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IntroductionDetermining the causal relationship between polycystic ovary syndrome (PCOS) and gestational diabetes mellitus (GDM) holds significant implications for GDM prevention and treatment. Despite numerous observational studies suggesting an association between PCOS and GDM, it remains unclear whether a definitive causal relationship exists between these two conditions and which specific features of PCOS contribute to increased incidence of GDM.MethodsThe causal relationship between polycystic ovary syndrome (PCOS), its characteristic indices, and gestational diabetes mellitus (GDM) was investigated using a two-sample Mendelian randomization study based on publicly available statistics from genome-wide association studies (GWAS). The inverse-variance weighted method was employed as the primary analytical approach to examine the association between PCOS, its characteristic indices, and GDM. MR Egger intercept was used to assess pleiotropy, while Q values and their corresponding P values were utilized to evaluate heterogeneity. It is important to note that this study adopts a two-sample MR design where PCOS and its characteristic indices are considered as exposures, while GDM is treated as an outcome.ResultsThe study results indicate that there is no causal relationship between PCOS and GDM (all methods P > 0.05, 95% CI of OR values passed 1). The IVW OR value was 1.007 with a 95% CI of 0.906 to 1.119 and a P value of 0.904. Moreover, the MR Egger Q value was 8.141 with a P value of 0.701, while the IVW Q value was also 8.141 with a P value of 0.774, indicating no significant heterogeneity. Additionally, the MR Egger intercept was 0.0004, which was close to zero with a P value of 0.988, suggesting no pleiotropy. However, the study did find a causal relationship between several other factors such as testosterone, high-density lipoprotein, sex hormone-binding globulin, body mass index, waist-hip ratio, apolipoprotein A-I, number of children, diabetes illnesses of mother, father and siblings, hemoglobin A1c, fasting insulin, fasting blood glucose, years of schooling, and GDM based on the IVW method.ConclusionWe observed no association between genetically predicted PCOS and the risk of GDM, implying that PCOS itself does not confer an increased susceptibility to GDM. The presence of other PCOS-related factors such as testosterone, high-density lipoprotein, and sex hormone-binding globulin may elucidate the link between PCOS and GDM. Based on these findings, efforts aimed at preventing GDM in individuals with PCOS should prioritize those exhibiting high-risk features rather than encompassing all women with PCOS.

Focusing on the complex market environment of Guangdong Province, this research pioneers a model for assimilating external power sources into the intra-provincial power market, a critical move to support the region’s high demand for electricity driven by robust economic activities. Our study innovatively tackles the province’s fluctuating power supply and demand by employing a novel power supply equivalence and co-modulation technique, enhancing system modeling efficiency and computational effectiveness. Comparative evaluations demonstrate that our approach significantly reduces the power market’s purchase costs by 15% while safeguarding power supply stability and security. The proposed model presents a breakthrough in optimizing Guangdong’s power market’s economic and reliability aspects, offering a promising pathway for future market clearing practices.

To address the growing complexity of urban traffic congestion and its associated environmental impacts, this study presents a pioneering application of the Gaussian plume model to investigate the carbon dioxide emission reduction efficacy of various reinforcement learning algorithms within a traffic signal control framework. By employing an insightful fusion of the traditional environmental science tool with contemporary reinforcement learning strategies - specifically Independent Partially Observable Policy Optimization (IPPO), Independent Delay Q-Network (IDQN), and MPLight - this research marks a novel intersection of methodologies. By quantitatively simulating and analyzing the diffusion dynamics of carbon dioxide pollutants under different traffic signal control scenarios, the study not only highlights the innovative use of the Gaussian plume model to assess the environmental impact of traffic signal control, but also provides critical insights into the selection and optimization of traffic signal control algorithms for improved urban environmental sustainability.

Background: Acute ischemic stroke (AIS) is a major global public health issue. There is limited research on the relationship between ambient temperature and AIS hospital admissions, and the results are controversial. Our objective is to assess the short-term impact of ambient temperature on the risk of AIS hospital admissions in Yancheng, China. Methods: We collected data on daily AIS hospital admissions, meteorological factors, and air quality in Yancheng from 2014 to 2019. We used Poisson regression to fit generalized linear models and distributed lag non-linear models to explore the association between ambient temperature and AIS hospital admissions. The effects of these associations were evaluated by stratified analysis by sex and age. Results: From 2014 to 2019, we identified a total of 13,391 AIS hospital admissions. We observed that the influence of extreme cold and heat on admissions for AIS manifests immediately on the day of exposure and continues for a duration of 3–5 days. Compared to the optimal temperature (24.4 °C), the cumulative relative risk under extreme cold temperature (-1.3 °C) conditions with a lag of 0–5 days was 1.88 (95%CI: 1.28, 2.78), and under extreme heat temperature (30.5 °C) conditions with a lag of 0–5 days was 1.48 (95%CI: 1.26, 1.73). Conclusions: There is a non-linear association between ambient temperature and AIS hospital admission risk in Yancheng, China. Women and older patients are more vulnerable to non-optimal temperatures. Our findings may reveal the potential impact of climate change on the risk of AIS hospital admissions. [ABSTRACT FROM AUTHOR]

Background: The role of tumor inflammatory microenvironment in the advancement of cancer, particularly prostate cancer, is widely acknowledged. ELL-associated factor 2 (EAF2), a tumor suppressor that has been identified in the prostate, is often downregulated in prostate cancer. Earlier investigations have shown that mice with EAF2 gene knockout exhibited a substantial infiltration of inflammatory cells into the prostatic stroma. Methods: A cohort comprising 38 patients who had been diagnosed with prostate cancer and subsequently undergone radical prostatectomy (RP) was selected. These patients were pathologically graded according to the Gleason scoring system and divided into two groups. The purpose of this selection was to investigate the potential correlation between EAF2 and CD163 using immunohistochemistry (IHC) staining. Additionally, in vitro experimentation was conducted to verify the relationship between EAF2 expression, macrophage migration and polarization. Results: Our study demonstrated that in specimens of human prostate cancer, the expression of EAF2 was notably downregulated, and this decrease was inversely associated with the number of CD163-positive macrophages that infiltrated the cancerous tissue. Cell co-culture experiments revealed that the chemotactic effect of tumor cells towards macrophages was intensified and that macrophages differentiated into tumor-associated macrophages (TAMs) when EAF2 was knocked out. Additionally, the application of cytokine protein microarray showed that the expression of chemokine macrophage migration inhibitory factor (MIF) increased after EAF2 knockout. Conclusions: Our findings suggested that EAF2 was involved in the infiltration of CD163-positive macrophages in prostate cancer via MIF. Highlights: EAF2 is associated with the progression of prostate cancer. EAF2 is correlated with Gleason score of prostate cancer. EAF2 is closely related to macrophages recruitment and polarition. EAF2 recruits M2 macrophages through upregulation of MIF. [ABSTRACT FROM AUTHOR]

Prior literature finds senior executives can influence auditor decision making. However, few studies have discussed the impact of employee's personal characteristics. Our research aims to fill the above research gaps by examining the impact of employee level education on audit costs. Taking A-share listed companies in Shanghai and Shenzhen from 2006 to 2021 as the research object, this paper examines the impact of employee education on audit fees. It is found that highly educated employees can effectively reduce the audit fees borne by the company, but the implementation of the Labor Protection Law weakens this inhibitory effect. In the case of low marketization level and weak Confucian culture intensity, employee education level has a more significant inhibitory effect on audit fees of listed companies. This study provides a basis for empirical research on the impact of employee attributes on auditor decision making, provides a new research perspective on the impact of labor protection law at the corporate micro level, and enriches the theoretical research on corporate governance rooted in traditional Chinese culture. We contribute to the practice that implications for evaluating the effectiveness of adopting labor protection. [ABSTRACT FROM AUTHOR]

Persistence reinforces continuous action, which benefits animals in many aspects. Diverse external or internal signals may trigger animals to start a persistent movement. However, it is unclear how the brain decides to persist with current actions by selecting specific information. Using single-unit extracellular recordings and opto-tagging in awake mice, we demonstrated that a group of dorsal mPFC (dmPFC) motor cortex projecting (MP) neurons initiate a persistent movement by selectively encoding contextual information rather than natural valence. Inactivation of dmPFC MP neurons impairs the initiation and reduces neuronal activity in the insular and motor cortex. After the persistent movement is initiated, the dmPFC MP neurons are not required to maintain it. Finally, a computational model suggests that a successive sensory stimulus acts as an input signal for the dmPFC MP neurons to initiate a persistent movement. These results reveal a neural initiation mechanism on the persistent movement. How the brain decides to persist with current action remains unclear. Here, the authors show that a prefrontal circuit utilizes contextual signals to initiate such persistent movement. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN)-based and Transformer-based methods for hyperspectral image (HSI) classification have rapidly advanced due to their unique characterization capabilities. However, the fixed kernel sizes in convolutional layers limit the comprehensive utilization of multi-scale features in HSI land cover analysis, while the Transformer's multi-head self-attention (MHSA) mechanism faces challenges in effectively encoding feature information across various dimensions. To tackle this issue, this article introduces an HSI classification method, based on multi-scale convolutional features and multi-attention mechanisms (i.e., MSCF-MAM). Firstly, the model employs a multi-scale convolutional module to capture features across different scales in HSIs. Secondly, to enhance the integration of local and global channel features and establish long-range dependencies, a feature enhancement module based on pyramid squeeze attention (PSA) is employed. Lastly, the model leverages a classical Transformer Encoder (TE) and linear layers to encode and classify the transformed spatial–spectral features. The proposed method is evaluated on three publicly available datasets—Salina Valley (SV), WHU-Hi-HanChuan (HC), and WHU-Hi-HongHu (HH). Extensive experimental results have demonstrated that the MSCF-MAM method outperforms several representative methods in terms of classification performance. [ABSTRACT FROM AUTHOR]

Land use profoundly impacts the sustainable development of the ecological environment. Optimizing land use patterns is a vital approach to mitigate climate change and achieve carbon neutrality. Using Shandong Province as a case study, this research evaluates the impacts of land use and land cover change (LUCC) on regional carbon storage and emissions. Employing a coupled PLUS–InVEST–GM(1,1) model, simulations were conducted for scenarios including the natural scenario (NS), cropland protection scenario (CPS), high-speed development scenario (HDS), and low-carbon scenario (LCS), to assess LUCC and changes in carbon storage and emissions from 2030 to 2060 under these scenarios. The findings indicate that due to the expansion of construction land and significant declines in arable and grassland areas, carbon emissions increased by 40,436.44 × 104 t over a 20-year period, while carbon storage decreased by 4881.13 × 104 t. Notably, forests contributed the most to carbon sequestration, while construction land emerged as the primary source of carbon emissions. Simulating four scenarios demonstrates that measures such as protecting cropland, expanding forest, grassland, and aquatic areas, controlling construction land expansion, and promoting intensive development positively affect emission reductions and carbon sequestration in Shandong. These findings underscore the importance of rational planning of land use patterns, which can enhance contributions to carbon neutrality by harmonizing the relationships among cropland protection, ecological conservation, and economic development. [ABSTRACT FROM AUTHOR]

Purpose: Lung cancer (LC) and breast cancer (BC) are the most common causes of brain metastases (BMs). Time from primary diagnosis to BM (TPDBM) refers to the time interval between initial LC or BC diagnosis and development of BM. This research aims to identify clinical, molecular, and therapeutic risk factors associated with shorter TPDBM. Methods: We retrospectively reviewed all diagnosed LC and BC patients with BM at Harbin Medical University Cancer Hospital from 2016 to 2020. A total of 570 patients with LC brain metastasis (LCBM) and 173 patients with breast cancer brain metastasis (BCBM) patients who met the inclusion criteria were enrolled for further analysis. BM free survival time curves were generated using Kaplan–Meier analyses. Univariate and multivariate Cox regression analyses were applied to identify risk factors associated with earlier development of BM in LC and BC, respectively. Results: The median TPDBM was 5.3 months in LC and 44.4 months in BC. In multivariate analysis, clinical stage IV and M1 stage were independent risk factors for early development of LCBM. LC patients who received chemotherapy, targeted therapy, pulmonary radiotherapy, and pulmonary surgery had longer TPDBM. For BC patients, age ≥ 50 years, Ki67 ≥ 0.3, HER2 positive or triple‐negative breast cancer subtype, advanced N stage, and no mastectomy were correlated with shorter TPDBM. Conclusions: This single‐institutional study helps identify patients who have a high risk of developing BM early. For these patients, early detection and intervention could have clinical benefits. [ABSTRACT FROM AUTHOR]

Green infrastructures (e.g., forests, parks, and other types of green spaces) in urban areas provide people with a huge volume of ecosystem benefits. However, the quality of urban green infrastructure varies among cities in different countries/regions, and key ecological processes, maintaining mechanisms, and policy decision routes remain unclear. Here, we recognize four themes that link studies from the Asia-Pacific and European regions presented in this Editorial: (1) indicators and services of urban green spaces; (2) assembly of and changes in diverse plant communities; (3) utilization and evaluation of urban forest landscape; and (4) patterns and drivers of urban agro-forestry systems. These investigations enlarge our understanding on the theoretical exploration and methodological innovation of urban forestry studies in response to the changing environment, and shed some light on routes to achieve sustainable development goals in the context of rapid urbanization. [ABSTRACT FROM AUTHOR]

Objective: Brachytherapy has been indicated as an alternative option for treating cystic craniopharyngiomas (CPs). The potential benefits of brachytherapy for CPs have not yet been clarified. The purpose of this work was to conduct a meta-analysis to analyze the long-term efficacy and adverse reactions profile of brachytherapy for CPs. Materials and methods: The relevant databases were searched to collect the clinical trials on brachytherapy in patients with CPs. Included studies were limited to publications in full manuscript form with at least 5-year median follow-up, and adequate reporting of treatment outcomes and adverse reactions data. Stata 12.0 was used for data analysis. Results: According to the inclusion and exclusion criteria, a total of 6 clinical trials involving 266 patients with CPs were included in this meta-analysis. The minimum average follow-up was 5 years. The results of the meta-analysis showed that 1-year, 2–3 years and 5 years progression free survival rates (PFS) are 75% (95%CI: 66-84%), 62% (95%CI: 52-72%) and 57% (95%CI: 22-92%), respectively. At the last follow-up, less than 16% of patients with visual outcomes worser than baseline in all included studies. While, for endocrine outcomes, less than 32% of patients worser than baseline level. Conclusion: In general, based on the above results, brachytherapy should be considered as a good choice for the treatment of CP. [ABSTRACT FROM AUTHOR]

Multi-horizontal submerged jets stilling basins have been utilized in large-scale water conservancy and hydropower projects due to its stable flow pattern, high energy dissipation rate and less atomization. This study employs vorticity criterion, Q criterion, λ2 criterion and Ω criterion to investigate the characteristics of vortex formation and turbulent dissipation in multi-horizontal submerged jets stilling basins with various configurations, including crest overflowing orifice alone (COO), combination of crest overflowing orifice and mid-discharge orifice (COO-MO) and mid-discharge orifice alone (MO). The results indicate that the Q criterion and λ2 criterion are effective in identifying vortex structure within multi-horizontal submerged jets stilling basin. Specifically, the stronger intensity of vortex structure and vortex dissipation are mainly distributed in the vicinity of the vertical drop, which gradually weakens for the increasing distance to the vertical drop. Furthermore, the intensity and number of vortexes with COO-MO are the largest. This conclusion can provide guidance for energy dissipation and bottom protection of stilling pool. [ABSTRACT FROM AUTHOR]

High‐entropy alloys (HEAs) materials, as promising nanomaterials, have garnered significant attention from researchers due to their excellent performance in the field of hydrogen evolution reaction (HER). The four core effects of HEAs, including the high‐entropy effect, severe lattice distortion effect, sluggish diffusion effect, and cocktail effect, are pivotal in underpinning their remarkable mechanical and thermodynamic properties. Nevertheless, the intricate geometric and electronic structures of HEAs make their catalytic mechanisms exceptionally complex and challenging to decipher. In particular, a thorough analysis of the underlying factors responsible for the outstanding catalytic activity, selectivity, and the ability to maintain stable hydrogen production, even at high current densities, in HEAs is lacking. To provide a systematic exploration of the design and application of HEAs in HER systems, this review commences with an examination of the physicochemical properties of HEAs. It covers a wide range of topics, including the synthesis methods of HEAs, and the major reaction mechanisms of HERs, and presents innovative methods and approaches for designing HEAs specifically in the context of HERs. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance. [ABSTRACT FROM AUTHOR]

The work focuses on the optimization of management strategies of energy supply chain in oil–gas enterprises to find out the optimal management strategy and overcome the fragility of the traditional energy supply system. Back propagation neural network (BPNN) is applied to energy supply predicting and energy security early warning in oil–gas enterprises, and the security early warning system is designed based on the auxiliary cognitive system. The security early warning system is used to predict the peak value of oil and gas supply, and then the detection performances and the data security transmission performances of different models are compared and analysed. Finally, a comprehensive evaluation model for security management of oil–gas supply chain based on the neural network is established. It is found that compared with convolutional neural network (CNNs), the improved BPNN model algorithm proposed shows increased accuracy by 5.4%, reduced model training time and test time, and improved accuracy and recall rate. The accuracy, recall rate, and F1 value of the proposed model are 88.48%, 75.88%, and 96.21%, respectively, which were obviously higher than those of the CNN. It suggests that the improved security management model of oil–gas supply chain shows better recognition and prediction accuracy. Based on the quantitative and qualitative analysis results, the optimization suggestions for management strategies in oil–gas enterprises are put forward, which is of strategic significance for improving the security of oil–gas supply. [ABSTRACT FROM AUTHOR]