Miracle-dz/UniFAR-FTUs huggingface dataset:
Insulin resistance and hypersecretion in obesity. European Group for the Study of Insulin Resistance (EGIR).

Insulin resistance and insulin hypersecretion are established features of obesity. Their prevalence, however, has only been inferred from plasma insulin concentrations. We measured insulin sensitivity (as the whole-body insulin-mediated glucose uptake) and fasting posthepatic insulin delivery rate (IDR) with the use of the euglycemic insulin clamp technique in a large group of obese subjects in the database of the European Group for the Study of Insulin Resistance (1,146 nondiabetic, normotensive Caucasian men and women aged 18-85 yr, with a body mass index (BMI) ranging from 15 to 55 kg.m-2). Insulin resistance, defined as the lowest decile of insulin sensitivity in the lean subgroup (608 subjects with a mean BMI of 29 kg.m-2). Insulin sensitivity declined linearly with BMI at an age- and sex-adjusted rate of 1.2 micromol.min-1.kg FFM-1 per BMI unit (95% confidence intervals = 1.0-1.4). Insulin hypersecretion, defined as the upper decile of IDR, was significantly (P<0.0001) more prevalent (38%) than insulin resistance in the obese group. In the whole dataset, IDR rose as a function of both BMI and insulin resistance in a nonlinear fashion. Neither the waist circumference nor the waist-to-hip ratio, indices of body fat distribution, was related to insulin sensitivity after adjustment for age, gender, and BMI; both, however, were positively associated (P<0.001) with insulin hypersecretion, particularly in women. In nondiabetic, normotensive obese subjects, the prevalence of insulin resistance is relatively low, and is exceeded by the prevalence of insulin hypersecretion, particularly in women with central obesity. In the obese with preserved insulin sensitivity, risk for diabetes, cardiovascular risk, and response to treatment may be different than in insulin resistant obesity.
Payload Caching: High-Speed Data Forwarding for Network Intermediaries

Large-scale network services such as data delivery often incorporate new functions by interposing intermediaries on the network. Examples of forwarding intermediaries include rewalls, content routers, protocol converters, caching proxies, and multicast servers. With the move toward network storage, even static Web servers act as intermediaries to forward data from storage to clients. This paper presents the design, implementation, and measured performance of payload caching, a technique for improving performance of host-based intermediaries. Our approach extends the functions of the network adapter to cache portions of the incoming packet stream, enabling the system to forward data directly from the cache. We prototyped payload caching in a programmable highspeed network adapter and a FreeBSD kernel. Experiments with TCP/IP traÆc ows show that payload caching can improve forwarding performance by up to 60% in realistic scenarios.
Systematic Review and Stratified Meta-Analysis of the Efficacy of Rho GTPase kinase ( ROCK ) Inhibitors and RhoA Inhibitors on Ischemic Stroke Animal Models Protocol Ischemic stroke

s will be independently screened by two reviewers to identify those meeting our inclusion criteria (see below), with differences resolved by discussion with a third reviewer. Inclusion criteria and data extraction Studies will be included if they reported the effects of a ROCK inhibitor in an in vivo animal model of focal cerebral Ischemia and reported the number of animals per group, a lesion size outcome (infarct volume or infarct area; primary outcome) or a neurobehavioral score (secondary outcome), and the mean and its variance (standard error of the mean or standard deviation). Data will be extracted to the CAMARADES database. Quality assessment Studies will be assessed against the CAMARADES published ten item quality check list [12] and given one point for each item reported. 1) A peer reviewed publication, 2) control of temperature stated, 3) were the animal models randomly allocated to groups, 4) was there a blinded induction of ischemia, 5) a blinded assessment of outcome, 6) the anaesthetic used didn’t have intrinsic neuroprotective activity, 7) the use of co morbid animals, 8) sample size calculation, 9) compliance with animal welfare regulations, 10) any statements of potential conflicts of interest.
Magnetization Transfer Ratio Relates to Cognitive Impairment in Normal Elderly

Magnetization transfer imaging (MTI) can detect microstructural brain tissue changes and may be helpful in determining age-related cerebral damage. We investigated the association between the magnetization transfer ratio (MTR) in gray and white matter (WM) and cognitive functioning in 355 participants of the Austrian stroke prevention family study (ASPS-Fam) aged 38–86 years. MTR maps were generated for the neocortex, deep gray matter structures, WM hyperintensities, and normal appearing WM (NAWM). Adjusted mixed models determined whole brain and lobar cortical MTR to be directly and significantly related to performance on tests of memory, executive function, and motor skills. There existed an almost linear dose-effect relationship. MTR of deep gray matter structures and NAWM correlated to executive functioning. All associations were independent of demographics, vascular risk factors, focal brain lesions, and cortex volume. Further research is needed to understand the basis of this association at the tissue level, and to determine the role of MTR in predicting cognitive decline and dementia.
The Wisdom of Bookies? Sentiment Analysis Versus. the NFL Point Spread

The American Football betting market provides a particularly attractive domain to study the nexus between public sentiment and the wisdom of crowds. In this paper, we present the first substantial study of the relationship between the NFL betting line and public opinion expressed in blogs and microblogs (Twitter). We perform a large-scale study of four distinct text streams: LiveJournal blogs, RSS blog feeds captured by Spinn3r, Twitter, and traditional news media. Our results show interesting disparities between the first and second halves of each season. We present evidence showing usefulness of sentiment on NFL betting. We demonstrate that a strategy betting roughly 30 games per year identified winner roughly 60% of the time from 2006 to 2009, well beyond what is needed to overcome the bookie's typical commission(53%).
Author ' s personal copy Combining stated and revealed choice research to simulate the neighbor effect : The case of hybrid-electric vehicles

According to intuition and theories of diffusion, consumer preferences develop along with technological change. However, most economic models designed for policy simulation unrealistically assume static preferences. To improve the behavioral realism of an energy–economy policy model, this study investigates the ‘‘neighbor effect,’’ where a new technology becomes more desirable as its adoption becomes more widespread in the market. We measure this effect as a change in aggregated willingness to pay under different levels of technology penetration. Focusing on hybrid-electric vehicles (HEVs), an online survey experiment collected stated preference (SP) data from 535 Canadian and 408 Californian vehicle owners under different hypothetical market conditions. Revealed preference (RP) data was collected from the same respondents by eliciting the year, make and model of recent vehicle purchases from regions with different degrees of HEV popularity: Canada with 0.17% new market share, and California with 3.0% new market share. We compare choice models estimated from RP data only with three joint SP–RP estimation techniques, each assigning a different weight to the influence of SP and RP data in coefficient estimates. Statistically, models allowing more RP influence outperform SP influenced models. However, results suggest that because the RP data in this study is afflicted by multicollinearity, techniques that allow more SP influence in the beta estimates while maintaining RP data for calibrating vehicle * Corresponding author at: Institute of Transportation Studies, University of California at Davis, 2028 Academic Surge, One Shields Avenue, Davis, CA 95616, USA. Tel. : +1 530 231 5007; fax: +1 530 752 6572. E-mail address: jaxsen@ucdavis.edu (J. Axsen).
Robust endpoint detection algorithm based on the adaptive band-partitioning spectral entropy in adverse environments

In speech processing, endpoint detection in noisy environments is difficult, especially in the presence of nonstationary noise. Robust endpoint detection is one of the most important areas of speech processing. Generally, the feature parameters used for endpoint detection are highly sensitive to the environment. Endpoint detection is severely degraded at low signal-to-noise ratios (SNRs) since those feature parameters cannot adequately describe the characteristics of a speech signal. As a result, this study seeks the banded structure on speech spectrogram to distinguish a speech from a nonspeech, especially in adverse environments. First, this study proposes a feature parameter, called band-partitioning spectral entropy (BSE), which exploits the use of the banded structure on speech spectrogram. A refined adaptive band selection (RABS) method is extended from the adaptive band selection method proposed by Wu et al., which adaptively selects useful bands not corrupted by noise. The successful RABS method is strongly depended on an on-line detection with minimal processing delay. In this paper, the RABS method is combined with the BSE parameter. Finally, a novel robust feature parameter, adaptive band-partitioning spectral entropy (ABSE), is presented to successfully detect endpoints in adverse environments. Experimental results indicate that the ABSE parameter is very effective under various noise conditions with several SNRs. Furthermore, the proposed algorithm outperforms other approaches and is reliable in a real car.
Spatial organisation and behaviour of the parental chromosome sets in the nuclei of Saccharomyces cerevisiae × S. paradoxus hybrids

We demonstrate that the genomes of Saccharomyces cerevisiae and S. paradoxus are sufficiently divergent to allow their differential labeling by genomic in situ hybridisation (GISH). The cytological discrimination of the genomes allowed us to study the merging of the two genomes during hybrid mating. GISH revealed that in hybrid nuclei the two genomes are intermixed. In hybrid meiosis, extensive intraspectific nonhomologous pairing takes place. GISH on chromosome addition and substitution strains (with chromosomes of S. paradoxus added to or replacing the homoeologous chromosome of an otherwise S. cerevisiae background) was used to delineate individual chromosomes at interphase and to examine various aspects of chromosome structure and arrangement.
Triclabendazole Sulfoxide Causes Stage-Dependent Embryolethality in Zebrafish and Mouse In Vitro

Background Fascioliasis and paragonimiasis are widespread foodborne trematode diseases, affecting millions of people in more than 75 countries. The treatment of choice for these parasitic diseases is based on triclabendazole, a benzimidazole derivative which has been suggested as a promising drug to treat pregnant women and children. However, at the moment, this drug is not approved for human use in most countries. Its potential adverse effects on embryonic development have been scarcely studied, and it has not been assigned a pregnancy category by the FDA. Thus, to help in the process of risk-benefit decision making upon triclabendazole treatment during pregnancy, a better characterization of its risks during gestation is needed. Methodology The zebrafish embryo test, a preimplantation and a postimplantation rodent whole embryo culture were used to investigate the potential embryotoxicity/teratogenicity of triclabendazole and its first metabolite triclabendazole sulfoxide. Albendazole and albendazole sulfoxide were included as positive controls. Principal Findings Triclabendazole was between 10 and 250 times less potent than albendazole in inducing dysmorphogenic effects in zebrafish or postimplantation rodent embryos, respectively. However, during the preimplantation period, both compounds, triclabendazole and triclabendazole sulfoxide, induced a dose-dependent embryolethal effect after only 24 h of exposure in rodent embryos and zebrafish (lowest observed adverse effect concentrations = 10 μM). Conclusions/Significance In humans, after ingestion of the recommended doses of triclabendazole to treat fascioliasis and paragonimiasis (10 mg/kg), the main compound found in plasma is triclabendazole sulfoxide (maximum concentration 38.6 μM), while triclabendazole concentrations are approximately 30 times lower (1.16 μM). From our results it can be concluded that triclabendazole, at concentrations of the same order of magnitude as the clinically relevant ones, does not entail teratogenic potential in vitro during the organogenesis period, but its first metabolite triclabendazole sulfoxide has a high embryotoxic capacity in vitro during the preimplantation stage.
Dedoop: Efficient Deduplication with Hadoop

We demonstrate a powerful and easy-to-use tool called Dedoop ( De duplication with Ha doop ) for MapReduce-based entity resolution (ER) of large datasets. Dedoop supports a browser-based specification of complex ER workflows including blocking and matching steps as well as the optional use of machine learning for the automatic generation of match classifiers. Specified workflows are automatically translated into MapReduce jobs for parallel execution on different Hadoop clusters. To achieve high performance Dedoop supports several advanced load balancing strategies.
Using Multiple Knowledge Sources for Word Sense Discrimination

This paper addresses the problem of how to identify the intended meaning of individual words in unrestricted texts, without necessarily having access to complete representations of sentences. To discriminate senses, an understander can consider a diversity of information, including syntactic tags, word frequencies, collocations, semantic context, role-related expectations, and syntactic restrictions. However, current approaches make use of only small subsets of this information. Here we will describe how to use the whole range of information. Our discussion will include how the preference cues relate to general lexical and conceptual knowledge and to more specialized knowledge of collocations and contexts. We will describe a method of combining cues on the basis of their individual specificity, rather than a fixed ranking among cue-types. We will also discuss an application of the approach in a system that computes sense tags for arbitrary texts, even when it is unable to determine a single syntactic or semantic representation for some sentences.
Exploiting mobile crowdsourcing for pervasive cloud services: challenges and solutions

With the proliferation of increasingly powerful mobile devices, mobile users can collaboratively form a mobile cloud to provide pervasive services, such as data collecting, processing, and computing. With this mobile cloud, mobile crowdsourcing, as an emerging service paradigm, can enable mobile users to take over the outsourced tasks. By leveraging the sensing capabilities of mobile devices and integrating human intelligence and machine-computation, mobile crowdsourcing has the potential to revolutionize the approach of data collecting and processing. In this article we investigate the mobile crowdsourcing architecture and applications, then discuss some research challenges and countermeasures for developing mobile crowdsourcing. Some research orientations are finally envisioned for further studies.
On the Detectability of Node Grouping in Networks

In typical studies of node grouping detection, the grouping is presumed to have a certain type of correlation with the network structure (e.g., densely connected groups of nodes that are loosely connected in between). People have defined different fitness measures (modularity, conductance, etc.) to quantify such correlation, and group the nodes by optimizing a certain fitness measure. However, a particular grouping with desired semantics, as the target of the detection, is not promised to be detectable by each measure. We study a fundamental problem in the process of node grouping discovery: Given a particular grouping in a network, whether and to what extent it can be discovered with a given fitness measure. We propose two approaches of testing the detectability, namely ranking-based and correlation-based randomization tests. Our methods are evaluated on both synthetic and real datasets, which shows the proposed methods can effectively predict the detectability of groupings of various types, and support explorative process of node grouping discovery.
Shape Classification Based on Skeletons

Most of the traditional methods for shape classification are based on contour. They often encounter difficulties when dealing with classes that have large nonlinear variability, especially when the variability is structural or due to articulation. It is well-known that shape representation based on skeletons is superior to contour based representation in such situations. However, approaches to shape similarity based on skeletons suffer from the instability of skeletons and matching of skeleton graphs is still an open problem. Using a new skeleton pruning method, we are able to obtain stable pruned skeletons even in the presence of significant contour distortions. We also propose a new method for matching of skeleton graphs. In contrast to most existing methods, it does not require converting of skeleton graphs to trees and it does not require any graph editing. Shape classification is done with Bayesian classifier. We present excellent classification results for complete shapes. Keyword: Shape classification; Skeletons; Shape similarity; Skeleton pruning; Bayesian classifier
Localization in Wireless Sensor Networks

A fundamental problem in wireless sensor networks is localization - the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile. MSL and MSL* are range-free algorithms - they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors. We present simulation results to demonstrate that MSL and MSL* outperform existing algorithms in terms of localization error in very different mobility conditions. MSL* outperforms MSL in most scenarios, but incurs a higher communication cost. MSL outperforms MSL* when there is significant irregularity in the radio range. We also point out some problems with a well known lower bound for the error in any range-free localization algorithm in static sensor networks.
Battery Dynamics Exploration: Insights and Implications of Relaxation Time in Electrochemical Impedance Spectroscopy

Understanding Li-ion batteries' dynamic and complex electrochemical behavior presents significant challenges in battery diagnostics and prognostics. To deal with these challenges, researchers rely on electrochemical impedance spectroscopy (EIS), which offers valuable insights into the electrochemical phenomena within the battery. This paper investigates the significance of resting or relaxation time in obtaining stable and reproducible EIS measurements. This paper identifies the impact of battery rest time by analyzing the voltage and temperature behavior for different impulse discharge rates at various battery states of charge (SOC) levels. In addition, the influence of test current level and battery rest time on EIS measurements is studied. Adequate rest time enables the battery to reach a stable equilibrium, minimizing transient effects and ensuring reproducibility. At the same time, optimal current amplitude selection enhances the signal-to-noise ratio and facilitates accurate impedance spectrum interpretation, minimizing the influence on the battery state. The findings of our study improve the accuracy and reliability of battery diagnostics and contribute to the understanding of battery system dynamics.
Exploiting Attribute Dependency for Attribute Assignment in Crowded Scenes

Attributes now play a vital role for characterizing a crowded scene. Compared to low-level visual features, processing informed by attributes can capture rich semantic information. However, to effectively assign attributes to a crowded scene still remains a challenging task. In this letter, inspired by a recently proposed zero-shot learning framework, a novel attribute assignment method that maps low-level features to predefined attributes is proposed. In particular, we propose to exploit the attribute dependency during the phase of attribute assignment, which can be regarded as our main contribution. In addition, to further enhance the performance, an effective low-level feature extraction mechanism is also proposed. More precisely, appearance and motion features are first simultaneously extracted from several sampled video frames and corresponding optical flow fields via deep convolutional neural network and then, respectively, aggregated by using Fisher vector encoding to form the low-level representation of crowded scenes. Experimental results on the challenging WWW dataset demonstrate that both the proposed attribute assignment method and the low-level feature extraction mechanism outperform the state of the art.
Application of personnel flow simulation in the analysis of infection risk in ships with medical functions

This study aims at the immature and imperfect epidemic prevention specifications related to the general arrangement design of ships, an infection risk analysis method based on personnel flow simulation is proposed. Taking a ship with medical functions as the target ship, typical working conditions are selected, and the cellular-based automaton method is used to model and simulate the flow of personnel to obtain the personnel trajectory; combined with the infection probability model, the probability of infection in various parts of the ship is obtained, the risk cloud map is drawn, and the effect of the general arrangement on reducing the risk of infection is discussed. The cloud map shows that the risk of cabin infection in the living area of the target ship is less than 0.001% in typical working conditions. The results show that this method can be used to analyze the effectiveness of the general arrangement design related to ship epidemic prevention.