Miracle-dz/UniFAR-FTUs huggingface dataset:
Lack of Efficacy of Ulinastatin Therapy During Cardiopulmonary Bypass Surgery

Background:It was believed that inflammatory response induced by cardiopulmonary bypass (CPB) was blamed for complications after cardiac surgery. To improve the outcome, many pharmacological interventions have been applied to attenuate inflammatory response during CPB. The objective of this study was to investigate the effect of ulinastatin (urinary trypsin inhibitor [UTI]) on outcome after CPB surgery. Methods:Totally, 208 patients undergoing elective valves replacement between November 2013 and September 2014 were divided into Group U (n = 70) and Group C (n = 138) based on they received UTI or not. Categorical variables were compared between groups using Fisher's exact test, and continuous variables using unpaired Student's t-test or Mann–Whitney U-test. One-way analysis of variance and Dunnett's or Tukey's tests were used to compare values at different time points within the same group. The risk of outcomes was estimated and adjusted by multivariable logistic regression, propensity scoring, and mixed-effect models for all measured variables. Results:Both the serious complications in total, including death, acute lung injury, acute respiratory distress syndrome and acute kidney injury, and the other complications, including hemodialysis, infection, re-incubation, and tracheotomy were similar between the two groups (P > 0.05). After adjusted by multivariable logistic regression and the propensity score, UTI still cannot be found any benefit to improve any outcomes after cardiac surgery. Also, no statistical differences with regard to duration of postoperative mechanical ventilation, the length of Intensive Care Unit and hospital stays (P > 0.05). Conclusion:UTI did not improve postoperative outcomes in our patients after cardiopulmonary bypass surgery.
A Prefiltered Cuckoo Search Algorithm with Geometric Operators for Solving Sudoku Problems

The Sudoku is a famous logic-placement game, originally popularized in Japan and today widely employed as pastime and as testbed for search algorithms. The classic Sudoku consists in filling a 9 × 9 grid, divided into nine 3 × 3 regions, so that each column, row, and region contains different digits from 1 to 9. This game is known to be NP-complete, with existing various complete and incomplete search algorithms able to solve different instances of it. In this paper, we present a new cuckoo search algorithm for solving Sudoku puzzles combining prefiltering phases and geometric operations. The geometric operators allow one to correctly move toward promising regions of the combinatorial space, while the prefiltering phases are able to previously delete from domains the values that do not conduct to any feasible solution. This integration leads to a more efficient domain filtering and as a consequence to a faster solving process. We illustrate encouraging experimental results where our approach noticeably competes with the best approximate methods reported in the literature.
CABOB: A Fast Optimal Algorithm for Winner Determination in Combinatorial Auctions

Combinatorial auctions where bidders can bid on bundles of items can lead to more economically efficient allocations, but determining the winners is \scr{N}\scr{P}-complete and inapproximable. We present CABOB, a sophisticated optimal search algorithm for the problem. It uses decomposition techniques, upper and lower bounding (also across components), elaborate and dynamically chosen bid-ordering heuristics, and a host of structural observations. CABOB attempts to capture structure in any instance without making assumptions about the instance distribution. Experiments against the fastest prior algorithm, CPLEX 8.0, show that CABOB is often faster, seldom drastically slower, and in many cases drastically faster---especially in cases with structure. CABOB's search runs in linear space and has significantly better anytime performance than CPLEX. We also uncover interesting aspects of the problem itself. First, problems with short bids, which were hard for the first generation of specialized algorithms, are easy. Second, almost all of the CATS distributions are easy, and the run time is virtually unaffected by the number of goods. Third, we test several random restart strategies, showing that they do not help on this problem---the run-time distribution does not have a heavy tail.
Molecular and Cellular Pathobiology A Unique Metastasis Gene Signature Enables Prediction of Tumor Relapse in Early-Stage Hepatocellular Carcinoma Patients

Metastasis-related recurrence often occurs in hepatocellular carcinoma (HCC) patients who receive curative therapies. At present, it is challenging to identify patients with high risk of recurrence, which would warrant additional therapies. In this study, we sought to analyze a recently developedmetastasis-related gene signature for its utility in predictingHCC survival, using 2 independent cohorts consisting of a total of 386 patientswho received radical resection. Cohort 1 contained 247 predominantly HBV-positive cases analyzed with an Affymetrix platform, whereas cohort 2 contained 139 cases with mixed etiology analyzed with the NCI Oligo Set microarray platform. We employed a survival risk prediction algorithm with training, test, and independent cross-validation strategies and found that the gene signature is predictive of overall and disease-free survival. Importantly, risk was significantly predicted independently of clinical characteristics and microarray platform. In addition, survival predictionwas successful in patientswith early disease, such as small (<5 cm indiameter) and solitary tumors, and the signature predictedparticularlywell for early recurrence risk (<2 years), especiallywhen combinedwith serum alpha fetoprotein or tumor staging. In conclusion, we have shown in 2 independent cohorts withmixed etiologies and ethnicity that the metastasis gene signature is a useful tool to predict HCC outcome, suggesting the general utility of this classifier.We recommend theuse of this classifier as amolecular diagnostic test to assess the risk that anHCC patient will develop tumor relapse within 2 years after surgical resection, particularly for those with earlystage tumors and solitary presentation. Cancer Res; 70(24); 10202–12. 2010 AACR.
Enhancing by saliency-guided decolorization

This paper introduces an effective decolorization algorithm that preserves the appearance of the original color image. Guided by the original saliency, the method blends the luminance and the chrominance information in order to conserve the initial color disparity while enhancing the chromatic contrast. As a result, our straightforward fusing strategy generates a new spatial distribution that discriminates better the illuminated areas and color features. Since we do not employ quantization or a per-pixel optimization (computationally expensive), the algorithm has a linear runtime, and depending on the image resolution it could be used in real-time applications. Extensive experiments and a comprehensive evaluation against existing state-of-the-art methods demonstrate the potential of our grayscale operator. Furthermore, since the method accurately preserves the finest details while enhancing the chromatic contrast, the utility and versatility of our operator have been proved for several other challenging applications such as video decolorization, detail enhancement, single image dehazing and segmentation under different illuminants.
Design considerations for a network of information

The existing Internet ecosystem is a result of decades of evolution. It has managed to scale well beyond the original aspirations. Evolution, though, highlighted a certain degree of inadequacies that is well documented. In this position paper we present the design considerations for a re-architected global networking architecture which delivers dissemination and non-dissemination objects only to consenting recipients, reducing unwanted traffic, linking information producers with consumers independently of the hosts involved, and connects the digital with the physical world. We consider issues ranging from the proposed object identifier/locator split to security and trust as we transition towards a Network of Information and relate our work with the emerging paradigm of publish/subscribe architectures. We introduce the fundamental components of a Network of Information, i.e., name resolution, routing, storage, and search, and close this paper with a discussion about future work.
R&D employee's intention to exchange knowledge within open innovation projects

The existing literature on open innovation strongly emphasizes on the organizational level, while neglecting the people side and especially the perspective of employees working in OIprojects. This study analyzes determinants of R&D employees' knowledge exchange in OIprojects by means of the theory of planned behavior (TPB) and a literature review regarding motivational factors influencing individuals' attitude toward knowledge exchange. An online survey amongst 133 R&D employees was conducted and data was analyzed through variancebased structural equation modeling (PLS). In our sample, subjective norm had by far the strongest impact on employees' intention to exchange their knowledge in OI-projects, although attitude and perceived behavioral control also showed highly significant and positive effects on intention. From all five identified motivational factors, enjoyment in helping was found to have the strongest influence on attitude, followed by intrinsic rewards and sense of self-worth. Extrinsic rewards and reciprocity did not show any effect on attitude.
Domain Specific Speech Acts for Spoken Language Translation

We describe a coding scheme for machine translation of spoken taskoriented dialogue. The coding scheme covers two levels of speaker intention − domain independent speech acts and domain dependent domain actions. Our database contains over 14,000 tagged sentences in English, Italian, and German. We argue that domain actions, and not speech acts, are the relevant discourse unit for improving translation quality. We also show that, although domain actions are domain specific, the approach scales up to large domains without an explosion of domain actions and can be coded with high inter-coder reliability across research sites. Furthermore, although the number of domain actions is on the order of ten times the number of speech acts, sparseness is not a problem for the training of classifiers for identifying the domain action. We describe our work on developing high accuracy speech act and domain action classifiers, which is the core of the source language analysis module of our NESPOLE machine translation system.
Real-time cloth simulation with sparse particles and curved faces

In this paper, we present a novel technique for real-time cloth simulation. The method combines dynamic simulation and geometric techniques. Only a small number of particles (a few hundred at maximum) are controlled using dynamic simulation to simulate global cloth behaviors such as waving and bending. The cloth surface is then smoothed based on the elastic forces applied to each particle and the distance between each pair of adjacent particles. Using this geometric smoothing, local cloth behaviors such as twists and wrinkles are efficiently simulated. The proposed method is very simple, and is easy to implement and integrate with existing particle-based systems. We also describe a particle-based simulation system for efficient simulation with sparse particles. The proposed method has animated a skirt with rich details in real-time.
Genome-Wide Association Mapping of Phenotypic Traits Subject to a Range of Intensities of Natural Selection in Timema cristinae*

The genetic architecture of adaptive traits can reflect the evolutionary history of populations and also shape divergence among populations. Despite this central role in evolution, relatively little is known regarding the genetic architecture of adaptive traits in nature, particularly for traits subject to known selection intensities. Here we quantitatively describe the genetic architecture of traits that are subject to known intensities of differential selection between host plant species in Timema cristinae stick insects. Specifically, we used phenotypic measurements of 10 traits and 211,004 single-nucleotide polymorphisms (SNPs) to conduct multilocus genome-wide association mapping. We identified a modest number of SNPs that were associated with traits and sometimes explained a large proportion of trait variation. These SNPs varied in their strength of association with traits, and both major and minor effect loci were discovered. However, we found no relationship between variation in levels of divergence among traits in nature and variation in parameters describing the genetic architecture of those same traits. Our results provide a first step toward identifying loci underlying adaptation in T. cristinae. Future studies will examine the genomic location, population differentiation, and response to selection of the trait-associated SNPs described here.
General bias/variance decomposition with target independent variance of error functions derived from the exponential family of distributions

An important theoretical tool in machine learning is the bias/variance decomposition of the generalization error. It was introduced for the mean square error. The bias/variance decomposition includes the concept of the average predictor. The bias is the error of the average predictor, and the systematic part of the generalization error, while the variability around the average predictor is the variance. We present a large group of error functions with the same desirable properties as the bias/variance decomposition. The error functions are derived from the exponential family of distributions via the statistical deviance measure. We prove that this family of error functions contains all error functions decomposable in that manner. We state the connection between the bias/variance decomposition and the ambiguity decomposition and present a useful approximation of ambiguity that is quadratic in the ensemble coefficients.
Mucosal NOD2 expression and NF‐&kgr;B activation in pediatric Crohn's disease

Background: Recent advances in the pathogenesis of Crohn's disease (CD) have suggested that an aberrant innate immune response initiates the cascade of events leading to T‐cell activation and to disease development. NOD2 protein, which is mainly expressed by innate immunity cells, appears to play a key role against bacteria by triggering a host defense response through the activation of the transcriptor factor NF‐&kgr;B and a consequent proinflammatory cytokine production. The present study was aimed at investigating the expression and activity of NOD2, NF‐&kgr;B, and of 2 proinflammatory cytokines, TNF&agr; and IL‐1&bgr;, in mucosal biopsies of CD affected children compared to healthy controls. Methods: In all, 22 children with active CD and 10 matched controls were entered in the study. mRNA and protein expressions were detected using reverse‐transcriptase polymerase chain reaction (RT‐PCR) and Western blot; NF‐&kgr;B binding activity was assessed by electromobility gel shift assay (EMSA). Results: NOD2 and IL‐1&bgr; mRNAs were upregulated in CD children. Protein levels of NOD2, TNF&agr;, and nuclear NF‐&kgr;B, as well as the binding activity of NF‐&kgr;B to a consensus DNA sequence, were significantly increased in inflamed mucosa of patients as compared to controls. Moreover, NF‐&kgr;B activity was strongly upregulated in patients also when bound to the NOD2 promoter site. No difference was seen between patients and controls when NF‐&kgr;B binding activity was determined in the uninflamed tissue. Conclusions: This study suggests that altered mechanisms regulating NOD2 induction, NF‐&kgr;B activation and cytokine production may contribute to dysregulate the innate immune response underlying pediatric CD.
Highly concurrent shared storage

Switched system-area networks enable thousands of storage devices to be shared and directly accessed by end hosts, promising databases and file systems highly scalable, reliable storage. In such systems, hosts perform access tasks (read and write) and management tasks (storage migration and reconstruction of data on failed devices.) Each task translates into multiple phases of low-level device I/Os, so that concurrent host tasks accessing shared devices can corrupt redundancy codes and cause hosts to read inconsistent data. Concurrent control protocols that scale to large system sizes are required in order to coordinate on-line storage management and access tasks. In this paper we identify, the tasks that storage controllers must perform, and propose an approach which allows these tasks to be composed from basic operations-called base storage transactions (BSTs)-such that correctness requires only the serializability of the BSTs and not of the parent tasks. We present highly scalable distributed protocols which exploit storage technology trends and BST properties to achieve serializability while coming within a few percent of ideal performance.
Curriculum-Based Measurement of Reading: Is 6 Weeks of Daily Progress Monitoring Enough?

Abstract. Curriculum-based measurement of reading (CBM-R) is used in research and practice to estimate the level and trend of student achievement. Although there is limited empirical or psychometric support to guide CBM-R progress monitoring practices, derived trend estimates are used to inform a variety of educational decisions including evaluations of program effects and a student's response to reading interventions. This study evaluated the quality of trend estimates derived from CBM-R progress monitoring data and evaluated the external validity of the results from previous simulation studies. Forty second-grade students participated in a 6-week daily schedule of CBM-R progress monitoring. The data were used to evaluate the validity, reliability, precision, and general quality of trend estimates. The results suggested that progress monitoring outcomes were more reliable, valid, and precise as the duration of progress monitoring and number of data points increased; however, the quality of estimates was only marginal after 6 weeks of progress monitoring. The results and corresponding conclusions provide evidence to support the external validity of previous simulation-based studies. Implications and future directions for research are discussed.
Nodal mutant eXtraembryonic ENdoderm (XEN) stem cells upregulate markers for the anterior visceral endoderm and impact the timing of cardiac differentiation in mouse embryoid bodies

Summary Interactions between the endoderm and mesoderm that mediate myocardial induction are difficult to study in vivo because of the small size of mammalian embryos at relevant stages. However, we and others have demonstrated that signals from endodermal cell lines can influence myocardial differentiation from both mouse and human embryoid bodies (EBs), and because of this, assays that utilize embryonic stem (ES) cells and endodermal cell lines provide excellent in vitro models to study early cardiac differentiation. Extraembryonic endoderm (XEN) stem cells have a particular advantage over other heart-inducing cell lines in that they can easily be derived from both wild type and mutant mouse blastocysts. Here we describe the first isolation of a Nodal mutant XEN stem cell line. Nodal−/− XEN cell lines were not isolated at expected Mendelian ratios, and those that were successfully established, showed an increase in markers for the anterior visceral endoderm (AVE). Since AVE represents the heart-inducing endoderm in the mouse, cardiac differentiation was compared in EBs treated with conditioned medium (CM) collected from wild type or Nodal−/− XEN cells. EBs treated with CM from Nodal−/− cells began beating earlier and showed early activation of myocardial genes, but this early cardiac differentiation did not cause an overall increase in cardiomyocyte yield. By comparison, CM from wild type XEN cells both delayed cardiac differentiation and caused a concomitant increase in overall cardiomyocyte formation. Detailed marker analysis suggested that early activation of cardiac differentiation by Nodal−/− XEN CM caused premature differentiation and subsequent depletion of cardiac progenitors.
Some spinor-curvature identities

We describe a class of spinor-curvature identities which exist for Riemannian or Riemann--Cartan geometries. Each identity relates an expression quadratic in the covariant derivative of a spinor field with an expression linear in the curvature plus an exact differential. Certain special cases in three and four dimensions which have been or could be used in applications to general relativity are noted.
Robust Demographic Inference from Genomic and SNP Data

We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with , the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets.
Coins make quantum walks faster

We show how to search <i>N</i> items arranged on a √<i>N</i> × √<i>N</i> grid in time <i>O</i>(√<i>N</i> log <i>N</i>), using a discrete time quantum walk. This result for the first time exhibits a significant difference between discrete time and continuous time walks without coin degrees of freedom. since it has been shown recently that such a continuous time walk needs time Ω(<i>N</i>) to perform the same task. Our result improves on a previous bound for quantum local search by Aaronson and Ambainis. We generalize our result to 3 and more dimensions where the walk yields the optimal performance of <i>O</i>(√N) and give several extensions of quantum walk search algorithms and generic expressions for its performance for general graphs. The coin-flip operation needs to be chosen judiciously: we show that another "natural" choice of coin gives a walk that takes Ω(<i>N</i>) steps. We also show that in 2 dimensions it is sufficient to have a two-dimensional coin-space to achieve the time <i>O</i>(√<i>N</i> log <i>N</i>).