Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevention Effects and Possible Molecular Mechanism of Mulberry Leaf Extract and its Formulation on Rats with Insulin-Insensitivity

For centuries, mulberry leaf has been used in traditional Chinese medicine for the treatment of diabetes. This study aims to test the prevention effects of a proprietary mulberry leaf extract (MLE) and a formula consisting of MLE, fenugreek seed extract, and cinnamon cassia extract (MLEF) on insulin resistance development in animals. MLE was refined to contain 5% 1-deoxynojirimycin by weight. MLEF was formulated by mixing MLE with cinnamon cassia extract and fenugreek seed extract at a 6:5:3 ratio (by weight). First, the acute toxicity effects of MLE on ICR mice were examined at 5 g/kg BW dose. Second, two groups of normal rats were administrated with water or 150 mg/kg BW MLE per day for 29 days to evaluate MLE’s effect on normal animals. Third, to examine the effects of MLE and MLEF on model animals, sixty SD rats were divided into five groups, namely, (1) normal, (2) model, (3) high-dose MLE (75 mg/kg BW) treatment; (4) low-dose MLE (15 mg/kg BW) treatment; and (5) MLEF (35 mg/kg BW) treatment. On the second week, rats in groups (2)-(5) were switched to high-energy diet for three weeks. Afterward, the rats were injected (ip) with a single dose of 105 mg/kg BW alloxan. After four more days, fasting blood glucose, post-prandial blood glucose, serum insulin, cholesterol, and triglyceride levels were measured. Last, liver lysates from animals were screened with 650 antibodies for changes in the expression or phosphorylation levels of signaling proteins. The results were further validated by Western blot analysis. We found that the maximum tolerance dose of MLE was greater than 5 g/kg in mice. The MLE at a 150 mg/kg BW dose showed no effect on fast blood glucose levels in normal rats. The MLE at a 75 mg/kg BW dose and MLEF at a 35 mg/kg BW dose, significantly (p < 0.05) reduced fast blood glucose levels in rats with impaired glucose and lipid metabolism. In total, 34 proteins with significant changes in expression and phosphorylation levels were identified. The changes of JNK, IRS1, and PDK1 were confirmed by western blot analysis. In conclusion, this study demonstrated the potential protective effects of MLE and MLEF against hyperglycemia induced by high-energy diet and toxic chemicals in rats for the first time. The most likely mechanism is the promotion of IRS1 phosphorylation, which leads to insulin sensitivity restoration.
Climate Change and Macro-Economic Cycles in Pre-Industrial Europe

Climate change has been proven to be the ultimate cause of social crisis in pre-industrial Europe at a large scale. However, detailed analyses on climate change and macro-economic cycles in the pre-industrial era remain lacking, especially within different temporal scales. Therefore, fine-grained, paleo-climate, and economic data were employed with statistical methods to quantitatively assess the relations between climate change and agrarian economy in Europe during AD 1500 to 1800. In the study, the Butterworth filter was adopted to filter the data series into a long-term trend (low-frequency) and short-term fluctuations (high-frequency). Granger Causality Analysis was conducted to scrutinize the associations between climate change and macro-economic cycle at different frequency bands. Based on quantitative results, climate change can only show significant effects on the macro-economic cycle within the long-term. In terms of the short-term effects, society can relieve the influences from climate variations by social adaptation methods and self-adjustment mechanism. On a large spatial scale, temperature holds higher importance for the European agrarian economy than precipitation. By examining the supply-demand mechanism in the grain market, population during the study period acted as the producer in the long term, whereas as the consumer in the short term. These findings merely reflect the general interactions between climate change and macro-economic cycles at the large spatial region with a long-term study period. The findings neither illustrate individual incidents that can temporarily distort the agrarian economy nor explain some specific cases. In the study, the scale thinking in the analysis is raised as an essential methodological issue for the first time to interpret the associations between climatic impact and macro-economy in the past agrarian society within different temporal scales.
Control of a 9-DoF Wheelchair-Mounted Robotic Arm System

A wheelchair-mounted robotic arm (WMRA) system was designed and built to meet the needs of mobility-impaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The control of this 9DoF system expands on the conventional control methods and combines the 7-DoF robotic arm control with the 2-DoF power wheelchair control. The 3-degrees of redundancy are optimized to effectively perform activities of daily living (ADLs) and overcome singularities, joint limits and some workspace limitations. The control system is designed for teleoperated or autonomous coordinated Cartesian control, and it offers expandability for future research, such as voice or sip and puff control operations and sensor assist functions.
An Updated Evidence-Based Practice Review on Teaching Mathematics to Students With Moderate and Severe Developmental Disabilities

The purpose of this review was to examine the body of research on teaching mathematics to students with moderate and severe developmental disability that has been published since 2005, reflecting changes in both the academic expectations for this population and research and design standards in the evidence-based practice (EBP) era. We examined research on teaching mathematical skills for students with moderate and severe developmental disability from 2005–2016 and found 36 studies (33 single-case and three group-experimental studies), updating the Browder, Spooner, Ahlgrim-Delzell, Harris, and Wakeman analysis. Of the 36 studies included in the review, 22 single-case and three group-design studies received a rating of high or adequate quality using the National Technical Assistance Center on Transition (NTACT) indicator criteria. In addition to systematic instruction, instructional procedures of technology-aided instruction, graphic organizers, manipulatives, and explicit instruction were found to be EBPs in teaching mathematics to this population.
When to Schedule an Application? An Energy-Aware Decision

Making mobile applications energy efficient immensely builds user satisfaction. Apart from the fact that there are not many efficient techniques for evaluating energy consumption for applications on mobile devices, the methods used are static in nature. Static techniques assume that during the running of an application, no other process can run concurrently, and the concerned application has the entire CPU at its disposal. In this paper, we propose a novel idea of measuring the energy consumption of an application running on a mobile device considering the fact that not always the entire CPU is available. This is because the application may sometimes run in the foreground when the mobile is idle and therefore, use the maximum CPU available, at other times, there maybe other tasks being run (apart from the routine background tasks) by the user for which this application is forced to run in the background. The major highlight of this paper is in considering the concept of variable CPU availability in energy analysis. We have also suggested to model the energy consumption problem of a mobile phone as a finite state automaton, where our aim is to find if a state can be reached where the entire battery of the mobile phone is exhausted.
A heavy traffic approach for queue-aware power control in interfering wireless links

In this work, we address the problem of power allocation for interfering transmitter-receiver pairs so that the probability that each queue length exceeds a specified threshold is fixed at a desired value. One application is satisfying QoS requirements in a dense cellular network. We address this problem using heavy traffic approximation techniques which lead to an asymptotic model described by a (controlled) stochastic differential equation. The power control strategy consists in allocating most of the power according to the wireless channel state and a smaller fraction according to the queue lengths. Simulation results in a simple setting illustrate that the proposed control policy can yield desirable results in practical systems.
A psychophysically validated metric for bidirectional texture data reduction

Bidirectional Texture Functions (BTF) are commonly thought to provide the most realistic perceptual experience of materials from rendered images. The key to providing efficient compression of BTFs is the decision as to how much of the data should be preserved. We use psychophysical experiments to show that this decision depends critically upon the material concerned. Furthermore, we develop a BTF derived metric that enables us to automatically set a material's compression parameters in such a way as to provide users with a predefined perceptual quality. We investigate the correlation of three different BTF metrics with psychophysically derived data. Eight materials were presented to eleven naive observers who were asked to judge the perceived quality of BTF renderings as the amount of preserved data was varied. The metric showing the highest correlation with the thresholds set by the observers was the mean variance of individual BTF images. This metric was then used to automatically determine the material-specific compression parameters used in a vector quantisation scheme. The results were successfully validated in an experiment with six additional materials and eighteen observers. We show that using the psychophysically reduced BTF data significantly improves performance of a PCA-based compression method. On average, we were able to increase the compression ratios, and decrease processing times, by a factor of four without any differences being perceived.
Change in Visceral Adiposity Independently Predicts a Greater Risk of Developing Type 2 Diabetes Over 10 Years in Japanese Americans

OBJECTIVE —Visceral adiposity is an important risk factor for cardiovascular disease and type 2 diabetes. We sought to determine whether change in intraabdominal fat area (IAF) over time predicts subsequent development of diabetes. RESEARCH DESIGN AND METHODS —We followed up 436 nondiabetic Japanese-American subjects (mean age 51.9 years, mean BMI 24.2 kg/m2, 54% male) for development of diabetes. We fit a logistic regression model to examine the association over a 10-year follow-up between change in IAF at 5-year follow-up and other fat areas (measured by computed tomography) and development of incident diabetes, adjusted for age, sex, family history of diabetes in a first-degree relative, second-generation versus third-generation Japanese American (Nisei vs. Sansei), baseline IAF, BMI, weight change over time, smoking status, physical activity level, and subcutaneous fat (SCF) depot areas. RESULTS —Cumulative incidence of diabetes was 20.4% at 10 years. Mean change in IAF was 10.9 cm2. An increase of 1 SD in IAF was associated with a 1.65-fold increase in the odds of diabetes over 10 years (OR = 1.65, 95% CI 1.21–2.25) after adjusting for the above covariates. This association was also independent of changes in thoracic, thigh, and abdominal SCF, as well as change in weight. CONCLUSIONS —We conclude that baseline IAF and accumulation of fat in this area over time are independent predictors of the development of type 2 diabetes in Japanese Americans.
Synaptic weighting circuits for Cellular Neural Networks

Cellular Neural Network (CNN) that can provide parallel processing in massive scale is known suitable to neuromorphic applications such as vision systems. In this paper, we propose a new synaptic weighting circuit that can perform analog multiplication for CNN applications. The common-mode feedback is used in the new weighting circuit to minimize the output offset. The multiplication accuracy can be degraded by finite High Resistance State (HRS) and non-zero Low Resistance State (LRS) of real memristors. To improve the multiplication accuracy, we added two MOSFET switches to the memristor weighting circuit and decided the weighting memristance very carefully considering the leakage current. Variations in memristance are analyzed to estimate how much they can affect the accuracy of analog multiplication. Finally, the Average and Laplacian template were tested and verified by the circuit simulation using the proposed weighting circuit.
Linking with Meaning: Ontological Hypertext for Scholars

The links in ontological hypermedia are defined according to the relationships between real-world objects. An ontology that models the significant objects in a scholar’s world can be used toward producing a consistently interlinked research literature. Currently the papers that are available online are mainly divided between subject- and publisher-specific archives, with little or no interoperability. This paper addresses the issue of ontological interlinking, presenting two experimental systems whose hypertext links embody ontologies based on the activities of researchers and scholars.
Mining Disjunctive Association Rules

Association rule mining is one of the most important and well-researched techniques of data mining, since the seminal papers by R. Agrawal et al. [1, 2]. It aims to induce associations among sets of items in the transaction databases or other data repositories. Ever since, several algorithms for specialized association tasks have appeared: quantitative association rules, generalized association rules, association rules extended with negation, among many other specializations. To the best of our knowledge all these existent approaches that enhance the classic model of association rules do not still cover other important specialized association tasks. In this paper, we propose and justify a model of disjunctive association rules, accompanied by an algorithm that induces rules in accordance with this model. In at least one type of practical application, the algorithm showed that it is efficient and even indispensable: it is the problem of software change impact analysis.
/ 06 07 22 6 v 2 2 2 N ov 2 00 6 D-term inflation in non-minimal supergravity

D-term inflation is one of the most interesting and versatile models of inflation. It is possible to implement naturally D-term inflation within high energy physics, as for example SUSY GUTs, SUGRA, or string theories. D-term inflation avoids the η-problem, while in its standard form it always ends with the formation of cosmic strings. Given the recent three-year WMAP data on the cosmic microwave background temperature anisotropies, we examine whether D-term inflation can be successfully implemented in non-minimal supergravity theories. We show that for all our choices of Kähler potential, there exists a parameter space for which the predictions of D-term inflation are in agreement with the measurements. The cosmic string contribution on the measured temperature anisotropies is always dominant, unless the superpotential coupling constant is fine tuned; a result already obtained for D-term inflation within minimal supergravity. In conclusion, cosmic strings and their rôle in the angular power spectrum cannot be easily hidden by just considering a non-flat Kähler geometry. D-term inflation in non-minimal supergravity 2
Computational verb color image interpolation

In computational verb(CV) image processing, the evolving function of a spatial verb denotes the changes of gray-values along spatial coordinates. The spatial configurations and the changes of the brightness function as two kinds of the evolving functions of spatial verbs. In our previous work, gray level profiles and contour shapes were used to construct edge models for describing edges of image, and then, achieved the CV interpolation algorithm by using computational verb similarities. In this paper, CV interpolation will be extended to color images, make up the current lack of effective edge detection algorithm and interpolation algorithm for color images. Experiments show that, CV interpolation algorithm has good performance for color images, in the case of a fixed magnification, high resolution image quality is better than other algorithms.
Modelling the Stoichiometric Regulation of C-Rich Toxins in Marine Dinoflagellates

Toxin production in marine microalgae was previously shown to be tightly coupled with cellular stoichiometry. The highest values of cellular toxin are in fact mainly associated with a high carbon to nutrient cellular ratio. In particular, the cellular accumulation of C-rich toxins (i.e., with C:N > 6.6) can be stimulated by both N and P deficiency. Dinoflagellates are the main producers of C-rich toxins and may represent a serious threat for human health and the marine ecosystem. As such, the development of a numerical model able to predict how toxin production is stimulated by nutrient supply/deficiency is of primary utility for both scientific and management purposes. In this work we have developed a mechanistic model describing the stoichiometric regulation of C-rich toxins in marine dinoflagellates. To this purpose, a new formulation describing toxin production and fate was embedded in the European Regional Seas Ecosystem Model (ERSEM), here simplified to describe a monospecific batch culture. Toxin production was assumed to be composed by two distinct additive terms; the first is a constant fraction of algal production and is assumed to take place at any physiological conditions. The second term is assumed to be dependent on algal biomass and to be stimulated by internal nutrient deficiency. By using these assumptions, the model reproduced the concentrations and temporal evolution of toxins observed in cultures of Ostreopsis cf. ovata, a benthic/epiphytic dinoflagellate producing C-rich toxins named ovatoxins. The analysis of simulations and their comparison with experimental data provided a conceptual model linking toxin production and nutritional status in this species. The model was also qualitatively validated by using independent literature data, and the results indicate that our formulation can be also used to simulate toxin dynamics in other dinoflagellates. Our model represents an important step towards the simulation and prediction of marine algal toxicity.
2 Alternating Direction Algorithm for NMF 2 . 1 The classic ADM approach

We extend the classic alternating direction method for convex optimization to solving the non-convex, nonnegative matrix factorization problem and conduct several carefully designed numerical experiments to compare the proposed algorithms with the most widely used two algorithms for solving this problem. In addition, the proposed algorithm is also briefly compared with two other more recent algorithms. Numerical evidence shows that the alternating direction algorithm tends to deliver higher-quality solutions with faster computing times on the tested problems. A convergence result is given showing that the algorithm converges to a Karush-KuhnTucker point whenever it converges.
Treadmill protocols for determination of maximum oxygen uptake in runners.

Four testing protocols were completed by each of 10 runners using a common speed for protocols 1 and 2 (P1 and P2), each runner's training pace for protocol 3 (P3) and a speed selected manually by the runner for protocol 4 (P4). Stages were increased by 2.5% grade every 2 min for each protocol except for P1, which had 1 min stages. There were no significant differences in maximum oxygen uptake (VO2 max) between protocols (P1, 65.0 +/- 5.6 ml.kg-1 min-1; P2, 64.5 +/- 5.3 ml.kg-1 min-1; P3, 66.2 +/- 3.9 ml.kg-1 min-1; P4, 64.7 +/- 5.8 ml.kg-1 min-1). Treadmill time was significantly less for P1 than for the other protocols. The rate of perceived exertion obtained at maximal exercise during P1 was less than that obtained during the other three protocols. Heart rate was significantly lower (P less than 0.05) at any level of submaximal VO2 during P3 than during the other protocols. We recommend a testing protocol using speeds approximating the runner's training pace and 1 min stages. This may result in lower perception of difficulty and HR throughout the test and shorter testing times.
Scheduling Workflow in Cloud Computing Based on Ant Colony Optimization Algorithm

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. So how to schedule applications to cloud resources and the execution time should be taken into account. In this paper, we propose a scheduling strategy based on ant colony optimization (ACO) and two-way ants mechanism is introduced. Setting the pheromone threshold is to avoid the premature phenomenon, in addition, taking a two-tier search strategy and introducing pre-execution time is to avoid the local optimum so that tasks can be assigned to highest efficient computing resources. The simulation results show that the algorithm can greatly shorten the time to find the computing resource in cloud computing environment and significantly improve the efficiency.
Heuristic Lemmatization for Arabic Texts Indexation and Classification

Problem statement: This study proposed a system based on a heuristic l emmatization for Arabic text indexation and classification. This res earch is needed for a lot of NLP applications such as the research of information and automatic abstract. This system was not related to any linguistic rule . The proposed method was limited to five different d omains: Sports, medicine, politics, economics and agriculture. The main idea is collecting different texts related to the chosen domains and studying th em by extracting the pertinent terms. Approach: Every entered text had the formatting stage in whi ch we can remove some words and letters that do not have any importance for the meaning. After that, the frequencies' average is calculated to classify the text and its related domain. Results: The main finality of the System of Indexation and Classification of A rabic Texts (SICAT) is to classify finally an unknown text in its suitable domain. So, it's to de tect the text theme. To do this task, we applied a method by pertinent terms correspondence. It is abo ut testing the correspondence of all pertinent term s of the text to classify with the keywords of every domain of the corpus. The domain, that constitutes the majority of terms having a correspondence with terms of the text, represents the theme that we loo k for to classify our unknown text. Conclusion: It holds two main parts: the indexation and the classification. The indexation stage is composed of three main parts: the pre-learning, the lemmatization and the frequencies' calculation. The classification stage is composed of two main components: the extraction of keywords and classifi cation of new text. We have made many tests of verification to test the validation of the system. The system performance was evaluated on the differe nt chosen domains, achieves 90% precision and 85% recall.
Distributed PCA and k-Means Clustering

This paper proposes a distributed PCA algorithm, with the theoretical guarantee that any good approximation solution on the projected data for k-means clustering is also a good approximation on the original data, while the projected dimension required is independent of the original dimension. When combined with the distributed coreset-based clustering approach in [3], this leads to an algorithm in which the number of vectors communicated is independent of the size and the dimension of the original data. Our experiment results demonstrate the effectiveness of the algorithm.