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Hepatitis W virus infection as well as probability of nasopharyngeal carcinoma inside the southern area of China.
Arsenic (As), a geogenic and extremely toxic metalloid can jeopardize terrestrial and aquatic ecosystems through environmental partitioning in natural soil-water compartment, geothermal and marine environments. Although, many researchers have investigated the decontamination potential of different mesoporous engineered bio sorbents for a suite of contaminants, still the removal efficiency of various pyrolyzed agricultural residues needs special attention. In the present study, rice straw derived biochar (RSBC) produced from slow pyrolysis process at 600 °C was used to remove As (V) from aqueous medium. Batch experiments were conducted at room temperature (25 ± 2 °C) under different initial concentrations (10, 30, 50, 100 μg L-1), adsorbent dosages (0.5-5 μg L-1), pH (4.0-10.0) and contact times (0-180 min). The adsorption equilibrium was established in 120 min. Adsorption process mainly followed pseudo-second order kinetics (R2 ≥ 0.96) and Langmuir isotherm models (R2 ≥ 0.99), and the monolayer sorption capacity of 25.6 μg g-1 for As (V) on RSBC was achieved. Among the different adsorbent dosages and initial concentrations used in the present study, 0.2 g L-1 (14.8 μg g-1) and 100 μg L-1 (13.1 μg g-1) were selected as an optimum parameters. A comparative analysis of RSBC with other pyrolyzed waste materials revealed that RSBC had comparable adsorption ability (per unit area). These acidic groups are responsible for the electron exchange (electrostatic attraction, ion-exchange, π-π/n-πinteractions) with the anionic arsenate, which facilitates optimum removal (>60%) at 7 less then pH less then pHPZC. The future areas of research will focus on decontamination of real wastewater samples containing mixtures of different emerging contaminants and installation of biofilter beds that contains different spent adsorbents/organic substrates (including biochar) for biopurification study in real case scenario.An 1H NMR-based metabolomics approach was conducted to holisticly explore the effect of Xue Fu Zhu Yu (XFZY) capsule (a well-known Chinese herbal medicine) on high-fat diets combined with coronary artery ligation induced coronary heart disease (CHD) model rats. 1H NMR-based metabolomics approach combined with multivariate analysis was performed to explore potential biomarkers, a total of 20 metabolites were confirmed as contributors to the discrimination of model group and sham group. We investigated the dynamic metabolic characteristics of XFZY capsule on CHD rats, lactate, glutamine, pyruvate, citrate, choline and taurine were potential biomarkers of early effect. More potential biomarkers changed after 28 days of medication, XFZY capsules primarily influenced the taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, glyoxylate and dicarboxylate metabolism, purine metabolism, glycolysis/gluconeogenesis, amino sugar and nucleotide sugar metabolism, primary bile acid biosynthesis.The traceability of mineral element fingerprints to mutton in a small area of China was studied. The element data of 104 sheep and 24 goat samples from Inner Mongolia were measured, and the data were analyzed by multivariate statistical analysis from different origins, species and feeding patterns. The results shows that 11 elements (Mg, Al, K, Ca, Mn, Fe, Cu, Zn, Rb, Sr, Ba) in sheep meat had significant differences between different regions (P less then 0.05), and the results of linear discriminant analysis (LDA) showed that the accuracy of the original classification rate was 95.2%, and the cross-validation rate was 85.9%. Goat meat and sheep meat samples from Alxa League were also clearly identified with LDA results showing that the cross-validation accuracy of the two species was 70.2%. Then the feeding patterns of sheep meat were effectively classified. The results showed that the multi-element analysis has certain potential as a method to distinguish mutton in a small area.In recent years, deep learning has emerged as a powerful tool for developing Brain-Computer Interface (BCI) systems. However, for deep learning models trained entirely on the data from a specific individual, the performance increase has only been marginal owing to the limited availability of subject-specific data. To overcome this, many transfer-based approaches have been proposed, in which deep networks are trained using pre-existing data from other subjects and evaluated on new target subjects. This mode of transfer learning however faces the challenge of substantial inter-subject variability in brain data. Addressing this, in this paper, we propose 5 schemes for adaptation of a deep convolutional neural network (CNN) based electroencephalography (EEG)-BCI system for decoding hand motor imagery (MI). learn more Each scheme fine-tunes an extensively trained, pre-trained model and adapt it to enhance the evaluation performance on a target subject. We report the highest subject-independent performance with an average (N=54) accuracy of 84.19% (±9.98%) for two-class motor imagery, while the best accuracy on this dataset is 74.15% (±15.83%) in the literature. Further, we obtain a statistically significant improvement (p=0.005) in classification using the proposed adaptation schemes compared to the baseline subject-independent model.
The objective of this study was to investigate whether three dimentional (3D)- Coronary CT angiography (CCTA)- feature tracking (FT) can measure global myocardial strain of the left ventricle (LV) in patients with heart failure using cardiac MR (CMR) as reference.

Consecutive patients (n = 44) with variable degrees of heart failure who underwent an ECG-gated CCTA and CMR within 24 h were included. Both modalities were compared for 2D/3D LV global radial strain (2D/3D-GRS), circumferential strain (2D/3D-GCS), longitudinal strain (2D/3D-GLS) and conventional functional parameters.

Compared to CMR, CCTA-derived 3D-GLS and LVEF showed no significant difference (p > 0.05). Bland-Altman plots showed a small bias (0.3 %) between CCTA-derived 3D-GLS and CMR 3D-GLS. Close correlations were observed between the two modalities regarding LV global strain (3D-GRS, r = 0.89; 3D-GCS, r = 0.86; 3D-GLS, r = 0.79, respectively, p < 0.001 for all). However, CCTA-derived 3D-GRS and 3D-GCS were statistically different compared with CMR.
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