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Abstract Uterine corpus endometrial carcinoma (UCEC) is a significant cause of cancer-related mortality among women worldwide. Prior research has demonstrated an association between cyclin-dependent kinase inhibitor 2 A (CDKN2A) and various tumors. As a member of the INK4 family, CDKN2A is involved in cell cycle regulation by controlling CDKs. In the present study, bioinformatics was used to analyze public datasets. The expression levels, signaling pathways, and copy number variations of CDKN2A in UCEC were explored, along with its immune cell subset associations. CDKN2A expression was found to be elevated in UCEC, particularly in the signaling pathways involved in cell proliferation and inflammation. Analysis of somatic copy number alterations in the TCGA (The Cancer Genome Atlas)-UCEC dataset revealed a connection between CDKN2A and drug metabolism in UCEC. Assessment of the relationship between CDKN2A and genes involved in immunotherapy for UCEC patients showed a negative correlation between CDKN2A and CD8+ T cell activity, as well as IL-2 and TP53. Collectively, these insights suggest that CDKN2A may be a potential biomarker for prognosis and treatment strategies in UCEC.

Abstract Background As the population ages, hypertension has become the leading risk factor for cardiovascular diseases (CVDs) and premature deaths worldwide. Accurate monitoring of CVD risks and planning community-based public health interventions require reliable estimates of hypertension prevalence and management. While the validity of self-reporting in assessing hypertension prevalence has been debated, the concordance between self-reports and clinical measurements of hypertension control remains underexplored, particularly in large, community-based older populations. This study aims to examine the agreement between self-reported and objectively measured data on both hypertension diagnosis and control, as well as the associated factors, among community-dwelling middle-aged and older Chinese adults. Methods Data from the China Health and Retirement Longitudinal Study were utilized, with household survey responses combined with biomedical data. Sensitivity, specificity, and kappa coefficients were used to assess the agreement between self-reported and objectively measured hypertension diagnosis in the general sample, and the agreement on hypertension control among individuals who reported having hypertension. Binary and multinomial logistic regression analyses were conducted to identify individual, household, and community-level factors associated with the agreement. Results Self-reports exhibited substantial sensitivity, excellent specificity, and moderate agreement with objective measurements for hypertension diagnosis, while demonstrating fair sensitivity, excellent specificity, but low agreement for hypertension control. The odds of agreement on hypertension diagnosis were negatively associated with older age and heavy drinking, but positively related to marital status, higher education, chronic kidney disease, recent healthcare service utilization, and higher household economic levels. Meanwhile, the likelihood of agreement on hypertension control was negatively associated with older age, comorbid diabetes or cardiovascular disease, heavy drinking, BMI over 25, and antihypertensive medication adherence, but positively associated with recently healthcare service utilization. Conclusions Self-reporting underestimated hypertension prevalence but significantly overestimated the hypertension control rates. For middle-aged and older Chinese adults, individual-level factors including age, multimorbidity, behavioural risks, and healthcare-seeking behaviours were identified as significant predictors of agreement between self-reported and objectively measured hypertension data. Recognizing these factors is essential for improving the accuracy of chronic condition estimates and facilitating targeted chronic disease management programs for China’s aging population and other developing countries with similar demographic and health challenges.

Abstract Background The growing prevalence of non-communicable chronic diseases poses a significant public health challenge globally, particularly impacting the well-being of aging populations. This study aims to assess the impact of community-based public health service (PHS) on the health-related quality of life (HRQoL) among middle-aged and older adults with chronic diseases in China. Methods Utilizing data from the China Health and Retirement Longitudinal Study, we constructed a novel scale based on the 36-Item Short Form Health Survey (SF-36) to measure the HRQoL of middle-aged and older patients with hypertension and/or type-2 diabetes. Multivariate linear regression models with Instrument Variables and Propensity Score Matching techniques were applied to examine the effect of PHS on the HRQoL of identified chronic disease patients. Results Among 8,403 hypertensive and/or diabetic patients, only 10.98% had received PHS. After adjusting for covariates, PHS exhibited a significant association with an elevated overall SF-36 score (β = 3.539, p

Abstract Biofilms serve as crucial cues for settlement and metamorphosis in marine invertebrates. Within bacterial systems, c-di-GMP functions as a pivotal signaling molecule regulating both biofilm formation and dispersion. However, the molecular mechanism of how c-di-GMP modulates biofilm-induced larval metamorphosis remains elusive. Our study reveals that the deletion of a c-di-GMP related gene in Pseudoalteromonas marina led to an increase in the level of bacterial c-di-GMP by knockout technique, and the mutant strain had an enhanced ability to produce more outer membrane vesicles (OMVs) and lipopolysaccharides (LPS). The mutant biofilms had higher induction activity for larval metamorphosis in mussels Mytilus coruscus, and OMVs play a major role in the induction activity. We further explored the function of LPS in OMVs. Extracted LPS induced high larval metamorphosis rate, and LPS content were subject to c-di-GMP and LPS-biosynthesis gene. Thus, we postulate that the impact of c-di-GMP on biofilm-induced metamorphosis is mediated through OMVs and LPS.

A new online scheduling algorithm is proposed for photovoltaic (PV) systems with battery-assisted energy storage systems (BESS). The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather conditions. The proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging activities. The scheduling algorithm is developed by using deep deterministic policy gradient (DDPG), a deep reinforcement learning (DRL) algorithm that can deal with continuous state and action spaces. One of the main contributions of this work is a new DDPG reward function, which is designed based on the unique behaviors of energy systems. The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation. The new scheduling algorithm is tested through case studies using real world data, and the results indicate that it outperforms existing algorithms such as Deep Q-learning. The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

Abstract Background Expanding health insurance is a critical step towards universal health coverage due to its positive effect on reducing unmet health care needs and enhancing equitable access to health care. Despite previous studies on the socioeconomic factors associated with unmet health care needs, few studies have analysed the inequalities in such needs and the impact of universal health insurance coverage on addressing them. This study aimed to measure the contribution of social health insurance (SHI) coverage to inequalities in financially and non-financially constrained unmet health care needs among middle-aged and elderly Chinese adults. Methods The study data were obtained from the China Health and Retirement Longitudinal Study (2011–2015). A total of 11,592 respondents reporting outpatient care needs and 6320 reporting inpatient care needs were included. The concentration index (CI) was employed to measure the extent of income-related inequalities in unmet health care needs. A decomposition method based on a probit model was used to investigate the contribution of SHI to the inequalities. Results The incidence rates of unmet outpatient needs due to financial and non-financial constraints were 4.68% and 24.78%, respectively; these rates were 18.69% and 15.73% for unmet inpatient needs. The CIs of unmet outpatient needs due to financial and non-financial constraints were − 0.1872 and 0.0195, respectively; these values were − 0.1558 and 0.0352 for unmet inpatient needs. The percentages of the contribution of SHI to the CIs of financially constrained unmet outpatient and inpatient needs were 0.2639% and 1.8898%, respectively. Moreover, the percentages of the contribution of SHI to the CIs of non-financially constrained unmet outpatient and inpatient needs were − 0.4513% and − 6.4192%, respectively. Conclusion The universal coverage of SHI in China increased pro-poor inequalities in financially constrained unmet health care needs but decreased pro-rich inequalities in non-financially constrained unmet needs. Additionally, the contribution of SHI to inequalities in financially constrained unmet needs for inpatient care was stronger than that for outpatient care. Policy-makers are advised to introduce favourable reimbursement policies for patients with poor socioeconomic conditions and address both financial and non-financial barriers to promote equitable access to health care for the entire population.

Summary: Centromere localization of the chromosome passenger complex (CPC) is paramount for achieving accurate sister chromosome segregation in mitosis. Although it has been widely recognized that the recruitment of CPC is directly regulated by two histone codes, phosphorylation of histone H3 at threonine 3 (H3T3ph) and phosphorylation of histone H2A at threonine 120 (H2AT120ph), the regulation of CPC localization by other histone codes remains elusive. We show that dysfunction of disruptor of telomeric silencing 1 like (DOT1L) leads to mislocation of the CPC in prometaphase, caused by disturbing the level of H3T3ph and its reader Survivin. This cascade is initiated by over-dephosphorylation of H3T3ph mediated by the phosphatase RepoMan-PP1, whose scaffold RepoMan translocalizes to chromosomes, while the level of H3K79me2/3 is diminished. Together, our findings uncover a biological function of DOT1L and H3K79 methylation in mitosis and give insight into how genomic stability is coordinated by different histone codes.

To evaluate the role of serum human epididymis secretory protein 4 (HE4) and carbohydrate antigen 125 (CA125) levels for predicting and monitoring the recurrence of endometrial endometrioid carcinoma (EEC) and assessing preoperative risk stratification in EEC patients. A total of 434 EEC patients were selected for this retrospective study between May 2011 and August 2018. Serum HE4 and CA125 levels were analyzed before the initial treatment, at the first postoperative follow-up, and at recurrence or the last follow-up. Patients were risk stratified according to the European Society for Medical Oncology (ESMO), European Society for Radiotherapy & Oncology (ESTRO) and European Society of Gynaecological Oncology (ESGO) guideline. We compared the ability of these biomarkers for prediction and monitoring by performing receiver operating characteristic curve analysis and identified optimal cut-off values by determining the Youden index. Kaplan-Meier analyses were also performed to determine prognostic value. Preoperative serum HE4 was identified as a significant predictor for the recurrence of EEC (p = 0.014). Preoperative serum HE4 and CA125 levels were related to depth of myometrial invasion, lymph node status and FIGO stage. Serum HE4 and CA125 levels were both statistically significant markers for monitoring the recurrence of EEC (P = 0.000 for each biomarker). When combined, the two markers showed higher levels of sensitivity and specificity. The two biomarkers were also significant biomarkers for evaluating the risk stratification of patients undergoing lymphadenectomy (P = 0.000 for each biomarker). For premenopausal stage I patients, preoperative serum HE4 and CA125 levels were significant predictors of the need for ovarian preservation (P = 0.000 and P = 0.002, respectively). For premenopausal patients with stage I intramucosal differentiation, preoperative serum levels of HE4 were significant predictors for fertility preservation (P = 0.024). Preoperative serum HE4 level can be used to predict the recurrence of EEC. Postoperative serum HE4 and CA125 levels can be used to monitor the recurrence of EEC and are more sensitive when combined. Preoperative serum levels of CA125 and HE4 levels are of significant value for risk stratification in EEC patients.

ABSTRACT: The duck is a representative and good model for studying the development and physiological mechanisms of the nervous system (NS) in waterfowl. Neurons are the basic structural and functional units of NS, but there is no detailed method for cultured duck neurons in vitro. An efficient and simple method for duck neuron culture is reported in this study. First, the sfigpecific markers (NSE and GFAP, respectively) were used to explore the timing of the development of neurons and astrocytes during the duck embryonic stage (E5–E18). The cytomorphology of tissues and cells was tracked with the microscope at different time points. The brain tissues from 10-day-old duck embryos were determined as the optimal sampling embryo age for neuron culture. Then, the brain tissue isolation method (papain digestion) and cell suspension inoculation density (7 × 105 cells/mL) were identified as the culture protocol to obtain target cells with high viability and high density. The purity of the cultured neurons was more than 95%. This experiment provides a supplement for the study of in vitro culture of waterfowl neurons and lays a good foundation for various subsequent studies.

Duck Tembusu virus (DTMUV) is an emerging avian flavivirus characterized by causing severe ovaritis and neurological symptoms in ducks. The pathology of the central nervous system (CNS) caused by DTMUV is rarely studied. This study aimed to systematically investigate the ultrastructural pathology of the CNS of ducklings and adult ducks infected with DTMUV via transmission electron microscopy technology at a cytopathological level. The results showed that DTMUV caused extensive lesions in the brain parenchyma of ducklings and slight damage in adult ducks. The neuron was the target cell of DTMUV, and virions were mainly observed in their cisternae of rough endoplasmic reticulum and the saccules of Golgi apparatus. The neuron perikaryon showed degenerative changes where the membranous organelles gradually decomposed and disappeared with DTMUV infection. Besides neurons, DTMUV infection induced marked swelling in astrocytic foot processes in ducklings and evident myelin lesions in ducklings and adult ducks. The activated microglia were observed phagocytizing injured neurons, neuroglia cells, nerve fibers, and capillaries after the DTMUV infection. Affected brain microvascular endothelial cells were surrounded by edema and had increased pinocytotic vesicles and cytoplasmic lesions. In conclusion, the above results systematically describe the subcellular morphological changes of the CNS after DTMUV infection, providing an ultrastructural pathological research basis for DTMUV-induced neuropathy.

Many studies provided evidence regarding the influence of built environment (BE) on commuting time. However, few studies have considered the spatial heterogeneity of such impacts. Using data from Nanjing, China, this study employs two-step clustering and gradient boosted regression trees (GBRT) to segment the neighborhoods into different types and investigate the effects of BE characteristics on the commuting time of active users. The results show a strong effect of BE characteristics on commuting time, involving active modes. The importance of BE characteristics varies among neighborhood types. For active commuters in the internal region of Nanjing, commuting time is affected mostly by the land use mix at the work end. The lowest impact of BE in internal regions is associated with metro station density. For active commuters in external region of the city, the relative importance of intersection density at the home end is the largest (as high as 5.76%). Moreover, other significant differences are found in the associations between BE characteristics and active commuting time in the two regions.

Mobile Edge Computing (MEC) is one of the most promising techniques for next-generation wireless communication systems. In this paper, we study the problem of dynamic caching, computation offloading, and resource allocation in cache-assisted multi-user MEC systems with stochastic task arrivals. There are multiple computationally intensive tasks in the system, and each Mobile User (MU) needs to execute a task either locally or remotely in one or more MEC servers by offloading the task data. Popular tasks can be cached in MEC servers to avoid duplicates in offloading. The cached contents can be either obtained through user offloading, fetched from a remote cloud, or fetched from another MEC server. The objective is to minimize the long-term average of a cost function, which is defined as a weighted sum of energy consumption, delay, and cache contents’ fetching costs. The weighting coefficients associated with the different metrics in the objective function can be adjusted to balance the tradeoff among them. The optimum design is performed with respect to four decision parameters: whether to cache a given task, whether to offload a given uncached task, how much transmission power should be used during offloading, and how much MEC resources to be allocated for executing a task. We propose to solve the problems by developing a dynamic scheduling policy based on Deep Reinforcement Learning (DRL) with the Deep Deterministic Policy Gradient (DDPG) method. A new decentralized DDPG algorithm is developed to obtain the optimum designs for multi-cell MEC systems by leveraging on the cooperations among neighboring MEC servers. Simulation results demonstrate that the proposed algorithm outperforms other existing strategies, such as Deep Q-Network (DQN).

ABSTRACT: The Duck Tembusu virus (DTMUV) is a novel flavivirus that occurs mainly in poultry. DTMUV infection can cause common neurological symptoms in ducklings, but the pathogenesis of DTMUV has not been elucidated yet. In this study, a DTMUV-infected duckling model was constructed to investigate the apoptosis in the duckling brains. After DTMUV infection, apoptotic cells were observed by transmission electron microscopy. It was found that the abundances of apoptosis-related genes and proteins were not obviously changed in the early stage of infection but significantly changed in the middle and late stages of the disease. Terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling assay staining results were also consistent with the above phenomena. Interestingly, although apoptosis occurred in the duckling brains infected by DTMUV, some antiapoptotic genes in the brain increased in varying degrees. In conclusion, DTMUV infection could induce apoptosis in ducklings’ brains, and the occurrence of apoptosis was accompanied by the virus infection process with certain regularity. This study provides a scientific basis for elucidating the apoptotic mechanism of brain lesions induced by DTMUV infection.

Abstract Background In this paper, an unsupervised Bayesian learning method is proposed to perform rice panicle segmentation with optical images taken by unmanned aerial vehicles (UAV) over paddy fields. Unlike existing supervised learning methods that require a large amount of labeled training data, the unsupervised learning approach detects panicle pixels in UAV images by analyzing statistical properties of pixels in an image without a training phase. Under the Bayesian framework, the distributions of pixel intensities are assumed to follow a multivariate Gaussian mixture model (GMM), with different components in the GMM corresponding to different categories, such as panicle, leaves, or background. The prevalence of each category is characterized by the weights associated with each component in the GMM. The model parameters are iteratively learned by using the Markov chain Monte Carlo (MCMC) method with Gibbs sampling, without the need of labeled training data. Results Applying the unsupervised Bayesian learning algorithm on diverse UAV images achieves an average recall, precision and F 1 score of 96.49%, 72.31%, and 82.10%, respectively. These numbers outperform existing supervised learning approaches. Conclusions Experimental results demonstrate that the proposed method can accurately identify panicle pixels in UAV images taken under diverse conditions.

Low latency change detection aims to minimize the detection delay of an abrupt change in probability distributions of a random process, subject to certain performance constraints such as the probability of false alarm (PFA). In this paper, we study the low latency detection of bearing faults of direct-drive wind turbines (WT), by analyzing the statistical behaviors of stator currents generated by the WT in real-time. It is discovered that the presence of fault will affect the statistical distribution of WT stator current amplitude at certain frequencies. Since the signature of a fault can appear in one of the multiple possible frequencies, we need to monitor the signals on multiple frequencies simultaneously, and each possible frequency is denoted as a candidate. Based on the unique properties of WT bearing faults, we propose a new multi-candidate low latency detection algorithm that can combine the statistics of signals from multiple candidate frequencies. The new algorithm does not require a separate training phase, and it can be directly applied to the stator current data and perform online detection of various possible bearing faults. The theoretical performance of the proposed algorithm is analytically identified in the form of upper bounds of the PFA and average detection delay (ADD). The algorithm allows flexible parametric adjustment of the tradeoff between PFA and ADD.

The phenomenon of affordable housing emerges in Chinese cities to meet low-income residents’ living needs in the city. Because affordable housing projects tend to be located far away from the city centre, their residents tend to face long commuting times to go to work. Although several studies have analysed commuting travel times, none have considered the commuting pattern of residents living in these affordable housing projects. This study employs a decision tree classifier to examine the commuting time patterns of affordable housing residents, fusing the data from the 2010 Nanjing Household Travel Survey and supplementary data collected through Google maps. Results show that attributes of the built environment and distance to work are the factors mostly influencing commuting time patterns of affordable housing residents in Nanjing. The availability of a subway service, job type, household car ownership, job location, travel mode choice, and departure time have logical but varying effects on commuting trip duration. These results provide a better understanding of these residents’ commuting patterns and provide urban planners insights about the effects of their affordable housing policies on travel behaviour.

Diabetes mellitus, a metabolic disease mainly characterized by hyperglycemia, is becoming a serious social health problem worldwide with growing prevalence. Many natural compounds have been found to be effective in the prevention and treatment of diabetes, with negligible toxic effects. Ferulic acid (FA), a phenolic compound commonly found in medicinal herbs and the daily diet, was proved to have several pharmacological effects such as antihyperglycemic, antihyperlipidemic and antioxidant actions, which are beneficial to the management of diabetes and its complications. Data from PubMed, EM-BASE, Web of Science and CNKI were searched with the keywords ferulic acid and diabetes mellitus. Finally, 28 articles were identified after literature screening, and the research progress of FA for the management of DM and its complications was summarized in the review, in order to provide references for further research and medical applications of FA.

Abstract Background Health workforce turnover remains a global concern, particularly in rural and remote areas. Western rural areas are the least developed in China, where it faces the serious challenge on the rural health worker (RHW) management. This study aimed to investigate job satisfaction, work stress, and turnover intentions of RHWs, and to explore prominent factors associated with turnover intentions of RHWs in rural western China. Methods From June to September 2013, based on a three-stage random sampling method, a cross-sectional survey was conducted among RHWs in 11 western provinces in China. A brief, structured questionnaire filled in by RHWs was used for data collection. A total of 5046 RHWs participated in the study. The response rate was approximately 93.1%. Exploratory factor analyses, Pearson’s chi-squared tests, one-way ANOVA, binary logistic regression analyses, and mediating effect tests were performed for data analyses. Results Approximately 29.1% of the 5046 RHWs indicated turnover intentions. Most of the RHWs disclosed low educational levels, income levels, and professional technical titles. The RHWs expressed slight job satisfaction (mean 3.20) and moderate work stress (mean 3.22). Age, income, medical institution, and job satisfaction (i.e., organizational management, reward, and occupation satisfaction) were significant predictors of the RHWs’ turnover intentions. The RHWs, who were younger (less than 41 years), receiving an income of $326.8–$490.1 per month, working in township hospitals, and having low job satisfaction, were more likely to have turnover intentions. Work stress had an indirect and positive effect on RHWs’ turnover intentions. Job satisfaction weakened the positive effect of work stress on turnover intentions of RHWs by playing a total mediating role. Reward satisfaction was the strongest mediator. Conclusions The turnover intentions of RHWs in western China are significantly associated with job satisfaction, work stress, age, income, and medical institution. Appropriate strategies should be implemented to improve RHWs’ job satisfaction and reduce their work stress. Meanwhile, providing more attractive wages and non-monetary support, improving working conditions, etc. could be effective to reduction in RHWs’ turnover intentions.

Abstract This paper studies the performance of antenna selection for high-speed railway (HSR) distributed antenna systems with imperfect channel state information (CSI). In HSR systems, distributed antennas mounted on top of the train carriage are connected to a mobile relay, which serves as an intermediate node between the base station (BS) and the users inside the train. An important feature of high mobility networks is the fast time-variation of the fading channel caused by the large Doppler spread. It is difficult to accurately estimate, track, and predict the fast time-varying fading coefficients; thus, channel estimation error is usually inevitable and it may seriously degrade system performance. In order to offer a good tradeoff between system performance, cost, and overhead, antenna selection is performed by considering the impacts of both channel estimation errors and noise, such that the antenna with the best link to the BS will be selected to serve users inside the train. The channel estimation error is quantified through the estimation mean squared error (MSE), which is expressed as a closed-form function of the maximum Doppler spread and signal-to-noise ratio (SNR). Several performance metrics, such as outage probability, symbol error rate, spectral efficiency lower bound, and switching rate, are developed as a function of the channel estimation MSE and the geometric layout of the antennas. The analytical and simulation results quantify the significant impacts of imperfect CSI on antenna selection for HSR systems in practical applications.

Urban rail transit trips usually involve multiple stages, which can be differentiated in terms of transfers that may involve distinct access and egress modes. Most studies on access and egress mode choices of urban rail transit have separately examined the two mode choices. However, in reality, the two choices are temporally correlated. This study, therefore, has sequentially applied the mixed logit to examine the contributors of access and egress mode choices of urban metro commuters using the data from a recent survey conducted in Nanjing, China. 9 typical multimodal combinations constituted by 5 main access modes (walk, bike, electric bike, bus, and car) and 2 main egress modes (walk and bus) are included in the study. The result proves that the model is reliable and reproductive in analyzing access/egress mode choices of metro commuters. Estimation results prove the existence of time constraint and service satisfaction effect of access trip on commuters’ egress mode choice and reveal the importance of transfer infrastructure and environments that serve for biking, walking, bus riding, and car parking in commuter’s connection choice. Also, policy implications are segmentally concluded for the transfer needs of commuters in different groups to encourage the use of metro multimodal trips.

The outage probability of an interference-limited linear vehicular ad hoc network (VANET) with random multiple access protocol is studied in this paper. All nodes in the VANET, including the intended transmitter-receiver pair and the interferers, are assumed to form a homogeneous Poisson point process. Communications between any two nodes in the network are carried out by using nodes between them as relays. For a given network section, more nodes between the source and the destination means more relays, thus a stronger signal component due to smaller pathloss between two neighboring nodes. Meanwhile, more nodes generate more mutual interference. A random multiple access protocol, where each node transmits with a certain probability, is adopted to reduce interference. To characterize the competing effects between pathloss and interference, we derive analytically the upper and lower bounds of the outage probability for a cluster of nodes over a fixed section of the network under the interference-limited environment. The outage probability bounds are derived as explicit functions of various system parameters, including the pathloss exponent, node mobility, transmission probability, and cluster size. Optimum designs are then performed to minimize the outage probability with respect to several system parameters, such as the node density, cluster size, and node mobilities.

We study low-latency detections of sparse false data injection attacks in power grids, where an adversary can maliciously manipulate power grid operations by modifying measurements at a small number of smart meters. When a power grid is under attack, the detection delay, which is defined as the time difference between the occurrence and detection of the attack, is critical to power grid operations. A shorter detection delay can shorten the response time, thus prevent catastrophic impacts from the attack. The objective of this paper is to develop low-latency false data detection algorithms that can minimize the detection delay subject to constraints on false alarm probability. The false data injection can be modeled with a sparse attack vector, with each non-zero element corresponding to one meter under attack. Since neither the support nor the values of the sparse attack vector is known, a new orthogonal matching pursuit cumulative sum (OMP-CUSUM) algorithm is proposed to identify the meters under attack while minimizing the detection delay. In order to recover the support of the sparse vector, we develop a new stopping condition for the iterative OMP algorithm by analyzing the statistical properties of the power grid measurements. Theoretical analysis and simulation results show that the proposed OMP-CUSUM algorithm can efficiently identify the meters under attack, and reliably detect false data injections with low delays while maintaining good detection accuracy.

This paper studies the fundamental performance limits of a single-input multiple-output (SIMO) high mobility wireless communication systems with imperfect channel state information (CSI). The fundamental limits are quantified in terms of the maximum diversity order (including both Doppler diversity and antenna diversity) that can be achieved by a high mobility system, and the loss in coding gain due to channel estimation errors. Due to severe Doppler effects caused by fast time-varying fading in high mobility systems, channel estimation errors are usually inevitable and might cause significant performance losses. On the other hand, Doppler effect provides potential Doppler diversity that can be harnessed to improve system performance. To quantify this fundamental tradeoff between Doppler diversity and channel estimation errors, we perform analytical studies of the asymptotic error performance of SIMO high mobility systems when the signal-to-noise ratio and the coding block length is large. The analytical results are obtained by analyzing and quantifying the impacts of channel estimation errors on the system performance. With the help of pilot-aided channel estimation and a simple repetition code, we identified the maximum diversity order achievable by a SIMO high mobility system with imperfect CSI and the loss in coding gains due to the inevitable channel estimation errors. The analytical results are then used to optimally allocate transmission energy to pilot symbols and data symbols to simultaneously maximize the Doppler diversity order and minimize the loss coding gain loss due to channel estimation errors. The results reveal the fundamental tradeoff between Doppler diversity and channel estimation errors and can be used to serve as a guide in the design of practical high mobility systems.

Chlorophyll content is an important indicator of the growth status of japonica rice. The objective of this paper is to develop an inversion model that can predict japonica rice chlorophyll content by using hyperspectral image of rice canopy collected with unmanned aerial vehicle (UAV). UAV-based hyperspectral remote sensing can provide timely and cost-effective monitoring of chlorophyll content over a large region. The study was based on hyperspectral data collected at the Shenyang Agricultural College Academician Japonica Rice Experimental Base in 2018 and 2019. In order to extract the salient information embedded in the high-dimensional hyperspectral data, we first perform dimension reduction by using a successive projection algorithm (SPA). The SPA extracts the characteristic hyperspectral bands that are used as input to the inversion model. The characteristic bands extracted by SPA are 410 nm, 481 nm, 533 nm, 702 nm, and 798 nm, respectively. The inversion model is developed by using an extreme learning machine (ELM), the parameters of which are optimized by using particle swarm optimization (PSO). The PSO-ELM algorithm can accurately model the nonlinear relationship between hyperspectral data and chlorophyll content. The model achieves a coefficient of determination R2 = 0.791 and a root mean square error of RMSE = 8.215 mg/L. The model exhibits good predictive ability and can provide data support and model reference for research on nutrient diagnosis of japonica rice.

Providing reliable broadband wireless communications in high mobility environments, such as high-speed railway systems, remains one of the main challenges faced by the development of the next generation wireless systems. This paper provides a systematic review of high mobility communications. We first summarize a list of key challenges and opportunities in high mobility communication systems, then provide comprehensive reviews of techniques that can address these challenges and utilize the unique opportunities. The review covers a wide spectrum of communication operations, including the accurate modeling of high mobility channels, the transceiver structures that can exploit the properties of high mobility environments, the signal processing techniques that can harvest the benefits (e.g., Doppler diversity) and mitigate the impairments (e.g., carrier frequency offset, intercarrier interference, channel estimation errors) in high mobility systems, and the mobility management and network architectures that are designed specifically for high mobility systems. The survey focuses primarily on physical layer operations, which are affected the most by the mobile environment, with some additional discussions on higher layer operations, such as handover management and control-plane/user-plane decoupling, which are essential to high mobility operations. Future research directions on high mobility communications are summarized at the end of this paper.

In this paper, we study the optimum estimation of a band-unlimited continuous-time random process using discrete-time samples taken by a sensor powered by energy harvesting devices. In order to accurately represent a band-unlimited random process, a large sampling rate is needed, and this may yield a huge amount of data to be collected and transmitted. In the mean time, the energy required for sensing and transmitting the data must satisfy the constraints imposed by stochastic energy harvesting sources. To cope with these challenges, we propose a family of the best-effort random sensing policies. The best-effort random sensing schemes define a set of randomly chosen candidate sensing instants, and the sensor performs sensing at a given candidate sensing instant only if there is sufficient energy available. Otherwise an energy outage is declared and the sensor remains silent. It is shown through asymptotic analysis that the probability of energy outage during sensing is determined by the ratio between the energy harvesting rate and the energy consumption rate. For a given average energy harvesting rate, less samples per-unit time means a weaker temporal correlation between two adjacent samples, but a smaller energy outage probability, less data, and more energy per sample. Such a tradeoff relationship is captured by developing a closed-form expression of the estimation mean squared error (MSE), which analytically identifies the interactions among the various system parameters. The estimation MSE is minimized by optimizing the tradeoff among system parameters. The proposed optimum sensing scheme can asymptotically achieve the same performance as a system with the conventional energy sources, and significantly reduce the amount of data to be collected and transmitted.

Level set estimation (LSE) is the process of using noisy observations of an unknown function to estimate the region(s) where the function values lie above a given threshold. It has a wide range of applications in many scientific and engineering areas, such as spectrum sensing or environment monitoring. In this paper, we study the energy-efficient LSE of a time-varying random field under a total power constraint. The fusion center of a wireless sensing system performs LSE by using discrete-time samples collected by a sensor. An accurate LSE usually requires a large number of samples to be collected and transmitted. However, most wireless sensing systems operate with a stringent power constraint that may not be able to meet the high energy demands imposed by the large amount of data. The gap between energy demands and supplies is a direct result of the so-called big data problem. It is critical to develop energy-efficient sampling schemes that can bridge this gap by reducing the amount of data required by LSE. Two sampling schemes are considered in this paper: 1) a dynamic active sampling scheme that sequentially and adaptively selects the next sampling instant in a myopic manner with knowledge learned from previous samples and 2) a uniform sampling scheme that employs a fixed sampling rate to minimize the LSE error probability in the long term. The exact analytical cost functions and their respective upper bounds of both sampling schemes are developed by using an optimum thresholding-based LSE algorithm. The design parameters of both sampling schemes are optimized by minimizing their respective cost functions. The analytical and simulation results demonstrate that both sampling schemes can significantly reduce the amount of data collected by the system while obtain accurate LSE under a stringent power constraint. In addition, the uniform sampling scheme slightly outperforms the dynamic active sampling scheme.

Background: Nod-like receptor protein 3 (NLRP3) inflammasome is a crucial contributor in the inflammatory process during cerebral ischemia/reperfusion (I/R) injury. ATF4 plays a pivotal role in the pathogenesis of cerebral I/R injury, however, its function and underlying mechanism are not fully characterized yet. In the current study, we examined whether ATF4 ameliorates cerebral I/R injury by inhibiting NLRP3 inflammasome activation and whether mitophagy is involved in this process. In addition, we explored the role of parkin in ATF4-mediated protective effects. Method: To address these issues, healthy male adult Sprague-Dawley rats were exposed to middle cerebral artery occlusion for 1 h followed by 24 h reperfusion. Adeno-associated virus (AAV) and siRNA were injected into rats to overexpress and knockdown ATF4 expression, respectively. After pretreatment with AAV, mdivi-1(mitochondrial division inhibitor-1) was injected into rats to block mitophagy activity. Parkin expression was knockdown using specific siRNA after AAV pretreatment. Result: Data showed that ATF4 overexpression induced by AAV was protective against cerebral I/R injury, as evidenced by reduced cerebral infraction volume, decreased neurological scores and improved outcomes of HE and Nissl staining. In addition, overexpression of ATF4 gene was able to up-regulate Parkin expression, enhance mitophagy activity and inhibit NLRP3 inflammasome-mediated inflammatory response. ATF4 knockdown induced by siRNA resulted in the opposite effects. Furthermore, ATF4-mediated inhibition of NLRP3 inflammasome activation was strongly affected by mitophagy blockage upon mdivi-1 injection. Besides, ATF4-mediated increase of mitophagy activity and inhibition of NLRP3 inflammasome activation were effectively reversed by Parkin knockdown using siRNA. Conclusion: Our study demonstrated that ATF4 is able to alleviate cerebral I/R injury by suppressing NLRP3 inflammasome activation through parkin-dependent mitophagy activity. These results may provide a new strategy to relieve cerebral I/R injury by modulating mitophagy-NLRP3 inflammasome axis.

Oxidative damage plays a critical role in many diseases of the central nervous system. This study was conducted to determine the molecular mechanisms involved in the putative anti-oxidative effects of curcumin against experimental stroke. Oxygen and glucose deprivation/reoxygenation (OGD/R) was used to mimic ischemic insult in primary cultured cortical neurons. A rapid increase in the intracellular expression of NAD(P)H: quinone oxidoreductase1 (NQO1) induced by OGD was counteracted by curcumin post-treatment, which paralleled attenuated cell injury. The reduction of phosphorylation Akt induced by OGD was restored by curcumin. Consequently, NQO1 expression and the binding activity of nuclear factor-erythroid 2-related factor 2 (Nrf2) to antioxidant response element (ARE) were increased. LY294002 blocked the increase in phospho-Akt evoked by curcumin and abolished the associated protective effect. Adult male Sprague-Dawley rats were subjected to transient middle cerebral artery occlusion for 60 minutes. Curcumin administration significantly reduced infarct size. Curcumin also markedly reduced oxidative stress levels in middle cerebral artery occlusion (MCAO) rats; hence, these effects were all suppressed by LY294002. Taken together, these findings provide evidence that curcumin protects neurons against ischemic injury, and this neuroprotective effect involves the Akt/Nrf2 pathway. In addition, Nrf2 is involved in the neuroprotective effects of curcumin against oxidative damage.

BackgroundIschemia/reperfusion (I/R) injury is associated with systemic inflammatory response. Macrophage migration inhibitory factor (MIF) has been implicated in many inflammatory processes. Tanshinone IIA (TSA) is one of the active ingredients in danshen, which derived from the dried root or rhizome of Salviae miltiorrhizae Bge. Recent studies have demonstrated that TSA has protective effects against focal cerebral I/R injury. However, little is known about the underlying mechanisms. Here we put forward the hypothesis that TSA acts through inhibition of MIF expression during focal cerebral I/R injury in rats.Methodology/principal findingsRats were subjected to middle cerebral artery occlusion (MCAO) for 2 hours. This was followed by reperfusion. We measured neurological deficits, brain water content, and infarct volume, and found that neurological dysfunction, brain edema, and brain infarction were significantly attenuated by TSA 6 hours after reperfusion. We also measured myeloperoxidase (MPO) activity at 6 and 24 hours, and found that neutrophil infiltration was significantly higher in the vehicle+I/R group than in the TSA+I/R group. ELISA demonstrated that TSA could inhibit MIF expression and the release of TNF-α and IL-6 induced by I/R injury. Western blot analysis and immunofluorescence staining showed that MIF expression was significantly lower in the TSA+I/R group than in the vehicle+I/R group. MIF was found almost all located in neurons and hardly any located in astrocytes in the cerebral cortex. Western blot analysis and EMSA demonstrated that NF-κB expression and activity were significantly increased in the vehicle+I/R group. However, these changes were attenuated by TSA.Conclusion/significanceOur results suggest that TSA helps alleviate the proinflammatory responses associated with I/R-induced injury and that this neuroprotective effect may occur through down-regulation of MIF expression in neurons.