Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical study on butylphthalide combined with urinary kallidinogenase in treatment of acute cerebral infarction

Objective To study the effect of Butylphthalide Soft Capsules combined with Urinary Kallidinogenase for injection in treatment of acute cerebral infarction. Methods Patients (150 cases) with acute cerebral infarction in Tianjin Huanhu Hospital from April 2014 to March 2014 were randomly divided into control and treatment groups according to the extraction method, and each group had 75 cases. Patients in the control group were iv administered with Urinary Kallidinogenase for injection, 0.15 PNA added into 250 mL normal saline, finished during 30 min, once daily. The patients in treatment group were po administered with Butylphthalide Soft Capsules on the basis of the control group, 0.2 g/time, four times daily. The patients in two groups were treated for 14 d. After treatment, endothelial function index, ability of daily life (ADL) score, and nerve function score in two groups were observed, and efficacy and the incidence of adverse reactions in two groups during the process of treatment was evaluated. Results After treatment, levels of plasma endothelin-1 in two groups were significantly decreased, but levels of plasma nitric oxide in two groups were obviously increased, and the difference was statistically significant in the same group (P < 0.05). After treatment, the observational indexes in the treatment group were significantly better than those in the control group, with significant difference between two groups (P < 0.05). After treatment, ADL scores in two groups were significantly increased, but nerve function scores in two groups were obviously decreased, and the difference was statistically significant in the same group (P < 0.05). After treatment, the observational indexes in the treatment group were significantly better than those in the control group, with significant difference 收稿日期:2016-02-23 作者简介::王 虹(1969—),女,天津人,本科,主管药师,主要研究方向为药学。 Tel: 18920076199 E-mail: nabi_l@163.com *通信作者 李骏峰(1969—),男,台湾人,本科,主管药师,主要研究方向为药学。 现代药物与临床 Drugs & Clinic 第 31 卷 第 5 期 2016 年 5 月 ·641· between two groups (P < 0.05). After treatment, the efficacies in the control and treatment groups were 74.67% and 90.67%, respectively, and there were differences between two groups (P < 0.05). Adverse reaction rates of the treatment group and the control group were 4.00% and 2.67%, respectively, but there was no difference between two groups. Conclusion Butylphthalide Soft Capsules combined with Urinary Kallidinogenase for injection has clinical curative effect in treatment of acute cerebral infarction, can significantly improve endothelial function, ability of daily life, and nerve function, and has less adverse reactions, which has a certain clinical application value.
Vous êtes invités à participer à une enquête internationale organisée par « Birkenwood International Pty

Meat science toward 2030: global strategic directions initiative background paper Birkenwood International Pty Ltd is inviting you to contribute to the international research agenda in meat sciences. PHASE 1. We invite you to submit your views in response to the following questions: 1) What are the key challenges and opportunities for meat science toward 2030? 2) How do we harness meat science and related expertise into the future? Please submit online at: Meat Science Toward 2030 (https://docs.google.com/forms/d/e/1FAIpQLSf4b30TP1Ftr6x99JFuR7t7fpAnYxv4hCvgLvyQMy-kHZDmA/viewform). We welcome responses until June 20th, 2018. PHASE 2. Review of all submissions. The submissions will inform the setting up of a Global Meat Science Expert Group and high level discussions within this group when convened in Phase 3. PHASE 3. Convening of the Global Meat Science Expert Group for a 2-day meeting (location to be advised). Members of the Meat Science Expert Group will receive funding towards personal time spent and travel. It is anticipated that the outcomes of this meeting will include agreement on high-level strategies that address the identified challenges and opportunities out to 2030. These will include potential strategic approaches to encourage collaborative application of global resources and development of research talent together with an acceleration of knowledge transfer through enhanced industry and research interaction. La revue scientifique
DESIGN OF A VACUUM IMPRINTING SYSTEM FOR HIGH RESOLUTION PATTERNING ON NONPLANAR SURFACES

In this paper, we present the design and development of an automated vacuum microimprinting system that enables high resolution patterning on both planar and nonplanar surfaces over a large area, e.g. 4” x 4”, in a noncleanroom environment. This system is applicable to many contact printing processes including microcontact printing (MCP), nano/micro-imprinting, and micro-molding etc. In this work we selected MCP and UV micro-imprinting to demonstrate the versatility and unique capability of our vacuum imprinting system.
Effect of discriminate and indiscriminate use of oxytetracycline on residual status in broiler soft tissues

Aim: This study aimed to evaluate the effects of discriminate and indiscriminate use of oxytetracycline on hematological parameters, residual status in soft tissue of broiler and of thermal effect on oxytetracycline residual status. Materials and Methods: Eighteen, day-old male broiler chickens were purchased and were divided into three different groups (control group, discriminate group, and indiscriminate group). The control group received no antibiotics. The discriminate group received oxytetracycline 1 g/L drinking water for 5 consecutive days, and 10 days’ withdrawal period was maintained before sacrifice. The indiscriminate group received oxytetracycline 1 g/L drinking water till the sacrificed day. Blood samples were collected before sacrificing for hematological analysis. After sacrificing liver, kidney, spleen, and muscle samples were collected for analysis of oxytetracycline residues in raw soft tissues. Since meat is used to cook by traditional method in Bangladesh before consumption that is why positive meat samples were cooked by traditional cooking method to evaluate the thermal effect on oxytetracycline residual status as well. Thin-layer chromatography (TLC) was done for screening of oxytetracycline residues in soft tissues. Results: Mean differences of total erythrocyte count (million/mm3), hemoglobin estimation (gm%), and packed cell volume (%) estimation were not statistically significant among the groups. TLC analysis of raw samples showed 100% positive results of all samples collected from the indiscriminate group. In contrast, samples collected from the discriminate group were negative for oxytetracycline residues. In the control group, all samples were negative for oxytetracycline residue. There was a significant (p<0.05) relationship of oxytetracycline residues among three different groups for liver, kidney, spleen, and muscle samples. Positive liver and muscle samples from the indiscriminate group were subjected to thermal treatment by traditional cooking method of Bangladesh. Oxytetracycline residues had found in cooked meat, liver, and juice part, suggesting that antibiotic residues disseminated to juice part from flesh part after cooking. Conclusion: Evidence suggests that proper maintenance of withdrawal period would minimize oxytetracycline residues in broiler soft tissues, whereas antibiotics retained in soft tissues of broiler in case of indiscriminate use. Traditional cooking does not change oxytetracycline residual status in edible tissues. Therefore, awareness regarding the proper maintenance of withdrawal period after antibiotic treatment of broiler is one of the best strategies which may positively reduce the risk of antimicrobial drugs residue in meat.
Beslenme ve diyetetik bölümü kiz öğrencileri arasinda görülen ortoreksiya nervosa riski The risk of orthorexia nervosa for female students studying nutrition and dietetics

Objective: Studies indicate that orthorexia nervosa is more frequently seen in medical students, physicians, dietitians, people with anxiety, obsessive compulsive people, and people who are concerned with their body image. The objective of this study was to evaluate healthy eating obsessions, eating attitudes, and eating behaviors of female students studying nutrition and dietetics. Metarial-Method: This study was conducted on 106 randomly selected female students of Near East University, Faculty of Health Sciences; Department of Nutrition and Dietetics. A questionnaire was used to obtain general information on the subjects, Diet Quality Index-International (DQI-I), Orthorexia Nervosa Evaluation Scale (ORTO-11), Eating Attitude Test (EAT-40), and Maudsley Obsessive Compulsive Inventory (MOCI) were applied to evaluate the subjects’ obsession with healthy eating, eating attitudes, and eating behaviors, furthermore weight, height, waist and hip circumferences were recorded. Test scores and antropometric measurements were analysed using student t-test and one way anova test. Correlation between test scores were determined using Pearson correlation coefficient. Results: Mean ORTO-11 score of the subjects was found to be 30.7±4.45. As eating behavior disorder risk increases, orthorexia nervosa and obsessive compulsive disorder risks decreases with statistical significance (p<0.05). In addition, the increase of orthorexia nervosa risk is related with increased obsessive compulsive behavior risk and decreased eating behavior disorder risk (p<0.01).
Providing a Link Prediction Model based on Structural and Homophily Similarity in Social Networks

In recent years, with the growing number of online social networks, these networks have become one of the best markets for advertising and commerce, so studying these networks is very important. Forecasting new edges in online social networks can give us a better understanding of the growth of these networks. There have been many studies of link prediction in the field of engineering and humanities. Scientists attribute the existence of a new relationship between two individuals for two reasons: 1) Proximity to the graph (structure) 2) Similar properties of the two individuals (Homophile law). However, studying the impact of the two approaches working together to create new edges remains an open problem. Similarity metrics can also be divided into two categories; Neighborhood-based and path-based. So far, the above two theoretical approaches (proximity and homophile) have not been found together in the neighborhood-based metrics. In this paper, we first attempt to provide a solution to determine importance of the proximity to the graph and similar features in the connectivity of the graphs. Then obtained weights are assigned to both proximity and homophile. Then the best similarity metric in each approach are obtained. Finally, the selected metric of homophily similarity and structural similarity are combined with the obtained weights. The results of this study were evaluated on two datasets; Zanjan University Graduate School of Social Sciences and Pokec online Social Network. The first data set was collected for this study and then the questionnaires and data collection methods were filled out. Since this dataset is one of the few Iranian datasets that has been compiled with its users' specifications, it can be of great value. In this paper, we have been able to increase the accuracy of Neighborhood-based similarity metric by using two proximity in graph and homophily approaches.
Benchmarking Knowledge Graphs on the Web

The growing interest in making use of Knowledge Graphs for developing explainable artificial intelligence, there is an increasing need for a comparable and repeatable comparison of the performance of Knowledge Graph-based systems. History in computer science has shown that a main driver to scientific advances, and in fact a core element of the scientific method as a whole, is the provision of benchmarks to make progress measurable. This paper gives an overview of benchmarks used to evaluate systems that process Knowledge Graphs.
Transformer on a Diet

Transformer has been widely used thanks to its ability to capture sequence information in an efficient way. However, recent developments, such as BERT and GPT-2, deliver only heavy architectures with a focus on effectiveness. In this paper, we explore three carefully-designed light Transformer architectures to figure out whether the Transformer with less computations could produce competitive results. Experimental results on language model benchmark datasets hint that such trade-off is promising, and the light Transformer reduces 70% parameters at best, while obtains competitive perplexity compared to standard Transformer. The source code is publicly available.
Hodge and Podge: Hybrid Supervised Sound Event Detection with Multi-Hot MixMatch and Composition Consistence Training

In this paper, we propose a method called Hodge and Podge for sound event detection. We demonstrate Hodge and Podge on the dataset of Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 Challenge Task 4. This task aims to predict the presence or absence and the onset and offset times of sound events in home environments. Sound event detection is challenging due to the lack of large scale real strongly labeled data. Recently deep semi-supervised learning (SSL) has proven to be effective in modeling with weakly labeled and unlabeled data. This work explores how to extend deep SSL to result in a new, state-of-the-art sound event detection method called Hodge and Podge. With convolutional recurrent neural networks (CRNN) as the backbone network, first, a multi-scale squeeze-excitation mechanism is introduced and added to generate a pyramid squeeze-excitation CRNN. The pyramid squeeze-excitation layer can pay attention to the issue that different sound events have different durations, and to adaptively recalibrate channel-wise spectrogram responses. Further, in order to remedy the lack of real strongly labeled data problem, we propose multi-hot MixMatch and composition consistency training with temporal-frequency augmentation. Our experiments with the public DCASE2019 challenge task 4 validation data resulted in an event-based F-score of 43.4%, and is about absolutely 1.6% better than state-of-the-art methods in the challenge. While the F-score of the official baseline is 25.8%.
Graph Prolongation Convolutional Networks: Explicitly Multiscale Machine Learning on Graphs, with Applications to Modeling of Biological Systems

We deﬁne a novel type of ensemble Graph Convolutional Network (GCN) model. Using optimized linear projection operators to map between spatial scales of graph, this ensemble model learns to aggregate information from each scale for its ﬁnal prediction. We calculate these linear projection operators as the inﬁma of an objective function relating the structure matrices used for each GCN. Equipped with these projections, our model (a Graph Prolongation-Convolutional Network) outperforms other GCN ensemble models at predicting the potential energy of monomer subunits in a coarse-grained mechanochemical simulation of microtubule bending. We demonstrate these performance gains by measuring an estimate of the FLOPs spent to train each model, as well as wall-clock time. Because our model learns at multiple scales, it is possible to train at each scale according to a predetermined schedule of coarse vs. ﬁne training. We examine several such schedules adapted from the Algebraic Multigrid (AMG) literature, and quantify the computational beneﬁt of each. Finally, we demonstrate how under certain assumptions, our graph prolongation layers may be decomposed into a matrix outer product of smaller GCN operations.
Groupes convexes-cocompacts en rang supérieur

The convex-cocompact subgroups are central in hyperbolic geometry and more generally in negative curvature. Labourie introduced in 2005 the notion of 'Anosov' subgroup which proves progressively to be the right generalizations of convex-cocompact groups, especially after the works of Kapovich, Leeb and Porti. This expose will review different caracteriations of those groups, emphasizing the parallel (or difference) with the negative curvature, and will give their basic properties.
Quantum Limits in Optical Communications

This tutorial reviews the Holevo capacity limit as a universal tool to analyze the ultimate transmission rates in a variety of optical communication scenarios, ranging from conventional optically amplified fiber links to free-space communication with power-limited optical signals. The canonical additive white Gaussian noise model is used to describe the propagation of the optical signal. The Holevo limit exceeds substantially the standard Shannon limit when the power spectral density of noise acquired in the course of propagation is small compared to the energy of a single photon at the carrier frequency per unit time-bandwidth area. General results are illustrated with a discussion of efficient communication strategies in the photon-starved regime.
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets

Skip connections are an essential component of current state-of-the-art deep neural networks (DNNs) such as ResNet, WideResNet, DenseNet, and ResNeXt. Despite their huge success in building deeper and more powerful DNNs, we identify a surprising \emph{security weakness} of skip connections in this paper. Use of skip connections \textit{allows easier generation of highly transferable adversarial examples}. Specifically, in ResNet-like (with skip connections) neural networks, gradients can backpropagate through either skip connections or residual modules. We find that using more gradients from the skip connections rather than the residual modules according to a decay factor, allows one to craft adversarial examples with high transferability. Our method is termed \emph{Skip Gradient Method} (SGM). We conduct comprehensive transfer attacks against state-of-the-art DNNs including ResNets, DenseNets, Inceptions, Inception-ResNet, Squeeze-and-Excitation Network (SENet) and robustly trained DNNs. We show that employing SGM on the gradient flow can greatly improve the transferability of crafted attacks in almost all cases. Furthermore, SGM can be easily combined with existing black-box attack techniques, and obtain high improvements over state-of-the-art transferability methods. Our findings not only motivate new research into the architectural vulnerability of DNNs, but also open up further challenges for the design of secure DNN architectures.
Gaussian Process Imputation of Multiple Financial Series

In Financial Signal Processing, multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market and therefore they are required to be jointly analysed. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process (MOGP) with expressive covariance functions. Learning these market dependencies among financial series is crucial for the imputation and prediction of financial observations. The proposed model is validated experimentally on two real-world financial datasets for which their correlations across channels are analysed. We compare our model against other MOGPs and the independent Gaussian process on real financial data.
Speech Enhancement Using Self-Adaptation and Multi-Head Self-Attention

This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly from the test utterance. Conventional studies of deep neural network (DNN)-based speech enhancement mainly focus on building a speaker independent model. Meanwhile, in speech applications including speech recognition and synthesis, it is known that model adaptation to the target speaker improves the accuracy. Our research question is whether a DNN for speech enhancement can be adopted to unknown speakers without any auxiliary guidance signal in test-phase. To achieve this, we adopt multi-task learning of speech enhancement and speaker identification, and use the output of the final hidden layer of speaker identification branch as an auxiliary feature. In addition, we use multi-head self-attention for capturing long-term dependencies in the speech and noise. Experimental results on a public dataset show that our strategy achieves the state-of-the-art performance and also outperform conventional methods in terms of subjective quality.
Bosonization of the Q=0 continuum of Dirac fermions

We develop a bosonization formalism that captures non-perturbatively the interaction effects on the $\mathbf{Q}=0$ continuum of excitations of nodal fermions above one dimension. Our approach is a natural extension of the classic bosonization scheme for higher dimensional Fermi surfaces to include the $\mathbf{Q}=0$ neutral excitations that would be absent in a single-band system. The problem is reduced to solving a boson bilinear Hamiltonian. We establish a rigorous microscopic footing for this approach by showing that the solution of such boson bilinear Hamiltonian is exactly equivalent to performing the infinite sum of Feynman diagrams associated with the Kadanoff-Baym particle-hole propagator that arises from the self-consistent Hartree-Fock approximation to the single particle Green's function. We apply this machinery to compute the interaction corrections to the optical conductivity of 2D Dirac Fermions with Coulomb interactions reproducing the results of perturbative renormalization group at weak coupling and extending them to the strong coupling regime.
A Method to Distinguish Quiescent and Dusty Star-forming Galaxies with Machine Learning

Large photometric surveys provide a rich source of observations of quiescent galaxies, including a surprisingly large population at z > 1. However, identifying large, but clean, samples of quiescent galaxies has proven difficult because of their near-degeneracy with interlopers such as dusty, star-forming galaxies. We describe a new technique for selecting quiescent galaxies based upon t-distributed stochastic neighbor embedding (t-SNE), an unsupervised machine-learning algorithm for dimensionality reduction. This t-SNE selection provides an improvement both over UVJ, removing interlopers that otherwise would pass color selection, and over photometric template fitting, more strongly toward high redshift. Due to the similarity between the colors of high- and low-redshift quiescent galaxies, under our assumptions, t-SNE outperforms template fitting in 63% of trials at redshifts where a large training sample already exists. It also may be able to select quiescent galaxies more efficiently at higher redshifts than the training sample.
Boltzmann hierarchies for self-interacting warm dark matter scenarios

We provide a general framework for self-interacting warm dark matter (WDM) in cosmological perturbations, by deriving from first principles a Boltzmann hierarchy which retains certain independence from a particular interaction Lagrangian. We consider elastic interactions among the massive particles, and obtain a hierarchy which is more general than the ones usually obtained for non-relativistic (as for cold DM) or for ultra-relativistic (as for neutrinos) approximations. The more general momentum-dependent kernel integrals in the Boltzmann collision terms, are explicitly calculated for different field-mediator models, including examples of a scalar field or a massive vector field. As an application, we study the evolution of the interaction rate per particle under the relaxation time approximation, and assess when a given self-interaction is relevant in comparison with the Hubble expansion rate. Our framework aims to be a useful tool to evaluate DM self-interaction effects in the linear power spectrum, with the consequent imprints on non-linear scales of structure formation.