Miracle-dz/UniFAR-FTUs huggingface dataset:
Pathogenic determinants of Kingella kingae disease

Kingella kingae is an emerging pediatric pathogen and is increasingly recognized as a leading etiology of septic arthritis, osteomyelitis, and bacteremia and an occasional cause of endocarditis in young children. The pathogenesis of K. kingae disease begins with colonization of the upper respiratory tract followed by breach of the respiratory epithelial barrier and hematogenous spread to distant sites of infection, primarily the joints, bones, and endocardium. As recognition of K. kingae as a pathogen has increased, interest in defining the molecular determinants of K. kingae pathogenicity has grown. This effort has identified numerous bacterial surface factors that likely play key roles in the pathogenic process of K. kingae disease, including type IV pili and the Knh trimeric autotransporter (adherence to the host), a potent RTX-family toxin (epithelial barrier breach), and multiple surface polysaccharides (complement and neutrophil resistance). Herein, we review the current state of knowledge of each of these factors, providing insights into potential approaches to the prevention and/or treatment of K. kingae disease.
When to say when: Using big data to support mobile communications

ABSTRACT As a result of the current mobile phenomenon, many libraries have rushed to provide mobile-compatible services in order to remain relevant in today's quickly evolving information services market. Beginning in 2010, the Laverne and Dorothy Brown Library at the University of St. Francis (USF) launched a data tracking initiative via Google Analytics to determine the extent that mobile-specific library resources were being utilized by students for access to library materials and services. Data analysis has shown that weighing an emphasis toward future development of mobile library resources should be balanced against development costs under a pragmatic anticipation of the longevity and sustainability of any mobile resource.
Learning Language-vision Correspondences

Given an unstructured collection of captioned images of cluttered scenes featuring a variety of objects, our goal is to simultaneously learn the names and appearances of the objects. Only a small fraction of local features within any given image are associated with a particular caption word, and captions may contain irrelevant words not associated with any image object. We propose a novel algorithm that uses the repetition of feature neighborhoods across training images and a measure of correspondence with caption words to learn meaningful feature configurations (representing named objects). We also introduce a graph-based appearance model that captures some of the structure of an object by encoding the spatial relationships among the local visual features. In an iterative procedure we use language (the words) to drive a perceptual grouping process that assembles an appearance model for a named object. We also exploit co-occurrences among appearance models to learn hierarchical appearance models. Results of applying our method to three data sets in a variety of conditions demonstrate that from complex, cluttered, real-world scenes with noisy captions, we can learn both the names and appearances of objects, resulting in a set of models invariant to translation, scale, orientation, occlusion, and minor changes in viewpoint or articulation. These named models, in turn, are used to automatically annotate new, uncaptioned images, thereby facilitating keyword-based image retrieval.
Teaching and language corpora

Starting with an account of the author’s experience of corpus applications in language teaching from the mid 1970s through to 2014, this chapter traces the transformation of corpus analysis from its origins on mainframe computers in university departments to the present day – where personal computers are used in research, materials development and DDL. Following this retrospective narrative, the results from a series of surveys of classroom applications of corpus analysis are presented. These findings address questions such as: Who is using corpora in language teaching, and in what contexts? What tools and resources are they using? What published resources do teachers find most useful? And what benefits do teachers feel arise from using corpora and corpus tools in language education?
Evaluating the role of type 2 diabetes mellitus in rotator cuff tendinopathy: Development and analysis of a novel rat model

Objective To establish and validate an intact rotator cuff rat model for exploring the pathophysiological effects of type 2 diabetes on the rotator cuff tendon in vivo. Methods A total of 45 adult male rats were randomly divided into a control group (n = 9) and type 2 diabetes group (n=36). The rats were sacrificed at 2 weeks (T2DM-2w group, n=9), 4 weeks (T2DM-4w group, n=9), 8 weeks (T2DM-8w group, n=9), and 12 weeks (T2DM-12w group, n=9) after successful modeling of type 2 diabetes. Bilateral shoulder samples were collected for gross observation and measurement, protein expression(enzyme-linked immunosorbent assay,ELISA), histological evaluation, biomechanical testing, and gene expression (real-time quantitative polymerase chain reaction, qRT-PCR). Results Protein expression showed that the expression of IL-6 and Advanced glycation end products (AGEs)in serum increased in type 2 diabetic group compared with the non-diabetic group. Histologically, collagen fibers in rotator cuff tendons of type 2 diabetic rats were disorganized, ruptured, and with scar hyperplasia, neovascularization, and extracellular matrix disturbances, while Bonar score showed significant and continuously aggravated tendinopathy over 12 weeks. The biomechanical evaluation showed that the ultimate load of rotator cuff tendons in type 2 diabetic rats gradually decreased, and the ultimate load was negatively correlated with AGEs content. Gene expression analysis showed increased expression of genes associated with matrix remodeling (COL-1A1), tendon development (TNC), and fatty infiltration (FABP4) in tendon specimens from the type 2 diabetic group. Conclusion Persistent type 2 diabetes is associated with the rupture of collagen fiber structure, disturbance in the extracellular matrix, and biomechanical decline of the rotator cuff tendon. The establishment of this new rat model of rotator cuff tendinopathy provides a valuable research basis for studying the cellular and molecular mechanisms of diabetes-induced rotator cuff tendinopathy.
The prominent role of the S100A8/S100A9-CD147 axis in the progression of penile cancer

Currently, no established biomarkers are recommended for the routine diagnosis of penile carcinoma (PeCa). The rising incidence of this human papillomavirus (HPV)–related cancer entity highlights the need for promising candidates. The Calprotectin subunits S100A8 and S100A9 mark myeloid-derived suppressor cells in other HPV-related entities while their receptor CD147 was discussed to identify patients with PeCa at a higher risk for poor prognoses and treatment failure. We thus examined their expression using immunohistochemistry staining of PeCa specimens from 74 patients on tissue microarrays of the tumor center, the invasion front, and lymph node metastases. Notably, whereas the tumor center was significantly more intensively stained than the invasion front, lymph node metastases were thoroughly positive for both S100 subunits. An HPV-positive status combined with an S100A8+S100A9+ profile was related with an elevated risk for metastases. We observed several PeCa specimens with S100A8+S100A9+-infiltrating immune cells overlapping with CD15 marking neutrophils. The S100A8+S100A9+CD15+ profile was associated with dedifferentiated and metastasizing PeCa, predominantly of HPV-associated subtype. These data suggest a contribution of neutrophil-derived suppressor cells to the progression of HPV-driven penile carcinogenesis. CD147 was elevated, expressed in PeCa specimens, prominently at the tumor center and in HPV-positive PeCa cell lines. CD147+HPV+ PeCa specimens were with the higher-frequency metastasizing cancers. Moreover, an elevated expression of CD147 of HPV-positive PeCa cell lines correlated negatively with the susceptibility to IgA-based neutrophil-mediated tumor cell killing. Finally, stratifying patients regarding their HPV/S100A8/S100A9/CD15/CD147 profile may help identify patients with progressing cancer and tailor immunotherapeutic treatment strategies.
The effect of three violent videogame engagement states on aggressive behavior: A partial least squares structural equation modeling approach

Debate on violent games and their effect on aggressive behavior remains inconclusive. This study aims to study the predicting role of cognitive, affective, and behavioral engagement states in violent videogames on aggressive behavior, which remains nebulous to date. We visited gaming zones and administered the study survey to collect data from violent videogame users. We collected 208 valid responses that were further analyzed. The present study used SmartPLS (3.3.3) software to perform partial least squares structural equation modeling (PLS-SEM) analysis in two stages. In the first stage, the measurement model assessment reported that cognitive, affective, behavioral, and aggressive behavior proved to be reliable reflective-formative composite constructs. Whereas, the second phase illustrated that cognitive engagement in violent videogames fails to impact aggressive behavior. The other two engagement states (affective and behavioral) in violent games showed a positive impact on aggressive behavior. Our study contributes to aggressive behavior literature by understanding how violent videogame engagement states impact aggressive behavior, which is crucial to recognize aggression so that steps can be taken toward addressing it. This study also contributes methodologically by utilizing the hierarchical component model (HCM) approach to estimate, specify, and validate the hierarchical structure of higher-order constructs (i.e., consumer violent videogame engagement dimensions (cognitive, affective, and behavioral) and aggressive behavior) as reflective-formative composite models.
Nonparametric Statistical Methods: Hollander/Nonparametric Statistical Methods

This Second Edition of Myles Hollander and Douglas A. Wolfe's successful Nonparametric Statistical Methods meets the needs of a new generation of users, with completely up-to-date coverage of this important statistical area. Like its predecessor, the revised edition, along with its companion ftp site, aims to equip readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for a given situation. An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. An ideal text for an upper-level undergraduate or first-year graduate course, Nonparametric Statistical Methods, Second Edition is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics.
“Be rational!” Epistemic aims and socio-cognitive tension in argumentation about dietary choices

Argumentation is a social practice that can lead to epistemic outcomes, that is, to the construction of knowledge. Recent research in collaborative learning has pointed out the significance of affective and motivational aspects, as well as the influence of socio-relational concerns, which have been found to frequently take priority over epistemic ones. Our research objective is to investigate how the epistemic and socio-relational dimensions of students' argumentative interactions are intertwined. We apply discourse analysis to examine the interactions in a small group of four 11th-graders evaluating the nutritional acceptability of omnivorous and vegetarian diets. The epistemic dimension is analyzed in terms of the aims pursued by the participants and the epistemic outcomes achieved. The socio-relational dimension is analyzed in terms of fluctuations of interpersonal tensions and their relaxations. The results show a convergence of participants' epistemic aims and the epistemic statuses of the options. Most of the epistemic outcomes are produced in sequences in which socio-cognitive tension arises and then relaxes. Enduring high socio-cognitive tension and overcoming conflict seem to have encouraged the adoption of epistemic aims. Moreover, our findings suggest that driven by epistemic aims in high socio-cognitive tensed contexts, students can refine the conditions by which they engage in argumentation. These results call for further investigating on what constitutes an appropriate or productive level of interpersonal tension for learning. Educational implications are related to the design of argumentative learning environments promoting epistemic aims and outcomes through the encouragement of suitable socio-cognitive climates leading to them.
Perubahan Epidemiologi Demam Berdarah Dengue Di Indonesia

Dengue berdarah dengue yang ditularkan melalui vektor nyamuk Aedes aegypty masih merupakan masalah kesehatan penting di dunia. Di Indonesia, demam berdarah dengue mulai dikenal pertama kali pada tahun 1968 di DKI Jakarta dan Surabaya, dan terus menyebar ke seluruh tiga puluh tiga propinsi di Indonesia. Pola epidemiologi infeksi dengue mengalami perubahan dari tahun ke tahun, jumlah kasus memuncak setiap siklus 10 tahunan. Dari tahun 1968-2008 angka kesakitan demam berdarah dengue terus meningkat. Pada tahun 2008 didapatkanangka kesakitan 58,85/ 100.000 penduduk. Angka kematian menurun dengan stabil dari 41% pada tahun 1968 menjadi kurang dari 2% sejak tahun 2000, dan pada tahun 2008 angka kematian menurun menjadi 0,86%. Semua serotipe virus dengue ditemukan di Indonesia, namun serotipe virus den-3 masih dominan menyebabkan kasus dengue yang berat dan fatal. Surveilans epidemiologi, dukungan edukasi masyarakat dan program pengendalian vektor diperlukan untuk mencegah transmisi. Pengembangan vaksin dengue merupakan salah satu upaya mencegah penyakit dengue.
Skill and Profit in Active Management

Greater skill of active investment managers can mean less fee revenue in a general equilibrium. Although more-skilled managers earn more revenue than less-skilled managers, greater skill for active managers overall can imply less revenue for their industry. Greater skill allows managers to identify mispriced securities more accurately and thereby make better portfolio choices. Greater skill also means, however, that active management corrects prices better and thus reduces managers' return opportunities. The latter effect can outweigh managers' better portfolio choices in equilibrium. Investors then rationally allocate less to active funds and more to index funds if active management is more skilled.
Factors Influencing Commercialization of Smallholder Agriculture

Commercialization of agriculture is a major challenge of smallholder farmers in Africa. Agricultural policies and programmes over the years have centered on resolving these challenges. The present study analyzes the trends in maize and cassava production by farm households; estimates the levels of commercialization of these two crops; and quantifies the factors influencing intensity of commercialization in the Effutu Municipality using the Tobit model. Results indicate a higher annual growth rate of cassava production relative to maize production. The extents of maize and cassava commercialization are 0.53 and 0.72 respectively. The study observes, inter alia, that output price and farm size are associated with a higher level of cassava and maize commercialization. It is recommended that policy should be aimed at improving the quality of agricultural extension delivery services, strengthening of market information provision and the functioning of the land lease market coupled with development of the land sales market.
Data Mining and Knowledge Extraction in the Risk Based Insurance Audit: A Case Study (City of Tehran)

Nowadays, data mining techniques are widely used in various industries. The Social Security Insurance Audit, with more than two million audit cases nationwide and limited manpower, cannot respond to all cases. The present research, using data mining techniques, aims to propose an efficient method for the detection of high-risk companies and fraud in the insurance industry. Materials and Methods: After randomly collecting 2000 records from the Social Security Audit database in 2014 and extracting various features for each company, a pre-processing of the data was conducted. The data was then divided into the two categories of Training Set and Test Set and given to the following four algorithms: Neural Network, Decision Tree, Bayesian and Support Vector Machine. Ultimately the accuracy of each algorithm was measured by the confusion matrix. Findings: By comparing the four algorithms mentioned above, it can be seen that all of these algorithms can provide a proper level of accuracy, with the neural network algorithm showing 90.6% accuracy, was found to be the best algorithm for the identification of high-risk companies. The findings of this study enable the Social Security Audit to develop software which can identify companies with high insurance risk in order to achieve higher levels of efficiency in the face of current limitations in manpower.
Nitrogen Use Efficiency of Rice Crop

The present study was conducted at the Dr. N.E. Borlaug’s Crop Research Centre, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, India with an objective to evaluate the effects of different N doses on growth dynamic, morphological, physiological parameters and nitrogen use efficiency in five genotypes of rice. The field experiment was conducted during rainy season of 2008 and 2009. Five rice genotypes (KRH-2- hybrid, Vasumati- aromatic, Tulsi, Kasturi and Krishna Hamsa- high yielding) were used as experimental material. The N fertilizer 0, 50, 100 and 200N kg ha-1 was sprouted in the field. The growth parameters were recorded at time of panicle initiation, maximum tillering and reproductive stage. The growth as well as physiological parameters and guttative fluid increases as the nitrogen levels increased. The grain and biological yieldalso increased with increasing nitrogen levels. The application of nitrogen fertilizers also affected the nitrogen and protein content of leaves, seeds and the soil significantly. The NUE increased up to N100 level and found to be highest for KRH-2.
An analysis of the effectiveness of curriculum embedded handwriting instruction and its impact on student learning

Common Core Standards bring increased educational demands with focused expectations for the production of quality writing however explicit handwriting instruction has minimal presence in today’s elementary school classrooms. The purpose of this study was to investigate the effect of handwriting instruction, using the Handwriting Without Tears handwriting curriculum, on student achievement. Performance on measures of handwriting legibility, written literacy, and basic literacy skills were compared between two groups; students receiving Handwriting Without Tears curriculum instruction and those who were receiving typical classroom instruction. Participants included a total of 789 Kindergarten and First-grade students from two Central Valley school districts in California. Analysis indicated significant differences between the two groups for handwriting legibility and Written Literacy. Students who received instruction using the Handwriting Without Tears curriculum demonstrated significantly higher performance on specific elements of written literacy as compared to those students who did not receive Handwriting Without Tears curriculum instruction. These findings support the use of specific handwriting instruction for promoting student achievement with written production and written literacy. Implications of
Adaptive, nonparametric markov models and information-theoretic methods for image restoration and segmentation

The regularity in data fundamentally distinguishes itself from random noise. Describing this regularity in generic, yet powerful, ways is one of the key problems in signal processing. One way of capturing image regularity is by incorporating a priori information into the image model itself. Approaches extracting such prior information from training data have limited utility because of the lack of effective training sets for most applications. Unsupervised approaches that, typically, encode prior information via parametric models work best only when the data conforms to that model. Certain kinds of problems do not adhere to strict models, entailing unsupervised approaches to be adaptive. Statistical-inference methodologies that allow us to learn the underlying structure and variability in the data form important tools in adaptive signal processing. This dissertation presents an adaptive Markov-random-field (MRF) image model that automatically learns the local statistical dependencies via data-driven nonparametric techniques. We use this model to create adaptive algorithms for processing images. We incorporate prior information, when available, through optimal Bayesian frameworks. We enforce optimality criteria based on fundamental information-theoretic concepts that capture the functional dependence and information content in the data. We employ this adaptive-MRF framework for effectively solving several classic problems in image processing, computer vision, and medical image analysis. Inferring the statistical structure underlying corrupted images enables us to restore images without enforcing strong models on the signal. The restoration iteratively improves the predictability of pixel intensities from their neighborhoods, by decreasing their joint entropy. When the nature of noise is known, we present an effective empirical-Bayesian reconstruction strategy. We also present a method to optimally estimate the uncorrupted-signal statistics from the observed corrupted-signal statistics by minimizing a KL-divergence measure. We apply this adaptive-MRF framework to classify tissues in magnetic resonance (MR) images of the human brain by maximizing the mutual information between the classification labels and image data, capturing their mutual dependency. The generic formulation enables the method to adapt to different MR modalities, noise, inhomogeneities, and partial-voluming. We incorporate a priori information via probabilistic brain-tissue atlases. We use a similar strategy for texture segmentation, using fast threshold-dynamics-based level-set techniques for regularization.
Evaluation of the Model for Analysing Anti-Phishing Authentication Ceremonies

Phishing takes advantage of the way humans interact with computers or interpret messages. A security ceremony is one way of extending the reach of current methods for social, technical and contextual analysis of security protocols to include humans. It is an extension of the concept of network security protocol and includes user interface and human-protocol interaction. We propose a model with which anti-phishing authentication ceremonies can be examined not only with a technical focus but by including the human into the analysis. The model examines anti-phishing authentication tasks that a human needs to apply, how users process these additional authentication tasks and how these tasks impact the human’s decision outcome. We outline the evaluation of the model and propose a ceremony called MultiStep Mutual Authentication (MSMA) that combines PIN, text password and dynamic image feedback as a help to foil phishing attacks. The MSMA ceremony is used as part of the evaluation of the model.