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Abstract Simple and practical tools for screening high-risk new-onset diabetes after percutaneous coronary intervention (PCI) (NODAP) are urgently needed to improve post-PCI prognosis. We aimed to evaluate the risk factors for NODAP and develop an online prediction tool using conventional variables based on a multicenter database. China evidence-based Chinese medicine database consisted of 249, 987 patients from 4 hospitals in mainland China. Patients ≥ 18 years with implanted coronary stents for acute coronary syndromes and did not have diabetes before PCI were enrolled in this study. According to the occurrence of new-onset diabetes mellitus after PCI, the patients were divided into NODAP and Non-NODAP. After least absolute shrinkage and selection operator regression and logistic regression, the model features were selected and then the nomogram was developed and plotted. Model performance was evaluated by the receiver operating characteristic curve, calibration curve, Hosmer–Lemeshow test and decision curve analysis. The nomogram was also externally validated at a different hospital. Subsequently, we developed an online visualization tool and a corresponding risk stratification system to predict the risk of developing NODAP after PCI based on the model. A total of 2698 patients after PCI (1255 NODAP and 1443 non-NODAP) were included in the final analysis based on the multicenter database. Five predictors were identified after screening: fasting plasma glucose, low-density lipoprotein cholesterol, hypertension, family history of diabetes and use of diuretics. And then we developed a web-based nomogram ( https://mr.cscps.com.cn/wscoringtool/index.html ) incorporating the above conventional factors for predicting patients at high risk for NODAP. The nomogram showed good discrimination, calibration and clinical utility and could accurately stratify patients into different NODAP risks. We developed a simple and practical web-based nomogram based on multicenter database to screen for NODAP risk, which can assist clinicians in accurately identifying patients at high risk of NODAP and developing post-PCI management strategies to improved patient prognosis.

Abstract Interspecific genomic introgression is an important evolutionary process with respect to the generation of novel phenotypic diversity and adaptation. A key question is how gene flow perturbs gene expression networks and regulatory interactions. Here, an introgression population of two species of allopolyploid cotton (Gossypium) to delineate the regulatory perturbations of gene expression regarding fiber development accompanying fiber quality change is utilized. De novo assembly of the recipient parent (G. hirsutum Emian22) genome allowed the identification of genomic variation and introgression segments (ISs) in 323 introgression lines (ILs) from the donor parent (G. barbadense 3–79). It documented gene expression dynamics by sequencing 1,284 transcriptomes of developing fibers and characterized genetic regulatory perturbations mediated by genomic introgression using a multi‐locus model. Introgression of individual homoeologous genes exhibiting extreme low or high expression bias can lead to a parallel expression bias in their non‐introgressed duplicates, implying a shared yet divergent regulatory fate of duplicated genes following allopolyploidy. Additionally, the IL N182 with improved fiber quality is characterized, and the candidate gene GhFLAP1 related to fiber length is validated. This study outlines a framework for understanding introgression‐mediated regulatory perturbations in polyploids, and provides insights for targeted breeding of superior upland cotton fiber.

In recent years, a large number of scholars have paid attention to the identification of vital nodes of cyber–physical power systems. However, in addition to these vital nodes, other relatively important nodes in the system have important research significance. For example, when a vital node in the system has been damaged, avoiding further damage is particularly important. We can obtain useful information from these vital nodes and find out which nodes are relatively important for these nodes to protect, which can prevent further cascading of failures to some extent. In this paper, we aim to identify the relatively important nodes and define the connection tightness index of any two nodes. By combining these indexes with relative entropy, we can correctly measure the degree of difference between any two nodes and sort them to determine the relative importance of nodes. The results show that the proposed strategy can evaluate relatively important nodes without distinguishing between power network and communication network. It is also possible to identify nodes that are relatively important to the network on one side of the cyber–physical power system by the importance of the network nodes on the other side, and this is superior to other strategies.

Abstract Background Diabetes is a predominant driver of coronary artery disease worldwide. This study aims to unravel the distinct characteristics of oral and gut microbiota in diabetic coronary heart disease (DCHD). Simultaneously, we aim to establish a causal link between the diabetes-driven oral-gut microbiota axis and increased susceptibility to diabetic myocardial ischemia–reperfusion injury (MIRI). Methods We comprehensively investigated the microbial landscape in the oral and gut microbiota in DCHD using a discovery cohort (n = 183) and a validation chohort (n = 68). Systematically obtained oral (tongue-coating) and fecal specimens were subjected to metagenomic sequencing and qPCR analysis, respectively, to holistically characterize the microbial consortia. Next, we induced diabetic MIRI by administering streptozotocin to C57BL/6 mice and subsequently investigated the potential mechanisms of the oral-gut microbiota axis through antibiotic pre-treatment followed by gavage with specific bacterial strains (Fusobacterium nucleatum or fecal microbiota from DCHD patients) to C57BL/6 mice. Results Specific microbial signatures such as oral Fusobacterium nucleatum and gut Lactobacillus, Eubacterium, and Roseburia faecis, were identified as potential microbial biomarkers in DCHD. We further validated that oral Fusobacterium nucleatum and gut Lactobacillus are increased in DCHD patients, with a positive correlation between the two. Experimental evidence revealed that in hyperglycemic mice, augmented Fusobacterium nucleatum levels in the oral cavity were accompanied by an imbalance in the oral-gut axis, characterized by an increased coexistence of Fusobacterium nucleatum and Lactobacillus, along with elevated cardiac miRNA-21 and a greater extent of myocardial damage indicated by TTC, HE, TUNEL staining, all of which contributed to exacerbated MIRI. Conclusion Our findings not only uncover dysregulation of the oral-gut microbiota axis in diabetes patients but also highlight the pivotal intermediary role of the increased abundance of oral F. nucleatum and gut Lactobacillus in exacerbating MIRI. Targeting the oral-gut microbiota axis emerges as a potent strategy for preventing and treating DCHD. Oral-gut microbial transmission constitutes an intermediate mechanism by which diabetes influences myocardial injury, offering new insights into preventing acute events in diabetic patients with coronary heart disease. Graphic Abstract

Objective: Several circulating microRNAs, including microRNA-126-3p, have been identified as diagnostic and prognostic biomarker of cardiovascular disease. However, whether microRNA-126-3p is an independent risk predictor for coronary artery calcification is unclear. Methods: In this prospective single-center study, we collected blood samples from coronary artery atherosclerosis patients ( n = 54), patients with coronary artery calcification ( n = 33) and controls ( n = 56). Total RNA was extracted from plasma and blood cells with TRIzol reagents. The microRNA-126-3p level was determined via quantitative real-time polymerase chain reaction (RT-PCR). Results: MicroRNA-126-3p levels were significantly increased in patients with coronary artery calcification than in coronary artery atherosclerosis patients or controls. The highest expression of microRNA-126-3p was observed in patients with moderate calcification who were diagnosed with Grade 2 calcification by coronary angiography. Age, microRNA-126-3p expression in veins, hypertension and diabetes significantly influence the occurrence of coronary artery calcification, among which diabetes and venous microRNA-126-3p expression were found to be independent risk factors for coronary artery calcification. Conclusions: Taken together, the data in this study suggest that circulating microRNA-126-3p may be a novel noninvasive biomarker for coronary artery calcification. Regulating microRNA-126-3p expression may be an effective and promising strategy for the diagnosis and treatment of cardiovascular diseases, especially coronary artery calcification.

Safely and appropriately disposing of metalworking fluids sludge (MFS) remains a challenge owing to its highly hazardous properties, this work investigated MFS pyrolysis at various temperatures (500, 600, 700, 800, and 900 °C) for energy recovery and safety treatment of MFS. The experimental results indicated that inherent minerals at higher temperatures could enhance the gas yields and promote the qualities of oil and gas from MFS pyrolysis. The highest pyrolysis gas yield was achieved at 18.86 wt% after MFS pyrolysis at 900 °C. GC-MS analysis revealed that the inherent minerals facilitated a decrease in oxygenated and nitrogenated compounds within the oil, while simultaneously leading to a substantial increase in hydrocarbon contents. Notably, the highest content of aromatics (61.16%) was attained during pyrolysis at 900 °C. Moreover, inherent minerals improved carbon sequestration and the characteristics of biochar during the MFS pyrolysis. The leaching contents of heavy metals in biochars were reduced, thereby reducing the heavy metals associated environmental risk. This research suggests that the pyrolysis process was a promising approach for simultaneous energy recovery and MFS disposal with low environmental risk.

The primary source of energy losses in distribution networks (DNs) is rooted in line losses, which is crucial to conduct a thorough and reasonable examination of any unusual sources of line losses to guarantee the power supply in a timely and safe manner. In recent studies, identifying and analyzing abnormal line losses in DNs has been a widely and challenging research subject. This article investigates a key technology for the line loss analyses of DNs and intelligent diagnosis of abnormal causes by implementing artificial intelligence (AI), resulting in several prominent results. The proposed algorithm optimizes the parameters of the support vector machine (SVM) and suggests an intelligent diagnosis algorithm called the Improved Sparrow Search Algorithm and Support Vector Machine (ISSA-SVM). The ISSA-SVM algorithm is trained to calculate the data anomalies of line losses when changing loads and exhibiting exceptional performance to identify abnormal line losses. The accuracy of abnormality identification employing the ISSA-SVM algorithm reaches an impressive 98%, surpassing the performances of other available algorithms. Moreover, the practical performance of the proposed approach for analyzing large volumes of abnormal line loss data daily in DNs is also noteworthy. The ISSA-SVM accurately identifies the root causes of abnormal line losses and lowers the error in calculating abnormal line loss data. By combining different types of power operation data and creating a multidimensional feature traceability model, the study successfully determines the factors contributing to abnormal line losses. The relationship between transformers and voltage among various lines is determined by using the Pearson correlation, which provides valuable insights into the relationship between these variables and line losses. The algorithm’s reliability and its potential to be applied to real-world scenarios bring an opportunity to improve the efficiency and safety of power supply systems. The ISSA that incorporates advanced techniques such as the Sobol sequence, golden sine algorithm, and Gaussian difference mutation appears to be a promising tool.

Summary: The phosphoinositide 3-kinase p110α is an essential mediator of insulin signaling and glucose homeostasis. We interrogated the human serine, threonine, and tyrosine kinome to search for novel regulators of p110α and found that the Hippo kinases phosphorylate p110α at T1061, which inhibits its activity. This inhibitory state corresponds to a conformational change of a membrane-binding domain on p110α, which impairs its ability to engage membranes. In human primary hepatocytes, cancer cell lines, and rodent tissues, activation of the Hippo kinases MST1/2 using forskolin or epinephrine is associated with phosphorylation of T1061 and inhibition of p110α, impairment of downstream insulin signaling, and suppression of glycolysis and glycogen synthesis. These changes are abrogated when MST1/2 are genetically deleted or inhibited with small molecules or if the T1061 is mutated to alanine. Our study defines an inhibitory pathway of PI3K signaling and a link between epinephrine and insulin signaling.

Abstract Photoelectrochemical device is a versatile platform for achieving various chemical transformations with solar energy. However, a grand challenge, originating from mass and electron transfer of triphase—reagents/products in gas phase, water/electrolyte/products in liquid phase and catalyst/photoelectrode in solid phase, largely limits its practical application. Here, we report the simulation-guided development of hierarchical triphase diffusion photoelectrodes, to improve mass transfer and ensure electron transfer for photoelectrochemical gas/liquid flow conversion. Semiconductor nanocrystals are controllably integrated within electrospun nanofiber-derived mat, overcoming inherent brittleness of semiconductors. The mechanically strong skeleton of free-standing mat, together with satisfactory photon absorption, electrical conductivity and hierarchical pores, enables the design of triphase diffusion photoelectrodes. Such a design allows photoelectrochemical gas/liquid conversion to be performed continuously in a flow cell. As a proof of concept, 16.6- and 4.0-fold enhancements are achieved for the production rate and product selectivity of methane conversion, respectively, with remarkable durability.

Semi-open and chaotic environments of building sites are considered primary challenges for the localization of mobile construction robots. To mitigate environmental limitations, an improved trilateral localization technique based on artificial landmarks fusing the extended Kalman filters (EKFs) is proposed in this paper. The reflective intensity of the onboard laser is employed to identify artificial landmarks arranged in the ongoing construction environment. A trilateral positioning algorithm is then adopted and evaluated based on artificial landmarks. Multi-sensor fusion, combined with the EKF, is included to improve the positioning accuracy and reliability of the robot in complex conditions. We constructed validation scenarios in the Gazebo simulation environment to verify the required localization functionality. Concurrently, we established simulated testing environments in real-world settings, where the practicality of the proposed technique was validated through the fitting of ideal and actual localization trajectories. The effectiveness of the proposed technique was corroborated through comparative experimental results.

The purpose of this study was to investigate the impact of need fulfillment given by opposite-sex friends (NFOF) on breakup considerations, the mediating role of love commitment in this relationship, and the moderating role of need fulfillment given by romantic partners (NFRP). A total of 334 unmarried individuals in romantic relationships from Northwest China were invited to participate in the study. The findings revealed the following: (1) NFOF significantly and positively predicted breakup considerations. (2) This relationship is mediated by love commitment (3) The association between NFOF and breakup considerations was moderated by NFRP (in terms of the first mediation path). Specifically, those who hold higher levels of NFRP are appreciably buffered against the negative impact of NFOF on love commitment. These findings emphasize the crucial role of NFOF and NFRP in shaping love commitment and breakup considerations. Moreover, our research has important realistic implications: NFOF, as a trigger, has a negative effects the quality of romantic relationships and leads to breakup considerations. And, the key to maintaining a romantic relationship is to focus on their partners' need fulfillment as much as possible and increasing the level of their love commitment.

Abstract A thermoelectric generation (TEG) system has the weakness of relatively low thermoelectric conversion efficiency caused by heterogeneous temperature distribution (HgTD). Dynamic reconfiguration is an effective technique to improve its overall energy efficiency under HgTD. Nevertheless, numerous combinations of electrical switches make dynamic reconfiguration a complex combinatorial optimization problem. This paper aims to design a novel adaptive coordinated seeker (ACS) based on an optimal configuration strategy for large-scale TEG systems with series–parallel connected modules under HgTDs. To properly balance global exploration and local exploitation, ACS is based on ‘divide-and-conquer’ parallel computing, which synthetically coordinates the local searching capability of tabu search (TS) and the global searching capability of a pelican optimization algorithm (POA) during iterations. In addition, an equivalent re-optimization strategy for a reconfiguration solution obtained by meta-heuristic algorithms (MhAs) is proposed to reduce redundant switching actions caused by the randomness of MhAs. Two case studies are carried out to assess the feasibility and superiority of ACS in comparison with the artificial bee colony algorithm, ant colony optimization, genetic algorithm, particle swarm optimization, simulated annealing algorithm, TS, and POA. Simulation results indicate that ACS can realize fast and stable dynamic reconfiguration of a TEG system under HgTDs. In addition, RTLAB platform-based hardware-in-the-loop experiments are carried out to further validate the hardware implementation feasibility.

Epigenetics modification is a process that does not change the sequence of deoxyribonucleic acid (DNA) in disease progression but can alter the genetic expression of the brain in Alzheimer’s disease (AD). In this study, we deployed the weighted gene co-expression network analysis (WGCNA) to explore the role of Class I histone deacetylases (HDACs) in AD, which included HDAC1, HDAC2, HDAC3, and HDAC8. The aim of the study was to find how Class I HDACs affected AD pathology by analyzing the Gene Expression Omnibus (GEO) microarray datasets GSE33000. We found that HDAC1 and HDAC8 were more highly expressed in the cortex of AD patients than in Controls, while HDAC2 and HDAC3 were lower expressed. By WGCNA analysis, we found the blue module was associated with HDAC1 and HDAC8, and the turquoise module was related to HDAC2 and HDAC3. Functional enrichment analysis revealed that the Wnt signaling pathway and synaptic plasticity played an important role in the modification of HDAC1 and HDAC8 while gap junction and cell-cell junction were involved in the regulation of HDAC2 and HDAC3 in the disease progression of AD. By Receiver Operating Characteristics (ROC) analysis, we concluded that HDAC1 might be the most probable diagnostic biomarker of Class I HDACs for AD. Our study provided a comprehensive understanding of Class I HDACs and provided new insight into the function of HDAC1 in AD disease progression.

IntroductionRhizosphere microbes perform critical functions for their hosts, and their structure is strongly influenced by vegetation type. Although studies on the effects of vegetation on rhizosphere microbial community structure have been conducted at large and global environment scales, studies at local environment scales would eliminate numerous external factors such as climate and soil type, while highlighting the potential influence of local vegetation type.MethodsHere, we compared rhizosphere microbial communities using 54 samples under three vegetation types (herb, shrubs, and arbors, with bulk soil as the control) at the campus of Henan University. 16S rRNA and ITS amplicons were sequenced using Illumina high throughput sequencing.Results and DiscussionRhizosphere bacterial and fungal community structures were influenced considerably by vegetation type. Bacterial alpha diversity under herbs was significantly different from that under arbors and shrubs. The abundance of phyla such as Actinobacteria was extremely higher in bulk soil than in the rhizosphere soils. Herb rhizosphere harbored more unique species than other vegetation type soils. Furthermore, bacterial community assembly in bulk soil was more dominated by deterministic process, whereas the rhizosphere bacterial community assembly was dominated by stochasticity and the construction of fungal communities was all dominated by deterministic processes. In addition, rhizosphere microbial networks were less complex than bulk soil networks, and their keystone species differed based on vegetation type. Notably, bacterial community dissimilarities were strongly correlated with plant phylogenetic distance. Exploring rhizosphere microbial community patterns under different vegetation types could enhance our understanding of the role of rhizosphere microbes in ecosystem function and service provision, as well as basic information that could facilitate plant and microbial diversity conservation at the local environment scale.

With the development of communication technology, power and information systems have become deeply coupled and have become the most massive and complex cyber-physical power systems (CPPSs), resulting in certain risks to the safe operation of power systems. In this paper, a CPPS security assessment method based on the expected failure method and considering combined information attacks is proposed. Due to the large number of nodes in modern information systems, enumerating all possible combinations of system failures for evaluation and analysis is unrealistic. By considering the topological relationship of the CPPS and the electrical properties of coupled physical nodes, the idea of first screening information nodes is proposed in this paper to filter the information nodes. The impact of combined information attacks on the expected physical fault handling process is analyzed, and a safety evaluation index for the distribution network is provided. In addition, the proposed method is applied to the IEEE-118 system and compared with previous evaluation methods to verify its effectiveness.

Blackouts have severely damaged cyber-physical power systems over the years, resulting in malfunctions that have rapidly spread within the electrical network. Selecting a set of critical nodes for human control can avoid similar situations. We propose a critical node evaluation method based on pinning control theory that uses a minimum nonzero eigenvalue of a modified Laplacian matrix as the evaluation index. Computational complexity can be markedly reduced using matrix analysis theory to sort screening conditions. Multiple nodes are controlled to form critical node groups in the directed weighted cyber-physical power system. We can thus more accurately find an optimal set of controlled nodes compared with other critical node evaluation strategies. Through theoretical and simulated verifications, we conclude that the addition of two nodes, which are used as active and standby dispatching centers of the communication network, is more effective and can result in a cyber-physical power system with better connectivity.

Wind power is a new energy source, for which the forecasting accuracy is related to the feasibility of various decision-making schemes for the power grid. The random, fluctuating and uncertain characteristics of wind power make accurate point forecasting very difficult and result in low practical application value. Therefore, a novel wind-power interval prediction method is proposed in this paper. First, variational model decomposition (VMD) is applied to the historical time sequence for wind power, followed by using an extreme learning machine (ELM) to establish a prediction model for the intrinsic mode function (IMF). An autoregressive integrated moving average (ARIMA) is used to establish a prediction model for the residual components, and the predicted IMF and residual components are reconstructed to predict the wind power. Finally, iterative self-organizing data analysis technique algorithm (ISODATA) are used with the error sequence to perform clustering segmentation, and adaptive diffusion kernel density estimation (ADKDE) is used to fit the probability density function for the prediction error of each segment and thereby obtain the cumulative distribution function and the final interval prediction under a given confidence level (CL). The results of a simulation based on real wind farm data from Hunan Province, China, from January to April 2020 show that the proposed method can significantly improve interval coverage.

PurposeTo evaluate the diagnostic performance of radiomics model based on fully automatic segmentation of pancreatic tumors from non-enhanced magnetic resonance imaging (MRI) for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC).Materials and methodsIn this retrospective study, patients with surgically resected histopathologically confirmed PASC and PDAC who underwent MRI scans between January 2011 and December 2020 were included in the study. Multivariable logistic regression analysis was conducted to develop a clinical and radiomics model based on non-enhanced T1-weighted and T2-weighted images. The model performances were determined based on their discrimination and clinical utility. Kaplan-Meier and log-rank tests were used for survival analysis.ResultsA total of 510 consecutive patients including 387 patients (age: 61 ± 9 years; range: 28–86 years; 250 males) with PDAC and 123 patients (age: 62 ± 10 years; range: 36–84 years; 78 males) with PASC were included in the study. All patients were split into training (n=382) and validation (n=128) sets according to time. The radiomics model showed good discrimination in the validation (AUC, 0.87) set and outperformed the MRI model (validation set AUC, 0.80) and the ring-enhancement (validation set AUC, 0.74).ConclusionsThe radiomics model based on non-enhanced MRI outperformed the MRI model and ring-enhancement to differentiate PASC from PDAC; it can, thus, provide important information for decision-making towards precise management and treatment of PASC.

A new meta-heuristic algorithm called like-attracts-like optimizer (LALO) is proposed in this article. It is inspired by the fact that an excellent person (i.e., a high-quality solution) easily attracts like-minded people to approach him or her. This LALO algorithm is an important inspiration for video robotics cluster control. First, the searching individuals are dynamically divided into multiple clusters by a growing neural gas network according to their positions, in which the topological relations between different clusters can also be determined. Second, each individual will approach a better individual from its superordinate cluster and the adjacent clusters. The performance of LALO is evaluated based on unimodal benchmark functions compared with various well-known meta-heuristic algorithms, which reveals that it is competitive for some optimizations.

ABSTRACT Enterococcus faecalis is a normal commensal of the human gastrointestinal tract (GIT). However, upon disruption of gut homeostasis, this nonmotile bacterium can egress from its natural niche and spread to distal organs. While this translocation process can lead to life-threatening systemic infections, the underlying mechanisms remain largely unexplored. Our prior work showed that E. faecalis migration across diverse surfaces requires the formation of matrix-covered multicellular aggregates and the synthesis of exopolysaccharides, but how enterococcal cells are reprogrammed during this process is unknown. Whether surface penetration endows E. faecalis with adaptive advantages is also uncertain. Here, we report that surface penetration promotes the generation of a metabolically and phenotypically distinct E. faecalis population with an enhanced capacity to endure various forms of extracellular stress. Surface-invading enterococci demonstrated major ultrastructural alterations in their cell envelope characterized by increased membrane glycolipid content. These changes were accompanied by marked induction of specific transcriptional programs enhancing cell envelope biogenesis and glycolipid metabolism. Notably, the surface-invading population demonstrated superior tolerance to membrane-damaging antimicrobials, including daptomycin and β-defensins produced by epithelial cells. Genetic mutations impairing glycolipid biosynthesis sensitized E. faecalis to envelope stressors and reduced the ability of this bacterium to penetrate semisolid surfaces and translocate through human intestinal epithelial cell monolayers. Our study reveals that surface penetration induces distinct transcriptional, metabolic, and ultrastructural changes that equip E. faecalis with enhanced capacity to resist external stressors and thrive in its surrounding environment. IMPORTANCE Enterococcus faecalis inhabits the GIT of multiple organisms, where its establishment could be mediated by the formation of biofilm-like aggregates. In susceptible individuals, this bacterium can overgrow and breach intestinal barriers, a process that may lead to lethal systemic infections. While the formation of multicellular aggregates promotes E. faecalis migration across surfaces, little is known about the metabolic and physiological states of the enterococci encased in these surface-penetrating structures. The present study reveals that E. faecalis cells capable of migrating through semisolid surfaces genetically reprogram their metabolism toward increased cell envelope and glycolipid biogenesis, which confers superior tolerance to membrane-damaging agents. E. faecalis’s success as a pathobiont depends on its antimicrobial resistance, as well as on its rapid adaptability to overcome multiple environmental challenges. Thus, targeting adaptive genetic and/or metabolic pathways induced during E. faecalis surface penetration may be useful to better confront infections by this bacterium in the clinic.

Background: Cells of the immune system can inhibit tumor growth and progression; however, immune cells can also promote tumor cell growth, survival, and angiogenesis as a result of the immunosuppressive microenvironments. In the last decade, a growing number of new therapeutic strategies focused on reversing the immunosuppressive status of tumor microenvironments (TMEs), to reprogram the TME to be normal, and to further activate the antitumor functions of immune cells. Most of the “hot tumors” are encompassed with M2 macrophages promoting tumor growth, and the accumulation of M2 macrophages into tumor islets leads to poor prognosis in a wide variety of tumors. Summary: Therefore, how to uncover more immunosuppressive signals and to reverse the M2 tumor-associated macrophages (TAMs) to M1-type macrophages is essential for reversing the immunosuppressive state. Except for reeducation of TAMs in the cancer immunotherapy, macrophages as central effectors and regulators of the innate immune system have the capacity of phagocytosis and immune modulation in macrophage-based cell therapies. Key Messages: We review the current macrophage-based cell therapies that use genetic engineering to augment macrophage functionalities with antitumor activity for the application of novel genetically engineered immune cell therapeutics. A combination of TAM reeducation and macrophage-based cell strategy may bring us closer to achieving the original goals of curing cancer. In this review, we describe the characteristics, immune status, and tumor immunotherapy strategies of macrophages to provide clues and evidences for future macrophage-based immune cell therapies.

There is mounting evidence demonstrating that oral dysbiosis causes periodontal disease and promotes the development of cardiovascular disease. The advancement of omics techniques has driven the optimization of oral microbiota species analysis and has provided a deeper understanding of oral pathogenic bacteria. A bi-directional relationship exists between the oral microbiota and the host, and oral-gut microbiota transfer is known to alter the composition of the gut microbiota and may cause local metabolic disorders. Furthermore, cardiovascular health can also be highly affected by oral microbiota functions and metabolites, including short-chain fatty acids (SCFAs), nitric oxide (NO), hydrogen sulfide (H2S), and some lipid metabolites. Studies have found that trimethylamine oxide (TMAO) may have adverse effects on cardiovascular health, whereas SCFAs, NO, and H2S have cardioprotective effects. SCFAs and H2S exert varying oral and cardiovascular effects, however reports on this specific topic remain controversial. Previous evidences are accustomed to summarizing the functions of oral microbiota in the context of periodontitis. The direct relationship between oral microbiota and cardiovascular diseases is insufficient. By systematically summarizing the methods associated with oral microbiota transplantation (OMT), this review facilitates an investigation into the causal links between oral microbiota and cardiovascular disease. The concomitant development of omics, bioinformatics, bacterial culture techniques, and microbiota transplantation techniques is required to gain a deeper understanding of the relationship between oral microbiota and cardiovascular disease occurrence.

DC electronic current transformer is the data source of the system control and protection device, so its measurement delay has always been concerned. In this paper, the DC current transformer is modelled equivalently and the key factors affecting the delay of shunt and the remote module are analyzed. A field test method for the delay characteristics of the DC electronic current transformer is proposed. Moreover, a complex multi-state large current generator and integrated test system are developed. On the basis of the IEC 60044-8 standard transmission protocol, the accuracy of standard signal acquisition with the shunt is better than 0.2%. The maximum output current of the established testing system is 600 A. Based on the field test in an actual HVDC project, transient step and various frequency signal components are applied for analyzing.

With the steady progress of the intelligent development of power systems, as well as the higher demand for power supply reliability. It is essential to achieve the effective monitoring of substations 24 h a day. The vigorous development of deep learning network brings strong theoretical and technical support to the unmanned and intelligent construction of the substation. To identify the on/off state of the isolation switch in the substation robot inspection image, this paper proposes a method for identifying the isolation switch state of YOLOv4 (You Only Look Once V4) network based on transfer learning. Firstly, for the insufficient number of samples, transfer learning is introduced, and the network feature extraction layer is pre-trained by using public data sets. Secondly, images of isolation switch are obtained by a fixed camera and inspection robot in the substation, and data set of isolation switch is constructed. Finally, the isolation switch data set is used to train the YOLOv4 network. The test results show that compared with YOLOv3 and YOLOv4, the network can improve the identification precision of the isolation switch.

Electronic current transformer (ECT) is responsible for collecting and transmitting measurement, protection, and control signals of substations, which is regarded as a critical device for the connection of primary and secondary equipment in an intelligent substation. Naturally, a well-designed ECT is essential to ensure a safe and stable operation of power systems. This paper attempts to provide a detailed discussion and analysis of the principle, structure and characteristic of ECT, which mainly focuses on the following three aspects, i.e., steady-state accuracy, frequency response test and transient spread characteristics test. For a comprehensive performance test, a laboratory test platform is established in intelligent substations to simulate actual operation condition of ECT and further implement error accuracy analysis, frequency response test, transient response test and long-term test. Simulation results show the influence of ECT on measurement and protection accuracy, which can provide theoretical basis and evaluation criteria for further improvement of digital measurement performance and large-scale application in intelligent substations.

Abstract Schizophrenia is a neurocognitive illness of synaptic and brain network-level dysconnectivity that often reaches a persistent chronic stage in many patients. Subtle language deficits are a core feature even in the early stages of schizophrenia. However, the primacy of language network dysconnectivity and language-related genetic variants in the observed phenotype in early stages of illness remains unclear. This study used two independent schizophrenia dataset consisting of 138 and 53 drug-naïve first-episode schizophrenia (FES) patients, and 112 and 56 healthy controls, respectively. A brain-wide voxel-level functional connectivity analysis was conducted to investigate functional dysconnectivity and its relationship with illness duration. We also explored the association between critical language-related genetic (such as FOXP2) mutations and the altered functional connectivity in patients. We found elevated functional connectivity involving Broca’s area, thalamus and temporal cortex that were replicated in two FES datasets. In particular, Broca’s area - anterior cingulate cortex dysconnectivity was more pronounced for patients with shorter illness duration, while thalamic dysconnectivity was predominant in those with longer illness duration. Polygenic risk scores obtained from FOXP2-related genes were strongly associated with functional dysconnectivity identified in patients with shorter illness duration. Our results highlight the criticality of language network dysconnectivity, involving the Broca’s area in early stages of schizophrenia, and the role of language-related genes in this aberration, providing both imaging and genetic evidence for the association between schizophrenia and the determinants of language.

ObjectiveTo establish an artificial intelligence-based method to quantitatively evaluate subtle pathological changes in retinal nerve cells and synapses in monosodium glutamate (MSG) mice and provide an effective animal model and technique for quantitative evaluation of retinal neurocytopathies.MethodsICR mice were subcutaneously injected with MSG to establish a model of metabolic syndrome. We then established a mouse model of type 1 diabetes, type 2 diabetes, and KKAy mouse model as control. The HE sections of the retina were visualized using an optical microscope. AI technology was used for quantitative evaluation of the retinal lesions in each group of rats. The surface area custom parameters of the retinal nerve fiber layer (RNFL), inner plexiform layer (IPL), inner nuclear layer (INL), and outer plexiform layer (OPL) were defined as SR, SIPL, SINL, and SOPL, respectively. Their heights were defined as HR, HIPL, HINL, and HOPL, and the number of ganglion cells was defined as A. Then, the attention-augmented fully convolutional Unet network was used to segment the retinal HE images, and AI technology to identify retinal neurocytopathies quantitatively.ResultsThe attention-augmented fully convolutional Unet network increased PA and IOU parameters for INL, OPL, RNFL, and ganglion cells and was superior in recognizing fine structures. A quantitative AI identification of the height of each layer of the retina showed that the heights of the IPL and INL of the MSG model were significantly less than those of the control groups; the retinas of the other diabetic models did not exhibit this pathological feature. The RNFLs of type 2 diabetes were thinner, and the characteristics of retinopathy were not obvious in the other animal models. The pathological changes seen on HE images were consistent with the results of the quantitative AI evaluation. Immunohistochemistry results showed that NMDAR2A, GluR2, and NRG1 were significantly downregulated in the retina of MSG mice.ConclusionsThe MSG retinopathy model is closely associated with neurotransmitter abnormalities and exhibits important characteristics of retinal neurodegeneration, making it suitable for studying retinal neurocytopathies. The AI recognition technology for retinal images established in the present study can be used for the quantitative and objective evaluation of drug efficacy.

Depression is a common comorbidity of type 2 diabetes mellitus (T2DM). T2DM with comorbid depression increases the risk of cardiovascular events and death. Depression and T2DM and its macrovascular complications exhibited a two-way relationship. Regarding treatment, antidepressants can affect the development of T2DM and cardiovascular events, and hypoglycemic drugs can also affect the development of depression and cardiovascular events. The combination of these two types of medications may increase the risk of the first myocardial infarction. Herein, we review the latest research progress in the exacerbation of cardiovascular disease due to T2DM with comorbid depression and provide a rationale and an outlook for the prevention and treatment of cardiovascular disease in T2DM with comorbid depression.

Abstract The Chimera antigen receptor (CAR)-T cell therapy has gained great success in the clinic. However, there are still major challenges for its wider applications in a variety of cancer types including lack of effectiveness due to the highly complex tumor microenvironment, and the forbiddingly high cost due to the personalized manufacturing procedures. In order to overcome these hurdles, numerous efforts have been spent focusing on optimizing Chimera antigen receptors, engineering and improving T cell capacity, exploiting features of subsets of T cell or NK cells, or making off-the-shelf universal cells. Here, we developed induced pluripotent stem cells (iPSCs)-derived, CAR-expressing macrophage cells (CAR-iMac). CAR expression confers antigen-dependent macrophage functions such as expression and secretion of cytokines, polarization toward the pro-inflammatory/anti-tumor state, enhanced phagocytosis of tumor cells, and in vivo anticancer cell activity. This technology platform for the first time provides an unlimited source of iPSC-derived engineered CAR-macrophage cells which could be utilized to eliminate cancer cells.

Scan matching methods have been widely applied in the fields of autonomous localization and mapping. However, in structured environments where feature differences are less significant, such as long straight corridors, conventional positioning algorithms often suffer from characteristic mismatching, resulting in lower accuracy. As such, a new global localization algorithm, based on environmental difference evaluation and correlation scan matching fusion, is proposed in this study. In this process, the surrounding space was evaluated using a priori understanding of the environment based on a linear fit. Corresponding evaluation and positioning results from correlation scan matching were then modified using dynamic selection and a posture updating strategy. The performance of the proposed technique was compared with other conventional methods using open datasets exhibiting long straight features and a series of tests conducted in a physical corridor. Results showed that the proposed algorithm could effectively improve localization accuracy in narrow environments. The translation and rotation absolute pose errors were reduced by an average of 27.29% and 25.82%, respectively, compared with a correlation matching approach that does not consider the surrounding geometry. These results suggest the proposed technique offers higher adaptability and positioning accuracy in narrow environments.

African swine fever (ASF) is a highly detrimental viral disease caused by African swine fever virus (ASFV). The occurrence and prevalence of this disease have become a serious threat to the global swine industry and national economies. At present, the detection volume of African swine fever is huge, more sensitive and accurate detection techniques are needed for the market. pp62 protein, as a protein in the late stage of infection, has strong antigenicity and a high corresponding antibody titer in infected pigs. In this study, the CP530R gene was cloned into expression vector pET-28a to construct a prokaryotic expression plasmid, which was induced by IPTG to express soluble pp62 protein. Western blot analysis showed that it had great reactivity. Using the purified recombinant protein as an antigen, an indirect ELISA method for detecting ASFV antibody was established. The method was specific only to ASFV-positive serum, 1:1600 diluted positive serum could still be detected, and the coefficients of variation (CV) of the intra assay and inter assay were both

Increased Nogo-B receptor (NGBR) expression in the liver improves insulin sensitivity by reducing endoplasmic reticulum stress (ER stress) and activating the AMPK pathway, although it remains elusive the mechanisms by which NGBR is induced. In this study, we found that PPARγ ligands (rosiglitazone or pioglitazone) increased NGBR expression in hepatic cells and HUVECs. Furthermore, promoter analysis defined two PPREs (PPARγ-responsive elements) in the promoter region of NGBR, which was further confirmed by the ChIP assay. In vivo, using liver-specific PPARγ deficient (PPARγLKO) mice, we identified the key role of PPARγ expression in pioglitazone-induced NGBR expression. Meanwhile, the basal level of ER stress and inflammation was slightly increased by NGBR knockdown. However, the inhibitory effect of rosiglitazone on inflammation was abolished while rosiglitazone-inhibited ER stress was weakened by NGBR knockdown. Taken together, these findings show that NGBR is a previously unrecognized target of PPARγ activation and plays an essential role in PPARγ-reduced ER stress and inflammation.

Bufalin (Buf), an active ingredient of the traditional Chinese medicine Chansu, is known to have anticancer effects for breast cancer. However, its poor solubility, high toxicity, and extensive side effects limit its use. Metal-organic frameworks (MOFs) are a class of promising drug delivery systems known for their high porosity. Here, we designed and constructed pH-sensitive and redox-responsive folic acid–modified MOFs as drug carriers of Buf (FA-MOF/Buf). Moreover, the anticancer activity of nanomedicines was also explored in vitro and in vivo. Compared to free Buf, the FA-MOF/Buf nanoparticles demonstrated improved water solubility and stability, higher intracellular uptake, and enhanced cytotoxicity in breast cancer cells in vitro. Furthermore, it displayed improved accumulation in the tumor site, enhanced anticancer activity, and reduced side effects in vivo. Our results demonstrated that FA-MOF could be developed as a potential delivery system for Buf to improve its antitumor activity for breast cancer treatment.

In this paper, the finite element and cellular automaton coupling (CAFE) model is used to simulate the solidification process of a large ingot during vacuum consumable arc melting (VAR). The effects of melting temperature, melting rate, and mold cooling coefficient on temperature field distribution and solidification structure were studied by simulation. The results show that the microstructure predicted by the numerical method is consistent with the experimental results. As the melting temperature increases from 1500 °C to 1800 °C, the depth of the molten pool increases from 14 mm to 24 mm, the width of the mushy zone decreases from 10 mm to 9 mm, and the average radius of the grains increases from 584.3 μ m to 679 μ m. With the increase in melting rate from 6 kg min ^−1 to 12 kg min ^−1 , the maximum depth of the molten pool increases from 4 mm to 32 mm, the width of the mushy zone increases from 8 mm to 13 mm, and the average grain radius decreases from 943 μ m to 497 μ m. As the cooling coefficient of the mold increases from 1000 W m ^−2 ·K ^−1 to 5000 W m ^−2 ·K ^−1 , the depth of the molten pool decreases from 16.7 mm to 12 mm, the width of the mushy zone decreases from 7.3 mm to 5.9 mm, and the average radius of the grains increases from 630 μ m to 1303.5 μ m.

PurposeTo develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor–stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsIn this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility.ResultsWe observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively.ConclusionsThe CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.

Atherosclerosis is a major pathology for cardiovascular diseases (CVDs). Clinically, the intermittent fasting (IF) has been observed to reduce the risk of CVDs. However, the effect of IF on the development of atherosclerosis has not been fully elucidated. Herein, we determined the protection of IF against high-fat diet–induced atherosclerosis in pro-atherogenic low-density lipoprotein receptor deficient (LDLR-/-) mice and the potentially involved mechanisms. The LDLR-/- mice were scheduled intermittent fasting cycles of 3-day HFD feeding ad libitum and 1 day fasting, while the mice in the control group were continuously fed HFD. The treatment was lasted for 7 weeks (∼12 cycles) or 14 weeks (∼24 cycles). Associated with the reduced total HFD intake, IF substantially reduced lesions in the en face aorta and aortic root sinus. It also increased plaque stability by increasing the smooth muscle cell (SMC)/collagen content and fibrotic cap thickness while reducing macrophage accumulation and necrotic core areas. Mechanistically, IF reduced serum total and LDL cholesterol levels by inhibiting cholesterol synthesis in the liver. Meanwhile, HFD-induced hepatic lipid accumulation was attenuated by IF. Interestingly, circulating Ly6Chigh monocytes but not T cells and serum c-c motif chemokine ligand 2 levels were significantly reduced by IF. Functionally, adhesion of monocytes to the aortic endothelium was decreased by IF via inhibiting VCAM-1 and ICAM-1 expression. Taken together, our study indicates that IF reduces atherosclerosis in LDLR-/- mice by reducing monocyte chemoattraction/adhesion and ameliorating hypercholesterolemia and suggests its potential application for atherosclerosis treatment.

Synovitis, acne, pustulosis, hyperostosis, and osteitis (SAPHO) syndrome is known as a rare disease characterized by inflammatory lesions on bones and skin. Polymorphism of clinical manifestation and lack of molecular biomarkers have both limited its diagnosis. Our study performed RNA sequencing (RNA-seq) and integrative bioinformatics analysis of long noncoding RNA (lncRNA)-messenger RNA (mRNA) profile in patients with SAPHO syndrome and healthy controls. A total of 4,419 differentially expressed (DE) mRNAs and 2,713 lncRNAs were identified (p < 0.05, fold change > 2) and a coexpression network was constructed to further investigate their regulatory interactions. The DE lncRNAs were predicted to interact with mRNAs in both cis and trans manners. Functional prediction found that the lncRNA-targeted genes may function in SAPHO syndrome by participating in biological process such as adipocytokine signaling pathway, ErbB signaling pathway, FoxO signaling pathway, as well as production and function of miRNAs. The expression levels of three pairs of coexpressed lncRNA-mRNAs were validated by qRT-PCR, and their relative expression levels were consistent with the RNA-seq data. The deregulated RNAs GAS7 and lnc-CLLU1.1-1:2 may serve as potential diagnostic biomarkers, and the combined receiver operating characteristic (ROC) curve of the two showed more reliable diagnostic ability with an AUC value of 0.871 in distinguishing SAPHO patients from healthy controls. In conclusion, this study provides a first insight into long noncoding RNA transcriptome profile changes associated with SAPHO syndrome and inspiration for further investigation on clinical biomarkers and molecular regulators of this inadequately understood clinical entity.

Abstract Background Micropeptides are small proteins with length

Abstract Background SAPHO syndrome is a rare disease characterized by inflammatory lesions on skin and bones. Diversified manifestation and inadequate understanding of etiology has limited its diagnosis and treatment. The co-occurrence of other immune-mediated diseases strongly suggests an involvement of autoimmunity in SAPHO syndrome. However, the role of the largest population of circulating immune cells, neutrophils, is still not well explored. In this study, we performed RNA sequencing to profile the mRNA expression of neutrophils purified from peripheral blood of SAPHO patients to identify key genes associated with SAPHO syndrome, trying to find new functional molecules or biomarkers for this rare disease. Results A total of 442 differentially expressed genes were identified (p 2), in which 294 genes were upregulated and 148 genes were downregulated. Five differentially expressed genes of interest were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR), among which S100A12 was upregulated and positively related to high-sensitivity C-reactive protein (hsCRP), while the downregulated gene MYADM was positively related to osteocalcin. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that differentially expressed genes were enriched in “systemic lupus erythematosus” and “ECM-receptor interaction”. Gene ontology (GO) enrichment showed that differentially expressed genes may participate in biological processes such as “cell migration” and “cell adhesion”. Conclusions In conclusion, this study provides a first insight into transcriptome characteristics of SAPHO syndrome, indicating an over-active neutrophil recruitment in patients and possibly suggesting molecular candidates for further study on diagnosis and pathology of this disease.

It is acknowledged that human activities have initiated and increased steppe habitat loss and fragmentation in Northwest China. Carabid beetles are often used as bioindicators of environmental change because they are extremely sensitive to disturbance. We chose 42 landscapes (18 fragmented and 24 continuous) in two steppe types in Ningxia, Northwest China, to examine the influence of steppe fragmentation on carabid beetles. The results showed that the canonical correlation coefficient between carabid beetles and landscape fragmentation was greatest at a 7-km spatial scale in both desert and typical steppes. Furthermore, the response of carabid beetles to steppe fragmentation was species-specific in both desert and typical steppes. Steppe fragmentation in the desert steppe could significantly enhance the richness and abundance of carabid beetles, which contrasts with the typical steppe. Additionally, steppe fragmentation significantly increased the abundance of two dominant carabid beetles in the desert steppe only. Therefore, the effects of steppe fragmentation on carabid beetles were species-dependent and were also mediated by the plant community and microclimate in the different steppes. The redundancy analysis (RDA) indicated that plant richness and cover were important factors affecting carabid diversity. Finally, these results provide experimental evidence and technical data for biodiversity conservation management in the steppes of Northwest China. Keywords: Steppe, Habitat fragmentation, Carabid beetles, Community, Richness, Abundance

The data presented in this article are related to the research paper entitled “Changes in N supply pathways under different long-term fertilization regimes in Northeast China” [1]. Seasonal dynamics of soil NH4+–N, NO3−–N, soil microbial biomass nitrogen (N) and fixed NH4+ were provided on the basis of a 26-year long-term experiment, including six treatments: no fertilizer (CK), recycled manure (M), N and P fertilizers (NP), P and K fertilizers (PK), N, P and K fertilizers (NPK), and NPK fertilizers with recycled manure (NPKM). The presentation of potential N retention and supply through soil microbial biomass N and fixed NH4+ pools at different growth stages is helpful for comparing the effects of different N pools on soil N transformation and assessing synchronies between crop N demand and soil N supply through different N pools.

A facile sol-gel method was proposed to prepare fluorine-containing hydrophobic SiO2 coating using trifluoroethanol (TFE) as the dispersant and hydrophobic modifier simultaneously. FTIR and XPS analysis indicated that CF3 groups were incorporated onto the surface of SiO2 particles. The resultant SiO2 coating possessed excellent hydrophobicity with a water contact angle (WCA) of 145°. This provides a new method for the preparation of fluorine-containing sol-gel SiO2 coating without the expensive long-chain alkyl fluorosilane.

Heterosis is an interesting topic for both breeders and biologists due to its practical importance and scientific significance. Cultivated rice (Oryza sativa L.) consists of two subspecies, indica and japonica, and hybrid rice is the predominant form of indica rice in China. However, the molecular mechanism underlying heterosis in japonica remains unclear. The present study determined the genome sequence and conducted quantitative trait locus (QTL) analysis using backcross recombinant inbred lines (BILs) and BILF1 lines to uncover the heterosis-related loci for rice yield increase under a japonica genetic background. The BIL population was derived from an admixture variety Habataki and japonica variety Sasanishiki cross to improve the genetic diversity but maintain the genetic background close to japonica. The results showed that heterosis in F1 mainly involved grain number per panicle. The BILF1s showed an increase in grain number per panicle but a decrease in plant height compared with the BILs. Genetic analysis then identified eight QTLs for heterosis in the BILF1s; four QTLs were detected exclusively in the BILF1 population only, presenting a mode of dominance or super-dominance in the heterozygotes. An additional four loci overlapped with QTLs detected in the BIL population, and we found that Grains Height Date 7 (Ghd7) was correlated in days to heading in both BILs and BILF1s. The admixture genetic background of Habataki was also determined by subspecies-specific single nucleotide polymorphisms (SNPs). This investigation highlights the importance of high-throughput sequencing to elucidate the molecular mechanism of heterosis and provides useful germplasms for the application of heterosis in japonica rice production.

The broad-spectrum and durable resistance gene Xa3/Xa26 against Xanthomonas oryzae pv. oryzae (Xoo) has been widely exploited in rice production in China. But the cytological features of the Xa3/Xa26-mediated resistance reaction have been rarely reported. This study reveals the cytological characteristics of the Xa3/Xa26-mediated resistance reaction against Xoo to uncover the functions of hypersensitive response programmed cell death (HR-PCD) in rice. Autophagy-like cell death, which was characterized by double-membrane bodies appearance in xylem parenchyma cell and mesophyll cell, was inhibited by autophagy inhibitor 3-methyladenin (3-MA). The autophagy-related genes were induced to reach a high level in resistance reaction. The hydrogen peroxide (H2O2) maintained a low concentration on the plasma membrane. The calcium ions localized on the apoplast were transferred into the vacuole. The autophagy inhibitor (3-MA) impaired Xa3/Xa26-mediated resistance by promoting the accumulation of H2O2, and inhibited the transfer of extracellular calcium ions into the vacuole in the xylem parenchyma cells and mesophyll cells. Therefore, the HR-PCD belongs to autophagy-like cell death in the Xa3/Xa26-mediated resistance reaction. These results suggest that the autophagy-like cell death participates in the Xa3/Xa26-mediated resistance by negatively regulating H2O2 accumulation, in order to abolish oxidative stress and possibly activate calcium ion signals in xylem parenchyma cells of the rice leaf.

Stomata, microscopic pores in leaf surfaces through which water loss and carbon dioxide uptake occur, are closed in response to drought by the phytohormone abscisic acid (ABA). This process is vital for drought tolerance and has been the topic of extensive experimental investigation in the last decades. Although a core signaling chain has been elucidated consisting of ABA binding to receptors, which alleviates negative regulation by protein phosphatases 2C (PP2Cs) of the protein kinase OPEN STOMATA 1 (OST1) and ultimately results in activation of anion channels, osmotic water loss, and stomatal closure, over 70 additional components have been identified, yet their relationships with each other and the core components are poorly elucidated. We integrated and processed hundreds of disparate observations regarding ABA signal transduction responses underlying stomatal closure into a network of 84 nodes and 156 edges and, as a result, established those relationships, including identification of a 36-node, strongly connected (feedback-rich) component as well as its in- and out-components. The network's domination by a feedback-rich component may reflect a general feature of rapid signaling events. We developed a discrete dynamic model of this network and elucidated the effects of ABA plus knockout or constitutive activity of 79 nodes on both the outcome of the system (closure) and the status of all internal nodes. The model, with more than 1024 system states, is far from fully determined by the available data, yet model results agree with existing experiments in 82 cases and disagree in only 17 cases, a validation rate of 75%. Our results reveal nodes that could be engineered to impact stomatal closure in a controlled fashion and also provide over 140 novel predictions for which experimental data are currently lacking. Noting the paucity of wet-bench data regarding combinatorial effects of ABA and internal node activation, we experimentally confirmed several predictions of the model with regard to reactive oxygen species, cytosolic Ca2+ (Ca2+c), and heterotrimeric G-protein signaling. We analyzed dynamics-determining positive and negative feedback loops, thereby elucidating the attractor (dynamic behavior) repertoire of the system and the groups of nodes that determine each attractor. Based on this analysis, we predict the likely presence of a previously unrecognized feedback mechanism dependent on Ca2+c. This mechanism would provide model agreement with 10 additional experimental observations, for a validation rate of 85%. Our research underscores the importance of feedback regulation in generating robust and adaptable biological responses. The high validation rate of our model illustrates the advantages of discrete dynamic modeling for complex, nonlinear systems common in biology.

The objective was to examine the protective effect of metformin (Met) on myocardial ischemia-reperfusion (IR) injury and whether the mechanism was related to the AMPK/ antioxidant enzymes signaling pathway. Rat Langendorff test and H2O2-treated rat cardiomyocytes (H9c2) were used in this study. Met treatment significantly improved left ventricular (LV) function, reduced infarct size and CK-MB release in comparison with IR group. Decreased TUNEL staining positive cells were also observed in IR+Met group ex vivo. Met treatment markedly inhibited IR inducing cell death and significantly decreased apoptosis with few generations of reactive oxygen species (ROS) in H9c2 cells in comparison with IR group. Up-regulated expressions of phosphorylated LKB1/AMPK/ACC, as well as down-regulated expressions of apoptotic proteins (Bax and cleaved caspase 3) were found in IR+Met group when compared to the IR group. Importantly, Met significantly up-regulated the expression of antioxidant enzymes (MnSOD and catalase) during IR procedure either ex vivo or in vitro. Compound C, a conventional inhibitor of AMPK, abolished the promoting effect of Met on antioxidant enzymes, and then attenuated the protective effect of Met on IR injury in vitro. In conclusion, Met exerted protective effect on myocardial IR injury, and this effect was AMPK/ antioxidant enzymes dependent.

In the past few decades, a great number of hardware and software reliability models have been proposed to address hardware failures in hardware subsystems and software failures in software subsystems, respectively. The interactions between hardware and software subsystems are often neglected in order to simplify reliability modeling, and hence, most existing reliability models assumed hardware subsystems and software subsystem are independent of each other. However, this may not be true in reality. In this study, system failures are classified into three categories, which are hardware failures, software failures, and hardware-software interaction failures. The main contribution of our research is that we further classify hardware-software interaction failures into two groups: software-induced hardware failures and hardware-induced software failures. A Markov-based unified system reliability modeling incorporating all three categories of system failures is developed in this research, which provides a novel and practical perspective to define system failures and further improve reliability prediction accuracy. Comparison of system reliability estimation between the reliability models with and without considering hardware-software interactions is elucidated in the numerical example. The impacts on system reliability prediction as the changes of transition parameters are also illustrated by the numerical examples.

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by symmetric polyarthritis with swelling and pain at synovial joints. In RA patients, delayed neutrophil apoptosis amplifies the inflammatory response and massively released neutrophil extracellular traps (NETs) induce tissue damage and provide self-antigens. Andrographolide (AD) is the major active labdane diterpenoid derived from Andrographis paniculata, which has multiple pharmacological effects, including hepatoprotection, anti-angiogenesis, anti-thrombosis, and anti-inflammation. In the present study, we investigated the effect of AD on an adjuvant-induced arthritis (AA) murine model of RA and found that AD alleviated murine arthritis by reducing neutrophil infiltration and NETosis in the ankle joints and relieved the systematic inflammation. In vitro experiments showed that AD accelerated the apoptosis of lipopolysaccharide-activated neutrophils and inhibited autophagy-dependent extracellular traps formation of neutrophils. These findings suggest that AD has considerable potential for RA therapy.