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With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a user's blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.

Abstract Background To investigate the prognosis of patients with Multiple Myeloma (MM) after surgery, analyze the risk factors leading to adverse postoperative outcomes, and establish a nomogram. Methods Clinical data from 154 patients with MM who underwent surgery at our institution between 2007 and 2019 were retrospectively analyzed. Assessing and comparing patients’ pain levels, quality of life, and functional status before and after surgery (P

A type of reduced graphene oxide (rGO)/SnIn4S8 (SIS) nanocomposites was successfully fabricated by a facile and efficient microwave-assisted method. Their morphology, structure, composition, and optical performance were investigated by different analytical techniques, respectively. The results indicated that rGO had an important effect on the structure of photocatalysts. As the content of rGO increases, the unchin-like microsphere structure of rGO/SIS evolved gradually into aggregated nanosheet structure. The photocatalytic degradation experiments were carried out under visible light irradiation. The results demonstrated rGO/SIS-2 nanocomposites exhibited higher photocatalytic activity toward the degradation of RhodamineB than that of SIS, rGO/SIS-1 as well as rGO/SIS-3. Meanwhile, the mineralization efficiency of Rhodamine B (RhB) can reach up to 73.8% within 30 min. Interestingly, rGO/SIS-2 nanocomposites exhibited excellent recycle stability with 96% removal efficiency of RhB after three cycles. The enhanced photocatalytic activity of rGO/SIS-2 nanocomposites should be attributed to the strong interaction of rGO and SIS, the decrease of band gap energy, the enhanced visible light absorption, and excellent electrons’ mobility of rGO, thereby promoting charge separation. Finally, the mechanism of rGO/SIS nanocomposites for the degradation of RhB has been also proposed. This work provides a facile and efficient pathway to prepare SIS-based nanocomposites with enhanced catalytic efficiency by combining the advantages of microwave-assisted heating and incorporating carbon-based nanomaterials for the degradation of organic pollutants in visible light irradiation.

Grain refinement is a crucial technique for enhancing the mechanical properties and corrosion resistance of magnesium alloys. In this study, the addition of 0.3 wt% CaO and 0.3 wt% CaCO3 successfully refined the AM60 alloy, resulting in a significant reduction in the average grain size from 326 μm to 96 μm (when adding CaCO3). The refined grain size of the as-cast AM60 alloy led to an increase in both tensile strength and yield strength to 118.2 MPa and 72.6 MPa, representing a respective increase of 35% and 23% compared to the alloy without the refiner. As results of EBSD, it is verified that the basal slip system (0001) has a higher Schmid factor after adding CaO, which indicates that this system has higher activity. The substitution reaction between CaO and Mg can produce synergistic effects from the solute effect of Ca and the nucleating effect of residual CaO, which play an important role in grain refinement. Due to the fine grain size and diffuse distribution of the second phase in the AM60-CaCO3 alloy and the thermal decomposition of CaCO3, which consumes the aluminum element in the matrix, the content of the β-phase (Mg17Al12) is reduced. Consequently, this reduces the electric coupling effect between the β-phase and the magnesium matrix. The AM60-CaCO3 alloy exhibited the most superior corrosion resistance, achieving a corrosion rate of only 1.01 mm y−1, representing a 48.9% reduction in corrosion rate compared to the AM60 alloy.

Abstract UV-crosslinking of protein and RNA in direct contacts has been widely used to study protein-RNA complexes while our understanding of the photo-crosslinking mechanisms remains poor. This knowledge gap is due to the challenge of precisely mapping the crosslink sites in protein and RNA simultaneously in their native sequence and structural contexts. Here we systematically analyze protein-RNA interactions and photo-crosslinking by bridging crosslinked nucleotides and amino acids mapped using different assays with protein-RNA complex structures. We developed a computational method PxR3D-map which reliably predicts crosslink sites using structural information characterizing protein-RNA interaction interfaces. Analysis of the informative features revealed that photo-crosslinking is facilitated by base stacking with not only aromatic residues, but also dipeptide bonds that involve glycine, and distinct mechanisms are utilized by different RNA-binding domains. Our work suggests protein-RNA photo-crosslinking is highly selective in the cellular environment, which can guide data interpretation and further technology development for UV-crosslinking-based assays.

Abstract Background Atrial fibrillation (AF) is a prevalent arrhythmic condition resulting in increased stroke risk and is associated with high mortality. Electrolyte imbalance can increase the risk of AF, where the relationship between AF and serum electrolytes remains unclear. Methods A total of 15,792 individuals were included in the observational study, with incident AF ascertainment in the Atherosclerosis Risk in Communities (ARIC) study. The Cox regression models were applied to calculate the hazard ratio (HR) and 95% confidence interval (CI) for AF based on different serum electrolyte levels. Mendelian randomization (MR) analyses were performed to examine the causal association. Results In observational study, after a median 19.7 years of follow-up, a total of 2551 developed AF. After full adjustment, participants with serum potassium below the 5th percentile had a higher risk of AF relative to participants in the middle quintile. Serum magnesium was also inversely associated with the risk of AF. An increased incidence of AF was identified in individuals with higher serum phosphate percentiles. Serum calcium levels were not related to AF risk. Moreover, MR analysis indicated that genetically predicted serum electrolyte levels were not causally associated with AF risk. The odds ratio for AF were 0.999 for potassium, 1.044 for magnesium, 0.728 for phosphate, and 0.979 for calcium, respectively. Conclusions Serum electrolyte disorders such as hypokalemia, hypomagnesemia and hyperphosphatemia were associated with an increased risk of AF and may also serve to be prognostic factors. However, the present study did not support serum electrolytes as causal mediators for AF development.

Abstract Background Asthma is a chronic respiratory disease affecting millions of people worldwide, but early detection can be challenging due to the time-consuming nature of the traditional technique. Machine learning has shown great potential in the prompt prediction of asthma. However, because of the inherent complexity of asthma-related patterns, current models often fail to capture the correlation between data samples, limiting their accuracy. Our objective was to use our novel model to address the above problem via an Affinity Graph Enhanced Classifier (AGEC) to improve predictive accuracy. Methods The clinical dataset used in this study consisted of 152 samples, where 24 routine blood markers were extracted as features to participate in the classification due to their ease of sourcing and relevance to asthma. Specifically, our model begins by constructing a projection matrix to reduce the dimensionality of the feature space while preserving the most discriminative features. Simultaneously, an affinity graph is learned through the resulting subspace to capture the internal relationship between samples better. Leveraging domain knowledge from the affinity graph, a new classifier (AGEC) is introduced for asthma prediction. AGEC’s performance was compared with five state-of-the-art predictive models. Results Experimental findings reveal the superior predictive capabilities of AGEC in asthma prediction. AGEC achieved an accuracy of 72.50%, surpassing FWAdaBoost (61.02%), MLFE (60.98%), SVR (64.01%), SVM (69.80%) and ERM (68.40%). These results provide evidence that capturing the correlation between samples can enhance the accuracy of asthma prediction. Moreover, the obtained $$p$$ p values also suggest that the differences between our model and other models are statistically significant, and the effect of our model does not exist by chance. Conclusion As observed from the experimental results, advanced statistical machine learning approaches such as AGEC can enable accurate diagnosis of asthma. This finding holds promising implications for improving asthma management.

Background and purpose: Robotic thyroid surgery has been carried out worldwide since Kang first applied it to the thyroid in 2007. The proportion of axillary approach is increasing rapidly in China due to the continuous verification of safety, radicalness and cosmetic properties of cancer treatment. The advantages and clinical value of Da Vinci Xi robotic unilateral axillary approach in the treatment of unilateral papillary thyroid microcarcinoma (PTMC) was investigated by comparing and analyzing the clinical efficacy in 95 patients undergoing a gasless, unilateral axillary approach Da Vinci Xi robotic thyroid surgery for PTMC and 91 patients undergoing conventional surgery. Methods: The clinical data of 186 cases of unilateral PTMC undergoing thyroidectomy in the Department of Head and Neck Surgery of Shanghai cancer Hospital from March 2020 to November 2021 were retrospectively analyzed, and they were divided into the robotic group (95 cases) and the open group (91 cases) according to the surgical methods. The general clinical data, surgical results, postoperative complications, satisfaction of incision, learning curve and surgical costs were analyzed and compared between the two groups. Results: There was no statistically significant difference between the two groups in gender, age, incidence of Hashimoto’s thyroiditis, tumor diameter, location and number of lesions (P>0.05). However, the operation time was significantly longer in the robotic group [(126.65±34.76) min]than in the open group [(60.99±21.12) min], and after 36 operations, the operation time reached a stable level. The operation cost was significantly higher in the robotic group [(47 940.66±4 404.30) yuan] than in the open group [(15 807.73±2 295.35) yuan]. There was no significant difference in intraoperative hemorrhage [robotic group: (20.48±18.83) mL; open group: (16.15±13.95) mL], the number of removed lymph nodes (robotic group: 2.61±2.77; open group: 2.97±2.20) and the number of positive lymph nodes (robotic group: 1.01±1.61; open group: 0.78±1.36) between the two groups (P>0.05). The incidence of postoperative temporary vocal hoarseness and transient hypocalcemia was lower in the robotic group (1.10%, 2.10%, respectively) than in the open group (2.20%, 3.30%, respectively), however there was no statistically significant difference (P>0.05). The postoperative satisfaction of incision was significantly higher in the robotic group (very satisfied 87.4%, satisfied 11.6%) than in the open group (very satisfied 71.4%, satisfied 26.4%), and the difference was statistically significant (P

C20H16FNO5, monoclinic, P21/c (no. 14), a = 9.5235(2) Å, b = 22.1323(2) Å, c = 8.95249(19) Å, β = 101.445(3)°, V = 1779.66(7) Å3, Z = 4, R gt(F) = 0.0410, wR ref(F 2) = 0.1194, T = 279.5 K.