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Large-scale optimization, which has received much attention in recent years, is inherently a challenging problem. This paper proposes a particle swarm optimizer with multi-level population sampling and dynamic p -learning mechanisms to address the problem. The multi-level sampling mechanism in the proposed method is developed for supporting a balanced evolutionary search. The mechanism works by partitioning the particles of swarm into multi-levels based on their fitness before each generation of evolution. A subset of swarm is then dynamically sampled from the particles at various levels for evolution such that encouraging exploration at the beginning of evolution while exploitation towards the end of evolution, thus appropriately searching the space. The dynamic p -learning mechanism, on the other hand, is introduced to allow efficient particle learning while preserving the swarm diversity during evolution. In this mechanism, each particle is devised to learn from one of the top 100 p% particles of the sub-swarm and the value of p associated with each particle is dynamically adjusted during evolution. By employing the above two mechanisms, the resulting method aims to appropriate search the solution space of large-scale global optimization problem for identifying the optimal or near-optimal solution. The performance of the proposed method has been evaluated on CEC'2010 and CEC'2013 benchmark suites for large-scale optimization and compared with related methods. Our results confirm the merits of the devised mechanisms in the proposed method. The results also show that our method can achieve a superior performance and outperform related methods. [ABSTRACT FROM AUTHOR]

The literature has provided inconsistent findings on the relationship between interleukin IL-1 gene polymorphisms and susceptibility to ankylosing spondylitis (AS). Therefore, a systematic review and meta-analysis were conducted. Online electronic database searches were performed for relevant research published as of May 2021. Meta-analysis was performed to compare alleles and multiple genetic models (including dominant, recessive, heterozygous, and homozygous models) using random-effects models to reduce the impact of heterogeneity. A 95% confidence interval (95% CI) odds ratio (OR) was used to assess potential relationships. Nineteen studies including 6235 patients with AS and 5919 healthy controls were recruited. IL-1A-889 (rs1800587) had statistical significance in the allelic model (OR 1.38, 95% CI 1.08–1.77, P = 0.010) (I2 = 51%.1, P = 0.0001); homozygous model (OR 1.92, 95% CI 1.27–2.89, P = 0.002); heterozygous model (OR 1.49, 95% CI 1.02–2.17, P = 0.163); dominant genetic model (OR 1.53, 95% CI 1.05–2.24, P = 0.026); and recessive model (OR 1.54, 95% CI 1.04–2.28, P = 0.031). Further stratified analysis showed that the allele model (OR 1.35, 95% CI 1.08–1.69, P = 0.008), heterozygous model (OR 1.45, 95% CI 1.07–1.96, P = 0.017), and dominant model (OR 1.49, 95% CI 1.11–1.99, P = 0.007) in the English population and allele model (OR 2.21, 95% CI 1.45–3.37, P = 0.0001), homozygous model (OR 3.85, 95% CI 1.38–10.76, P = 0.010), heterozygous model (OR 3.42, 95% CI 1.85–6.32, P = 0.0001), and dominant model (OR 3.49, 95% CI 1.93–6.30, P = 0.001) in Tunis were significantly associated with susceptibility to AS. Analysis of the IL1F7 exon 2 (rs3811047) showed that the G allele frequency was higher in the normal population than in the AS population (OR 0.76, 95% CI (0.64, 0.91)). Further stratified analysis concluded that the allele model was significantly associated with AS susceptibility in Canadian (OR 0.76, 95% CI 0.61–0.94, P = 0.011) and Chinese patients (OR 0.64, 95% CI 0.41–0.98, P = 0.041). The meta-analysis showed that the IL-1 gene polymorphism IL-1A-889 (rs1800587) increases the risk of AS in English and Tunisian populations. IL1F7 exon 2 (rs3811047) is negatively correlated with susceptibility to AS in Canadian and Chinese populations, but additional studies are needed for further exploration. [ABSTRACT FROM AUTHOR]

[Display omitted] • Investigates a novel EEG fading data classification problem. • Proposes a novel fading data classification method based on manifold learning. • Shows the effectivness of the proposed method via BCI Competition 2008 - Graz data set A. In electroencephalogram (EEG) signal analysis, data fading problem exists from signal production to collection by brain-computer interface (BCI) device, which can be raised by BCI device deficiency, dynamic network limitation and subject issue. EEG data fading problem changes the distribution of data, which results in the movement of the cluster center and fuzzy class boundary after feature extraction with negative effects in EEG classification results. To decrease the adverse influence of data fading, a novel fading data classification method based on manifold learning and adaptive neighborhood selection is proposed in this paper to mitigate this adverse effect of data fading. In the proposed method, after neighborhood selection according to local linearity, data are mapped into manifold space through local tangent space alignment (LTSA) for dimensionality reduction. The method is carried out on BCI Competition 2008 – Graz data set A of four-class EEG data of motor imagery (MI) experiments. The experimental results are compared with conventional LTSA and indicate that the proposed method effectively improves the classification accuracy of fading data. [ABSTRACT FROM AUTHOR]

Purpose: The Fremantle back awareness questionnaire (FreBAQ) was recently developed as simple and quick tool to assess back-specific body perception in Low back pain (LBP) patients. The aim of the present study was to translate and cross-culturally adapt the Fremantle back awareness questionnaire (FreBAQ) into a Simplified Chinese version (FreBAQ-C), and evaluate the reliability and validity of the FreBAQ-C in patients with non-specific Chronic Low back pain (CLBP). Methods: The FreBAQ was translated into Chinese according to established methods. Internal consistency was assessed according to Cronbach's alpha. Test–retest reliability was estimated by Intraclass correlation coefficient (ICC). Construct validity was evaluated by correlations between the FreBAQ-C and Visual analogue scale (VAS), Roland-Morris disability questionnaire (RDQ), Pain catastrophizing scale (PCS), Tampa scale for kinesiophobia (TSK) as well as Hospital anxiety and depression scale (HADS). Results: A total of 105 participants (38 males and 67 females) were included in this study with the mean age of 54.1 ± 15.6 years, mean duration of LBP of 6.8 ± 4.6 years. The FreBAQ-C total scores were well distributed, with no floor or ceiling effects. Internal consistency was excellent (Cronbach's alpha = 0.833). ICC of test–retest reliability was good (0.897, 95% confidence interval: 0.852–0.929). The limits of agreement (LOA) ranged from − 5.8 to 6.3. The Standard error of measurement (SEM) and Minimum detectable change (MDC) were 2.16 and 5.99. Construct validity was confirmed by significant correlation of The FreBAQ-C and VAS during motion (r = 0.274, p = 0.005) and rest (r = 0.243, p = 0.012), RDQ (r = 0.377, p < 0.001), PCS (r = 0.439, p < 0.001), and TSK(r = 0.311, p = 0.001). Conclusions: The FreBAQ-C was demonstrated to have acceptable reliability and validity for patients with non-specific CLBP in Chinese mainland. It will allow evaluating body preception of the back in the Chinese population with CLBP. [ABSTRACT FROM AUTHOR]

• A trade-driven model of "Hydropower for Rice" cooperation was established. • "Hydropower for Rice" cooperation is feasible. • Dominant factors are rice trade of downstream countries and China's reservoir operation. There have been intense conflicts over water resources between upstream and downstream countries in the Lancang-Mekong River Basin (LMRB) over the past years, primarily due to the trade-off relationship between hydropower and rice production. Great potential for transboundary cooperation has been shown, such as China's emergency water release in 2016 to alleviate downstream severe drought. However, there is still a lack of systematic cooperation pattern. To address this issue, the study proposes a trade-driven model of "Hydropower for Rice" cooperation, and then derives three key political factors including price weight, export rate, and terminal water level of reservoirs. Our results show that (1) The hydropower and rice production under the trade-driven model can approximate the centralized model, and the "Hydropower for Rice" cooperation is feasible. (2) For downstream countries, rice is the main product, and the rice production and economic benefit can be increased with a higher price weight or export rate. Compared to Thailand and Cambodia, Vietnam needs to engage in deeper trade with China. (3) For upstream China, hydropower is the main product, and the economic benefit of riparian countries can be increased with higher export rates. The discharge in the dry season is increased, which is driven by the trade of rice. Additionally, China's initiative to reduce the terminal water level of reservoir Nuozhadu has the potential to further increase rice production in downstream countries. This study indicates that the transboundary cooperation in LMRB requires joint efforts of upstream and downstream countries. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to examine the associations between body composition and temporal eating patterns, including time of first eating occasion, time of last eating occasion, eating window, and eating jet lag (the variability in meal timing between weekdays and weekends).A total of 131 participants were included in the study. Temporal eating pattern information was collected through consecutive 7-day eat timing questionnaires and photographic food records. Body composition was assessed by bioelectrical impedance analysis. Multiple linear regression models were used to evaluate the relationships of temporal eating patterns with body composition, and age was adjusted. Eating midpoint was additionally adjusted in the analysis of eating window.On weekdays, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with lower body fat percentage. On weekends, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with higher FFMI. Longer first eating occasion jet lag was associated with lower lean mass.Our study suggested that earlier and more regular eating patterns may have a benefit on body composition.Methods: The aim of this study was to examine the associations between body composition and temporal eating patterns, including time of first eating occasion, time of last eating occasion, eating window, and eating jet lag (the variability in meal timing between weekdays and weekends).A total of 131 participants were included in the study. Temporal eating pattern information was collected through consecutive 7-day eat timing questionnaires and photographic food records. Body composition was assessed by bioelectrical impedance analysis. Multiple linear regression models were used to evaluate the relationships of temporal eating patterns with body composition, and age was adjusted. Eating midpoint was additionally adjusted in the analysis of eating window.On weekdays, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with lower body fat percentage. On weekends, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with higher FFMI. Longer first eating occasion jet lag was associated with lower lean mass.Our study suggested that earlier and more regular eating patterns may have a benefit on body composition.Results: The aim of this study was to examine the associations between body composition and temporal eating patterns, including time of first eating occasion, time of last eating occasion, eating window, and eating jet lag (the variability in meal timing between weekdays and weekends).A total of 131 participants were included in the study. Temporal eating pattern information was collected through consecutive 7-day eat timing questionnaires and photographic food records. Body composition was assessed by bioelectrical impedance analysis. Multiple linear regression models were used to evaluate the relationships of temporal eating patterns with body composition, and age was adjusted. Eating midpoint was additionally adjusted in the analysis of eating window.On weekdays, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with lower body fat percentage. On weekends, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with higher FFMI. Longer first eating occasion jet lag was associated with lower lean mass.Our study suggested that earlier and more regular eating patterns may have a benefit on body composition.Conclusion: The aim of this study was to examine the associations between body composition and temporal eating patterns, including time of first eating occasion, time of last eating occasion, eating window, and eating jet lag (the variability in meal timing between weekdays and weekends).A total of 131 participants were included in the study. Temporal eating pattern information was collected through consecutive 7-day eat timing questionnaires and photographic food records. Body composition was assessed by bioelectrical impedance analysis. Multiple linear regression models were used to evaluate the relationships of temporal eating patterns with body composition, and age was adjusted. Eating midpoint was additionally adjusted in the analysis of eating window.On weekdays, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with lower body fat percentage. On weekends, both later first eating occasion and last eating occasion were associated with lower lean mass, and longer eating window was associated with higher FFMI. Longer first eating occasion jet lag was associated with lower lean mass.Our study suggested that earlier and more regular eating patterns may have a benefit on body composition. [ABSTRACT FROM AUTHOR]

The decarbonization of energy systems requires the deployment of intermittent renewables (wind and solar) with increasingly low costs. The growth of these renewables impacts power prices, which may influence hydropower investments and the adoption of fossil-fuel options for baseload electricity. It thus remains unclear whether deploying more intermittent renewables would reduce the net carbon emissions across energy systems in the absence of well-designed policy. This study first developed a techno-economic optimization model to explore the impacts of future projections of intermittent renewables on long-run electricity prices and the subsequent hydropower investments, using Hubei Province of China as a case study. The carbon emissions associated with the change in hydropower investment were then quantified. Results indicate that the new policy-driven renewables deployment reduces the electricity prices by 36 % on average over 2021–2100, compared to the case with the policy specified in the prior (13th) National Five-Year Plan. Without intervention, the lower electricity price could result in a 42 % reduction in hydropower investments by 2050. With increased intermittent renewables and decreased investments in hydropower, the energy system is expected to deploy more backup fossil-fuel options, which could increase carbon emissions by 45 %. Therefore, deploying more renewable energy would depress the investments in hydropower plants and further hinder decarbonization unexpectedly, suggesting a possible paradox in the energy transition. This possible paradox calls for a new, and robust policy to ensure the benefits of decarbonization are realized regardless of potential changes electricity prices. [Display omitted] • A dynamic techno-economic optimization model is developed to derive hydropower investments. • Long-term electricity prices are shaped by policy-driven projections of renewable energy. • Ambitious intermittent renewable projections depress hydropower investment by reducing electricity prices. • Carbon emissions relating to energy system evolution are quantified. [ABSTRACT FROM AUTHOR]

Aiming at limitations in fully exploiting the temporal correlation features of the original signals, expensive cost in parameter tuning, and difficulties in obtaining sufficient training data under multiple working conditions, this paper proposes an optimized Convolutional Neural Network (CNN) with Bi-directional Long Short-Term Memory (BiLSTM) scheme for bearing fault diagnosis under multiple working conditions with limited training samples. A CNN-BiLSTM network is developed to obtain precise fault features and high detection accuracy by extracting high-dimensional and temporal correlation features of raw vibration signals. An improved particle swarm optimization (PSO) algorithm is leveraged to optimize the training hyperparameters of the CNN-BiLSTM network for further advances in fault diagnosis performance. The optimized CNN-BiLSTM network is regarded as a pre-trained model and transferred to new working conditions to achieve satisfactory fault diagnosis results based on limited training samples. Several comprehensive experiments are implemented to confirm the excellent performance of the proposed schemes, especially efficiently addressing the challenges of model training and fault diagnosis in new working conditions with scarce samples. [ABSTRACT FROM AUTHOR]

• An improved runoff forecasting method is proposed based on time-varying parameters. • The proposed method outperforms the TVPF method in the water balance index notably. • The proposed method can update parameters with newly measured rainfall and runoff. The time-varying parameter functional (TVPF) method has shown promise in improving runoff forecasting under climate change and human activities. However, it has three notable limitations: data availability, increased model complexity, and the inability to update with newly measured data. To address these challenges, this study proposes an improved runoff forecasting method that relies solely on time-varying parameters from the same period in previous years. The proposed method comprises four steps: (1) identification of time-varying parameters from historical observations; (2) estimation of the current-year parameters by averaging the time-varying parameters from the same period in previous years; (3) runoff forecasts using the estimated current-year parameters; (4) continuous identification and estimation of parameters with newly measured data for each year, enabling a rolling forecast of runoff. The proposed method is compared with both the constant parameter method and TVPF method, using the Xun River and Zuo River basins as case studies. The results reveal that (1) parameters from the same period in the previous three years yield the best runoff forecasts in the Xun River basin, while parameters from the same period in the previous five years produce the best runoff forecasts in the Zuo River basin; (2) the proposed method significantly improves the water balance index (WBI) compared to the TVPF method, providing either superior or comparable performance in structural risk minimization (SRM) evaluation. Overall, this study provides an approach to forecast runoff in a changing environment that reduces data requirements, simplifies model complexity, and improves WBI performance. [ABSTRACT FROM AUTHOR]

Through the application of deep learning and multi-sensor data, fault features can be automatically extracted and valuable information can be integrated to tackle intricate challenges in motor bearing fault diagnosis. Most existing fusion models focus primarily on the original time series signal with information extraction largely restricted to the time domain (without extensions into multiple transformation domains). Also, in most fusion models, the sensor fusion level is kept relatively simple which could lead to the oversight of correlations and complementarities among the information. To enhance the recognition capability of diagnostic network features, in this paper, we propose a novel framework for motor bearing fault diagnosis from the perspectives of multi-transformation domain and multi-source data fusion. Within this framework, feature extraction and fusion from various source data are achieved in the time domain, frequency domain, and time–frequency domain. Distinct independent networks are set up within these domains: one network is designated for overseeing feature fusion, while the others are dedicated to extracting features from individual sensors. To support the extraction of pivotal features across multiple fusion layers in various transformation domains, several fusion nodes are inserted between the layers of the multiple feature extraction networks and the feature summarization network. Furthermore, a channel attention mechanism is introduced as a fusion strategy that serves to pinpoint the significance of different features, thus enhancing the efficiency of feature extraction. Experimental evaluation reveals the efficacy of the proposed model and highlights its noteworthy performance attributes such as scalability and universality. [ABSTRACT FROM AUTHOR]

Background: Adolescents with scoliosis consistently demonstrate lower body weight, lean muscle mass, and bone mineral density than healthy adolescent counterparts. Recent studies have focused on understanding how leptin and ghrelin signaling may play a role in adolescent idiopathic scoliosis (AIS). In our current study, we aim to evaluate the serum levels of leptin, soluble leptin receptor (sOB-R), and ghrelin in AIS patients through systematic review and meta-analysis. Methods: We conducted our systematic review by searching the keywords in online databases including PubMed, Embase, Cochrane, Elsevier, Springer, and Web of Science from the time of database inception to January 2020. Inclusion criteria were studies that measure leptin, soluble leptin receptor (sOB-R), and ghrelin levels in AIS patients. Selection of studies, assessment of study quality, and data extraction were performed by two reviewers independently. Then, data was analyzed to calculate the mean difference and 95% confidence interval (CI). Results: Seven studies concerning leptin/sOB-R and three studies concerning ghrelin were qualified for meta-analysis (one study concerning both leptin and ghrelin). Serum leptin of patients with AIS were significantly lower when compared with healthy controls, with the weighted mean difference (WMD) of − 0.95 (95% CI − 1.43 to − 0.48, p < 0.0001) after reducing the heterogeneity using six studies for meta-analysis, while sOB-R and ghrelin level was significantly higher in AIS group when compared with control group, with the WMD of 2.64 (95% CI 1.60 to 3.67, p < 0.001) and 1.42 (95% CI 0.48 to 2.35, p = 0.003), respectively. Conclusion: Our current meta-analysis showed that serum level of leptin in AIS patients was significantly lower when compared with control subjects, while serum sOB-R and ghrelin levels were significantly higher in AIS patients. More clinical studies are still required to further validate the predictive value of leptin or ghrelin for the curve progression for AIS patients. [ABSTRACT FROM AUTHOR]

• Feature extraction : Aiming at the problems existing in the fault feature extraction methods, it decomposed the fault signal into several IMF components using the VMD algorithm with adaptive characteristics. Then the IMF components with transient fault characteristics are accurately selected to achieve fault feature extraction. • Wave-head calibration: The discrete kurtosis value is used to detect the arrival time of the traveling wave head. The discrete kurtosis value is calculated for the IMF component with the highest frequency. The time corresponding to the maximum kurtosis value is the traveling wave head moment, and the wave-head calibration is realized. • Wave velocity selection : Given the problem of low fault location accuracy due to different wave speeds of mixed lines, it is proposed to convert the length of cable lines to overhead lines and used unified wave velocity to reduce location errors. There are many affected factors for the traditional traveling wave detection method applied to distribution networks, and the wave head calibration needs to be more accurate. This paper proposes a new method for fault location in resonant grounding systems based on variable modal decomposition (VMD) and kurtosis calibration. Firstly, the feeder currents are collected for phase mode transformation to obtain the aerial mode components. Secondly, the VMD algorithm decomposes the aerial mode components into several intrinsic mode functions (IMFs). Then, the discrete kurtosis is calculated for the high-frequency IMF, and the moment of maximum kurtosis value is determined as the first arrival moment of the faulty wave head. Finally, the faulty section is preliminarily determined using the calibrated moments of the head and end devices of the main line. If the fault is located on the main line, the fault location is determined directly. If it is located on a branch line, the distance of the fault is calculated using the wave head between the head and branch line ends to achieve a precise location. The PSCAD simulation results show that the proposed method can accurately calibrate the arrival moment of the fault wave head and achieve fault location. It is not affected by the fault location and the initial angle of the fault. [ABSTRACT FROM AUTHOR]

Quantifying the uncertainty of stormwater inflow is critical for improving the resilience of urban drainage systems (UDSs). However, the high computational complexity and time consumption obstruct the implementation of uncertainty-addressing methods for real-time control of UDSs. To address this issue, this study developed a machine learning-based surrogate model (MLSM) that maintains high-fidelity descriptions of drainage dynamics and meanwhile diminishes the computational complexity. With stormwater inflow and controls as inputs and system overflow as the output, MLSM is able to fast evaluate system performance, and therefore stochastic optimization becomes feasible. Thus, a real-time control strategy was built by combining MLSM with the stochastic model predictive control. This strategy used stochastic stormwater inflow scenarios as input and aimed to minimize the expected overflow under all scenarios. An ensemble of stormwater inflow scenarios was generated by assuming the forecast errors follow normal distributions. To downsize the ensemble, representative scenarios with their probabilities were selected using the simultaneous backward reduction method. The proposed control strategy was applied to a combined UDS of China. Results are as follows. (1) MLSM fit well with the original high-fidelity urban drainage model, while the computational time was reduced by 99.1%. (2) The proposed strategy consistently outperformed the classical deterministic model predictive control in both magnitude and duration dimensions of system resilience, when the consumed time compatible is with the real-time operation. It is indicated that the proposed control strategy could be used to inform the real-time operation of complex UDSs and thus enhance system resilience to uncertainty. • Machine learning-based surrogate model (MLSM) replaced high-fidelity process model. • Stochastic model predictive control (MPC) became feasible with MLSM. • MLSM reduced computational time significantly without compromising MPC efficacy. • Stochastic MPC based on MLSM achieved more resilience than deterministic one. [ABSTRACT FROM AUTHOR]

Study Design: A retrospective study.Objective: To describe a new surgical technique for total en bloc spondylectomy (TES) of the fifth lumbar (L5) tumor and evaluate the efficacy and safety of this new technique.Summary Of Background Data: TES has been considered an optimal treatment for tumor, including certain spinal tumors, but it requires a combined posterior-anterior approach, which is often complicated by a long operation time, considerable blood loss and severe trauma.Methods: Seven patients with primary or solitary metastatic tumors of L5 were treated with this new technique in our center between March 2014 and November 2017. The critical points were fabrication of the iliac graft, dissection, resection, and reconstruction. Other parameters including surgical time, blood loss, complications, pre- and postoperative neurological function, tumor control, and overall survival (OS) were presented and analyzed.Results: All the included patients received one stage TES. The mean surgical time was 365.7 minutes with an average blood loss of 2514.3 mL. No serious perioperative complication was observed or reported during the mean follow-up period of 27.4 months. Wound disruption occurred in one patient and numbness of the left lower limb in another, but both recovered rapidly after appropriate management. Adventitial avulsion of the abdominal aorta occurred during dissection in one patient. Two patients died during the follow-up period due to advanced malignancy. One patient was alive but developed a newly diagnosed thoracolumbar tumor in 40 months. The other four patients recovered well without evidence of disease. All patients were able to walk independently 3 to 4 weeks after operation, with satisfied fusion of the iliac grafts in a mean period of 6.7 months after operation. No evidence of internal fixation failure occurred.Conclusion: This new technique offers satisfactory surgical exposure, total en bloc spondylectomy, reliable reconstruction, and good tumor control for certain L5 tumors through the posterior-only approach.Level Of Evidence: 4. [ABSTRACT FROM AUTHOR]

Abstract The crushed-rock interlayer (CRI) has been used to cool the railway embankment subbase and subgrade of the Qinghai-Tibet Railway (QTR) in permafrost regions. However, in addition to the climate change, aeolian sand clogging has been affecting this mitigation techniques aimed to increase heat extraction and reduce subsurface heat penetration by air convection through the voids of the CRI. Its long-term performance is evaluated herein using a numerical modelling. Simulation results demonstrate an unclogged CRI can mitigate permafrost warming for the first 20 years with a simulated increase in average air temperature by 0.052 °C a−1. But after sand clogging, including both filling of voids in the CRI and covering of embankment slopes, its cooling performance is weakened. In such conditions, permafrost table is increasing and the permafrost is warming up under different mean annual air temperature (MAAT) of −4.5, −5.5, and −6.5 °C. The water content of the aeolian sand can also affect the cooling effectiveness of CRI. A 1 m-thick dry sand cover can induce an increase in ground temperature. But wet sand layer can mitigate and delay the effect of climate change on the permafrost degradation. The sand-control measures and additional engineering techniques should be adopted to prevent the long-term thermal stability of the QTR in sandy permafrost zone of Qinghai-Tibet Plateau. Highlights • Crushed-rock interlayer can counteract impacts of climate warming on permafrost degradation. • Performance of crushed-rock interlayer embankment declines after aeolian sand clogging. • Warm ice-rich permafrost is sensitive to climate change and aeolian sand clogging scenarios. [ABSTRACT FROM AUTHOR]

The medical grid including hospitals at all levels is a new hierarchical diagnosis and treatment system. It is assumed to provide health services for residents in a certain area, allowing free referral of patients, so as to effectively utilize medical resources. Therefore, from the perspective of the government, the key issue is how to divide the medical grid in a robust and balanced manner. In this paper, various deterministic factors, such as hospital level, location and department, as well as uncertain factors, including patient distribution or population density, are considered in decision-making. To solve this problem, a dual-clustering algorithm based on K-means and K-medoids (DCKK) is developed with local search methods to minimize the average patient waiting and travelling time. The experimental results show that DCKK algorithm can generate better and more robust grid partition solutions than the existing mainstream algorithms in different scenarios. In addition, the rules between the number of medical grids and the number of patients, as well as the hospital sharing between medical grids, are also studied. Finally, a real medical grid partition case of Ji'nan, China, with forty hospitals in four urban areas, is studied, and five medical grids are recommended. • Develop a robust dual-clustering algorithm associated with six local search methods for medical grid partition. • Patient referral can improve the utilization of medical resources and shorten the patients' waiting and travelling time. • Investigate the law between the number of medical grids and the number of patients. • The sharing and marginal hospitals from different grids can significantly reduce patient waiting and travelling time. [ABSTRACT FROM AUTHOR]

Aeolian sand is an important factor, which can affect the permafrost environment and infrastructures on the Tibetan Plateau. The thermal conductivity of aeolian sand is an important parameter for analyzing the thermal regime of the ground in cold regions. This paper presents the test results of the thermal conductivity of aeolian sand sampled from the Tibetan Plateau in unfrozen state with temperature 20 °C and in frozen state. The aeolian sand samples were measured with water content ranging from 3% to 15% and dry density ranging from 1400 to 1650 kg·m − 3 . The results show that the thermal conductivity increased with water content and dry density. Some methods that have been developed for calculating the thermal conductivity of soils are applied to predict the thermal conductivity of aeolian sand. However, these methods all have significant deviations. From the test data, a simple model is proposed as a function of the degree of saturation and the volume fraction of each phase to calculate the thermal conductivity of aeolian sand. The calculated results obtained from the proposed model agree well with the test data. The mean deviations are 2.15% and 4.28% for the unfrozen and frozen states, respectively. [ABSTRACT FROM AUTHOR]

• Quantify changes in hydropower efficiency for HWPESs. • Average hydropower efficiency decreases from 9.04 to 9.02 compared with separate operation. • Extract short-term hydropower efficiency changes and power curtailment patterns. • Propose an accurate mid- and long-term power generation calculation method. • System power generation calculation errors reduce from 0.60 % to 0.03 % in validation period. Complementary operation with hydropower can facilitate the integration of intermittent wind and photovoltaic (PV) power by the regulation ability of reservoirs and the flexibility of hydro units. However, changes in operating patterns of hydropower stations in hydro-wind-PV complementary energy systems (HWPESs) induce potential impacts on hydropower efficiency, which has been seldom studied. To quantify the changes in hydropower efficiency in HWPESs, the complementary and separate short-term operation models are constructed to identify the changes in hydropower efficiency. Then, an accurate calculation method for power generation of HWPESs in the mid- and long-term operation is proposed by considering the short-term hydropower efficiency changes and power curtailment patterns. The JP-I HWPES in the Yalong River Basin is selected as a case study. Results indicate that the average daily hydropower efficiency under complementary operation decreases from 9.04 to 9.02 compared with separate operation, which results in a 3.73 % (1.36 billion kWh) decrease in the total hydropower generation. The proposed method can reduce calculation errors of hydropower and system total power generation by 99.59 million kWh (from 0.65 % to 0.09 %) and 126.78 million kWh (from 0.60 % to 0.03 %) in the validation period, respectively. From the hydropower perspective, this study quantified the changes in short-term hydropower efficiency, which can provide a practical tool for the accurate mid- and long-term operation of HWPESs. [ABSTRACT FROM AUTHOR]

Canals play an extremely important role in irrigation and conveyance of water. In cold regions, composite geomembranes are usually utilized as cushions in canals to mitigate frost heaving and seepage damages. In this study, a mechanical model for trapezoidal canals lined with double-layer geomembranes was developed to clarify the mechanisms of action of double-layer geomembranes for trapezoidal canals during freezing-thawing processes. The external and internal forces of a traditional concrete canal and two composite canals (i.e. a double-layer geomembranes canal and an anti-slip canal) were calculated using the mechanical model, and the tangential and internal forces on the slabs of three canals were compared, and analyzed. Furthermore, The reasons for the cracks in canal slabs and the sliding of slope slabs were explained using the freeze-thaw mechanical model. The results show that the tangential and internal forces in the double-layer geomembranes canal are minimal during the freezing period, while the tangential and internal forces in the traditional concrete canal are maximum. Conversely, during the thawing period, the anti-slip force of the anti-slip canal is larger than those of the double-layer geomembranes canal and traditional concrete canal subsequently. The mechanical model developed in this study will benefit the design of canals in cold regions. • The frost-heaving damage and collapse mechanisms of the slope slabs of canals in cold regions were explored. • A new composite canal in trapezoidal is proposed, including the double-layer geomembranes canal and the anti-slip canal. • A mechanical model was established to deal with the frost heaving and slab-slipping of a composite canal. • The causes of crack formation in canal slabs and the sliding of slope slabs were explained using the mechanical model. • Judgment criteria for the frost-heaving damage of concrete slabs and the collapse of slope slabs were proposed. [ABSTRACT FROM AUTHOR]

• It proposed a novel noninvasive location method based on MF. It can collect line information in a low-cost, non-contact manner, and is not affected by the complex topology of the distribution network. No power cut is required for installation and maintenance. At the same time, the MF is directly used to reflect the physical characteristics of the fault, and the analysis process is simple, reducing the introduction of complex algorithms for feature extraction and other steps, which has greater advantages in practical applications. • It proposed a multi criteria fusion location method based on dual-axis MF component. We deeply studied the application of MF in fault location, and analyzes the MF waveform from two dimensions of x -axis and y -axis. We measured the fault information from the perspective of five criteria, which is more comprehensive and suitable for all kinds of operation conditions. D-S evidence theory is introduced to improve the fault location reliability through multi criteria fusion. • The variation of MF in each axis and its influence on the method are further considered when the sag, galloping and sampling data window are different. The distribution network has many branches and complex structure, and the existing protection is difficult to achieve accurate fault section location. Moreover, the monitoring device based on electrical quantity has some problems, such as high cost, power failure in installation and maintenance, and the information collected can not meet the requirements of distribution network for operation monitoring. Therefore, fault location is still a difficult problem in the existing distribution network. The magnetic field (MF) under the overhead line can reflect the change of line current, locate the fault position between two pole towers, and the magnetoresistive sensor has the advantages of low cost and uninterrupted installation and maintenance. Therefore, in recent years, the non-contact fault feeder detection and location method based on MF has begun to develop rapidly. Based on the similarity analysis of MF waveform under overhead line from different aspects by using five criteria: relative entropy, Frobenius norm, maximum mean discrepancy, cosine similarity and correlation coefficient, it proposed a multi criteria fusion fault location method based on D-S evidence theory, which makes full use of the advantages of each criterion and overcomes the limitations of single criterion. Simulation results show that the method is not affected by conductor sag, galloping and data window length, and it fit for the noise interference and high impedance fault (HIF). Compared with the other MF method, the method has stronger adaptability. [ABSTRACT FROM AUTHOR]

• Temporal sensitivity for hydrological processes enhances identifiability of process structure. • Quickflow and soil evaporation are more easily identified than infiltration, baseflow, and snow balance. • The model identified by sensitivity-weighted MAE outperforms that by MAE without sensitivity-weights. Structure identifiability of hydrological processes refers to the ability to select appropriate mathematical representation from multiple structural options. Although the influence of hydrological processes on model behavior varies in time, this temporal relative influence has not been used to identify process structures. In this work, a framework is proposed to investigate the impacts of temporal relative influence of hydrological processes on structure identification. The framework consists of four elements: (1) a blended model, where hydrological process options and parameter values are regarded as integer and continuous decision variables, respectively, is built based on the flexible modeling framework Raven; (2) the temporal sensitivities of each process are derived by using Sobol' sensitivity analysis on a moving window; (3) performance metrics with and without sensitivity-weights, which are derived from the temporal sensitivities, are used to identify process structures, respectively; and (4) models identified by these two methods are compared to evaluate performance. A synthetic experiment is designed to validate the proposed framework. Three case studies are carried out in the Tongtianhe, Xunhe, and Wudinghe basins in China. Results are summarized as follows: (1) sensitivity-weighted performance metrics improve the identifiability of process structures by considering the temporal relative influence of hydrological processes on model behavior; (2) quickflow and soil evaporation processes are the two most identifiable processes, followed by infiltration, baseflow, and snow balance processes; and (3) the model identified by sensitivity-weighted performance metrics is more efficient than that selected by the performance metric without sensitivity-weights. It is recommended that the temporal sensitivity of hydrological processes be used to identify model structures based on flexible modeling frameworks. [ABSTRACT FROM AUTHOR]

• Optimally using the in-pipe storage space can improve the performances of UDSs. • Improved DP for active stormwater controls by considering time-lags of flow routing. • The proposed method can outperform the passive, RBC, and EA strategies. The active control of urban drainage systems (UDSs) is playing an increasingly important role in the world threatened by urban flooding and associated disasters caused by insufficient drainage capacity. However, little research has recognized the importance of the optimal use of in-pipe storage space. To address this issue, the use of the in-pipe storage capacity was optimized in this study. A novel approach, that is, dynamic programming with successive approximation considering the time lag of flow routing (DPSA-TL), was developed to determine the control policies, in addition to the commonly used passive, rule-based control (RBC), and evolutionary algorithm (EA) strategies. A real-life urban catchment considering flooding control and combined sewer overflow (CSO) reduction was used as the case study. First of all, the potential benefit of maximizing the use of in-pipe storage space was tested using the four control strategies in three storm events, including a 3-year, 2-hour design (46.5 mm), a 5-year, 2-hour design (56.0 mm) and a 7-h historical (152.5 mm) storm events. Results indicate that DPSA-TL performed best in all cases. Without compromising the goal of flooding control, it provided 16.5%, 12.6%, and 3.0% reductions in CSO volume for the three storm events when compared with the passive strategy. Due to the limited capacity of in-pipe storage, the relative improvement diminished as the total rainfall depth increased. Then, control strategies were further applicated to the real-time operation. DPSA-TL was found to be the best alternative for CSO control, with the CSO volume reduced by 14.7%, 11.4%, and 2.5% in the three storm events, respectively. The findings suggest that the performance of UDS can be significantly improved by optimizing the use of in-pipe storage capacity, and the proposed method is effective in the offline optimization and real-time control of UDSs. [Display omitted] [ABSTRACT FROM AUTHOR]

• Model structural deficiencies can be diagnosed by testing whether parameters are time-varying. • The temporal variations of parameters provide guidance for model structure improvements. • Modifications of model structure outperform models with functional forms of parameters. Time-varying parameters, which are often expressed in functional forms, have been widely used to improve hydrological modeling. Here, we assume that the time-varying parameters may be caused by deficiencies of the model structures. To justify this point, the SIMHYD model was selected as the base model, and the ensemble Kalman filter (EnKF) was used to identify the temporal variation of parameters. A synthetic experiment was designed to prove that model parameters can partly compensate for the structure deficiencies, while the parameters' time-varying is unavoidable when the structure error has been involved. Two real case studies in the Tongtianhe and Xunhe basins were then conducted to explore the modifications of model structures based on the implications of time-varying parameters. The results are summarized as follows. (1) Model structural deficiencies can be diagnosed by testing whether parameters are time-varying. (2) Guided by the identified time-varying parameters, the energy-based snow module was added and the interception module was removed in the Tongtianhe basin, and the infiltration was modified to follow a power function of the soil moisture in the Xunhe basin. As a result, the model performance has been improved, and the parameter variations have been diminished. (3) The modifications of the SIMHYD model structure outperformed the models with time-varying parameters in functional forms. This study provides insights into the compensatory relationship between model structural deficiencies and parameters, and proposes a method to improve model structures by time-varying parameters. [ABSTRACT FROM AUTHOR]

Aeolian sand is sometimes used as geomaterial in cold regions of China. This paper tries to investigate the unfrozen water, matric suction of aeolian sand, which is related to its thermal and mechanical properties. The pF-meter matric suction sensor coupled with 5TM moisture content sensor that can operate in the subfreezing environment was used to measure the moisture content and matric suction of aeolian sand under different conditions. Combined with thermal resistor temperature probe, the relationship between unfrozen water and temperature is analyzed. The results indicate a significant phenomenon of latent heat absorption and release at freezing and thawing points. In addition, the phenomenon of the supercooling was observed with a value of −1.96 °C during stepwise temperature change. Most water in the sand freezes at freezing point 0 °C under rapid and stepwise temperature change. There is approximately 3% of unfrozen water content retained when the sand temperature drops to −15 °C. The soil matric suction increases very slowly at the stable freezing stage. These results provide the theoretical support for future research on the moisture migration mechanism and engineering application of aeolian sand in cold regions. • Matric suction and unfrozen water of aeolian sand were studied. • Most water in aeolian sand freezes at freezing point 0 °C. • Significant supercooling effect was observed under the rapid freezing process. • Matric suction shows dramatic change at freezing and thawing points. [ABSTRACT FROM AUTHOR]

Most toughening methods for epoxy resins are usually used at the expense of other properties. Some polyhedral oligomeric silsesquioxanes (POSSs) with both a rigid Si-O-Si structure and flexible organic chain segments could be expected to be effective toughening agents. In this study, three reactive polyhedral oligomeric silsesquioxanes with a thiol group (OMPPS), a carboxyl group (OCOPS), and an epoxy group (OGCPS) were synthesized and characterized. They were utilized as modifiers to toughen 3-(oxiran-2-ylmethoxy)- N , N -bis(oxiran-2-ylmethyl)aniline (AFG-90MH)/4,4'-methylenebis(2-ethylaniline) (MOEA) (epoxy resin) with different molar ratios to obtain hybrid resins named OMPPS-EP-i, OCOPS-EP-j, and OGCPS-EP-k. The effects of the amount of modifier added and the length of the organic chain on the cage structure on various properties of the hybrid resins were investigated. The results show that all three modifiers show good compatibility with the epoxy resin. The hybrid resins have a low viscosity at 45~85 °C and can be cured at a low temperature (110 °C). The cured hybrid resins display improved toughness. Typically, the critical stress intensity factor (K IC ) and impact strength of OGCPS-EP-0.6-C are 2.54 MPa∙m -1/2 and 19.33 kJ∙m -2 , respectively, which increased by 58.75% and 22.48% compared with the pristine epoxy resin, respectively. In addition, the glass transition temperature and flexural strength of the hybrid resins are basically unchanged.

Background: To systematically evaluate the relationship between the expression level of long noncoding RNA NEAT1 and the clinical characteristics and prognostic value of rectal cancer patients., Methods: PubMed, EMBASE, Cochrane library database and case-control studies on the correlation between abnormal expression of lncRNA NEAT1 and prognosis of rectal cancer patients published by the American clinical trials registry before May 1, 2023 were searched. The search time was from the establishment of the database to May 30, 2023., Results: A total of 7 case-control studies were included, including 1063 cancer patients. The results of meta-analysis showed that the high expression of lncRNA NEAT1 was significantly correlated with the degree of differentiation [or=0.45, 95%CI=0.32-0.63, P<0.01], tumor size [or=0.59, 95%CI=0.42-0.82, P<0.01], and overall survival [HR=1.34, 95%CI=1.21-1.48, P<0.001]; However, it was not associated with gender [or=1.23, 95%CI= 0.88-1.72, P=0.23] and lymph node metastasis [or=0.87, 95%CI=0.45-1.66, P=0.67]., Conclusions: The high expression of lncRNA NEAT1 may be a risk factor for poor prognosis in patients with malignant tumors, and lncRNA NEAT1 can be used as a potential biomarker to evaluate its prognosis., Competing Interests: All the authors declare that they have no conflict of interest in this work., (2024 Qiyi Lin, Jianpeng Pan, Huaishuai Wang, Yinlin Li, Yixiang Zhuang, Zhicong Cai, Gaofeng Lin, Weibo Liu, Guoxi Xu, published by CEON/CEES.)

Osteosarcoma is the most common type of primary malignant bone tumor. Due to the lack of selectivity and sensitivity of chemotherapy drugs to tumor cells, coupled with the use of large doses, chemotherapy drugs often have systemic toxicity. The use of modern sequencing technology to screen tumor markers in a large number of tumor samples is a common method for screening highly specific and selective anti-tumor drugs. This study aims to identify potential biomarkers using the latest reported gene expression signatures of oncogene-induced replication stress (ORS) in aggressive cancers, and potential anti-osteosarcoma drugs were screened in different drug databases. In this study, we obtained 89 osteosarcoma-related samples in the TARGET database, all of which included survival information. According to the median expression of each of six reported ORS gene markers (NAT10/DDX27/ZNF48/C8ORF33/MOCS3/MPP6), we divided 89 osteosarcoma gene expression datasets into a high expression group and a low expression group and then performed a differentially expressed gene (DEG) analysis. The coexisting genes of 6 groups of DEGs were used as replication stress-related genes (RSGs) of osteosarcoma. Then, key RSGs were screened using LASSO regression, a Cox risk proportional regression prognostic model and a tenfold cross-validation test. GSE21257 datasets collected from the Gene Expression Omnibus (GEO) database were used to verify the prognostic model. The final key RSGs selected were used in the L1000PWD and DGIdb databases to mine potential drugs. After further validation by the prognostic model, we identified seven genes associated with ORS in osteosarcoma as key RSGs, including transcription factor 7 like 2 (TCF7L2), solute carrier family 27 member 4 (SLC27A4), proprotein convertase subtilisin/kexin type 5 (PCSK5), nucleolar protein 6 (NOL6), coiled-coil-coil-coil-coil-helix domain containing 4 (CHCHD4), eukaryotic translation initiation factor 3 subunit B (EIF3B), and synthesis of cytochrome C oxidase 1 (SCO1). Then, we screened the seven key RSGs in two drug databases and found six potential anti-osteosarcoma drugs (D GIdb database: repaglinide, tacrolimus, sirolimus, cyclosporine, and hydrochlorothiazide; L1000PWD database: the small molecule VU-0365117-1). Seven RSGs (TCF7L2, SLC27A4, PCSK5, NOL6, CHCHD4, EIF3B, and SCO1) may be associated with the ORS gene signatures in osteosarcoma. Repaglinide, tacrolimus, sirolimus, cyclosporine, hydrochlorothiazide and the small molecule VU-0365117-1 are potential therapeutic drugs for osteosarcoma., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Tidal flats are of great importance to coastal residents and environments, which are recently facing unprecedented challenges due to massive urban expansions. While some case studies have been conducted in small areas, it is yet to come a picture to examine the issue at the nationwide level. To fill this void, it is necessary to reconsider whether the analytical and statistical methods used in the previous studies are still appropriate to the larger scales, which accordingly needs to be refined and updated. Aiming at this issue, this study implemented a justification for the conterminous United States, in which nearly 70 % of the counties by the seashore with intensified tidal flats in 1985 were selected to conduct a comprehensive assessment. Based on the 156 selected counties, this paper firstly analyzed the spatiotemporal change patterns of tidal flats and urban extents from 1985 to 2015, and then combined these results and implemented a series of correlation assessments between tidal flat loss and urban expansion. As a result, we found that urban expansions in the conterminous coastal United States have not only substantially squeezed the space of tidal flats, but also significantly affected the surrounding tidal flat environments during the three decades., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Copy number variation (CNV) is associated with progression of esophageal cancer (EC), a common gastrointestinal neoplasm., Methods: Using sequencing data, CNV data, and clinical data of EC transcriptome samples obtained from public databases, we performed differential expression analysis on sequencing data. Differentially expressed CNV-driven lncRNAs were screened using the chi-square test, and CNV-driven lncRNA-associated miRNAs and mRNAs were predicted. Cytoscape software was then used to construct ceRNA networks. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to investigate biological functions of mRNAs in the ceRNA network. Survival curves were plotted to explore correlations between lncRNAs in the ceRNA network and overall survival of CNV patients. Multiple databases were used to predict lncRNAs-related drugs., Results: A dysregulated lncRNA-associated ceRNA network driven by CNV in EC, including 11 lncRNAs, 11 miRNAs and 159 mRNAs, was constructed. Downstream enrichment of mRNAs was related to biological processes such as extracellular matrix organization, indicating that these mRNAs mainly participate in intercellular exchange between tumor cells. Additionally, expression of all lncRNAs in the ceRNA network, except LINC00950, LINC01270 and MIR181A1HG, was correlated with patients' CNV. In addition, none of the 11 lncRNAs was significantly correlated with overall survival of CNV patients. CH5424802 and PD-033299CNV mainly affected the RTK signaling pathway and the cell cycle of tumor cells via RP11-180N14.1 and RP11-273 G15.2 in the ceRNA network., Conclusions: This study identified 11 CNV-driven lncRNAs that might affect EC development, 2 of which have promising effects if applied to drug treatment. These findings might assist in identifying novel treatments for EC.

The imbalanced development between deep learning-based model design and motor imagery (MI) data acquisition raises concerns about the potential overfitting issue-models can identify training data well but fail to generalize test data. In this study, a Spatial Variation Generation (SVG) algorithm for MI data augmentation is proposed to alleviate the overfitting issue. In essence, SVG generates MI data using variations of electrode placement and brain spatial pattern, ultimately elevating the density of the raw sample vicinity. The proposed SVG prevents models from memorizing the training data by replacing the raw samples with the proper vicinal distribution. Moreover, SVG generates a uniform distribution and stabilizes the training process of models. In comparison studies involving five deep learning-based models across eight datasets, the proposed SVG algorithm exhibited a notable improvement of 0.021 in the area under the receiver operating characteristic curve (AUC). The improvement achieved by SVG outperforms other data augmentation algorithms. Further results from the ablation study verify the effectiveness of each component of SVG. Finally, the studies in the control group with varying numbers of samples show that the SVG algorithm consistently improves the AUC, with improvements ranging from approximately 0.02 to 0.15.

Osteosarcoma (OS) is a primary malignant tumor of the bone characterized by poor prognosis due to chemotherapy resistance and high recurrence rates. DJ-1 (PARK7) is known as an oncogene and its abnormal expression is related to the poor prognosis of various types of malignant tumors. It was found in this study that upregulated expression of DJ-1 was closely correlated with the prognosis of OS patients by promoting the proliferation, migration and chemotherapy resistance of OS cells in vitro through regulating the activity of CDK4 but not through the oxidation mechanism or AKT pathway. The combination of DJ-1 and CDK4 promoted RB phosphorylation, leading to the dissociation of E2F1 into the nucleus to regulate the expression of cell cycle-related genes. The tumor xenograft mouse model demonstrated that DJ-1 knockout suppressed tumor growth in vivo . All these findings indicate that DJ-1 can affect the occurrence and progression of OS by regulating the CDK/RB/E2F1axis, suggesting a novel therapeutic opportunity for OS patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Han, Wang, Wang, Zhang, Bi, Zheng, Liu, Bai, Wang, Wan, Ma, Cai, Liu and Jia.)

Low back pain (LBP) seriously affects human quality of life. Intervertebral disc degeneration (IVDD) is the main pathological factor that leads to LBP, but the pathological mechanism underlying IVDD has not been fully elucidated. Neuropathic pain caused by IVDD is an important pathological factor affecting people's daily lives. Therefore, it is very important to identify therapeutic drugs to ameliorate IVDD and secondary neuropathic pain. Hydroxytyrosol (HT) is a natural compound derived from olive leaves and oil and has anti-inflammatory, antioxidant, and antitumor activities and other properties. In this study, TNF- α -stimulated human nucleus pulposus cells (HNPCs) were used to simulate the local inflammatory microenvironment observed in IVDD in vitro to explore the role of HT in alleviating various pathological processes associated with IVDD. A rat needle puncture model was used to further explore the role of HT in alleviating IVDD. Lipopolysaccharide (LPS) was used to stimulate microglia in vitro to comprehensively explore the role of HT in alleviating neuropathic pain, and a rat model involving chronic compression of the dorsal root ganglion (CCD) was established to simulate the neuropathic pain caused by IVDD. This study suggests that HT reduces the expression of cyclooxygenase-2 (COX-2), inducible nitric oxide synthase (iNOS), the NOD-like receptor thermal protein domain associated protein 3 (NLRP3) inflammasome, a disintegrin and metalloproteinase with thrombospondin motifs-4 (ADAMTS-4) and matrix metalloproteinase-13 (MMP-13); inhibits the production of mitochondrial reactive oxygen species (ROS); and maintains mitochondrial homeostasis. Thus, HT appears to reduce the rate of apoptosis and mitigate the loss of major intervertebral disc components by inhibiting the nuclear factor kappa-B (NF- κ B) signaling pathway. Moreover, HT inhibited the secretion of COX-2, tumor necrosis factor- α (TNF- α ), interleukin (IL)-6, IL-1 β , and iNOS and activation of the NLRP3 inflammasome in microglia by inhibiting the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) and extracellular regulated protein kinase (ERK) signaling pathways. In conclusion, HT plays a protective role against IVDD and secondary neuropathic pain by inhibiting the NF- κ B, PI3K/AKT, and ERK signaling pathways., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 Haichao Yu et al.)

The Chinese space station will be built around 2020. As a national space laboratory, it will offer unique opportunities for studying the physiological effects of weightlessness and the efficacy of the countermeasures against such effects. In this paper, we described the development of countermeasure systems in the Chinese space program. To emphasize the need of the Chinese space program to implement its own program for developing countermeasures, we reviewed the literature on the negative physiological effects of weightlessness, the challenges of completing missions, the development of countermeasure devices, the establishment of countermeasure programs, and the efficacy of the countermeasure techniques in American and Russian manned spaceflights. In addition, a brief overview was provided on the Chinese research and development on countermeasures to discuss the current status and goals of the development of countermeasures against physiological problems associated with weightlessness.

The study focused on the predictive value of baseline metabolite ratios in bilateral hippocampus of first-episode schizophrenia by using proton magnetic resonance spectroscopy ((1)H MRS). (1)H MRS data were acquired from 23 hallucination and 17 non-hallucination first-episode schizophrenia patients compared with 17 healthy participants. Clinical characteristics of patients were rated using the Positive and Negative Syndrome Scale (PANSS) before and after 3-month treatment. The schizophrenia patients showed lower NAA/Cr ratio than healthy participants respectively (p=0.024; p=0.001), and non-hallucination patients had even lower NAA/Cr ratio than hallucination patients (p=0.033). After 3-month treatment, hallucination patients had greater improvement in negative symptoms than non-hallucination patients (p=0.018). The reduction of PANSS total score and negative factor score was positively correlated with the left NAA/Cr in both group patients (p<0.05). Given that the bilateral hippocampal baseline NAA/Cr had predictive value for the whole treatment response, and the left hippocampal NAA/Cr can predict the prognosis of negative symptoms during acute phase medication in first-episode schizophrenia., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Objective: To investigate the correlation of neurochemical metabolism in hippocampus with memory function in young adult patients with first-episode depression., Methods: Twenty patients with first-episode depression (patient group) and fifteen health subjects (control group) were enrolled in the study. The neurochemical metabolism, including the levels of N-acetylaspartate (NAA), Choline (Cho), Creatine (Cr), Myoinositol (mI) were measured by proton magnetic resonance spectroscope (1H-MRS) in bilateral hippocampus. Wechsler Memory Scale (WMS) were used to examine the memory function in both groups., Results: The memory quotient (89.15 ±6.62) of patient group was significantly lower than that of controls (P <0.01),the scores of long-term memory,short-term memory and immediate memory in patients were also lower than those of controls (P<0.05 or 0.01). In patient group, the ratio of NAA/Cr (1.34 ±0.08) in the left hippocampus was significantly lower than that of control group (P<0.01); and the ratio of mI/Cr in the bilateral hippocampus [(0.63 ±0.13) in left and (0.6 ±0.1) in right] was significantly higher than those in control group (P<0.05). In patient group,the ratio of NAA/Cr in the left hippocampus was positively correlated with WMS scores (P<0.01), and the ratio of mI/Cr in the left hippocampus was negatively correlated with WMS scores (P<0.05)., Conclusion: The memory deficit and abnormal metabolism function of neuron cell in hippocampus coexist in young adult patients with first-episode depression, and the lower NAA/Cr and higher mI/Cr ratio in the left hippocampus may result in the memory deficit.