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Purpose: To establish an early clinical diagnosis model based on the retinal vascular features associated with POI, supplying a non-invasive way for accurately and early predicted the risk of POI. Methods: A total of 78 women with spontaneous POI and 48 healthy women were recruited from the Affiliated Shenzhen Maternity & Child Healthcare Hospital in the study. Retinal characteristics were analyzed using an automated retinal image analysis system. Binary logistic regression was used to identify POI cases and develop predictive models. Results: Compared to the normal group, the POI group had larger central retinal artery equivalent (CRAE) (P = 0.006), central retinal vein equivalent (CRVE) (P = 0.001), index of venules asymmetry (Vasym) (P = 0.000); larger bifurcation angles of arterioles (Aangle) (P = 0.001), bifurcation coefficient of venule (BCV) (P = 0.001) and more obvious arteriovenous nipping (Nipping) (P = 0.005), but lower arteriole-to-venule ratio (AVR) (P = 0.012). In the POI group, the odds ratio (OR) of Vasym was 6.72e-32 (95% C.I. 4.62e-49–9.79e-15, P = 0.000), the OR of BCV was 5.66e-20 (95% C.I. 1.93e-34–.0000, P = 5.66e-20) and the OR of Nipping was 6.65e-06 (95% C.I. 6.33e-10–.0698, P = 0.012). Moreover, the area under the ROC curve for the binary logistic regression with retinal characteristics was 0.8582, and the fitting degree of regression models was 60.48% (Prob > chi-square = 0.6048). Conclusion: This study demonstrated that retinal image analysis can provide useful information for POI identification and certain characteristics may help with early clinical diagnosis of POI. [ABSTRACT FROM AUTHOR]

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers' financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid's low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers' distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms. • Data Empowered Appliance-level Utility Modeling. • Demand Response Design to Offer Appliance-level Incentive. • Heterogeneity Characterization to Improve Scalability and Efficiency. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the relationship between vaginal microbial community structure and premature ovarian insufficiency (POI). Methods: Twenty-eight women with POI and 12 healthy women were recruited at Shenzhen Maternity and Child Healthcare Hospital between August and September 2020. Blood samples were collected for glucose tests and detection of sex hormone levels and vaginal secretions were collected for microbial group determination. Vaginal microbial community profiles were analysed by 16S rRNA gene sequencing using the Illumina MiSeq system (Illumina Inc., San Diego, CA, USA). Results: Compared to the controls, the serum levels of follicle-stimulating hormone, luteinizing hormone, testosterone, and the follicle-stimulating hormone/luteinizing hormone ratio, significantly increased, and oestradiol and anti-Müllerian hormone levels significantly decreased in women with POI. Higher weighted UniFrac values were observed in women with POI than in healthy women. Bacteria in the genera Lactobacillus, Brevundimonas, and Odoribacter were more abundant in the microbiomes of healthy women, while the quantity of bacteria in the genus Streptococcus was significantly increased in the microbiomes of women with POI. Moreover, these differences in microbes in women with POI were closely related to follicle-stimulating hormone, luteinizing hormone, oestradiol, and anti-Müllerian hormone levels and to the follicle-stimulating hormone/luteinizing hormone ratio. Conclusions: Women with POI had altered vaginal microbial profiles compared to healthy controls. The alterations in their microbiomes were associated with serum hormone levels. These results will improve our understanding of the vaginal microbial community structure in women with POI. Trial registration: CHICTR, ChiCTR2000029576. Registered 3 August 2020 - Retrospectively registered, https://www.chictr.org.cn/showproj.aspx?proj=48844. [ABSTRACT FROM AUTHOR]

Background: Premature ovarian insufficiency (POI) is characterized by impairment of ovarian function on a continuum before the age of 40 years. POI is affected by multiple factors. Considering new insights from recent gut microbiome studies, this study aimed to investigate the relationship between gut microbial community structure and POI.Methods: Subjects were recruited at the Shenzhen Maternity & Child Healthcare Hospital. Fecal microbial community profiles of healthy women (n = 18), women with POI (n = 35) were analyzed using 16S rRNA gene sequencing based on Illumina NovaSeq platform.Results: Compared to the controls, the serum levels of FSH, LH, T and FSH/LH ratio significantly increased in women with POI, whereas E2 and AMH decreased significantly. Higher weighted UniFrac value was observed in POI women compared with healthy women. Phylum Firmicutes, genera Bulleidia and Faecalibacterium were more abundant in healthy women, while phylum Bacteroidetes, genera Butyricimonas, Dorea, Lachnobacterium and Sutterella enriched significantly in women with POI. Moreover, these alterations of the gut microbiome in women with POI were closely related to FSH, LH, E2, AMH level and FSH/LH ratio.Conclusions: Women with POI had altered microbial profiles in their gut microbiome, which were associated with serum hormones levels. These results will shed a new light on the pathogenesis and treatment for POI. [ABSTRACT FROM AUTHOR]

The increasingly popular electric vehicles (EVs) are changing the control paradigm of the power grid due to their uncoordinated charging behaviors. However, if well coordinated, smart homes, workplaces, and other locations that support EV charging could provide the grid with the urgently required flexibility via virtual power plants (VPP). In this paper, we develop the EV charging schedule model by capturing the unwillingness of EV drivers to alter their initial charging behaviors, referred to as the discomfort function. Predictability and the value of charging time, which represent the electricity consumption stability and the time value of EV drivers, characterize the discomfort function. Rather than existing works capturing discomfort by a direct simple parameter, such a computable data-driven quantification of discomfort enables us to customize an efficient VPP dispatch mechanism for EVs. In addition, to deal with the unknown charging efficiencies of EVs, we apply chance constraints only with the knowledge about mean and standard deviation of charging efficiencies, rather than their specific distribution. Using the concept of conditional value-at-risk (CVaR), we build an effective algorithm to solve the practical non-convex VPP dispatch model considering charging efficiencies. The effectiveness of our proposed models and associated algorithms are validated by numerical studies. • Data-driven computable model to quantify discomfort of EV drivers' charging schedule adjustment. • Practical generalization of EVs embedded VPP dispatch by considering uncertain charging efficiency. • Efficient algorithm design for non-convex chance-constrained dispatch model based on CVaR. [ABSTRACT FROM AUTHOR]

Price competition among electric vehicle (EV) charging stations is as fierce as the competition among gas stations. Nash equilibrium (NE) is a solution concept that can characterize a competition's efficient and stable state. However, calculation of the equilibrium is often time-consuming and requires complete information on the charging stations. Rapidly changing charging stations often hinder reaching equilibrium. In this study, we analyze price competition with service capacity constraints and use an ordinal potential game framework to investigate the structure of the competition. By constructing the ordinal potential function, the equilibrium characterization is converted to identifying the solution through a single-objective optimization. We further propose a decentralized algorithm to enable effective price coordination to achieve equilibrium with maximized social welfare. To preserve the privacy of charging stations from internal collusion and external attacks, an advanced secure multi-party computation technology known as the Paillier Cryptosystem is customized for our proposed decentralized algorithm. Numerical studies based on field data suggest the significance of our framework. • Proofs of the Existence of unique Nash equilibrium for general EVCS pricing game with charging capacity constraints. • Efficient characterization for the Nash equilibrium by a single-objective optimization based on the potential game theory. • Centralized and decentralized algorithm design for charging price coordination. • Using Paillier Cryptosystem to preserve the privacy of EV charging stations during the price coordination process. [ABSTRACT FROM AUTHOR]

As renewable energy becomes more prevalent in the power grid, energy storage systems (ESSs) are playing an ever-increasingly crucial role in mitigating short-term supply–demand imbalances. However, the operation and control of ESS are not straightforward, given the ever-changing electricity prices in the market environment and the stochastic and intermittent nature of renewable energy generations, which respond to real-time load variations. In this paper, we propose a deep reinforcement learning (DRL) approach to address the electricity arbitrage problem associated with optimal ESS management. First, we analyze the structure of the optimal offline ESS control problem using the mixed-integer linear programming (MILP) formulation. This formulation identifies optimal control actions to absorb excess renewable energy and perform price arbitrage strategies. To tackle the uncertainties inherent in the prediction data, we then recast the online ESS control problem into a Markov Decision Process (MDP) framework and develop the DRL approach, which involves integrating the optimal offline control solution obtained from the training data into the training process and introducing noise to the state transitions. Unlike typical offline DRL training over a long time interval, we employ the Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) algorithms with smaller neural networks training over a short time interval. Numerical studies demonstrate the promising potential of the proposed DRL-enabled approach for achieving better online control performance than the model predictive control (MPC) method under different price errors. This highlights the sample efficiency and robustness of our DRL approaches in managing ESS for electricity arbitrage. • Incorporate optimal strategy as prior knowledge by revising the DRL reward. • Employ small neural networks to efficiently train samples and update models. • Utilize noise to enhance error tolerance and reduce distribution shifts' impact. [ABSTRACT FROM AUTHOR]

Background: Abortion prone (AP) is a common clinical event. The underlying mechanism remains unclear. Traditional Chinese formulas are known to be efficient in the management of abortion. The purpose of this study is to observe the effects of Anzitiaochongtang (AZT), a traditional formulation of Chinese medicine, on improving AP in mice by regulating immune tolerance. Methods: An established abortion model (CBA/J×DBA/2) was employed. AZT was prepared and administered to mice in a manner consistent with clinical practice. Tolerogenic dendritic cells (tDC) and type 1 regulatory T cells (Tr1 cell) in mice were analyzed by immunological approaches to be used as representative immune tolerant parameters. Results: An AP model was established with CBA/J × DBA/2 mice. The expression of IL‐10 in tDC and Tr1 cell frequency in the mouse decidua tissues were lower in the AP group than that in the normal pregnancy (NP) group. Administration of AZT up regulated the expression of IL‐10 in tDCs and Tr1 cell generation in the decidua tissues, and improved the pregnancy and tissue structure in AP mice. The main mechanism by which AZT improves pregnancy in AP mice is that AZT enhanced the expression of galectin‐9 in the epithelial cells of decidua tissues. Galectin 9 activates TIM3 on DCs to promote the IL‐10 expression. The DCs induced more Tr1 cells in the decidua tissues. Conclusions: Dysfunctional tDCs were detected in the AP decidua tissues. Administration of AZT improved pregnancy in AP mice by regulating tDC function and generation of Tr1 cells in the maternal‐fetal interface. [ABSTRACT FROM AUTHOR]

Rationale: Paclitaxel-induced acute pain syndrome (P-APS), characterized by deep muscle aches and arthralgia, occurs in more than 70% of patients who receive paclitaxel. P-APS can be debilitating for patients and lead to reductions and discontinuation of potentially curable therapy. Despite being relatively common in clinical practice, no clear treatment exists for P-APS and the underlying mechanisms remain poorly defined. Regulation of glutamatergic transmission by metabotropic glutamate receptors (mGluRs) has received growing attention with respect to its role in neuropathic pain. To our knowledge, no study has been conducted on alterations and functions of group III mGluR7 signaling in P-APS. Objectives: In the present study, we determined whether a single administration of paclitaxel induces glutamatergic alterations and whether mGluR7 activation blocks paclitaxel-induced neuropathic pain by suppressing glial reactivity in the spinal cord. Results: A single paclitaxel injection dose-dependently induced acute mechanical and thermal hypersensitivity, and was associated with increased glutamate level accompanied by reduction in mGluR7 expression in the spinal cord. Selective activation of mGluR7 by its positive allosteric modulator, AMN082, blocked the development of paclitaxel-induced acute mechanical and thermal hypersensitivity, without affecting the normal pain behavior of control rats. Moreover, activation of mGluR7 by AMN082 inhibited glial reactivity and decreased pro-inflammatory cytokine release during P-APS. Abortion of spinal glial reaction to paclitaxel alleviated paclitaxel-induced acute mechanical and thermal hypersensitivity. Conclusions: There results support the hypothesis that spinal mGluR7 signaling plays an important role in P-APS; Selective activation of mGluR7 by its positive allosteric modulator, AMN082, blocks P-APS in part by reducing spinal glial reactivity and neuroinflammatory process. [ABSTRACT FROM AUTHOR]