Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study on the Relationship between Educational Achievement and Emotional Engagement in a Gameful Interface for Video Lecture Systems

An increasing number of students are learning in digitalized education environments. For younger generations of students who have grown up participating in digital environments and virtual reality, E-learning is considered to be an appropriate educational platform. In particular, video lectures are regarded as key component of E-learning systems. However, online lecture systems have limitations in sustainability due to a lack of motivation on the part of the learner. To provide motivation for users, we focus on enhancing emotional engagement with 'gameful design. ' By gameful design, we mean extending game thinking to non-game contexts. In this study, we apply gameful design to video lecturing systems, and we expect this design to enhancing emotional engagement in learning, thereby increasing educational achievement.
Critical Role of TLR4 Response in the Activation of Microglia Induced by Ethanol1

Microglial cells are the primary immune effector cells in the brain and play a pivotal role in the neuroinflammatory processes associated with a variety of neurological and pathological disorders. Alcohol consumption induces brain damage, although the neuropathological processes are poorly understood. We previously suggested that ethanol promotes inflammatory processes in the brain, up-regulating inflammatory mediators and signaling pathways associated with IL-1RI/TLR4 receptors. In the present study we investigate whether ethanol induces microglia activation by stimulating TLR4 response and whether this response causes neuronal death and contributes to ethanol-induced neuroinflammatory damage. We demonstrate that ethanol activates microglía and stimulates NF-κB, MAPKs, and MyD88-independent (IFN regulatory factor-3, IFN-β) pathways to trigger the production of inflammatory mediators, causing neuronal death. The inflammatory response induced by ethanol is completely abrogated in microglia of TLR4-deficient mice (TLR4−/−), thus supporting the role of these receptors in microglia activation and neuronal death. In accord with the in vitro findings, acute ethanol administration induces microglia activation (CD11b+ cells) in cerebral cortex of TLR4+/+ mice, but not in TLR4−/− mice. Taken together, our results not only provide the first evidence of the critical role of the TLR4 response in the ethanol-induced microglia activation, but also new insight into the basic mechanisms participating in ethanol-induced neuroinflammatory damage.
Highly correlated calculations with a polynomial cost algorithm: A study of the density matrix renormalization group

We study the recently developed Density Matrix Renormalization Group (DMRG) algorithm in the context of quantum chemistry. In contrast to traditional approaches, this algorithm is believed to yield arbitrarily high accuracy in the energy with only polynomial computational effort. We describe in some detail how this is achieved. We begin by introducing the principles of the renormalization procedure, and how one formulates an algorithm for use in quantum chemistry. The renormalization group algorithm is then interpreted in terms of familiar quantum chemical concepts, and its numerical behavior, including its convergence and computational cost, are studied using both model and real systems. The asymptotic convergence of the algorithm is derived. Finally, we examine the performance of the DMRG on widely studied chemical problems, such as the water molecule, the twisting barrier of ethene, and the dissociation of nitrogen. In all cases, the results compare favorably with the best existing quantum chemical methods, and particularly so when the nondynamical correlation is strong. Some perspectives for future development are given.
Task-Dependent Masked Priming Effects in Visual Word Recognition

A method used widely to study the first 250 ms of visual word recognition is masked priming: These studies have yielded a rich set of data concerning the processes involved in recognizing letters and words. In these studies, there is an implicit assumption that the early processes in word recognition tapped by masked priming are automatic, and masked priming effects should therefore be invariant across tasks. Contrary to this assumption, masked priming effects are modulated by the task goal: For example, only word targets show priming in the lexical decision task, but both words and non-words do in the same-different task; semantic priming effects are generally weak in the lexical decision task but are robust in the semantic categorization task. We explain how such task dependence arises within the Bayesian Reader account of masked priming (Norris and Kinoshita, 2008), and how the task dissociations can be used to understand the early processes in lexical access.
Prevalence and smokers' profile: comparisons between the psychiatric population and the general population*

Objectives: to identify the prevalence of smokers between the psychiatric population and the general population; to compare the personal, socio-demographic and clinical profile of smokers and non-smokers in the psychiatric population and the general population; to compare the reasons for smoking of these two population groups. Method: this is a cross-sectional descriptive-analytical epidemiological study with 378 patients from three services: Ambulatory Mental Health, Psychiatric Hospital, and Basic Health Unit. Interviews were conducted with three questionnaires. The Chi-square and Kruskal-Wallis tests were applied. Results: in the total of the 378 participants, 67% were women and 69% were over 40 years old. There was a higher prevalence of smokers among men, young people, illiterates, singles and with more than one government benefit. Smokers prevailed among schizophrenics, chronic patients, who used ≥ 3 psychotropic drugs and had a history of ≥ 4 psychiatric hospitalizations and/or suicide attempts. The main reason for smoking was the improvement of negative feelings. Conclusion: the prevalence of smokers is higher in the psychiatric population (especially among severely ill patients) and among men, young people, unmarried and with socioeconomic losses. The main reason for smoking is tension/relaxation relief. This study provides nurses and other professionals with knowledge capable of subsidizing the planning of smoking interventions in the Brazilian population.
Current cervical screening technology considerations: liquid-based cytology and automated screening.

Introduction After the introduction of the Papanicolaou (Pap) smear, both the incidence of, and mortality from, cervical cancer has decreased significantly, particularly for women in developed countries with the medical infrastructure available for mass screening programs. However, the Pap test still has limitations, including a false-negative rate that has been reported to be as high as 50%. Several sources contribute to this potentially high false-negative rate. Sampling error may occur during collection of the Pap test when the cervical lesion is not adequately sampled; these errors can account for up to 90% of false-negative cases. Another source of error occurs when the diagnostic material does not transfer from the sampling device to the slide in conventional smears or to the fluid medium in liquid-based cytology. This is particularly true for smeared (conventional) slides. The reported percentage of cells transferred to the slide for conventional smears varies from 6.5% to 62.5%. Inability to fully evaluate the slide due to obscuring material, whether it is blood, mucus, overlapping cells, or inflammation, is also a source of error. Finally, but least commonly, false-negative results Correspondence: Keith J. Kaplan, MD, Medical Director: Cytopathology, Department of Pathology,Walter Reed Army Medical Center, 6900 Georgia Avenue NW, Washington, DC 20307-5001. E-mail: keith.kaplan@na.amedd. army.mil The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or reflecting the views of the Department of the Army or the Department of Defense.
Changes in hip fracture rates in southeastern Australia spanning the period 1994–2007

Hip fracture incidence rates appear to be declining in Western populations. Utilizing comprehensive incident hip fracture data from radiology reports, we determined changes in hip fracture rates in southeastern Australia between 1994–1996 and 2006–2007 for residents aged 55 years or older. During this period, the population at risk increased by 47% for men and 40% for women. Although the absolute number of hip fractures increased by 53% in men and 4.4% in women, standardized hip fracture ratios were 0.92 [95% confidence interval (CI) 0.79–1.08] and 0.69 (95% CI 0.62–0.77), respectively. Marked reductions in hip fracture rates were observed for women: 32% for ages 75 to 84 years and 29% for ages 85 years or older. Data from the Geelong Osteoporosis Study were used to identify changes in body composition and lifestyle that might have influenced hip fracture risk in women during this period. Between 1993–1997 and 2004–2008, there was an increase in adiposity, bone mineral density (BMD), healthy lifestyles, and exposure to bone‐active drugs; use of hormone therapy declined. Thus hip fracture incidence rates have decreased from the mid‐1990 s to the mid‐2000 s, the effect being greater among women. Our data also suggest that the recent increase in adiposity and consequent increase in BMD at the hip may have contributed to this decline. However, improved efficacy and increased uptake of antifracture drug treatments, other cohort effects, or other environmental influences cannot be excluded. © 2011 American Society for Bone and Mineral Research.
Optimizing Distribution-based Matching by Random Subsampling

We boost the efficiency and robustness of distribution-based matching by random subsampling which results in the minimum number of samples required to achieve a specified probability that a candidate sampling distribution is a good approximation to the model distribution. The improvement is demonstrated with applications to object detection, mean-shift tracking using color distributions and tracking with improved robustness for low-resolution video sequences. The problem of minimizing the number of samples required for robust distribution matching is formulated as a constrained optimization problem with the specified probability as the objective function. We show that surprisingly mean-shift tracking using our method requires very few samples. Our experiments demonstrate that robust tracking can be achieved with even as few as 5 random samples from the distribution of the target candidate. This leads to a considerably reduced computational complexity that is also independent of object size. We show that random subsampling speeds up tracking by two orders of magnitude for typical object sizes.
Where Do Features Come From?

It is possible to learn multiple layers of non-linear features by backpropagating error derivatives through a feedforward neural network. This is a very effective learning procedure when there is a huge amount of labeled training data, but for many learning tasks very few labeled examples are available. In an effort to overcome the need for labeled data, several different generative models were developed that learned interesting features by modeling the higher order statistical structure of a set of input vectors. One of these generative models, the restricted Boltzmann machine (RBM), has no connections between its hidden units and this makes perceptual inference and learning much simpler. More significantly, after a layer of hidden features has been learned, the activities of these features can be used as training data for another RBM. By applying this idea recursively, it is possible to learn a deep hierarchy of progressively more complicated features without requiring any labeled data. This deep hierarchy can then be treated as a feedforward neural network which can be discriminatively fine-tuned using backpropagation. Using a stack of RBMs to initialize the weights of a feedforward neural network allows backpropagation to work effectively in much deeper networks and it leads to much better generalization. A stack of RBMs can also be used to initialize a deep Boltzmann machine that has many hidden layers. Combining this initialization method with a new method for fine-tuning the weights finally leads to the first efficient way of training Boltzmann machines with many hidden layers and millions of weights.
Human Activity in the Web

The recent information technology revolution has enabled the analysis and processing of large-scale data sets describing human activities. The main source of data is represented by the web, where humans generally use to spend a relevant part of their day. Here, we study three large data sets containing the information about web activities of humans in different contexts. We study in details interevent and waiting-time statistics. In both cases, the number of subsequent operations which differs by tau units of time decays powerlike as tau increases. We use nonparametric statistical tests in order to estimate the significance level of reliability of global distributions to describe activity patterns of single users. Global interevent time probability distributions are not representative for the behavior of single users: the shape of single users' interevent distributions is strongly influenced by the total number of operations performed by the users and distributions of the total number of operations performed by users are heterogeneous. A universal behavior can be anyway found by suppressing the intrinsic dependence of the global probability distribution on the activity of the users. This suppression can be performed by simply dividing the interevent times with their average values. Differently, waiting-time probability distributions seem to be independent of the activity of users and global probability distributions are able to significantly represent the replying activity patterns of single users.
Evaluation of a New Integrated Discharge Prescription Form

OBJECTIVE: To determine whether a new discharge prescription form which integrates admission medications, in-hospital changes, and discharge medications could enhance the accuracy of information in patient profiles in community pharmacies after hospital discharge. DESIGN: Nonrandomized, prospective, multi-site study. SETTINGS: Internal medicine wards of the three teaching hospitals (1200 beds) of the Centre Hospitalier de l'Université de Montréal. SUBJECTS: Patients admitted to the internal medicine wards between January 4 and 31, 1999, at St.-Luc and Notre-Dame Hospitals formed the control group and received a usual discharge form (UD). Those admitted between February 1 and 28, 1999, received the new discharge prescription form (DPF) capturing the list of admission medications and revisions during hospitalization; they served as the experimental group. METHODS: Patient profiles were reviewed to calculate conformity rates of community pharmacy patient profiles after discharge and the rate of overall conformity for each group in the study. Each drug in the patient profile was assessed according to six criteria. Healthcare providers' satisfaction with the DPF was assessed via a written questionnaire. RESULTS: Eighty-nine patients and 669 discharge medications were studied. The patient profiles had a higher conformity rate in the DPF group than in the UD group (82% vs. 40%; p < 0.001); improvement could be attributed to higher conformity rates, particularly for two criteria (medications stopped in hospital and dose changes in hospital). CONCLUSIONS: Integration of admission medications, in-hospital changes, and discharge medications on a single form increases the conformity rates of community pharmacy patient profiles after hospitalization. This tool is well accepted by both pharmacists and physicians and may lead to a major decrease in drug-related problems.
Families of nodal curves on projective threefolds and their regularity via postulation of nodes

The main purpose of this paper is to introduce a new approach to study families of nodal curves on projective threefolds. Precisely, given X a smooth projective threefold, E a rank-two vector bundle on X, L a very ample line bundle on X and k � 0, � > 0 integers and denoted by V�(E L k ) the subscheme of P(H 0 (E L k )) parametrizing global sections of E L k whose zero-loci are irreducible and �- nodal curves on X, we present a new cohomological description of the tangent space T(s)(V�(E L k )) at a point (s) 2 V�(E L k ). This description enable us to determine effective and uniform upper- bounds for �, which are linear polynomials in k, such that the family V�(E Lk) is smooth and of the expected dimension (regular, for short). The almost-sharpness of our bounds is shown by some interesting examples. Furthermore, when X is assumed to be a Fano or a Calaby-Yau threefold, we study in detail the regularity property of a point (s) 2 V�(E Lk) related to the postulation of the nodes of its zero-locus C = V (s) � X. Roughly speaking, when the nodes of C are assumed to be in general position either on X or on an irreducible divisor of X having at worst log-terminal singularities or to lie on a l.c . i. and subcanonical curve in X, we find upper-bounds onwhich are, respectively, cubic, quadratic and linear polynomials in k ensuring the regularity of V�(E L k ) at (s). Finally, when X = P 3 , we also discuss some interesting geometric properties of the curves given by sections parametrized by V�(E OX(k)).
Melatonin therapy for circadian rhythm sleep disorders in children with multiple disabilities: what have we learned in the last decade?

In this discussion, children are considered to have multiple disabilities and low functioning when they have a combination of severe neurodevelopmental disabilities, such as visual impairment, epilepsy, intellectual deficits, cerebral palsy, attention-deficit–hyperactivity disorder (ADHD), autism, eating difficulties, and other neuropsychiatric problems.
Learning from Logged Implicit Exploration Data

We provide a sound and consistent foundation for the use of nonrandom exploration data in "contextual bandit" or "partially labeled" settings where only the value of a chosen action is learned. The primary challenge in a variety of settings is that the exploration policy, in which "offline" data is logged, is not explicitly known. Prior solutions here require either control of the actions during the learning process, recorded random exploration, or actions chosen obliviously in a repeated manner. The techniques reported here lift these restrictions, allowing the learning of a policy for choosing actions given features from historical data where no randomization occurred or was logged. We empirically verify our solution on two reasonably sized sets of real-world data obtained from Yahoo!
Generation of a ToxA knockout strain of the wheat tan spot pathogen Pyrenophora tritici-repentis.

The necrotrophic fungal pathogen Pyrenophora tritici-repentis causes tan spot, a major disease of wheat, throughout the world. The proteinaceous effector ToxA is responsible for foliar necrosis on ToxA-sensitive wheat genotypes. The single copy ToxA gene was deleted from a wild-type race 1 P. tritici-repentis isolate via homologous recombination of a knockout construct. Expression of the ToxA transcript was found to be absent in transformants (toxa), as was ToxA protein production in fungal culture filtrates. Plant bioassays were conducted to test transformant pathogenicity. The toxa strains were unable to induce necrosis on ToxA-sensitive wheat genotypes. To our knowledge, this is the first demonstration of a targeted gene knockout in P. tritici-repentis. The ability to undertake gene deletions will facilitate the characterization of other pathogenicity effectors of this economically significant necrotroph.
Tree-Pattern Similarity Estimation for Scalable Content-based Routing

With the advent of XML as the de facto language for data publishing and exchange, scalable distribution of XML data to large, dynamic populations of consumers remains an important challenge. Content-based publish/subscribe systems offer a convenient design paradigm, as most of the complexity related to addressing and routing is encapsulated within the network infrastructure. To indicate the type of content that they are interested in, data consumers typically specify their subscriptions using a tree-pattern specification language (an important subset of XPath), while producers publish XML content without prior knowledge of any potential recipients. Discovering semantic communities of consumers with similar interests is an important requirement for scalable content-based systems: such "semantic clusters" of consumers play a critical role in the design of effective content-routing protocols and architectures. The fundamental problem underlying the discovery of such semantic communities lay in effectively evaluating the similarity of different tree-pattern subscriptions based on the observed document stream. In this paper, we propose a general framework and algorithmic tools for estimating different tree-pattern similarity metrics over continuous streams of XML documents. In a nutshell, our approach relies on continuously maintaining a novel, concise synopsis structure over the observed document stream that allows us to accurately estimate the fraction of documents satisfying various Boolean combinations of different tree-pattern subscriptions. To effectively capture different branching and correlation patterns within a limited amount of space, our techniques use ideas from hash-based sampling in a novel manner that exploits the hierarchical structure of our document synopsis. Experimental results with various XML data streams verify the effectiveness of our approach.
Depression, anxiety, and risk factor control in patients after hospitalization for coronary heart disease: the EUROASPIRE III Study

Objective: To assess in coronary heart disease (CHD) patients: (1) differences in the prevalence of depression and anxiety between samples selected from 22 countries; (2) the association of depression and anxiety with age, education, diagnostic category, favourable behaviours, use of cardioprotective drugs, and reaching the secondary prevention treatment targets. Design: Cross-sectional study. Methods: The study group consisted of 8580 patients from 22 European countries examined at least 6 months after hospitalization due to CHD. Depression and anxiety were assessed using Hospital Anxiety and Depression Scale (HADS). Results: Prevalence of depression (HADS depression score ≥8) varied from 8.2% to 35.7% in men and from 10.3% to 62.5% in women. Prevalence of anxiety (HADS anxiety score ≥8) varied from 12.0% to 41.8% in men and from 21.5% to 63.7% in women. Older age, female sex, low education, and no history of invasive treatment were associated with more frequent depression and anxiety. Depression and anxiety were associated with less frequent modification of lifestyle. Depression was related with body mass index, waist circumference, fasting glucose, and more frequent self-reported diabetes but not with reaching the treatment targets for blood pressure and lipids. Conclusions: High prevalence of depression and anxiety in CHD patients, and relation with less frequent lifestyle modification, call to integrate methods of identification and minimizing unfavourable effects of depression and anxiety into the cardiac rehabilitation and prevention programmes.