Miracle-dz/UniFAR-FTUs huggingface dataset:
Nutrient-Independent and Nutrient-Dependent Factors Stimulate Protein Synthesis in Colostrum-Fed Newborn Pigs

ABSTRACT: We hypothesized that nonnutrient components, including growth factors, present in colostrum contribute to the stimulation of protein synthesis in colostrum-fed neonatal pigs. We studied neonatal pigs fed mature milk, colostrum, or a formula containing a macronutrient composition comparable to that of colostrum for 24 h. We measured the circulating concentrations of insulin, insulin-like growth factor I, glucose, and amino acids at intervals throughout the 24-h period, after which we measured in vivo protein synthesis using a flooding dose of [3H]phenylalanine. The rates of protein synthesis in several tissues measured after 24 h of feeding were greater than those we reported previously after 6 h of feeding. The acute (within 6 h) stimulation of protein synthesis in visceral and skeletal muscle tissues of neonatal pigs fed milk, colostrum, or formula was primarily influenced by nutrient intake and associated with rapid secretion of insulin. Indirect evidence suggests that intestinal absorption of ingested colostral insulin was minimal. However, the sustained increase in tissue protein synthesis between 6 and 24 h coincided with an increase in circulating insulin-like growth factor I. We found a novel, specific stimulation of skeletal muscle and jejunal protein synthesis in colostrum-fed pigs that can be attributed to some nonnutrient component of colostrum.
Multidimensional convolutional code: progresses and bottlenecks

Multidimensional (m-D, m/spl ges/2) finite support convolutional code has become a new area of research in the multidimensional system and signal processing. While the one-dimensional convolutional code and its variants have been thoroughly understood, the m-D counterpart still lacks unified notation and practical implementation. In this document, the issues related to equivalent encoder, syndrome decoder and the realization of m-D convolutional code are discussed. The application of Grobner basis theory for finding a syndrome decoder matrix is given. In addition, the formulations of optimal realization problem and m-D convolutional code design problem is stated. Recent development and one open-problem are also reported.
Defining Success within a "Successful" Men's NCAA Division I Sport Program.

A coaching philosophy is a personal doctrine, or individual set of experiences and values, that guides a coach’s beliefs, actions, and coaching style (Huber, 2013). The humanistic coaching philosophy involves a collaborative coach/athlete process which considers individual athlete differences, abilities, and goals, with the long-term aim of developing a self-confident and selfregulated athlete (Lyle, 1999). Opposite of the traditional model where success is determined by wins or losses, within the humanistic approach winning is redefined so that the process is emphasized and achievement of individual athlete goals indicates success. Through coach interviews, athlete interviews, training session observations, and artifact collection, the aim of this case study was to explore the coaching philosophy and methods of a successful men’s NCAA Division I cross country coach and explore to what extent they are humanistic. Definition of success emerged as a primary theme where results indicated that while the participant coach was extrinsically motivated by outcome measures (i.e., winning NCAA national championships), his methods ascribed to the humanistic values of striving for individual athlete potential, holistic development, and self-actualization. Findings suggest that while the NCAA espouses to holistic development of the student-athlete, it is hard to separate athletic outcome measures as at least a portion of the definition of success for coaches working within this setting.
Modifications of myofilament protein phosphorylation and function in response to cardiac arrest induced in a swine model

Cardiac arrest is a prevalent condition with a poor prognosis, attributable in part to persistent myocardial dysfunction following resuscitation. The molecular basis of this dysfunction remains unclear. We induced cardiac arrest in a porcine model of acute sudden death and assessed the impact of ischemia and reperfusion on the molecular function of isolated cardiac contractile proteins. Cardiac arrest was electrically induced, left untreated for 12 min, and followed by a resuscitation protocol. With successful resuscitations, the heart was reperfused for 2 h (IR2) and the muscle harvested. In failed resuscitations, tissue samples were taken following the failed efforts (IDNR). Actin filament velocity, using myosin isolated from IR2 or IDNR cardiac tissue, was nearly identical to myosin from the control tissue in a motility assay. However, both maximal velocity (25% faster than control) and calcium sensitivity (pCa50 6.57 ± 0.04 IDNR vs. 6.34 ± 0.07 control) were significantly (p < 0.05) enhanced using native thin filaments (actin+troponin+tropomyosin) from IDNR samples, suggesting that the enhanced velocity is mediated through an alteration in muscle regulatory proteins (troponin+tropomyosin). Mass spectrometry analysis showed that only samples from the IR2 had an increase in total phosphorylation levels of troponin (Tn) and tropomyosin (Tm), but both IR2 and IDNR samples demonstrated a significant shift from mono-phosphorylated to bis-phosphorylated forms of the inhibitory subunit of Tn (TnI) compared to control. This suggests that the shift to bis-phosphorylation of TnI is associated with the enhanced function in IDNR, but this effect may be attenuated when phosphorylation of Tm is increased in tandem, as observed for IR2. There are likely many other molecular changes induced following cardiac arrest, but to our knowledge, these data provide the first evidence that this form cardiac arrest can alter the in vitro function of the cardiac contractile proteins.
FCM Clustering from the View Point of Iteratively Reweighted Least Squares

By alleviating theoretical strictness, a broad class of membership functions can be used in fuzzy c-means (FCM) clustering from the viewpoint of iteratively reweighted least-squares (IRLS) techniques. Clustering characteristics of regular FCM, entropy regularized FCM or deterministic annealing by Rose and our proposed IRLS approaches are compared by using 3D graphics and contour maps. Though an in-depth analysis of theoretical aspect is beyond the scope of this paper, numerical comparisons reveal that IRLS algorithm using different membership functions do not share the same property with the regular FCM and entropy regularized FCM or DA
A subpopulation model to analyze heterogeneous cell differentiation dynamics

MOTIVATION Cell differentiation is steered by extracellular signals that activate a cell type specific transcriptional program. Molecular mechanisms that drive the differentiation can be analyzed by combining mathematical modeling with population average data. For standard mathematical models, the population average data is informative only if the measurements come from a homogeneous cell culture. In practice, however, the differentiation efficiencies are always imperfect. Consequently, cell cultures are inherently mixtures of several cell types, which have different molecular mechanisms and exhibit quantitatively different dynamics. There is an urgent need for data-driven mathematical modeling approaches that can detect possible heterogeneity and, further, recover the molecular mechanisms from heterogeneous data. RESULTS We develop a novel method that models a heterogeneous population using homogeneous subpopulations that evolve in parallel. Different subpopulations can represent different cell types and each subpopulation can have cell type specific molecular mechanisms. We present statistical methodology that can be used to quantify the effect of heterogeneity and to infer the subpopulation specific molecular interactions. After a proof of principle study with simulated data, we apply our methodology to analyze the differentiation of human Th17 cells using time-course RNA sequencing data. We construct putative molecular networks driving the T cell activation and Th17 differentiation and allow the cell populations to be split into two subpopulations in the case of heterogeneous samples. Our analysis shows that the heterogeneity indeed has a statistically significant effect on observed dynamics and, furthermore, our statistical methodology can infer both the subpopulation specific molecular mechanisms and the effect of heterogeneity. AVAILABILITY AND IMPLEMENTATION An implementation of the method is available at http://research.ics. aalto.fi/csb/software/subpop/ CONTACT: jukka.intosalmi@aalto.fi or harri.lahdesmaki@aalto.fiSupplementary information: Supplementary data are available at Bioinformatics online.
Statistical Tests for Network Classifier Evaluation

Recently a number of modeling techniques have been developed for data mining and machine learning in relational and network domains where the instances are not independent and identically distributed (i.i.d.). These methods specifically exploit the statistical dependencies among instances in order to improve classification accuracy. However, there has been little focus on how these same dependencies affect our ability to draw accurate conclusions about the performance of the models. More specifically, the complex link structure and attribute dependencies in network data violate the assumptions of many conventional statistical tests and make it difficult to use these tests to assess the models in an unbiased manner. In this work, we examine the task of within-network classification and the question of whether two algorithms will learn models which will result in significantly different levels of performance. We show that the commonly-used form of evaluation (paired t-test on overlapping network samples) can result in an unacceptable level of Type I error. Furthermore we show that Type I error increases as (1) the correlation among instances increases and (2) the size of the evaluation set increases (i.e., the proportion of labeled nodes in the network decreases). We propose a method for network cross-validation that combined with paired t-tests produces more acceptable levels of Type I error while still providing reasonable levels of statistical power (i.e., Type II error).
Expression of human BRE in multiple isoforms.

BRE, a putative stress-modulating gene, found able to down-regulate TNF-alpha-induced NF-kappaB activation upon overexpression, is now shown in human cells expressed as multiple mRNA isoforms. A total of six isoforms are produced by alternative splicing predominantly at either end of the gene. Predicted from the cDNA sequences of these isoforms, three of them (alpha(a), alpha(b), and alpha(c)) code for BRE of different C-terminus, and the other three (beta(a), beta(b), and beta(c)) may possibly be the nonfunctional counterparts. All human cells examined coexpress all the predominant splice variants, albeit at different ratios. Comparing with normal cells, immortalized human cell lines uniformly express higher levels of BRE. Interestingly, peripheral blood monocytes responded to LPS by down-regulating the expression of all the BRE isoforms, which was however less obvious in the cell line counterpart, THP-1. Isoform alpha(a), which codes for the canonical BRE with a C-terminal peroxisomal targeting sequence, is the most abundant transcript. We propose that the function of BRE and its isoforms is to regulate peroxisomal activities.
Region-specific protein misfolding cyclic amplification reproduces brain tropism of prion strains

Human prion diseases such as Creutzfeldt-Jakob disease are transmissible brain proteinopathies, characterized by the accumulation of a misfolded isoform of the host cellular prion protein (PrP) in the brain. According to the prion model, prions are defined as proteinaceous infectious particles composed solely of this abnormal isoform of PrP (PrPSc). Even in the absence of genetic material, various prion strains can be propagated in experimental models. They can be distinguished by the pattern of disease they produce and especially by the localization of PrPSc deposits within the brain and the spongiform lesions they induce. The mechanisms involved in this strain-specific targeting of distinct brain regions still are a fundamental, unresolved question in prion research. To address this question, we exploited a prion conversion in vitro assay, protein misfolding cyclic amplification (PMCA), by using experimental scrapie and human prion strains as seeds and specific brain regions from mice and humans as substrates. We show here that region-specific PMCA in part reproduces the specific brain targeting observed in experimental, acquired, and sporadic Creutzfeldt-Jakob diseases. Furthermore, we provide evidence that, in addition to cellular prion protein, other region- and species-specific molecular factors influence the strain-dependent prion conversion process. This important step toward understanding prion strain propagation in the human brain may impact research on the molecular factors involved in protein misfolding and the development of ultrasensitive methods for diagnosing prion disease.
Can we do better than using ‘mean GCSE grade’ to predict future outcomes? An evaluation of Generalised Boosting Models

A key predictor of young people’s future outcomes is their level of academic achievement whilst at school. In England this is most commonly measured by achievement in GCSEs. However, not all pupils will have taken the same set of GCSE examinations as, for example, they may make different subject choices. For this reason, GCSE performance is often aggregated into a simple measure such as ‘mean GCSE grade’ before being used in statistical models. This paper investigates the merits of using an alternative method, based upon the relatively new technique of Generalised Boosting Models, which does not require for GCSE results in different subjects to be aggregated together. The importance of this research is that by evaluating the predictive performance of such a method we can ascertain how much useful information is lost in the process of GCSE aggregation. The results show that traditional predictions based upon simple aggregated measures of GCSE attainment are fairly similar to those based upon the more complex approach. This provides some confidence that, for the majority of outcomes, only a small amount of predictive information will be lost through the use of aggregated measures of GCSE performance.
Endothelial Cell-Specific Molecule 2 (ECSM2) Localizes to Cell-Cell Junctions and Modulates bFGF-Directed Cell Migration via the ERK-FAK Pathway

Background Despite its first discovery by in silico cloning of novel endothelial cell-specific genes a decade ago, the biological functions of endothelial cell-specific molecule 2 (ECSM2) have only recently begun to be understood. Limited data suggest its involvement in cell migration and apoptosis. However, the underlying signaling mechanisms and novel functions of ECSM2 remain to be explored. Methodology/Principal Findings A rabbit anti-ECSM2 monoclonal antibody (RabMAb) was generated and used to characterize the endogenous ECSM2 protein. Immunoblotting, immunoprecipitation, deglycosylation, immunostaining and confocal microscopy validated that endogenous ECSM2 is a plasma membrane glycoprotein preferentially expressed in vascular endothelial cells (ECs). Expression patterns of heterologously expressed and endogenous ECSM2 identified that ECSM2 was particularly concentrated at cell-cell contacts. Cell aggregation and transwell assays showed that ECSM2 promoted cell-cell adhesion and attenuated basic fibroblast growth factor (bFGF)-driven EC migration. Gain or loss of function assays by overexpression or knockdown of ECSM2 in ECs demonstrated that ECSM2 modulated bFGF-directed EC motility via the FGF receptor (FGFR)-extracellular regulated kinase (ERK)-focal adhesion kinase (FAK) pathway. The counterbalance between FAK tyrosine phosphorylation (activation) and ERK-dependent serine phosphorylation of FAK was critically involved. A model of how ECSM2 signals to impact bFGF/FGFR-driven EC migration was proposed. Conclusions/Significance ECSM2 is likely a novel EC junctional protein. It can promote cell-cell adhesion and inhibit bFGF-mediated cell migration. Mechanistically, ECSM2 attenuates EC motility through the FGFR-ERK-FAK pathway. The findings suggest that ECSM2 could be a key player in coordinating receptor tyrosine kinase (RTK)-, integrin-, and EC junctional component-mediated signaling and may have important implications in disorders related to endothelial dysfunction and impaired EC junction signaling.
An Empirical Comparison of Radiosity Algorithms

Abstract : This report presents an extensive empirical comparison of matrix, progressive, and wavelet radiosity algorithms for simulating diffuse interreflection in three-dimensional scenes. The algorithms are tested in their basic forms, without advanced variations such as clustering, discontinuity meshing, or Monte Carlo techniques. The three algorithms were implemented in a common code base to facilitate direct empirical comparison. A number of parameterized scenes were designed to test the basic methods' ability to deal with such issues as singularities, occlusion, high reflectance, and scene complexity. Each algorithm was run on the set of scenes at several parameter settings, and results were examined in terms of their error, speed, and memory consumption. For the basic algorithms as we implemented them, our results show: Progressive radiosity with substructuring is beat for simple scenes, but for moderately complex scenes it is outperformed by wavelet radiosity using the Hoar basis. Wavelet methods use an immense amount of memory; without clustering they become totally impractical for complex scenes. The problem is particularly severe for higher order bases, less so for Haar. Visibility handling was also found to be a critical problem with higher order wavelets. This study also provides a general framework for comparisons of global illumination techniques.
Framework of Designing Automata Capable of Modeling Reversibility in Concurrent and Probabilistic Environment

A framework of automata along with its detailed road-map has been proposed here. This automaton will be capable of modeling reversibility, concurrency and probabilistic environment. Along with this, motivations behind the different constructs of frame-work have been provided. The novelty of this framework is in respect of providing a formalism to model full non-determinism along with full probabilistic characterization irrespective of any distinctions made among states and actions, a notation of reversible transitions having a memory structure, and last but not least; a notation of bisimulation which grounds on the field of natural computing. Latter one also draws a new line of discussion.
From Climbing Stairs to Riding Waves

This article provides a model, measure, and theoretical background for understanding group critical thinking. A research study was designed to test group critical thinking and its relationship to the learning style composition of teams. Groups were composed using a randomized block design based on learning style, and responses to a group exercise were coded for type of critical thinking. Results showed support for a group-level measure of critical thinking based on interrater agreement on codes. Some support was found for differences in critical thinking based on learning style. However, critical thinking was not shown to relate to group cohesion, psychological safety, or efficacy. This study marks the first empirical evidence supporting critical thinking at the group level. It provides a measure that can be used in assessing the level of critical thinking for vocational education and assessing team performance on ill-structured problems.
Knowledge Contribution Motivators - An Expectation-Confirmation Approach

Individual knowledge needs to be shared across IS developing organizations to provide information for all types of decisions. Considering knowledge management (KM) as a two-part process of knowledge contribution and knowledge seeking, we focus on the former one as it is (1) the required condition for knowledge sharing and (2) the greater challenge to accomplish by organizations compared to implementing successful knowledge seeking. Distinguishing different types of individual and organizational extrinsic motivators based on self-determination theory, we use expectationconfirmation theory (ECT) to analyze the extent to which software developers’ expectations towards knowledge contributions are fulfilled by organizations. Additionally, showing extrinsic motivators’ importance for software developers to contribute to KM systems, we provide organizations a roadmap for setting favorable conditions. Whereas our consolidation of previous research on knowledge contribution provides guidelines for future research on extrinsic motivators, we contribute to existing theory by applying ECT to the context of KM contribution.
MBest Struct: M-Best diverse sampling for structured tracker

We approach the problem of model-free visual tracking of objects in videos. Modelfree tracking has its state-of-the-art in a class of methods called tracking-by-detection, as shown in recent benchmarks. Some top-performing methods use deep neural networks (i.e., convnets) to solve the learning-based steps of the tracking algorithm (e.g., bounding-box prediction and evaluation). Despite improving accuracy, convnets impose a high computational cost on trackers, limiting their real-time applications. In this paper, we propose to use deep features from a pre-learned deep-convolutional network in a computationally efficient way. Here, we use M-Best diverse-sampling to sample a small yet diverse set of bounding boxes that are likely to contain the tracked object. Given these bounding boxes, our method performs detection using deep features. The resulting tracker, named MBestStruck, uses a high-quality feature representation while being computationally efficient. Our tracking approach compares very well to the state-of-the-art, as shown by experiments done on popular benchmark datasets.
Diagnosis and Assessment of Alcohol Use Disorders Among Adolescents

The diagnostic criteria for alcohol use disorders (AUDs) (i.e., alcohol abuse and alcohol dependence) as defined in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM–IV) were developed largely from research and clinical experience with adults. Little is known about the validity of these criteria when applied to adolescents. Recent epidemiological and clinical studies of AUDs and their symptoms among adolescents have indicated that the DSM–IV criteria have significant limitations when applied to this age group. Diagnostic interviews and screening tools for adolescent AUDs are discussed. Numerous instruments are available that have shown moderate-to-high reliability and validity in assessing AUDs among adolescents.
Assessment of Gestational Age of Nigerian Newborns Using Teeth Calcification

The relationship between fetal teeth calcification and gestational age was evaluated in 54 Nigerian newborn chest radiographs. The first deciduous molar was found to calcify at the 33rd week of gestation and the second molar at the 36th week. The pattern of calcification of fetal teeth is similar to those of Caucasian infants, and there is no significant difference in teeth calcification between the sexes.