Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of dual antiplatelet therapy prescribed as one-pill versus two-pill regimen

Summary Fixed-dose combination (FDC) drugs can simplify the medication regimen and potentially improve adherence. However, evidence is lacking about the efficacy and safety of FDC drugs of clopidogrel plus aspirin. Individual data from the three independent MR-CAPCIS trials were pooled and analysed. In those trials, subjects who had been treated with either dual antiplatelet therapy (DAPT) or aspirin alone after drug-eluting stent (DES) implantation were randomly assigned to one-pill or to two-pill DAPT group. Platelet reactivity was measured with VerifyNow-P2Y12 and aspirin point-of-care assays at baseline and eight weeks after treatment. In the present study, primary efficacy endpoint was changes in platelet reactivity unit (PRU) between baseline and eight weeks. A total of 965 subjects were analysed. In prior clopidogrel and aspirin users, PRU was well maintained regardless of switching to either one-pill or two-pill DAPT (ΔPRU=0.4 vs 0.0, p=0.939). In prior aspirin users, PRU was decreased by 73.7 in one-pill DAPT and 77.5 in two-pill DAPT group, with no differences between them (p=0.499). The incidence of high on-treatment platelet reactivity at eight weeks, defined as PRU≥235 in Western people, was 34.8 % in one-pill DAPT group and 37.6 % in two-pill DAPT group (p=0.380), and that defined as PRU ≥275 in Oriental people was 17.7 vs 21.7 % (p=0.129). Independent predictors of high platelet reactivity on clopidogrel were female gender, increasing age, and diabetes. Study drugs were well tolerated. In conclusion, FDC one-pill DAPT showed similar efficacy to two-pill DAPT in terms of platelet reactivity in patients receiving DES in Korea.
Kalman Prediction Based Proportional Fair Resource Allocation for a Solar Powered Wireless Downlink

Optimization of a Wireless Sensor Network (WSN) downlink with an energy harvesting transmitter (base station) is considered. The base station (BS), which is attached to the central controller of the network, sends control information to the gateways of individual WSNs in the downlink. This paper specifically addresses the case where the BS is supplied with solar energy. Leveraging the daily periodicity inherent in solar energy harvesting, the schedule for delivery of maintenance messages from the BS to the nodes of a distributed network is optimized. Differences in channel gain from the BS to sensor nodes make it a challenge to provide service to each of them while efficiently spending the harvested energy. Based on PTF (Power-Time-Fair), a close-to-optimal solution for fair allocation of harvested energy in a wireless downlink proposed in previous work, we develop an online algorithm, PTF-On, that operates two algorithms in tandem: A prediction algorithm based on a Kalman filter that operates on solar irradiation measurements, and a modified version of PTF. PTF-On can predict the energy arrival profile throughout the day and schedule transmission to nodes to maximize total throughput in a proportionally fair way.
DL CSI acquisition and feedback in FDD massive MIMO via path aligning

Unlike the time-division duplexing (TDD) systems, the downlink (DL) and uplink (UL) channels are not reciprocal anymore in the case of frequency-division duplexing (FDD). However, some long-term parameters, e.g. the time delays and angles of arrival (AoAs) of the channel paths, still enjoy reciprocity. In this paper, by efficiently exploiting the aforementioned limited reciprocity, we address the DL channel state information (CSI) feedback in a practical wideband massive multiple-input multiple-output (MIMO) system operating in the FDD mode. In particular, the base station (BS) can transmit the FFT-based pilots with the carefully-selected phase shifts to align the DL paths. Then the user can rely on the so-called time-domain aggregate channel (TAC) to derive the estimate of the DL CSI. Next, with the knowledge about the second-order statistics of the DL channels, the user is also able to compute feedback of reduced dimensionality. We demonstrate that each user can just feed back one scalar number per DL channel path for the BS to recover the DL CSIs. Comprehensive numerical results corroborate our designs.
Saliency Enhanced Robust Visual Tracking

Discrete correlation filter (DCF) based trackers have shown considerable success in visual object tracking. These trackers often make use of low to mid level features such as histogram of gradients (HoG) and mid-layer activations from convolution neural networks (CNNs). We argue that including semantically higher level information to the tracked features may provide further robustness to challenging cases such as viewpoint changes. Deep salient object detection is one example of such high level features, as it make use of semantic information to highlight the important regions in the given scene. In this work, we propose an improvement over DCF based trackers by combining saliency based and other features based filter responses. This combination is performed with an adaptive weight on the saliency based filter responses, which is automatically selected according to the temporal consistency of visual saliency. We show that our method consistently improves a baseline DCF based tracker especially in challenging cases and performs superior to the state-of-the-art. Our improved tracker operates at 9.3 fps, introducing a small computational burden over the baseline which operates at 11 fps.
Behaviour of Cellular Structures under Impact Loading a Computational Study

New multiphysical computational models for simulation of regular open and closed-cell cellular structures behaviour under compressive impact loading are presented. The behaviour of cellular structures with fluid fillers under uniaxial impact loading and large deformations has been analyzed with the explicit nonlinear finite element code LS-DYNA. The behaviour of closed-cell cellular structure has been evaluated with the use of the representative volume element, where the influence of residual gas inside the closed pores has been studied. Open-cell cellular structure was modelled as a whole to properly account for considered fluid flow through the cells, which significantly influences macroscopic behaviour of cellular structure. The fluid has been modelled by applying a Smoothed Particle Hydrodynamics (SPH) method. Computational simulations showed that the base material has the highest influence on the behaviour of cellular structures under impact conditions. The increase of the relative density and strain rate results in increase of the cellular structure stiffness. Parametrical numerical simulations have also confirmed that filler influences the macroscopic behaviour of the cellular structures which depends on the loading type and the size of the cellular structure. In open-cell cellular structures with higher filler viscosity and higher relative density, increased impact energy absorption has been observed.
Dorsal root ganglion neurons become hyperexcitable and increase expression of voltage-gated T-type calcium channels (Cav3.2) in paclitaxel-induced peripheral neuropathy

Abstract Here, it is shown that paclitaxel-induced neuropathy is associated with the development of spontaneous activity (SA) and hyperexcitability in dorsal root ganglion (DRG) neurons that is paralleled by increased expression of low-voltage–activated calcium channels (T-type; Cav3.2). The percentage of DRG neurons showing SA and the overall mean rate of SA were significantly higher at day 7 in rats receiving paclitaxel treatment than in rats receiving vehicle. Cav3.2 expression was increased in L4-L6 DRG and spinal cord segments in paclitaxel-treated rats, localized to small calcitonin gene-related peptide and isolectin B4 expressing DRG neurons and to glial fibrillary acidic protein–positive spinal cord cells. Cav3.2 expression was also co-localized with toll-like receptor 4 (TLR4) in both the DRG and the dorsal horn. T-type current amplitudes and density were increased at day 7 after paclitaxel treatment. Perfusion of the TLR4 agonist lipopolysaccharide directly activated DRG neurons, whereas this was prevented by pretreatment with the specific T-type calcium channel inhibitor ML218 hydrochloride. Paclitaxel-induced behavioral hypersensitivity to mechanical stimuli in rats was prevented but not reversed by spinal administration of ML218 hydrochloride or intravenous injection of the TLR4 antagonist TAK242. Paclitaxel induced inward current and action potential discharges in cultured human DRG neurons, and this was blocked by ML218 hydrochloride pretreatment. Furthermore, ML218 hydrochloride decreased firing frequency in human DRG, where spontaneous action potentials were present. In summary, Cav3.2 in concert with TLR4 in DRG neurons appears to contribute to paclitaxel-induced neuropathy.
Path-Loss-Based Fingerprint Localization Approach for Location-Based Services in Indoor Environments

Wireless local area network fingerprint-based indoor localization schemes have been widely studied because of the increasing requirements of location-based services (LBSs). The features of fingerprint-based localization are known to have higher precision in indoor environments than traditional methods, such as triangulation. However, the precision depends on the amount of pre-created received signal strength (RSS) fingerprints, which is associated with the number of reference points (RPs) of the RSS measurements and the available signal sources in the environment. In this paper, we consider the resource limitations of todays’ wireless environment and propose an improved fingerprint-based localization approach that adapts a path loss model for fingerprint creation and localization. Based on the proposed approach, we present two related localization schemes. The first is a path-loss-based fingerprint localization (PFL) scheme and the second is a dual-scanned fingerprint localization (DFL) scheme. The PFL attempts to improve positioning precision, and the DFL attempts to guarantee positioning reliability. Several simulations are performed, and they show that the proposed schemes improve the positioning precision and reliability in resource-limited environments, which would improve the practicability of fingerprint-based localizations in indoor LBSs.
The Association Between Protein Intake by Source and Osteoporotic Fracture in Older Men: A Prospective Cohort Study

Dietary protein is a potentially modifiable risk factor for fracture. Our objectives were to assess the association of protein intake with incident fracture among older men and whether these associations varied by protein source or by skeletal site. We studied a longitudinal cohort of 5875 men (mean age 73.6 ± 5.9 years) in the Osteoporotic Fractures in Men (MrOS) study. At baseline, protein intake was assessed as percent of total energy intake (TEI) with mean intake from all sources = 16.1%TEI. Incident clinical fractures were confirmed by physician review of medical records. There were 612 major osteoporotic fractures, 806 low‐trauma fractures, 270 hip fractures, 193 spine fractures, and 919 non‐hip non‐spine fractures during 15 years of follow‐up. We used Cox proportional hazards models with age, race, height, clinical site, TEI, physical activity, marital status, osteoporosis, gastrointestinal surgery, smoking, oral corticosteroids use, alcohol consumption, and calcium and vitamin D supplements as covariates to compute hazard ratios (HRs) with 95% confidence intervals (CIs), all expressed per unit (SD = 2.9%TEI) increase. Higher protein intake was associated with a decreased risk of major osteoporotic fracture (HR = 0.92; 95% CI, 0.84 to 1.00) with a similar association found for low‐trauma fracture. The association between protein and fracture varied by protein source; eg, increased dairy protein and non‐dairy animal protein were associated with a decreased risk of hip fracture (HR = 0.80 [95% CI, 0.65 to 0.98] and HR = 0.84 [95% CI, 0.72 to 0.97], respectively), whereas plant‐source protein was not (HR = 0.99 [95% CI, 0.78 to 1.24]). The association between protein and fracture varied by fracture site; total protein was associated with a decreased risk of hip fracture (HR = 0.84 [95% CI, 0.73 to 0.95]), but not clinical spine fracture (HR = 1.06 [95% CI, 0.92 to 1.22]). In conclusion, those with high protein intake (particularly high animal protein intake) as a percentage of TEI have a lower risk of major osteoporotic fracture. © 2016 American Society for Bone and Mineral Research.
Tuning Curves for Arm Posture Control in Motor Cortex Are Consistent with Random Connectivity

Neuronal responses characterized by regular tuning curves are typically assumed to arise from structured synaptic connectivity. However, many responses exhibit both regular and irregular components. To address the relationship between tuning curve properties and underlying circuitry, we analyzed neuronal activity recorded from primary motor cortex (M1) of monkeys performing a 3D arm posture control task and compared the results with a neural network model. Posture control is well suited for examining M1 neuronal tuning because it avoids the dynamic complexity of time-varying movements. As a function of hand position, the neuronal responses have a linear component, as has previously been described, as well as heterogeneous and highly irregular nonlinearities. These nonlinear components involve high spatial frequencies and therefore do not support explicit encoding of movement parameters. Yet both the linear and nonlinear components contribute to the decoding of EMG of major muscles used in the task. Remarkably, despite the presence of a strong linear component, a feedforward neural network model with entirely random connectivity can replicate the data, including both the mean and distributions of the linear and nonlinear components as well as several other features of the neuronal responses. This result shows that smoothness provided by the regularity in the inputs to M1 can impose apparent structure on neural responses, in this case a strong linear (also known as cosine) tuning component, even in the absence of ordered synaptic connectivity.
Conflict Resolution as Near-Threshold Decision-Making: A Spiking Neural Circuit Model with Two-Stage Competition for Antisaccadic Task

Automatic responses enable us to react quickly and effortlessly, but they often need to be inhibited so that an alternative, voluntary action can take place. To investigate the brain mechanism of controlled behavior, we investigated a biologically-based network model of spiking neurons for inhibitory control. In contrast to a simple race between pro- versus anti-response, our model incorporates a sensorimotor remapping module, and an action-selection module endowed with a “Stop” process through tonic inhibition. Both are under the modulation of rule-dependent control. We tested the model by applying it to the well known antisaccade task in which one must suppress the urge to look toward a visual target that suddenly appears, and shift the gaze diametrically away from the target instead. We found that the two-stage competition is crucial for reproducing the complex behavior and neuronal activity observed in the antisaccade task across multiple brain regions. Notably, our model demonstrates two types of errors: fast and slow. Fast errors result from failing to inhibit the quick automatic responses and therefore exhibit very short response times. Slow errors, in contrast, are due to incorrect decisions in the remapping process and exhibit long response times comparable to those of correct antisaccade responses. The model thus reveals a circuit mechanism for the empirically observed slow errors and broad distributions of erroneous response times in antisaccade. Our work suggests that selecting between competing automatic and voluntary actions in behavioral control can be understood in terms of near-threshold decision-making, sharing a common recurrent (attractor) neural circuit mechanism with discrimination in perception.
Echinococcus multilocularis in Kyrgyzstan: similarity in the Asian EmsB genotypic profiles from village populations of Eastern mole voles (Ellobius tancrei) and dogs in the Alay valley

Abstract Echinococcus multilocularis is a cestode that causes human alveolar echinococcosis, a lethal zoonosis of public health concern in central Asia and western China. In the present study, one of 42 Eastern mole voles (Ellobius tancrei) caught in Sary Mogol (Alay valley, southern Kyrgyzstan) presented liver lesions with E. multilocularis from which the EmsB target was amplified. The Asian profile obtained was almost identical to one amplified from domestic dog faeces collected in a nearby village. This observation adds additional information to the potential role of E. tancrei in the transmission of E. multilocularis, and to the known distribution range of E. multilocularis (Asian strain) in central Asia.
A Microwave Microfluidic Sensor Based on a Dual-Mode Resonator for Dual-Sensing Applications

In this paper, we propose a novel microwave microfluidic sensor with dual-sensing capability. The sensor is based on a dual-mode resonator that consists of a folded microstrip line loaded with interdigital lines and a stub at the plane of symmetry. Due to the specific configuration, the resonator exhibits two entirely independent resonant modes, which allows simultaneous sensing of two fluids using a resonance shift method. The sensor is designed in a multilayer configuration with the proposed resonator and two separated microfluidic channels—one intertwined with the interdigital lines and the other positioned below the stub. The circuit has been fabricated using low-temperature co-fired ceramics technology and its performance was verified through the measurement of its responses for different fluids in the microfluidic channels. The results confirm the dual-sensing capability with zero mutual influence as well as good overall performance. Besides an excellent potential for dual-sensing applications, the proposed sensor is a good candidate for application in mixing fluids and cell counting.
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.
A Dual-Modal Virtual Reality Kitchen for (Re)Learning of Everyday Cooking Activities in Alzheimer's Disease

Everyday action impairment is one of the diagnostic criteria of Alzheimer's disease and is associated with many serious consequences, including loss of functional autonomy and independence. It has been shown that the (re)learning of everyday activities is possible in Alzheimer's disease by using error reduction teaching approaches in naturalistic clinical settings. The purpose of this study is to develop a dual-modal virtual reality platform for training in everyday cooking activities in Alzheimer's disease and to establish its value as a training tool for everyday activities in these patients. Two everyday tasks and two error reduction learning methods were implemented within a virtual kitchen. Two patients with Alzheimer's disease and two healthy elderly controls were tested. All subjects were trained in two learning sessions on two comparable cooking tasks. Within each group (i.e., patients and controls), the order of the training methods was counterbalanced. Repeated measure analysis before and after learning was performed. A questionnaire of presence and a verbal interview were used to obtain information about the subjective responses of the participants to the VR experience. The results in terms of errors, omissions, and perseverations (i.e., repetitive behaviors) indicate that the patients performed worse than the controls before learning, but that they reached a level of performance similar to that of the controls after a short learning session, regardless of the learning method employed. This finding provides preliminary support for the value of the dual-modal virtual reality platform for training in everyday cooking activities in Alzheimer's disease. However, further work is needed before it is ready for clinical application.
A Direct Coarray Interpolation Approach for Direction Finding

Sparse arrays have gained considerable attention in recent years because they can resolve more sources than the number of sensors. The coprime array can resolve O(MN) sources with only O(M+N) sensors, and is a popular sparse array structure due to its closed-form expressions for array configuration and the reduction of the mutual coupling effect. However, because of the existence of holes in its coarray, the performance of subspace-based direction of arrival (DOA) estimation algorithms such as MUSIC and ESPRIT is limited. Several coarray interpolation approaches have been proposed to address this issue. In this paper, a novel DOA estimation approach via direct coarray interpolation is proposed. By using the direct coarray interpolation, the reshaping and spatial smoothing operations in coarray-based DOA estimation are not needed. Compared with existing approaches, the proposed approach can achieve a better accuracy with lower complexity. In addition, an improved angular resolution capability is obtained by using the proposed approach. Numerical simulations are conducted to validate the effectiveness of the proposed approach.
Let's Agree to Disagree: Fixing Agreement Measures for Crowdsourcing

In the context of micro-task crowdsourcing, each task is usually performed by several workers. This allows researchers to leverage measures of the agreement among workers on the same task, to estimate the reliability of collected data and to better understand answering behaviors of the participants. While many measures of agreement between annotators have been proposed, they are known for suffering from many problems and abnormalities. In this paper, we identify the main limits of the existing agreement measures in the crowdsourcing context, both by means of toy examples as well as with real-world crowdsourcing data, and propose a novel agreement measure based on probabilistic parameter estimation which overcomes such limits. We validate our new agreement measure and show its flexibility as compared to the existing agreement measures.
Topic diffusion analysis of a weighted citation network in biomedical literature

In this study, we propose a framework for detecting topic evolutions in weighted citation networks. Citation networks are important in studying knowledge flows; however, citation network analysis has primarily focused on binary networks in which the individual citation influences of each cited paper in a citing paper are considered identical, even though not all cited papers have a significant influence on the cited publication. Accordingly, it is necessary to build and analyze a citation network comprising scholarly publications that notably impact one another, thus identifying topic evolution in a more precise manner. To measure the strength of citation influence and identify paper topics, we employ a citation influence topic model primarily based on topical inheritance between cited and citing papers. Using scholarly publications in the field of the protein p53 as a case study, we build a citation network, filter it using citation influence values, and examine the diffusion of topics not only in the field but also in the subfields of p53.