Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting adherence of adults to a 12-month exercise intervention.

BACKGROUND The purpose of this study was to (a) examine demographic, psychosocial, and physiological predictors of exercise adherence in a yearlong exercise intervention and (b) describe the trajectory of adherence over time. METHODS Participants were 51 men and 49 women aged 40 to 75 years. The supervised and home-based intervention consisted of 60 minutes/day, 6 days/ week of moderate-to-vigorous intensity exercise. Three adherence measures were used: (1) minutes/week, (2) MET-hours/week, and (3) change in cardiopulmonary fitness (VO2max). Predictors of adherence were determined separately by sex using mixed models and multivariable regression. RESULTS Participants performed 287 ± 98 minutes/week of moderate-to-vigorous activity with 71% adhering to at least 80% (288 minutes/week) of the prescription. Men adhered better than women (P < .001). Among women, adiposity-related variables were significantly related to poorer adherence on all 3 measures (P < .05). A less consistent pattern was observed among men but in follow-up analyses, adiposity was associated with fewer MET-hours/week of exercise. Social support, pain, and perceived benefits were predictive in some models. Men and nonobese women experienced peak adherence at 4 to 6 months, while obese women peaked during months 0 to 3. CONCLUSIONS When provided with supervision and support, previously sedentary men and women can achieve and maintain high levels of aerobic activity.
Risk Factors for Severe Respiratory Syncytial Virus Lower Respiratory Tract Infection

RSV infection is a leading cause of lower respiratory tract infection, especially in High-risk infants with a history of prematurity, bronchopulmonary dysplasia (BPD), congenital heart disease (CHD), neuromusculair impairment, immunodeficiency, and Down syndrome. Host related risk factors that have been identified to be associated with severe RSV related lower respiratory tract infection include young age below 6 months at the beginning of RSV season, multiple birth, male sex, low socioeconomic status and parental education, crowded living conditions, young siblings, maternal smoking and indoor smoke pollution, malnutrition/small for gestational age, family history of atopy or asthma, low cord serum RSV antibody titers, and living at altitude. Risk factors increasing the risk of acquisition of RSV have been identified to be birth before and/or during RSV season, day care attendance, presence of older siblings in school or day-care, and lack of breast feeding. Some of these risk factors are discussed controversially and some of them are found continuously throughout the literature. Given the high cost of RSV prophylaxis, especially for the large population of late preterm infants, algorithms and risk score systems have been published that could identify high-risk infants for treatment with palivizumab out of this gestational age group. Several models reported on an average sensitivity and specificity of 70 percent and, thus, are helpful to identify infants at high risk for severe RSV infection and need for prophylaxis with palivizumab.
Dual Transfer Learning

Transfer learning aims to leverage the knowledge in the source domain to facilitate the learning tasks in the target domain. It has attracted extensive research interests recently due to its effectiveness in a wide range of applications. The general idea of the existing methods is to utilize the common latent structure shared across domains as the bridge for knowledge transfer. These methods usually model the common latent structure by using either the marginal distribution or the conditional distribution. However, without exploring the duality between these two distributions, these single bridge methods may not achieve optimal capability of knowledge transfer. In this paper, we propose a novel approach, Dual Transfer Learning (DTL), which simultaneously learns the marginal and conditional distributions, and exploits the duality between them in a principled way. The key idea behind DTL is that learning one distribution can help to learn the other. This duality property leads to mutual reinforcement when adapting both distributions across domains to transfer knowledge. The proposed method is formulated as an optimization problem based on joint nonnegative matrix trifactorizations (NMTF). The two distributions are learned from the decomposed latent factors that exhibit the duality property. An efficient alternating minimization algorithm is developed to solve the optimization problem with convergence guarantee. Extensive experimental results demonstrate that DTL is more effective than alternative transfer
Convolutional Compressed Sensing Using Decimated Sidelnikov Sequences

In many applications of compressed sensing, the data acquisition involves convolution by a filter followed by subsampling. In this letter, we propose to construct a filter with real-valued coefficients by taking the discrete Fourier transform of a decimated binary Sidelnikov sequence. With a random subsampler, we prove that stable recovery can be guaranteed if a signal is sparse in the canonical or the FFT basis. Besides, simulation results also show that if a deterministic subsampler is used, the proposed system can offer similar reconstruction performance as that of a random Gaussian operator for a wide range of signal length.
Alanine Aminotransferase Variants Conferring Diverse NUE Phenotypes in Arabidopsis thaliana

Alanine aminotransferase (AlaAT, E.C. 2.6.1.2), is a pyridoxal-5’-phosphate-dependent (PLP) enzyme that catalyzes the reversible transfer of an amino group from alanine to 2-oxoglutarate to produce glutamate and pyruvate, or vice versa. It has been well documented in both greenhouse and field studies that tissue-specific over-expression of AlaAT from barley (Hordeum vulgare, HvAlaAT) results in a significant increase in plant NUE in both canola and rice. While the physical phenotypes associated with over-expression of HvAlaAT have been well characterized, the role this enzyme plays in vivo to create a more N efficient plant remains unknown. Furthermore, the importance of HvAlaAT, in contrast to other AlaAT enzyme homologues in creating this phenotype has not yet been explored. To address the role of AlaAT in NUE, AlaAT variants from diverse sources and different subcellular locations, were expressed in the wild-type Arabidopsis thaliana Col-0 background and alaat1;2 (alaat1-1;alaat2-1) knockout background in various N environments. The analysis and comparison of both the physical and physiological properties of AlaAT over-expressing transgenic plants demonstrated significant differences between plants expressing the different AlaAT enzymes under different external conditions. This analysis indicates that the over-expression of AlaAT variants other than HvAlaAT in crop plants could further increase the NUE phenotype(s) previously observed.
Efficient Internalization of MHC I Requires Lysine-11 and Lysine-63 Mixed Linkage Polyubiquitin Chains

The downregulation of cell surface receptors by endocytosis is a fundamental requirement for the termination of signalling responses and ubiquitination is a critical regulatory step in receptor regulation. The K5 gene product of Kaposi's sarcoma‐associated herpesvirus is an E3 ligase that ubiquitinates and downregulates several cell surface immunoreceptors, including major histocompatibility complex (MHC) class I molecules. Here, we show that K5 targets the membrane proximal lysine of MHC I for conjugation with mixed linkage polyubiquitin chains. Quantitative mass spectrometry revealed an increase in lysine‐11, as well as lysine‐63, linked polyubiquitin chains on MHC I in K5‐expressing cells. Using a combination of mutant ubiquitins and MHC I molecules expressing a single cytosolic lysine residue, we confirm a functional role for lysines‐11 and ‐63 in K5‐mediated MHC I endocytosis. We show that lysine‐11 linkages are important for receptor endocytosis, and that complex mixed linkage polyubiquitin chains are generated in vivo.
An inverse approach to determining spatially varying arterial compliance using ultrasound imaging

The mechanical properties of arteries are implicated in a wide variety of cardiovascular diseases, many of which are expected to involve a strong spatial variation in properties that can be depicted by diagnostic imaging. A pulse wave inverse problem (PWIP) is presented, which can produce spatially resolved estimates of vessel compliance from ultrasound measurements of the vessel wall displacements. The 1D equations governing pulse wave propagation in a flexible tube are parameterized by the spatially varying properties, discrete cosine transform components of the inlet pressure boundary conditions, viscous loss constant and a resistance outlet boundary condition. Gradient descent optimization is used to fit displacements from the model to the measured data by updating the model parameters. Inversion of simulated data showed that the PWIP can accurately recover the correct compliance distribution and inlet pressure under realistic conditions, even under high simulated measurement noise conditions. Silicone phantoms with known compliance contrast were imaged with a clinical ultrasound system. The PWIP produced spatially and quantitatively accurate maps of the phantom compliance compared to independent static property estimates, and the known locations of stiff inclusions (which were as small as 7 mm). The PWIP is necessary for these phantom experiments as the spatiotemporal resolution, measurement noise and compliance contrast does not allow accurate tracking of the pulse wave velocity using traditional approaches (e.g. 50% upstroke markers). Results from simulations indicate reflections generated from material interfaces may negatively affect wave velocity estimates, whereas these reflections are accounted for in the PWIP and do not cause problems.
Distinguishing Electronic and Vibronic Coherence in 2D Spectra by Their Temperature Dependence

Long-lived oscillations in 2D spectra of chlorophylls are at the heart of an ongoing debate. Their physical origin is either a multipigment effect, such as excitonic coherence, or localized vibrations. We show how relative phase differences of diagonal- and cross-peak oscillations can distinguish between electronic and vibrational (vibronic) effects. While direct discrimination between the two scenarios is obscured when peaks overlap, their sensitivity to temperature provides a stronger argument. We show that vibrational (vibronic) oscillations change relative phase with temperature, while electronic oscillations are only weakly dependent. This highlights that studies of relative phase difference as a function of temperature provide a clear and easily accessible method to distinguish between vibrational and electronic coherences.
Visualization research of the tweet diffusion in the microblog network

With the development of big data technology, information visualization plays an increasingly important role in the social network, especially the microblog network. The path of the tweet diffusion shows the relationships among microblog users and constructs a directed network among them. The microblog data mining project and the visualization process of the tweet propagation are described in detail. The visualization results of the tweet propagation indicate that there are two typical transmission path patterns: the dandelion pattern and the double-star pattern. The visualization research may easily discover the key retweeting nodes in the tweet diffusion network.
PERFORMANCE OF OPTICAL NETWORKS : A SHORT SURVEY

In the information technology era, there are relentless needs for networks with extremely high capacity. Due to the increasing internet usage and upsurge in users, the end users were longing for the revolution in the network. At this juncture to make the dreams of the network users come true, tremendous research has resulted in optical technology which is a great boon to the world of communication. Optical Packet Switching (OPS) presents new avenues for high speed interconnection networks. There is a rapidly growing demand for high-throughput networks to transmit heterogeneous traffic services such as communication of voice, images and data, multimedia interaction and advanced digital service which warranted OPS. These heterogeneous traffic services in OPS networks require appropriate treatments (e.g. delay or bandwidth guarantees). Quality of Service (QoS) is the only way through which optimization can be attained in the OPS. OPS networks place a particular attention to the QoS provisioning problem from end-to-end perspective. QoS provisioning is therefore a mandatory task in optical packet switching networks. The goal of QoS is to provide guarantees on the ability of a network to deliver predictable results. A network monitoring system must typically be deployed as part of QoS, to insure that networks are performing at the desired level. Definitely, optical networks with improved QoS capabilities are the main goal for the next generation telecommunication infrastructure. One of the proper ways of improving QoS is enhancing the network parameters in each layer. The major parameters that affect the performance of QoS in OPS are: (1) Bit Error Rate (BER), (2) Packet Loss Rate (PLR), (3) Bandwidth, (4) Delay, (5) Jitter, (6) Recovery time, (7) Response time, (8) Reliability, (9) Availability, (10) Fault tolerance and (11) Throughput and so on.
DeFrag: Defragmentation for Efficient Runtime Resource Allocation in NoC-Based Many-core Systems

Efficient runtime resource allocation is critical to the overall performance and energy consumption of many-core systems. However, due to the applications' unknown arrival and departure time under dynamic workloads, the runtime system resource management is challenging. The frequent allocations and deal locations of the applications might leave on-chip free cores scattered due to the lack of design-time knowledge of their finishing time. This situation is referred to as fragmentation. In order to optimize the performance and energy consumption of the system in such situations, in this paper, we propose a runtime defragmentation approach that collects and reshapes the scattered cores in close proximity. We also propose a fragmentation metric which is able to evaluate the scatteredness of the free cores. Based on this, the proposed algorithm will be executed to bring the scattered free cores together when the metric is over a certain predefined threshold. In this way, the contiguous free core region is formed to facilitate efficient mapping of the incoming applications. Moreover, the proposed algorithm is also aware of the existing applications and minimizes their performance impact. Experimental results demonstrated that the proposed defragmentation approach reduces the overall execution time and energy consumption by 42% and 41%, respectively when compared to some of the existing approaches. Moreover, a negligible overhead, accounting for only less than 2.6% of the overall execution time, is required for the defragmentation process.
ANTSREC: A Semantic Recommender System Based on Ant Colony Meta-Heuristic in Electronic Commerce

A recommender system is a guide and assistance for choosing the required product or service for improving the electronic commerce systems. Most of the recommender systems use the history of customer purchase and a few are based on Semantic relatedness of purchased commodities. In this paper a semantic recommender system based on Ant Colony and Ontology dependencies is used for improvement of electronic commerce. This system comprises heuristic, stochastic, reinforcement learning in Ant Colony theory and semantic dependency in ontology characteristics. The presented system is able to recommend similar, complement and bundled products. This characteristic can overcome problems such as cold start, scalability and scarcity of information. In this paper applied tests results show the performance and efficiency of presented algorithms.
Maneuverability performance of tracked vehicles on soft terrains

Unmanned ground vehicles are widely used in industries where repetitive tasks or high risk missions are required. Such vehicles usually operate on soft deformable terrains and still require human supervision due to the complexity of the interaction between the vehicle and the terrain. A traversability prediction simulator has recently been developed. The simulator is used to investigate the influence of vehicle design and soil properties on the maneuverability performance of tracked vehicles on soft terrains. Results of the simulations carried out give insight into the behavior of such vehicles with different operating conditions and how they can be controlled.
Performance Evaluation for Cloud Computing in u-Health Environment

As aging population increases, the demand of medical systems for elder and chronic disease is also increasing. Our research evaluates on performance of TCP/UDP in order to assist cloud computing. We optimize communication between smart device and server using TCP. The sampling rate presents the number of data per second. The sampling rate is important factor in resolution of waveform, though high sampling rate causes decline of network performance. The smart device performs three threads such as Bluetooth, main, TCP/UDP. The Bluetooth thread collects vital sign from sensing node. The main thread performs computation for drawing waveform and transmits data to TCP/UDP thread. The TCP/UDP thread transmits vital sign to server. We expect to contribute to development of WBAN and cloud computing.
Systèmes de lois de conservation hyperbolique-elliptique Hyperbolic-elliptic systems of conservation laws ?

One strategy for proving the global existence of admissible solutions to the Cauchy problem for first-order systems of conservation laws, is to introduce a small amount of diffusion and then pass to the limit. Under some structural assumption, this task was achived by DiPerna when the diffusion is given by artificial viscosity. We showed recently that this program works also for the Jin–Xin relaxation. We prove here that it works well when the diffusion is given by a coupling with an elliptic equation. Such coupling arises in models for radiating gases.
Macrophage-Derived Human Resistin Is Induced in Multiple Helminth Infections and Promotes Inflammatory Monocytes and Increased Parasite Burden

Parasitic helminth infections can be associated with lifelong morbidity such as immune-mediated organ failure. A better understanding of the host immune response to helminths could provide new avenues to promote parasite clearance and/or alleviate infection-associated morbidity. Murine resistin-like molecules (RELM) exhibit pleiotropic functions following helminth infection including modulating the host immune response; however, the relevance of human RELM proteins in helminth infection is unknown. To examine the function of human resistin (hResistin), we utilized transgenic mice expressing the human resistin gene (hRetnTg+). Following infection with the helminth Nippostrongylus brasiliensis (Nb), hResistin expression was significantly upregulated in infected tissue. Compared to control hRetnTg− mice, hRetnTg+ mice suffered from exacerbated Nb-induced inflammation characterized by weight loss and increased infiltration of inflammatory monocytes in the lung, along with elevated Nb egg burdens and delayed parasite expulsion. Genome-wide transcriptional profiling of the infected tissue revealed that hResistin promoted expression of proinflammatory cytokines and genes downstream of toll-like receptor signaling. Moreover, hResistin preferentially bound lung monocytes, and exogenous treatment of mice with recombinant hResistin promoted monocyte recruitment and proinflammatory cytokine expression. In human studies, increased serum resistin was associated with higher parasite load in individuals infected with soil-transmitted helminths or filarial nematode Wuchereria bancrofti, and was positively correlated with proinflammatory cytokines. Together, these studies identify human resistin as a detrimental factor induced by multiple helminth infections, where it promotes proinflammatory cytokines and impedes parasite clearance. Targeting the resistin/proinflammatory cytokine immune axis may provide new diagnostic or treatment strategies for helminth infection and associated immune-mediated pathology.
Outcome of pulmonary endarterectomy in symptomatic chronic thromboembolic disease

Chronic thromboembolic disease is characterised by persistent pulmonary thromboembolic occlusions without pulmonary hypertension. Early surgical treatment with pulmonary endarterectomy may improve symptoms and prevent disease progression. We sought to assess the outcome of pulmonary endarterectomy in symptomatic patients with chronic thromboembolic disease. Patients with symptomatic chronic thromboembolic disease and a mean pulmonary artery pressure <25 mmHg at baseline with right heart catheterisation and treated with pulmonary endarterectomy between January 2000 and July 2013 were identified. Patients were reassessed at 6 months and at 1 year following surgery. A total of 42 patients underwent surgery and the median length of stay in hospital was 11 days. There was no in-hospital mortality but complications occurred in 40% of patients. At 1 year, following surgery, 95% of the patients remained alive. There was a significant symptomatic improvement with 95% of patients in the New York Heart Association functional classes I or II at 6 months. There was a significant improvement in quality of life assessed by the Cambridge pulmonary hypertension outcome review questionnaire. In this carefully selected cohort of chronic thromboembolic disease patients, pulmonary endarterectomy resulted in significant improvement in symptoms and quality of life. Appropriate patient selection is paramount given the known surgical morbidity and mortality, and surgery should only be performed in expert centres. Pulmonary endarterectomy improves symptoms and quality of life in patients with chronic thromboembolic disease http://ow.ly/AeECt
A role of the sodium pump in spreading ischemia in rats

In rats, spreading depolarization induces vasodilation/hyperemia in naïve tissue but the inverse response when artificial cerebrospinal fluid is topically applied to the brain containing (a) a nitric oxide–lowering agent and (b) elevated K+. The inverse response is characterized by severe vasoconstriction/ischemia. The perfusion deficit runs together with the depolarization in the tissue (=spreading ischemia). Here, we found in male Wistar rats that pre-treatment with artificial cerebrospinal fluid containing elevated K+ in vivo led to a selective decline in α2/α3 Na+/K+-ATPase activity, determined spectrophotometrically ex vivo. Moreover, spreading ischemia, recorded with laser-Doppler flowmetry and electrocorticography, resulted from artificial cerebrospinal fluid containing a nitric oxide–lowering agent in combination with the Na+/K+-ATPase inhibitor ouabain at a concentration selectively inhibiting α2/α3 activity. Decline in α2/α3 activity results in increased Ca2+ uptake by internal stores of astrocytes, vascular myocytes, and pericytes since Ca2+ outflux via plasmalemmal Na+/Ca2+-exchanger declines. Augmented Ca2+ mobilization from internal stores during spreading depolarization might enhance vasoconstriction, thus, contributing to spreading ischemia. Accordingly, spreading ischemia was significantly shortened when intracellular Ca2+ stores were emptied by pre-treatment with thapsigargin, an inhibitor of the sarco(endo)plasmic reticulum Ca2+-ATPase (SERCA). These findings might have relevance for clinical conditions, in which spreading ischemia occurs such as delayed cerebral ischemia after subarachnoid hemorrhage.