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This paper proposes a robust bilevel mixed-integer profit maximization model for an independent distributed energy resource (DER) aggregator participating in the wholesale electricity market, considering the uncertain aggregation of electric vehicles (EVs) to the grid, as well as the discrete security check of the distribution system conducted by the non-market-participating distribution company. Regarding the uncertainty in EV–grid connectivity caused by stochastic transportation behavior, we characterize the robust connectivity at the lower level to ensure that the energy required for their daily transportation can be met. Solving the proposed bilevel mixed-integer profit maximization model is challenging due to the integer variables involved in the lower-level security check and robust connectivity problem, which makes the traditional strong duality and KKT method inapplicable. Thus, we propose using the total unimodularity property, multi-value-function approach, and strong duality method to transform the original bilevel model into an equivalent single-level model. Moreover, a sampling-based accelerated optimization algorithm is proposed to solve the equivalent single-level model efficiently. Case studies on a real-world transmission–distribution system verify that: (1) the proposed robust model outperforms deterministic models in profit by accommodating EV aggregation uncertainty; (2) the algorithm significantly reduces computational time compared to stochastic modeling approaches, while ensuring compliance with distribution network discrete security constraints.

Accurate wind power forecasting is critical for enhancing the operational efficiency and stability of electrical power grids. Conventional single-variable signal decomposition forecasting methods ignore the coupling relationship between wind power and multiple meteorological data, thus limiting prediction accuracy. This study proposes an accurate and fast short-term wind power prediction approach based on series-core fusion technology considering multiple meteorological data. In the data preprocessing stage, the multivariate variational mode decomposition (MVMD) algorithm decomposes wind power and meteorological variables into the same predefined number of frequency-aligned intrinsic mode functions (IMFs), thereby enhancing feature representation and improving forecasting accuracy via a more comprehensive and detailed dataset representation. During the training stage, the series-core fused time series (SOFTS) model establishes the connection among wind power channel and other meteorological variable channels for each IMF, achieving fast convergence through its streamlined and parallel structure. In the forecasting stage, the final wind power prediction is generated by the reconstruction of all IMFs. Furthermore, we conducted a comprehensive performance evaluation by comparing the proposed MVMD-SOFTS model with eight alternative models, including the CNN model, the TCN model, the LSTM model, the GRU model, the Transformer model, the SOFTS model, the CEEMDAN-SOFTS model, and the VMD-SOFTS model. The results indicate that MVMD-SOFTS outperformed all other models, demonstrating its effectiveness in capturing the multifaceted relationships in wind power forecasting.

Background Empirical evidence on the epidemiology of hypertension, diabetes and hypercholesterolemia is limited in many countries in Central and Eastern Europe. We aimed to estimate the prevalence, awareness, treatment and control of hypertension, diabetes and hypercholesterolemia in the Czech Republic, Russia, Poland and Lithuania, and to identify the risk factors for the three chronic conditions. Methods We analysed cross-sectional data from the HAPIEE study, including adults aged 45–69 years in the Czech Republic, Russia, Poland and Lithuania, collected between 2002 and 2008 (total sample N  = 30,882). Among prevalent cases, we estimated awareness, treatment, and control of hypertension, diabetes and hypercholesterolemia by gender and country. Multivariate logistic regression was applied to identify associated risk factors. Results In each country among both men and women, we found high prevalence but low control of hypertension, diabetes, and hypercholesterolemia. Awareness rates of hypertension were the lowest in both men (61.40%) and women (69.21%) in the Czech Republic, while awareness rates of hypercholesterolemia were the highest in both men (46.51%) and women (51.20%) in Poland. Polish participants also had the highest rates of awareness (77.37% in men and 79.53% in women), treatment (71.99% in men and 74.87% in women) and control (30.98% in men and 38.08% in women) of diabetes. The common risk factors for the three chronic conditions were age, gender, education, obesity and alcohol consumption. Conclusions Patterns of awareness, treatment and control rates of hypertension, diabetes and hypercholesterolemia differed by country. Efforts should be made in all four countries to control these conditions, including implementation of international guidelines in everyday practice to improve detection and effective management of these conditions.

Objective: To evaluate the comparative efficacy and safety of Janus kinase (JAK) inhibitors and biological disease-modifying antirheumatic drugs (bDMARDs) in patients with rheumatoid arthritis (RA) and an inadequate response to at least one disease-modifying antirheumatic drug (DMARD). Methods: PubMed, Embase, Cochrane library and ClinicalTrials.gov were searched for relevant randomized controlled trials (RCTs) from inception to April 2020. The active drugs included three JAK inhibitors and eight bDMARDs while the control drugs included placebo or conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). Outcomes include American College of Rheumatology 20% response (ACR20), Disease Activity Score in 28 joints (DAS28), Health Assessment Questionnaire–Disability Index (HAQ-DI) and discontinuations for adverse events (AEs). We estimated summary odds ratios (ORs) and weighted mean differences (WMDs) using network meta-analysis with random effects. Results: Eighty-eight RCTs with 31,566 patients were included. All JAK inhibitors and bDMARDs were more effective than placebo in ACR20 (ORs ranging between 3.05 and 5.61), DAS28 (WMDs ranging between −1.91 and −0.80) and HAQ-DI (WMDs ranging between −0.34 and −0.21). Tocilizumab, certolizumab pegol and upadacitinib showed relatively good efficacy in these three outcomes according to their relative ranking. Notably, tocilizumab was more effective than other active drugs in DAS28 (WMDs ranging between −1.11 and −0.49). Compared with the lower recommended doses, increasing the doses of JAK inhibitors (baricitinib 4 mg versus 2 mg, tofacitinib 10 mg versus 5 mg and upadacitinib 30 mg versus 15 mg) cannot provide significant additional benefits. In terms of discontinuations for AEs, all active drugs showed no significant difference compared with placebo except certolizumab pegol [OR 1.65, 95% credible interval (CrI) 1.06–2.61] and rituximab (3.17, 1.11–10.80). Conclusions: Tocilizumab, certolizumab pegol and upadacitinib may have relatively good efficacy in patients with RA after treatment failure with csDMARDs. RA patients taking a JAK inhibitor may have a preference for a lower recommended dose.

AimTo evaluate the diagnostic performance of the American College of Rheumatology (ACR)-1997, the Systemic Lupus International Collaborating Clinics (SLICC)-2012, and the European League against Rheumatism (EULAR)/ACR-2019 classification criteria in adult patients with systemic lupus erythematosus (SLE).MethodsPubMed, Embase, Web of Science and Cochrane Library databases were searched for literature comparing the three classification criteria of ACR-1997, SLICC-2012 and EULAR/ACR-2019, which took clinical diagnosis as reference. Meta-analysis was used to evaluate and compare the sensitivity, specificity and diagnostic odds ratio of ACR-1997, SLICC-2012 and EULAR/ACR-2019. To assess the early diagnosis capability of the classification criteria, subgroups of patients with disease duration < 3 years and < 1 year were selected for comparison of sensitivity and specificity based on the inclusion of the original study. The sensitivity and specificity of each item in three sets of classification criteria were evaluated. In addition, the clinical and immunological characteristics of patients who did not meet the three classification criteria were compared.ResultsNine original studies were included in the analysis, including 6404 SLE patients and 3996 controls. Results showed that the diagnostic odds ratios (95% confidence interval) of the SLICC-2012 [136.35 (114.94, 161.75)] and EULAR/ACR-2019 [187.47 (158.00, 222.42)] were higher than those of the ACR-1997 [67.53 (58.75, 77.63)]. Compared with ACR-1997[(0.86 (0.82, 0.89)], SLICC-2012[(0.96 (0.93, 0.97)] and EULAR/ACR-2019[(0.95 (0.92, 0.97)] had higher sensitivity. The specificity of the three classification criteria was similar: ACR-1997, SLICC-2012, and EULAR/ACR-2019 were 0.93 (0.89, 0.95), 0.86 (0.79, 0.91), and 0.91 (0.85, 0.95), respectively. The sensitivity of SLICC-2012 and EULAR/ACR-2019 were higher than that of ACR-1997 in early-course subgroups. Patients who did not meet ACR-1997 had more hypocomplementemia, patients who did not meet SLICC-2012 had more cutaneous lupus and photosensitivity, and patients who did not meet EULAR/ACR-2019 had more cutaneous lupus and leucopenia.ConclusionsSLICC-2012 and EULAR/ACR-2019 have better diagnostic ability than the ACR-1997, and the sensitivity of the former two criteria is also higher than that of the latter; Moreover, the SLICC-2012 and EULAR/ACR-2019 for patients in the early stages of disease performed equally excellent.

To address the limitations of existing power system low-carbon dispatching studies—such as over-reliance on generation-side carbon mitigation, price-oriented demand response (DR) failing to guide carbon reduction, and the low solution efficiency of traditional carbon emission flow (CEF)-based two-stage models—this paper proposes a data-driven CEF framework integrated with a bi-level economic and low-carbon dispatching model. First, a data-driven CEF calculation method is developed: It eliminates the need for complex power flow post-processing while maintaining calculation accuracy through multiple linear regression. On this basis, a bi-level optimization model is constructed: The upper level focuses on optimizing the economic and low-carbon objectives of power grid operation, while the lower level regulates industrial, commercial, and residential load aggregators (LAs) via carbon-intensity-oriented DR strategies and economic compensation mechanisms. Finally, a sample-based optimization algorithm combined with convex relaxation is proposed to solve the model, avoid the static setting of power flow and carbon intensity, and improve solution efficiency. Case studies demonstrate the following: the proposed method reduces the calculation time of node carbon intensity from 5 min to less than 100 ms, with the coefficient of determination (R2) ranging from 0.969 to 0.998; compared with the two-stage method, it achieves a 4.26% reduction in total scheduling cost, a 3.80% decrease in total carbon emissions, a 53.27% drop in carbon trading cost, and a 21.6% shortening in iteration time. These results verify that the proposed method can effectively enhance the source−load interaction and improve the accuracy and efficiency of low-carbon scheduling. This study provides a feasible technical path for the low-carbon transition of new-type power systems.

Tubulo-interstitial injury is a poor prognostic factor for lupus nephritis (LN). Here, we tested whether iguratimod could inhibit tubulo-interstitial injury in LN. MRL/lpr mice, an animal model of lupus, were treated with iguratimod or vehicle solution. Pathological changes of kidney were evaluated blindly by the same pathologist. Renal type I collagen (COL-I), IgG, E-cadherin, fibroblast-specific protein 1 (FSP-1) were detected by immunofluorescence, immunohistochemical staining or quantitative real-time PCR. After treated with transforming growth factor β1 (TGF-β1) and iguratimod, E-cadherin, fibronectin, Smad2/3, p38 MAPK, p-Smad2/3, and p-p38 MAPK, β-catenin and TGF-β type II receptor (TGFβRII) in HK2 cells were measured by western blotting, quantitative real-time PCR or immunofluorescence. Iguratimod reduced immune deposition along the tubular basement membrane, inhibited the tubulo-interstitial infiltration of inflammatory cells, and alleviated tubular injury in MRL/lpr mice. Moreover, Iguratimod eased the tubulo-interstitial deposition of collagen fibers, which was confirmed by decreased expression of COL-I. Furthermore, iguratimod suppressed the expression of FSP-1 and increased that of E-cadherin in renal tubular epithelial cells. In HK2 cells cultured with TGF-β1, iguratimod treatment not only reversed cellular morphological changes, but also prevented E-cadherin downregulation and fibronectin upregulation. In addition, iguratimod inhibited phosphorylation of TGFβRII, Smad2/3 and p38 MAPK in HK2 cells treated with TGF-β1, and also blocked nuclear translocation of β-catenin. Iguratimod eased tubulo-interstitial lesions in LN, especially tubulo-interstitial fibrosis, and might have potential as a drug for inhibiting the progression of tubulo-interstitial fibrosis in LN.

This paper addresses the challenge of fostering cooperation among virtual power plant (VPP) operators in competitive electricity markets, focusing on the application of evolutionary game theory (EGT) and static reward–punishment mechanisms. This investigation resolves four critical questions: the minimum reward–punishment thresholds triggering stable cooperation, the influence of initial market composition on equilibrium selection, the sufficiency of static versus dynamic mechanisms, and the quantitative mapping between regulatory parameters and market outcomes. The study establishes the mathematical conditions under which static reward–punishment mechanisms transform competitive VPP markets into stable cooperative systems, quantifying efficiency improvements of 15–23% and renewable integration gains of 18–31%. Through rigorous evolutionary game-theoretic analysis, we identify critical parameter thresholds that guarantee cooperation emergence, resolving longstanding market coordination failures documented across multiple jurisdictions. Numerical simulations and sensitivity analysis demonstrate that static reward–punishment systems enhance cooperation, optimize resources, and increase renewable energy utilization. Key findings include: (1) Reward–punishment mechanisms effectively promote cooperation and system performance; (2) A critical region exists where cooperation dominates, enhancing market outcomes; and (3) Parameter adjustments significantly impact VPP performance and market behavior. The theoretical contributions of this research address documented market failures observed across operational VPP implementations. Our findings provide quantitative foundations for regulatory frameworks currently under development in seven national energy markets, including the European Union’s proposed Digital Single Market for Energy and Japan’s emerging VPP aggregation standards. The model’s predictions align with successful cooperation rates achieved by established VPP operators, suggesting practical applicability for scaled implementations. Overall, through evolutionary game-theoretic analysis of 156 VPP implementations, we establish precise conditions under which static mechanisms achieve 85%+ cooperation rates. Based on this, future work could explore dynamic adjustments, uncertainty modeling, and technologies like blockchain to further improve VPP resilience.

Altered expression and activation of Epidermal Growth Factor Receptor (EGFR) is implicated in acute and chronic kidney injury. One of the important cellular sources of EGFR is the myeloid compartment, which plays roles in both acute kidney injury and subsequent fibrosis. Here we show in a murine ischemic acute kidney injury (AKI) model that myeloid deletion of EGFR promotes a pro-resolving, anti-inflammatory phenotype and increased efferocytotic capacity in macrophages. This leads to accelerated recovery in response to AKI and inhibited subsequent development of tubulointerstitial fibrosis. We find that selective EGFR deletion in neutrophils also accelerates recovery from ischemic kidney injury and reduces subsequent fibrosis. EGFR activation plays an essential role in increasing the life span of neutrophils in the injured kidney. Deletion of EGFR expression either in all murine myeloid cells or selectively in neutrophils decreases kidney neutrophil Mcl-1 expression and promotes neutrophil apoptosis, which is accompanied by accelerated recovery from organ injury and reduced subsequent fibrosis. These studies thus identify coordinated and complementary roles for EGFR activation in neutrophils and macrophages to exacerbate kidney injury. Kidney-infiltrating myeloid cells play important roles in acute kidney injury and post-injury fibrosis. Here authors show that genomic deletion of Epidermal Growth Factor Receptor specifically in myeloid cells or in neutrophils alleviates acute kidney injury in a mouse model, via limiting the life span of these pro-inflammatory cells.

The distribution of elite soybean ( Glycine max ) cultivars is limited due to their highly sensitive to photoperiod, which affects the flowering time and plant architecture. The recent emergence of CRISPR/Cas9 technology has uncovered new opportunities for genetic manipulation of soybean. The major maturity gene E1 of soybean plays a critical role in soybean photoperiod response. Here, we performed CRISPR/Cas9-mediated targeted mutation of E1 gene in soybean cultivar Tianlong1 carrying the dominant E1 to investigate its precise function in photoperiod regulation, especially in plant architecture regulation. Four types of mutations in the E1 coding region were generated. No off-target effects were observed, and homozygous trans-clean mutants without T-DNA were obtained. The photoperiod sensitivity of e1 mutants decreased relative to the wild type plants; however, e1 mutants still responded to photoperiod. Further analysis revealed that the homologs of E1 , E1 - La , and E1 - Lb , were up-regulated in the e1 mutants, indicating a genetic compensation response of E1 and its homologs. The e1 mutants exhibited significant changes in the architecture, including initiation of terminal flowering, formation of determinate stems, and decreased branch numbers. To identify E1 -regulated genes related to plant architecture, transcriptome deep sequencing (RNA-seq) was used to compare the gene expression profiles in the stem tip of the wild-type soybean cultivar and the e1 mutants. The expression of shoot identity gene Dt1 was significantly decreased, while Dt2 was significantly upregulated. Also, a set of MADS-box genes was up-regulated in the stem tip of e1 mutants which might contribute to the determinate stem growth habit.