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In order to analyze the four main properties of propellant, the performance prediction model based on BP neural network is established with LM algorithm and CG algorithm as the core algorithm, and the simulation calculation is carried out with the actual production process data and the performance of the model is analyzed. The results show that the mass prediction model based on BP neural network algorithm can quickly predict the properties of propellant. Compared with the prediction model based on CG algorithm, the prediction model based on LM algorithm is more suitable for propellant performance prediction and analysis.

According to Marxist philosophy, everything in the world is universally connected and in perpetual motion. Chinese society has gone through a long history of civilizational development. Since the modern era (1840-1919), great changes have taken place in the civilizational development of China. The transformation of social forms depends on the transformation of culture, and the approximation of culture to modernity is a prerequisite for the modernization of society. Thus, the development of society inevitably causes changes in art and culture; and vice versa, the progress of art and culture, in turn, contributes to the progress of society. A clear and complex line has been traced in the development of Chinese painting over the centuries: from a high degree of integration to gradual differentiation leading to conflict, and then to integration. The development of Chinese painting as a whole shows a transformational trend from tradition to modernity, developing in a tortuous way. In this paper, a combination of research methods is used for research, mainly documentary research, descriptive research, comparative research and case study. Considering the features and trends of Chinese painting at different stages of development, we can more accurately judge the direction of development of modern Chinese painting and find ways to integrate tradition and modernity.

Backpropagation has been successfully generalized to optimize deep spiking neural networks (SNNs), where, nevertheless, gradients need to be propagated back through all layers, resulting in a massive consumption of computing resources and an obstacle to the parallelization of training. A biologically motivated scheme of local learning provides an alternative to efficiently train deep networks but often suffers a low performance of accuracy on practical tasks. Thus, how to train deep SNNs with the local learning scheme to achieve both efficient and accurate performance still remains an important challenge. In this study, we focus on a supervised local learning scheme where each layer is independently optimized with an auxiliary classifier. Accordingly, we first propose a spike-based efficient local learning rule by only considering the direct dependencies in the current time. We then propose two variants that additionally incorporate temporal dependencies through a backward and forward process, respectively. The effectiveness and performance of our proposed methods are extensively evaluated with six mainstream datasets. Experimental results show that our methods can successfully scale up to large networks and substantially outperform the spike-based local learning baselines on all studied benchmarks. Our results also reveal that gradients with temporal dependencies are essential for high performance on temporal tasks, while they have negligible effects on rate-based tasks. Our work is significant as it brings the performance of spike-based local learning to a new level with the computational benefits being retained.

Recent years have witnessed the big success deep reinforcement learning achieved in the domain of card and board games, such as Go, chess and Texas Hold’em poker. However, Dou Di Zhu, a traditional Chinese card game, is still a challenging task for deep reinforcement learning methods due to the enormous action space and the sparse and delayed reward of each action from the environment. Basic reinforcement learning algorithms are more effective in the simple environments which have small action spaces and valuable and concrete reward functions, and unfortunately, are shown not be able to deal with Dou Di Zhu satisfactorily. This work introduces an approach named Two-steps Q-Network based on DQN to playing Dou Di Zhu, which compresses the huge action space through dividing it into two parts according to the rules of Dou Di Zhu and fills in the sparse rewards using inverse reinforcement learning (IRL) through abstracting the reward function from experts’ demonstrations. It is illustrated by the experiments that two-steps Q-network gains great advancements compared with DQN used in Dou Di Zhu. &nbsp

Explosives and propellants are common basic energy for weapons and equipment to achieve delivery, damage, and control, and are an important manifestation of national defense power. However, due to the wide variety of products and complex performance, the traditional serial design optimization mode can no longer meet the requirements of product diversification and rapid development. In this paper, the design process of explosives is deeply studied, and a method for optimizing the comprehensive performance of explosives and propellants oriented to the whole process is proposed. The method uses a comprehensive performance optimization engine to numerically model different design links in a unified parameter space, connects different links horizontally, and adopts a multi-objective optimization algorithm to comprehensively consider the optimization objectives of different links, realizing the automatic execution of the optimization process according to the design process. In order to verify the effectiveness of the method, this paper optimizes the formula of explosives and propellants based on two different types of explosives, gun propellant and rocket propellant. Experimental results show that this method improves the design efficiency and improves product quality under the premise of ensuring safety and manufacturability.

The SM-MSR (small modular molten salt reactor) has a good prospect for development with regards to combining the superiority of the molten salt reactor and modularization technologies, showing the advantages of safety, reliability, low economic cost and flexibility of site selection. However, because its internal structural parts are not easily replaced, and the outer shielding structure is limited, the lifespan of the reactor vessel and its in-reactor shielding design needs to be addressed. In order to find an optimal shielding model with both high fuel efficiency and strong radiation shielding capability, five different design schemes were proposed in this work, which varied in thickness and boron concentration in inner-shielding materials. The neutron/gamma flux and DPA (displacements per atom)/helium production rates were evaluated to obtain an appropriate scheme. Several beneficial results were obtained. Considering the above factors and the actual manufacturing process, 20 cm-thick boron graphite with a 5 wt% Boron-10 concentration combined with a 1 cm-thick Hastelloy barrel was chosen as the in-reactor shielding structure. Outside the reactor, the neutron flux was reduced to 8.33 × 1010 cm−2 s−1, and the gamma flux was decreased to 1.13 × 1011 cm−2 s−1. The vessel/barrel material could maintain a lifespan of more than 10 years, while the burnup depth was 6.25% lower than that of a model without inner-shielding. The conclusions of this research can provide important references for the shielding design and parameter selections of small molten salt reactors in the future.

Today, timestamped web documents related to a general news query flood the Internet, and timeline summarization targets this concisely by summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this article we propose our Unified Timeline Summarizer, which can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information retained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting a summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that our Unified Timeline Summarizer achieves state-of-the-art performance in terms of both automatic and human evaluations. 1

Organic disulfide-modified folate carbon dots for tumor-targeted synergistic chemodynamic/photodynamic therapy have been fabricated.

Photo-induced synthesis, stereochemistry and antitumor activity of valine-based small cyclopeptides were studied.

Graphene oxide modulated dual S-scheme ultrathin heterojunctions with iron phthalocyanine and phase-mixed bismuth molybdate as wide visible-light catalysts for tetracycline degradation.

We study privacy-utility trade-offs where users share privacy-correlated useful information with a service provider to obtain some utility. The service provider is adversarial in the sense that it can infer the users' private information based on the shared useful information. To minimize the privacy leakage while maintaining a desired level of utility, the users carefully perturb the useful information via a probabilistic privacy mapping before sharing it. We focus on the setting in which the adversary attempting an inference attack on the users' privacy has potentially biased information about the statistical correlation between the private and useful variables. This information asymmetry between the users and the limited adversary leads to better privacy guarantees than the case of the omniscient adversary under the same utility requirement. We first identify assumptions on the adversary's information so that the inference costs are well-defined and finite. Then, we characterize the impact of the information asymmetry and show that it increases the inference costs for the adversary. We further formulate the design of the privacy mapping against a limited adversary using a difference of convex functions program and solve it via the concave-convex procedure. When the adversary's information is not precisely available, we adopt a Bayesian view and represent the adversary's information by a probability distribution. In this case, the expected cost for the adversary does not admit a closed-form expression, and we establish and maximize a lower bound of the expected cost. We provide a numerical example regarding a census data set to illustrate the theoretical results.

Unipolar depression (UD) and bipolar depression (BD) showed convergent and divergent cognitive impairments. Neural oscillations are linked to the foundational cognitive processes. We aimed to investigate the underpinning spectral neuronal power patterns by magnetoencephalography (MEG), which combinates high spatial and temporal resolution. We hypothesized that patients with UD and BD exhibit common and distinct patterns, which may contribute to their cognitive impairments.Group cognitive tests were performed. Eyes closed resting-state MEG data were collected from 61 UD, 55 BD, and 52 healthy controls (HC). Nonparametric cluster-based permutation tests were performed to deal with the multiple comparison problem on channel-frequency MEG data. Correlation analysis of cognitive dysfunction scores and MEG oscillation were conducted by Spearman or partial correlation analysis.Wisconsin Card Sorting Test showed similar cognitive impairment in patients with UD and BD. Moreover, patients with BD exhibited extensive cognitive deficits in verbal executive functions and visuospatial processing. Compare to HC, both patients with UD and BD showed increased frontal-central beta power while high gamma power was decreased in UD groups during the resting-state. The significant correlations between cognitive function and average beta power were observed.Patients with BD had more cognitive impairments on different dimensions than those with UD, involving disrupted beta power modulations. Our investigation provides a better understanding of the neuroelectrophysiological process underlying cognitive impairments in patients with UD and BD.

Red carbon dot-doped Cu-MOF nanoplatform was fabricated and used in combination with immune checkpoint blockade antitumor therapy.

Due to advances in medical treatments, a substantial proportion of heart failure (HF) patients with reduced left ventricular ejection fraction (EF, HFrEF) have experienced partial or complete recovery of EF, termed HFrecEF, and markedly improved clinical outcomes. In the present study, we sought to investigate the relationship between glycemic control and the incidence of HFrecEF in hospitalized HFrEF patients with type 2 diabetes mellitus (T2DM).A total of 463 hospitalized T2DM patients with HFrEF were consecutively enrolled. Follow-up echocardiogram was performed after around 12 months. Patients who had an absolute EF improvement ≥10% and a second EF 40% were classified into HFrecEF, and those who did not meet these criteria were defined as persistent HFrEF.During the 12-month follow-up, 44.5% of T2DM patients developed HFrecEF. Patients with HFrecEF had significantly lower HbA1c level than those with persistent HFrEF (6.5% [IQR 5.8% ∼ 7.2%] vs. 6.7% [IQR 6.1% ∼ 7.8%], P = 0.003), especially in HF of an ischemic etiology. HbA1c levels were inversely correlated with changes in EF during follow-up. After multivariate adjustment, every 1% increase in HbA1c conferred a 17.4% (OR: 0.826 [95% CI 0.701-0.968]) lower likelihood of HFrecEF. Compared to patients with good glycemic control (HbA1c ≤ 6.2%), those with poor glycemic control (HbA1c 7.1%) had a 52.0% (OR: 0.480 [95% CI 0.281-0.811] decreased likelihood of HFrecEF.This study demonstrates that uncontrolled HbA1c level is associated with compromised development of HFrecEF in T2DM patients with HF, especially in those with an ischemic etiology.

Mental disorders caused by stress have become a worldwide public health problem. These mental disorders are often the results of a combination of genes and environment, in which epigenetic modifications play a crucial role. At present, the genetic and epigenetic mechanisms of mental disorders such as posttraumatic stress disorder or depression caused by environmental stress are not entirely clear. Although many epigenetic modifications affect gene regulation, the most well-known modification in eukaryotic cells is the DNA methylation of CpG islands. Stress causes changes in DNA methylation in the brain to participate in the neuronal function or mood-modulating behaviors, and these epigenetic modifications can be passed on to offspring. Ten-eleven translocation (Tet) enzymes are the 5-methylcytosine (5mC) hydroxylases of DNA, which recognize 5mC on the DNA sequence and oxidize it to 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC), and 5-carboxylcytosine (5caC). Tet regulates gene expression at the transcriptional level through the demethylation of DNA. This review will elaborate on the molecular mechanism and the functions of Tet enzymes in environmental stress-related disorders and discuss future research directions.

The monoamine oxidase type B inhibitors plus channel blockers (MAO-BIs plus) are a new class of anti-parkinsonian drug with additional mechanisms of action for their property as ion channel blockers. The present study aimed to compare the efficacy and safety of MAO-BIs plus and conventional MAO-BIs, as well as their corresponding doses, as adjuvant therapy to levodopa in the treatment of Parkinson's disease (PD).Randomized controlled trials (RCTs) enrolling PD patients treated with selegiline, rasagiline, safinamide, or zonisamide as adjuvant therapy to levodopa were identified. Bayesian network meta-analysis (NMA) was conducted.Thirty-one RCTs comprising 7142 PD patients were included. Compared with levodopa monotherapy, the combination therapy of MAO-BIs and levodopa was significantly more effective, with a mean difference of 2.74 (1.26-4.18) on Unified Parkinson's Disease Rating Scale (UPDRS) III score change for selegiline, 2.67 (1.45-3.87) for safinamide, 2.2 (0.98-3.64) for zonisamide, and 2.04 (1.24-2.87) for rasagiline. No significant difference was detected among MAO-BIs. Surface under the cumulative ranking (SUCRA) results showed safinamide-100mg and rasagiline-1mg ranked first in improving UPDRS III and UPDRS II, respectively. Zonisamide-100mg ranked first in reducing OFF time. For safety outcomes, rasagiline was associated with a higher incidence of adverse events than placebo and safinamide. MAO-BIs plus had higher probability of being safer agents compared to conventional MAO-BIs .MAO-BIs plus, conventional MAO-BIs, and the corresponding doses are similar in efficacy in PD treatment. MAO-BIs plus might be safer than conventional MAO-BIs. Head-to-head comparisons are in need for further investigation.

In recent years, the resurgence of pertussis has posed a public health challenge in many countries. This study aimed to evaluate the immunity levels against pertussis among populations of different ages in China.We conducted a cross-sectional serological survey in Zhejiang Province, China in 2020. Serum IgG antibodies against pertussis toxin (anti-PT), filamentous hemagglutinin (anti-FHA), and pertactin (anti-PRN) were quantitatively measured. The geometric mean concentration (GMC) of three antibodies was calculated. An anti-PT level 5 IU/mL was considered undetectable, ≥20 IU/mL as seropositive and ≥80 IU/mL as an indicator of recent infection. Mathematical models were fitted for anti-PT concentrations over time in children after four doses of the pertussis vaccination.A total of 4459 participants aged 0-59 years were included in the analyses. The overall positivity rate of anti-PT was 29.80% with the highest (81.44%) rate in the 1-2 years old and the lowest (4.72%) in 10-14 years old. The GMCs of anti-PT, anti-FHA and anti-PRN for the whole participants were 9.67 (95%CI: 9.25-10.10),18.93 (18.24-19.67), and 8.99 (8.61-9.38) IU/mL, respectively. Over 50% of subjects aged ≥ 7 years had undetectable anti-PT IgG antibodies (5IU/mL). The proportions of the populations with anti-PT IgG ≥ 80 IU/mL were approximately 0.9%, 0.3% and 1.1% among the 10-14, 15-29, and 40-59 years old groups, respectively. The power regression equation of the attenuation model after last dose of pertussis vaccine was y = 41.088xThe prevalence of pertussis decreased during the study period in the COVID-19 pandemic; however, there was still a certain proportion of adolescents and adults with evidence of recent infection. The decline in antibody levels after pertussis vaccination was observed, and booster doses are in urgent need in China.

Background and ObjectivesLimbic predominant age-related TAR DNA binding protein 43 (TDP-43) encephalopathy neuropathologic change (LATE-NC) is a prevalent degenerative pathology in the oldest-old who are the fastest-growing segment of our population with the highest rates of dementia. We aimed to determine the relationship between LATE-NC and cognitive impairment and to identify its potential risk factors by studying its relationship with common past medical histories in an oldest-old cohort.MethodsParticipants from The 90+ Study with longitudinal evaluations and autopsy data were included. Dementia status and impairment in 5 main cognitive domains were determined at postmortem conferences leveraging all clinical and neuropsychological data blind to neuropathologic diagnosis. Medical history information was obtained from patients and their informants. LATE-NC and Alzheimer disease neuropathologic change (ADNC) were considered present in those with TDP-43 pathology in the hippocampus and/or neocortex and those with high likelihood of ADNC according to NIA-AA guidelines, respectively. We examined the association of degenerative pathologies with cognitive outcomes and multiple comparisons–adjusted relationship of medical history variables with LATE-NC and ADNC using logistic regressions adjusted for age at death, sex, and education.ResultsThree hundred twenty-eight participants were included in this study. LATE-NC was present in 32% of the participants. It had a significant association with the presence of dementia (OR 2.8, 95% CI 1.7–4.6) and impairment in memory (OR 3.0, 95% CI 1.8–5.1), language (OR 2.6, 95% CI 1.6–4.3), and orientation (OR 3.5, 95% CI 2.1–5.9). The association with impaired orientation was unique to LATE-NC, and the strength and significance of the other associations were comparable to ADNC. Furthermore, we found that history of osteoarthritis (OR 0.37, adjusted 95% CI 0.21–0.66) and hypertension (OR 0.52, adjusted 95% CI 0.28–0.98) were associated with a reduced likelihood of LATE-NC, but not ADNC.DiscussionOur results suggest that LATE-NC is a prevalent degenerative pathology in the oldest-old and has significant associations with dementia and impairment in cognitive domains with magnitudes that are comparable to ADNC. We also found that past medical histories of hypertension and osteoarthritis were associated with a lower likelihood of LATE-NC. This might help identify upstream mechanisms leading to this important pathology.

Gait recognition can be used in person identification and re-identification by itself or in conjunction with other biometrics. Although gait has both spatial and temporal attributes, and it has been observed that decoupling spatial feature and temporal feature can better exploit the gait feature on the fine-grained level. However, the spatial-temporal correlations of gait video signals are also lost in the decoupling process. Direct 3D convolution approaches can retain such correlations, but they also introduce unnecessary interferences. Instead of common 3D convolution solutions, this paper proposes an integration of decoupling process into a 3D convolution framework for cross-view gait recognition. In particular, a novel block consisting of a Parallel-insight Convolution layer integrated with a Spatial-Temporal Dual-Attention (STDA) unit is proposed as the basic block for global spatial-temporal information extraction. Under the guidance of the STDA unit, this block can well integrate spatial-temporal information extracted by two decoupled models and at the same time retain the spatial-temporal correlations. In addition, a Multi-Scale Salient Feature Extractor is proposed to further exploit the fine-grained features through context awareness extension of part-based features and adaptively aggregating the spatial features. Extensive experiments on three popular gait datasets, namely CASIA-B, OULP and OUMVLP, demonstrate that the proposed method outperforms state-of-the-art methods.

Event cameras sense changes in light intensity and record them as an asynchronous event stream. Efficiently encoding and learning spatiotemporal information of the event streams remain challenging. In this paper, we propose a novel event descriptor to encode the spatio-temporal features for event streams and a local-search based multi-spike learning algorithm for spiking neural networks to classify encoded features. The spatio-temporal event surface (STES) descriptor explicitly captures both spatial and temporal correlations among events, and thus can characterize spatiotemporal features more accurately than existing feature descriptors that focus only on temporal or spatial information. In classification with multi-spike learning, we introduce a local search and gradient clipping mechanism to ensure the efficiency and stability of learning, which avoids other multi-spike learning rules' time-consuming global search and the gradient explosion problem. Experimental results demonstrate the superior classification performance of our proposed model, especially for event streams with rich spatiotemporal dynamics.

The chromatin-modifying enzyme ATAD2 confers oncogenic competence and proliferative advantage in malignances. We previously identified ATAD2 as a marker and driver of cell proliferation in ovarian cancer (OC); however, the mechanisms whereby ATAD2 is regulated and involved in cell proliferation are still unclear. Here, we disclose that ATAD2 displays a classical G2/M gene signature, functioning to facilitate mitotic progression. ATAD2 ablation caused mitotic arrest and decreased the ability of OC cells to pass through nocodazole-arrested mitosis. ChIP-seq data analyses demonstrated that DREAM and MYBL2-MuvB (MMB), two switchable MuvB-based complexes, bind the CHR elements in the ATAD2 promoter, representing a typical feature and principle mechanism of the periodic regulation of G2/M genes. As a downstream target of MYBL2, ATAD2 deletion significantly impaired MYBL2-driven cell proliferation. Intriguingly, ATAD2 silencing also fed back to destabilize the MYBL2 protein. The significant coexpression of MYBL2 and ATAD2 at both the bulk tissue and single-cell levels highlights the existence of the MYBL2-ATAD2 signaling in OC patients. This signaling is activated during tumorigenesis and correlated with TP53 mutation, and its hyperactivation was found especially in high-grade serous and drug-resistant OCs. Disrupting this signaling by CRISPR/Cas9-mediated ATAD2 ablation inhibited the in vivo growth of OC in a subcutaneous tumor xenograft mouse model, while pharmacologically targeting this signaling with an ATAD2 inhibitor demonstrated high therapeutic efficacy in both drug-sensitive and drug-resistant OC cells. Collectively, we identified a novel MYBL2-ATAD2 proliferative signaling axis and highlighted its potential application in developing new therapeutic strategies, especially for high-grade serous and drug-resistant OCs.

Brief therapy for insomnia (BTI) is a short-term cognitive behavioral therapy for insomnia. At present, there is no study combining BTI with digital technology. However, in the context of the outbreak of coronavirus disease 2019 (COVID-19), patients with acute insomnia may need an online treatment which can quickly improve insomnia symptoms. Our team built a digital BTI (dBTI) platform based on the WeChat mini program. This research provides a framework design and a course design of dBTI, and evaluates the system via recruiting participants suffering from acute insomnia in pandemic. What's more, it explores patients' adherence, the efficiency of the system and their relationship. As the result demonstrates, 68% of participants have completed more than half of the course with medium to high adherence. Gender, pre-sleep arousal scale (PSAS) somatic score and insomnia severity index (ISI) score have affected participants' adherence, and higher adherence has led to better improvement in the severity of insomnia and somatic pre-sleep arousal. It is proved that the platform we built is effective, which not only offers an entry point for the study of how to set up a dBTI platform, but also provides theoretical basis for its clinical application.

Yi-Qi Cong-Ming (YQCM) decoction has been widely used to prevent age-related hearing loss (ARHL), the most prevalent neurodegenerative disease in the elderly.To explore the mechanism of YQCM decoction in the treatment of ARHL.The chemical constituents of YQCM were screened from the Traditional Chinese Medicine Systems Pharmacology Database. Potential targets of YQCM against ARHL were predicted by DrugBank, GeneCards, and OMIM database. Protein-protein network and enrichment analysis were used for exploring possible molecular mechanisms. Molecular docking and anThe main compounds have good affinities with hub targets, especially AKT1, PTGS2, and CASP3. GO and KEGG analysis showed that the main biological process and key targets were related to negative regulation of the apoptotic process. HOur study suggested that YQCM prevents ARHL by modulating the apoptosis process in auditory hair cells. Moreover, this study proved that bioinformatics analysis combined with molecular docking and cell model is a promising method to explore other possible pharmacological interventions of ARHL.

This paper is concerned with the problem of adaptive event-triggered tracking control for a class of uncertain stochastic nonlinear systems in strict-feedback form. Firstly, with the help of fuzzy logic systems (FLSs) to approximate the unknown nonlinear functions, a robust fuzzy state observer is constructed to estimate all the unmeasurable states. Next, an adaptive output feedback controller which can adjust the variables on-line is designed by using the backstepping scheme. Concomitantly, in order to reduce the computation of communication process, a new event triggering condition involving the decreasing function of tracking errors is introduced. Moreover, the desired closed-loop stability of the resulting systems can be achieved by exploiting Lyapunov function analysis. Finally, simulation results verify the effectiveness of the proposed method.

Structure-from-motion (SfM) techniques have been widely used for three-dimensional (3D) scene reconstruction from sequential video frames. However, for reconstructing narrow and confined spaces such as the interior of drainage pipes, selecting geometrically optimal frames is a major challenge, not only to reduce the number of needed frames but also to yield better geometry. This paper introduces a coarse-to-fine method to optimize the selection of monocular video frames based on a geometric criterion called feature-based parallax analysis for 3D pipe reconstruction. The proposed method was applied in two experiments with a monocular camera fixed on a customized robot. Experimental results show that our approach only requires respectively 9.66% and 3.15% of the number of frames. The spatial distribution of the retrieved frames was uniform and reasonable, enabling the successful SfM process to achieve a complete reconstruction of the pipe geometry.

Objective. To investigate the effects of peroxisome proliferator-activated receptor (PPARγ) expression on renal podocyte in diabetic mice by conditionally knockout mouse PPARγ gene. Methods. Wild-type C57BL mice and PPARγ gene knockout mice were used as research objects to establish the diabetic mouse model, which was divided into normal control group (NC group), normal glucose PPARγ gene knockout group (NK group), diabetic wild-type group (DM group), and diabetic PPARγ gene knockout group (DK group), with 8 mice in each group. After 16 weeks, the mice were sacrificed for renal tissue collection. Morphological changes of renal tissue were observed by HE and Masson staining, and ultrastructure of renal tissue was observed by transmission electron microscope. Protein expressions of PPARγ, podocin, nephrin, collagen IV, and fibronectin (FN) in renal tissues were detected by immunohistochemistry and Western blot, and mRNA changes of PPARγ, podocin, and nephrin in renal tissues were detected by qRT-PCR. Results. Compared with the NC group, the protein and mRNA expressions of PPARγ, podocin, and nephrin decreased in the kidney tissue of mice in the DM group, while the protein expressions of collagen IV and FN increased. The expression of various proteins in kidney tissues of the DK group was consistent with that of the DM group, and the difference was more obvious. The expression of PPARγ protein and mRNA decreased in the NK group, while the expression of podocin, nephrin protein and mRNA, collagen IV, and FN protein showed no significant difference. Conclusion. In diabetic renal tissue, the loss of PPARγ can aggravate podocellular damage and thus promote the occurrence of diabetic renal fibrosis. Increasing the expression of PPARγ may effectively relieve renal podocyte impairment in diabetic patients, which can be used for the treatment of diabetic nephropathy.

To explore the risk factors for adverse pregnancy outcomes (APOs) in women with rheumatoid arthritis (RA) and the influence on their offspring health.Pregnant women with RA (n = 67) who were hospitalized in the Department of Obstetrics, Peking University People's Hospital between January 2007 and September 2021 were included in this study. Participants were evaluated at least once in each trimester and postpartum. Fetal outcomes and RA disease activity were extracted from medical records, and the offspring of enrolled patients were followed up. Associations between RA disease activity, medication use, and pregnancy outcomes were analyzed.The incidence of APOs in our cohort was 43.3%. Postpartum hemorrhage (20.9%) was the commonest complication, followed by premature delivery (11.9%). Previous miscarriages [odds ratio (OR): 1.869, 95% confidence interval (CI): 1.053-3.318, P = 0.033] and antinuclear antibody (ANA) positivity (OR:3.168, 95% CI: 1.068-9.768, P = 0.045) were risk factors for APOs. Compared to patients with APOs, the remission rate of disease activity during pregnancy was higher in patients without APOs (P = 0.027). There were no significant differences between patients with and without APOs with respect to daily and cumulative doses of prednisone (P 0.05). The average age of the offspring was 4.9 years (range 0.3-14 years). Long-term follow-up showed no significant differences in offspring health between the two groups (P 0.05).Previous miscarriages and ANA positivity are independent risk factors for APOs in RA patients, while adverse pregnancy outcomes and low-dose prednisone have no effect on offspring health. Key points • Previous miscarriages and ANA positivity are risk factors for APOs in RA patients. • Adverse pregnancy outcomes and low-dose prednisone during pregnancy have no effect on offspring health.

Chinese painting has always been a unique product of our ancient civilization, which has survived to the present day. During its 2000-year development, Chinese painting has gradually turned into a unique and non-reproducible art form capable of expressing the thoughts and feelings of the artist. Over time, Chinese painting also demonstrates a new trend of inheritance and innovative development. Changes in Chinese painting occur simultaneously with the processes of globalization. This gives rise to new research on the trends of diversification of artistic creativity. The focus of artistic expression has shifted from the depiction of direct natural objects towards the spiritual plane, which is a direct result of the creative activity of artists whose aspirations are manifested in the use of traditional Chinese ink painting in combination with new methods of figurative representation and deep understanding of spiritual content. Since ancient times, artists have actively sought to harmonize and balance the subjective and objective, as well as to achieve their organic combination. This article examines traditional Chinese painting as a source and driving force of the transformation of Chinese painting、prints of the era and integration in the content of paintings、attempts to introduce the Western concept of frontal composition, as well as updating the language of painting and artistic concepts. It is argued that the fundamental driving force and the main support for the development of Chinese painting in the context of globalization should be traditions integrated with new creative elements and techniques, thus it is possible to better promote Chinese culture and inherit the essence of traditional Chinese painting.

PurposeClear cell renal cell carcinoma (ccRCC) is the most common pathology type in kidney cancer. However, the prognosis of advanced ccRCC is unsatisfactory. Thus, early diagnosis becomes one of the most important research priorities of ccRCC. However, currently available studies about ccRCC lack urine-related further studies. In this study, we applied proteomics to search urinary biomarkers to assist early diagnosis of ccRCC. In addition, we constructed a prognostic model to assist judge patients’ prognosis.Materials and methodsUrine which was used to perform 4D label-free quantitative proteomics was collected from 12 ccRCC patients and 11 non-tumor patients with no urinary system diseases. The urine of 12 patients with ccRCC confirmed by pathological examination after surgery was collected before operatoin. Bioinformatics analysis was used to describe the urinary proteomics landscape of these patients with ccRCC. The top ten proteins with the highest expression content were selected as the basis for subsequent validation. Urine from 46 ccRCC patients and 45 control patients were collected to use for verification by enzyme linked immunosorbent assay (ELISA). In order to assess the prognostic value of urine proteomics, a prognostic model was constructed by COX regression analysis on the intersection of RNA-sequencing data in The Cancer Genome Atlas (TCGA) database and our urine proteomic data.Results133 proteins differentially expressed in the urinary samples were found and 85 proteins (Fold Change, FC>1.5) were identified up-regulated while 48 down-regulated (FCPPConclusionOur study indicated that the application of urine proteomics to explore diagnostic biomarkers and to construct prognostic models of renal clear cell carcinoma is of certain clinical value. PKHD1L1, ANGPTL6, FABP4 and C3 can assist to diagnose ccRCC. The prognostic model constituted of VSIG4, HLA-DRA, SERPINF1, and IGLV2-23 can significantly predict the prognosis of ccRCC patients, but this still needs more clinical trials to verify.