Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender and Ultimatum in Pakistan: Revisited

Razzaque (2009) studied the role of gender in the ultimatum game by running experiments on students in various cities in Pakistan. He used standard confirmatory data analysis techniques, which work well in familiar contexts, where relevant hypotheses of interest are known in advance. Our goal in this paper is to demonstrate that exploratory data analysis is much better suited to the study of experimental data where the goal is to discover patterns of interest. Our exploratory re-analysis of the original data set of Razzaque (2009) leads to several new insights. While we re-confirm the main finding of Razzaque regarding the greater generosity of males, additional analysis suggests that this is driven by student subculture in Pakistan, and would not generalise to the population at large. In addition, we find strong effect of urbanisation. Our exploratory data analysis also offers considerable additional insights into the learning process that takes place over the course of a sequence of games.
Business Models for Sustainability

The relevance of business models for corporate performance in general and corporate sustainability in particular has been widely acknowledged in the literature while sustainable entrepreneurship research has started to explore contributions to the sustainability transformation of markets and society. Particularities of the business models of sustainable niche market pioneers have been identified in earlier research, but little is known about the dynamic role of business models for sustainable entrepreneurship processes aiming at upscaling ecologically and socially beneficial niche models or sustainability upgrading of conventional mass market players. Informed by evolutionary economics, we develop a theoretical framework to analyze co-evolutionary business model development for sustainable niche pioneers and conventional mass market players aiming at the sustainability transformation of markets. Core evolutionary processes of business model variation, selection and retention, and evolutionary pathways are identified to support structured analyses of the dynamics between business model innovation and sustainability transformation of markets.
Trading Off Rate and Power Consumption in DSL Systems

DSL systems have traditionally been tuned to maximize rate or margin, subject to constraints on transmitted power. Recently attention is focusing on reducing power consumption. We formulate a new DSL tuning framework in which the line driver power consumption is part of the objective function. The framework uses a model for line driver power consumption as a function of transmitted power, along with the traditional model for data rate as a function of transmitted power. Using this framework, we show that it is possible to achieve significant reductions in line driver power consumption while incurring modest reductions in data rate. To solve the resulting nonlinear problem efficiently, we propose a new heuristic algorithm based on the combination of linearization of the objective and subsequent linear programming techniques. Numerical studies illustrate the tradeoffs that are available in multi-line systems.
Improving the antibody-based evaluation of autoimmune encephalitis

Objective: We tested whether antibody screening samples of patients with suspected autoimmune encephalitis with additional research assays would improve the detection of autoimmune encephalitis compared with standard clinical testing alone. Methods: We examined 731 samples (333 CSF, 182 sera, and 108 pairs) from a cohort of 623 patients who were tested for CNS autoantibodies by the University of Pennsylvania clinical laboratory over a 24-month period with cell-based assays (CBAs) on commercially obtained slides of fixed cells for antibodies to NMDA receptor (NMDAR), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR), γ-aminobutyric acid-B receptor (GABABR), leucine-rich glioma-inactivated 1 (LGI1), contactin-associated protein-like 2 (Caspr2), and glutamic acid decarboxylase (GAD65). In parallel, our research laboratory screened all samples for reactivity to brain sections and for anti-NMDAR using in-house CBAs. Samples with brain reactivity or positive clinical studies were examined with CBAs for a larger panel of antibodies. Results: The clinical laboratory reported positive findings for NMDAR (80 samples), GAD65 (8), LGI1 (5), Caspr2 (2), and GABABR (4). Sixty-five serum samples and 32 CSF samples were indeterminate for one or more antibodies. In our research laboratory, all but 4 positive results were confirmed, 88 of 97 indeterminate results were resolved, and 15 additional samples were found positive (10 NMDAR, 1 AMPAR, 3 LGI1, and 1 Caspr2). Clinical information supported these diagnoses. Overall, informative autoantibodies were detected in 15.5% of cases. Conclusions: Standard clinical laboratory kits were specific, but some tests were insensitive and prone to indeterminate results. Screening with immunohistochemistry for reactivity to brain sections, followed by additional CBAs for cases with brain reactivity, improves the diagnostic accuracy of testing for autoimmune encephalitis.
Toponym matching through deep neural networks

ABSTRACT Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics.
Robust Estimation of Texture Flow via Dense Feature Sampling

Texture flow estimation is a valuable step in a variety of vision related tasks, including texture analysis, image segmentation, shape-from-texture and texture remapping. This paper describes a novel and effective technique to estimate texture flow in an image given a small example patch. The key idea consists of extracting a dense set of features from the example patch where discrete orientations are encapsulated into the feature vector such that rotation can be simulated as a linear shift of the vector. This dense feature space is then compressed by PCA and clustered using EM to produce a set of small set of principal features. Obtaining these principal features at varying image scales, we can compute the per-pixel scale and orientation likelihoods for the distorted texture. The final texture flow estimation is formulated as the MAP solution of a labeling Markov network which is solved using belief propagation. Experimental results on both synthetic and real images demonstrate good results even for highly distorted examples.
Combine non-binary LDPC codes with M-ary orthogonal spread spectrum modulation

Low-density parity-check (LDPC) codes are playing more and more important roles in wireless communications due to their excellent error correction performance. In this paper, we study the combination of non-binary LDPC codes with M-ary orthogonal spread spectrum modulation for bandwidth efficient transmission over AWGN channel. Specifically, we investigate an algorithm for computing symbol soft metric based on Maximum a Posteriori Probability (MAP) criterion. To justify the performance of our proposed scheme, we made extensive simulations for various code rates and codeword lengths.
Different metabolic features of Bacteroides fragilis growing in the presence of glucose and exopolysaccharides of bifidobacteria

Bacteroides is among the most abundant microorganism inhabiting the human intestine. They are saccharolytic bacteria able to use dietary or host-derived glycans as energy sources. Some Bacteroides fragilis strains contribute to the maturation of the immune system but it is also an opportunistic pathogen. The intestine is the habitat of most Bifidobacterium species, some of whose strains are considered probiotics. Bifidobacteria can synthesize exopolysaccharides (EPSs), which are complex carbohydrates that may be available in the intestinal environment. We studied the metabolism of B. fragilis when an EPS preparation from bifidobacteria was added to the growth medium compared to its behavior with added glucose. 2D-DIGE coupled with the identification by MALDI-TOF/TOF evidenced proteins that were differentially produced when EPS was added. The results were supported by RT-qPCR gene expression analysis. The intracellular and extracellular pattern of certain amino acids, the redox balance and the α-glucosidase activity were differently affected in EPS with respect to glucose. These results allowed us to hypothesize that three general main events, namely the activation of amino acids catabolism, enhancement of the transketolase reaction from the pentose-phosphate cycle, and activation of the succinate-propionate pathway, promote a shift of bacterial metabolism rendering more reducing power and optimizing the energetic yield in the form of ATP when Bacteroides grow with added EPSs. Our results expand the knowledge about the capacity of B. fragilis for adapting to complex carbohydrates and amino acids present in the intestinal environment.
Vasoconstrictors in Spinal Anesthesia with Tetracaine—A Comparison of Epinephrine and Phenylephrine

A randomized double-blind, study was conducted in 50 orthopedic patients to determine the effect of epinephrine and phenylephrine on the anesthetic properties of intrathecally administered tetracaine. Two doses of each vasoconstrictor agent were studied: 0.2 mg of epinephrine, 0.3 mg of epinephrine, 1 mg of phenylephrine, and 2 mg of phenylephrine. The results show that both vasoconstrictor agents in the doses used significantly prolong duration of sensory anesthesia and motor blockade produced by the subarachnoid administration of tetracaine. At equipotent doses no differences existed between the ability of epinephrine and phenylephrine to prolong the duration of spinal anesthesia produced by detracting.
School autonomy, leadership and learning: a reconceptualisation

Purpose – The purpose of this paper is to develop a framework for reconceptualising research on school autonomy to redress the limitations of traditional research, strengthen the conceptual links between school autonomy and learning outcomes and offer a range of new strategies for studying the interplay of school autonomy, leadership and learning. Design/methodology/approach – Based on a review of international studies and the findings of the Programme for International Student Assessment (PISA) and the Teaching and Learning International Study (TALIS), the conceptual limitations of and gaps in traditional research on school autonomy in relation to leadership and learning are discussed, and their implications for the development of a new framework are outlined. Findings – The conceptual limitations of traditional research on school autonomy are as follows: internal school autonomy is insufficiently differentiated; too little attention is paid to cultural autonomy and internal structural autonomy at indivi.
Global teleseismic earthquake relocation with improved travel times and procedures for depth determination

We relocate nearly 100,000 events that occurred during the period 1964 to 1995 and are well-constrained teleseismically by arrival-time data reported to the International Seismological Centre (ISC) and to the U.S. Geological Survey's National Earthquake Information Center (NEIC). Hypocenter determination is significantly improved by using, in addition to regional and teleseismic P and S phases, the arrival times of PKiKP, PKPdf, and the teleseismic depth phases pP, pwP, and sP in the relocation procedure. A global probability model developed for later-arriving phases is used to independently identify the depth phases. The relocations are compared to hypocenters reported in the ISC and NEIC catalogs and by other sources. Differences in our epicenters with respect to ISC and NEIC estimates are generally small and regionally systematic due to the combined effects of the observing station network and plate geometry regionally, differences in upper mantle travel times between the reference earth models used, and the use of later-arriving phases. Focal depths are improved substantially over most other independent estimates, demonstrating (for example) how regional structures such as downgoing slabs can severely bias depth estimation when only regional and teleseismic P arrivals are used to determine the hypocenter. The new data base, which is complete to about Mw 5.2 and includes all events for which moment-tensor solutions are available, has immediate application to high-resolution definition of Wadati-Benioff Zones (WBZs) worldwide, regional and global tomographic imaging, and other studies of earth structure.
INFERRING GENE DEPENDENCY NETWORKS FROM GENOMIC LONGITUDINAL DATA : A FUNCTIONAL DATA APPROACH

• A key aim of systems biology is to unravel the regulatory interactions among genes and gene products in a cell. Here we investigate a graphical model that treats the observed gene expression over time as realizations of random curves. This approach is centered around an estimator of dynamical pairwise correlation that takes account of the functional nature of the observed data. This allows to extend the graphical Gaussian modeling framework from i.i. d. data to analyze longitudinal genomic data. The new method is illustrated by analyzing highly replicated data from a genome experiment concerning the expression response of human T-cells to PMA and ionomicin treatment.
A female‐specific desaturase gene responsible for diene hydrocarbon biosynthesis and courtship behaviour in Drosophila melanogaster

Drosophila melanogaster shows sexually dimorphic cuticular hydrocarbons, with monoenes produced in males and dienes produced in females. Here we describe a female‐specific desaturase gene, desatF. RNAi knock‐down led to a dramatic decrease in female dienes and increase in monoenes paralleled with an increase in copulation latency and a decrease in courtship index and copulation attempts by the males. The desatF gene was also expressed in females from D. sechellia, rich in dienes, but not D. simulans, which produce only monoenes. When hydrocarbons were feminized in D. melanogaster males by targeted expression of the transformer gene, the expression of desatF occurred. These results strongly suggest that desatF is a crucial enzyme for female pheromone biosynthesis and courtship behaviour in D. melanogaster.
Genealogical face recognition based on UB KinFace database

In this paper, we consider a challenging problem raised in biometric recently, genealogical face recognition. Practically, kinship can be proved via several methods, i.e., gene match. We argue in this paper that merely based on facial appearance can we present an accepted result on kinship verification, though there is remarkable appearance variance between kinship members. Essentially, as an extension of our former study, we proceed with the following work. First, an extended kinship database named “UB KinFace Ver2.0” is introduced. Second, we propose to use robust local Gabor filters to extract genetic-invariant features. Finally, metric learning and transfer subspace learning are adopted to abridge the great discrepancy between children and their old parents. The key idea is to take advantage of some intermediate data, e.g. young parents, close to both source and target data. Experimental results demonstrate the effectiveness of our kinship verification methods.
MMM-QSAR Recognition of Ribonucleases without Alignment: Comparison with an HMM Model and Isolation from Schizosaccharomyces pombe, Prediction, and Experimental Assay of a New Sequence

The study of type III RNases constitutes an important area in molecular biology. It is known that the pac1+ gene encodes a particular RNase III that shares low amino acid similarity with other genes despite having a double-stranded ribonuclease activity. Bioinformatics methods based on sequence alignment may fail when there is a low amino acidic identity percentage between a query sequence and others with similar functions (remote homologues) or a similar sequence is not recorded in the database. Quantitative structure-activity relationships (QSAR) applied to protein sequences may allow an alignment-independent prediction of protein function. These sequences of QSAR-like methods often use 1D sequence numerical parameters as the input to seek sequence-function relationships. However, previous 2D representation of sequences may uncover useful higher-order information. In the work described here we calculated for the first time the spectral moments of a Markov matrix (MMM) associated with a 2D-HP-map of a protein sequence. We used MMMs values to characterize numerically 81 sequences of type III RNases and 133 proteins of a control group. We subsequently developed one MMM-QSAR and one classic hidden Markov model (HMM) based on the same data. The MMM-QSAR showed a discrimination power of RNAses from other proteins of 97.35% without using alignment, which is a result as good as for the known HMM techniques. We also report for the first time the isolation of a new Pac1 protein (DQ647826) from Schizosaccharomyces pombe strain 428-4-1. The MMM-QSAR model predicts the new RNase III with the same accuracy as other classical alignment methods. Experimental assay of this protein confirms the predicted activity. The present results suggest that MMM-QSAR models may be used for protein function annotation avoiding sequence alignment with the same accuracy of classic HMM models.
Comparative analysis of NK/T‐cell lymphoma and peripheral T‐cell lymphoma in Korea: Clinicopathological correlations and analysis of EBV strain type and 30‐bp deletion variant LMP1

Natural killer/T‐cell lymphoma (NKTL) and peripheral T‐cell lymphomas (PTCL) are prevalent in the Asian population and exhibit a high association with the Epstein–Barr virus (EBV). Moreover, differentiation of these two groups is often difficult and problematic. We investigated 35 cases of NKTL (22 nasal cases and 13 extranasal cases) and 30 cases of PTCL in terms of their clinical features, immunohistology, EBV positivity, EBV strain‐type polymorphism and latent membrane protein 1 (LMP1) deletion variant distribution. Eighteen cases (82%) of nasal NKTL and seven (54%) of extranasal NKTL showed EBV positivity by EBV in situ hybridization. Fifteen cases (50%) of PTCL revealed EBV positivity. EBV strain type A was predominant in NKTL (18:5), and EBV strain types A and B were distributed evenly in PTCL (6:6). EBV‐positive patients had significantly shorter survival than EBV‐negative patients (P < 0.05), and EBV positivity correlated with advanced clinical stage (P < 0.05). Patients harboring type A EBV showed slightly poorer prognoses than those having type B, though it was not obviously statistically different (P = 0.07). The LMP1 deletion variant was prevalent in both NKTL (three wild‐type LMP1, 15 deletion variants) and PTCL (three wild‐type LMP1, eight deletion variants, two coexistent forms) patients, but did not have prognostic impact. Our results indicate that EBV acts as a negative prognostic factor in NKTL and PTCL, and that the intrinsic properties of a specific viral strain might influence the clinical behavior of these diseases.
Comparing Active Shape Models with Active Appearance Models

Statistical models of the shape and appearance of image structures can be matched to new images using both the Active Shape Model [7] algorithm and the Active Appearance Model algorithm [2]. The former searches along profiles about the current model point positions to update the current estimate of the shape of the object. The latter samples the image data under the current instance and uses the difference between model and sample to update the appearance model parameters. In this paper we compare and contrast the two algorithms, giving the results of experiments testing their performance on two data sets, one of faces, the other of structures in MR brain sections. We find that the ASM is faster and achieves more accurate feature point location than the AAM, but the AAM gives a better match to the texture.
Dynamic Moral Judgments and Emotions

We may experience strong moral outrage when we read a news headline that describes a prohibited action, but when we gain additional information by reading the main news story, do our emotional experiences change at all, and if they do in what way do they change? In a single online study with 80 participants the aim was to examine the extent to which emotional experiences (disgust, anger) and moral judgments track changes in information about a moral scenario. The evidence from the present study suggests that we systematically adjust our moral judgments and our emotional experiences as a result of exposure to further information about the morally dubious action referred to in a moral scenario. More specifically, the way in which we adjust our moral judgments and emotions appears to be based on information signalling whether a morally dubious act is permitted or prohibited.
MicroRNA-449a Overexpression, Reduced NOTCH1 Signals and Scarce Goblet Cells Characterize the Small Intestine of Celiac Patients

MiRNAs play a relevant role in regulating gene expression in a variety of physiological and pathological conditions including autoimmune disorders. MiRNAs are also important in the differentiation and function of the mouse intestinal epithelium. Our study was aimed to look for miRNA-based modulation of gene expression in celiac small intestine, and particularly for genes involved in cell intestinal differentiation/proliferation mechanisms. A cohort of 40 children (20 with active CD, 9 on a gluten-free diet (GFD), and 11 controls), were recruited at the Paediatrics Department (University of Naples Federico II). The expression of 365 human miRNAs was quantified by TaqMan low-density arrays. We used bioinformatics to predict putative target genes of miRNAs and to select biological pathways. The presence of NOTCH1, HES1, KLF4, MUC-2, Ki67 and beta-catenin proteins in the small intestine of CD and control children was tested by immunohistochemistry. The expression of about 20% of the miRNAs tested differed between CD and control children. We found that high miR-449a levels targeted and reduced both NOTCH1 and KLF4 in HEK-293 cells. NOTCH1, KLF4 signals and the number of goblet cells were lower in small intestine of children with active CD and in those on a GFD than in controls, whereas more nuclear beta-catenin staining, as a sign of the WNT pathway activation, and more Ki67 staining, as sign of proliferation, were present in crypts from CD patients than in controls. In conclusion we first demonstrate a miRNA mediated gene regulation in small intestine of CD patients. We also highlighted a reduced NOTCH1 pathway in our patients, irrespective of whether the disease was active or not. We suggest that NOTCH pathway could be constitutively altered in the celiac small intestine and could drive the increased proliferation and the decreased differentiation of intestinal cells towards the secretory goblet cell lineage.