Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Vitamin K Antagonist Oral Anticoagulants in Patients with Atrial Fibrillation and Valvular Heart Disease

Background: There are limited data for non-vitamin K antagonist oral anticoagulants (NOACs) impact on outcomes for patients with atrial fibrillation (AF) and valvular heart diseases (VHDs). Methods: We identified patients with AF and associated Evaluated Heartvalves, Rheumatic or Artificial (EHRA) type 2 VHDs, and who had been naïve from the oral anticoagulants in the Korean National Health Insurance Service database between 2014 and 2016 (warfarin: n = 2671; NOAC: n = 3058). For analyzing the effect of NOAC on primary prevention, we excluded those with a previous history of ischemic stroke, intracranial hemorrhage (ICH), and gastrointestinal (GI) bleeding events. To balance covariates, we used the propensity score weighting method. Ischemic stroke, ICH, GI bleeding, major bleeding, all-cause death, and their composite outcome and fatal clinical events were evaluated. Results: During a follow-up with a mean duration of 1.4 years, NOACs were associated with lower risks of ischemic stroke (hazard ratio (HR): 0.71, 95% confidence interval (CI): 0.53–0.96), GI bleeding (HR: 0.50, 95% CI: 0.35–0.72), fatal ICH (HR: 0.28, 95% CI: 0.07–0.83), and major bleeding (HR: 0.61, 95% CI: 0.45–0.80) compared with warfarin. Overall, NOACs were associated with a lower risk of the composite outcome (HR: 0.68, 95% CI: 0.58–0.80). Conclusions: In this nationwide Asian AF population with EHRA type 2 VHDs, NOAC use was associated with lower risks of ischemic stroke, major bleeding, all-cause death, and the composite outcome compared to warfarin use.
Preparation and Electrochemical Characterization of Organic–Inorganic Hybrid Poly(Vinylidene Fluoride)-SiO2 Cation-Exchange Membranes by the Sol-Gel Method Using 3-Mercapto-Propyl-Triethoxyl-Silane

A new synthesis method for organic–inorganic hybrid Poly(vinylidene fluoride)-SiO2 cation-change membranes (CEMs) is proposed. This method involves mixing tetraethyl orthosilicate (TEOS) and 3-mercapto-propyl-triethoxy-silane (MPTES) into a polyvinylidene fluoride (PVDF) sol-gel solution. The resulting slurry was used to prepare films, which were immersed in 0.01 M HCl, which caused hydrolysis and polycondensation between the MPTES and TEOS. The resulting Si-O-Si polymers chains intertwined and/or penetrated the PVDF skeleton, significantly improving the mechanical strength of the resulting hybrid PVDF-SiO2 CEMs. The -SH functional groups of MPTES oxidized to-SO3H, which contributed to the excellent permeability of these CEMs. The surface morphology, hybrid structure, oxidative stability, and physicochemical properties (IEC, water uptake, membrane resistance, membrane potential, transport number, and selective permittivity) of the CEMs obtained in this work were characterized using scanning electron microscope and Fourier transform infrared spectroscopy, as well as electrochemical testing. Tests to analyze the oxidative stability, water uptake, membrane potential, and selective permeability were also performed. Our organic–inorganic hybrid PVDF-SiO2 CEMs demonstrated higher oxidative stability and lower resistance than commercial Ionsep-HC-C membranes with a hydrocarbon structure. Thus, the synthesis method described in this work is very promising for the production of very efficient CEMs. In addition, the physical and electrochemical properties of the PVDF-SiO2 CEMs are comparable to the Ionsep-HC-C membranes. The electrolysis of the concentrated CoCl2 solution performed using PVDF-SiO2-6 and Ionsep-HC-C CEMs showed that at the same current density, Co2+ production, and current efficiency of the PVDF-SiO2-6 CEM membrane were slightly higher than those obtained using the Ionsep-HC-C membrane. Therefore, our novel membrane might be suitable for the recovery of cobalt from concentrated CoCl2 solutions.
Neural crest development: insights from the zebrafish

Our understanding of the neural crest, a key vertebrate innovation, is built upon studies of multiple model organisms. Early research on neural crest cells (NCCs) was dominated by analyses of accessible amphibian and avian embryos, with mouse genetics providing complementary insights in more recent years. The zebrafish model is a relative newcomer to the field, yet it offers unparalleled advantages for the study of NCCs. Specifically, zebrafish provide powerful genetic and transgenic tools, coupled with rapidly developing transparent embryos that are ideal for high‐resolution real‐time imaging of the dynamic process of neural crest development. While the broad principles of neural crest development are largely conserved across vertebrate species, there are critical differences in anatomy, morphogenesis, and genetics that must be considered before information from one model is extrapolated to another. Here, our goal is to provide the reader with a helpful primer specific to neural crest development in the zebrafish model. We focus largely on the earliest events—specification, delamination, and migration—discussing what is known about zebrafish NCC development and how it differs from NCC development in non‐teleost species, as well as highlighting current gaps in knowledge.
Exposure to Ambient Air Pollution and Cognitive Impairment in Community-Dwelling Older Adults: The Korean Frailty and Aging Cohort Study

The aim of this study was to investigate the associations between ambient air pollutants and cognitive impairment in Korean older adults. The cognitive function of 2,896 participants aged 70 to 84 years was measured using the Korean version of the mini-mental state examination, the digit span test, the word list learning test, and the frontal assessment battery. After matching the average concentrations of particulate matter (PM) <10 μm in size (PM10) and <2.5 μm (PM2.5), NO2, CO, SO2, and O3 between 2013 and 2017, the association between air pollutants and cognitive scales was analyzed using a linear mixed regression and a multiple logistic regression analysis (after adjusting for age, sex, health related behaviors, socioeconomic status, comorbidity, and meteorological data). Exposure to PM2.5, PM10, NO2, SO2, and CO was associated with cognitive impairment above and beyond age or education level effects. Specifically, PM2.5 was negatively associated with most components of the cognitive scales (interquartile range for PM2.5: 2.0 μg/m3, odds ratio for poor global cognition: 2.28, 95% confidence interval: 1.60–3.26). These associations may be affected by sex, residence area, or alcohol intake. Conclusively, air pollutants, especially PM2.5, were associated with cognitive impairment, including global cognition, attention, memory, and executive function in Korean older adults aged ≥70 years.
Effect of Size and Concentration of PLGA-PEG Nanoparticles on Activation and Aggregation of Washed Human Platelets

Nanotechnology is being increasingly utilised in medicine as diagnostics and for drug delivery and targeting. The small size and high surface area of nanoparticles (NPs), desirable properties that allow them to cross biological barriers, also offer potential for interaction with other cells and blood constituents, presenting possible safety risks. While NPs investigated are predominantly based on the biodegradable, biocompatible, and FDA approved poly-lactide-co-glycolide (PLGA) polymers, pro-aggregatory and antiplatelet effects have been reported for certain NPs. The potential for toxicity of PLGA based NPs remains to be examined. The aims of this study were to determine the impact of size-selected PLGA-PEG (PLGA-polyethylene glycol) NPs on platelet activation and aggregation. PLGA-PEG NPs of three average sizes of 112, 348, and 576 nm were formulated and their effect at concentrations of 0.0–2.2 mg/mL on the activation and aggregation of washed human platelets (WP) was examined. The results of this study show, for the first time, NPs of all sizes associated with the surface of platelets, with >50% binding, leading to possible internalisation. The NP-platelet interaction, however, did not lead to platelet aggregation nor inhibited aggregation of platelets induced by thrombin. The outcome of this study is promising, suggesting that these NPs could be potential carriers for targeted drug delivery to platelets.
L-Alanine Exporter, AlaE, of Escherichia coli Functions as a Safety Valve to Enhance Survival under Feast Conditions

The intracellular level of amino acids is determined by the balance between their anabolic and catabolic pathways. L-alanine is anabolized by three L-alanine synthesizing enzymes and catabolized by two racemases and D-amino acid dehydrogenase (DadA). In addition, its level is regulated by L-alanine movement across the inner membrane. We identified the novel gene alaE, encoding an L-alanine exporter. To elucidate the physiological function of L-Alanine exporter, AlaE, we determined the susceptibility of alaE-, dadA-, and alaE/dadA-deficient mutants, derived from the wild-type strain MG1655, to L-alanyl-L-alanine (Ala-Ala), which shows toxicity to the L-alanine-nonmetabolizing variant lacking alaE. The dadA-deficient mutant has a similar minimum inhibitory concentration (MIC) (>1.25 mg/mL) to that observed in MG1655. However, alaE- and alaE/dadA-deficient mutants had MICs of 0.04 and 0.0025 mg/mL, respectively. The results suggested that the efficacy of AlaE to relieve stress caused by toxic intracellular accumulation of L-alanine was higher than that of DadA. Consistent with this, the intracellular level of alanine in the alaE-mutant was much higher than that in MG1655 and the dadA-mutant. We, therefore, conclude that AlaE functions as a ‘safety-valve’ to prevent the toxic level accumulation of intracellular L-alanine under a peptide-rich environment, such as within the animal intestine.
Non-Invasive Device for Blood Pressure Wave Acquisition by Means of Mechanical Transducer

Blood pressure wave monitoring provides interesting information about the patient’s cardiovascular function. For this reason, this article proposes a non-invasive device capable of capturing the vibrations (pressure waves) produced by the carotid artery by means of a pressure sensor encapsulated in a closed dome filled with air. When the device is placed onto the outer skin of the carotid area, the vibrations of the artery will exert a deformation in the dome, which, in turn, will lead to a pressure increase in its inner air. Then, the sensor inside the dome captures this pressure increase. By combining the blood pressure wave obtained with this device together with the ECG signal, it is possible to help the screening of the cardiovascular system, obtaining parameters such as heart rate variability (HRV) and pulse transit time (PTT). The results show how the pressure wave has been successfully obtained in the carotid artery area, discerning the characteristic points of this signal. The features of this device compare well with previous works by other authors. The main advantages of the proposed device are the reduced size, the cuffless condition, and the potential to be a continuous ambulatory device. These features could be exploited in ambulatory tests.
Popularity-based top-k spatial-keyword preference query

Applications based on spatial data has become present in our daily lives. Spatial data can be used to represent objects such as roads, bus stops, restaurants and schools. Some of these objects maybe associated with a text (e.g. menu of a restaurant). The objects that have spatial location (latitude and longitude) and text are named spatio-textual objects. There are a large number of interesting spatio-textual queries that can be posed. For example, a tourist maybe interested in hotels (spatial objects) that have a lot of restaurants in its vicinity. In this paper, we propose a new query type named Popularity-based Top-k Spatial-keyword Preference Query. Giving a set of query keywords and a spatial vicinity of interest; this query returns the k best spatial objects of interest in terms of the number (popularity) of spatio-textual objects of reference in their vicinity that are textually relevant for the given query keywords. We propose new algorithms to process this query efficiently and evaluate the algorithms proposed in real datasets. The results show the efficiency of spatial-based algorithms for radius bellow 5km and the efficiency of algorithms with hybrid indexes (spatio-textual indexes) for the majority of the experiments.
ECAx

Approximate computing has emerged as a design paradigm amenable to error-tolerant applications. It enables trading the quality of results for efficiency improvement in terms of delay, power, and energy consumption under user-provided tolerable quality degradation. Approximate accelerators have been proposed to expedite frequently executing code sections of error-resilient applications while meeting a defined quality level. However, these accelerators may produce unacceptable errors at run time if the input data changes or dynamic adjustments are made for a defined output quality constraint. State-of-the-art approaches in approximate computing address this issue by correctly re-computing those accelerator invocations that produce unacceptable errors; this is achieved by using the host processor or an alternate exact accelerator, which is activated on-demand. Nevertheless, such approaches can nullify the benefits of approximate computing, especially when input data variations are high at run time and errors due to approximations are above a tolerable threshold. As a robust and general solution to this problem, we propose ECAx, a novel methodology to explore low-overhead error correction in approximate accelerators by selectively correcting most significant errors, in terms of their magnitude, without losing the gains of approximations. We particularly consider the case of approximate accelerators built with approximate functional units such as approximate adders. Our novel methodology reduces the required exact re-computations on the host processor, achieving up to 20% performance gain compared to state-of-the-art approaches.
Real-Time Multimodal Classification of Internal and External Attention

The current attentional state can be divided into several categories, for example, the direction of attention. Often, this state is subconscious or its constant report impossible. Thus, an automated surveillance of the attentional state could be beneficial. In this paper, we performed a classification of multimodal data (EEG and eye tracking) to model internally- and externally-directed attention. 10 participants performed 6 different tasks of which 3 were associated with internal and 3 with external attention. In the first step, we showed that a combination of the two modalities led to an improvement of classification accuracy (average 72.67%) compared to single modality classifications. In a second step, the analysis was performed in real-time. The system was tested on one participant with an average accuracy of 60.87%. These results allow for an optimistic outlook on a reliable real-time multimodal classification system of internal and external attention.
Trajectory tracking of a differentially driven wheeled mobile robot in the presence of obstacles

A trajectory following and obstacle avoidance mechanism for a mobile robot is presented for situations where the robot has to follow a speciﬁc target trajectory but the task might not be completely possible due to obstacles in the way, which the robot must avoid. After avoiding an obstacle, the robot should catch up with the target trajectory. In the proposed system, this objective is reached by combining a nonlinear control method with an Artiﬁcial Potential Function method, leading to trajectory tracking control with obstacle avoidance capabilities.
Next learning topic prediction for learner's guidance in informal learning environment

Estimating the learning needs of learners in a Social Learning Network (SLN) is very important in proper planning for improving learning space. This paper presented a predictor to estimate the learning needs of learners in SLNs. In Question & Answer Networks, estimating the need for learning means estimating the future subject of the question. The significance of the similarity of the sequence of previous learning subjects with the future subjects of learners is one of the most important areas for estimating the subject of future learning. Hence, this predictor estimates the next learning subject based on the similarity of the subjects about which the learner asks questions. The estimation method introduced in this study is based on the Bayesian solution method. The performance of this method was evaluated in the dataset extracted from one of the most widely used SLNs. The results showed that the proposed method was able to detect future tag of each learner with 78% precision in the informal learning environment using the tags of the questions asked by learners.
Leaf Image Recognition Based Identification of Plants: Supportive Framework for Plant Systematics

Plant life is incomplete without proper leaf orientation and morphology. Leaf is the first identity of any plant species. Identification of leaf is the first choice for plant taxonomist as well as common men. Generally, taxonomists and common people face difficulties of different nature during plant identification. Botanists are overwhelmed with fresh data e.g. DNA sequencing to the complex appearance of traits, species phylogeny, environmental influences and outcomes, molecular phenotyping etc. Advancements in biological sciences over last three decades and amplified significance of determining the relationship between structure and function have proved worth of imaging technology as an emerging discipline. Leaf image classification is the best choice for plant taxonomy. Generally, one can easily make over the images of leaves to a system and that computer automatically generates attributes by processing images. Earlier, input images were first converted into grayscale and then transformed to a binary image. Other than plant classification studies with help of image recognition, the same technique can be very supportive for detecting diseased plants in a field. In this article, we have focused on the developments related to image processing and its implementation for plant science research. Leaf image processing has been focused with the help of reported literature in related scientific domains.
State of Water in β-Cycliodextrin/Epichlorohydrin Hydrogel and the Swelling Behavior of the Crosslinked Polymer

Synthetic techniques of β cycliodextrin/epichlorohydrin (β CD/EPI ) hydrogels were reported. The hydrogel synthesized has good transparency, and shows fast swelling behavior in water. No glass transition for the polymer was observed in 40-200 ℃ range. Differential scanning calorimetry (DSC) was employed to probe the water swelling process of the polymer and the state of water in the hydrogel. The results indicate that water is first accommodated in the hydrophilic network of the outside of β CD in the order of nonfreezing bound, freezable bound and freezing free water, and then in the hydrophobic cavity of β CD. When the swelling equilibrium is reached, water distributs both in the outside and inside of the cone shaped structure of β CD.
The influence of Thyroxine on wool growth

IT HAS LONG BEEN KNOWN that the thyroid glands are necessary for normal wool growth. Thyroidectomy reduces wool growth to about 40 to 50% of normal, if thyroidectomy is complete, (Simpson, 1924; Marston and Pierce, 1932; Ferguson, 1951; Ferguson et. aZ., 1956). If even a small fragment of thyroid tissue remains after the operation, wool growth is normal. This suggests that thyroxine may have a permissive role and not a regulatory one so far as wool growth is concerned. More recently evidence has been produced that iodinated casein or thyroxine administered to normal sheep causes a stimulation of wool growth (Maqsood, 1950, 1955; Hart, 1954. 1955, 1957). It is not clear whether such stimulation occurs within the physiological range of thyroid secretion rate or whether it occurs only when the dose of thyroxine reaches hyperthyroid levels, causing a su’bstantial increase in metabolic rate. However, whether or not thyroxine determines the normal level of wool growth, the question arises as to whether the administration of thyroxine to normal sheep is a means of increasing wool production commercially. The present experiment was carried out to determine the dose range of thyroxine which stimulated wool growth and the extent to which any stimulation produced was associated with changes in appetite and body weight.
Low cost inkjet fabrication of glucose electrochemical sensors based on copper oxide

The availability of low cost, efficient and wearable glucose sensors is one of the prerequisites for managing diabetes, one of the most diffuse chronic disease worldwide. Efficient monitoring of glucose levels in diabetics patients is essential to control symptoms and prevent severe complications. Starting from this principle, wet metallization and low cost inkjet printing were employed in the present work to manufacture non-enzymatic electrochemical sensors. CuO nanoparticles were inkjet printed on platinum, which was electrodeposited on stainless steel. The active layer obtained in this way showed an acceptable linear range for glucose detection and a good sensitivity when used as sensor. The influence on performances of interfering species and curvature were investigated, demonstrating a negligible effect for the first and a decrease in linearity of the response and sensitivity for the latter. A second CuO based sensor was realized using a PET flexible substrate. Electroless deposition was employed for the formation of a conductive NiP layer on top of which the CuO nanoparticles were inkjet printed. Glucose sensing was demonstrated by amperometric measurement, but the manufacturing process still has to be optimized to achieve a linear response and acceptable values of sensitivity. Finally, the effectiveness of CuO nanoparticles towards glucose sensing was studied by printing CuO based inks on a Cu/graphene electrochemical sensor.
GDP: A Greedy Based Dynamic Power Budgeting Method for Multi/Many-Core Systems in Dark Silicon

Dark silicon phenomenon is significant in today's multi/many-core systems manufactured using new generation technology. In order to enhance performance of dark silicon systems, power budget constrained dynamic optimizations are performed in various ways including dynamic voltage and frequency scaling (DVFS) and task scheduling. However, power budgets given by existing methods are generally over pessimistic, which greatly limit the capability of dynamic performance optimization methods. In order to resolve this problem, we propose a dynamic power budgeting method, called Greedy based Dynamic Power (GDP). Different from existing methods, which are steady state based and ignore active core distributions, GDP formulates the power budgeting problem as a thermal-constrained combinational power optimization problem. To efficiently solve this problem, we propose two new ideas: first, we transform the original power-optimization problem to an easier solving temperature-optimization problem; second, we employ a more efficient greedy based algorithm that finds a sub-optimal active core distribution which maximizes power budget. The new method can consider current temperature states and transient thermal effects, which were ignored by existing methods. Both theoretical studies and experimental results show that GDP outperforms existing methods by providing a higher and less pessimistic power budget with low computing cost and guaranteed thermal safety.