices (such as smartphones) operating under the Android system. Advanced functions of Android-based devices are hardly accessible to the blind using a touchscreen; one aim of such integration is to enable accessing them with a notetaker. Another is to allow the blind who work with notetakers on a daily basis and use common touchscreen-based smartphones and tablets to write using the physical Braille keyboard of a notetaker as well as its editing functions; this would solve many problems encountered and prevent numerous errors made by the blind when using the virtual QWERTY keyboard of a touchscreen-based device. Pilot implementation of the concept included developing a communication protocol for a notetaker operated under Windows CE and an Android-based smartphone; services to be provided to notetakers by smartphones have been developed as well. The implemented services dealt with managing contacts and composing messages - operations that normally require considerable interaction with a QWERTY keyboard. Favourable results of initial research on pilot implementation conducted among the blind indicate a need for further development of this concept.
Surgery for intractable pain in a patient with chronic pancreatitis complicated with biliary obstruction, portal vein stenosis and mesenteric venous collaterals

Pancreatic head resection for chronic pancreatitis is a challenging procedure, in the presence of venous collaterals, cavernous transformation, extensive fibrosis or porto-mesenteric stenosis or thrombosis. We present a surgically treated patient for the intractable pain of chronic pancreatitis. Complications with biliary obstruction and portal vein stenosis/thrombosis resulted in cavernous transformation. A pancreaticoduodenectomy combined with portal vein resection was intended in a 51 year-old male, but the procedure was terminated due to the high risk associated with intraoperative bleeding. The surgical procedure was switched to a Frey procedure, wherein partial pancreatic head resection, drainage of the pancreatic canal and sufficient pain palliation, without an increased risk of intraoperative hemorrhage, was ensured. The procedure was successfully combined with bilio-enteric anastomosis.
The Knowledge Gradient for Sequential Decision Making with Stochastic Binary Feedbacks

We consider the problem of sequentially making decisions that are rewarded by "successes" and "failures" which can be predicted through an unknown relationship that depends on a partially controllable vector of attributes for each instance. The learner takes an active role in selecting samples from the instance pool. The goal is to maximize the probability of success, either after the offline training phase or minimizing regret in online learning. Our problem is motivated by real-world applications where observations are time consuming and/or expensive. With the adaptation of an online Bayesian linear classifier, we develop a knowledge-gradient type policy to guide the experiment by maximizing the expected value of information of labeling each alternative, in order to reduce the number of expensive physical experiments. We provide a finite-time analysis of the estimated error and demonstrate the performance of the proposed algorithm on both synthetic problems and benchmark UCI datasets.
A Random Text Model for the Generation of Statistical Language Invariants

Abstract A novel random text generation model is introduced. Unlike in previous random text models, that mainly aim at producing a Zipfian distribution of word frequencies, our model also takes the properties of neighboring co-occurrence into account and introduces the notion of sentences in random text. After pointing out the defi-ciencies of related models, we provide a generation process that takes neither the Zipfian distribution on word frequencies nor the small-world structure of the neighboring co-occurrence graph as a constraint. Nevertheless, these distribu-tions emerge in the process. The distribu-tions obtained with the random generation model are compared to a sample of natu-ral language data, showing high agree-ment also on word length and sentence length. This work proposes a plausible model for the emergence of large-scale characteristics of language without as-suming a grammar or semantics. 1 Introduction G. K. Zipf (1949) discovered that if all words in a sample of natural language are arranged in de-creasing order of frequency, then the relation be-tween a words frequency and its rank in the list follows a power-law. Since then, a significant amount of research in the area of quantitative lin-guistics has been devoted to the question how this property emerges and what kind of processes gen-erate such Zipfian distributions. The relation between the frequency of a word at rank r and its rank is given by f(r) µ r
The endophenotype concept in psychiatric genetics

The idea that some phenotypes bear a closer relationship to the biological processes that give rise to psychiatric illness than diagnostic categories has attracted considerable interest. Much effort has been devoted to finding such endophenotypes, partly because it is believed that the genetic basis of endophenotypes will be easier to analyse than that of psychiatric disease. This belief depends in part on the assumption that the effect sizes of genetic loci contributing to endophenotypes are larger than those contributing to disease susceptibility, hence increasing the chance that genetic linkage and association tests will detect them. We examine this assumption by applying meta-analytical techniques to genetic association studies of endophenotypes. We find that the genetic effect sizes of the loci examined to date are no larger than those reported for other phenotypes. A review of the genetic architecture of traits in model organisms also provides no support for the view that the effect sizes of loci contributing to phenotypes closer to the biological basis of disease is any larger than those contributing to disease itself. While endophenotype measures may afford greater reliability, it should not be assumed that they will also demonstrate simpler genetic architecture.
Time-Domain Multi-bit Delta\Sigma Analog-to-Digital Converter

A multi-bit representation in the time domain has been applied to a Delta\Sigma analog-to-digital converter (ADC), which consists of an asynchronous Delta\Sigma modulator (ADSM) and a time-to-digital converter (TDC). Current-mode circuits are included in the ADSM to suppress the variation in the node voltage. The TDC is based on a ring oscillator-based TDC comprised of four stages of differential delay element followed by a counter and a phase detector. The 1st-order noise-shaping was experimentally obtained for the TDC fabricated by using 0.18-µ m standard CMOS technology. A successful operation of the ADC has been obtained by transistor-level circuit simulation.
Refining the Openness–Performance Relationship: Construct Specificity, Contextualization, Social Skill, and the Combination of Trait Self- and Other-Ratings

abstract Scholars have raised concerns that openness to experience has ambiguous relationships with performance. In this study, we examine both openness and one of its more narrow dimensions, learning approach. In addition, the research context was made narrow (i.e., higher education academic performance in science), and social skill was interactively combined with peer- and self-rated personality in the prediction of academic performance (i.e., grades). We found that those high on learning approach, but not openness, 1 year later performed better academically than those lower on learning approach. Furthermore, for those high and average on social skill, increased peer-rated learning approach was associated with higher performance. Finally, the combination of self- and other-ratings of learning approach was a better predictor of academic performance than the combination of self- and other-ratings of openness. The relationship of openness with academic performance benefits from narrowing predictors and criteria, framing the study within a relevant context, accounting for social skill, and combining self- and other trait ratings.
Topology-aware resource allocation for data-intensive workloads

This paper proposes an architecture for optimized resource allocation in Infrastructure-as-a-Service (IaaS)-based cloud systems. Current IaaS systems are usually unaware of the hosted application's requirements and therefore allocate resources independently of its needs, which can significantly impact performance for distributed data-intensive applications. To address this resource allocation problem, we propose an architecture that adopts a "what if" methodology to guide allocation decisions taken by the IaaS. The architecture uses a prediction engine with a lightweight simulator to estimate the performance of a given resource allocation and a genetic algorithm to find an optimized solution in the large search space. We have built a prototype for Topology-Aware Resource Allocation (TARA) and evaluated it on a 80 server cluster with two representative MapReduce-based benchmarks. Our results show that TARA reduces the job completion time of these applications by up to 59% when compared to application-independent allocation policies.
On the Statistical Invariance for Adaptive Radar Detection in Partially Homogeneous Disturbance Plus Structured Interference

This paper deals with the problem of adaptive vector subspace signal detection in partially homogeneous Gaussian disturbance and structured (unknown) deterministic interference within the framework of invariance theory. It is first proved that the Maximal Invariant Statistic (MIS) for the problem at hand is scalar-valued and coincides with the well-known adaptive normalized matched filter evaluated after data projection in the complementary subspace of the interfering signal. Second, the statistical characterization of the MIS under both hypotheses is derived. Then, it is shown the statistical equivalence of (two-step) generalized-likelihood ratio test, Rao and Wald tests, as well as the more recently considered Durbin and Gradient test, to the above statistic. Finally, simulation results are provided to confirm our findings and analyze the performance trend of the MIS with the relevant parameters.
Principal component analysis and radiative transfer modelling of Spitzer Infrared Spectrograph spectra of ultraluminous infrared galaxies

The mid-infrared spectra of ultraluminous infrared galaxies (ULIRGs) contain a variety of spectral features that can be used as diagnostics to characterize the spectra. However, such diagnostics are biased by our prior prejudices on the origin of the features. Moreover, by using only part of the spectrum they do not utilize the full information content of the spectra. Blind statistical techniques such as principal component analysis (PCA) consider the whole spectrum, find correlated features and separate them out into distinct components. We further investigate the principal components (PCs) of ULIRGs derived in Wang et al. We quantitatively show that five PCs are optimal for describing the Infrared Spectrograph spectra. These five components (PC1–PC5) and the mean spectrum provide a template basis set that reproduces spectra of all z < 0.35 ULIRGs within the noise. For comparison, the spectra are also modelled with a combination of radiative transfer models of both starbursts and the dusty torus surrounding active galactic nuclei (AGN). The five PCs typically provide better fits than the models. We argue that the radiative transfer models require a colder dust component and have difficulty in modelling strong polycyclic aromatic hydrocarbon features. Aided by the models we also interpret the physical processes that the PCs represent. The third PC is shown to indicate the nature of the dominant power source, while PC1 is related to the inclination of the AGN torus. Finally, we use the five PCs to define a new classification scheme using 5D Gaussian mixture modelling and trained on widely used optical classifications. The five PCs, average spectra for the four classifications and the code to classify objects are made available at:
Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitus

OBJECTIVE To evaluate data fragmentation across healthcare centers with regard to the accuracy of a high-throughput clinical phenotyping (HTCP) algorithm developed to differentiate (1) patients with type 2 diabetes mellitus (T2DM) and (2) patients with no diabetes. MATERIALS AND METHODS This population-based study identified all Olmsted County, Minnesota residents in 2007. We used provider-linked electronic medical record data from the two healthcare centers that provide >95% of all care to County residents (ie, Olmsted Medical Center and Mayo Clinic in Rochester, Minnesota, USA). Subjects were limited to residents with one or more encounter January 1, 2006 through December 31, 2007 at both healthcare centers. DM-relevant data on diagnoses, laboratory results, and medication from both centers were obtained during this period. The algorithm was first executed using data from both centers (ie, the gold standard) and then from Mayo Clinic alone. Positive predictive values and false-negative rates were calculated, and the McNemar test was used to compare categorization when data from the Mayo Clinic alone were used with the gold standard. Age and sex were compared between true-positive and false-negative subjects with T2DM. Statistical significance was accepted as p<0.05. RESULTS With data from both medical centers, 765 subjects with T2DM (4256 non-DM subjects) were identified. When single-center data were used, 252 T2DM subjects (1573 non-DM subjects) were missed; an additional false-positive 27 T2DM subjects (215 non-DM subjects) were identified. The positive predictive values and false-negative rates were 95.0% (513/540) and 32.9% (252/765), respectively, for T2DM subjects and 92.6% (2683/2898) and 37.0% (1573/4256), respectively, for non-DM subjects. Age and sex distribution differed between true-positive (mean age 62.1; 45% female) and false-negative (mean age 65.0; 56.0% female) T2DM subjects. CONCLUSION The findings show that application of an HTCP algorithm using data from a single medical center contributes to misclassification. These findings should be considered carefully by researchers when developing and executing HTCP algorithms.
Unlocking the Influence of Leadership Network Structures on Team Conflict and Viability

This article seeks to test whether a leader's position in the team's informal network strengthens or weakens the leader's team. Based on data collected from 231 employees working in 19 teams in a manufacturing organization, the study tested whether two different leader network centralities in teams' advice networks predicted team conflict and viability. Teams with more prestigious formal leaders (i.e., leaders whom a high proportion of subordinates sought out for advice) experienced lower levels of team conflict and had higher levels of team viability. In contrast, teams with leaders who brokered across subordinates within a team's advice network (i.e., leaders who had advice ties with subordinates who did not have advice ties with each other) reported elevated levels of team conflict and lower levels of team viability, even when controlling for the team leader's prestige. Team conflict mediated the effects of the two leader network positions on team viability.
The Impacts of Automatic Scaffolding on Students' Acquisition of Data Collection Inquiry Skills

We explore in this paper if automated scaffolding delivered via a pedagogical agent within a simulation can help students acquire data collection inquiry skills. Our initial analyses revealed that such scaffolding was effective for helping students who initially did not know two specific skills, designing controlled experiments and testing stated hypotheses, acquire those skills. These results provide evidence towards realizing rigorous, scalable, performance-based assessment of scientific inquiry skills and the efficacy of an automated scaffolding approach. Introduction Science educators and reseachers agree that cultivating inquiry and critical thinking skills are necessary for students to become scientifically literate [1], [2], [3], [4] and to be well-poised for the demands of the knowledge-based economy of the 21st century [5]. However, learning inquiry skills is challenging for students (e.g. [6], [7], [8], [9], [10]). These challenges can lead to many false starts [11], misconceptions [12], and failure to learn targeted science principles [13]. Given the importance of inquiry and these challenges, it is important to understand how best to foster learning of these skills (cf. [13], [14]). Relevant to this paper, we consider skills at designing and conducting experiments (cf. [1]). Some studies showed that explicitly teaching strategies like controlling for variables [15] can lead to acquisition [16], [17], [18], [19], retention [18], [20], and transfer [16], [17], [18] of the strategy over pure discovery methods. Others showed that long-term, repeated practice also promotes skill acquisition, without instruction [21], [22], [23]. Scaffoldingbased approaches, on the other hand, may strike a balance between these extremes by providing help only when students need it (cf. [24], [25]). For example, providing structure (scaffolding) during open-ended inquiry activities can foster learning (e.g. [26]). Similarly, individualized, real-time feedback may also help students learn inquiry skills [27]. Towards promoting the learning of data collection inquiry skills, we determine if scaffolding can help students acquire two such skills [1]: testing one’s stated hypothesis, and designing controlled experiments [28], [29]. This work is conducted within the context of a web-based inquiry learning environment, called Inq-ITS, in which students conduct inquiry using simulations [29]. The learning environment was augmented to provide real-time, automated scaffolding as students collect data with the simulation. This scaffolding is driven, in part, by data-mined models that determine when students are off-track [30], [28]. A randomized, controlled experiment was conducted with two groups of students, those who received data collection scaffolds and those who did not, to determine if scaffolding impacted data collection skill acquisition in one set of inquiry activities on phase change, a middle school physical science topic. We hypothesize that our automated scaffolding approach that provides just-in-time feedback can improve students’ data collection skills.