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A serious of phenol and amine bifunctionalized polyacrylonitrile fibers have been prepared and used to catalyze the tandem deprotection-Knoevenagel reaction. Among them, the phenol and tertiary amine bifunctionalized polyacrylonitrile fiber (PANP-TAF) achieved the best catalysis activity. The PANP-TAF was characterized by Fourier-transfer infrared spectroscopy (FTIR), X-ray diffraction spectra (XRD) and elemental analysis (EA). The PANP-TAF can efficiently catalyze Knoevenagel condensation reaction of aromatic aldehydes and methyl cyanoacetateethyl cyanoacetate or malononitrile with high yields of 85%-99% and extensive substrate scope in water. Moreover, the PANP-TAF shows excellent reusablity (up to 15 times) without significant decrease of catalytic activity. [ABSTRACT FROM AUTHOR]

Acupuncture treatment (AT) of depressive insomnia by traditional Chinese medicine has the advantages of fewer side effects, quicker results, and lower prices compared to medication and psychological and cognitive therapy. Clinicians often select multiple acupoints, such as Bai Hui (GV20), San Yin Jiao (SP6), and Shen Men (HT7), for combined treatment in a single AT session to improve sleep quality. Since the ancient literature on AT often only records the general order of acupoints, there needs to be more discussion on the influence of the multiple acupoint sequence on the priority of efficacy for a specific disease. At the same time, determining the ranking of acupoints in-patient treatment in clinical practice is mainly dependent on the treatment experience of practitioners, and there is no transparent quantitative model or evaluation method for generating credible acupoint sequences from a small and limited scale of cases. Therefore, it is essential to explore the optimization of the order of multiple acupoints in treating depressive insomnia by Traditional Chinese Medicine acupuncture both for the symptom relief of depressive insomnia patients and for the efficient use of national health care resources. This paper proposes a reinforcement learning-based method for optimizing the acupoint sequence for depressive insomnia AT to address these issues. This paper provides a post-AT EEG signal prediction model with related interpretable models to construct a reinforcement learning framework to represent the state transfer of the AT environment and a quantitative EEG signal-based AT efficacy model to represent the reward function. Finally, 30 patients with depressive insomnia were recruited to collect EEG signals during AT for depressive insomnia, and the case data were used to quantify the efficacy of AT and to model the post-AT EEG signal prediction. The above two models were applied to optimize the acupoint sequence based on reinforcement learning. Satisfactory results were obtained, verifying the effectiveness and feasibility of the method proposed in this paper. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate the mediating roles of parental and romantic attachment in the relationship between interparental conflict (IPC) and young adults' romantic relationship quality, and whether their mediating roles differ between males and females. A total of 160 heterosexual romantic couples (mean age = 21.82 years, SD = 1.72 years) from a capital city in western China volunteered to take part in an online survey, which encompassed self-report questionnaires of IPC, parental attachment, romantic attachment, and romantic relationship quality. Structural equation modeling was conducted to analyze data among males and females, respectively. Results suggested that male adults' attachment to mother independently mediated the relationship between IPC and male adults' romantic relationship quality, whereas romantic anxiety independently mediated the relationship between IPC and female adults' romantic relationship quality. In addition, for both males and females, parental and romantic attachment did not sequentially mediate the association between IPC and romantic relationship quality. These findings illustrate the differential roles that parental and romantic attachment play in the mediation mechanisms for male and female young adults. [ABSTRACT FROM AUTHOR]

The virtual try-on technology can satisfy the demands for online shopping and help consumers experience online clothes through image generation technology. Compared with image-based try-on, the 3D virtual try-on methods can realize the multi-perspective of try-on simulation and get the attention of many researchers. The current 3D virtual try-on methods mainly base on the thin-plate spline method and depth information-based 3D reconstruction, which increases the costs of implementing 3D virtual try-on and the results lack of clothing details such as folds and patterns. To solve those problems, we propose a novel 3D virtual try-on network based on appearance flow and shape field called AFSF-3DVTON. Specifically, this network consists of three modules. First, the appearance flow warping module generates the desired warped clothes according to the appearance flow of the original clothes. Then, the flat try-on module facilitates geometric matching between the warped clothes and reference person images and synthesizes 2D try-on results. Third, to increase the image's details to 3D try-on synthesis, the shape field-based reconstruction is adopted, which extracts shape features of 2D try-on results to improve the quality of 3D try-on reconstruction. We evaluate the proposed method on the VITON and MPV3D datasets, and several state-of-the-art virtual try-on algorithms are used as comparisons. The qualitative analyses verify the superiority of the proposed method, and the evaluation indexes, including Abs., Sq., and RMSE, demonstrate the outperformance of the proposed network. [ABSTRACT FROM AUTHOR]

In public goods games, it is common for agents to learn strategies from those who possess the highest utility. However, in reality, because of the lack of information, strategies and utilities from others cannot be obtained or predicted during learning and updating. To address this issue, we introduce a learning update mechanism based on aspirations. To make this model more universal, we study goods that can be shared with k-hop neighbors. Additionally, when a free rider accesses an investor, it is required to pay an access cost to him. We investigate the influence of aspiration, shared scope k, and access cost on the social invest level and utility. It is shown that large shared scope k, moderate aspiration, and moderate access cost are conducive to the maximum utilization of social benefits. However, with low aspiration, the utilities of investors are very close and limited, while both the high aspiration and high access cost could disrupt the social stability. [ABSTRACT FROM AUTHOR]

A growing of evidence has showed that patients with osteoarthritis (OA) had a higher coronavirus 2019 (COVID-19) infection rate and a poorer prognosis after infected it. Additionally, scientists have also discovered that COVID-19 infection might cause pathological changes in the musculoskeletal system. However, its mechanism is still not fully elucidated. This study aims to further explore the sharing pathogenesis of patients with both OA and COVID-19 infection and find candidate drugs. Gene expression profiles of OA (GSE51588) and COVID-19 (GSE147507) were obtained from the Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) for both OA and COVID-19 were identified and several hub genes were extracted from them. Then gene and pathway enrichment analysis of the DEGs were performed; protein–protein interaction (PPI) network, transcription factor (TF)-gene regulatory network, TF-miRNA regulatory network and gene-disease association network were constructed based on the DEGs and hub genes. Finally, we predicted several candidate molecular drugs related to hub genes using DSigDB database. The receiver operating characteristic curve (ROC) was applied to evaluate the accuracy of hub genes in the diagnosis of both OA and COVID-19. In total, 83 overlapping DEGs were identified and selected for subsequent analyses. CXCR4, EGR2, ENO1, FASN, GATA6, HIST1H3H, HIST1H4H, HIST1H4I, HIST1H4K, MTHFD2, PDK1, TUBA4A, TUBB1 and TUBB3 were screened out as hub genes, and some showed preferable values as diagnostic markers for both OA and COVID-19. Several candidate molecular drugs, which are related to the hug genes, were identified. These sharing pathways and hub genes may provide new ideas for further mechanistic studies and guide more individual-based effective treatments for OA patients with COVID-19 infection. [ABSTRACT FROM AUTHOR]

Betavoltaic batteries can meet long-term energy supply needs. However, due to the decay of the radioactive source, the output performance of battery will change over time, and the laws of this change require elucidation to aid the battery engineer. In this study, the influences of time on the apparent power density and β-energy spectrum of the 63 Ni source were clarified. Moreover, the structural parameters, including the p-region, n-region doping concentrations, and junction depth of GaN-based (hexagonal) p–n junction, were optimized, which are 2 × 1017 cm–3, 1014 cm–3, and 0.1 μm, respectively. In particular, the time-related performance trends were analyzed, and a formula for the maximum output power density deterioration was obtained, which can be used to evaluate the performance of 63 Ni GaN-based p–n junction betavoltaic battery within 200 years. The simulation results showed that the maximum output power density of battery after 50 years is 0.243 μW cm−2, which is only 60% of the initial value. In addition, the other output performances, namely, the short-circuit current density, open-circuit voltage, fill factor, and conversion efficiency are 0.096 μA cm−2, 2.67 V, 94.3%, and 20.5%, respectively. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to introduce a novel all-inside arthroscopic anterior talofibular ligament (ATFL) repair for chronic ankle instability (CAI) with a knotless suture anchor technique—Hugging Repair, evaluate clinical outcomes, and analyze the associated risk factors. Methods: A total of 84 patients (42 males and 42 females, mean age: 36.1 ± 11.2 years, range: 19–68 years) who underwent Hugging Repair from January 1, 2016 to December 31, 2018, were enrolled in this retrospective study. The American Orthopedic Foot and Ankle Society (AOFAS) score, Karlsson Ankle Functional Score (KAFS), Foot and Ankle Outcome Score (FAOS), Tegner score, and Numerical Rating Scale (NRS) were evaluated pre-operatively and at final follow-up. The potential risk factors such as age, body mass index (BMI), sex, post-injury duration (time from injury to surgery), follow-up time, number of anchors, concomitant injuries [e.g., osteochondral defects (OCD), sinus tarsi syndrome (STS), anterior ankle impingement (AAI)], first-time treatment, and number of ankle sprains were also analyzed through multiple regression analysis. Results: There were 68 (81%) patients followed up for a mean time of 42 (range: 35–50) months. The median AOFAS score increased from 65 (35–72) to 90 (77–100), KAFS increased from 64 (38–71) to 90 (62–100), FAOS increased from 68 (50–70) to 97 (68–100), Tegner score increased from 1 (1–3) to 4 (2–7), and NRS increased from 3.5 (2–5) to 1 (0–3). No correlation was found between the functional scores and risk factors mentioned above. In the multivariate model, age was significantly negatively associated with KAFS, FAOS, and Tegner activity scale (P = 0.013; P = 0.002; P = 0.000); female was significantly associated with poorer Tegner activity scale (P = 0.004); and the presence of concomitant injuries was significantly negatively associated with AOFAS score (P = 0.033). Conclusion: The novel all-inside arthroscopic ATFL repair for CAI with a knotless suture anchor technique—Hugging Repair is a safe and suitable technique that achieves satisfactory clinical outcomes and provides an effective option for the treatment of CAI. Risk factors for patients who underwent all-inside ATFL repair were older age, female sex, and concomitant injuries. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA), the most common type of arthritis, is a complex biological response caused by cartilage wear and synovial inflammation that links biomechanics and inflammation. The progression of OA correlates with a rise in the number of senescent cells in multiple joint tissues. However, the mechanisms by which senescent cells and their involvement with immune infiltration promote OA progression are not fully understood. The gene expression profiles and clinical information of OA and healthy control synovial tissue samples were retrieved from the Gene Expression Omnibus database, and then differential analysis of senescence regulators between OA and normal samples was performed. The random forest (RF) was used to screen candidate senescence regulators to predict the occurrence of OA. The reverse transcription quantitative real-time PCR experiments at tissue's level was performed to confirm these biomarkers. Moreover, two distinct senescence patterns were identified and systematic correlation between these senescence patterns and immune cell infiltration was analyzed. The senescence score and senescence gene clusters were constructed to quantify senescence patterns together with immune infiltration of individual OA patient. 73 senescence differentially expressed genes were identified between OA patients and normal controls. The RF method was utilized to build an OA risk model based on two senescence related genes: BCL6 and VEGFA. Next, two distinct aging patterns were determined in OA synovial samples. Most patients from senescence cluster A were further classified into gene cluster B and high senescence score group correlated with a non-inflamed phenotype, whereas senescence cluster B were classified into gene cluster A and low senescence score group correlated with an inflamed phenotype. Our study revealed that senescence played an important role in in OA synovial inflammation. Evaluating the senescence patterns of individuals with OA will contribute to enhancing our cognition of immune infiltration characterization, providing novel diagnostic and prognostic biomarkers, and guiding more effective immunotherapy strategies. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is the one of most common joint diseases worldwide. Cuproptosis, which had been discovered lately, is a novel form of cell death induced by copper. Our purpose is to study the relationship between cuproptosis-related genes (CRGs) and inflammatory microenvironments in patients with OA and identify characteristic cuproptosis-related biomarkers. First, the combinatory analysis of OA transcriptome data from five datasets identified differentially expressed CRGs associated with OA. Then, we applied single-sample gene set enrichment analysis (ssGSEA) to evaluate immune-cell infiltration and immune-function levels in OA patients and normal controls, respectively. Hub CRGs for OA were mined based on the random forest (RF) model, and a nomogram prediction model was constructed based on them. In total, four differentially expressed CRGs were identified through bioinformatics analysis and confirmed by RT-qPCR. FDX1 and LIPT1 were expressed at a high level in OA, while DBT and DLST were expressed higher in the normal group. In total, 10 CRGs were found to be significantly correlated with immune landscape. Four hub CRGs were subsequently obtained by the RF analysis as potential biomarkers for OA. We constructed an OA predictive model based on these four CRGs (DBT, DLST, FDX1, and LIPT1). [ABSTRACT FROM AUTHOR]

The fourth paradigm of science has achieved great success in material discovery and it highlights the sharing and interoperability of data. However, most material data are scattered among various research institutions, and a big data transmission will consume significant bandwidth and tremendous time. At the meanwhile, some data owners prefer to protect the data and keep their initiative in the cooperation. This dilemma gradually leads to the "data island" problem, especially in material science. To attack the problem and make full use of the material data, we propose a new strategy of neural network training based on multi-source databases. In the whole training process, only model parameters are exchanged and no any external access or connection to the local databases. We demonstrate its validity by training a model characterizing material structure and its corresponding formation energy, based on two and four local databases, respectively. The results show that the obtained model accuracy trained by this method is almost the same to that obtained from a single database combining all the local ones. Moreover, different communication frequencies between the client and server are also studied to improve the model training efficiency, and an optimal frequency is recommended. [ABSTRACT FROM AUTHOR]

Nuclear battery is a promising long-life power source. Selecting semiconductors with high limit efficiency and appropriate device structures effectively improves their output performance. In this work, a GaN-based (hexagonal) betavoltaic battery with N i 63 source was simulated by Monte Carlo codes and COMSOL Multiphysics, and the energy converters, including p–n junction and p–i–n junction, were compared and optimized. We analyzed the effects of thickness and doping concentration of each region on the battery performances. The p-region and n-region thickness and doping concentration of the p–n junction-based battery are 0.5, 9.5 µm, 1017 cm−3, and Nd = 1016 cm−3, which can achieve 3.77% conversion efficiency, and the short-circuit current density, open-circuit voltage, and maximum output power density are 0.074 µA/cm2, 2.01 V, and 0.125 µW/cm2, respectively. For the p–i–n junction-based battery, when the thickness and the doping concentration of p-region, i-region, and n-region are 0.5, 3, 6.5 µm, 1017, 1014, and 1016 cm−3, respectively, the conversion efficiency, short-circuit current density, open-circuit voltage, and maximum output power density are 5.03%, 0.099 µA/cm2, 2.0 V, and 0.167 µW/cm2, respectively. By comparing the output parameters of the two types of batteries, the results indicate that the p–i–n junction has a wider depletion region and better output performance compared with the p–n junction. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has driven public health intervention strategies, including keeping social distance, wearing masks in crowded places, and having good health habits, to prevent the transmission of the novel coronavirus (SARS-CoV-2). However, it is unknown whether the use of these intervention strategies influences morbidity in other human infectious diseases, such as tuberculosis.Methods: In this study, three prediction models were constructed to compare variations in PTB incidences after January 2020 without or with intervention includes strict and regular interventions, when the COVID-19 outbreak began in China. The non-interventional model was developed with an autoregressive integrated moving average (ARIMA) model that was trained with the monthly incidence of PTB in China from January 2005 to December 2019. The interventional model was established using an ARIMA model with a continuing intervention function that was trained with the monthly PTB incidence in China from January 2020 to December 2020.Results: Starting with the assumption that no COVID-19 outbreak had occurred in China, PTB incidence was predicted, and then the actual incidence was compared with the predicted incidence. A remarkable overall decline in PTB incidence from January 2020 to December 2020 was observed, which was likely due to the potential influence of intervention policies for COVID-19. If the same intervention strategy is applied for the next 2 years, the monthly PTB incidence would reduce on average by about 1.03 per 100,000 people each month compared with the incidence predicted by the non-interventional model. The annual incidence estimated 59.15 under regular intervention per 100,000 in 2021, and the value would decline to 50.65 with strict interventions.Conclusions: Our models quantified the potential knock-on effect on PTB incidence of the intervention strategy used to control the transmission of COVID-19 in China. Combined with the feasibility of the strategies, these results suggested that continuous regular interventions would play important roles in the future prevention and control of PTB. [ABSTRACT FROM AUTHOR]

Objective: Individuals with chronic ankle instability experience recurring sprains and impaired proprioception, and the effect of external support in the proprioception of injured ankles is still inconsistent in existing studies. Therefore, this study aimed to investigate whether external support could enhance the proprioception of injured ankles in patients with chronic ankle instability. Design: This was a systematic review and meta-analysis. Results: Eight studies from PubMed, Embase, Cochrane Library, Web of Science, SPORTDiscus, Scopus, and CINAHL were finally included after applying the exclusion criteria. Meta-analyses revealed a significantly higher joint position sense on inversion with a weighted mean difference of 1.25 degrees and plantar flexion (weighted mean difference, 1.74 degrees) and lower kinesthesia in eversion (weighted mean difference = −0.70 degrees) with the application of external support in the injured ankles of patients with chronic ankle instability. Conclusion: Applying external support has statistically significant negative effects on kinesthesia and a positive effect on the active joint position sense in the injured ankles of patients with chronic ankle instability. However, this study did not support the restoration of proprioception deficits as a mechanism of external support in preventing reinjuries, owing to its potentially negative effect on kinesthesia, clinically small changes in proprioception, and poor methodologic quality of existing studies. [ABSTRACT FROM AUTHOR]

• We propose a cascading Transformer to address the computational bottleneck of the Transformer in handling complex tasks with blurry images. • We propose the Deep Separable Attention (DSA) mechanism to replace the vanilla self-attention mechanism, which can capture specific areas of blur in the image and calculate attention scores across feature dimensions. • We propose the Double-Flow Gate (DFG) to enhance the model's nonlinearity, control the output of specific blurry information, and improve deblurring performance. In dynamic scenes, motion blur can often occur, which is non-uniform and can be difficult to remove. Recently, the Transformer has shown excellent performance in various image-related tasks such as classification, recognition, and segmentation. Using a Transformer-based backbone network has also shown potential advantages in image deblurring. However, the computational complexity of Transformers increases quadratically with spatial resolution, making it difficult to apply to high-resolution images. To address the above issue, we propose a cascade Transformer (Casformer) that consists of two key modules: Deep Separable Attention (DSA) and Double-Flow Gate (DFG). Our approach effectively reduces computational complexity while suppressing blurry information. Additionally, we discovered an inconsistency between training and testing images during the image restoration process. We addressed this issue by experimentally verifying an inference aggregation method (IAM) that independently predicts patches during inference to address the problem of imbalanced information distribution. Experimental results demonstrate that our design performs well on GoPro and other datasets, e.g. 29.20 dB PSNR on RealBlur-J, exceeding the previous state-of-the-art (SOTA) 0.14 dB. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate the transcriptomic response to virus simulant (polyinosinic-polycytidylic acid, poly I:C), and to determine if the aqueous leech (Whitmania pigra) extract has the function of interfering with the transcriptomic response in primary culture cells from the embryo of Chinese soft-shelled turtle (Pelodiscus sinensis). The results showed that the transfection with poly I:C and treatment with leech extract reduced cell viability. A total of 468 differentially expressed genes (DEGs) was induced by poly I:C, including 272 upregulated genes and 196 downregulated genes. A total of 965 DEGs were induced by leech extract, including 209 upregulated genes and 756 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that 43 GO terms and 12 pathways were significantly enriched by poly I:C, including immune response and immune system processes, nucleotide-binding and oligomerization domain (NOD)–like receptor signaling pathway, RIG-I-like receptor signaling pathway, and Toll-like receptor signaling pathway. Leech extract significantly enriched 50 GO terms and 56 pathways, including translation, peptide biosynthetic process, ribosome, and oxidative phosphorylation pathway. In addition, qRT-PCR of seven DEGs from poly I:C transfection and four DEGs from leech extract treatment verified the reliability of transcriptome analysis. Immunofluorescence analysis indicated poly I:C transfection enhanced the generation of DDX58 protein in cytoplasm and the nuclear translocation of NF-κB(p65), while the leech extract inhibited above action to DDX58 and NF-κB(p65). In conclusion, this study improved the understanding of immune system response to the virus in primary culture cells of Chinese soft-shelled turtle, and suggested that aqueous leech extract from W. pigra may inhibit immune response genes induced by poly I:C. [ABSTRACT FROM AUTHOR]

In this paper, we have compared and analyzed the effect of laparoscopic and open surgical treatments in children with congenital megacolon. To address this, a total of 64 children with congenital megacolon who underwent surgery in the hospital, particularly from April 2014 to December 2020, were selected as the research objects. They were divided into control and observation groups by the random number table method, with 32 cases in each group. The control and observation groups were treated with open surgical and laparoscopic treatments, respectively. The treatment effects of the two groups were compared. The enema time, operation time, blood loss, anal defecation time, and duration of postoperative hospital stay of the observation group were lower than those of the control group. The comparison between the two groups was statistically significant (P < 0.05). There was no significant difference in CRP and WBC between the two groups before surgery (P > 0.05). The CRP level and WBC of the two groups were both increased after operation, the CRP level of the observation group was lower than that of the control group, the difference was statistically significant (P < 0.05), the WBC of the two groups was not statistically significant (P > 0.05). The rate of excellent and good defecation in the observation group on the 7th day after surgery was higher than that in the control group, and the difference was statistically significant (P < 0.05). There was no significant difference in Krickenbeck scores between the two groups before surgery (P > 0.05); 6 months after the surgery, the score of Krickenbeck in both groups increased, and that of the observation group was higher than that of the control group, indicating a difference in the overall score (P < 0.05). The total complication rate within 7 days after surgery in the observation group was lower than that in the control group, and the difference was not statistically significant (P > 0.05). Laparoscopic treatment of congenital megacolon could improve surgical indicators and reduce stress response in children, improve defecation and anal function, reduce the risk of complications, and promote recovery. [ABSTRACT FROM AUTHOR]

Nervonic acid is a natural component of breast milk and is frequently used as a food additive due to its excellent neuroprotective effects. Although it has been reported that nervonic acid may play a role in the recovery of human cognitive impairment, its specific mechanism of action is still unclear. In this study, the results of serum biochemical indexes showed that nervonic acid improved inflammation and reduced amyloid β peptide (Aβ) deposition and tau protein phosphorylation in Alzheimer's disease (AD) rats. Subsequently, we further used a metabolomics approach to investigate the potential mechanism of action of nervonic acid in the treatment of AD. The results of serum and urine metabolomics study showed that the intervention of nervonic acid significantly reversed the metabolic profile disorder in AD rats. A total of 52 metabolites were identified. They mainly involved linoleic acid metabolism, alpha-linolenic acid metabolism, phenylalanine metabolism and arachidonic acid metabolism, and all these metabolic pathways were associated with the emergence of inflammation in vivo. It suggests that the therapeutic effect of nervonic acid on AD is likely to be produced by ameliorating inflammation. The results obtained in this study provide new insights into the mechanism of nervonic acid treatment of AD and lay a foundation for the clinical application of nervonic acid in the treatment of Alzheimer's disease. [Display omitted] • Nervonic acid has a therapeutic effect on Alzheimer's disease. • Nervonic acid alleviates inflammation in Alzheimer's rats. • Nervonic acid modulates serum and urine metabolism in Alzheimer's disease rats. [ABSTRACT FROM AUTHOR]

Licorice is a medicinal herb with a 2000-year history of applications in traditional Chinese medicine. Isoliquiritigenin (ISL) is a bioactive chalcone compound isolated from licorice. It has attracted increasing attention in recent years due to its excellent anti-inflammatory activity. This study is to provide a comprehensive summary of the anti-inflammatory activity of ISL and the underlying molecular mechanisms, and discuss new insights for its potential clinical applications as an anti-inflammation agent. We examined literatures published in the past twenty years from PubMed, Research Gate, Web of Science, Google Scholar, and SciFinder, with single or combined key words of "isoliquiritigenin", "inflammation", and "anti-inflammatory". ISL elicits its anti-inflammatory activity by mediating various cellular processes. It inhibits the upstream of the nuclear factor kappa B (NF- κ B) pathway and activates the nuclear factor erythroid related factor 2 (Nrf2) pathway. In addition, it suppresses the NOD-like receptor protein 3 (NLRP3) pathway and restrains the mitogen-activated protein kinase (MAPK) pathway. Current studies indicate a great therapeutical potential of ISL as a drug candidate for treatment of inflammation-associated diseases. However, the pharmacokinetics, biosafety, and bioavailability of ISL remain to be further investigated. [Display omitted] • The anti-inflammatory activity of isoliquiritigenin and the underlying mechanism were summarized. • Multiple signaling pathways mediated by isoliquiritigenin were illustrated. • Bioactivities of isoliquiritigenin in inflammation-associated diseases were mentioned. [ABSTRACT FROM AUTHOR]

Tissue immune cells have long been recognized as important regulators for the maintenance of balance in the body system. Quantification of the abundance of different immune cells will provide enhanced understanding of the correlation between immune cells and normal or abnormal situations. Currently, computational methods to predict tissue immune cell compositions from bulk transcriptomes have been largely developed. Therefore, summarizing the advantages and disadvantages is appropriate. In addition, an examination of the challenges and possible solutions for these computational models will assist the development of this field. The common hypothesis of these models is that the expression of signature genes for immune cell types might represent the proportion of immune cells that contribute to the tissue transcriptome. In general, we grouped all reported tools into three groups, including reference-free, reference-based scoring and reference-based deconvolution methods. In this review, a summary of all the currently reported computational immune cell quantification tools and their applications, limitations, and perspectives are presented. Furthermore, some critical problems are found that have limited the performance and application of these models, including inadequate immune cell type, the collinearity problem, the impact of the tissue environment on the immune cell expression level, and the deficiency of standard datasets for model validation. To address these issues, tissue specific training datasets that include all known immune cells, a hierarchical computational framework, and benchmark datasets including both tissue expression profiles and the abundances of all the immune cells are proposed to further promote the development of this field. [ABSTRACT FROM AUTHOR]

A novel coronavirus named SARS-CoV-2 is causing the severe acute pneumonia (COVID-19) and rapid spread nationally and internationally, resulting in a major global health emergency. Chinese governments and scientists have implemented a series of rigorous measures and scientific research to prevent and control the SARS-CoV-2 infection. However, there is still no specific antiviral drug or vaccine against SARS-CoV-2. It has been proven that traditional Chinese medicine (TCM) exerts an important role in the prevention and treatment of the COVID-19 caused by SARS-CoV-2 during the outbreak. Although the therapeutic effects of these TCM formulas are attractive, the molecular mechanism of action has not been fully elucidated. An emerging strategy of systems pharmacology has been proposed to be a promising method to interpret drug action in complex biological systems and quickly screen out the bioactive compounds from TCM to treat treatment of COVID-19 caused by SARS-CoV-2. Therefore, in this study, the epidemiology, TCM therapy, and the systems pharmacology-based method for TCM are reviewed for COVID-19 to provide a perspective for the prevention and treatment of SARS-CoV-2 infection. Further efforts should be made to reduce disease burden and improve the ability to design antiviral drugs and vaccines, which will benefit the health care system, economic development and even social stability. [ABSTRACT FROM AUTHOR]

Combining Dirichlet Mixture Models (DMM) with deep learning models for road extraction is an attractive study topic. Benefiting from DMM, the manually labeling work is alleviated. However, DMM suffers from high computational complexity due to pixel by pixel computations. Also, traditional constant parameter settings of DMM may not be suitable for different target images. To address the above problems, we propose an improved DMM which embeds superpixel strategy and sparse representation into DMM. In our road extraction framework, we first use improved DMM to filter out most backgrounds. Then, a trained deep CNN model is used for further precise road area recognition. To further promote the processing speed, we also apply the superpixel scanning strategy for CNN models. We tested our method on a Shaoshan dataset and proved that our method not only can achieve better results than other compared state-of-the-art image segmentation methods, but the processing speed and accuracy of DMM are also improved. [ABSTRACT FROM AUTHOR]

Submonolayer-supported catalysts exhibited excellent activity for ammonia selective catalytic reduction (NH3–SCR) and have attracted great interest. Here, a catalyst based on three-dimensional ordered mesoporous (3DOM) CeO2 supported by a submonolayer of vanadium and manganese bimetal oxides (3DOM VOx–MnOx/CeO2) was successfully synthesized. The optimal catalytic temperature was found to shift to lower temperature with increasing VOx–MnOx loading amount under the submonolayer coverage. The 5 wt% 3DOM VOx–MnOx/CeO2 catalyst with submonolayer coverage exhibited the best catalytic performance, achieving a NO conversion of more than 95% at 250 °C. It was found that three-dimensional CeO2 endowed the material with a high specific surface and highly ordered mesoporous channels, which could highly disperse VOx–MnOx below one monolayer coverage. Based on NH3–TPD, H2–TPR, Raman and XPS characterization, an increase in VOx–MnOx content under the submonolayer coverage resulted in an enhancement of the surface acidity, redox capacity, number of oxygen vacancy defects and charge transfer. In particular, the 5 wt% 3DOM VOx–MnOx/CeO2 catalyst showed the highest number of acidic sites that for NH3 activation and the fastest electron transfer required for oxygen to be regenerated. Therefore, the 5 wt% 3DOM VOx–MnOx/CeO2 catalyst with submonolayer coverage generated a significantly enhanced catalytic performance, which made it desirable for NO removal applications. [ABSTRACT FROM AUTHOR]

Herein, we develop an accelerated DNA tetrahedron based molecular beacon for efficient detection and imaging of miRNA in living cells. Our study provides an improved strategy for fluorescence amplified RNA imaging in early clinical diagnosis. [ABSTRACT FROM AUTHOR]

Citri Reticulatae Pericarpium (CRP), also known as Chenpi in Chinese, is the dry mature peel of Citrus reticulata Blanco or its cultivated varieties. CRP as the health-care food and dietary supplement has been widely used in various diseases. However, the potential pharmacological mechanisms of CRP to predict and treat various diseases have not yet been fully elucidated. A systems pharmacology-based approach is developed by integrating absorption, distribution, metabolism, and excretion screening, multiple target fishing, network pharmacology, as well as pathway analysis to comprehensively dissect the potential mechanism of CRP for therapy of various diseases. The results showed that 39 bioactive components and 121 potential protein targets were identified from CRP. The 121 targets are closely related to various diseases of the cardiovascular system, respiratory system, gastrointestinal system, etc. These targets are further mapped to compound-target, target-disease, and target-pathway networks to clarify the therapeutic mechanism of CRP at the system level. The current study sheds light on a promising way for promoting the discovery of new botanical drugs. [ABSTRACT FROM AUTHOR]

Due to the wide distribution of crosswalks over the road nets, the finding of impaired crosswalk marks is usually long-time delayed, which may put crosswalk pedestrians into danger. To reduce the repairing cost and improve the finding speed of damaged crosswalks, this paper uses remote sensing images to automatically detect crosswalks. The detection results can be used for further examination of crosswalks. However, the detection of crosswalks from remote sensing images suffers from serious interferes of many other kinds of ground targets. Besides, there are rare openly available datasets for the research of crosswalk detection from remote sensing images. To conquer the above problems, this study provides an openly available dataset for the research of crosswalk detection. To improve the robustness, we propose a crosswalk detection framework which uses a U-Net based road area guidance. First, we use CNN models to detect crosswalks. Then, we use U-Net to extract potential road areas. Third, we propose a mixture classification strategy which combines the detection confidence and potential road area guidance for final crosswalk detection. Experimental results show that the road area guidance for crosswalks' detection is effective and can improve the detection performance. [ABSTRACT FROM AUTHOR]

• A comprehensive topology integration scheme for Zero-Shot Learning (ZSL). • A strong topology-based ZSL benchmark with state-of-the-art performance. • Exploration of the topological validity within/across different modalities. Zero-shot learning (ZSL) aims to discriminate object categories through the identification of their attributes and has received much attention for its capability to predict unseen categories without collecting training data. Recently, excellent works have been devoted to optimizing the model inference by mining the topology among categories/attributes, which proves that the topology learning is beneficial and important for ZSL. However, existing works focus almost exclusively on the construction of semantic topological knowledge with textual descriptions, which, though effective, still suffer from two deficiencies: first, the semantic gap between modalities makes it difficult for the category attributes to accurately describe the corresponding visual characters, resulting in the topology constructed in the semantic modality being distorted in the visual modality; second, it is difficult for one to enumerate all the attributes hidden in images, resulting in an incomplete topology mined only from the defined attributes. Therefore, we propose a C ross-Modality T opology P ropagation M atcher (CTPM) to construct a more complete topology system by collaborative mining of topological knowledge in both the visual and semantic modalities. We stand at the dataset level to construct sample-based visual topological knowledge based on the global image features to preserve the integrity of visual information. Meanwhile, we exploit the matching relationship between visual and semantic modalities to make topological knowledge propagate effectively across modalities, and fully enjoy the benefits of multi-modality topological knowledge in category/attribute reasoning. We validate the effectiveness of our CTPM through extensive experiments and achieve state-of-the-art performance on four ZSL datasets. [ABSTRACT FROM AUTHOR]

• STC1, VEGFA, KDELR3, DDIT4 and PGAM1 were selected as candidate genes to predict the probability of OA. • Two glycolysis patterns in OA were identified and glycolysis scoring system was constructed to show distinct individual immune characteristics. • Glycolysis cluster A and higher glycolysis score were revealed to be related to an inflamed phenotype. Osteoarthritis (OA) is a degenerative arthritis with high levels of clinical heterogeneity. Aberrant metabolism such as shifting from oxidative phosphorylation to glycolysis is a response to changes in the inflammatory microenvironment of OA. Therefore, there is a pressing need to identify novel glycolysis regulators during OA progression. We systematically studied glycolysis patterns mediated by 141 glycolysis regulators in 74 human synovial samples and discussed the characteristics of the immune microenvironment modified by glycolysis. The random forest (RF) method was applied to screen candidate hub glycolysis regulators in OA. RT-qPCR was performed to validate these key regulators. Then distinct glycolysis patterns were identified, and systematic correlation between these glycolysis patterns and immune cell infiltration was analyzed. The glycolysis score was constructed to quantify glycolysis patterns together with immune infiltration of individual OA patient. 56 glycolysis-related differentially expressed genes (DEGs) were identified between OA and non-OA samples. STC1, VEGFA, KDELR3, DDIT4 and PGAM1 were selected as candidate genes to predict the probability of OA. Two glycolysis patterns in OA were identified. Glycolysis cluster A with higher glycolysis score was related to an inflamed phenotype. Taken together, our results established a glycolysis-based genetic signature for OA, guided in-depth studies on the metabolic mechanism of OA, and facilitated to explore new clinical treatment strategies. [ABSTRACT FROM AUTHOR]

• An OA predictive model based on 4 m7G hub genes (SNUPN, METTL1, EIF4E2 and CYFIP1) was constructed. • Two m7G methylation patterns in OA were found to show distinct biological characteristics, and an m7G score were generated. • M7G cluster A and a higher m7G score were found to be related to an inflamed phenotype. Accumulating evidence has shown that aberrant N7-methylguanosine (m7G) RNA methylation played an important role in the occurrence and development of cancer. However, knowledge of m7G modifications in inflammatory diseases is limited. Osteoarthritis (OA) is the most common arthritic disease with poor prognosis. Our research aimed to identify m7G-related hub biomarkers and investigate m7G regulator expression pattern in immune landscape of OA patients. Gene expression profiles and their clinical information were obtained from the Gene Expression Omnibus (GEO) database, and differential analysis of 14 m7G-related regulators between elective OA and normal samples was performed. M7G-related hub genes for OA were mined based on single-sample gene set enrichment analysis (ssGSEA) and the random forest (RF) algorithm, and qRT-PCR was performed to confirm the abnormal expression of hub genes. Enrichment, protein–protein interaction (PPI), transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory analysis based on m7G hub genes were performed. Then we predicted several candidate drugs related to m7G hub genes using DSigDB database. Moreover, we comprehensively evaluated m7G methylation patterns in OA samples and systematically correlated these modification patterns with the characteristics of immune cell infiltration. The m7G score was generated to quantify m7G methylation patterns for individual OA patients by the application of principal component analysis (PCA) algorithm. We constructed an OA predictive model based on 4 m7G hub genes (SNUPN, METTL1, EIF4E2 and CYFIP1). Two m7G methylation patterns in OA were discovered to show distinct biological characteristics, and an m7G score were generated. M7G cluster A and a higher m7G score were found to be related to an inflamed phenotype. Our study was the first to comprehensively investigate the m7G methylation dysregulations in immune landscape during the progression of OA. These 4 m7G gene-related signatures can be used as novel OA biomarkers to predict the occurrence of OA. Evaluating the m7G methylation patterns of OA individuals will contribute to enhancing our cognition of immune infiltration characterization and guiding more effective immunotherapy strategies. [ABSTRACT FROM AUTHOR]

Maotai‐flavor liquor, one of the world's most famous natural fermentation food products plays a significant role in traditional Chinese culture and people's daily lives for a long time due to its good taste. However, the pharmacological activities of flavor compounds in Maotai liquor have not been fully elucidated. In answering this question, a system‐level analysis was developed by combining in silico ADME evaluation, multi‐target prediction, GO enrichment analysis, network pharmacology technology, pathway analysis, as well as experimental verification to elucidate the pharmacological effects of flavor compounds in Maotai liquor. Finally, 55 active compounds and 80 targets were identified to interpret the pharmacological effect of the flavor compounds. Moreover, the key active compounds were verified by in vitro experiments to validate the reliability of our approaches. Our study provides a novel integrated strategy to comprehensively understand the pharmacological activities of complex components in Maotai liquor. Practical applications: We proposed an integrative strategy by systems pharmacology to investigate the potential active compounds and their related targets, as well as to understand the potential pharmacological mechanism of flavor compounds in Maotai liquor. The present work will not only shed light on the mechanism of active compounds in Maotai liquor at the system level, but also provide a novel approach for discovery of the active compounds that may benefit human health. [ABSTRACT FROM AUTHOR]

Resting‐state fMRI has shown the ability to predict task activation on an individual basis by using a general linear model (GLM) to map resting‐state network features to activation z‐scores. The question remains whether the relatively simplistic GLM is the best approach to accomplish this prediction. In this study, several regression‐based machine‐learning approaches were compared, including GLMs, feed‐forward neural networks, and random forest bootstrap aggregation (bagging). Resting‐state and task data from 350 Human Connectome Project subjects were analyzed. First, the effect of the number of training subjects on the prediction accuracy was evaluated. In addition, the prediction accuracy and Dice coefficient were compared across models. Prediction accuracy increased with the training number up to 200 subjects; however, an elbow in the prediction curve occurred around 30–40 training subjects. All models performed well with correlation matrices, which displayed correlation between actual and predicted task activation for all subjects, exhibiting a strong diagonal trend for all tasks. Overall, the neural network and random forest bagging techniques outperformed the GLM. These approaches, however, require additional computing power and processing time. These results show that, while the GLM performs well, resting‐state fMRI prediction of task activation could benefit from more complex machine learning approaches. [ABSTRACT FROM AUTHOR]

Motivation Many methods have been developed to estimate immune cell composition from tissue transcriptomes. One common characteristic of these methods is that they are trained using a set of general immune cell transcriptomes that ignores tissue specificities. However, as immune cells are localized in different tissues, they may have distinct expression profiles. Hence, calculations that use general signature matrices may hinder the deconvolution accuracy. Results This study used single cell RNA-sequencing (scRNA-Seq) data from different mouse tissues instead of general signature expression values to generate tissue-specific signature gene matrices that are used as the input of the deconvolution model. First, the transcriptome of immune cells in each tissue was extracted from scRNA-Seq data and used to construct the entire expression matrix of tissue immune cells. Then, after comparing different gene selection strategies, the expressions of 162 seq-ImmuCC derived signature genes in tissue immune cell scRNA-Seq data were regarded as the tissue specific signature matrices. Finally, a modest improvement in performance was observed in multiple tissues that refer to a traditional general signature matrix in the deconvolution model. With the fast accumulation of scRNA-Seq data, the introduction of these data into an estimation of immune cell compositions for different tissues will open a new window for avoiding tissue bias for immune cell expression. Availability and implementation The signature matrices were available at https://github.com/wuaipinglab/ImmuCC/tree/master/tissue%5fimmucc/SignatureMatrix). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract In this work, an innovative aptasensor based on electrochemiluminescence resonance energy transfer (ECL-RET) from CdTe quantum dots (QDs) to a cyanine dye (Cy5) fluorophore for the determination of Ochratoxin A (OTA) was fabricated. A strong cathodic ECL emission was obtained by the CdTe QDs modified glassy carbon electrode (GCE). After the immobilization with the capture DNA (cDNA) and the sequential hybridization with the probe DNA-modified Cy5 (pDNA, the aptamer of OTA), the ECL signal enhanced obviously through the ECL-RET. Meanwhile, the spectrum- and distance-related ECL enhancement effect was investigated. When the target OTA was in the presence, the pDNA-Cy5 molecules were released from the electrode surface owing to the specific interaction between OTA and aptamer, resulting in an evident decrease of ECL signal. Under optimal conditions, the developed aptasensor displayed the linear response toward OTA in the wide range of 0.0005–50 ng/mL with a low detection limit of 0.17 pg/mL. With the excellent selectivity, stability and repeatability, the strategy provided an efficient and universal method for the sensitive detection of target in practical application. Graphical abstract fx1 Highlights • A simple and convenient aptasensor for OTA was prepared. • The ECL-RET aptasensor based on CdTe QDs and Cy5 has high sensitivity. • This sensor has a good specificity since the great affinity of aptamer to target. • The aptasensor could be applied to detect OTA in corn. [ABSTRACT FROM AUTHOR]

Maotai liquor is one of the most famous traditional Chinese distilled liquor enjoyed by many people all over the world for its unique production method, impressive liquor quality, and soy sauce‐like and roasted aroma style. It is known that aroma characteristics of liquor mainly depend on the aroma compounds. Alcohols as one of the most important categories of aroma components have been determined in Maotai liquor. However, the systemic analysis of alcoholic compounds in liquor is limited, especially the active alcoholic components and their pharmacological effects. Therefore, in this study, a systemic analysis method was proposed by combining in silico absorption, distribution, metabolism, and excretion (ADME) evaluation, target fishing, network pharmacology technology, pathway analysis, and experimental verification to interpret the pharmacological mechanism of alcoholic compounds in Maotai liquor. Finally, 15 compounds with favorable pharmacokinetic profiles were screened through in silico ADME models. Thirty‐eight related targets of these active compounds were identified by target prediction method. The network pharmacology and pathway analysis were developed to clarify the pharmacological effect of alcoholic compounds in Maotai liquor at the system and pathway level. Moreover, the key active compounds were validated by in vitro experiments that verified the effectiveness of our methods. Our study provides a novel approach to systematically analyze the pharmacological effect of alcoholic compounds in Maotai liquor, which would be beneficial for promoting the in‐depth study of various liquors. Practical Application: Maotai liquor is popularly enjoyed in the world for a very long time. However, the systemic analysis of compounds in liquor is limited. Our systematic analysis approach was developed to explore the bioactive ingredients and their related target proteins as well as the pharmacological effects of Maotai liquor. This will provide a new method to understand the pharmacological mechanisms of compounds in various liquors at the systems level, so as to promote the development of liquors and to increase the public awareness of science about alcohol consumption. [ABSTRACT FROM AUTHOR]

The overall study design is illustrated. [Display omitted] • A four-gene signature (ZBTB16, TNFSF11, SCRG1 and KDELR3) was identified to be involved OA progression based on WGCNA, LASSO, SVM-RFE and RF algorithms combined with bioinformatic analyses. • The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell, natural killer cell and CD8 + T cell. Although osteoarthritis (OA) is one of the most prevalent joint disorders, effective biomarkers to diagnose OA are still unavailable. This study aimed to acquire some key synovial biomarkers (hub genes) and analyze their correlation with immune infiltration in OA. Gene expression profiles and clinical characteristics of OA and healthy synovial samples were retrieved from the Gene Expression Omnibus (GEO) database. Hub genes for OA were mined based on a combination of weighted gene co-expression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms. A diagnostic nomogram model for OA prediction was developed based on the hub genes. Receiver operating characteristic curves (ROC) were performed to confirm the abnormal expression of hub genes in the experimemtal and validation datasets. qRT-PCR using patients' samples were conducted as well. In addition, the infiltration level of 28 immune cells in the expression profile and their relationship with hub genes were analyzed using single-sample GSEA (ssGSEA). 4 hub genes (ZBTB16, TNFSF11, SCRG1 and KDELR3) were obtained by WGCNA, lasso, SVM-RFE, RF algorithms as potential biomarkers for OA. The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell and natural killer cell. A machine learning model to diagnose OA based on ZBTB16, TNFSF11, SCRG1 and KDELR3 using synovial tissue was constructed, providing theoretical foundation and guideline for diagnostic and treatment targets in OA. [ABSTRACT FROM AUTHOR]

• A new method to convert waste shield slurry into high-strength material was proposed. • Better pumpability and dewatering of slurry pre-treated by GBFS-FSB were obtained. • Multi-stage pressure filtration can improve the strength of the cakes. • The mechanisms of dewatering and strength growth were discussed. • Dense structure and low water content limited the strength improvement. A huge amount of waste slurry generated in the shield tunnelling construction process needs to be utilized or disposed properly. A geopolymeric flocculation-filtration-solidification method (G-FFS) was proposed in this paper. The properties and the dewatering efficiency of the waste slurry conditioned by the cementitious agents and the geopolymeric agents were investigated through filtration under multi-stage pressures. The strength of the filter cake and its microstructures were then investigated. Subsequently, the mechanisms of dewatering and solidification of the waste slurry and the advantages of the G-FFS method in terms of environmental friendliness were discussed. The results show that, with the G-FFS method, the waste slurry with a high water content could be dewatered rapidly and transformed into high-strength building materials (2.47–10.31 MPa). The conditioned slurry also demonstrated a low kinematic viscosity and a low capillary suction time, indicating a better pumpability and dewatering ability. The addition of the sodium dodecyl sulfate (SDS) could improve the dewatering performance further. The hydration of the geopolymeric agent and the high-pressure filtration enhanced the strength of the cake. The densified micro-structure of the filter cake and limited water content led to the difficulty of hydration and, thus, limited the increase of the strength. Compared to the traditional method of utilizing the waste slurry through a cementitious agent, the G-FFS method demonstrates its advantages in efficiency, cost-effectiveness, and environmental friendliness. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • MicroRNAs (miRNAs) play a critical role in regulating the extracellular signal regulated kinase (ERK) pathway. • The mechanisms of miRNA-mediated regulations on signal pathway require further investigation systematically. • We develop a mathematical model by analyzing the precise dynamic mechanisms with miRNAs involved in the ERK pathway. • Our study will provide a rich area of future research with the regulatory roles of miRNAs in cell signaling pathway. Abstract MicroRNAs (miRNAs) play a critical role in regulating the signaling network such as the extracellular signal regulated kinase (ERK) pathway. However, the mechanisms of miRNA-mediated posttranscriptional regulations with regard to their impacts on signaling require further investigation from a systematic level. Therefore, we develop a mathematical model by analyzing the precise dynamic mechanisms with a bevy of miRNAs involved in the Ras/Raf/MEK/ERK pathway. A systems-based analysis approach is introduced into this model and the dynamics have been implemented deterministically and stochastically. Our analysis reveals that miRNAs are key participants regulating the gene expression of ERK network, and the cooperative actions of miRNAs are important in keeping the normal biological characteristics and amplification effects of the system. Meanwhile, the appearance of system disorder in the absence of miRNAs suggests that miRNAs may play a role in the pathological processes, such as tumor and inflammation. The sensitivity analysis and the kinetic parameter perturbation show that the binding of receptor (EGFR) and adaptor protein (Shc, Grb2 and Sos) under normal physiological conditions is crucial for the robustness of the whole pathway. In addition, the stochastic dynamic patterns are in a good agreement with the deterministic results, further demonstrating that the variability of the system due to the presence of some stochastic noise is low. All these will be helpful for a deeper understanding of the dynamic mechanism of miRNA-mediated ERK signal network, which might present a rich area of future research with the relevant regulatory roles of miRNAs in cell signaling pathway. [ABSTRACT FROM AUTHOR]

• Geopolymeric flocculation and solidification of the tail slurry was conducted. • Model test simulated the dewatering process of the slurry on construction sites. • Geopolymeric flocculation-solidification agent performed better in treating slurry. • The effects of flocculation and solidification mutually interacted with each other. Geopolymeric flocculation-solidification of the tail slurry of shield tunnelling spoil after sand separation was investigated to replace the low-efficient flocculation and filter-pressing method currently adopted on site. The geopolymeric flocculation-solidification agent (GBFS-FS) consisting of flocculation component and geopolymeric solidification component (granulated blast-furnace slag (GBFS) activated by alkali) was prepared and investigated through flocculation-sedimentation test, dewatering model test, unconfined compression test, SEM test, etc. In the flocculation-sedimentation test, the GBFS-FS agent demonstrated the best flocculation effect as compared with other cementitious flocculation-solidification agents. The dewatering model tests revealed that adding the GBFS-FS agent into the slurry can not only dewater the slurry efficiently, but also generate solidified clayey sediments with quickly-increased strength. The solidified clayey sediments with GBFS-FS showed strengths 3.5 times higher than others with cementitious agents at a 28-day curing age. The GBFS-FS agent can also facilitate to make controlled low-strength material (CLSM) using the tail slurry. The fluidity and strength of CLSM with GBFS-FS were distinctly better than those of other groups. SEM test shows that the components of the GBFS-FS agent can interact to avoid large flocs and support each other in strength growth. [ABSTRACT FROM AUTHOR]

Increased aerobic glycolysis is a metabolic hallmark of proinflammatory leukocytes including macrophages and T cells. To take up glucose from the environment and fuel glycolysis, activated leukocytes upregulate the glucose transporter GLUT1. The orally bioavailable selective GLUT1 inhibitor BAY-876 was developed primarily as an anti-tumor drug. Our study assessed its activity on activated macrophages and CD4+ T cells. BAY-876 significantly attenuated glucose uptake by cultured CD4+ T cells and macrophages by 41% and 15%, respectively. Extracellular flux analysis of activated CD4+ T cells in vitro showed that BAY-876 significantly decreases glycolytic proton flux rate and lactate production, effects that are accompanied by an increased oxidative phosphorylation-mediated ATP production rate, leaving intracellular ATP levels per cell unchanged. However, GLUT1 inhibition reduced CD4+ T cell proliferation without compromising cell viability and reduced IFN-γ secretion by 20%. Moreover, TNF secretion from macrophages was reduced by 27%. We conclude that GLUT1-specific inhibitors, like BAY-876, deserve further in vivo testing in a broad range of (auto-) inflammatory disease models. [ABSTRACT FROM AUTHOR]

The photovoltaic performances of n-i-p or inverted p-i-n BaSi2 homojunction solar cells are limited by the parasitic high light absorption of n-BaSi2 or p-BaSi2 window layer. In this work, diverse electron transport materials (ETMs) and hole transport materials (HTMs) are employed instead of BaSi2 as window layers to promote the performances of BaSi2 solar cells, and the effects of different ETMs and HTMs were simulated and comprehensively studied. For n-ETM/i-BaSi2/p-BaSi2 solar cells, the solar cell with ZnO exhibits the highest efficiency of 28.21%. As increasing the thicknesses of ETM layer from 5 nm to 100 nm, the efficiency for n-BaSi2/i-BaSi2/p-BaSi2 solar cell decreases seriously from 24.52% to 15.99%, while the solar cells with wide band gap ETMs including SnO2, TiO2 and ZnO show neglectable change in performances. The performances n-ETM/i-BaSi2/p-BaSi2 solar cells are affected by donor like defects more than acceptor like defects in i-BaSi2. For p-HTM/i-BaSi2/n-BaSi2 solar cells, employing Cu2O, CuI, CuSCN or NiO as HTM layer produces evident valence band discrepancies at HTM/i-BaSi2 interfaces. Heavy doping in HTM layer is necessary desired to overcome the energy band barriers. As increasing the thicknesses of HTM layer from 5 nm to 100 nm, the efficiency for p-BaSi2/i-BaSi2/n-BaSi2 solar cell also decreases from 24.52% to 15.99%, while the solar cells with wide band gap HTMs including CuI, CuSCN and NiO show neglectable change, and the efficiency for p-Cu2O/i-BaSi2/n-BaSi2 solar cells decreases slightly from 25.86% to 24.88%. The performances of p-HTM/i-BaSi2/n-BaSi2 solar cells are affected by acceptor like defects more than donor like defects in i-BaSi2. [ABSTRACT FROM AUTHOR]

SET domain containing protein 2 (SETD2, also known as HYPB) is a 230-kD protein which is located at cytogenetic band p21.31 of chromosome 3. SETD2 is usually transformed or eradicated in multiple forms of tumours in humans. However, its primary function in gastric cancer (GC) remains unclear. In the current study, we investigated the mRNA and protein expression levels of SETD2 using immunohistochemistry, qPCR, RT-PCR, and immunoblotting. The function of SETD2 in GC cells was investigated by MTT and transwell assays. Our results revealed remarkably lower levels of SETD2 mRNA and protein in the tumour samples than in tumour-adjacent tissues. Decreased expression of SETD2 mRNA was observed in 122 (79.7%) of 153 primary tumour tissue samples. On the basis of the overall survival analysis, we could interpret that a low expression of SETD2 was correlated with a poor prognosis ( P  < 0.001, log-rank test). Multivariate survival analysis indicated that SETD2 was an obvious prognostic factor in patients with GC. SETD2 overexpression in GC cell lines significantly inhibited cell proliferation, migration, and invasion. Altogether, the investigation demonstrated the clinical significance of SETD2 expression, supporting the fundamental principle that a decrease in its level is an unfavourable event in the progression and prognosis of GC. Therefore, down-regulated SETD2 can presumably be a potential negative prognostic and progression marker for GC. [ABSTRACT FROM AUTHOR]

Valproic acid(VPA) is a classic drug that used to treat epilepsy in monotherapy or combination with other anticonvulsant drugs such as lamotrigine (LTG). Although it was reported that VPA could increase lamotrigine trough concentration in clinical practice, there was no report about the effect of LTG on the trough concentration of VPA and its main metabolites, such as 4-ene-VPA, 3-OH-VPA, 4-OH-VPA, 3-keto-VPA, 2-PGA which are related to the therapeutic and toxic effects of VPA. In this study, a simple and rapid method for the simultaneous determination of VPA and its five metabolites in human plasma using HPLC–MS/MS was developed for the first time. Benzoic acid was used as an internal standard (IS). Separation was performed on a Hypersil GOLD C 18 column by isocratic elution using acetonitrile: 10 mM ammonium acetate solution (90:10, v/v) as mobile phase at a flow rate of 0.3 mL/min. A triple quadrupole mass spectrometer operating in the negative ion-switching, electron spray ionization mode with selection reaction monitoring (SRM) was employed to determine transitions of m/z 143.183 → 143.183, 157.033 → 113.165, 173.017 → 129.074, 159.058 → 101.082, 140.870 → 140. 870, 159.049 → 123.076, 121.035 → 77.136 for VPA, 2-PGA, 3-keto-VPA, 3-OH-VPA, 4-ene-VPA, 4-OH-VPA and IS, respectively. The method also afforded satisfactory results in terms of sensitivity, specificity, precision (intra- and inter-batch), accuracy, recovery, matrix effect and stability. This method was successfully applied to evaluate the effect of LTG on the trough concentration of VPA, 2-PGA, 3-keto-VPA, 3-OH-VPA, 4-ene-VPA, 4-OH-VPA in Chinese epilepsy patients. The result showed that there was no significant difference in the concentration of VPA and its five metabolites between patients in VPA monotherapy and patients in therapy combining VPA with LTG. [ABSTRACT FROM AUTHOR]

Introduction: It has been suggested that uncontrolled epilepsy might not necessarily equate to drug resistance when antiepileptic drugs (AEDs) are used at relatively low doses, a practice frequently observed in rural areas of China.Objective: To assess the clinical benefits of further drug manipulation in this situation, we prospectively followed up the outcomes of patients with uncontrolled epilepsy while taking relatively low doses of AEDs.Methods: The study included patients aged 16 years and older with uncontrolled epilepsy and who were receiving at least one AED at a dosage below 50% of the World Health Organization (WHO) defined daily dose (DDD) (50% DDD) (Group 1). Patients with drug-resistant epilepsy were included for comparison (Group 2). Both groups were followed-up for at least 2 years. Seizure outcomes after further drug manipulations were recorded at the last follow-up.Results: A total of 197 patients (55.3% male) were included in Group 1 and 32 (46.9% male) in Group 2; their mean duration of follow-up was 28.85 ± 1.90 and 30.91 ± 2.04 months, respectively. At the last follow-up, 16.8% (33/197) of patients in Group 1 had become seizure-free compared with none in Group 2 (p < 0.001). Seventeen of 93 (18.3%) patients in Group 1 became seizure free after increasing the dosage of baseline AED(s) alone. Only 5.5% (3/55) of patients who had failed to respond to an AED at ≥50% DDD at baseline became seizure free compared with 21.1% (30/142) who did not have such a history (p = 0.001). The number of AEDs taken at a dosage below 50% DDD at baseline was not associated with seizure outcome.Conclusions: Uncontrolled epilepsy could become controlled in a substantial proportion of patients by dose increase alone, particularly if there is no history of drug failure at ≥50% DDD. [ABSTRACT FROM AUTHOR]

Osteoporosis a common disease in postmenopausal women which contains significant impact on the living quality of women. With the aging of the population, the number of patients suffer from osteoporosis has shown a significant increase. Given the limitations of clinical drugs for the treatment of osteoporosis, natural extracts with small side effects have a great application prospect in the treatment of osteoporosis. Praeruptorin B (Pra-B), is one of the main components found in the roots of Peucedanum praeruptorum Dunn and exhibits anti-inflammatory effects. However, there is no research on the influence of Pra-B on osteoporosis. Here, we showed that Pra-B can dose-dependently suppress osteoclastogenesis without cytotoxicity. Receptor activator of nuclear factor kappa-B (NF-κB) ligand (RANKL)-induced the nuclear import of P65 was inhibited by Pra-B, which indicated the suppressive effect of Pra-B on NF-κB signaling. Further, Pra-B enhanced the expression of Glutathione S-transferase Pi 1 (GSTP1) and promoted the S-glutathionylation of IKKβ to inhibit the nuclear translocation of P65. Moreover, in vivo experiments showed that Pra-B considerably attenuated the bone loss in ovariectomy (OVX)-induced mice. Collectively, our studies revealed that Pra-B suppress the NF-κB signaling targeting GSTP1 to rescued RANKL-induced osteoclastogenesis in vitro and OVX-induced bone loss in vivo, supporting the potential of Pra-B for treating osteoporosis in the future. [Display omitted] [ABSTRACT FROM AUTHOR]

The quaternary chalcogenide LiGaSiS 4 was prepared by the reaction of Li 2 S, Si, Ga 2 S 3 and S at high temperature. Single-crystal X-ray diffraction analysis revealed that LiSiGaS 4 crystallizes in the monoclinic space group P 2 1 / c (No. 14) with the lattice parameters (a ​= ​6.8001(5) Å, b ​= ​60.067(4) Å, c ​= ​6.7948(7) Å, β ​= ​119.580(2)°). The three-dimensional framework of LiGaSiS 4 is composed of M1S 4 (M1 ​= ​Ga1/Si1) tetrahedra with shared edges, MS 4 and SiS 4 tetrahedra with shared corners. Li cations occupy the intervening voids of the framework for charge-balance. LiGaSiS 4 exhibits good thermal stability at high temperature up to 828 ​°C. The optical band gap of 4.11 ​eV is obtained by UV–Visible diffuse reflection spectrum, corresponding to the off-white color of the crystals. Moreover, the theoretical calculations confirm that the large band gap is mainly ascribed to the Si–S and Ga−S hybrid chemical bonds. The new compound LiGaSiS 4 was synthesized by solid-state reaction for the first time. LiGaSiS 4 crystallizes in the monoclinic space group P 2 1 / C (No. 14) and shows a three-dimensional framework consisting of MS 4 and SiS 4 tetrahedra shared angles or edges. The diffuse-reflectance spectroscopy measurement reveals that the optical band gap is 4.11 ​eV, which is consistent with the off-white color of LiGaSiS 4 crystal. [Display omitted] • Quaternary sulfide LiGaSiS 4 was synthesized by solid-state reaction, and crystallizes in space group P 2 1 / C. • The three-dimensional framework of LiGaSiS 4 is composed of MS 4 and SiS 4 tetrahedra shared angles or edges. • The energy gap of LiGaSiS 4 is measured to be 4.11 ​eV, corresponding to the off-white color of the crystals. • Theoretical calculations confirm that the optical response mainly originates from the Si–S and Ga-S chemical bonds. [ABSTRACT FROM AUTHOR]

This paper presents a study of vehicle detection from high-resolution aerial images. In this paper, a superpixel segmentation method designed for aerial images is proposed to control the segmentation with a low breakage rate. To make the training and detection more efficient, we extract meaningful patches based on the centers of the segmented superpixels. After the segmentation, through a training sample selection iteration strategy that is based on the sparse representation, we obtain a complete and small training subset from the original entire training set. With the selected training subset, we obtain a dictionary with high discrimination ability for vehicle detection. During training and detection, the grids of histogram of oriented gradient descriptor are used for feature extraction. To further improve the training and detection efficiency, a method is proposed for the defined main direction estimation of each patch. By rotating each patch to its main direction, we give the patches consistent directions. Comprehensive analyses and comparisons on two data sets illustrate the satisfactory performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A series of transition metal (M)‐promoted carbon‐silicon (C−M−Si; M=Mn, Fe, Co, Ni, Cu, Zn, Zr) solid acid catalysts with designated molar ratio of M/Si=1 : 8 were fabricated and exploited for acetalization of benzaldehyde (BzH) with ethylene glycol (EG). The physical and chemical properties of these C−M−Si catalysts prepared by sol‐gel method were characterized by various techniques, namely, SEM, EDS, TGA‐DTG, BET, XRD, FTIR, XPS, and NH3‐TPD. Among various examined acidic C−M−Si catalysts, the C−Fe−Si catalyst exhibited the optimal catalytic activity with the benzaldehyde glycol acetal (BEGA) yield of 97.67 %, in excellent agreement with the value (97.88 %) predicted by the response surface methodology (RSM) based on a Box‐Behnken design (BBD). C−Fe−Si catalyst with the high catalytic activities and excellent stability and reusability may be ascribed to the suitable acidity and uniform surface distribution of active sites requisite for the acid‐catalyzed acetalization reaction. [ABSTRACT FROM AUTHOR]

Objective In this study on the analysis of EEG signals for seizure prediction, we used a combination of statistically relevant theory and nonlinear dynamics to maximize the sensitivity of nonlinear analysis and improved prediction accuracy (PA) and effectiveness. Methods First, a physiological reference range of approximate entropy (ApEn) was set up based on normal EEG data. Second, using the concept of global optimization, all EEG electrodes were used in the study regardless of the location of epileptic foci, and the five-electrode group with the strongest synchronization discharge was employed as the optimal electrode group for the next prediction. We set a warning signal when the ApEn values of the data were below the reference range in five electrodes at the same time. Results From the overall 142.7-h EEG signal containing 37 seizures from nine epileptics, our PA was 94.59%, the false prediction rate was 0.084/h, and the mean prediction time was 26.64 min. Conclusion Combining statistically relevant theory and nonlinear dynamics can significantly improve the sensitivity of the nonlinear analysis in seizure prediction. Significance This method may provide a theoretical foundation for the development of a clinical real-time warning system for patients with partial epilepsy. [ABSTRACT FROM AUTHOR]

Metal halide perovskite (MHP) materials have become a research hotspot in recent years due to their unique optical and electrical properties. In the process of preparing MHP materials into optoelectronic devices, patterning is often a key step. The ability to achieve high‐precision and high‐quality patterned films determines whether the corresponding high‐quality applications can be made. At present, there are a lot of patterning researches. Based on the classification of operation methods, this review divides the current MHP patterning methods into three categories: template patterning, inkjet printing patterning, and laser patterning, which describes the operation means, research status, pattern accuracy, method advantages, and future development prospect of each method in detail. This review provides a detailed reference for the research and application of MHP materials, and provides a method basis for future development. [ABSTRACT FROM AUTHOR]