Effects of Polyethylene Glycol/Porous Silica Form-Stabilized Phase Change Materials on the Performance of Asphalt Binders.

The road performance and temperature-regulating properties of asphalt binders modified with novel polyethylene glycol (PEG)/porous silica (PS) form-stabilized phase-change materials (PEG/PS-fs-PCMs) were studied. PS and PEG were used as the supporting substance and PCMs. The results showed that PEG/PS-fs-PCMs could maintain a maximum weight percentage of 70% without leakage, at temperatures as high as 90 °C. The PEG/PS-fs-PCMs exhibited stable chemical structures, excellent thermal stability, high heat storage density, and suitable phase-change temperature. Based on conventional physical tests, the addition of PEG/PS-fs-PCMs can increase the viscosity and the degree of hardness of asphalt binders; thus, achieving an excellent comprehensive performance of the modified asphalt binder depends on determining the optimal dosage of PEG/PS-fs-PCMs. Additionally, incorporating PEG/PS-fs-PCM particles into the asphalt binder can enhance its ability to withstand permanent deformation at elevated temperatures, while PEG/PS-fs-PCMs mainly act as a filler, weakening the cohesive force of the asphalt molecules, and preventing the ductility of asphalt from expansion, according to DSR and BBR tests. Moreover, the use of PEG/PS-fs-PCMs can enhance the heat transfer properties of the asphalt binders, resulting in an improved temperature regulation performance. However, the accumulation of PEG/PS-fs-PCM particles on asphalt binders can negatively impact the storage stability of the modified asphalt binders, because of the difference in density between the two materials.

Ultra-Broadband, Omnidirectional, High-Efficiency Metamaterial Absorber for Capturing Solar Energy.

In this study, we investigated an absorber based on a center-aligned tandem nanopillar array for ultra-broadband solar energy harvesting theoretically. A high-efficiency, omnidirectional absorber was obtained by introducing the center-aligned tandem nanopillar array embedded in an Al2O3 dielectric layer. The multi-coupling modes at different wavelengths were interpreted. The strong absorption can be adjusted by changing the radii and heights of nanopillars. According to the simulation results, the average absorptance of the absorber exceeded 94% in the wavelength range from 300 nm to 2000 nm. In addition, the high-efficiency absorption was insensitive to the incident angle and polarization state. The research not only proposed an absorber which possesses a huge potential value for application areas, such as thermal photovoltaic systems, infrared detection, and isotropic absorption sensors, but also pointed out a new way to design an absorber with high efficiency in an ultrabroad wavelength range.

Association between transcription factor 7-like 2 C/T polymorphism and diabetic retinopathy risk: a meta-analysis.

BACKGROUND: Previous studies have suggested a close association between transcription factor 7-like 2 (TCF7L2) polymorphisms and diabetic retinopathy (DR) susceptibility. However, the published results were inconsistent. This meta-analysis was conducted to review and examine the relationship between TCF7L2 rs7903146 C/T polymorphism and DR risk. MATERIALS AND METHODS: Online databases were searched and the related studies were identified in this meta-analysis. Odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were calculated to examine the statistical power. Moreover, heterogeneity test, sensitivity accumulative analysis and publication bias were conducted to measure the statistical effect. RESULT: 6 studies involving 12,982 subjects were included in this meta-analysis to assess the association between rs7903146 C/T polymorphism and DR susceptibility. The synthetic results indicated that the mutation of rs7903146 C/T polymorphism maybe accompany with an increased risk for DR (T vs. C: OR=1.26, 95%CI=1.00-1.60, P=0.05, I2=83.5%; TT vs. CC: OR=1.79 95%CI=1.12-2.86, P=0.02, I2=80.2%; TT vs. CC+CT: OR=1.62, 95%CI=1.38-1.92, P<0.01, I2=32.3%). Moreover, the subgroup analysis also demonstrated an increasing risk for DR with T mutations in Caucasian descendants. CONCLUSION: The current evidences meta-analysis suggested that the TCF7L2 rs7903146 C/T polymorphism might be play an important role in DR susceptibility.

Qualitative study of the learning and studying process of resident physicians in China.

BACKGROUND: Clinical medical education is essential in physician training. This study developed recommendations for medical residency course design on the basis of the perspectives of learners in China and how they interact with their environment. The central research topic was the professional development and learning process of residents, including the obstacles that hinder and factors that promote their learning, their views on existing teaching methods, interaction between teachers and medical teams, and suggestions for designing future residency training programs. METHODS: This study had a qualitative research design. Interviews were conducted between July and October 2019 with 17 specialist residents and 12 assistant general practitioner residents from the department of education of the hospital. The participants were recruited from Qingyuan People's Hospital in Guangdong Province, China. The interview outlines focused on the following four themes: clinical learning experiences and reflections on learning, experience of interaction with patients, experience of working with other medical personnel, and future learning directions. RESULTS: To overcome challenges in clinical learning, the residents mainly learned from their teachers and focused specifically on their own experiences. Regarding teaching methods and designs in clinical medicine, the residents preferred large-group, small-group, and bedside teaching and reported that bedside teaching enables the resolution of clinical problems, initiates self-learning, and improves diagnostic thinking. They disliked teachers with low teaching motivation or who were reluctant to interact with them and favored teachers who had strong teaching skills and respect for their students. CONCLUSIONS: The residents suggested that clinical and active learning must be the main learning method for developing general medical competencies. Residency training must be conducted in an environment that facilitates residents' learning and meaningful learning activities. The interdependent symbiotic relationships in the education ecosystem can serve as a reference for designing residency courses.

GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions.

MOTIVATION: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network. RESULTS: In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale. AVAILABILITY: Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI.

Heterogeneous graph embedding model for predicting interactions between TF and target gene.

MOTIVATION: Identifying the target genes of transcription factors (TFs) is of great significance for biomedical researches. However, using biological experiments to identify TF-target gene interactions is still time consuming, expensive and limited to small scale. Existing computational methods for predicting underlying genes for TF to target is mainly proposed for their binding sites rather than the direct interaction. To bridge this gap, we in this work proposed a deep learning prediction model, named HGETGI, to identify the new TF-target gene interaction. Specifically, the proposed HGETGI model learns the patterns of the known interaction between TF and target gene complemented with their involvement in different human disease mechanisms. It performs prediction based on random walk for meta-path sampling and node embedding in a skip-gram manner. RESULTS: We evaluated the prediction performance of the proposed method on a real dataset and the experimental results show that it can achieve the average area under the curve of 0.8519 ± 0.0731 in fivefold cross validation. Besides, we conducted case studies on the prediction of two important kinds of TF, NFKB1 and TP53. As a result, 33 and 32 in the top-40 ranking lists of NFKB1 and TP53 were successfully confirmed by looking up another public database (hTftarget). It is envisioned that the proposed HGETGI method is feasible and effective for predicting TF-target gene interactions on a large scale. AVAILABILITY AND IMPLEMENTATION: The source code and dataset are available at https://github.com/PGTSING/HGETGI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tunable dielectric metasurfaces by structuring the phase-change material.

Metasurfaces have made great progress in the last decade for generating miniature and integrated optical devices. The optical properties of metasurfaces can be tuned dynamically by integrating with phase-change materials. However, the efficiency of tunable metasurfaces remains a bit low, which is a disadvantage for the realistic applications of metasurfaces. Here, we demonstrate the tunable dielectric metasurfaces by structuring the phase-change material Ge2Sb2Te5. The unit cell of metasurface is composed of several Ge2Sb2Te5 nanopillars with different geometric parameters, and the incident light interacts with different nanopillars at diverse phases of Ge2Sb2Te5, leading to various functions. By elaborately arranging the Ge2Sb2Te5 nanopillars, various tunable optical devices have been realized, including tunable beam steering, reconfigurable metalens and switchable wave plate. The refractive direction, focal length and polarization state can be tuned through the phase transition of Ge2Sb2Te5. The phase-change metasurfaces based on Ge2Sb2Te5 nanostructures could be used in cameras, optical microscopy and adaptive optics.

Switchable transmissive and reflective metadevices based on the phase transition of vanadium dioxide.

Metasurfaces have made great progress in the past decade in generating various planar optical devices. However, most metasurfaces exhibit their functions in either reflection mode or transmission mode, with the other mode unutilized. In this work, we demonstrate switchable transmissive and reflective metadevices by combining metasurfaces with vanadium dioxide. The composite metasurface can work as a transmissive metadevice, with one function for vanadium dioxide in the insulating phase, and is changed to a reflective metadevice with another function for vanadium dioxide in the metallic phase. By carefully designing the structures, the metasurface can be switched from a transmissive metalens to a reflective vortex generator, or between a transmissive beam steering and a reflective quarter-wave plate through the phase transition of vanadium dioxide. The switchable transmissive and reflective metadevices have potential applications in imaging, communication, and information processing.

Identification of Network Topology Variations Based on Spectral Entropy.

Based on the fact that the traditional probability distribution entropy describing a local feature of the system cannot effectively capture the global topology variations of the network, some indicators constructed by the network adjacency matrix and Laplacian matrix come into being. Specifically, these measures are based on the eigenvalues of the scaled Laplace matrix, the eigenvalues of the network communicability matrix, and the spectral entropy based on information diffusion that has been proposed recently, respectively. In this article, we systematically study the dependence of these measures on the topological structure of the network. We prove from various aspects that spectral entropy has a better ability to identify the global topology than the traditional distribution entropy. Furthermore, the indicator based on the eigenvalues of the network communicability matrix achieves good results in some aspects while, overall, the spectral entropy is able to identify network topology variations from a global perspective.

Finite-size scaling of geometric renormalization flows in complex networks.

Some characteristics of complex networks need to be derived from global knowledge of the network topologies, which challenges the practice for studying many large-scale real-world networks. Recently, the geometric renormalization technique has provided a good approximation framework to significantly reduce the size and complexity of a network while retaining its "slow" degrees of freedom. However, due to the finite-size effect of real networks, excessive renormalization iterations will eventually cause these important "slow" degrees of freedom to be filtered out. In this paper, we systematically investigate the finite-size scaling of structural and dynamical observables in geometric renormalization flows of both synthetic and real evolutionary networks. Our results show that these observables can be well characterized by a certain scaling function. Specifically, we show that the critical exponent implied by the scaling function is independent of these observables but depends only on the structural properties of the network. To a certain extent, the results of this paper are of great significance for predicting the observable quantities of large-scale real systems and further suggest that the potential scale invariance of many real-world networks is often masked by finite-size effects.

Non-Causal Effects of Asthma on COVID-19 Susceptibility and Severity.

Background: Asthma is observationally associated with an increased risk of COVID-19, but the causality remains unclear. We aim to determine whether there is a casual role of asthma in susceptibility to SARS-CoV-2 infection or COVID-19 severity. Methods: Instrumental variables (IVs) for asthma and moderate-to-severe asthma were obtained from publicly available summary statistics from the most recent and largest genome-wide association study (GWAS), including 394 283 and 57 695 participants of European ancestry, respectively. The corresponding data for COVID-19 susceptibility, hospitalization and severe-disease were derived from the COVID-19 Host Genetics Initiative GWAS meta-analysis of up to 1 683 768 individuals of European descent. Causality was inferred between correlated traits by Mendelian Randomization analyses. Inverse-variance weighted method was used as the primary MR estimates and multiple alternate approaches and several sensitivity analyses were also conducted. Results: Our MR analysis revealed no causal effects of asthma on COVID-19 susceptibility, hospitalization or severe disease, with odds ratio (OR) of 0.994 (95% CI: 0.962-1.027), 1.020 (95% CI: 0.955-1.089), and 0.929 (95% CI: 0.836-1.032), respectively. Furthermore, using genetic variants for moderate-to-severe asthma, a similar pattern of results was observed for COVID-19 susceptibility (OR: 0.988, 95% CI: 0.946-1.031), hospitalization (OR: 0.967, 95% CI: 0.906-1.031), and severe disease (OR: 0.911, 95% CI: 0.823-1.009). The association of asthma and moderate-to-severe asthma with COVID-19 was overall robust to sensitivity analyses. Conclusion: Genetically predicted asthma was not associated with susceptibility to, or severity of, COVID-19 disease, indicating that asthma is unlikely to be a causal factor in the development of COVID-19.

Retraction: ASIC1a involves acidic microenvironment-induced activation and autophagy of pancreatic stellate cells.

[This retracts the article DOI: 10.1039/C8RA03679A.].

[Killing Effect of A CD7 Chimeric Antigen Receptor-Modified NK-92MI Cell Line on CD7-Positive Hematological Malignant Cells].

OBJECTIVE: To investigate the killing effect of NK-92MI cells modified by chimeric antigen receptor (CD7-CAR) and specifically targeting CD7 to CD7+ hematological malignant cells. METHODS: Three types of hematological malignant tumor cells, including 5 cases of CD7+ acute T-lymphoblastic leukemia (T-ALL), 10 cases of acute myeloid leukemia (AML) and 6 cases of T-cell lymphoma were collected, centrifuged, cultured and used to detect the expression levels of tumor cell surface targets; 7-AAD, CD56-APC, CD3-FITC, IgG Fc-PE flow cytometry were used to detected the transfection efficiency of NK-92MI and CD7-CAR-NK-92MI cells, killing efficiencies of CD7-CAR-NK-92MI cells to CD7+ hematological tumor cells in vitro were determined by flow cytometry using PE Annexin V Apoptosis Detection Kit. Secretion differences of NK-92MI and CD7-CAR-NK-92MI cytokines interleukin (IL)-2, interferon (IFN)-γ, and granzyme B detection were estimated by using CBA kit. RESULTS: The killing efficiencies of CD7-CAR-modified NK-92MI cells to CD7+ T-ALL, AML, T-cell lymphoma tumor cells were significantly higher than those of NK-92MI cells without genetical modification. The difference showed statistically significant (P＜0.05). The level of IFN-γ and granzyme B were significantly increased among cytokines secreted by CD7-CAR-modified NK-92MI cells as compared with those of NK-92MI cells without genetical modification (P＜0.05) . CONCLUSION: CD7-CAR-modified NK-92MI cells have significantly improved killing efficiency against CD7+ T-ALL, AML and T lymphoma cells, and shows specific targeting effects, which provides a clinical basis for the treatment of CD7+ hematological malignancies.

Characterization of network complexity by communicability sequence entropy and associated Jensen-Shannon divergence.

Characterizing the structural complexity of networks is a major challenging work in network science. However, a valid measure to quantify network complexity remains unexplored. Although the entropy of various network descriptors and algorithmic complexity have been selected in the previous studies to do it, most of these methods only contain local information of the network, so they cannot accurately reflect the global structural complexity of the network. In this paper, we propose a statistical measure to characterize network complexity from a global perspective, which is composed of the communicability sequence entropy of the network and the associated Jensen-Shannon divergence. We study the influences of the topology of the synthetic networks on the complexity measure. The results show that networks with strong heterogeneity, strong degree-degree correlation, and a certain number of communities have a relatively large complexity. Moreover, by studying some real networks and their corresponding randomized network models, we find that the complexity measure is a monotone increasing function of the order of the randomized network, and the ones of real networks are larger complexity-values compared to all corresponding randomized networks. These results indicate that the complexity measure is sensitive to the changes of the basic topology of the network and increases with the increase of the external constraints of the network, which further proves that the complexity measure presented in this paper can effectively represent the topological complexity of the network.

Effects of passive smoking and its duration on the prevalence of prediabetes and type 2 diabetes mellitus in Chinese women.

Several studies have shown that active smoking is a risk factor for type 2 diabetes mellitus (T2DM). However, the effects of passive smoking on T2DM remains unknown. In this study, we investigated the effects of passive smoking and its duration on the prevalence of prediabetes and T2DM. According to passive smoking status, participants were divided into Group A (passive smokers) and Group B (controls). Furthermore, Group A was divided into three subgroups according to the duration of passive smoking: Group A1 (≤10 years), Group A2 (10-20 years), and Group A3 (>20 years). We found that the prevalence of impaired glucose tolerance (IGT) in Group A (26.6%), Group A2 (28%), and Group A3 (37.8%) was significantly higher than that in Group B (19.6%), and the prevalence gradually increased with an increase in the duration of passive smoking. Multiple logistic regression analysis showed that passive smoking for >10 years was a risk factor for impaired fasting glucose (IFG), IGT, and T2DM. Therefore, passive smoking not only increases the prevalence of IGT in a time-dependent manner, but also a risk factor for IFG, IGT, and T2DM when its duration is over 10 years.

[Network Meta-analysis of effect of traditional Chinese medicine injections in adjuvant treatment of mycoplasma pneumonia in children].

To assess the clinical efficacy of traditional Chinese medicine injection for adjuvant treatment of mycoplasma pneumoniae pneumonia in children by network Meta-analysis method. We retrieved CNKI, WanFang, CBM, Cochrane Library, PubMed from the establishment to September 2018. Two reviewers independently screened out literatures, extracted data and assessed the methodological quality of included studies. The data were analyzed by Stata 13.0 software. Totally 89 RCTs were included, involving 8 kinds of traditional Chinese medical injections and 8 936 patients. According to the results of network Meta-analysis, the order by the total effective rate from high to low was Huangqi Injection>Xiyanping Injection>Tanreqing Injection>Compound Danshen Injection>Reduning Injection>Yanhuning Injection>Qingkailing Injection>Xixinnao Injection; the order by cooling time from high to low was Reduning Injection> Yanhuning Injection>Qingkailing Injection>Tanreqing Injection>Huangqi Injection>Xiyanping Injection>Xiexinnao Injection>Compound Danshen Injection; the order by the cough disappeared time from high to low was Compound Danshen Injection>Qingkailing Injection>Xiyanping Injection>Huangqi Injection>Yanhuning Injection>Reduning Injection>Tanreqing Injection>Xixinnao Injection; the order by the rales disappearing time from high to low was Qingkailing Injection>Yanhuning Injection>Reduning Injection>Huangqi Injection>Tanreqing Injection>Xiyanping Injection>Xixinnao Injection. The results show that traditional Chinese medicine injection has a significant clinical efficacy in the adjuvant treatment of various symptoms of mycoplasma pneumoniae pneumonia in children. Due to the small sample size, more studies are required to verify the strength of evidence.

The key regulation of miR-124-3p during reprogramming of primary mouse hepatocytes into insulin-producing cells.

Based on the action of small molecule compounds, the efficiency of differentiation of mouse primary hepatocytes into insulin-producing cells (IPCs) was improved by changing the expression of miR-124-2p. Hepatocytes were transfected with microRNA-124-3p (miR-124-3p) mimic or inhibitor, followed by a chemical-defined culture system for maturation of IPCs. Then, detect the expression of insulin-related genes and protein and insulin secretion of each stage during differentiation. The expression of Foxa2, PDX1, NeuroD, insulin1, and insulin2 in IPCs in the miR-124-3p inhibition expression group was significantly upregulated, while the results were opposite in the miR-124-3p overexpression group. The results of cell immunofluorescence and glucose stimulation in vitro of the miR-124-3p inhibition expression group showed that the expression of insulin, PDX1, and C-peptide was increased, and the differentiation efficiency was higher than those of the control group and overexpression group. The primary mouse hepatocytes were successfully reprogrammed into IPCs by small-molecule compounds. We found that miR-124-3p plays a negative regulatory role in the differentiation of hepatocytes into IPCs in vitro. Inhibition of miR-124-3p expression significantly increased the expression of FOXA2 and PDX1, promoted the differentiation of hepatocytes into IPCs, and increased the induction efficiency.

Correlations between communicability sequence entropy and transport performance in spatially embedded networks.

We investigate electric current transport performances in spatially embedded networks with total cost restriction introduced by Li et al. [Phys. Rev. Lett. 104, 018701 (2010)10.1103/PhysRevLett.104.018701]. Precisely, the network is built from a d-dimensional regular lattice to be improved by adding long-range connections with probability P_{ij}∼r_{ij}^{-α}, where r_{ij} is the Manhattan distance between sites i and j, and α is a variable exponent, the total length of the long-range connections is restricted. In addition, each link has a local conductance given by g_{ij}∼r_{ij}^{-C}, where the exponent C is to measure the impact of long-range connections on network flow. By calculating mean effective conductance of the network for different exponent α, we find that the optimal electric current transport conditions are obtained with α_{opt}=d+1 for all C. Interestingly, the optimal transportation condition is identical to the one obtained for optimal navigation in spatially embedded networks with total cost constraint. In addition, the phenomenon can be possibly explained by the communicability sequence entropy; we find that when α=d+1, the spatial network with total cost constraint can obtain the maximum communicability sequence entropy. The results show that the transport performance is strongly correlated with the communicability sequence entropy, which can provide an effective strategy for designing a power network with high transmission efficiency, that is, the transport performance can be optimized by improving the communicability sequence entropy of the network.

P2X7 receptor: A potential therapeutic target for autoimmune diseases.

P2X7 receptor (P2X7R), a distinct ligand-gated ion channel, is a member of purinergic type 2 receptor family with ubiquitous expression in human body. Previous studies have revealed a pivotal role of P2X7R in innate and adaptive immunity. Once activated, it will meditate some vital cascaded responses including the assembly of nucleotide-binding domain (NOD) like receptor protein 3 (NLRP3) inflammasome, non-classical secretion of IL-1ß, modulation of cytokine-independent pathways in inflammation such as P2X7R- transglutaminase-2 (TG2) and P2X7R-cathepsin pathway, activation and regulation of T cells, etc. In fact, above responses have been identified to be involved in the development of autoimmunity, specifically, the NLRP3 inflammasome could promote inflammation in massive autoimmune diseases and TG2, as well as cathepsin may contribute to joint destruction and degeneration in inflammatory arthritis. Recently, numerous evidences further suggested the significance of P2X7R in the pathogenesis of autoimmune diseases, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), inflammatory bowel disease (IBD), multiple sclerosis (MS), etc. In this review, we will succinctly discuss the biological characteristics and summarize the recent progress of the involvement of P2X7R in the development and pathogenesis of autoimmune diseases, as well as its clinical implications and therapeutic potential.

Metal-Organic-Framework-Based Gel Polymer Electrolyte with Immobilized Anions To Stabilize a Lithium Anode for a Quasi-Solid-State Lithium-Sulfur Battery.

A lithium-sulfur (Li-S) battery is widely regarded as one of the most promising technologies for energy storage because of its high theoretical energy density and cost advantage. However, the shuttling of soluble polysulfides between the cathode and the anode and the consequent lithium anode degradation strongly limit the safety and electrochemical performance in the Li-S battery. Herein, a metal-organic-framework (MOF)-modified gel polymer electrolyte (GPE) is employed in a Li-S battery in order to stablize the lithium anode. In view of the abundant pores in the MOF skeleton, the as-prepared GPE not only immobilizes the large-size polysulfide anions but also cages electrolyte anions into the pores, thus facilitating a uniform flux of Li ions and homogeneous Li deposition. Cooperated with a sulfur-carbon composite cathode, the lithium with MOF-modified GPE exhibits a uniform surface morphology and dense solid electrolyte interphase (SEI) film, thus delivering good cycle stability and high-rate capability.