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Celastrol, the primary constituent of Tripterygium wilfordii, has demonstrated neuroprotective properties in rats with dementia by reducing inflammation. A high-fat diet and streptozotocin injection were utilized to establish a diabetic rat model, which was then employed to investigate the possible protective effect of celastrol against the development of diabetes-induced learning and memory deficits. Afterwards, the experimental animals received a dose of celastrol by gavage (4 mg/kg/d). An animal study showed that celastrol enhanced insulin sensitivity and glucose tolerance in diabetic rats. In the Morris water maze test, rats with diabetes performed poorly in terms of spatial learning and memory; treatment with celastrol improved these outcomes. Additionally, administration of celastrol downregulated the expression of inflammatory-related proteins (NF-κB, IKKα, TNF-α, IL-1β, and IL-6) and greatly reduced the generation of Aβ in the diabetic hippocampus tissue. Moreover, the insulin signaling pathway-related proteins PI3K, AKT, and GSK-3β were significantly upregulated in diabetic rats after celastrol was administered. Also, celastrol prevented damage to the brain structures and increased the synthesis of synaptic proteins like PSD-95 and SYT1. In conclusion, celastrol exerts a neuroprotective effect by modulating the insulin signaling system and reducing inflammatory responses, which helps to ameliorate the cognitive impairment associated with diabetes.

BackgroundUnder the backdrop of the national innovation-driven development strategy, the increasing occupational stress and job burnout among employees are noteworthy for their impact on employees' subjective well-being. ObjectiveTo clarify the status, distribution characteristics, and the relationship between subjective well-being, occupational stress, and job burnout of employees in research and development (R&D) enterprises, in order to improve their subjective well-being. MethodsA total of 3366 employees from R&D departments at 7 enterprises in Minhang District of Shanghai were selected. The well-being level of the research subjects was investigated by using the World Health Organization Well-Being Index (WHO-5) that yielded total scores from 0 to 25, and a higher total score indicated a higher well-being level; the levels of occupational stress and job burnout were investigated by using the Chinese version of the Job Content Questionnaire, and the Chinese version of the Maslach Burnout Inventory-General Survey (MBI-GS). The scores of WHO-5, JDC, and MBI-GS were incorporated into structural equation modeling (SEM) as numerical variables to analyze their relationship. ResultsThe scores of subjective well-being, occupational stress, and job burnout of employees in the R&D enterprises were 13.30±6.09, 1.12±0.45, and 2.18±1.12, respectively. The positive rates of occupational stress and job burnout were 44.4% and 70.9% respectively, and the positive rate of severe job burnout was 11.7%. There were statistically significant differences in the score of subjective well-being among the participants by gender, age, educational level, marital status, registered residence, working seniority, and jobs (P

Abstract Background Gastric emptying (GE), with wide inter-individual but lesser intra-individual variations, is a major determinant of postprandial glycaemia in health and type 2 diabetes (T2D). However, it is uncertain whether GE of a carbohydrate-containing liquid meal is predictive of the glycaemic response to physiological meals, and whether antecedent hyperglycaemia influences GE in T2D. We evaluated the relationships of (i) the glycaemic response to both a glucose drink and mixed meals with GE of a 75 g glucose drink, and (ii) GE of a glucose drink with antecedent glycaemic control, in T2D. Methods Fifty-five treatment-naive Chinese adults with newly diagnosed T2D consumed standardised meals at breakfast, lunch and dinner with continuous interstitial glucose monitoring. On the subsequent day, a 75 g glucose drink containing 150 mg 13C-acetate was ingested to assess GE (breath test) and plasma glucose response. Serum fructosamine and HbA1c were also measured. Results Plasma glucose incremental area under the curve (iAUC) within 2 hours after oral glucose was related inversely to the gastric half-emptying time (T50) (r = −0.34, P = 0.012). The iAUCs for interstitial glucose within 2 hours after breakfast (r = −0.34, P = 0.012) and dinner (r = −0.28, P = 0.040) were also related inversely to the T50 of oral glucose. The latter, however, was unrelated to antecedent fasting plasma glucose, 24-hour mean interstitial glucose, serum fructosamine, or HbA1c. Conclusions In newly diagnosed, treatment-naive, Chinese with T2D, GE of a 75 g glucose drink predicts the glycaemic response to both a glucose drink and mixed meals, but is not influenced by spontaneous short-, medium- or longer-term elevation in glycaemia.

Abstract Background Cetaceans, having experienced prolonged adaptation to aquatic environments, have undergone evolutionary changes in their respiratory systems. This process of evolution has resulted in the emergence of distinctive phenotypic traits, notably the abundance of elastic fibers and thickened alveolar walls in their lungs, which may facilitate alveolar collapse during diving. This structure helps selective exchange of oxygen and carbon dioxide, while minimizing nitrogen exchange, thereby reducing the risk of DCS. Nevertheless, the scientific inquiry into the mechanisms through which these unique phenotypic characteristics govern the diving behavior of marine mammals, including cetaceans, remains unresolved. Results This study entails an evolutionary analysis of 42 genes associated with pulmonary fibrosis across 45 mammalian species. Twenty-one genes in cetaceans exhibited accelerated evolution, featuring specific amino acid substitutions in 14 of them. Primarily linked to the development of the respiratory system and lung morphological construction, these genes play a crucial role. Moreover, among marine mammals, we identified eight genes undergoing positive selection, and the evolutionary rates of three genes significantly correlated with diving depth. Specifically, the SFTPC gene exhibited convergent amino acid substitutions. Through in vitro cellular experiments, we illustrated that convergent amino acid site mutations in SFTPC contribute positively to pulmonary fibrosis in marine mammals, and the presence of this phenotype can induce deep alveolar collapse during diving, thereby reducing the risk of DCS during diving. Conclusions The study unveils pivotal genetic signals in cetaceans and other marine mammals, arising through evolution. These genetic signals may influence lung characteristics in marine mammals and have been linked to a reduced risk of developing DCS. Moreover, the research serves as a valuable reference for delving deeper into human diving physiology.

Aside from antibodies, peptides show great potential as immune checkpoint inhibitors (ICIs) due to several advantages, such as better tumor penetration and lower cost. Lymphocyte-activation gene 3 (LAG-3) is an immune checkpoint which can induce T cell dysfunction through interaction with its soluble ligand fibrinogen like protein-1 (FGL1). Here, we found that LAG-3 expression was higher than programmed cell death protein 1 (PD-1) in multiple human cancers by TCGA databases, and successfully identified a LAG-3 binding peptide LFP-6 by phage display bio-panning, which specifically blocks the interaction of LAG-3/FGL1 but not LAG-3/MHC-II. Subsequently, d-amino acids were introduced to substitute the N- and C-terminus of LFP-6 to obtain the proteolysis-resistant peptide LFP-D1, which restores T cell function in vitro and inhibits tumor growth in vivo. Further, a bispecific peptide LFOP targeting both PD-1/PD-L1 and LAG-3/FGL1 was designed by conjugating LFP-D1 with PD-1/PD-L1 blocking peptide OPBP-1(8–12), which activates T cell with enhanced proliferation and IFN-γ production. More importantly, LFOP combined with radiotherapy significantly improve the T cell infiltration in tumor and elevate systemic antitumor immune response. In conclusion, we developed a novel peptide blocking LAG-3/FGL1 which can restore T cell function, and the bispecific peptide synergizes with radiotherapy to further enhance the antitumor immune response.

Abstract Alzheimer's disease (AD) is the leading cause of dementia worldwide, with recent studies highlighting the potential role of immunogenic cell death (ICD) in the pathogenesis of this neurodegenerative disorder. A total of 52 healthy controls and 64 patients with AD were included. Compared to the controls, the patients with AD exhibited 2392 differentially expressed genes (DEGs), of which 1015 and 1377 were upregulated and downregulated genes, respectively. Among them, nine common genes were identified by intersecting the AD-related module genes with the DEGs and ICD-associated genes. Gene ontology (GO)analysis further revealed “positive regulation of cytokine production” as the most significant term. Moreover, the enriched molecular functions were primarily related to the inflammatory body complex, while the overlapping genes were significantly enriched in lipopolysaccharide binding. Kyoto encyclopedia of genes and genomes (KEGG) analysis also indicated that these overlapping genes were mainly enriched in immunity, inflammation, and lipid metabolism pathways. Furthermore, the following four hub genes were detected using machine learning algorithms: P2RX7, HSP90AA1, NT5E, and NLRP3. These genes demonstrated significant differences in expression between the AD and healthy control groups (P 0.7, indicating their potential diagnostic value for AD. We further validated the protein levels of these four genes in the hippocampus of 3xTg-AD and C57BL/6J mice, showing P2RX7 and HSP90AA1 expression levels consistent with the previously analyzed trends. Finally, the single-sample gene set enrichment analysis (ssGSEA) algorithm provided additional evidence by demonstrating the crucial role of immune cell infiltration and its link with the hub genes in AD progression. Our study results suggest that ICD-mediated elevation of HSP90AA1 and P2RX7 levels and the resulting induction of tau hyperphosphorylation and neuroinflammation are vital in the AD pathogenic mechanism.

Abstract AXIN1, has been initially identified as a prominent antagonist within the WNT/β-catenin signaling pathway, and subsequently unveiled its integral involvement across a diverse spectrum of signaling cascades. These encompass the WNT/β-catenin, Hippo, TGFβ, AMPK, mTOR, MAPK, and antioxidant signaling pathways. The versatile engagement of AXIN1 underscores its pivotal role in the modulation of developmental biological signaling, maintenance of metabolic homeostasis, and coordination of cellular stress responses. The multifaceted functionalities of AXIN1 render it as a compelling candidate for targeted intervention in the realms of degenerative pathologies, systemic metabolic disorders, cancer therapeutics, and anti-aging strategies. This review provides an intricate exploration of the mechanisms governing mammalian AXIN1 gene expression and protein turnover since its initial discovery, while also elucidating its significance in the regulation of signaling pathways, tissue development, and carcinogenesis. Furthermore, we have introduced the innovative concept of the AXIN1-Associated Phosphokinase Complex (AAPC), where the scaffold protein AXIN1 assumes a pivotal role in orchestrating site-specific phosphorylation modifications through interactions with various phosphokinases and their respective substrates.

Abstract Lattice thermal conductivity is important for many applications, but experimental measurements or first principles calculations including three-phonon and four-phonon scattering are expensive or even unaffordable. Machine learning approaches that can achieve similar accuracy have been a long-standing open question. Despite recent progress, machine learning models using structural information as descriptors fall short of experimental or first principles accuracy. This study presents a machine learning approach that predicts phonon scattering rates and thermal conductivity with experimental and first principles accuracy. The success of our approach is enabled by mitigating computational challenges associated with the high skewness of phonon scattering rates and their complex contributions to the total thermal resistance. Transfer learning between different orders of phonon scattering can further improve the model performance. Our surrogates offer up to two orders of magnitude acceleration compared to first principles calculations and would enable large-scale thermal transport informatics.

Abstract Multi‐layered plasmonic nanostructures are able to highly promote the near‐field confinement and effectively activate analytes, which are of predominate significance but are extremely challenging. Herein, the semi‐open Au core@carved AuAg multi‐shell superstructure nanoparticles (multi‐Au@Ag‐Au NPs, multi = mono, bi, tri, tetra, and penta) are reported with a high designability on electromagnetic field and capability of effectively capturing analytes. By controlling synthetic parameters such as the number of galvanic exchange and Ag growth, multi‐Au@Ag‐Au NPs are successfully obtained, with tunable layer numbers and asymmetric nanoholes. Due to collective plasmon oscillations of multi‐layered built‐in nanogaps, the electromagnetic field strength of a single penta‐Au@Ag‐Au entity reach 48841. More importantly, the penta‐Au@Ag‐Au NPs show a remarkable light‐harvesting capability, which is adaptive to different Raman lasers, supporting high‐diversity detection. Additionally, the structural specificity allows analytes to be sufficiently captured into interior hotspots, and further achieve highly sensitive detection with limit of detection down to 3.22 × 10−12 M. This study not only provides an effective pathway for integrating abundant hotspots and activating target molecules in single plasmonic superstructure, but stimulates advancements in SERS substrates for various applications.

BackgroundPast research indicates that occupational stress negatively predicts health-related productivity. Simultaneously, sleep problem among workers may stem from job stress, subsequently leading to a decline in sleep quality and resulting in reduced health productivity. Therefore, this study aims to idenitify whether the sleep quality of employees functions as a mediator in the process through which job stress impacts health productivity.ObjectivesThis study aimed to assess the status and analyze differences in quality of sleep, job stress, and health-related productivity loss (HRPL) among workers in research and development (R&D) enterprises in Minhang District, Shanghai. We also assessed the mediating effect of sleep quality on the relationship between job stress and HRPL.MethodsA total of 3,216 workers in R&D firms aged between 18 and 60 years participated in this study (mean age 35.15 years; standard deviation 8.44; male-to-female ratio≈2:1). The Nakata Insomnia Questionnaire, the Chinese version of the Brief Job Stress Questionnaire revised edition, and the Chinese version of the Work Productivity and Activity Impairment Questionnaire were used in this study. And the Kruskal–Wallis test, Hierarchical Multiple Regression Analysis, and Path Analysis were utilized for data analysis in this study.ResultsThere were significant differences in the positive detection rate of insomnia among participants according to age, educational level, marital status, position, length of service, and level of financial difficulties (all P < 0.05). We also found significant differences in the positive detection rate of HRPL among participants according to age, marital status, length of service, and level of financial difficulties (all P < 0.05); participants with insomnia scored higher for HRPL than those without insomnia (6.00 vs. 4.20, P < 0.001). Additionally, participants with job stress problems had higher HRPL than those without these issues (7.00 vs. 4.20, P < 0.001). Our findings suggest that sleep quality plays a mediating role between job stress and HRPL (all P < 0.05).ConclusionsOccupational health professionals must pay particular attention to job stress, sleep quality, and their influencing factors to positively influence the wellbeing of workers while improving productivity.

BackgroundAt present, domestic research on job burnout and health-related productivity is limited to medical workers, and the impact of job burnout on health-related productivity of enterprise staff deserves attention. ObjectiveTo explore the association between job burnout and health-related productivity loss among enterprise staff. MethodsA cross-sectional online questionnaire survey was conducted among enterprise staff who were selected from seven enterprises in Minhang District of Shanghai. The Chinese version of Maslach Burnout Inventory-General Survey (MBI-GS) was used to assess job burnout, and a questionnaire based on and modified from the WHO Health and Work Performance Questionnaire was used to assess the loss of health-related productivity. Logistic regression was used to analyze the impact of job burnout on health-related productivity under the control of selected demographic characteristics, socio-economic factors, and occupational factors. ResultsA total of 3489 questionnaires were recovered, and 3156 valid questionnaires were included in the statistical analysis. Among the 3156 valid questionnaires, 2228 (70.8%) respondents were assessed as suffering from job burnout, in which 1858 (59.0%) were mild to moderate job burnout, and 370 (11.7%) were severe job burnout; the median score (interquartile range) of MBI-GS was 2.18（2.69）, the median rates (interquartile range) of absenteeism and presenteeism were 0.00% (0.00%) and 20.00% (50.00%), respectively. The prevalence of presenteeism significantly varied by gender, education, marital status, working years, job category, exhaustion, cynicism, professional efficacy, and job burnout (P

In medical imaging, detecting tissue anomalies is vital for accurate diagnosis and effective treatment. Electrical impedance tomography (EIT) is a non-invasive technique that monitors the changes in electrical conductivity within tissues in real time. However, the current challenge lies in simply and accurately reconstructing multi-conductivity distributions. This paper introduces a layered fusion framework for EIT to enhance imaging in multi-conductivity scenarios. The method begins with pre-imaging and extracts the main object from the fuzzy image to form one layer. Then, the voltage difference in the other layer, where the local anomaly is located, is estimated. Finally, the corresponding conductivity distribution is established, and multiple layers are fused to reconstruct the multi-conductivity distribution. The simulation and experimental results demonstrate that compared to traditional methods, the proposed method significantly improves multi-conductivity separation, precise anomaly localization, and robustness without adding uncertain parameters. Notably, the proposed method has demonstrated exceptional accuracy in local anomaly detection, with positional errors as low as 1% and size errors as low as 33%, which significantly outperforms the traditional method with respective minimum errors of 9% and 228%. This method ensures a balance between the simplicity and accuracy of the algorithm. At the same time, it breaks the constraints of traditional linear methods, struggling to identify multi-conductivity distributions, thereby providing new perspectives for clinical EIT.

Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.

IntroductionThyroid cancer is the most prevalent endocrine malignancy, with its global incidence increasing annually in recent years. Papillary carcinoma is the most common subtype, frequently accompanied by cervical lymph node metastasis early on. Central lymph node metastasis (CLNM) is particularly the common metastasis form in this subtype, and the presence of lymph node metastasis correlates strongly with tumor recurrence. However, effective preoperative assessment methods for CLNM in patients with papillary thyroid carcinoma (PTC) remain lacking.MethodsData from 400 patients diagnosed with PTC between January 1, 2018, and January 1, 2022, at the Shandong Provincial Hospital were retrospectively analyzed. This data included clinicopathological information of the patients, such as thyroid function, BRAF V600E mutation, whether complicated with Hashimoto’s thyroiditis, and the presence of capsular invasion. Univariate and multivariate logistic regression analyses were performed to assess the risk factors associated with cervical CLNM in patients with PTC. Subsequently, a clinical prediction model was constructed, and prognostic risk factors were identified based on univariate and multivariate Cox regression analyses.ResultsUnivariate and multivariate analyses identified that age >45 years (P=0.014), body mass index ≥25 (P=0.008), tumor size ≥1 cm (P=0.001), capsular invasion (P=0.001), and the presence of BRAF V600E mutation (P45 years, body mass index ≥25, tumor size ≥1 cm, BRAF V600E mutation, and capsular invasion are the related risk factors for CLNM in patients with PTC. For patients with clinically nodal-negative (cN0) papillary thyroid microcarcinoma, accurately identifying the BRAF V600E mutation is essential for guiding the central lymph node dissection approach and subsequent treatments.

BackgroundJob burnout is an early mental health condition caused by job stress and contributes to many negative effects on work and life. Employees of research and development (R&D) enterprises are exposed to constant pressure from innovation, production speed and sales expansion, and they are prone to burnout symptoms if such factors are not under effective control. ObjectiveTo evaluate the current situation of job burnout among employees of R&D enterprises in Minhang District of Shanghai and explore its influencing factors. MethodsDuring November to December 2021, a cross-sectional study was developed and a convenient sampling method was used to enroll employees from 7 R&D enterprises in Minhang District of Shanghai. On the basis of voluntary participation with informed consent, a survey was conducted by using a self-made questionnaire (collecting data about general demographic characteristics, occupational characteristics, behavior and lifestyle), the Chinese version of the Concise Occupational Stress Questionnaire, and the Chinese version of the Maslach Burnout Inventory-General Survey. Occupational stress and its dimensions (job demand, job control, and social support) were divided into high, medium, and low levels according to tertiles. The positive rate of job burnout was reported according to score categorization (

Epithelial ovarian cancer (EOC) is one of the most lethal gynecologic malignancies, and effective treatments are still lacking due to drug tolerance and tumor recurrence. In this study, we aimed to investigate the effects of sonodynamic therapy (SDT) on ovarian cancer and its potential mechanism. Folate receptor-targeted and ultrasound-responsive nanoparticles (NPs) were constructed using PLGA-PEG-FA (PLGA: poly (lactic-co-glycolic) acid, polyethylene glycol (PEG), FA: folate), the reactive oxygen species (ROS)-generating sonosensitizer IR780 and the oxygen-carrying material perfluorohexane (PFH), termed IRO@FA NPs. The antitumor effect of NPs triggered by ultrasound (US) was measured by an apoptosis assay in a C57/BL6 mouse model. Immunochemistry and flow cytometry were used to detect the proportion of CD3+ T, CD4+ T, CD8+ T cells and activated dendritic cells (DCs) in spleens and tumor tissues to assess variation in the immune response. Moreover, endoplasmic reticulum (ER) stress and immunogenic cell death (ICD) markers (high mobility group protein box-1, ATP and calreticulin) were detected to identify potential mechanisms. The results showed that IRO@FA NP-mediated SDT promoted ID8 cell apoptosis both in vitro and in vivo. The densities of CD3+ and CD8+ T lymphocytes and inflammatory markers were upregulated in tumor tissues. IRO@FA NP-mediated SDT prompted DC maturation and T lymphocyte infiltration by inducing ID8 cell ICD.

Abstract Materials discovery from the infinite earth repository is a major bottleneck for revolutionary technological progress. This labor‐intensive and time‐consuming process hinders the discovery of new materials. Although machine learning techniques show an excellent capability for speeding up materials discovery, obtaining effective material feature representations is still challenging, and making a precise prediction of the material properties is still tricky. This work focuses on developing an automatic material design and discovery framework enabled by data‐driven artificial intelligence (AI) models. Multiple types of material descriptors are first developed to promote the representation and encoding of the materials’ uniqueness, resulting in improved performance for different molecular properties predictions. The material's thermoelectric (TE) properties prediction is then utilized as a baseline to demonstrate the investigation logistic. The proposed framework achieves more than 90% accuracy for predicting materials' TE properties. Furthermore, the developed AI models identify 6 promising p‐type TE materials and 8 promising n‐type TE materials. The prediction results are evaluated by density functional theory calculations and agree with the material's TE property provided by experimental results. The proposed framework is expected to accelerate the design and discovery of the new functional materials.

This paper introduces a sensitivity matrix decomposition regularization (SMDR) method for electric impedance tomography (EIT). Using k-means clustering, the EIT-reconstructed image can be divided into four clusters, derived based on image features, representing posterior information. The sensitivity matrix is then decomposed into distinct work areas based on these clusters. The elimination of smooth edge effects is achieved through differentiation of the images from the decomposed sensitivity matrix and further post-processing reliant on image features. The algorithm ensures low computational complexity and avoids introducing extra parameters. Numerical simulations and experimental data verification highlight the effectiveness of SMDR. The proposed SMDR algorithm demonstrates higher accuracy and robustness compared to the typical Tikhonov regularization and the iterative penalty term-based regularization method (with an improvement of up to 0.1156 in correlation coefficient). Moreover, SMDR achieves a harmonious balance between image fidelity and sparsity, effectively addressing practical application requirements.

Arecoline is a pyridine alkaloid derived from areca nut in the Arecaceae family. It has extensive medicinal activity, such as analgesic, anti-inflammatory, and anti-allergic. However, the toxicity of Arecoline limits its application. Most current studies on its toxicity mainly focus on immunotoxicity, carcinogenesis, and cancer promotion. However, there are few systematic studies on its hepatotoxicity and mechanisms. Therefore, this research explored the mechanism of hepatotoxicity induced by Arecoline in rats and analyzed endogenous metabolite changes in rat plasma by combining network toxicology with metabolomics. The differential metabolites after Arecoline exposure, such as D-Lysine, N4-Acetylaminobutanal, and L-Arginine, were obtained by metabolomics study, and these differential metabolites were involved in the regulation of lipid metabolism, amino acid metabolism, and vitamin metabolism. Based on the strategy of network toxicology, Arecoline can affect the HIF-1 signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, and other concerning pathways by regulating critical targets, such as ALB, CASP3, EGFR, and MMP9. Integration of metabolomics and network toxicology results were further analyzed, and it was concluded that Arecoline may induce hepatotoxicity by mediating oxidative stress, inflammatory response, energy and lipid metabolism, and cell apoptosis.

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

Abstract Background Metastasis is the leading cause of mortality in human cancers, including esophageal squamous cell carcinoma (ESCC). As a pro-inflammatory cytokine, IL-32 was reported to be a poor prognostic factor in many cancers. However, the role of IL-32 in ESCC metastasis remains unknown. Methods ESCC cells with ectopic expression or knockdown of IL-32 were established and their effects on cell motility were detected. Ultracentrifugation, Transmission electron microscopy and Western blot were used to verify the existence of extracellular vesicle IL-32 (EV-IL-32). Coculture assay, immunofluorescence, flow cytometry, and in vivo lung metastasis model were performed to identify how EV-IL-32 regulated the crosstalk between ESCC cells and macrophages. Results Here, we found that IL-32 was overexpressed and positively correlated to lymph node metastasis of ESCC. IL-32 was significantly higher in the tumor nest compared with the non-cancerous tissue. We found that IL-32β was the main isoform and loaded in EV derived from ESCC cells. The shuttling of EV-IL-32 derived from ESCC cells into macrophages could promote the polarization of M2 macrophages via FAK-STAT3 pathway. IL-32 overexpression facilitated lung metastasis and was positively correlated with the proportion of M2 macrophages in tumor microenvironment. Conclusions Taken together, our results indicated that EV-IL-32 derived from ESCC cell line could be internalized by macrophages and lead to M2 macrophage polarization via FAK-STAT3 pathway, thus promoting the metastasis of ESCC. These findings indicated that IL-32 could serve as a potential therapeutic target in patients with ESCC.

Tryptophan 2,3-dioxygnease 2 (TDO2) is specific for metabolizing tryptophan to kynurenine (KYN), which plays a critical role in mediating immune escape of cancer. Although accumulating evidence demonstrates that TDO2 overexpression is implicated in the development and progression of multiple cancers, its tumor-promoting role in esophageal squamous cell carcinoma (ESCC) remains unclear. Here, we observed that TDO2 was overexpressed in ESCC tissues and correlated significantly with lymph node metastasis, advanced clinical stage, and unfavorable prognosis. Functional experiments showed that TDO2 promoted tumor cell proliferation, migration, and colony formation, which could be prevented by inhibition of TDO2 and aryl hydrocarbon receptor (AHR). Further experimentation demonstrated that TDO2 could promote the tumor growth of KYSE150 tumor-bearing model, tumor burden of C57BL/6 mice with ESCC induced by 4-NQO, enhance the expression of phosphorylated AKT, with subsequent phosphorylation of GSK3β, and polarization of M2 macrophages by upregulating interleukin-8 (IL-8) to accelerate tumor progression in the tumor microenvironment (TME). Collectively, our results discovered that TDO2 could upregulate IL-8 through AKT/GSK3β to direct the polarization of M2 macrophages in ESCC, and suggested that TDO2 could represent as an attractive therapeutic target and prognostic marker to ESCC.

Background The development of cancer is largely dependent on the accumulation of somatic mutations, indicating the potential to develop cancer chemoprevention agents targeting mutation drivers. However, ideal cancer chemoprevention agents that can effectively inhibit the mutation drivers have not been identified yet.Methods The somatic mutation signatures and expression analyses of APOBEC3B were performed in patient with pan-cancer. The computer-aided screening and skeleton-based searching were performed to identify natural products that can inhibit the activity of APOBEC3B. 4-nitroquinoline-1-oxide (4-NQO)-induced spontaneous esophageal squamous cell carcinoma (ESCC) and azoxymethane/dextran sulfate sodium (AOM/DSS)-induced spontaneous colon cancer mouse models were conducted to investigate the influences of APOBEC3B inhibitor on the prevention of somatic mutation accumulation and cancer progression.Results Here, we discovered that the cytidine deaminase APOBEC3B correlated somatic mutations were widely observed in a variety of cancers, and its overexpression indicated poor survival. SMC247 (3, 5-diiodotyrosine), as a source of kelp iodine without side effects, could strongly bind APOBEC3B (KD=65 nM) and effectively inhibit its deaminase activity (IC50=1.69 µM). Interestingly, 3, 5-diiodotyrosine could significantly reduce the clusters of mutations, prevent the precancerous lesion progression, and prolong the survival in 4-NQO-induced spontaneous ESCC and AOM/DSS-induced spontaneous colon cancer mouse models. Furthermore, 3, 5-diiodotyrosine could reduce colitis, increase the proportion and function of T lymphocytes via IL-15 in tumor microenvironment. The synergistic cancer prevention effects were observed when 3, 5-diiodotyrosine combined with PD-1/PD-L1 blockade.Conclusions This is the first prove-of-concept study to elucidate that the natural product 3, 5-diiodotyrosine could prevent somatic mutation accumulation and cancer progression through inhibiting the enzymatic activity of APOBEC3B. In addition, 3, 5-diiodotyrosine could reduce the colitis and increase the infiltration and function of T lymphocytes via IL-15 in tumor microenvironment. 3, 5-diiodotyrosine combined with PD-1/PD-L1 blockade could elicit synergistic cancer prevention effects, indicating a novel strategy for both prevent the somatic mutation accumulation and the immune-suppressive microenvironment exacerbation.

The tumor immune microenvironment has been a research hot spot in recent years. The cytokines and metabolites in the microenvironment can promote the occurrence and development of tumor in various ways and help tumor cells get rid of the surveillance of the immune system and complete immune escape. Many studies have shown that the existence of tumor microenvironment is an important reason for the failure of immunotherapy. The impact of the tumor microenvironment on tumor is a systematic study. The current research on this aspect may be only the tip of the iceberg, and a relative lack of integrity, may be related to the heterogeneity of tumor. This review mainly discusses the current status of glucose metabolism and lipid metabolism in the tumor microenvironment, including the phenotype of glucose metabolism and lipid metabolism in the microenvironment; the effects of these metabolic methods and their metabolites on three important immune cells Impact: regulatory T cells (Tregs), tumor-associated macrophages (TAM), natural killer cells (NK cells); and the impact of metabolism in the targeted microenvironment on immunotherapy. At the end of this article,the potential relationship between Ferroptosis and the tumor microenvironment in recent years is also briefly described.

ObjectivesPromoting equity in healthcare resource allocation (EHRA) has become a critical political agenda of governments at all levels since the ambitious Universal Health Coverage was launched in China in 2009, while the role of an important institutional variable—fiscal autonomy of subnational governments—is often overlooked. The present study was designed to determine the effect of FASG on EHRA and its potential mechanism of action and heterogeneity characteristics to provide empirical support for the research field expansion and relative policies making of EHRA.MethodsFrom the start, we utilized the Theil index and the entropy method to calculate the EHRA index of 22 provinces (2011–2020) based on the medical resource data of 287 prefecture-level cities. Furthermore, we used the two-way fixed effects model (FE) to identify and analyze the impact of FASG on EHRA and then used three robustness test strategies and two-stage least squares (2SLS) regression to verify the reliability of the conclusions and deal with potential endogeneity problems, respectively. At last, we extend the baseline regression model and obtain the two-way FE threshold model for conducting heterogeneity analysis, which makes us verify whether the baseline model has nonlinear characteristics.ResultsThe static value and the trend of interannual changes in the EHRA values in different provinces are both very different. The regression results of the two-way FE model show that FASG has a significant positive impact on EHRA, and the corresponding estimated coefficient is – 0.0849 (P < 0.01). Moreover, this promotion effect can be reflected through two channels: enhancing the intensity of government health expenditure (IGHE) and optimizing the allocation of human resources for health (AHRH). At last, under the different economic and demographic constraints, the impact of FASG on EHRA has nonlinear characteristics, i.e., after crossing a specific threshold of per capita DGP (PGDP) and population density (PD), the promotion effect is reduced until it is not statistically significant, while after crossing a particular threshold of dependency ratio (DR), the promotion effect is further strengthened and still statistically significant.ConclusionsFASG plays an essential role in promoting EHRA, which shows that subnational governments need to attach great importance to the construction of fiscal capability in the allocation of health care resources, effectively improve the equity of medical and health fiscal expenditures, and promote the sustainable improvement of the level of EHRA.

High-voltage pulse discharge (HVPD) in liquid technology, when applied to the demolition of concrete structures, has the advantages of green environmental protection, saving energy, emission reduction, safety, reliability, etc. However, research on the influence law of various factors on the effect of crushing is still insufficient. Therefore, this manuscript equated the shock load caused by HVPD to the blasting load and introduced a cohesive zone model based on ABAQUS. The whole process of a concrete column being subjected to a shock wave generated by pulse power discharge was simulated and analyzed. To validate the model’s reasonableness, a comparison and analysis were conducted with the results of experimental studies on concrete column fractures caused by HVPD in liquid. The study further investigated the influence of three parameters—one-side longitudinal reinforcement ratio, volume hoop ratio, and concrete grade strength—on the degree of fracture of the concrete column with a single row of holes (i.e., the width of transverse cracks or longitudinal cracks around the drilled holes). The simulation results revealed that the width of transverse cracks decreases significantly with the increase in the one-side longitudinal reinforcement ratio of the column, while the width of longitudinal cracks decreases substantially with the increase in the volume hoop ratio of the column. In addition, the degree of fracture of concrete columns decreases slightly with the increase in the concrete grade strength. Based on the simulation results, the mathematical expressions between the crack widths (transverse crack width and longitudinal crack width) and the key parameters, such as the one-side longitudinal reinforcement ratio of the column, volume hoop ratio of the column, and concrete grade strength, were established, respectively. These expressions facilitate their practical application in engineering practice.

Study region: Panjiakou Reservoir, Hebei province, China Study focus: Based on our multiple measurements of dissolved organic matter (DOM) and collected published papers, DOM itself stratification has been identified. Then, this stratification is adopted to analyze vertical distribution of DOM and its influencing factors during the thermal stratification period in the Panjiakou Reservoir, a deep and channel type reservoir. New hydrological insights for the region: It was found that DOM itself was stratified due to thermal stratification and other factors, but its stratification boundaries were not fully consistent with the thermal stratification ones. This strongly indicated that DOM characteristics of different layers should be appropriately and accurately analyzed based on its own stratification rather than the thermal stratification. In the upper layer, DOM characteristics were not only affected by endogenous input, but also affected by an external contribution and photodegradation, resulting in low aromaticity and low molecular weight of DOM. In the middle layer, influenced by runoff, phytoplankton secretion, and microbial activities, DOM quantity first increased and then decreased. Meanwhile, DOM had properties of higher aromaticity, molecular weight, and humification. In the lower layer, endogenous input had the maximum influence on DOM properties, resulting in a decrease in aromaticity and humification of DOM. However, endogenous input produced less DOM because microbial activity and phytoplankton growth were limited by the low temperature and low oxygen environment.

Abstract Background TIGIT, as a novel immune checkpoint molecule involved in T cell and NK cell anergy, could induce the immune tolerance and escape through binding with its ligand PVR. Blockade of TIGIT/PVR is considered as a promising strategy in cancer immunotherapy. However, to facilitate the design of inhibitors targeting TIGIT/PVR, the structural characteristics and binding mechanism still need to be further studied. Methods In this study, molecular dynamics (MD) simulations and in silico mutagenesis were used to analyze the interaction between TIGIT and its ligand PVR. Then, PVR mutants were designed and their activities were determined by using TIGIT overexpressed Jurkat cells. Results The results suggested that the loops of PVR (CC′ loop, C′C″ loop, and FG loop) underwent a large intra-molecular rearrangement, and more hydrogen bond crosslinking between PVR and TIGIT were formed during MD simulations. The potential residues for PVR to interact with TIGIT were identified and utilized to predict high affinity PVR mutants. Through the biological activity evaluation, four PVR mutants (PVRS72W, PVRS72R, PVRG131V and PVRS132Q) with enhanced affinity to TIGIT were discovered, which could elicit more potent inhibitory effects compared with the wild type PVR. Conclusions The MD simulations analysis provided new insights into the TIGIT/PVR interaction model, and the identified PVR mutants (PVRS72W, PVRS72R, PVRG131V and PVRS132Q) could serve as new candidates for immunotherapy to block TIGIT/PVR. Video Abstract

Ovarian hyperandrogenism is one of the characteristics of polycystic ovary syndrome (PCOS) and androgen receptor (AR) in ovarian granulosa cells (GCs) functions as an important element to the accumulation of androgens. This study verified the existence of alternative splicing variant of AR (AR-SVs) in the GCs of PCOS patients and found that the function of AR decreased significantly in the presence of AR-SVs. And compared to the normal individuals, the expression of Serine/arginine-rich splicing factor 2(SRSF2) was higher and the expression of SRSF3 was lower in the GCs of patients with AR-SVs. More importantly, we found that the expression of SRSF2 was inhibited and that the expression of AR was decreased after the successful upregulation of miRNA-183, and testostrone (T) concentrations in the culture medium were increased. The results also showed that the expression of SRSF3 decreased when miRNA-124 was successfully upregulated, while the expression of AR significantly increased; however, the function of AR was also inhibited when T concentration in the culture medium was increased. This study has proved that SRSFs are regulated by corresponding miRNAs, and the altered expression of SRSFs interferenced the alternative splicing process of AR and ultimately decreased the function of AR, leading to the accumulation of androgens in the ovary.

Vision-based approaches are widely used in steel crack detection. After processing the images taken by the camera, the superficial defects can be detected. Due to the common limitation of the nature of photographic images, internal features of objects cannot be fully discovered. In order to overcome the drawbacks of vision-based methods, this work presents an approach for detecting cracks in infrared thermal imaging steel sheets using Convolutional Neural Networks (CNN). Firstly, a horizontal heat conduction method is researched to thermally excite the surface of the steel sheet, and a rolling electric heating device is developed to be a thermal excitation source. Secondly, we analyze the temperature difference between normal area and different crack depths, and study the influence of temperature change. Thirdly, 3000 infrared thermograms labeled for penetrating cracks, non-penetrating cracks, and surface scratches are fabricated into a databank. Then, the CNN is trained and validated on the data bank. Finally, improving the original Faster Region-based Convolutional Neural Network (Faster R-CNN) network, the feature maps of multiple levels in the feature extraction network are aggregated, and the anchor selection scheme of the Region Proposal Network (RPN) is adjusted from 9 to 25. The robustness of the improved Faster R-CNN is demonstrated by evaluating the detection results on the 125 images outside of the data bank, the accuracy and Mean Average Precision (mAP) are 95.54% and 92.41%, respectively, which outperform the original algorithm by increasing the accuracy of 3.18% and mAP of 1.88%.

Background: There is a variety of mortality prediction models for patients in intensive care units (ICU) to guide appropriate clinical management. Advances in machine learning methodologies typically employ classifiers such as Neural Network and Random Forest which are often regarded by healthcare professionals as black boxes. These models often do not provide clear links between the input model features and output clinical event. We investigate whether features identified by Cox-Proportional Hazards (CPH) model can be used for ICU mortality prediction. Methods: We employ the PhysioNet Challenge 2012 dataset, a subset of MIMIC-II Clinical Database data of ICU patients admitted to Boston's Beth Israel Deaconess Medical Center from 2001 to 2008. The dataset is split into train set A, test set B and unseen set C, with 4000 patients each. Python is the programming language used alongside scikit-learn, and lifelines packages. Besides white-box feature selection methods (logistic regression and decision tree), we also explore using Cox-Proportional Hazards model for feature selection. We then trained the machine learning model using classifiers such as logistic regression and variants of decision tree. Extreme gradient boosted trees models performed better than other classifiers. The model is validated using 5-fold cross-validation and evaluated against unseen set C. The model performance is assessed using area under the precision-recall curve (AUC-PR) as the main metric. Findings: The data of about 12,000 patients is used, providing a high degree of generalizability. The number of statistically significant features identified by CPH (n = 16) is significantly smaller than logistic regression (n = 36), decision tree (n = 26) and all features (n = 42). With only 16 features used, the model achieves a performance of AUC-PR 0·438 on test set B, which is close to decision tree (AUC-PR 0·442) and logistic regression (AUC-PR 0·446) and all features (AUC-PR 0·446). Interpretation: The significantly fewer features identified by CPH allows the building of a model that is easily interpretable by clinicians whilst still achieving comparable results to other models. This finding allows clinicians to use CPH as an alternative method to determine and act on features that need to be closely monitored for ICU patients.

Background Immunotherapy has achieved remarkable advances via a variety of strategies against tumor cells that evade immune surveillance. As important innate immune cells, macrophages play important roles in maintaining homeostasis, preventing pathogen invasion, resisting tumor cells and promoting adaptive immune response. CD47 is found to be overexpressed on tumor cells and act as a don’t eat me’ signal, which contributes to immune evasion. Macrophages mediated phagocytosis via blockade CD47/SIRPα (signal regulatory protein alpha) interaction was proved to induce effective antitumor immune response.Methods A novel peptide pep-20, specifically targeting CD47 and blocking CD47/SIRPα interaction, was identified via high-throughput phage display library bio-panning. The capability to enhance the macrophage-mediated phagocytosis activities and antitumor effects of pep-20 were investigated. The mechanism of pep-20 to induce T-cell response was explored by ex vivo analysis and confirmed via macrophage depleting strategy. The structure-activity relationship and D-amino acid substitution of pep-20 were also studied. The antitumor effects and mechanism of a proteolysis resistant D-amino acid derivate pep-20-D12 combined with irradiation (IR) were also investigated.Results Pep-20 showed remarkable enhancement of macrophage-mediated phagocytosis to both solid and hematologic tumor cells in vitro, and inhibited tumor growth in immune-competent tumor-bearing mice. Furthermore, pep-20 promoted macrophages to mobilize the antitumor T-cell response with minimal toxicity. Furthermore, systemic administration of the derivate pep-20-D12 showed robust synergistic antitumor efficacy in combination with IR.Conclusion In summary, these results demonstrated that CD47/SIRPα blocking peptides, pep-20 and its derivate, could serve as promising candidates to promote macrophages-mediated phagocytosis and immune response in cancer immunotherapy.

Background: Nonalcoholic fatty liver disease (NAFLD) is associated with altered production of secreted proteins. Increased understanding of secreted proteins could lead to improved prediction and treatment of NAFLD. Here, we aimed to discover novel secreted proteins in humans that are associated with hepatic fat content using unbiased proteomic profiling strategy, and how the identified Thbs1 modulates lipid metabolism and hepatic steatosis. Method: NAFLD patients were enrolled and treated with lifestyle intervention. Patients who underwent liver biopsy were enrolled for analyzing the correlation between circulating Thbs1 and liver steatosis. Mice were fed on high-fat, high-sucrose diet and treated with recombinant Thbs1. Primary hepatocytes isolated from CD36 knockout (CD36-/-) mice and their wild-type littermates (controls) were treated with glucose plus insulin for 24 h together with or without recombinant Thbs1. Finding: Serum Thbs1 levels are increased in participants with NAFLD and positively associated with liver steatosis grades. Improvement of liver steatosis after lifestyle intervention was accompanied with significant reduction of serum Thbs1 levels. Pharmacological administration of recombinant human Thbs1 attenuates hepatic steatosis in diet-induced obese mice. Treatment with Thbs1 protein or stably overexpression of Thbs1 causes a significant reduction of lipid accumulation in primary hepatocytes or HepG2 cells exposed to high glucose plus insulin, suggesting that Thbs1 regulates lipid metabolism in a hepatocyte-autonomous manner. Mechanistically, Thbs1 inhibits cleavage and processing of SREBP-1, leading to a reduction of target lipogenic gene expression and hepatic steatosis. Inhibitory effects of Thbs1 on lipogenesis and triglyceride accumulation are abrogated in CD36 deficient primary hepatocytes exposed to high glucose plus insulin. Interestingly, beneficial effects of Thbs1 on lipid accumulation are observed in primary hepatocytes treated with a Thbs1 nonapeptide mimetic ABT-526. Interpretation: Thbs1 is a biomarker for NAFLD in humans, and pharmacological and genetic approaches for the modulation of Thbs1 activity may have the therapeutic potential for treating hepatic steatosis. Fund: A full list of funding bodies that contributed to this study can be found in the Funding Sources section.

Background. The purpose of this study was to investigate the accuracy of the continuously stored data from the Abbott FreeStyle Libre flash glucose monitoring (FGM) system in Chinese diabetes patients during standard meal tests when glucose concentrations were rapidly changing. Subjects and Methods. Interstitial glucose levels were monitored for 14 days in 26 insulin-treated patients with type 2 diabetes using the FGM system. Standard meal tests were conducted to induce large glucose swings. Venous blood glucose (VBG) was tested at 0, 30, 60, and 120 min after standard meal tests in one middle day of the first and second weeks, respectively. The corresponding sensor glucose values were obtained from interpolating continuously stored data points. Assessment of accuracy was according to recent consensus recommendations with median absolute relative difference (MARD) and Clarke and Parkes error grid analysis (CEG and PEG). Results. Among 208 paired sensor-reference values, 100% were falling within zones A and B of the Clarke and Parkes error grid analysis. The overall MARD was 10.7% (SD, 7.8%). Weighted least squares regression analysis resulted in high agreement between the FGM sensor glucose and VBG readings. The overall MTT results showed that FGM was lower than actual VBG, with MAD of 22.1 mg/dL (1.2 mmol/L). At VBG rates of change of -1 to 0, 0 to 1, 1 to 2, and 2 to 3 mg/dl/min, MARD results were 11.4% (SD, 8.7%), 9.4% (SD, 6.5%), 9.9% (SD, 7.5%), and 9.5% (SD, 7.7%). At rapidly changing VBG concentrations (>3 mg/dl/min), MARD increased to 19.0%, which was significantly higher than slow changing BG groups. Conclusions. Continuously stored interstitial glucose measurements with the FGM system were found to be acceptable to evaluate VBG in terms of clinical decision during standard meal tests. The continuously stored data from the FGM system appeared to underestimate venous glucose and performed less well during rapid glucose changes.

Using pulse power technology to break concrete structures can reduce environmental pollution, save energy, and increase safety and reliability. The whole process of concrete beam subjected to shock wave generated by pulse power discharge was simulated and analyzed. An experiment of breaking reinforced concrete beams by metal wire explosion in liquid was carried out. And the main parameters are pulse power discharge voltage, copper wires section size, concrete beam material strength, drilling parameters, etc. The results show that with the increase of discharge voltage and the total area of copper wire cross section between electrodes, the breaking effect of concrete beam is obviously improved. The breaking effect of the beam is slightly improved when the concrete strength is reduced. The breaking effect of concrete beams with 40 mm aperture is better than that of concrete beams with 50 mm aperture. As the distance between adjacent boreholes decreases, the fractures are easier to connect. According to the test results, the formulas for calculating the crack width of concrete beams were proposed, which takes the output voltage, the number of copper wires between electrodes, the hole spacing, the strength of concrete and other key parameters as independent variables. The calculated results agree well with the test results.

Strategies boosting both innate and adaptive immunity have great application prospects in cancer immunotherapy. Antibodies dual blocking the innate checkpoint CD47 and adaptive checkpoint PD-L1 or TIGIT could achieve durable anti-tumor effects. However, a small molecule dual blockade of CD47/SIRPα and TIGIT/PVR pathways has not been investigated. Here, an elevated expression of CD47 and PVR was observed in tumor tissues and cell lines analyzed with the GEO datasets and by flow cytometry, respectively. Compounds approved by the FDA were screened with the software MOE by docking to the potential binding pockets of SIRPα and PVR identified with the corresponding structural analysis. The candidate compounds were screened by blocking and MST binding assays. Azelnidipine was found to dual block CD47/SIRPα and TIGIT/PVR pathways by co-targeting SIRPα and PVR. In vitro, azelnidipine could enhance the macrophage phagocytosis when co-cultured with tumor cells. In vivo, azelnidipine alone or combined with irradiation could significantly inhibit the growth of MC38 tumors. Azelnidipine also significantly inhibits the growth of CT26 tumors, by enhancing the infiltration and function of CD8+ T cell in tumor and systematic immune response in the tumor-draining lymph node and spleen in a CD8+ T cell dependent manner. Our research suggests that the anti-hypertensive drug azelnidipine could be repositioned for cancer immunotherapy.

This paper presents the COMSOL simulations of magnetically coupled resonant wireless power transfer (WPT), using simplified coil models for embroidered planar two-coil and four-coil systems. The power transmission of both systems is studied and compared by varying the separation, rotation angle and misalignment distance at resonance (5 MHz). The frequency splitting occurs at short separations from both the two-coil and four-coil systems, resulting in lower power transmission. Therefore, the systems are driven from 4 MHz to 6 MHz to analyze the impact of frequency splitting at close separations. The results show that both systems had a peak efficiency over 90% after tuning to the proper frequency to overcome the frequency splitting phenomenon at close separations below 10 cm. The four-coil design achieved higher power efficiency at separations over 10 cm. The power efficiency of both systems decreased linearly when the axial misalignment was over 4 cm or the misalignment angle between receiver and transmitter was over 45 degrees.

The goal of crowd counting is to estimate the number of people in the image. Presently, use regression to count people number became a mainstream method. It is worth noting that, with the development of convolutional neural networks (CNN), methods that are based on CNN have become a research hotspot. It is a more interesting topic that how to locate the site of the person in the image than simply predicting the number of people in the image. The perspective transformation present is still a challenge, because perspective distortion will cause differences in the size of the crowd in the image. To devote perspective distortion and locate the site of the person more accuracy, we design a novel framework named Adaptive Learning Network (CAL). We use the VGG as the backbone. After each pooling layer is output, we collect the 1/2, 1/4, 1/8, and 1/16 features of the original image and combine them with the weights learned by an adaptive learning branch. The object of our adaptive learning branch is each image in the datasets. By combining the output features of different sizes of each image, the challenge of drastic changes in the size of the image crowd due to perspective transformation is reduced. We conducted experiments on four population counting data sets (i.e., ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF-QNRF), and the results show that our model has a good performance.

Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus (DM). However, the exact mechanism is not clearly understood. In this study, our results showed that 24 h urinary protein, kidney index, and glomerular area were decreased, while creatinine clearance ratio was increased in DN rats when the rats were treated with NAR 50 mg/d for 6 weeks. Mesangial cell (MMCs) proliferation was inhibited in the NAR group by 3,(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT), and the cell cycle analysis showed that cells stayed in G2 phase in NAR group. And NAR treatment attenuated the deposition of ECM in DN rats and MMCs. Moreover, our data showed that let-7a was downexpressed in both DN rats and MMCs under high glucose condition. Surprisingly, NAR affected the expressions of Col4 and FN through upregulating let-7a in MMCs. In addition, we found that let-7a negatively regulated the expression of transforming growth factor-β1 receptor 1 (TGFBR1), and TGFBR1 was required for the let-7a-mediated downregulation of TGF-β1/smad signaling. Interestingly, NAR inhibited TGF-β1/smads signaling activation by upregulating let-7a. Therefore, our findings indicated that NAR ameliorated kidney injury by regulating let-7a/TGFBR1 signaling.

In our previous studies, we presumed subtypes of Graves' disease (GD) may be caused by different major susceptibility genes or different variants of a single susceptibility gene. However, more evidence is needed to support this hypothesis. Single-nucleotide polymorphism (SNP) rs2476601 in PTPN22 is the susceptibility loci of GD in the European population. However, this polymorphism has not been found in Asian populations. Here, we investigate whether PTPN22 is the susceptibility gene for GD in Chinese population and further determine the susceptibility variant of PTPN22 in GD. We conducted an imputation analysis based on the results of our genome-wide association study (GWAS) in 1,536 GD patients and 1,516 control subjects. Imputation revealed that 255 common SNPs on a linkage disequilibrium (LD) block containing PTPN22 were associated with GD (P0.05) in either the genotype distribution or allelic frequencies between patients and controls in the replication study. Although the combined analysis exhibited a weak association signal (P(combined) = 0.003263 for rs3811021), the false positive report probability (FPRP) analysis indicated it was most likely a false positive finding. Our study did not support an association of common SNPs in PTPN22 LD block with GD in Chinese Han population. This suggests that GD in different ethnic population is probably caused by distinct susceptibility genes.