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Introduction: Guizhou Province, characterized by complex and diverse geographic and climatic environments, has rich genetic resources for the Chinese honeybee (Apis cerana cerana) and is one of the main bee-producing areas in China. However, research on the genetic diversity of Chinese honeybee in the Guizhou region is very limited, despite implications for conservation of biodiversity.Methods: In this study, we analyzed the genetic diversity, differentiation, and selection signals based on 116 Chinese honeybees from 12 regions in Guizhou Province using whole-genome sequencing.Results: We identified 1,400,430 high-quality SNPs across all samples. A population structure analysis revealed two independent genetic subgroups of Chinese honeybees in Guizhou, a Yunnan-Guizhou Plateau population in western Guizhou and a hilly-mountainous population in eastern Guizhou. The average nucleotide diversity (Pi) ranged from 0.00138 to 0.00161 and average expected heterozygosity (He) ranged from 0.2592 to 0.2604. The average genetic differentiation index (FST) for Chinese honeybees in pairwise comparisons of 12 regions ranged from 0.0094 to 0.0293. There was clear genetic differentiation between the western plateau and the eastern hilly mountainous areas of Guizhou; however, FST values between the eastern and western populations ranged from 0.0170 to 0.0293, indicating a low degree of differentiation. A genome-wide scan revealed a number of genes under selection in the Yunnan-Guizhou Plateau environment. These genes were related to growth and development, reproduction, and cold resistance, and several candidate genes involved in environmental adaptation were identified, including CTR, MAPK, MAST, HSF, and MKKK.Discussion: The results of the present study provide important theoretical bases for the conservation, evaluation, development, and utilization of genetic resources for Chinese honeybees in the Guizhou region and for further investigations of environmental adaptation and underlying mechanisms in the species.

IntroductionThe severity of flood disasters is increasing due to climate change, resulting in a significant reduction in the yield and quality of forage crops worldwide. This poses a serious threat to the development of agriculture and livestock. Hemarthria compressa is an important high-quality forage grass in southern China. In recent years, frequent flooding has caused varying degrees of impacts on H. compressa and their ecological environment.MethodsIn this study, we evaluated differences in flooding tolerance between the root systems of the experimental materials GY (Guang Yi, flood-tolerant) and N1291 (N201801291, flood-sensitive). We measured their morphological indexes after 7 d, 14 d, and 21 d of submergence stress and sequenced their transcriptomes at 8 h and 24 h, with 0 h as the control.ResultsDuring submergence stress, the number of adventitious roots and root length of both GY and N1291 tended to increase, but the overall growth of GY was significantly higher than that of N1291. RNA-seq analysis revealed that 6046 and 7493 DEGs were identified in GY-8h and GY-24h, respectively, and 9198 and 4236 DEGs in N1291-8h and N1291-24h, respectively, compared with the control. The GO and KEGG enrichment analysis results indicated the GO terms mainly enriched among the DEGs were oxidation-reduction process, obsolete peroxidase reaction, and other antioxidant-related terms. The KEGG pathways that were most significantly enriched were phenylpropanoid biosynthesis, plant hormone signal transduction etc. The genes of transcription factor families, such as C2H2, bHLH and bZIP, were highly expressed in the H. compressa after submergence, which might be closely related to the submergence adaptive response mechanisms of H. compressa.DiscussionThis study provides basic data for analyzing the molecular and morphological mechanisms of H. compressa in response to submergence stress, and also provides theoretical support for the subsequent improvement of submergence tolerance traits of H. compressa.

Herein, we report efficient visible light-induced photoredox reactions of C–H/N–H and C–X Bonds. These methods have provided access to varied portfolio of synthetically important γ-ketoesters, azaspirocyclic cyclohexadienones spirocyclohexadienones, multisubstituted benzimidazole derivatives, substituted N,2-diarylacetamide, 2-arylpyridines and 2-arylquinolines in good yields and under mild conditions. Moreover, we have successfully discussed the construction through visible light-induction by an intermolecular radical addition, dearomative cyclization, aryl migration and desulfonylation. Similarly, we also spotlight the visible light-catalyzed aerobic C–N bond activation from well-known building blocks through cyclization, elimination and aromatization. The potential use of a wide portfolio of simple ketones and available primary amines has made this transformation very attractive.

Existing studies on electric bus (EB) scheduling mainly focus on the arrangement of bus charging at the bus terminals, which can result in high scheduling costs and insufficient utilization of chargers and bus batteries. This paper proposes a bus replacement strategy during the operation of EB, i.e., during the operation, a bus with insufficient battery can be driven to a charging station, and will then be replaced at the station. Its passengers will be transferred to another fully charged “standby bus” at the charging station. The “standby bus” will execute the rest of the bus trip, and the replaced bus becomes a “standby bus” after it is fully charged. In this context, we consider the electric bus scheduling and charging infrastructure planning (charger location and quantity) problem, given level of service constraints, battery power limitation, charging capacity limitation. The proposed model is a nonlinear integer programming model. We then linearize the proposed model and obtain an equivalent mixed integer linear programming (MILP) model, which can be efficiently solved by commercial solvers (e.g., CPLEX). The case study results show that bus replacement can improve the operational efficiency of the EB system when compared with battery swapping, e.g., the charger utilization rate can be increased by 14.3%, and passengers’ travel time and the total cost can be reduced by 15.5% and 7.75%, respectively.

Abstract Paraspeckles are mammal-specific membraneless nuclear bodies that participate in various biological processes. NONO, a central paraspeckle component, has been shown to play pivotal roles in DNA double-strand breaks (DSB) repair, whereas its underlying mechanism needs to be further disclosed. Here, using co-immunoprecipitation and mass spectrum, we identified ribosomal protein P0 (RPLP0) as a DSB-induced NONO-binding protein; RPLP0 binds to the RRM1 and RRM2 domains of NONO. Similar to NONO, RPLP0 enhances non-homologous end joining-mediated DSB repair, which was ascribed to a ribosome-independent manner. Interestingly, paraspeckles were induced as early as 15 min after irradiation; it further recruited nuclear RPLP0 to enhance its interaction with NONO. Radiation-induced NONO/RPLP0 complex subsequently anchored at the damaged DNA and increased the autophosphorylation of DNA-PK at Thr2609, thereby enhancing DSB repair. Consistently, in vivo and in vitro experiments showed that depletion of NONO sensitizes tumor cells to radiation. For patients with locally advanced rectal cancer, NONO expression was remarkably increased in tumor tissues and correlated with a poor response to radiochemotherapy. Our findings suggest a pivotal role of radiation-induced paraspeckles in DNA repair and tumor radioresistance, and provide a new insight into the ribosome-independent function of ribosomal proteins.

Under the goal of “Emission peak, carbon-neutral”, it has become an inevitable choice to build a new power system with new energy as the mainstay in the “14th Five-Year Plan” period. Wind power has become the hot spot of new energy development with its green, non-polluting, and renewable characteristics. In this paper, the investment risk evaluation index system of wind power projects is constructed for the investment risk characteristics of wind power projects, and the fuzzy-gray clustering trigonometric function comprehensive evaluation model is used to study the investment risk of wind power projects in China. The research results show that the risk evaluation index system constructed in this paper is reasonable, the evaluation results are consistent with the actual, and the evaluation method can be used as a reference tool for wind power project investment decisions.

IntroductionSubmergence stress creates a hypoxic environment. Roots are the first plant organ to face these low-oxygen conditions, which causes damage and affects the plant growth and yield. Orchardgrass (Dactylis glomerata L.) is one of the most important cold-season forage grasses globally. However, their submergence stress-induced gene expression and the underlying molecular mechanisms of orchardgrass roots are still unknown.MethodsUsing the submergence-tolerant ‘Dianbei’ and submergence-sensitive ‘Anba’, the transcriptomic analysis of orchardgrass roots at different time points of submergence stress (0 h, 8 h, and 24 h) was performed.ResultsWe obtained 118.82Gb clean data by RNA-Seq. As compared with the control, a total of 6663 and 9857 differentially expressed genes (DEGs) were detected in Dianbei, while 7894 and 11215 DEGs were detected in Anba at 8 h and 24 h post-submergence-stress, respectively. Gene Ontology (GO) enrichment analysis obtained 986 terms, while Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis obtained 123 pathways. Among them, the DEGs in plant hormones, mitogen-activated protein kinase (MAPK) and Ca2+ signal transduction were significantly differentially expressed in Dianbei, but not in Anba.DiscussionThis study was the first to molecularly elucidate the submergence stress tolerance in the roots of two orchardgrass cultivars. These findings not only enhanced our understanding of the orchardgrass submergence tolerance, but also provided a theoretical basis 36 for the cultivation of submergence-tolerant forage varieties.

Submergence stress can severely affect plant growth. Orchardgrass (Dactylis glomerata L.) is an important forage grass, and the molecular mechanisms of orchardgrass to submergence stress are not well understood. The roots of the flood-tolerant cultivar “Dian Bei” were harvested at 0 h, 8 h and 24 h of submergence stress. The combined transcriptomic and metabolomic analyses showed that β-alanine metabolism, flavonoid biosynthesis, and biosynthesis of amino acid pathways were significantly enriched at 8 h and 24 h of submergence stress and were more pronounced at 24 h. Most of the flavonoid biosynthesis-related genes were down-regulated for the synthesis of metabolites such as naringenin, apigenin, naringin, neohesperidin, naringenin chalcone, and liquiritigenin in response to submergence stress. Metabolites such as phenylalanine, tyrosine, and tryptophan were up-regulated under stress. The predominant response of flavonoid and amino acids biosynthesis to submergence stress suggests an important role of these pathways in the submergence tolerance of orchardgrass.

Abstract Background Orchardgrass (Dactylis glomerata L.) is a popular cool-season perennial grass with a high production value, and orchardgrass seed is the fourth top-selling forage grass seed in the world. However, its yield and quality are often affected by flooding. To date, the molecular responses of orchardgrass to flooding were poorly understood. Results Here, we performed mRNA-seq to explore the transcriptomic responses of orchardgrass to a short term flooding (8 h and 24 h). There were 1454 and 565 differentially expressed genes identified in the 8 h and 24 h of flooding, respectively, compared to well control. GO functional enrichment analysis showed that oxidoreductase activity and oxidation-reduction process were highly present, suggesting that flooding induced the response to oxygen stress. Pathways enrichment analysis highlights the importance of glutathione metabolism, peroxidase, glycolysis and plant hormone signal transduction in response to flooding acclimation. Besides, the ROS clearance system is activated by significantly expressed glutathione S-transferase and genes encoding SOD and CAT (CAT1 and CDS2). The significant positive correlation between RNA sequencing data and a qPCR analysis indicated that the identified genes were credible. Conclusion In the process of orchardgrass response to flooding stress, multiple differential genes and biological processes have participated in its acclimation to flooding, especially the biological processes involved in the removal of ROS. These results provide a basis for further research on the adaptation mechanism of orchardgrass to flood tolerance.

Removing rain streaks from a single image continues to draw attentions today in outdoor vision systems. In this paper, we present an efficient method to remove rain streaks. First, the location map of rain pixels needs to be known as precisely as possible, to which we implement a relatively accurate detection of rain streaks by utilizing two characteristics of rain streaks.The key component of our method is to represent the intensity of each detected rain pixel using a linear model: p = αs + β, where p is the observed intensity of a rain pixel and s represents the intensity of the background (i.e., before rain-affected). To solve α and $\beta $ for each detected rain pixel, we concentrate on a window centered around it and form an L2-norm cost function by considering all detected rain pixels within the window, where the corresponding rain-removed intensity of each detected rain pixel is estimated by some neighboring non-rain pixels. By minimizing this cost function, we determine α and β so as to construct the final rain-removed pixel intensity. Compared with several state-of-the-art works, our proposed method can remove rain streaks from a single color image much more efficiently - it offers not only a better visual quality but also a speed-up of several times to one degree of magnitude compared to other conventional deraining methods.

Light-field raw data captured by a state-of-the-art light-field camera is limited in its spatial and angular resolutions due to the camera's optical hardware. In this paper, we propose an all-software algorithm to synthesize light-field raw data from a single RGB-D input image, which is driven largely by the need in the research area of light-field data compression. Our synthesis algorithm consists of three key steps: (1) each pixel of the input image is regarded as a spot lighting source that emits directional light rays with an equal strength; (2) the optical path of each directional light ray through the camera's main lens as well as the corresponding micro lens is considered as accurately as possible; and (3) the occlusion of light rays among objects at different distances within the input image is handled with the depth information. The spatial and angular resolutions of our synthesized light-field data can be scaled up when the input RGB-D image has a higher and higher spatial resolution. Meanwhile, for a given input image with a fixed size, we pay a special attention to what would be the extreme we can push the parameters involved in our synthesis algorithm, such as the number of rays emitted from each pixel, the number of micro lenses, and the number of sensors associated with each micro lens. The usefulness of our synthesized data is validated by refocusing, all-in-focus, and sub-aperture reconstructions. In particular, all-in-focus images are evaluated objectively by computing the structural similarity (SSIM) index, which allows us to reach the goal of pushing to the extreme through selecting various parameters mentioned above.

To investigate the static performance of precast segmental hollow piers, two precast segmental hollow pier specimens were designed for static loading tests on the top of piers. The finite element model of precast segmental hollow piers was established by the finite element software Abaqus and verified based on the test results. Based on the experimental and finite element models, three optimal design solutions were proposed, and the calculation results of each solution were analyzed. The results show that precast segmental hollow pier mechanical behavior is similar to that of cantilevered bending members. The specimens present brittle damage characteristics after the destruction of the structure at the bottom of the pier pressure edge as the axis of the rigid body rotation. Following the test loading process, the bonding between the segments is good, except for the pier bottom damage surface of the rest of the bonding surface, which has no relative displacement. The calculation results of the finite element model are in good agreement with the test results and can effectively predict the load–displacement response of precast piers. Three optimized design solutions are proposed. The finite element simulation proves all three optimized design solutions show better overall ductility than the original solution and can effectively improve the performance of segmental precast hollow piers.

Weakly supervised object detection (WSOD) has received increasing attention in object detection field, because it only requires image-level annotations to indicate the presence or absence of target objects, which greatly reduces the labeling costs. Existing methods usually focus on the current individual image to learn object instance representations, while ignoring instance correlations between different images. To address this problem, we propose an instance-level contrastive learning (ICL) framework to mine reliable instance representations from all learned images, and use the contrastive loss to guide instance representation learning for the current image. Due to the diversity of instances, with different appearances, sizes or shapes, we propose an instance-diverse memory updating (IMU) algorithm to mine different instance representations and store them in a memory bank with multiple representation vectors per class, which also considers background information to enhance foreground representations. With the help of memory bank, we further propose a memory-aware instance mining (MIM) algorithm that combines proposal confidence and instance similarity across images to mine more reliable object instances. In addition, we also propose a memory-aware proposal sampling (MPS) algorithm to sample more positive proposals and remove some negative proposals to balance the learning of positive-negative samples. We conduct extensive experiments on the PASCAL VOC2007 and VOC2012 datasets, which are widely used in WSOD, to demonstrate the effectiveness of our method. Compared to our baseline, our method brings 14.2% mAP and 13.4% CorLoc gains on PASCAL VOC2007 dataset, and 12.2% mAP and 8.3% CorLoc gains on PASCAL VOC2012 dataset.

Orchardgrass (Dactylis glomerata) is one of the top four perennial forages worldwide and, despite its large economic advantages, often threatened by various environmental stresses. WRKY transcription factors (TFs) can regulate a variety of plant processes, widely participate in plant responses to biotic and abiotic stresses, and are one of the largest gene families in plants. WRKYs can usually bind W-box elements specifically. In this study, we identified a total of 93 DgWRKY genes and 281 RGAs, including 65, 169 and 47 nucleotide-binding site-leucine-rich repeats (NBS-LRRs), leucine-rich repeats receptor-like protein kinases (LRR-RLKs), and leucine-rich repeats receptor-like proteins (LRR-RLPs), respectively. Through analyzing the expression of DgWRKY genes in orchardgrass under different environmental stresses, it was found that many DgWRKY genes were differentially expressed under heat, drought, submergence, and rust stress. In particular, it was found that the greatest number of genes were differentially expressed under rust infection. Consistently, GO and KEGG enrichment analysis of all genes showed that 78 DgWRKY TFs were identified in the plant–pathogen interaction pathway, with 59 of them differentially expressed. Through cis-acting element prediction, 154 RGAs were found to contain W-box elements. Among them, DG6C02319.1 (a member of the LRR-RLK family) was identified as likely to interact with 14 DGWRKYs. Moreover, their expression levels in susceptible plants after rust inoculation were first up-regulated and then down-regulated, while those in the resistant plants were always up-regulated. In general, DgWRKYs responded to both biotic stress and abiotic stress. DgWRKYs and RGAs may synergistically respond to the response of orchardgrass to rust. This study provides meaningful insight into the molecular mechanisms of WRKY proteins in orchardgrass.

The historical temperature data logged in the supervisory control and data acquisition (SCADA) system contains a wealth of information that can assist with the performance optimization of wind turbines (WTs). However, mining and using these long-term data is difficult and time-consuming due to their complexity, volume, etc. In this study, we tracked and analyzed the 5-year trends of major SCADA temperature rise variables in relation to the active power of four WTs in a real wind farm. To uncover useful information, an extended version of the bins method, which calculates the standard deviation (SD) as well as the average, is proposed and adopted. The implications of the analysis for engineering practice are discussed from multiple perspectives. The research results demonstrate a change in the patterns of the main temperature rise variables in a real wind farm, completeness of the monitoring of the WT internal temperature state, influence of wind turbine aging on temperature signals, a correlation between different measurement points, and a correlation between signals from different years. The knowledge gained from this research provides a reference for the development of more practical and comprehensive condition monitoring systems and methods, as well as better operation maintenance strategies.

Esophageal squamous cell cancer (ESCC) is the eighth most common cancer around the world. Several reports have focused on somatic mutations and common germline mutations in ESCC. However, the contributions of pathogenic germline alterations in cancer susceptibility genes (CSGs), highly frequently mutated CSGs, and pathogenically mutated CSG-related pathways in ESCC remain unclear. We obtained data on 571 ESCC cases from public databases and East Asian from the 1000 Genomes Project database and the China Metabolic Analytics Project database to characterize pathogenic mutations. We detected 157 mutations in 75 CSGs, accounting for 25.0% (143/571) of ESCC cases. Six genes had more than five mutations: TP53 (n = 15 mutations), GJB2 (n = 8), BRCA2 (n = 6), RECQL4 (n = 6), MUTYH (n = 6), and PMS2 (n = 5). Our results identified significant differences in pathogenic germline mutations of TP53, BRCA2, and RECQL4 between the ESCC and control cohorts. Moreover, we identified 84 double-hit events (16 germline/somatic double-hit events and 68 somatic/somatic double-hit events) occurring in 18 tumor suppressor genes from 83 patients. Patients who had ESCC with germline/somatic double-hit events were diagnosed at younger ages than patients with the somatic/somatic double-hit events, though the correlation was not significant. Fanconi anemia was the most enriched pathway of pathogenically mutated CSGs, and it appeared to be a primary pathway for ESCC predisposition. The results of this study identified the underlying roles that pathogenic germline mutations in CSGs play in ESCC pathogenesis, increased our awareness about the genetic basis of ESCC, and provided suggestions for using highly mutated CSGs and double-hit features in the early discovery, prevention, and genetic counseling of ESCC.

In this paper, we focus on the intrinsic image decomposition problem for stereoscopic image pairs. The existing methods cannot be applied directly to decompose stereoscopic images, as it often produces inconsistent reflectance (albedo) and 3D artifacts after the decomposition. We propose a straightforward yet effective framework that enables a high-quality decomposition for stereoscopic pairs. First, retinex-based constraints are employed to coarsely classify the observed image gradients into two categories that are caused by reflectance changes and illumination variations, respectively. Second, reflectance-consistent constraints are added to control the reflectance consistency between the left and right views. Since this problem is highly ill-posed, we further analyze local and non-local image textures regularized by super-pixels within and across two views to reduce reflectance ambiguity. Lastly, absolute-scale constraints are employed to normalize the decomposition results. Extensive experiments on the real-world stereoscopic images and synthetic stereoscopic images reveal that our method can readily achieve high-quality decomposition performance.

Sorghum-sudangrass hybrid silage has poor fermentation characteristics owing to a high moisture content. Accordingly, a 3 × 4+1 factorial design was applied to investigate the effects of adding different types and amounts of hay (corn stalk, wheat straw, and alfalfa hay at 12.5 kg t-1, 25 kg t-1, 37.5 kg t-1, and 50 kg t-1) on the nutritive value, fermentation quality, 72 h dry matter digestibility, and gas dynamics in vitro to simulate the rumen fermentation of sorghum-sudangrass hybrid silage. Separated silage of sorghum-sudangrass hybrids had a high butyric acid content and a FLIEG’s scores evaluation ranking of only “Fair.” The addition of hay significantly improved the fermentation quality of mixed silage. With respect to hay type, adding wheat straw had the best fermentation quality, alfalfa hay had the best nutritive value, in vitro dry matter digestibility (IVDMD) (662.41 g kg-1), constant fractional rate (C) (0.28 mL h-1), and the average gas production rate (AGPR) (32.70 mL h-1) content. There were no differences in the cumulative gas production at 72 h (GP72h), asymptotic gas production generated at a constant fractional rate (A), and lag time before gas production commenced (lag) among the three hay types. With respect to quantity, 25 kg t-1 hay had the best fermentation quality, 50 kg t-1 hay had the best nutritive value and highest IVDMD content (662.81 g kg-1), 37.5 kg t-1 hay had the highest C (0.28 mL h-1) and AGPR (31.48 mL h-1) contents, 25 kg t-1 hay had the highest Half time (2.20 h), and there were no significant differences in GP72h, A, and lag among the four amounts. Considering both nutritive value and fermentation quality, the best mixed silage mode was 37.5 kg t-1 wheat straw.

Ancylostomiasis is a fairly common small bowel parasite disease identified by capsule endoscopy (CE) for which a computer-aided clinical detection method has not been established. We sought to develop an artificial intelligence system with a convolutional neural network (CNN) to automatically detect hookworms in CE images. We trained a deep CNN system based on a YOLO-V4 (You Look Only Once-Version4) detector using 11236 CE images of hookworms. We assessed its performance by calculating the area under the receiver operating characteristic curve and its sensitivity, specificity, and accuracy using an independent test set of 10,529 small-bowel images including 531 images of hookworms. The trained CNN system required 403 seconds to evaluate 10,529 test images. The area under the curve for the detection of hookworms was 0.972 (95% confidence interval (CI), 0.967-0.978). The sensitivity, specificity, and accuracy of the CNN system were 92.2%, 91.1%, and 91.2%, respectively, at a probability score cut-off of 0.485. We developed and validated a CNN-based system for detecting hookworms in CE images. By combining this high-accuracy, high-speed, and oversight-preventing system with other CNN systems, we hope it will become an important supplement for detecting intestinal abnormalities in CE images. This trial is registered with ChiCTR2000034546 (a clinical research of artificial-intelligence-aided diagnosis for hookworms in small intestine by capsule endoscope images).

In order to conduct a further in-depth exploration of the role of temperature-related parameters in the condition monitoring of wind turbines, this paper proposes a method to assess the condition of wind turbines by analyzing the supervisory control and data acquisition system temperature-related parameters based on existing research. A prediction model of time-sequence regression is established, based on the key temperature signals of WTs, so as to reflect their health condition in the form of prediction residuals. A kind of health index from the perspective of temperature-related parameters is developed by separating the statistics concerning the conformity of the predicted values of key temperature parameters within a certain time window from the measured values in order to clearly present the implied information on the health condition of wind turbines contained in the model prediction residuals. The case study shows that the trend of health index from the perspective of temperature-related parameters is consistent with the health condition of wind turbines. In some instances, its decline obviously occurs earlier than the maintenance provided to address the stoppage, suggesting that such indexes can effectively reflect some early health problems of the wind turbines to provide a reference for their scientific maintenance.

Background/Aims: Long noncoding RNAs (lncRNAs) are key regulators of cancer initiation and progression. In this study, we investigated the clinical value and functional role of LncRNA DQ786243 (LncDQ) in the pathogenesis of hepatocellular carcinoma (HCC). Methods: To investigate the expression level of LncDQ in HCC, we performed quantitative real-time PCR using total RNA extracted from HCC tumor tissues and their matched non-neoplastic counterparts, as well as from the serum of HCC patients and healthy volunteers. The correlation of LncDQ expression with clinicopathologic features and prognosis was analyzed. The functional role of LncDQ in cell proliferation, migration, and invasion were evaluated by MTT cell viability, wound healing, and transwell assays in vitro and in vivo. RNA immunoprecipitation and chromatin immunoprecipitation assays were performed to analyze the potential mechanism of LncDQ in HCC cells. Results: LncDQ was upregulated in both HCC tissue samples and serum and was correlated with low survival rate and adverse clinical pathological characteristics. Multivariate analysis demonstrated that LncDQ expression was an independent prognostic factor for HCC. The area under the receiver operating characteristic curve was 0.804 with a sensitivity of 0.72 and a specificity of 0.8. Knockdown of LncDQ induced inhibition of cell proliferation, migration, and invasion in vitro and in vivo. Mechanistically, LncDQ regulated the epithelial–mesenchymal transition pathway by interacting with EZH2, to epigenetically repress the expression of E-cadherin in HCC cells. Conclusions: Taken together, the results of our study indicate that LncDQ plays a critical role in HCC progression, and may serve as a potential diagnostic and prognostic biomarker for HCC.

Transformer state forecasting and fault forecasting are important for the stable operation of power equipment and the normal operation of power systems. Forecasting of the dissolved gas content in oil is widely conducted for transformer faults, but its accuracy is affected by data scale and data characteristics. Based on phase space reconstruction (PSR) and weighted least squares support vector machine (WLSSVM), a forecasting model of time series of dissolved gas content in transformer oil is proposed in this paper. The phase spaces of time series of the dissolved gas content sequence are reconstructed by chaos theory, and the delay time and dimension are obtained by the C-C method. The WLSSVM model is used to forecast time series of dissolved gas content, the chemical reaction optimization (CRO) algorithm is used to optimize training parameters, the bootstrap method is used to build forecasting intervals. Finally, the accuracy and generalization ability of the forecasting model are verified by the analysis of actual case and the comparison of different models.

This paper proposes an evaluation index of wind turbine generator operating health based on the relationships with SCADA (Supervisory Control and Data Acquisition) data. First, the relationship among the data from a wind turbine SCADA system is thoroughly analyzed. Then, a time based sliding window model is used to process the SCADA data by the bin method, and a running state model of the wind turbine is established by data fitting. Taking the normal operation state model of the wind turbine as the standard reference and based on the Euclidean distance between the state model curve and the standard model curve, the health index of the wind turbine operation state is proposed. Finally, using SCADA data from two 2 MW direct-drive wind turbines as examples for analysis and discussion, the results show that: (1) health indicators have good stability and sensitivity to wind turbine operating conditions; (2) the width of the data window in the sliding window model must cover all operating conditions of the wind turbine to ensure that the health index depicts the operating state of the wind turbine; (3) the data window width, window increment, and data fitting modeling all affect the health indicators, and thus, the selection of the sliding window model parameters and the data relationship modeling methods should consider the accuracy and real-time performance of the health indicators; and (4) the data acquisition cycle does not affect the health indicators. Once the basic characteristics of the data relations are known, direct data fitting modeling is more efficient than bin preprocessing modeling.

Oil-immersed transformer is one of the most important components in the power system. The dissolved gas concentration prediction in oil is vital for early incipient fault detection of transformer. In this paper, a model for predicting the dissolved gas concentration in power transformer based on the modified grey wolf optimizer and least squares support vector machine (MGWO-LSSVM) with grey relational analysis (GRA) and empirical mode decomposition (EMD) is proposed, in which the influence of transformer load, oil temperature and ambient temperature on gas concentration is taken into consideration. Firstly, GRA is used to analyze the correlation between dissolved gas concentration and transformer load, oil temperature and ambient temperature, and the optimal feature set affecting gas concentration is extracted and selected as the input of the prediction model. Then, EMD is used to decompose the non-stationary series data of dissolved gas concentration into stationary subsequences with different scales. Finally, the MGWO-LSSVM is used to predict each subsequence, and the prediction values of all subsequences are combined to get the final result. DGA samples from two transformers are used to verify the proposed method, which shows high prediction accuracy, stronger generalization ability and robustness by comparing with LSSVM, particle swarm optimization (PSO)-LSSVM, GWO-LSSVM, MGWO-LSSVM, EMD-PSO-LSSVM, EMD-GWO-LSSVM, EMD-MGWO-LSSVM, GRA-EMD-PSO-LSSVM and GRA-EMD-GWO-LSSVM.

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

With the development of advanced manufacturing technology, more and more enterprises utilize automated guided vehicles to transfer materials for computer numerical control machines in the workshop. Because automated guided vehicle scheduling problem is non-deterministic polynomial–hard, highly efficient automated guided vehicle scheduling algorithms are necessary to improve productivity of the workshop. In order to solve this problem, a mathematical model with a new objective function is established firstly, which incorporates two indicators, that is, the standard deviation of the waiting time of computer numerical control material buffers and the total travel distance of automated guided vehicle, reflecting the overall capacity of computer numerical control machines and the energy efficiency of automated guided vehicles during the actual production, respectively. Then, an improved harmony search algorithm is proposed, which includes an effective discrete encoding scheme of harmony, a new initialization method for harmony memory based on opposition-based learning strategy, a dynamic harmony memory considering rate parameter and a local search strategy. The property of the proposed algorithm is evaluated through three cases from the real-world manufacturing system for producing back cover of smart phone. Compared with the competing algorithms, the results demonstrate the superiority of proposed algorithm for solving automated guided vehicle scheduling problem obviously.

Clustering and routing are two key techniques to improve the energy efficiency of wireless sensor networks. As clustering and routing for improving the energy efficiency of wireless sensor networks are NP-hard problems, increasing meta-heuristic algorithms are introduced for solving them. However, due to their discreteness and strong constraints, most meta-heuristics are unsuitable or inefficient to optimize them. Harmony search algorithm is one of the most suitable meta-heuristics for solving these problems. This article proposes a new energy-efficient clustering and routing algorithm based on harmony search algorithm to improve the energy efficiency of wireless sensor networks. The proposed approach contains two parts: clustering phase and routing phase. First, a new objective function model, which has considered balancing the energy consumption of both gateways and regular nodes as well as considered routing, is established for the clustering phase. Then, a new energy-efficient clustering algorithm is designed based on several improvements made to harmony search algorithm: (1) a discrete encoding scheme of a harmony for clustering is proposed; (2) a roulette wheel selection method is designed to choose a gateway for a regular sensor node to join, which is employed by two steps (i.e. initialization of harmony and improvisation of a new harmony); (3) the dynamically changed harmony memory considering rate is designed for improvisation of a new harmony; (4) a local search scheme is proposed to improve the best harmony within the harmony memory in iterations. In addition, the improved harmony search based energy-efficient routing algorithm that we proposed previously is employed to balance the energy consumption of gateways in the routing phase. The proposed approach is compared with several popular meta-heuristic-based clustering algorithms over extensive wireless sensor networks cases. The experimental results clearly demonstrate the superiority of the proposed approach.

The condition monitoring and fault diagnosis of power transformers plays a significant role in the safe, stable and reliable operation of the whole power system. Dissolved gas analysis (DGA) methods are widely used for fault diagnosis, however, their accuracy is limited by the selection of DGA features and the performance of fault diagnosis models, for example, the classical support vector machine (SVM), is easily affected by unbalanced training samples. This paper presents a transformer fault diagnosis model based on chemical reaction optimization and a twin support vector machine. Twin support vector machines (TWSVMs) are used as classifiers for solving problems involving unbalanced and insufficient samples. Restricted Boltzmann machines (RBMs) are used for data preprocessing to ensure the effective identification of feature parameters and improve the efficiency and accuracy of fault diagnosis. The chemical reaction optimization (CRO) algorithm is used to optimize TWSVM parameters to select the optimal training parameters. The cross-validation (CV) method is used to ensure the reliability and generalization ability of the diagnostic model. Finally, the validity of the model is verified using real fault samples and random testing.

The aim of present study was to profile the glucose-dependent and glutamine- dependent metabolism in pancreatic cancer.We performed Immunohistochemical staining of GLUT1, CAIX, BNIP3, p62, LC3, GLUD1, and GOT1. Based on the expression of metabolism-related proteins, the metabolic phenotypes of tumors were classified into two categories, including glucose- and glutamine-dependent metabolism. There were Warburg type, reverse Warburg type, mixed type, and null type in glucose-dependent metabolism, and canonical type, non-canonical type, mixed type, null type in glutamine-dependent metabolism.Longer overall survival was associated with high expression of BNIP3 in tumor (p = 0.010). Shorter overall survival was associated with high expression of GLUT1 in tumor (P = 0.002) and GOT1 in tumor (p = 0.030). Warburg type of glucose-dependent metabolism had a highest percentage of tumors with nerve infiltration (P = 0.0003), UICC stage (P = 0.0004), and activated autophagic status in tumor (P = 0.0167). Mixed type of glucose-dependent metabolism comprised the highest percentage of tumors with positive marginal status (P

Orchardgrass (Dactylis glomerata L.), is a well-known perennial forage species; however, rust diseases have caused a noticeable reduction in the quality and production of orchardgrass. In this study, genetic diversity was assessed and the marker-trait associations for rust were examined using 18 EST-SSR and 21 SCoT markers in 75 orchardgrass accessions. A high level of genetic diversity was detected in orchardgrass with an average genetic diversity index of 0.369. For the EST-SSR and SCoT markers, 164 and 289 total bands were obtained, of which 148 (90.24%) and 272 (94.12%) were polymorphic, respectively. Results from an AMOVA analysis showed that more genetic variance existed within populations (87.57%) than among populations (12.43%). Using a parameter marker index, the efficiencies of the EST-SSR and SCoT markers were compared to show that SCoTs have higher marker efficiency (8.07) than EST-SSRs (4.82). The results of a UPGMA cluster analysis and a STRUCTURE analysis were both correlated with the geographic distribution of the orchardgrass accessions. Linkage disequilibrium analysis revealed an average r2 of 0.1627 across all band pairs, indicating a high extent of linkage disequilibrium in the material. An association analysis between the rust trait and 410 bands from the EST-SSR and SCoT markers using TASSEL software revealed 20 band panels were associated with the rust trait in both 2011 and 2012. The 20 bands obtained from association analysis could be used in breeding programs for lineage selection to prevent great losses of orchardgrass caused by rust, and provide valuable information for further association mapping using this collection of orchardgrass.