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Abstract Cancer-associated fibroblasts (CAFs), integral components of the tumor microenvironment, play a pivotal role in tumor proliferation, metastasis, and clinical outcomes. However, its specific roles in Kidney Renal Clear Cell Carcinoma (KIRC) remain poorly understood. Employing the established Seurat single-cell analysis pipeline, we identified 21 CAFs marker genes. Subsequently, a prognostic signature consisting of 6 CAFs marker genes (RGS5, PGF, TPM2, GJA4, SEPT4, and PLXDC1) was developed in a cohort through univariate and LASSO Cox regression analyses. The model’s efficacy was then validated in an external cohort, with a remarkable predictive performance in 1-, 3-, and 5-year. Patients in the high-risk group exhibited significantly inferior survival outcomes (p

Abstract Syphilis, a sexually transmitted disease (STD) caused by Treponema pallidum (T. pallidum), has had a worldwide resurgence in recent years and remains a public health threat. As such, there has been a great deal of research into clinical strategies for the disease, including diagnostic biomarkers and possible strategies for treatment and prevention. Although serological testing remains the predominant laboratory diagnostic method for syphilis, it is worth noting that investigations pertaining to the DNA of T. pallidum, non-coding RNAs (ncRNAs), chemokines, and metabolites in peripheral blood, cerebrospinal fluid, and other bodily fluids have the potential to offer novel perspectives on the diagnosis of syphilis. In addition, the global spread of antibiotic resistance, such as macrolides and tetracyclines, has posed significant challenges for the treatment of syphilis. Fortunately, there is still no evidence of penicillin resistance. Hence, penicillin is the recommended course of treatment for syphilis, whereas doxycycline, tetracycline, ceftriaxone, and amoxicillin are viable alternative options. In recent years, efforts to discover a vaccine for syphilis have been reignited with better knowledge of the repertoire of T. pallidum outer membrane proteins (OMPs), which are the most probable syphilis vaccine candidates. However, research on therapeutic interventions and vaccine development for human subjects is limited due to practical and ethical considerations. Thus, the preclinical model is ideal for conducting research, and it plays an important role in clinical transformation. Different preclinical models have recently emerged, such as in vitro culture and mouse models, which will lay a solid foundation for clinical treatment and prevention of syphilis. This review aims to provide a comprehensive summary of the most recent syphilis tactics, including detection, drug resistance treatments, vaccine development, and preclinical models in clinical practice.

Abstract This study presents the first-ever synthesis of samarium-doped indium vanadate nanosheets (IVONSs:Sm) via microemulsion-mediated solvothermal method. The nanosheets were subsequently utilized as a nano-matrix in laser desorption/ionization mass spectrometry (LDI-MS). It was discovered that the as-synthesized IVONSs:Sm possessed the following advantages: improved mass spectrometry signal, minimal matrix-related background, and exceptional stability in negative-ion mode. These qualities overcame the limitations of conventional matrices and enabled the sensitive detection of small biomolecules such as fatty acids. The negative-ion LDI mechanism of IVONSs:Sm was examined through the implementation of density functional theory simulation. Using IVONSs:Sm-assisted LDI-MS, fingerprint recognitions based on morphology and chemical profiles of endogenous/exogenous compounds were also achieved. Notably, crucial characteristics such as the age of an individual’s fingerprints and their physical state could be assessed through the longitudinal monitoring of particular biomolecules (e.g., ascorbic acid, fatty acid) or the specific biomarker bilirubin glucuronide. Critical information pertinent to the identification of an individual would thus be facilitated by the analysis of the compounds underlying the fingerprint patterns. Graphical Abstract

Abstract "Clinical basic inspection technology" is one of the essential courses in the medical laboratory profession. Combining the characteristics of the discipline itself, the research and practice of the BOPPPS model based on the OBE concept in clinical basic laboratory experiment teaching are discussed, and the reform of in teaching objectives, teaching contents, and teaching design path is implemented. The "student-centered" teaching process is divided into six stages: before, during, and after class, and the teaching process is continuously improved to achieve the desired teaching effect. Results of the experiment teaching show that the model has improved students' active participation and developed their clinical thinking skills, and more than 95% of students are satisfied with this teaching model.

The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research.

Angiogenesis is the growth of new blood vessels on preexisting ones. It is the outcome of a multifactorial effect involving several cells, which can be brought on by different stress reactions.The accumulation of unfolded proteins in the endoplasmic reticulum occurs when cells are stressed due to environmental changes, where physical or chemical stimuli induce endoplasmic reticulum stress, thereby activating the unfolded protein response (UPR), a homeostasis response designed to re-establish protein balance. Ferroptosis is a planned death of lipid peroxidation and anomalies in metabolism that is dependent on iron. Large concentrations of iron ions accumulate there, along with high concentrations of lipid peroxides and reactive oxygen species, all of which can contribute to the development of several diseases. Through the production of growth factors, adhesion factors, and inflammatory factors that trigger the start of angiogenesis, both UPR and Ferroptosis can be implicated in angiogenesis.To set the stage for further research on angiogenesis, this work concentrated on the effects of Ferroptosis and UPR on angiogenesis, respectively.

This paper proposes a runoff-based hydroelectricity prediction method based on meteorological similar days and XGBoost model. Accurately predicting the hydroelectricity supply and demand is critical for conserving resources, ensuring power supply, and mitigating the impact of natural disasters. To achieve this, historical meteorological and runoff data are analyzed to select meteorological data that are similar to the current data, forming a meteorological similar day dataset. The XGBoost model is then trained and used to predict the meteorological similar day dataset and obtain hydroelectricity prediction results. To evaluate the proposed method, the hydroelectricity cluster in Yunnan, China, is used as sample data. The results show that the method exhibits high prediction accuracy and stability, providing an effective approach to hydroelectricity prediction. This study demonstrates the potential of using meteorological similar days and the XGBoost model for hydroelectricity prediction and highlights the importance of accurate hydroelectricity prediction for water resource management and electricity production.

As the penetration rate of renewable energy in modern power grids continues to increase, the assessment of renewable energy absorption capacity plays an increasingly important role in the planning and operation of power and energy systems. However, traditional methods for assessing renewable energy absorption capacity rely on complex mathematical modeling, resulting in low assessment efficiency. Assessment in a single scenario determined by the source-load curve is difficult because it fails to reflect the random fluctuation characteristics of the source-load, resulting in inaccurate assessment results. To address and solve the above challenges, this paper proposes a multi-scenario renewable energy absorption capacity assessment method based on an attention-enhanced time convolutional network (ATCN). First, a source-load scene set is generated based on a generative adversarial network (GAN) to accurately characterize the uncertainty on both sides of the source and load. Then, the dependence of historical time series information in multiple scenarios is fully mined using the attention mechanism and temporal convolution network (TCN). Finally, simulation and experimental verification are carried out using a provincial power grid located in southwest China. The results show that the method proposed in this article has higher evaluation accuracy and speed than the traditional model.

Alongside China’s new round of electricity reform, Yunnan has implemented medium- and long-term market reform in the electricity sector since 2015 with little progress seen in establishing an electricity spot market. To this end, this paper attempts to identify the pathway to an electricity spot market for Yunnan through its realities and lessons learned from Europe. As such, on the ground of the status quo of the energy mix, generation features, load structure and profiles, power supply–demand situations, and market progress in the Yunnan electricity system, this paper reviews two classical market frameworks commonly adopted worldwide, from the perspectives of market organizing and the core optimization model broadly used in coordinating system operation and clearing the market. Then the contrast of Yunnan with the European experience is analyzed with respect to the energy mix, generation, transmission, and exchange, as well as the political and economic institutional arrangements, market conditions, and driving forces. Moreover, the Yunnan case study scenarios and likely consequences and benefits under different market frameworks are further analyzed to verify the edge of a centralized optimal-power-flow-based regional market over a decentralized cross-province balancing market, in productively reducing price distortion, allocating resources, maintaining power supply–demand balance at all times, and promoting renewable energy integration across a wider region. Finally, policy suggestions concerning the pathway to an electricity spot market are provided for Yunnan in further deepening its electricity reform.

Abstract Topdressing time is crucial to achieving a high yield. To determine the optimum topdressing time for spring maize in the black soil of northeast China in the “one base and one topdressing” mode, the effects of topdressing time of nitrogen (N) fertilizer on maize yield, N utilization, and inorganic N residue and distribution were investigated by using 15N labeling technique. Four treatments were designed: no N fertilizer (N0), N fertilizer topdressing at jointing stage (N1), N fertilizer topdressing at belling stage (N2), and N fertilizer topdressing at tasseling stage (N3). The results showed that compared with N1 and N3, the maize yield, N uptake and N use efficiency (NUE) in N2 treatment significantly increased by 12.1% and 24.7%, 10.0% and 16.0%, and 26.4% and 38.9%, respectively (P

Ferroptosis is a type of programmed cell death that pathogens can leverage to enhance their replication, transmission, and pathogenicity. Hosts typically combat pathogenic infections by utilizing oxidative stress as a defense mechanism. Nonetheless, some pathogens can trigger considerable oxidative stress while infecting, inducing an intense inflammatory response in the host’s immune system and activating cell death. The process of ferroptosis is closely linked to oxidative stress, with their interaction exerting a substantial impact on the outcome of infectious diseases. This article presents an overview of the interrelated mechanisms of both Ferroptosis and oxidative stress in infectious diseases, identifying potential targets for treating such diseases in the context of their interaction.

As a clean energy source, solar power plays an important role in reducing the high carbon emissions of China’s electricity system. However, the intermittent nature of the system limits the effective use of photovoltaic power generation. This paper addresses the problem of low accuracy of ultra-short-term prediction of distributed PV power, compares various deep learning models, and innovatively selects the Informer model with multi-head probability sparse self-attention mechanism for prediction. The results show that the CEEMDAN-Informer model proposed in this paper has better prediction accuracy, and the error index is improved by 30.88% on average compared with the single Informer model; the Informer model is superior to other deep learning models LSTM and RNN models in medium series prediction, and its prediction accuracy is significantly better than the two. The power prediction model proposed in this study improves the accuracy of PV ultra-short-term power prediction and proves the feasibility and superiority of the deep learning model in PV power prediction. Meanwhile, the results of this study can provide some reference for the power prediction of other renewable energy sources, such as wind power.

Volcanic ash alteration supports chemolithotrophic activity at the seabed and contributes to the production of microbial biomass during diagenesis, suggest analyses of isoprenoid and branched glycerol dialkyl glycerol tetraethers distributions in Mariana Trough tuffs and adjacent sediments.

Frequency regulation services can be provided by emerging flexible resources such as electric vehicles and energy storage which can rapidly adjust the output in response to regulation signals, as long as there is an effective market to provide proper incentives. However, current regulation markets were designed for conventional thermal generating units, and it is difficult for the mileage-based bidding structure to express the cost characteristics of flexible resources. Therefore, the current mechanisms are not conducive to encouraging the participation of flexible resources in providing frequency regulation services. This paper proposes an incentive-compatible frequency regulation market for flexible resources, whose revisions to the existing mechanism include the bidding structure, clearing rules, and settlement rules that adapt to the characteristics of flexible resources. Compared with the current mechanism, the proposed mechanism can reduce the costs of deploying regulation and more reasonably allocate the revenues for regulation services. The case studies verify the effectiveness of the proposed mechanism.

This paper explores three new competitive heuristic procedures, which are compared with the mixed integer linear programming (MILP), all used to commit units within a hydroplant to give the maximal electrical production during a study horizon, given the cascade operation has already determined the quarter-hourly releases from individual reservoirs. The problem incorporates constraints on the unit up/down hours and its shutdown times. The procedures are applied to seven hydroplants, with 2, 3, 4, 5, 6, 9 and 18 units installed respectively. The application suggests that the MILP performs the best in minimizing spillages and improving generating efficiency but volatile in computation time when the number of units is more than 4 in a hydroplant, while the heuristic procedures can obtain fairly good results and have a significant advantage in computation time when committing more than 6 units in a hydroplant. The heuristic procedures are valuable when incorporating unit commitment into a large-scale operational problem of cascaded hydropower reservoirs.

To ensure the stability of the electricity spot market and the safety of the provincial and regional power systems, a safety check method is proposed to maximize the effective reserve resources in the power system by optimizing the power of each tie-line. This safety check method accurately models the tie-line equipment and the effective reserve resources and is coupled with each constraint of the electricity spot market clearing model to form a safety check algorithm to optimize the power of tie-line power. The model involved in this paper is a linear model, which has a clear implementation method in practical dispatching applications. Through this method, the power configuration scheme of each tie-line to meet the electricity spot market constraints can be obtained, and the safety check results have the executability of the power market. The rationality and feasibility of the safety check algorithm results are verified by simulating the provincial-scale electricity spot market. According to the simulation results, this method can release effective reserve resources and provide more guarantees for the safe operation of the power grid. In addition, this method can save up to 4.9% of the total operation cost of the power system and improve the dispatching economy of the power system. This method is of great significance to ensure the safe operation of the power system and the day-ahead market and real-time market scheduling in the actual power spot system. In addition, this method also has great guiding significance for the analysis of the actual reserve situation of the power market after the event.

China has made significant progress in developing renewable energy, but challenges in integrating variable sources like wind, solar, and hydro power persist. Existing research has a regional focus and lacks a comprehensive understanding of integration challenges in specific regions. Moreover, research concentrates on individual energy types rather than multiple sources. Addressing these challenges requires a deeper analysis of market rules, incentives, and mechanisms for integrating numerous renewable energy types. The existing research also overlooks crucial aspects like energy consumption, grid planning, and power market mechanisms. To promote renewable energy integration effectively, policymakers must address these interconnected factors through comprehensive investigations and research efforts. This study proposes tailored solutions to encourage integration in China based on international experiences, such as Brazil’s contract markets and electricity redistribution mechanism, the Nordic spot market, and California’s negative pricing mechanism. The strategies derived from global practices for China’s renewable energy integration include diverse trading mechanisms, expanded contract markets, optimized local consumption, fortified grid infrastructure, and improved market mechanisms.

One of the most critical tasks of China’s power sector reformation is re-designing transmission and distribution (T&D) prices, which are essential in establishing fair and ordered power markets and encouraging the efficient allocation of resources. In view of the problems in China’s power market, such as the lack of a market mechanism for the reasonable allocation of congestion revenue, unreasonable transmission cost composition, and the failure of the transmission pricing mechanism to reflect the utilization degree of transmission resources by users in different geographical locations, this study proposes a transmission price design under the application scenarios of regional power grids and provincial power grids dominated by meshed AC networks in China that considers the marginal benefit of the transmission and spatiotemporal information of electricity demand. The proposed method effectively tackles the above-mentioned problem, with the transmission cost being divided into two parts: the expansion cost reflecting the marginal benefits of transmission, and the residual cost reflecting the rest. The expansion cost is objective and based on power system economics; it is calculated as the congestion revenue under the location marginal price-based wholesale electricity markets, resulting in a more reasonable division of transmission costs and allocation of congestion revenue. Furthermore, allocating the residual cost with the power flow distribution factor and the postage stamp method also reflects the utilization degree of transmission resources by users in different geographical locations. The effectiveness of this transmission price design is confirmed by testing it against a 3-bus and an IEEE 30-bus system.

This study introduces a novel approach for optimizing the monthly hydropower scheduling of cascaded reservoirs by employing a special ordered set of type 2 (SOS2) formulation within a mixed integer linear programming (MILP) model. The proposed method linearizes the relationships between hydropower output, spillage, storage, and outflow, enabling controllable spillage. The objective is to minimize spillage, maximize firm hydropower output, and maximize energy production, all in priority while considering complex constraints such as reservoir storage and discharge bounds, upstream–downstream relationship, and water balance. The approach is applied to four cascaded reservoirs on the Lancang River. Results indicate that the SOS2 formulation effectively minimizes spillage, maximizes hydropower generation, and ensures maximum firm power output. Comparisons across different gridding resolutions reveal that more grid points yield greater benefits but with a longer solution time. Furthermore, a comparison with the Successive Quadratic Programming (SQP) method highlights the superior performance of the SOS2 model in terms of objective improvement and solution efficiency. This research offers valuable insights into optimizing monthly hydropower scheduling for cascaded reservoir systems, enhancing operational efficiency and decision-making in water resources management.

This study presents a genetic algorithm integrated with dynamic programming to address the challenges of the hydropower unit commitment problem, which is a nonlinear, nonconvex, and discrete optimization, involving the hourly scheduling of generators in a hydropower system to maximize benefits and meet various constraints. The introduction of a progressive generating discharge allocation enhances the performance of dynamic programming in fitness evaluations, allowing for the fulfillment of various constraints, such as unit start-up times, shutdown/operating durations, and output ranges, thereby reducing complexity and improving the efficiency of the genetic algorithm. The application of the genetic algorithm with dynamic programming and progressive generating discharge allocation at the Manwan Hydropower Plant in Yunnan Province, China, showcases increased flexibility in outflow allocation, reducing spillages by 79%, and expanding high-efficiency zones by 43%.

Wind power prediction (WPP) is necessary to the safe operation and economic dispatch of power systems. In order to improve the prediction accuracy of WPP, in this paper we propose a three-step model named SDAE-SVR-BA to be applied in short-term WPP based on stacked-denoising-autoencoder (SDAE) feature processing, bat algorithm (BA) optimization and support vector regression (SVR). First, we preprocessed the original NWP data input into the SDAE-SVR-BA model to adapt to the training and prediction of the proposed model. Second, we input the preprocessed features into the SDAE network, whose parameters are optimized by BA to obtain the depth-mapping features. Finally, we input the features of SDAE network mapping into SVR, whose parameters are optimized by BA for prediction, so as to obtain the SDAE-SVR-BA model. In this paper, we used BA during the training process to optimize the number of hidden layers and hidden layer nodes of SDAE, the penalty factor parameter C and the kernel function radius g of the SVR model. Additionally, we verified the model with a wind farm example and compared it to the traditional model. Based on the verification data applied in this article, in a forecast for the next twelve hours, the normalized root means square error (NRMSE) of SDAE-SVR was 11.97% and the NRMSE of SDAE-SVR-BA model was 11.54%, reduced by 1.24% compared with SDAE, which demonstrates the effectiveness of the proposed method.

The traditional power generation mix and the geographical distribution of units have faced structural reform with the increasing renewables. The existing scheduling schemes confront the optimization challenges of multi-source collaborative and multi-temporal coordination. This paper reviews the optimization of generation scheduling in power systems with renewables integration in different time scales, which are medium- and long-term, short-term and real-time, respectively. First, the scheduling model and method are summarized. The connections and differences of the multi-source mathematic model with uncertainty, as well as the market mechanism, including thermal power, hydroelectric power, wind power, solar energy, and energy storage, are also indicated. Second, the scheduling algorithm and approach are sorted out from the two dimensions of certainty and uncertainty. The innovation and difference in algorithm between the traditional scheduling and the scheduling problem with renewables are presented. Meanwhile, the interaction and coupling relationship among the different time scales are pointed out in each section. The challenges and shortcomings of current research and references future directions are also provided for dispatchers.

The Xin'anjiang model and the Sacramento model are two widely used short-term watershed rainfall-runoff forecasting models, each with their own unique model structure, strengths, weaknesses and applicability. This paper introduces a weight factor to integrate the two models into a combined model, and uses the cyclic coordinate method to calibrate the weight factor and the parameters of the two models to explore the possibility of the complementarity between the two models. With application to the Yuxiakou watershed in Qingjiang River, it is verified that the cyclic coordinate method, although simple, can converge rapidly to a satisfactory calibration accuracy, mostly after two iterations. Also, the results in case studies show that the forecast accuracy of the new combined rainfall-runoff model can improve the forecast precision by 4.3% in a testing period, better in runoff process fitting than the Xin'anjiang model that performs better than the Sacramento model. HIGHLIGHT This paper introduces a weight factor to integrate the two models into a combined model, and uses the cyclic coordinate method to calibrate the weight factor.; It is verified that the cyclic coordinate method can converge fast to a satisfactory calibration accuracy.; The results show that the forecast accuracy of the new combined rainfall-runoff model can improve the forecast precision.;

Abstract Recently, molybdenum dioxide (MoO2) has gained intensive attention as an eco‐friendly and earth abundant catalyst for electrocatalytic hydrogen evolution from water splitting. However, the catalytic activity of MoO2 catalyst for hydrogen evolution reaction (HER) is severely limited by the less exposed active sites. Herein, we present Co‐doped MoO2 for efficient HER through a facile wet chemistry synthesis followed by calcination treatment process. The optimized Co‐MoO2‐0.01 nanorods (NRs) delivers a very low overpotential of 26 mV at 10 mA cm−2 and a small Tafel slope of 30.9 mV dec−1, which is much better than that of pure MoO2 NRs and the commercial Pt/C catalyst in HER. Experimental and theoretical results reveal that Co doping not only produce more oxygen vacancies in MoO2, which can activate adjacent oxygen atoms as active sites and thus increase the exposed active sites on the surface of catalyst, but also enhance the electrical conductivity of catalyst during HER process. In a word, this work provides a new promising synthetic strategy for developing earth‐abundant and cost‐effective HER electrocatalysts through rational defect‐engineering design.

This work presents a novel stereo coded aperture gamma camera based on four detection modules placed within four quadrants. Each module includes 48 × 48 strips of Bi4Ge3O12 scintillators, 24 × 24 pieces of silicon photomultipliers (SiPMs), and a set of data acquisition circuits. All signals from the SiPMs are individually extracted and finally simplified into 144 outputs to calculate the positions and energies of gamma-rays. Two masks of modified uniformly redundant arrays are fixed in the front of the top-right and bottom-left modules and two anti-masks are fixed in the top-left and bottom-right modules to achieve simultaneous imaging without dual measure. Once the projections of radioactive sources are determined, their source-to-detector distances can be estimated by our new multocular ranging algorithm. A series of lab and field experiments, including flood histogram and energy spectrum testing, field-of-view and angular resolution testing, real-time imaging, long-distance imaging, and ranging ability testing, with different radioactive sources, such as 137Cs, 22Na, 241Am, 60Co, 133Ba, and 75Se, have been completed to evaluate the comprehensive ability of the stereo gamma camera. The results show that the stereo gamma camera has a strong ability for the three-dimensional localization of radioactive sources over long distances with a high image sensitivity. So far, the stereo gamma camera has been successfully used in practical nuclear security and safety tasks although the angular resolution and the ranging algorithm robustness should be more optimized in the future.

Fatal influenza (flu) virus infection often activates excessive inflammatory signals, leading to multi-organ failure and death, also referred to as cytokine storm. PPARγ (Peroxisome proliferator-activated receptor gamma) agonists are well-known candidates for cytokine storm modulation. The present study identified that influenza infection reduced PPARγ expression and decreased PPARγ transcription activity in human alveolar macrophages (AMs) from different donors. Treatment with PPARγ agonist Troglitazone ameliorated virus-induced proinflammatory cytokine secretion but did not interfere with the IFN-induced antiviral pathway in human AMs. In contrast, PPARγ antagonist and knockdown of PPARγ in human AMs further enhanced virus-stimulated proinflammatory response. In a mouse model of influenza infection, flu virus dose-dependently reduced PPARγ transcriptional activity and decreased expression of PPARγ. Moreover, PPARγ agonist troglitazone significantly reduced high doses of influenza infection-induced lung pathology. In addition, flu infection reduced PPARγ expression in all mouse macrophages, including AMs, interstitial macrophages, and bone-marrow-derived macrophages but not in alveolar epithelial cells. Our results indicate that the influenza virus specifically targets the PPARγ pathway in macrophages to cause acute injury to the lung.

In order to accurately analyze the load supply capability of power systems with high penetration of renewable energy generation, this paper proposes a probabilistic available load supply capability (ALSC) forecasting method. Firstly, the optimal input features are selected by calculating the maximal information coefficient (MIC) between the input features and the target output. Based on this, a stacking ensemble learning model is applied for the prediction of wind power, photovoltaic power and load power. Secondly, the distributions of the forecasting objects are obtained based on forecasting errors and the error statistics method. Finally, the forecasting distributions of wind power, photovoltaic power and load are set as the parameters of a power system, and then probabilistic ALSC is calculated using Latin hypercube sampling (LHS) and repeated power flow (RPF). In order to simulate a more realistic power system, multiple slack buses are introduced to conduct two types of power imbalance allocations with novel allocation principles during the RPF calculation, which makes the ALSC evaluation results more reasonable and accurate. The results of probabilistic ALSC forecasting can provide a reference for the load power supply capacity of a power system in the future, and they can also provide an early warning for the risk of ALSC threshold overlimit. Case studies carried out on the modified IEEE 39-bus system verify the feasibility and effectiveness of the proposed methods.

The third-monthly (about 10 days in a time-step) hydropower scheduling, typically a challenging nonlinear optimization, is one of the essential tasks in a power system with operational storage hydropower reservoirs. This work formulates the problem into quadratic programming (QP), which is solved successively, with the linearization updated on the nonlinear constraint of the firm hydropower yield from all the cascaded hydropower reservoirs. Notably, the generating discharge is linearly concaved with two planes, and the hydropower output is defined as a quadratic function of reservoir storage, release, and generating discharge. The application of the model and methods to four cascaded hydropower reservoirs on the Jinsha River reveals several things: the successive quadratic programming (SQP) presented in this work can derive results consistent with those by the dynamic programming (DP), typically with the difference in water level within 0.01m; it has fast convergence and computational time increasing linearly as the number of reservoirs increases, with the most significant improvement in the objective at the second iteration by about 20%; and it is capable of coordinating the cascaded reservoir very well to sequentially maximize the firm hydropower yield and the total hydropower production.

The hydropower unit commitment (HUC) is often indirectly considered by combining individual units in a hydropower plant into a plant-based generator to alleviate the dimensional difficulty in short-term hydropower scheduling of cascaded reservoirs. This work presents three procedures to combine operating zones of units in a hydropower plant into the plant-based operating zones, including an exhaustive method (M01) that enumerates all the possible combinations, a discrete method (M03) that investigates discrete values to see if they are in an operating zone, and a bound oriented method (M04) that explores new operating zones with an optimization solver. The procedures are compared with a previous one (M02) that merges the operating zones of a unit one by one in order. The experiments of the methods in 11 case studies involving a variety of hydropower plants reveal that the second method (M02) should always be recommended due to its strength, which is even more prominent than any other methods in dealing with large-scale problems, and errors occur when using interpolation to estimate the plant-based operating zones at a water head between two sampling water heads.

Abstract We noticed that syphilis patients seem to be more susceptible to diabetes and the lesions often involve the kidneys, but the pathogenesis is not yet completely understood. In this study, microarray analysis was performed to investigate the dysregulated expressed genes (DEGs) in rabbit model of syphilis combined with diabetes. A total of 1045 genes were identified to be significantly differentially expressed, among which 571 were up-regulated and 474 were down-regulated (≥ 2.0fold, p

Operators often have a dilemma in deciding what water levels the over-year hydropower reservoirs should drawdown at the end of dry seasons, either too high to achieve a large firm hydropower output during the dry seasons in the current year and minor spillage in coming flood seasons, or too low to refill to the full storage capacity at the end of the flood seasons and a greater firm hydropower output in the coming year. This work formulates a third-monthly (in an interval of about ten days) hydropower scheduling model, which is linearized by linearly concaving the nonlinear functions and presents a rolling strategy to simulate many years of reservoir operations to investigate how the water level at the end of dry seasons will impact the performances, including the energy production, firm hydropower output, full-refilling rate, etc. Applied to 11 cascaded hydropower reservoirs in a river in southwest China, the simulation reveals that targeting a drawdown water level between 1185–1214 m for one of its major over-year reservoirs and 774–791 m for another is the most favorable option for generating more hydropower and yielding larger firm hydropower output.

Summary: The hadal zone, mostly comprising of deep trenches and constituting of the deepest part of the world’s oceans, represents the least explored habitat but one of the last frontiers on our planet. The present scientific understanding of the hadal environment is still relatively rudimentary, particularly in comparison with that of shallower marine environments. In the last 30 years, continuous efforts have been launched in deepening our knowledge regarding the ecology of the hadal trench. However, the geological and environmental processes that potentially affect the sedimentary, geochemical and biological processes in hadal trenches have received less attention. Here, we review recent advances in the geology, biology, and environment of hadal trenches and offer a perspective of the hadal science involved therein. For the first time, we release high-definition images taken by a new full-ocean-depth manned submersible Fendouzhe that reveal novel species with an unexpectedly high density, outcrops of mantle and basaltic rocks, and anthropogenic pollutants at the deepest point of the world’s ocean. We advocate that the hydration of the hadal lithosphere is a driving force that influences a variety of sedimentary, geochemical, and biological processes in the hadal trench. Hadal lithosphere might host the Earth’s deepest subsurface microbial ecosystem. Future research, combined with technological advances and international cooperation, should focus on establishing the intrinsic linkage of the geology, biology, and environment of the hadal trenches. Public summary: • This paper provides a comprehensive review on hadal geology, environment, and biology, as well as potential interactions among them • For the first time, we release high-definition images taken by a new full-ocean-depth manned submersible Fendouzhe • The hydration of the hadal lithosphere is a driving force that influences a variety of sedimentary, geochemical, and biological processes in the hadal trench • The development of deep-sea technology and international cooperation will greatly promote the progress of hadal science

With the gradual opening of China’s electricity market, it is effective for cascade hydropower plants to simultaneously participate in both the monthly contract market and the day-ahead spot market to obtain higher power generation benefits. Hence, this paper studies the optimal decomposition model for the monthly contracted electricity of cascade hydropower plants considering the bidding space in the day-ahead spot market. The close hydraulic and electric connection between cascade hydropower plants, the implementation requirements of contracted electricity, and the uncertainty of the day-ahead market clearing price are all well considered. Several linearization techniques are proposed to address the nonlinear factors, including the objective function and the power generation function. A successive approximation (SA) approach, along with a mixed-integer linear programming (MILP) approach, is then developed to solve the proposed model. The presented model is verified by taking the decomposition of the monthly contracted electricity of cascade hydropower plants in China as an example. The results indicate that the developed model has high computational efficiency and can increase the power generation benefits compared with the conventional deterministic model. The effect of the penalty coefficient for imbalanced monthly contracted electricity is also evaluated, which provides a practical reference for market managers.

Hadal trenches have higher microbial carbon turnover rates as compared to adjacent abyssal plains. However, the source of organic carbon in the trench remains enigmatic. In this study, we show that a fraction of organic carbon is possibly derived in situ and correlated with chemoautotrophic communities supported by the fluid discharge of water-rock interaction in the trench wall, based on analysis of glycerol dialkyl glycerol tetraether (GDGT) membrane lipids, including archaeal isoprenoid GDGTs (IsoGDGTs) and bacterial branched GDGTs (BrGDGTs), in sediments and rocks of the Mariana and Yap Trenches, northwest Pacific Ocean. These trench sediments contained relative higher BrGDGTs ratios, which was a rare observation in the open ocean. The BrGDGT-to-IsoGDGT ratios ranged in 0.02–0.88 (mean = 0.10 ± 0.11) in sediments and 0.09–0.38 (mean = 0.17 ± 0.13) in altered rocks. The calculated values of branched and isoprenoid tetraether (BIT) index ranged from 0.02–0.73 (mean = 0.18 ± 0.11) in sediments and from 0.16–0.9 in altered rocks (mean = 0.37 ± 0.27). Moreover, these GDGTs exhibited similar characteristics to those of altered basalt rocks, indicating inputs of organic carbon from the trench subsurface environment. Thus, in addition to organic-rich material settling, we propose chemoautotrophic activity in oceanic crust could be an additional source of organic carbon in the deepest part of the ocean, with an important role in deep-sea carbon cycles.

The chlamydial plasmid, an essential virulence factor, encodes plasmid proteins that play important roles in chlamydial infection and the corresponding immune response. However, the virulence factors and the molecular mechanisms of Chlamydia psittaci are not well understood. In the present study, we investigated the roles and mechanisms of the plasmid-encoded protein CPSIT_P7 of C. psittaci in regulating the inflammatory response in THP-1 cells (human monocytic leukemia cell line). Based on cytokine arrays, CPSIT_P7 induces the expression of interleukin-6 (IL-6), interleukin-8 (IL-8), and monocyte chemoattractant protein-1 (MCP-1) in THP-1 cells. Moreover, the expression levels of IL-6, IL-8, and MCP-1 stimulated by CPSIT_P7 declined after silencing of the Toll-like receptor 4 (TLR4) gene using small interfering RNA and transfection of a dominant negative plasmid encoding TLR4 (pZERO-hTLR4). We further demonstrated that transfection with the dominant negative plasmid encoding MyD88 (pDeNy-hMyD88) and the dominant negative plasmid encoding Mal (pDeNy-hMal) could also abrogate the expression of the corresponding proteins. Western blot and immunofluorescence assay results showed that CPSIT_P7 could activate nuclear factor κB (NF-κB) signaling pathways in THP-1 cells. Altogether, our results indicate that the CPSIT_P7 induces the TLR4/Mal/MyD88/NF-κB signaling axis and therefore contributes to the inflammatory cytokine response.

Background: The lack of Treponema pallidum-specific antigens with highly accurate diagnosis makes the diagnosis of syphilis challenging. Methods: A soluble recombinant version of a new diagnostic protein Tp0663 has been produced. The serodiagnostic potential of this protein was assessed by screening 3326 serum samples simultaneously evaluated by rapid plasma reagin and T. pallidum particle agglutination tests. Kappa (κ) coefficients were used to compare the concordance between clinical diagnosis and the Tp0663-based ELISA or the ARCHITECT Syphilis TP chemiluminescent immunoassay (Abbott GmbH and Co. KG). Results: Using the results of clinical diagnosis as the gold standard, the sensitivity and specificity of Tp0663 were found to be 98.83% (95% confidence interval (CI) 96.61–99.60%) and 100% (95% CI 99.88–100%), respectively. In comparison, the ARCHITECT Syphilis TP assay was found to have a lower sensitivity (97.27%, 95% CI 94.46–98.67%) and specificity (99.61%, 95% CI 99.32–99.78%). In particular, the ARCHITECT Syphilis TP exhibited a false-positive rate of 0.39%. Moreover, the ELISA was in perfect agreement with the gold standard, with a κ value of 0.99, comparable to that of ARCHITECT Syphilis TP (0.96). Conclusion: These results identified Tp0663 as a novel serodiagnostic candidate with great potential for developing novel tests for the diagnosis of syphilis.