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BackgroundDistinct clinical features and molecular characteristics of left-sided colon cancer (LCC) and right-sided colon cancer (RCC) suggest significant variations in their tumor microenvironments (TME). These differences can impact the efficacy of immunotherapy, making it essential to investigate and understand these disparities.MethodsWe conducted a multi-omics analysis, including bulk RNA sequencing (bulk RNA-seq), single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing (WES), to investigate the constituents and characteristic differences of the tumor microenvironment (TME) in left-sided colon cancer (LCC) and right-sided colon cancer (RCC).ResultDeconvolution algorithms revealed significant differences in infiltrated immune cells between left-sided colon cancer (LCC) and right-sided colon cancer (RCC), including dendritic cells, neutrophils, natural killer (NK) cells, CD4 and CD8 T cells, and M1 macrophages (P < 0.05). Notably, whole-exome sequencing (WES) data analysis showed a significantly higher mutation frequency in RCC compared to LCC (82,187/162 versus 18,726/115, P < 0.01). Single-cell analysis identified predominant tumor cell subclusters in RCC characterized by heightened proliferative potential and increased expression of major histocompatibility complex class I molecules. However, the main CD8 + T cell subpopulations in RCC exhibited a highly differentiated state, marked by T cell exhaustion and recent activation, defined as tumor-specific cytotoxic T lymphocytes (CTLs). Immunofluorescence and flow cytometry results confirmed this trend. Additionally, intercellular communication analysis demonstrated a greater quantity and intensity of interactions between tumor-specific CTLs and tumor cells in RCC.ConclusionRCC patients with an abundance of tumor-specific cytotoxic T lymphocytes (CTLs) and increased immunogenicity of tumor cells in the TME may be better candidates for immune checkpoint inhibitor therapy.

With the significant breakthrough in the research of single-modal related deep learning tasks, more and more works begin to focus on multi-modal tasks. Multi-modal tasks usually involve more than one different modalities, and a modality represents a type of behavior or state. Common multi-modal information includes vision, hearing, language, touch, and smell. Vision and language are two of the most common modalities in human daily life, and many typical multi-modal tasks focus on these two modalities, such as visual captioning and visual grounding. In this paper, we conduct in-depth research on typical tasks of vision and language from the perspectives of generation, analysis, and reasoning. First, the analysis and summary with the typical tasks and some pretty classical methods are introduced, which will be generalized from the aspects of different algorithmic concerns, and be further discussed frequently used datasets and metrics. Then, some other variant tasks and cutting-edge tasks are briefly summarized to build a more comprehensive vision and language related multi-modal tasks framework. Finally, we further discuss the development of pre-training related research and make an outlook for future research. We hope this survey can help relevant researchers to understand the latest progress, existing problems, and exploration directions of vision and language multi-modal related tasks, and provide guidance for future research.

As the core component of photovoltaic grid-connected system, the performance improvement of inverter is an important means to improve the performance of photovoltaic grid-connected system. As the key components of the inverter system, capacitors and inductors play the role of energy storage and filtering in the system, and their electrical characteristics will directly affect the dynamic and static characteristics of the photovoltaic inverter system. The existing research showed that capacitors and inductors should be fractional in nature, and the modeling, analysis and control of inverter systems were carried out on the theoretical basis of integer calculus, which will inevitably produce some errors. Therefore, the fractional controller of three-phase photovoltaic inverter system based on the improved Oustaloup algorithm is introduced into the grid-connected photovoltaic inverter system in this paper for fractional modeling, analysis and control, which can provide a theoretical model for the simulation and analysis of fractional power electronic systems. The practical operation in Jining PV power generation base (installed capacity of 560MW) in Shandong Province, China, showed that the fractional-order PI $^{\mathrm {\lambda }}$ controller had a good control effect on the grid-connected PV system, and the power quality of grid-connected PV was also improved.

The application of computational fluid dynamics combined with 3D modeling of the hydraulic model was discussed in this paper. Through the complex calculation of the internal low field, dynamic simulation was used to predict the characteristics of water energy index. Based on the collection of a large number of different types of turbine operation data in different working conditions, the improved multi-layer neural network power prediction model based on the adaptive inverse normalization strategy and the improved grey wolf algorithm were used to optimize the least squares support vector machine power prediction model. Rlue activation function was used in the improved multi-layer neural network model uses to improve the computation speed and prevent gradient disappearance. Combined with L2 regularization to prevent data overfitting, Adam gradient optimizer was applied to achieve fast convergence of network output. The simulation results shown that the method can realize the high precision prediction of turbine output power.

In this paper, a Multi-Index Nonlinear Robust Adaptive Control (MINRAC) method was proposed for ultra-complex multi-input multi-output power systems with uncertain parameter factors and external disturbances. The controller designed by this method has excellent static and dynamic characteristics. Under the condition of the uncertainty of system parameters and external disturbance at the same time, the MINRAC method can ensure that the multiple indexes concerned by ultra-complex power systems can be controlled at their expected values. The simulation results showed that the control mechanism of MINRAC method was consistent in both single-machine infinite bus system and multi-machine interconnected coupling system. The output function chooses power angle, angular frequency and terminal voltage as constraints. When the system has parameter uncertainty and external interference, the uncertain parameter values are adjusted by adaptive control to force these indicators to tend to the given expected value. For three-phase short circuit, which is the most serious fault in power system, the use of multi index nonlinear robust controller can ensure that the system is stable in a wide range and has better dynamic performance.

BackgroundMental health problems are common among men who have sex with men (MSM) living with HIV and may negatively affect medication adherence. Psychosocial interventions designed to address these urgent needs are scarce in China. Incorporating behavioral health theories into intervention development strengthens the effectiveness of these interventions. The absence of a robust theoretical basis for interventions may also present challenges to identify active intervention ingredients. ObjectiveThis study aims to systematically describe the development of a mobile health–based intervention for MSM recently diagnosed with HIV in China, including the theoretical basis for the content and the considerations for its technological delivery. MethodsWe used intervention mapping (IM) to guide overall intervention development, a behavioral intervention technology model for technological delivery design, and a human-centered design and cultural adaptation model for intervention tailoring throughout all steps of IM. ResultsThe dialectical behavior therapy (DBT)–informed intervention, Turning to Sunshine, comprised 3 components: app-based individual skills learning, group-based skills training, and on-demand phone coaching. The theoretical basis for the intervention content is based on the DBT model of emotions, which fits our conceptualization of the intervention user’s mental health needs. The intervention aims to help MSM recently diagnosed with HIV (1) survive moments of high emotional intensity and strong action urges, (2) change emotional expression to regulate emotions, and (3) reduce emotional vulnerability, as well as (4) augment community resources for mental health services. Technological delivery considerations included rationale of the medium, complexity, and esthetics of information delivery; data logs; data visualization; notifications; and passive data collection. ConclusionsThis study laid out the steps for the development of a DBT-informed mobile health intervention that integrated app-based individual learning, group-based skills training, and phone coaching. This intervention, Turning to Sunshine, aims to improve mental health outcomes for MSM newly diagnosed with HIV in China. The IM framework informed by human-centered design principles and cultural adaptation considerations offered a systematic approach to develop the current intervention and tailor it to the target intervention users. The behavioral intervention technology model facilitated the translation of behavioral intervention strategies into technological delivery components. The systematic development and reporting of the current intervention can serve as a guide for similar intervention studies. The content of the current intervention could be adapted for a broader population with similar emotional struggles to improve their mental health outcomes.

Abstract Background RNA binding proteins (RBPs) have been implicated in oncogenesis and progression in various cancers. However, the potential value of RBPs as prognostic indicators and therapeutic targets in colorectal cancer (CRC) requires further investigation. Methods Four thousand eighty two RBPs were collected from literature. The weighted gene co-expression network analysis (WGCNA) was performed to identify prognosis-related RBP gene modules based on the data attained from the TCGA cohorts. LASSO algorithm was conducted to establish a prognostic risk model, and the validity of the proposed model was confirmed by an independent GEO dataset. Functional enrichment analysis was performed to reveal the potential biological functions and pathways of the signature and to estimate tumor immune infiltration. Potential therapeutic compounds were inferred utilizing CMap database. Expressions of hub genes were further verified through the Human Protein Atlas (HPA) database and RT-qPCR. Results One thousand seven hundred thirty four RBPs were differently expressed in CRC samples and 4 gene modules remarkably linked to the prognosis were identified, based on which a 12-gene signature was established for prognosis prediction. Multivariate Cox analysis suggested this signature was an independent predicting factor of overall survival (P

Abstract Background Surveys on Patient Safety Culture™ (SOPS®) Hospital Survey (HSOPS 1.0), developed by the U.S. Agency for Healthcare Research and Quality in 2004, has been widely adopted in the United States and internationally. An updated version, the SOPS Hospital Survey 2.0 (HSOPS 2.0), released in 2019, has not yet been applied in China. The aim of the present study was to translate HSOPS 2.0 into Chinese version with cross-cultural adaptations and test its psychometric properties. Methods A convenience sample was used. Hospital nurses (N = 1013) and a sub-set (n = 200) was invited for the re-test. A three-stage study was conducted. Firstly, the HSOPS 2.0 was translated by a panel. Secondly, the content validity was tested using the two-round Delphi method and cognitive interview. Next, the construct validity was tested by the confirmatory factor analysis and further demonstrated by the convergent validity, discriminant validity, and correlations with the outcome of patient safety. Thirdly, the reliability was tested by internal consistency reliability and re-test reliability. Results The “float or PRN” and “manager” words were deleted as considered unfitted for the Chinese health care system. The content validity index provided evidence of strong content validity (I-CVI = 0.84 ~ 1.00, S-CVI = 0.98). Confirmatory factor analysis revealed a good model fit (χ2/df = 4.05, RMSEA = 0.06, CFI = 0.94) and acceptable factor loadings (0.41 ~ 0.97). Convergent validity, and discriminant validity supported the factorial structure of the Chinese version of HSOPS 2.0. Further evidence for the construct validity was derived from correlations with the outcome of patient safety (r = 0.10 ~ 0.41). A good internal consistency (Cronbach’s α = 0.68 ~ 0.93, McDonald’s omega = 0.84 ~ 0.96) and test-retest reliability (ICC = 0.78 ~ 0.95) showed acceptable reliability. Additionally, Chinese nurses reported markedly lower scores for three dimensions, including “Response to Error”, “Communication Openness”, and “Reporting Patient Safety Events”, when comparing the findings of this study with those from U.S. research utilizing the HSOPS 2.0. Conclusion The Chinese version of HSOPS 2.0 demonstrated good validity and reliability in a Chinese sample of hospital nurses, which suggests that it can be used to measure nurse-perceived patient safety culture in future research and practice. Psychometric properties of the Chinese version of HSOPS 2.0 among other Chinese healthcare professionals remain to be confirmed.

Evaporation is a critical process involved in energy and water balance at the Earth’s surface and bears significant implications for water resource management, agricultural irrigation, and drought monitoring, among others. In this study, we focused on establishing a 1 km daily surface evaporation estimation for the Yangtze River Basin from 2010 to 2019 by using a machine learning method, and then analyzed its spatiotemporal patterns. The findings showed spatial heterogeneity in the Yangtze River Basin, indicating higher evaporation rates in the southwestern and southeastern regions in contrast to the western and northern areas. Additionally, the basin exhibited a strong spatial autocorrelation, indicating the influence of one spatial unit on the others. Furthermore, most regions in the basin displayed non-significant changes in surface evaporation, with some areas in the upper reaches exhibiting significant increases and a few regions near the source of the Yangtze River experiencing significant decreases. This study contributes to a better understanding of the spatial and temporal distribution of evaporation in the Yangtze River Basin, providing valuable insights for water resource management, environmental studies, and hydrological modeling in the region.

Abstract Background Delay in care seeking is one of the causes for neonatal death. Mothers’ knowledge of neonatal danger signs is imperative to promote early recognition of neonatal illness and reduce the delay in care seeking. Currently, no study has been conducted on the knowledge about neonatal danger signs in China, especially in economically less developed areas. This study aimed to examine the knowledge of neonatal danger signs and risk factors of poor knowledge among mothers in a rural county of southwest of China. Methods A cross-sectional study was conducted in Wenshan, a rural county of southwest of China. A total of 112 respondents were included from November 2020 to February 2021 among women who had babies aged 0–12 months and brought their babies to health care centers for immunization within the study period. A questionnaire with 18-item key neonatal danger signs was used to measure their knowledge about these signs. Mothers who scored above average were considered to have relatively good knowledge whereas those who scored below average were considered to have relatively poor knowledge. Independent predictors of mothers’ knowledge were identified by multivariable logistic regression analysis. Results The mean knowledge score of neonatal danger signs of mothers was 18.1 (SD = 8.6). Fifty-eight percentage of mothers (65/112) had poor knowledge of neonatal danger signs. Danger signs of “bluish or pale skin”, “chest indrawing”, and “convulsion” were mostly recognized, whereas danger signs of “not able to feed since birth, or stopped feeding well”, “excessive crying” and “eyes draining pus” were recognized poorly. Less than four antenatal visits [AOR = 4.348], younger than 25 years old [AOR = 3.839], ethnic minority [AOR = 3.956] and family financial difficulty [AOR = 4.944] were significant indicators of relatively poor knowledge. Conclusions Mothers’ knowledge about neonatal danger signs in rural China is poor even though the coverage of maternal and child health care services are expanded. Existing efforts should be enhanced for antenatal care visits, avoiding early marriage as well as early childbearing. More attention should be paid to low-income ethnic minority mothers. Educating and training should be strengthened for danger signs, especially those who are predicted to have insufficient knowledge.

With the development of modern communication technology and the large number of new controllable loads connected to the power grid, the new controllable loads with flexible regulation characteristics can participate in the emergency frequency stability control. However, the communication state differences and spatial distribution characteristics of controllable load will affect the actual effect of frequency control. In this paper, an emergency frequency control method based on deep reinforcement learning is proposed considering the response time of controllable load shedding. The proposed method evaluates response ability for emergency control of controlled loads through load response time, controllable load amount and controllable load buses. Then, the controllable load with smaller response time is cut out preferentially to ensure rapid control, and the Markov Decision Process (MDP) is used to model the emergency frequency control problem. Finally, Rainbow algorithm of Deep Reinforcement Learning (DRL) is used to optimize the emergency frequency stability control strategy involving controllable load resources. The formation of emergency load shedding instruction is directly driven by high-dimensional operation state data after power grid failure, so that, the aim of minimizing the economic cost is achieved under the constraint of system stability. The effectiveness of the proposed method is verified in the IEEE 39-bus system.

Abstract Pets have been indispensable members for many families in modern life, especially significant for the elderly and the blind. However, they may cause false alarm when misused as signal source in non-contact monitoring of the vital signs using ultra-wideband (UWB) radar. Distinguishing between humans and pets can help ensure the correct signal source. Nevertheless, existing solutions are few or only utilize a single feature, which can hinder robustness and accuracy because of individual differences. In this study, we proposed a robust multi-feature based method to solve the problem. First, 19 discriminative features were extracted to reflect differences in aspects of energy, frequency, wavelet entropy, and correlation coefficient. Second, the features were ranked by recursive feature elimination algorithm and the top eight were then selected to build an optimal support vector machine (SVM) model. The area under the receiver operating characteristic curve (AUC) of the optimal SVM model reached 0.9620. The false and missing alarms for identifying humans were 0.0962 and 0.0600, respectively. Finally, comparison with the state-of-the-art method that only employed one feature validated the advance and accuracy of the proposed method. The method is envisioned to facilitate the UWB radar applications in non-contact and continuous vital signs monitoring.

Ginkgolide B (GB), an important active constituent of Ginkgo biloba extract, has been used in clinical applications for the treatment of dementia, cerebral insufficiency or related cognitive decline. To investigate the main pharmacokinetic characteristics of three different GB formulations in beagle dogs, a simple, specific and sensitive LC-MS/MS method was established and validated. The separation of the analytes was achieved on an Agilent Eclipse Plus C18 column (1.8 μm, 2.1 × 50 mm) with a mobile phase consisting of water and acetonitrile. The flow rate was set at 0.4 mL/min. Quantitation was performed using multiple reaction monitoring (MRM) in negative ion mode, with the transitions at m/z (Q1/Q3) 423.1/367.1 for GB and m/z 269.3/170.0 for IS. The linear calibration curve of GB was obtained over the concentration range of 2–200 ng/mL. The intra- and inter-day precisions were

Hyperlipidemia is a major component of metabolic syndrome, and regarded as one of the main risk factors causing metabolic diseases. We have developed a therapeutic drug, akebia saponin D (ASD), and determined its anti-hyperlipidemia activity and the potential mechanism(s) of action by analyzing the metabolome and intestinal microbiota. Male Sprague-Dawley rats were fed a high fat diet to induce hyperlipidemia, and then given ASD orally for 8 weeks. Lipid levels in serum were determined biochemically. Metabolites in serum, urine and feces were analyzed by UPLC-Q/TOF-MS, and the structure of the intestinal microbiota was determined by 16S rRNA sequencing. The ASD treatment significantly decreased the levels of TC, TG and LDL-c and increased the serum level of HDL-c. Metabolomics analysis indicated that the ASD treatment mainly impacted seven differential metabolites in the serum, sixteen differential metabolites in the urine and four differential metabolites in feces compared to the model group. The ASD treatment significantly changed eight bacteria at the genus level compared to the model group. In conclusion, ASD treatment can significantly alleviate HFD-induced hyperlipidemia and the hypolipidemic effect of ASD treatment is certainly associated with a systematic change in the metabolism, as well as dynamic changes in the structure of the intestinal microbiota.

Etk is a non-receptor tyrosine kinase, which provides a strong survival signal in human prostate cancer cells. Src, another tyrosine kinase that cross-activates with Etk, has been shown to play an important role in prostate cancer metastasis. Herein, we discovered a new class of Etk inhibitors. Within those inhibitors, CTA095 was identified as a potent Etk and Src dual inhibitor. CTA095 was found to induce autophagy as well as apoptosis in human prostate cancer cells. In addition, CTA095 inhibited HUVEC cell tube formation and "wound healing" of human prostate cancer cells, implying its role in inhibition of angiogenesis and metastasis of human prostate cancer. More interestingly, CTA095 could overcome Src inhibitor resistance in prostate cancer cells. It induces apoptosis in Src inhibitor resistant prostate cancer cells, likely through a mechanism of down regulation of Myc and BCL2. This finding indicates that simultaneously targeting Etk and Src could be a promising approach to overcome drug resistance in prostate cancer.

The authors wish to correct the funding projects number of “Natural Science Foundation of Jiangsu Province” in the Acknowledgments section of this paper [1]: The correct funding projects number should be “No. BK20130403”, not “No. BK2013403”.[...]

BackgroundBurnout has become a prominent issue as the increase of workload in family doctor team members in primary healthcare institutions during the promotion of contracted family doctor services. There is still a lack of research comparing the differences in burnout among family doctor team members in different types of primary healthcare institutions.ObjectiveTo compare burnout prevalence and associated factors between family doctors in community/township health centers, and those in community health stations/village clinics, providing a basis for improving the mental health status and team stability of family doctors, as well as the quality of services provided by them.MethodsFrom August 1 to 21, 2020, a multistage cluster random sampling method was used to select 760 family doctor team members〔201 (26.4%) working at community/township health centers, and 559 (73.6%) working at community health stations/village clinics〕 as the participants from primary healthcare institutions in 6 counties/county-level cities /districts of Taian City, Shandong Province. They were invited to attend a survey to complete Demographic Questionnaire and the Chinese version of Maslach Burnout Inventory-General Survey (MBI-GS) .ResultsOverall, the prevalence of burnout among the participants was 68.9% (524/760) . Overall, the prevalence of burnout among the participants was 68.9% (524/760) , and the prevalence of burnoutof family doctor team members in community/township health centers and community health stations/village clinics was 63.7% (128/201) and 70.8% (396/559) , respectively. The levels of burnout of family doctor team members in community health stations/village clinics was higher than that of those in community/township health centers, with a statistically significant difference (P4 000 yuan is lower than that with monthly income