Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence and Determinants of Metabolic Syndrome among Adults in a Rural Area of Northwest China

Objectives To evaluate the prevalence and determinants of metabolic syndrome (MetS) among adults in a rural area of Northwest China. Methods A population-based cross-sectional study was conducted in 2010 among adults aged 18 to 80 years in rural areas of Hanzhong, in Northwest China. Interview, physical and clinical examinations, and fasting blood glucose and lipid measurements were completed for 2990 adults. The definitions of MetS proposed by the Third Report of the National Cholesterol Education Program Expert Panel (Adults Treatment Panel III, ATP III) and the International Diabetes Federation (IDF), and the modified ATP III definition for Asian population were used and compared. Proportions were adjusted for age and sex. Results The prevalence of MetS was 7.9%, 10.8% and 15.1% according to ATP III, IDF and modified ATP III criteria, respectively. Agreement between ATP III and IDF criteria and that between ATP III and modified ATP III criteria were moderate (Kappa = 0.52 and 0.64, respectively), whereas agreement between IDF and modified ATP III criteria was good (Kappa = 0.83). The prevalence of MetS increased with age, and was higher in women than in men (10.4% versus 5.4%, 13.6% versus 8.1% and 17.4% versus 12.8%, according to ATP III, IDF and modified ATP III criteria, respectively). The most common MetS component was high blood pressure. Having family history of hypertension, lack of physical activity, high economical level, overweight and obesity were positively associated with MetS. Conclusions MetS is prevalent among rural adults in Northwest China and high blood pressure is the most common MetS component. Prevention and treatment of hypertension and MetS should be a public health priority to reduce cardiovascular diseases in rural areas of Northwest China. More attention should be given to the elderly, women, people with family history of hypertension and obese people who are at high risk of MetS.
Efficacy of Nepeta Menthoides Boiss and Buhse Freeze-Dried Aqueous Extract on Anxiety of Patients with Depression: A Double-Blind Randomized Controlled Clinical Trial

Background: The efficacy and safety of Nepeta menthoides freeze-dried aqueous extract were assessed on the anxiety of patients suffering from depression. Method: Patients received either N. menthoides formulation (400 mg/BID) or Sertraline (50 mg/BID) for 4 weeks. The Beck Anxiety Inventory (BAI) scales were used to assess the anxiety in two-week intervals (2nd, 4th, and 6th weeks). Folin-Ciocalteu and Dowd methods were used to determine the formulation of total phenol and flavonoid contents. Results: Compared with Sertraline, N. menthoides showed a higher reduction in BAI scores in the 2nd (16.52±8.07 vs. 21.38±10.98, P<0.05) and 4th week (11.55±6.74 vs. 20.47±11.53, P<0.05) along with a reduction in the recurrence rate and side effects. Total phenolic and flavonoid contents revealed in the presence of 127.09±0.43 mg GAE/g and 16.93±0.09 mg Q/g of extract. Conclusion: N. menthoides could be effective in the control and introducing a delay in recurrence of anxiety in patients with depression.
The SETimes.HR Linguistically Annotated Corpus of Croatian

We present SETimes.HR ― the first linguistically annotated corpus of Croatian that is freely available for all purposes. The corpus is built on top of the SETimes parallel corpus of nine Southeast European languages and English. It is manually annotated for lemmas, morphosyntactic tags, named entities and dependency syntax. We couple the corpus with domain-sensitive test sets for Croatian and Serbian to support direct model transfer evaluation between these closely related languages. We build and evaluate statistical models for lemmatization, morphosyntactic tagging, named entity recognition and dependency parsing on top of SETimes.HR and the test sets, providing the state of the art in all the tasks. We make all resources presented in the paper freely available under a very permissive licensing scheme.
A Robust Image Hashing Algorithm Resistant Against Geometrical Attacks

This paper proposes a robust image hashing method which is robust against common image processing attacks and geometric distortion attacks. In order to resist against geometric attacks, the log-polar mapping (LPM) and contourlet transform are employed to obtain the low frequency sub-band image. Then the sub-band image is divided into some non-overlapping blocks, and low and middle frequency coefficients are selected from each block after discrete cosine transform. The singular value decomposition (SVD) is applied in each block to obtain the first digit of the maximum singular value. Finally, the features are scrambled and quantized as the safe hash bits. Experimental results show that the algorithm is not only resistant against common image processing attacks and geometric distortion attacks, but also discriminative to content changes.
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data

Peak detection is a central problem in genomic data analysis, and current algorithms for this task are unsupervised and mostly effective for a single data type and pattern (e.g. H3K4me3 data with a sharp peak pattern). We propose PeakSeg, a new constrained maximum likelihood segmentation model for peak detection with an efficient inference algorithm: constrained dynamic programming. We investigate unsupervised and supervised learning of penalties for the critical model selection problem. We show that the supervised method has state-of-the-art peak detection across all data sets in a benchmark that includes both sharp H3K4me3 and broad H3K36me3 patterns.
Adaptive admittance control: an approach to explicit force control in compliant motion

This paper addresses the problem of controlling a manipulator in compliant motion while in contact with an environment having an unknown stiffness. Admittance control is used as an explicit force control scheme in which a force setpoint is specified and is tracked by the force compensator. Two adaptive PID and PI force compensators are proposed. The compensators ensure robust tracking of step force setpoints and rejection of constant disturbances. Since the environmental stiffness can typically vary by several orders of magnitude, compensator adaptation is used to ensure a stable and uniform system performance. Dynamic simulation results for a 7 DOF Robotics Research arm are presented to demonstrate the efficacy of the proposed admittance control scheme in executing contact tasks. <<ETX>>
Short Text Authorship Attribution via Sequence Kernels, Markov Chains and Author Unmasking: An Investigation

We present an investigation of recently proposed character and word sequence kernels for the task of authorship attribution based on relatively short texts. Performance is compared with two corresponding probabilistic approaches based on Markov chains. Several configurations of the sequence kernels are studied on a relatively large dataset (50 authors), where each author covered several topics. Utilising Moffat smoothing, the two probabilistic approaches obtain similar performance, which in turn is comparable to that of character sequence kernels and is better than that of word sequence kernels. The results further suggest that when using a realistic setup that takes into account the case of texts which are not written by any hypothesised authors, the amount of training material has more influence on discrimination performance than the amount of test material. Moreover, we show that the recently proposed author unmasking approach is less useful when dealing with short texts.
Cannabinoids Inhibit the Formation of New Synapses between Hippocampal Neurons in Culture

The principal psychoactive ingredient in marijuana, Delta(9)-tetrahydrocannabinol, has been shown to inhibit adenylyl cyclase activity in vitro and can lead to impairment of memory in vivo. cAMP-induced changes in synaptic plasticity are thought to underlie memory formation. We examined the effects of cannabinoid receptor agonists on forskolin-induced formation of new synapses between rat hippocampal neurons in culture. Functional synaptic boutons were identified with FM1-43-based digital imaging. Cannabimimetic drugs prevented the recruitment of new synapses by inhibiting the formation of cAMP. The inhibition produced by Win55212-2, a synthetic cannabinoid receptor agonist, was stereoselective and was reversed by a selective CB1 receptor antagonist. Both Delta(9)-tetrahydrocannabinol and the endogenous ligand, anandamide, inhibited the formation of new synapses. Win55212-2 blocked the formation of new synapses induced by forskolin, but not those evoked by a membrane permeant cAMP analog. Thus, activation of cannabinoid receptors can modulate synaptic plasticity independent of direct effects on neurotransmitter release. Preventing the formation of new synapses may contribute to the impairment of memory produced by cannabinoids.
Efficient GPU synchronization without scopes: Saying no to complex consistency models

As GPUs have become increasingly general purpose, applications with more general sharing patterns and finegrained synchronization have started to emerge. Unfortunately, conventional GPU coherence protocols are fairly simplistic, with heavyweight requirements for synchronization accesses. Prior work has tried to resolve these inefficiencies by adding scoped synchronization to conventional GPU coherence protocols, but the resulting memory consistency model, heterogeneous-race-free (HRF), is more complex than the common data-race-free (DRF) model. This work applies the DeNovo coherence protocol to GPUs and compares it with conventional GPU coherence under the DRF and HRF consistency models. The results show that the complexity of the HRF model is neither necessary nor sufficient to obtain high performance. DeNovo with DRF provides a sweet spot in performance, energy, overhead, and memory consistency model complexity. Specifically, for benchmarks with globally scoped fine grained synchronization, compared to conventional GPU with HRF (GPU+HRF), DeNovo+DRF provides 28% lower execution time and 51% lower energy on average. For benchmarks with mostly locally scoped fine-grained synchronization, GPU+HRF is slightly better — however, this advantage requires a more complex consistency model and is eliminated with a modest enhancement to DeNovo+DRF. Further, if HRF's complexity is deemed acceptable, then DeNovo+HRF is the best protocol.
CCN Lab 1 : from single cells to psychophysics

In the behavioral part, the monkey had to report the direction of motion of random dot images (varying the level of coherence so that the task could be made easier or harder). During the same task, one neuron was recorded in area MT. Only two different possible directions of motion were used while a particular neuron was being recorded: the direction that produced the maximum response in that neuron, or the opposite direction. The monkey’s task was to discriminate between these 2 directions. The aim of the study was to compare the ability of the animal with that of single cortical neurons in this task.
Bacteria Isolations from Broiler and Layer Chicks in Zambia

Chick mortality (CM) is one of the major constraints to the expansion of the poultry industry in Zambia. Of the 2,829 avian disease cases submitted to the national diagnostic laboratory based at the Central Veterinary Research Institute in Lusaka between 1995 and 2007, 34.39% (973/2,829) were from CM cases. The disease accounted for 40.2% (218,787/544,903) mortality in the affected flocks with 89.6% (196,112/218,787) of the affected birds dying within seven days. Major bacteria species involved were Escherichia coli, Salmonella gallinarum, and Proteus species being isolated from 84.58%, 46.15%, and 26.93% of the reported CM cases (n = 973), respectively. Detection of Salmonella typhimurium, Salmonella enteritidis, and Salmonella dublin indicates that poultry has the potential of transmitting zoonotic pathogenic bacteria to humans. The proportion of Salmonella gallinarum reactors in the adult breeding stock was generally low (<0.5%) throughout the study period although its prevalence in CM cases was correlated (r = 0.68, P < 0.011) with seroprevalence of the same pathogen in the adult breeding stock. Given that the disease accounts for a large proportion of the avian diseases in Zambia as shown in the present study (34.39%, n = 2,829), it is imperative that an effective disease control strategy aimed at reducing its occurrence should be developed.
Bilinear Discriminant Component Analysis

Factor analysis and discriminant analysis are often used as complementary approaches to identify linear components in two dimensional data arrays. For three dimensional arrays, which may organize data in dimensions such as space, time, and trials, the opportunity arises to combine these two approaches. A new method, Bilinear Discriminant Component Analysis (BDCA), is derived and demonstrated in the context of functional brain imaging data for which it seems ideally suited. The work suggests to identify a subspace projection which optimally separates classes while ensuring that each dimension in this space captures an independent contribution to the discrimination.
Embedded real-time HD video deblurring

This paper explores a computational deblurring algorithm that will ultimately be implemented in an embedded system with a targeted form factor of 2"×2"×3". The deblurring algorithm completes a Fourier filtering step followed by a wavelet transform denoising step on a 1080×1920 Bayer input 30 frame per second video feed. A major challenge in performing this processing in real time is that the wavelet denoising process utilizes the stationary wavelet transform, thus exploding the bandwidth requirements of the algorithm. To reach the desired form-factor and performance rate, a hardware accelerator is required. While both GPU and FPGA implementations have been pursued, this paper limits itself to describing our successful implementation using a desktop GPU card. Additionally, we briefly highlight methods, left for future work, for improving GPU performance based on our FPGA implementation efforts that should aid in scaling from our current desktop implementation to an embedded implementation.
Demo abstract: Prototyping M2I communication system for underground and underwater networks

In the past few years, magnetic induction (MI) communication and networking have attracted significant attentions for wireless applications in underground, underwater, and many other complex and extreme environments. Although the technology is becoming more and more mature, there are still many research challenges, which prohibit MI from being widely applied. One of the most challenging task is to extend the limited MI communication range. Metamaterial-enhanced magnetic induction (M2I) has been theoretically proved to be an efficient way to achieve long distance communication without increasing antenna size. In this demo, we for the first time present a prototype for M2I communication and networking. The objective of this demo is to show the communication-range enhancement of M2I and the proposed communication and networking architecture.
Parsimonious Mobility Classification using GSM and WiFi Traces

Human mobility states, such as dwelling, walking or driving, are a valuable primary and meta data type for transportation studies, urban planning, health monitoring and epidemiology. Previous work focuses on fine-grained location-based mobility inference using global positioning system (GPS) data and external geo-indexes such as map information. GPS-based mobility characterization raises practical issues related to spotty coverage and battery drain, but the more fundamental concern addressed in this paper is that of privacy. For some applications and usage models we contend that it is desirable to adopt a more parsimonious approach to mobility characterization; one that avoids the collection and use of fine-grained location information by relying instead on GSM and WiFi connectivity data. Building upon previous work that demonstrated the utility of using GSM and WiFi beacons for localization applications, we demonstrate that this approach to mobility classification achieves promising results (with accuracy of 88%) using a sample data set collected across several populated areas in Los Angeles and is worthy of further research.
Identity management in IoT Clouds: A FIWARE case of study

Nowadays, the combination between Cloud computing and Internet of Things (IoT) is pursuing new levels of efficiency in delivering services, representing a tempting business opportunity for ICT operators of increasing their revenues. However, security is seen as one of the major factors that slows down the rapid and large scale adoption and deployment of both the IoT and Cloud computing. In this paper, considering such an IoT Cloud scenario, we present an authentication model that allow IoT devices to join IoT Clouds and users to access the system. Moreover, we discuss the issues involved in applying our authentication models in a real IoT Cloud based on the FIWARE technology.
Dislocation climb effects on particle bypass mechanisms

We examine the effects of dislocation climb on the mechanisms by which dislocations bypass particles. The analysis is based upon three-dimensional, level-set, dislocation dynamics simulations that include all elastic interactions, dislocation glide, cross-slip and climb, and particles that are either impenetrable or penetrable and either with or without misfit. When the particle is misfitting with respect to the matrix, the dislocation migration is strongly influenced by the elastic fields created by the misfit. An edge dislocation tends to climb towards either the top or bottom of the particle and may remain there if the stress is not too large. A screw dislocation may wrap around the particle several times, creating a helical dislocation structure at small applied stresses. If the stress is increased, these helices break into an array of loops. Without misfit, climb invariably lowers the threshold stress particle bypass. However, in the misfit case, climb can also lead to more stable dislocation structures and, hence, increases the threshold stress. This report emphasizes the detailed bypass and pinning mechanisms and provides insight into the conditions under which these mechanisms operate.
ANFIS Based Effluent pH Quality Prediction Model for an Activated Sludge Process

Activated sludge process is the most efficient technique used for municipal wastewater treatment plants. However, a pH value outside the limit of 6-9 could inhibit the activities of microorganisms responsible for treating the wastewater, and low pH value may cause damage to the treatment system. Therefore, prediction of pH value is essential for smooth and trouble-free operation of the process. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) model for effluent pH quality prediction in the process. For comparison, artificial neural network is used. The model validation is achieved through use of full-scale data from the domestic wastewater treatment plant in Kuala Lumpur, Malaysia. Simulation results indicate that the ANFIS model predictions were highly accurate having the root mean square error (RMSE) of 0.18250, mean absolute percentage deviation (MAPD) of 9.482% and the correlation coefficient (R) of 0.72706. The proposed model is efficient and valuable tool for the activated sludge wastewater treatment process.
A new architecture for low-power high-speed pipelined ADCs using double-sampling and opamp-sharing techniques

This paper presents a low-voltage low-power pipelined ADC with 1V supply voltage in a 90nm CMOS process. A new architecture is proposed to reduce the power consumption in high-speed pipelined analog-to-digital converters (ADCs). The presented architecture utilizes a combination of two current power-reduction techniques, double sampling and amplifier sharing. To decrease the power dissipation more efficiently, the stage scaling technique has been applied to the ADC and dynamic comparators have been used in sub-ADCs. Using this approach, a 10-bit 200MSample/s pipelined ADC has been designed in a 90nm CMOS technology. HSPICE simulation results show a signal-to-noise plus distortion ratio (SNDR) of 58.5dB with a 9.375MHz, 1-VP-P,diff input signal while consuming only 30.9mW power from a 1V supply voltage.
Energy-Efficiency Optimization for MIMO-OFDM Mobile Multimedia Communication Systems With QoS Constraints

It is widely recognized that, in addition to the quality-of-service (QoS), energy efficiency is also a key parameter in designing and evaluating mobile multimedia communication systems, which has catalyzed great interest in recent literature. In this paper, an energy-efficiency model is first proposed for multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) mobile multimedia communication systems with statistical QoS constraints. Employing the channel-matrix singular value decomposition (SVD) method, all subchannels are classified by their channel characteristics. Furthermore, the multichannel joint optimization problem in conventional MIMO-OFDM communication systems is transformed into a multitarget single-channel optimization problem by grouping all subchannels. Therefore, a closed-form solution of the energy-efficiency optimization is derived for MIMO-OFDM mobile multimedia communication systems. As a consequence, an energy-efficiency optimized power allocation (EEOPA) algorithm is proposed to improve the energy efficiency of MIMO-OFDM mobile multimedia communication systems. Simulation comparisons validate that the proposed EEOPA algorithm can guarantee the required QoS with high energy efficiency in MIMO-OFDM mobile multimedia communication systems.
Refined assessment of blood pressure instability after spinal cord injury.

BACKGROUND This study determined whether the Autonomic Dysfunction Following Spinal Cord Injury (ADFSCI) questionnaire, a measure of self-reported frequency and severity of symptoms during hypo- and hypertensive episodes, correlates with blood pressure (BP) instability. In addition, test-retest reliability of the ADFSCI questionnaire was assessed. METHODS Thirty individuals with spinal cord injury (SCI) (aged 42±12 years; level of lesion = C3-L1; American Spinal Injury Association Impairment Scale = A-C; lesion duration = 1 month to 30 years after injury) participated in this study. Twenty-four-hour ambulatory BP monitoring (ABPM) was used to assess BP instability. ABPM recorded systolic BP (SBP), diastolic BP (DBP), and heart rate at 15-minute intervals during the daytime and 1-hour intervals during the nighttime. Test-retest reliability was performed by completion of the ADFSCI questionnaire on 2 occasions (i.e., 9±4 days in between). RESULTS Individuals with SCI who self-reported autonomic dysreflexia (AD) episodes showed significantly higher SBP coefficient of variation (CV) (14%) and more AD events (n = 11) than individuals who reported never having AD symptoms (CV = 9%; AD events = 1). Both the number of AD events over the 24-hour period and the BP variability (SBP CV) were significantly related to the patients' self-reported total AD score (rho = 0.522, P = 0.005; rho = 0.584, P = 0.001, respectively) and daily AD frequency (rho = 0.553, P = 0.003; rho = 0.586, P = 0.001, respectively). Conversely, no significant correlations existed between the number of hypotensive events over the 24-hour period and self-reported frequency and severity in the ADFSCI questionnaire. CONCLUSIONS This study provides evidence that ABPM offers a strong clinical basis for documenting and understanding BP instability, such as AD, and related symptoms in individuals with SCI.