Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of management and guideline adherence in children with mild traumatic brain injury

ABSTRACT Aim: To evaluate the management and guideline adherence in children with mild traumatic brain injury (MTBI) in emergency departments (ED) in the Netherlands. Methods: A multicentre cohort study was conducted, including children younger than 18 years with MTBI who presented within 24 hours after trauma in the ED of hospitals in the southwest region of the Netherlands, in 2014. Primary outcome measures for management were percentages of performed computed tomography (CT) scans and hospital admissions. Guideline adherence was defined as percentages of correctly following the guideline. Secondary outcome measures were differences in management and guideline adherence between hospitals. Results: About 563 patients were analysed. Hospital admission was the most frequently performed management type (49.2% hospital admission vs. 30.9% CT). In only 49.7% of patients, the guideline was followed correctly. A substantial overuse of hospital admission (35%) and underuse of CT (40.1%) were found. Percentages of hospital admission and CT varied between 39.4–55.6% and 23.3–44.1%, respectively, across hospitals. Percentages of correctly following the guideline varied between 39.2–64.9% across hospitals. Conclusion: These findings suggest that physicians in the participating hospitals prefer hospital admission of children with MTBI instead of CT despite the current recommendations of the national MTBI guideline in the Netherlands.
Neural language representations predict outcomes of scientific research

Many research fields codify their findings in standard formats, often by reporting correlations between quantities of interest. But the space of all testable correlates is far larger than scientific resources can currently address, so the ability to accurately predict correlations would be useful to plan research and allocate resources. Using a dataset of approximately 170,000 correlational findings extracted from leading social science journals, we show that a trained neural network can accurately predict the reported correlations using only the text descriptions of the correlates. Accurate predictive models such as these can guide scientists towards promising untested correlates, better quantify the information gained from new findings, and has implications for moving artificial intelligence systems from predicting structures to predicting relationships in the real world.
Regulation of Withdrawals in Individual Account Systems

Funded mandatory pension systems based on individual accounts are spreading around the world. With the maturation of those systems, regulating the withdrawal of retirement savings will become increasingly important. Government regulation of withdrawals should mandate the purchase of inflation-indexed life annuities exceeding income available from government welfare programs for the retiree and potential survivors. However, proper functioning of insurance markets does not require annuitizing the entire account balance. Instead, more flexibility for the choice of withdrawals could be permitted for any remaining funds, helping to tailor income streams to individual needs and living arrangements.
Quantitative Detection of Group A Rotavirus in Humans and Animals by Real-Time Reverse Transcription-Polymerase Chain Reaction

The detection as well as quantification of Group A rotavirus in the diarrhoeic faecal samples from humans and animals was carried out by real time reverse transcription PCR assay (qRT-PCR) by using primers from the conserved region of gene segment 9 in Group A rotaviruses. The standardized test detected Group A rotaviruses in the range of 1.70x10 to 1.28x10 copies in humans, 2.21x10 to 4.78x10 copies in calves, 1.29x10 to 2.03x10 copies in kids and 7.21x10 to 3.52x10 copies/reaction in lambs.
RELATED TO OUTBREAKS OF HIGHLY PATHOGENIC H 5 N 1 AVIAN INFLUENZA 1

Outbreaks of highly pathogenic avian influenza (HPAI) H5N1 subtype have occurred in many countries across Asia, Europe, and Africa since 2003. Better understanding of the ecology and risk factors of HPAI is critical for surveillance, risk assessment, and public health policy. We introduce satellite remote sensing as one important tool, and highlight the potential of using satellite images to monitor dynamics of climate and landscapes that are related to wild bird migration and agriculture in the context of avian influenza transmission.
FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning

Understanding and following directions provided by humans can enable robots to navigate effectively in unknown situations. We present FollowNet, an end-to-end differentiable neural architecture for learning multi-modal navigation policies. FollowNet maps natural language instructions as well as visual and depth inputs to locomotion primitives. FollowNet processes instructions using an attention mechanism conditioned on its visual and depth input to focus on the relevant parts of the command while performing the navigation task. Deep reinforcement learning (RL) a sparse reward learns simultaneously the state representation, the attention function, and control policies. We evaluate our agent on a dataset of complex natural language directions that guide the agent through a rich and realistic dataset of simulated homes. We show that the FollowNet agent learns to execute previously unseen instructions described with a similar vocabulary, and successfully navigates along paths not encountered during training. The agent shows 30% improvement over a baseline model without the attention mechanism, with 52% success rate at novel instructions.
A Dual-Curve Short Rate Model with Multi-Factor Stochastic Volatility: I. Asymptotic Analysis

We present a stochastic-volatility, short rate term structure model, which extends the classic multi-factor Hull-White model. This model is designed to fit the swaption implied volatility cube and to incorporate the two-curve modeling paradigm. The model exhibits non-Gaussian forward swap rates whose distributions are parameterized across the dimensions of the volatility cube: underlying tenor, option strike and option expiration. To facilitate rapid model calibration, we establish suitable asymptotic expressions for the bond prices. Furthermore, we derive an effective SABR dynamics for each forward swap rate. Finally, we use the mean field approximation to match the effective SABR parameters corresponding to each swaption to the market levels.
Stabilization of serum antibody responses triggered by initial mucosal contact with the antigen independently of oral tolerance induction.

Initial contacts with a T-dependent antigen by mucosal routes may result in oral tolerance, defined as the inhibition of specific antibody formation after subsequent parenteral immunizations with the same antigen. We describe here an additional and permanent consequence of these initial contacts, namely, the blockade of secondary-type responsiveness to subsequent parenteral contacts with the antigen. When repeatedly boosted ip with small doses (3 microg) of ovalbumin (OVA) (or lysozyme), primed B6D2F1 mice showed progressively higher antibody responses. In contrast, mice primed after a single oral exposure to the antigen, although repeatedly boosted, maintained their secondary antibody titers on a level which was inversely proportional to the dose of antigen in the oral pretreatment. This phenomenon also occurred in situations in which oral tolerance was not induced. For example, senile 70-week-old B6D2F1 mice pretreated with a single gavage of 20 mg OVA did not become tolerant, i.e., they formed the same secondary levels of anti-OVA antibodies as non-pretreated mice. However, after 4 weekly challenges with 3 microg OVA ip, orally pretreated mice maintained the same anti-OVA serum levels, whereas the levels of control mice increased sequentially. This "stabilizing" effect of mucosal exposure was dose dependent, occurred with different proteins and was triggered by single or multiple oral or nasal exposures to the antigen.
Antinociception Induced by Copper Salt Revisited: Interaction with Ketamine in Formalin-Induced Intraplantar and Orofacial Pain in Mice.

AIMS To evaluate in mice the antinociceptive effect of copper in spinal and trigeminal nociceptive pathways by using the intraplantar and orofacial formalin tests, respectively, and to examine whether this effect may interact synergistically with ketamine-induced antinociception. METHODS Nociceptive behaviors (licking/biting of the formalin-injected limb and rubbing/scratching of the formalin-injected orofacial area) in male mice were evaluated during a 45-minute observation period post-formalin injection. Dose-response curves for intraperitoneal (ip) copper sulfate and ketamine allowed their combination in equi-effective doses, and their interaction was determined with isobolographic analysis. The results were examined with one-way analysis of variance followed by the Bonferroni post hoc test. Significance was accepted at an alpha level of . 05. RESULTS Irrespective of the region injected with formalin (upper lip or hindlimb), copper sulfate (0.3, 1.0, and 3.0 mg/kg) and ketamine (1.0, 3.0, and 10 mg/kg) dose-dependently decreased the nociceptive behaviors evoked by formalin injection. Isobolographic analysis showed a superadditive interaction between copper and ketamine at the spinal level, but this interaction was only additive at the trigeminal level. CONCLUSION The results suggest that copper salts could be used to synergistically improve the efficacy of some commercial centrally acting analgesic agents, such as ketamine, while reducing the possibility of side effects. However, a synergistic effect probably should not be expected if treatment is for orofacial pain.
Runtime Optimization of Identification Event in ECG Based Biometric Authentication

Biometric Authentication has become a very popular method for different state-of-the-art security architectures. Albeit the ubiquitous acceptance and constant development in trivial biometric authentication methods such as fingerprint, palm-print, retinal scan etc., the possibility of producing highly competitive performance from somewhat less-popular methods still remains. Electrocardiogram (ECG) based multi-staged biometric authentication is such a method, which, despite its limited appearance in earlier research works, are currently being observed as equally high-performing as other trivial popular methods. The identification stage of this method suffers from requiring a high runtime, due to cross-matching of the new data with every single template data stored in the database, especially when dealing with huge amount of data. To solve this unaddressed problem, in this paper, we have proposed a K-means clustering based novel method where all the template data are clustered based on similarity, and only the most similar data cluster is searched instead of whole dataset during the identification event. Using our method we have achieved a maximum of 79.26% time reduction with 100% accuracy.
Evaluation of a multi-channel algorithm for reducing transient sounds

Abstract Objective: The objective was to evaluate and select appropriate parameters for a multi-channel transient reduction (MCTR) algorithm for detecting and attenuating transient sounds in speech. Design: In each trial, the same sentence was played twice. A transient sound was presented in both sentences, but its level varied across the two depending on whether or not it had been processed by the MCTR and on the “strength” of the processing. The participant indicated their preference for which one was better and by how much in terms of the balance between the annoyance produced by the transient and the audibility of the transient (they were told that the transient should still be audible). Study sample: Twenty English-speaking participants were tested, 10 with normal hearing and 10 with mild-to-moderate hearing-impairment. Frequency-dependent linear amplification was provided for the latter. Results: The results for both participant groups indicated that sounds processed using the MCTR were preferred over the unprocessed sounds. For the hearing-impaired participants, the medium and strong settings of the MCTR were preferred over the weak setting. Conclusions: The medium and strong settings of the MCTR reduced the annoyance produced by the transients while maintaining their audibility.
Hybrid blind symbol rate estimation for linearly modulated signals

In this paper, a hybrid two-stage algorithm for blind symbol rate estimation is proposed, which applies for raised cosine pulse shaped, linearly modulated signals. In the first stage, the inverse (fast) Fourier transform (IFFT) of the averaged power spectrum is used to extract a coarse estimate based on the evaluation of the related baseband signal. In the second stage, a Maximum-likelihood (ML) based algorithm is used for the fine estimation due to its high accuracy. A prior estimation of the carrier frequency is proposed to allow for working in blind environment at low signal-to-noise ratio (SNR) levels. The performance of the proposed algorithm verified by simulation shows a high efficiency with less complexity comparing to the methods published in the open literature.
Returning is Believing: Optimizing Long-term User Engagement in Recommender Systems

In this work, we propose to improve long-term user engagement in a recommender system from the perspective of sequential decision optimization, where users' click and return behaviors are directly modeled for online optimization. A bandit-based solution is formulated to balance three competing factors during online learning, including exploitation for immediate click, exploitation for expected future clicks, and exploration of unknowns for model estimation. We rigorously prove that with a high probability our proposed solution achieves a sublinear upper regret bound in maximizing cumulative clicks from a population of users in a given period of time, while a linear regret is inevitable if a user's temporal return behavior is not considered when making the recommendations. Extensive experimentation on both simulations and a large-scale real-world dataset collected from Yahoo frontpage news recommendation log verified the effectiveness and significant improvement of our proposed algorithm compared with several state-of-the-art online learning baselines for recommendation.
Globalization and Welfare State: The effect on Health Systems

The appearance of the national state is the most important event in the passage from the Middle to the New Ages. The rapid growth of commerce and cities strengthens the exchanges between different regions of Europe, primarily, and the rest of the world, secondly. The cottage industry transforms into handicraft and that into manufacture. The industrial revolution in the 18th - 19th century creates an intense social issue. The consequence is the emersion of the welfare state since the early 1880. After the 1929-33 crisis, the state intervention enforces the welfare state as the dominant regime throughout Western Europe, and establishes a standard for many countries. After the oil crises in 1973 and 1979 and the rise of liberalism, the welfare state confronts a sharp criticism. The fall of existing (real) socialism accelerates the evolutions and the prevalence of globalization leads to its decline. Health systems are the main, perhaps, expression of the welfare state. The paradigms of Bismarck and Beveridge dominate in most countries of the world (whereas, the liberal model in the U.S.A.). The expenditure on health amounts already to 9% or more of GDP, in 1980. But after this decade, a decrease begins, particularly in the public shareholding. This decay leads to a continuous reduction of public expenditure and to the increase of private insurance. The health systems, for a variety of reasons (adverse demographic evolutions, decline in income share of Europe in world GDP, etc.), will suffer eclipse.
Cross Local Gabor Binary Pattern Descriptor with Probabilistic Linear Discriminant Analysis for Pose-Invariant Face Recognition

Automatic face recognition is a well researched area, but still many of the current face recognition methods sensitive to lighting and pose changes. In this paper we introduce a novel facial feature representation to enhance the robustness of face recognition against pose and illumination changes. Here we combined Gabor wavelets and Cross Lo- cal Binary Patterns for facial feature representation. Gabor wavelets are known to extract shape information by detecting shape attributes like edges, corners and blobs. Cross Local Binary Patterns can be used for better feature representation in two levels. Probabilistic Linear Discriminant Analysis (PLDA) minimizes the intra-class distance and maximizes the inter-class distances and generates a model for classification purposes. During recognition, PLDA estimates the likelihood of the probe image in the gallery image set. Experimental results on YALE, FERET and our internal datasets show the significance of this method.
Periodic Study of the Physico-Chemical Properties of the Non-Polar ( 010 ) Mg-Pure and Fe-Containing Olivine Surfaces

(Article begins on next page) Anyone can freely access the full text of works made available as "Open Access". Works made available under a Creative Commons license can be used according to the terms and conditions of said license. Use of all other works requires consent of the right holder (author or publisher) if not exempted from copyright protection by the applicable law. Availability: This is the author's manuscript
Repression Substitution: Shifting Human Rights Violations in Response to UN Naming and Shaming

ABSTRACT Does the United Nations naming and shaming of specific violations of human rights decrease government repression? In this article, we argue that international shaming of specific human rights violations can weaken the target government, bringing new challenges and making the government cessation of repression less feasible. When international naming and shaming campaigns target specific repressive tactics, they increase the costs of some – but not all – means of repression. Using original data on naming and shaming by the United Nations Human Rights Council (HRC), we show that the shaming of one physical integrity violation is jointly associated with decreases in that violation and increases in other violations of human rights.
Aerobic Interval Training Regulated SIRT3 Attenuates High-Fat-Diet-Associated Cognitive Dysfunction

Cognitive dysfunction is an important complicated disease in obesity. Exercise ameliorates obesity and the related cognitive dysfunction. However, the underlying mechanism is still unclear. In this study, we investigated whether aerobic interval training (AIT) could attenuate high-fat-diet- (HFD-) associated cognitive dysfunction and the possible mechanism of SIRT3-MnSOD pathway. C57BL/6 wild-type (WT) mice and SIRT3 knockout (KO) mice were randomized into control (Con) or HFD group with or without AIT training for 6 weeks. The spatial learning and memory ability were impaired in HFD group compared to the control group. The levels of mitochondrial protein acetylation were increased in the hippocampus of HFD group. The acetylation level of antioxidative MnSOD was increased as well. As a result, the ROS and MDA levels were significantly increased, which leads to the neuron apoptosis in the hippocampus. SIRT3 deficiency further aggravated HFD-induced cognitive dysfunction and susceptibility to oxidative stress injury. However, AIT upregulated neuron SIRT3 expression and decreased the acetylation of MnSOD. The hippocampus neuron oxidative stress and apoptosis were both decreased compared to untrained HFD group, which finally improved cognitive function of HFD mice. Collectively, AIT attenuates HFD-associated cognitive dysfunction through SIRT3 upregulation and improvement of antioxidative MnSOD activity.
Effect of Strategic Material Sourcing on Operational Performance of Manufacturing Firms: A Case of East African Breweries Ltd, Kenya

The objective of the study was to determine the effect of strategic Material Sourcing on operational performance of manufacturing firms, a case of East African Breweries Limited in Nairobi, Kenya. The study adopted descriptive research design to generate findings and made conclusions Strategic Material Sourcing and operational performance. Stratified sampling technique was adopted besides using closed ended questions to obtain data. Data was analyzed using SPSS version 21 where findings were presented in both descriptive and inferential analysis format. Descriptive analysis was presented using mean and standard deviation while inferential analysis utilized Karl Pearson correlation co-efficient to establish the relationships that exist between the independent and dependent variables. The study findings showed that strategic material sourcing entails developing sourcing strategy and that it involved improving and re-evaluating the purchasing activities at EABL. Findings also indicated that effective supplier relationship management helps in reducing monitoring costs and that it helped in conflict resolution and better communication between the company and the supplier thereby promoting operational performance. Recommendations were made based on the findings of the study including; that communication to be shared among supply chain partners should be commonly agreed upon majorly to enhance trust and understanding between the buyer and the supplier. Further, the study recommended that alternative strategies should be formulated to facilitate attempts by companies to promote their buyer supplier relationships with regard to enhanced operational performance.
Emerging roles of long non‐coding RNA in cancer

Since comprehensive analysis of the mammalian genome revealed that the majority of genomic products are transcribed in long non‐coding RNA (lncRNA), increasing attention has been paid to these transcripts. The applied next‐generation sequencing technologies have provided accumulating evidence of dysregulated lncRNA in cancer. The implication of this finding can be seen in many forms and at multiple levels. With impacts ranging from integrating chromatin remodeling complexes to regulating transcription and post‐transcriptional processes, aberrant expression of lncRNA may have repercussions in cell proliferation, tumor progression or metastasis. lncRNA may act as enhancers, scaffolds or decoys by physically interacting with other RNA species or proteins, resulting in a direct impact on cell signaling cascades. Even though their functional classification is well‐established in the context of cancer, clearer characterization in terms of their phenotypic outputs is needed to optimize and identify suitable candidates that enable the development of new therapeutic strategies and the design of novel diagnostic approaches. The present article aims to outline different cancer‐associated lncRNA according to their contribution to tumor suppression or tumor promotion based on their most current functional annotations.