Search

Abstract Background Breast cancer is the most prevalent cancer in women globally. Over-activated estrogen receptor (ER) α signaling is considered the main factor in luminal breast cancers, which can be effectively managed with selective estrogen receptor modulators (SERMs) like tamoxifen. However, approximately 30–40% of ER + breast cancer cases are recurrent after tamoxifen therapy. This implies that the treatment of breast cancer is still hindered by resistance to tamoxifen. Recent studies have suggested that post-translational modifications of ERα play a significant role in endocrine resistance. The stability of both ERα protein and its transcriptome is regulated by a balance between E3 ubiquitin ligases and deubiquitinases. According to the current knowledge, approximately 100 deubiquitinases are encoded in the human genome, but it remains unclear which deubiquitinases play a critical role in estrogen signaling and endocrine resistance. Thus, decoding the key deubiquitinases that significantly impact estrogen signaling, including the control of ERα expression and stability, is critical for the improvement of breast cancer therapeutics. Methods We used several ER positive breast cancer cell lines, DUB siRNA library screening, xenograft models, endocrine-resistant (ERα-Y537S) model and performed immunoblotting, real time PCR, RNA sequencing, immunofluorescence, and luciferase activity assay to investigate the function of USP36 in breast cancer progression and tamoxifen resistance. Results In this study, we identify Ubiquitin-specific peptidase 36 (USP36) as a key deubiquitinase involved in ERα signaling and the advancement of breast cancer by deubiquitinases siRNA library screening. In vitro and in vivo studies showed that USP36, but not its catalytically inactive mutant (C131A), could promote breast cancer progression through ERα signaling. Conversely, silencing USP36 inhibited tumorigenesis. In models resistant to endocrine therapy, silencing USP36 destabilized the resistant form of ERα (Y537S) and restored sensitivity to tamoxifen. Molecular studies indicated that USP36 inhibited K48-linked polyubiquitination of ERα and enhanced the ERα transcriptome. It is interesting to note that our results suggest USP36 as a novel biomarker for treatment of breast cancer. Conclusion Our study revealed the possibility that inhibiting USP36 combined with tamoxifen could provide a potential therapy for breast cancer.

Abstract Background Pilomatricoma (PM) is a cutaneous benign neoplasm derived from the hair matrix. It clinically presents as a solitary and firm nodule overlying normal epidermis and is usually not easy to be noticed at early stage. Nevertheless, when special bullous lesion occurs in a short time or even ulcerates, preoperative diagnosis by a dermatologist is often challenging especially when the pediatric patients refuse biopsy. Case presentation We present six bullous PM cases and particularly conduct correlation analysis on the dermotoscopy and histopathology detection data. The basic information, medical history, symptoms and lesion morphology results of the patients were also provided. We found that the incidence of bullous PM was higher in females than in males, and most patients were adolescents and the predilection location seem to be consistent in the vaccine injection site. The dermatoscopic features of bullous PM reported were luminous yellow structure below, with gray-blue homogeneous areas and branched capillary. The histological features were consistent with PM, and evident epidermis bullae were above the tumor with extraordinary dilation of lymphangion in the upper dermis. The patients described in this study were Chinese patients in Han population included 4 females and 2 males, coincidentally, they are almost teen-age, respectively are 5,11,17,19,21,22 year-old. Conclusions This study reported and analyzed the dermotoscopy and clinical characteristics of bullous PM, dermotoscopy may guide as a rapid and reliable technique in bullous PM diagnosis.

Apostichopus japonicus (A. japonicus) is known as one of the most popular varieties in aquaculture due to its rich nourishment and potential medicinal value. In recent years, with the abnormal change of marine environment and global climate, new challenges are arising continually in the culture process of A. japonicus. Intestinal flora plays an important role in host health and growth, and a lot of studies confirmed the close relationship between the composition change of intestinal flora and the growth of sea cucumbers. In this study, polypeptidin belonging to bacteriocins, was as an additive ingredient in sea cucumber feed. The growth of A. japonicus increased significantly after polypeptidin feeding, and the growth rate reached the highest with 1 % polypeptidin especially. Subsequently 16 S rRNA of gut microbiota from 1 % polypeptidin group was sequenced and analyzed for richness and diversity index, but the results showed that polypeptidin supplement did not markedly change the diversity and main composition of gut microbiota compared to the control. So the further analyze was performed by dependent t-test for paired samples, and found that the abundance of only a few of species at the class level changed significantly, including Alphaproteobacteria, Desulfotomaculia and Chlamydiae, but many species at the genus level changed significantly. Some important metabolic pathways were identified through function prediction, like TCA cycle, streptomycin biosynthesis and phenylalanine metabolism. At this basis, the analysis combined with the genuses and the metabolic pathways demonstrated that polypeptidin might regulate the richness of some bacteria, which could be involved in enzyme degradation, antibacterial substances secreting or underlying pathogenic tendency of gut microbiota ecosystem. These fine-tunings instead of main community structure change were probably more beneficial to the intestinal flora health, and finally contributed to the growth of A. japonicus. Thus, polypeptidin serves as a mild, non-stimulating feed supplement for A. japonicus, and has good prospects for application.

Potatoes are a globally important crop with high nutritional value. Different potato varieties display notable variations in color, texture, and nutrient composition. However, the influence of cooking on tuber color, texture, and metabolites has not been comprehensively explored. This study evaluated the color and texture of five potato varieties before and after cooking. Cooking significantly altered tuber color, decreased hardness and adhesiveness, and increased springiness, particularly after steaming. The metabolomic analysis of Zhongshu 49 (ZS49) and Shishu 3 (SH3) tubers was conducted using gas chromatography–mass spectrometry (GC-MS) and ultra-high performance liquid chromatography (UHPLC)-MS/MS. GC-MS identified 122 volatile metabolites, with 42 significantly varying between cooking treatments, while UHPLC-MS/MS detected 755 nonvolatile metabolites, 445 of which showed significant differences. Compared to ZS49, SH3 exhibited a marked increase in umami- and flavor-related metabolites, especially after cooking. This study provides new insights into how cooking affects the quality, texture, and metabolite profiles of potato tubers.

Abstract Age is a significant contributing factor to the occurrence and progression of cardiovascular disease (CVD). Pharmacological treatment can effectively alleviate CVD symptoms caused by aging. However, 90% of the drugs have failed in clinics because of the loss of drug effects or the occurrence of the side effects. One of the reasons is the disparity between animal models used and the actual physiological levels in humans. Therefore, we integrated multiple datasets from single‐cell and bulk‐seq RNA‐sequencing data in rats, monkeys, and humans to identify genes and pathways with consistent/differential expression patterns across these three species. An approach called “Cross‐species signaling pathway analysis” was developed to select suitable animal models for drug screening. The effectiveness of this method was validated through the analysis of the pharmacological predictions of four known anti‐vascular aging drugs used in animal/clinical experiments. The effectiveness of drugs was consistently observed between the models and clinics when they targeted pathways with the same trend in our analysis. However, drugs might have exhibited adverse effects if they targeted pathways with opposite trends between the models and the clinics. Additionally, through our approach, we discovered four targets for anti‐vascular aging drugs, which were consistent with their pharmaceutical effects in literatures, showing the value of this approach. In the end, software was established to facilitate the use of “Cross‐species signaling pathway analysis.” In sum, our study suggests utilizing bioinformatics analysis based on disease characteristics can help in choosing more appropriate animal models.

Phosphate-solubilizing bacteria (PSB) enhance plant phosphorus utilization through their ability to dissolve phosphorus. To address the low utilization of nitrogen, phosphorus, potassium, zinc, and selenium by tea plants in acidic, selenium-rich soils, the study aimed to investigate the impact of exogenous PSB on soil nutrients and the absorption of zinc and selenium by tea plants. Following the inoculation of potted Longjing and Huangjinya varieties with exogenous phosphorus-solubilizing bacteria, we determined the concentrations of AN, AP, AK, Zn, and Se in their rhizosphere soil, in addition to the Zn and Se contents in their aboveground and belowground parts. The results show that after respective treatment with the three PSB, the concentration of available P in the tea plant rhizosphere soil significantly increased, with PMS08 having the most pronounced effect.After the same treatment, In the rhizosphere soil of Longjing tea plants, the AN content increased by 26.47%, 18.41%, and 7.51%, respectively, relative to the control, while the AK content decreased in the rhizosphere soil of Huangjinya tea plants. Inoculation with the three PSB resulted in a greater content of available Se in both the aboveground and belowground parts of the two tea plants. After inoculation with PMS20, the available Zn content of the belowground parts of Longjing and Huangjinya tea plants respectively decreased by 13.42% and 15.69% in comparison with the control. Additionally, after inoculating Longjing tea plants with PSt09 and Huangjinya tea plants with PMS08, the content of available Zn in their belowground parts significantly decreased by 9.22% and 35.74%, respectively. Evidently, the inoculation with the three phosphorus-solubilizing bacteria is beneficial for the uptake of available P by tea plants, promoting the utilization and accumulation of available Se. However, the content of AN or AK in rhizosphere soil varies between different tea plant varieties inoculated with the same kind of phosphorus-solubilizing bacteria. Moreover, the content of available Zn in tea plants also differs, highlighting the need to further investigate the differential effects of phosphorus-solubilizing bacteria on different plant varieties.

Aircraft object detection in remote sensing images is a challenging task, especially for small objects, complex backgrounds, and aircraft objects in flight. Due to the lack of contextual relationship between the aircraft object and the surrounding background during flight and the small number of pixels in the object itself, the use of regular rectangular convolution for feature extraction results in many background pixels being sampled. This article proposes YOLO-FRS for aircraft object in flight detection, which uses a new module based on deformable convolution (DCN), the feature response separation deformable convolution (FRS-DCN) module. The FRS-DCN module adds semantic segmentation supervision on the basis of stacked DCNs, so that the background and object in the input of DCNs in this module are separated as much as possible. We design a soft label annotation and loss calculation method for semantic segmentation supervision. In addition, we propose a flight-state aircraft object dataset that includes multiple backgrounds and cloud interference. YOLO-FRS was tested on the proposed dataset, and the results showed that the FRS-DCN module improved the performance of aircraft object detection. Compared with multiple mainstream deep convolution models, the YOLO-FRS model also exhibits competitive performance.

A novel DC traction power supply system suitable for energy feeding and de-icing is proposed in this paper for an urban rail transit catenary on the basis of the full bridge submodule (FBSM) modular multilevel converter (MMC). The FBSM-MMC is a novel type of voltage source converter (VSC) and can directly control the output DC voltage and conduct bipolar currents, thus flexibly controlling the power flow of the urban rail transit catenary. The proposed topology can overcome the inherent disadvantages of the output voltage drop in the diode rectifier units, increase the power supply distance and reduce the number of traction substations. The flexible DC technology can coordinate multiple FBSM-MMCs in a wide area and jointly complete the bidirectional control of catenary power flow during the operation of the electric locomotive, so as to realize the local consumption and optimal utilization of the recovered braking energy of the train. In addition, the FBSM-MMCs can also adjust the output current when the locomotive is out of service to prevent the catenary from icing in winter. The working modes of the proposed topology are illustrated in detail and the control strategy is specially designed for normal locomotive operations and catenary de-icing. Simulation cases conducted by PSCAD/EMTDC validate the proposed topology and its control strategy.

Abstract Background To determine the differences in the microbial communities in the Camellia sinensis L. hairy root, lateral root, and main root rhizospheres in Anping Village, Kaiyang County, Guizhou Province, the community structure, diversity, and main dominant species of bacteria and fungi in different parts of the soil were analyzed by ITS and 16S sequencing. Results In the rhizosphere soil of the main root, lateral root, and hairy root of Camellia sinensis L., there were significant differences in the diversity and richness of the bacterial and fungal communities. The bacterial diversity was the highest and the fungal richness was the lowest in the rhizosphere soil of the main root. In the bacterial community, Proteobacteria, Actinobacteria, Acidobacteria, Chloroflexi, and Gemmatimonadetes were the common dominant bacteria. Rhodospirillaceae, Bradyrhizobiaceae, Hyphomicrobiaceae, Solibacteraceae, and Koribacteraceae were the common dominant bacteria in the rhizosphere soil of different parts of the root system, but the relative abundance of bacteria in different parts of the rhizosphere soil varied greatly. The dominant groups of fungal communities in different parts of the rhizosphere soil were Basidiomycota, Ascomycota, Mortierellomycota, and Sebacinaceae. The structure of the fungal community is similar in different parts. Conclusions Compared with the different parts of the hairy root, lateral root, and main root of rhizosphere soil of Camellia sinensis L. in Anping village, it was found that the abundance of fungal community decreased with the increase of bacterial community abundance, and there were significant differences in bacterial community diversity and structure. However, the fungal community maintained stability among different parts.

Abstract In order to further explore the effects of soil mercury pollution on soil microbial diversity and community structure, soil samples were randomly collected from 2 m, 20 m, 30 m, 500 m and 650 m periphery of Wanshan mining area, as 5 different treatments. Each treatment had 4 replicates. Soil microbial DNA was extracted from 20 soil samples, and then high-throughput sequencing technology was used to analyse the structure and distribution of bacterial and fungal communities. The results showed that the number of bacterial and fungal communities in T0–T30 treatments was significantly larger than that in T500–T650 treatments at order, family and genus level. Whatever, the number of uniquely distributed bacterial and fungal communities among 4 replicates soil samples was quite different at order, family and genus level. The results of the effect on the microbial community structure showed that there were both the same dominant bacterial and fungal communities, and the different dominant bacterial and fungal communities at any classification level, moreover, the number of same dominant bacterial and fungal communities was larger than that of different dominant bacterial and fungal communities. The results of relationship between soil environment factors and bacterial and fungal community structure showed that distance (Hg2+), EC and pH had a high correlation with community structure, especially the distance factor, that is, the content of mercury in soil had the highest effects on community structure. The internal heterogeneity of soil caused significant differences in bacterial and fungal community structure, and the emergence of dominant bacterial and fungal communities was a manifestation of better adaptability to long-term mercury stress and other stresses in soil, which will provide a scientific reference for further exploring the mechanism of mercury enrichment between microorganisms and plants.

Objectives: This study aimed to determine the epidemiological and genetic features of human metapneumovirus (HMPV) infection in children in southern China, and the effect of meteorological factors on infection. Methods: 14,817 children (≤14 years) with acute respiratory tract infections from 2010 to 2019 were examined for HMPV and other respiratory viruses by real-time quantitative polymerase chain reaction. Full-length F gene of 54 positive samples were sequenced and subjected to phylogenetic analysis. The correlation between the HMPV-positive rate and meteorological factors was analyzed by linear regression analysis. Results: HMPV was detected in 524 (3.5%) children, who were mostly younger than 1 year. The seasonal peak of HMPV prevalence mainly occurred in spring. Respiratory syncytial virus was the most common virus coinfected with HMPV (5.3%). Phylogenetic analysis revealed that the sequenced HMPV strains belonged to four sublineages, including A2b (1.9%), A2c (31.5%), B1 (50.0%), and B2 (16.7%). After adjusting for all meteorological factors, sunshine duration was inversely correlated with the HMPV-positive rate. Conclusion: HMPV is an important respiratory pathogen that causes acute respiratory tract infections in children in southern China, particularly in children ≤5 years old. The prevalence peak of HMPV in this area appeared in spring, and the predominant subtype was B1. Meteorological factors, especially long sunshine duration, might decrease the HMPV prevalence.

Abstract Background The Hippo pathway is crucial in organ size control and tumorigenesis. Dysregulation of the Hippo/YAP axis is commonly observed in gastric cancer, while effective therapeutic targets for the Hippo/YAP axis are lacking. Identification of reliable drug targets and the underlying mechanisms that could inhibit the activity of the Hippo/YAP axis and gastric cancer progression is urgently needed. Methods We used several gastric cancer cell lines and xenograft models and performed immunoblotting, qPCR, and in vivo studies to investigate the function of CXCR7 in gastric cancer progression. Results In our current study, we demonstrate that the membrane receptor CXCR7 (C-X-C chemokine receptor 7) is an important modulator of the Hippo/YAP axis. The activation of CXCR7 could stimulate gastric cancer cell progression through the Hippo/YAP axis in vitro and in vivo, while pharmaceutical inhibition of CXCR7 via ACT-1004–1239 could block tumorigenesis in gastric cancer. Molecular studies revealed that the activation of CXCR7 could dephosphorylate YAP and facilitate YAP nuclear accumulation and transcriptional activation in gastric cancer. CXCR7 functions via G-protein Gαq/11 and Rho GTPase to activate YAP activity. Interestingly, ChIP assays showed that YAP could bind to the promoter region of CXCR7 and facilitate its gene transcription, which indicates that CXCR7 is both the upstream signalling and downstream target of the Hippo/YAP axis in gastric cancer. Conclusion In general, we identified a novel positive feedback loop between CXCR7 and the Hippo/YAP axis, and blockade of CXCR7 could be a plausible strategy for gastric cancer.

Abstract Aims and objectives To construct a risk assessment scale for medical adhesive‐related skin injuries (MARSI) at the peripherally inserted central catheter (PICC) insertion site in oncology patients and test its reliability and validity. Design The STARD 2015 statement guided this study. Methods Literature research and a modified Delphi method were adopted in this study. A total of 31 experts participated in two rounds of consultation to build the assessment scale. A convenient sampling method was used to select 195 oncology patients at the PICC clinic from January to June 2022. Inter‐rater reliability was used to test the reliability of the scale. Validity was evaluated using the content validity index (CVI) and predictive validity. Results After the two rounds of consultation, the assessment scale with five dimensions and 13 primary entries and 36 secondary entries was developed, and the expert authority coefficients for both were 0.90. The inter‐rater reliability was 0.968. The CVIs of the items ranged from 0.83 to 1.00. The area under the subject's work characteristic curve was 0.757, and the sensitivity and specificity of the scale were 80.0% and 65.6%, respectively, at a cutoff score of 15.5.

Examining the spatiotemporal changes of territorial space is crucial for addressing the conflict between economic-social development and the natural environment and achieving optimal territorial space utilization. However, there is a research gap regarding the spatial characteristics and optimization in the mountain–flatland area. To address this gap, this paper focuses on the urban agglomeration in Central Yunnan (UACY) as a representative mountain-flatland area. A mountain–flatland classification model was established. Based on the evaluation of production– living– ecological functions, the economic models were introduced to measure the balance degree, and further researched the spatiotemporal evolution and coupling coordination characteristics by spatial analysis from 2010 to 2020. The findings indicate the following: (1) The study area exhibited distinct mountain-flatland differentiation, with ''western mountainous counties (MCs)/semi-mountainous and semi-flatland counties (SMSFCs), central flatland counties (FCs), and eastern SMSFCs''. production function (PF) primarily formed a cluster in the central-northeastern areas of FCs and of SMSFCs, living function (LF) was highly clustered in the central areas of FCs, remained stable, and ecological function (EF) was significantly clustered in the northwestern regions of MCs and of SMSFCs, significantly enhanced in the northeast. (2) The imbalance degree followed the order LF > PF > EF, showing a decreasing trend primarily driven by intra-group imbalances within FCs, SMSFCs, and MCs. The coordinate areas were mainly concentrated in central FCs, and the dysfunctional areas was largely located in MCs and SMSFCs, the degree was improved, especially in northwestern and southeastern MCs and SMSFCs. (3) The study area fell into 18 functional areas, optimized into 13 areas, with recommendations for differentiated development control paths to achieve an optimization of PLEFs. These results provide theoretical references for promoting sustainable utilization of territorial resources and facilitating high-quality regional development in UACY and other parts of the country.

[Objective] The simulation effects and land use prediction accuracy of three land use transition models (LogReg, MLP, and SimWeight) for multi-land change analysis were compared and analyzed in order to provide a reference for land space planning, soil and water conservation, and ecological restoration in the Yunnan-Guizhou Plateau. [Methods] Three models were used to simulate land use changes for Kunming City from 2000 to 2020. Various methods such as ROC curve, AUC coefficient, and kappa coefficient were used to evaluate model performance. [Results] The simulations of change trends for most land use classes by MLP and SimWeight were better than the simulations by LogReg, especially with regard to the change analysis of unused land where the AUC values for MLP and SimWeight were greater than 0.9. The kappa values for LogReg, MLP, and SimWeight were 0.906 6, 0.904 1, and 0.925 3, respectively. The overall prediction results for the three models were similar, and SimWeight was slightly better than the two other models. [Conclusion] For LUCC modeling of Kunming City, SimWeight is recommended when the user desires higher accuracy, MLP is recommended when the user cares more about calculation speed, and LogReg is recommended when the user wants to further analyze the relationship between driving factors and land use changes.

BackgroundSeveral observational studies have suggested a potential relationship between gut microbiome and psoriatic arthritis (PsA). However, the causality of this relationship still remains unclear. We aim to explore if the specific gut microbiome is causally associated with PsA at the genetic level and offer valuable insights into the etiology of PsA.MethodsIn this study, we employed a bidirectional two-sample Mendelian randomization (MR) analysis to investigate the causal effects of the gut microbiome on PsA. Publicly accessible genome-wide association study summary data of gut microbiome were obtained from the MiBioGen consortium (n = 14,306), while the summary statistics of psoriatic arthropathies were sourced from the FinnGen consortium R8 release data (2,776 cases and 221,323 controls). The primary analytical method employed was inverse variance weighted (IVW), complemented by supplementary methods including MR-Egger, weighted median, weighted mode, maximum likelihood, MR-PRESSO, and cML-MA. Reverse MR analysis was performed on the bacteria that were found to be causally associated with PsA in forward MR analysis. Cochran’s IVW Q statistic was utilized to assess the heterogeneity of instrumental variables among the selected single nucleotide polymorphisms.ResultsIVW estimates revealed that Ruminococcaceae_UCG-002 (odds ratio (OR) = 0.792, 95% confidence interval (CI), 0.643–0.977, p = 0.029) exhibited a protective effect on PsA. Conversely, Blautia (OR = 1.362, 95% CI, 1.008–1.842, p = 0.044), Eubacterium_fissicatena_group (OR = 1.28, 95% CI, 1.075–1.524, p = 0.006), and Methanobrevibacter (OR = 1.31, 95% CI, 1.059–1.621, p = 0.013) showed a positive correlation with the risk of PsA. No significant heterogeneity, horizontal pleiotropy, or outliers were observed, and the results of the MR analysis remained unaffected by any single nucleotide polymorphisms. According to the results of reverse MR analysis, no significant causal effect of PsA was found on gut microbiome.ConclusionThis study establishes for the first time a causal relationship between the gut microbiome and PsA, providing potential valuable strategies for the prevention and treatment of PsA. Further randomized controlled trials are urgently warranted to support the targeted protective mechanisms of probiotics on PsA.

Bronchopulmonary dysplasia (BPD) is a common complication in preterm infants, leading to chronic respiratory disease. There has been an improvement in perinatal care, but many infants still suffer from impaired branching morphogenesis, alveolarization, and pulmonary capillary formation, causing lung function impairments and BPD. There is an increased risk of respiratory infections, pulmonary hypertension, and neurodevelopmental delays in infants with BPD, all of which can lead to long-term morbidity and mortality. Unfortunately, treatment options for Bronchopulmonary dysplasia are limited. A growing body of evidence indicates that mesenchymal stromal/stem cells (MSCs) can treat various lung diseases in regenerative medicine. MSCs are multipotent cells that can differentiate into multiple cell types, including lung cells, and possess immunomodulatory, anti-inflammatory, antioxidative stress, and regenerative properties. MSCs are regulated by mitochondrial function, as well as oxidant stress responses. Maintaining mitochondrial homeostasis will likely be key for MSCs to stimulate proper lung development and regeneration in Bronchopulmonary dysplasia. In recent years, MSCs have demonstrated promising results in treating and preventing bronchopulmonary dysplasia. Studies have shown that MSC therapy can reduce inflammation, mitochondrial impairment, lung injury, and fibrosis. In light of this, MSCs have emerged as a potential therapeutic option for treating Bronchopulmonary dysplasia. The article explores the role of MSCs in lung development and disease, summarizes MSC therapy’s effectiveness in treating Bronchopulmonary dysplasia, and delves into the mechanisms behind this treatment.

Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries. Fully convolutional neural networks (FCNs) excel at pixel-wise fine segmentation, making them less susceptible to heterogeneous content, but they require fully annotated dense image patches, which may not be readily available in real-world scenarios. This paper proposes an object-based semi-supervised spatial attention residual UNet (OS-ARU) model. First, multiscale segmentation is performed to obtain segments from a remote sensing image, and segments containing sample points are assigned the categories of the corresponding points, which are used to train the model. Then, the trained model predicts class probabilities for all segments. Each unlabeled segment’s probability distribution is compared against those of labeled segments for similarity matching under a threshold constraint. Through label propagation, pseudo-labels are assigned to unlabeled segments exhibiting high similarity to labeled ones. Finally, the model is retrained using the augmented training set incorporating the pseudo-labeled segments. Comprehensive experiments on aerial image benchmarks for Vaihingen and Potsdam demonstrate that the proposed OS-ARU achieves higher classification accuracy than state-of-the-art models, including OCNN, 2OCNN, and standard OS-U, reaching an overall accuracy (OA) of 87.83% and 86.71%, respectively. The performance improvements over the baseline methods are statistically significant according to the Wilcoxon Signed-Rank Test. Despite using significantly fewer sparse annotations, this semi-supervised approach still achieves comparable accuracy to the same model under full supervision. The proposed method thus makes a step forward in substantially alleviating the heavy sampling burden of FCNs (densely sampled deep learning models) to effectively handle the complex issue of land cover information identification and classification.

The band gap is a key parameter in semiconductor materials that is essential for advancing optoelectronic device development. Accurately predicting band gaps of materials at low cost is a significant challenge in materials science. Although many machine learning (ML) models for band gap prediction already exist, they often suffer from low interpretability and lack theoretical support from a physical perspective. In this study, we address these challenges by using a combination of traditional ML algorithms and the ‘white-box’ sure independence screening and sparsifying operator (SISSO) approach. Specifically, we enhance the interpretability and accuracy of band gap predictions for binary semiconductors by integrating the importance rankings of support vector regression (SVR), random forests (RF), and gradient boosting decision trees (GBDT) with SISSO models. Our model uses only the intrinsic features of the constituent elements and their band gaps calculated using the Perdew–Burke–Ernzerhof method, significantly reducing computational demands. We have applied our model to predict the band gaps of 1208 theoretically stable binary compounds. Importantly, the model highlights the critical role of electronegativity in determining material band gaps. This insight not only enriches our understanding of the physical principles underlying band gap prediction but also underscores the potential of our approach in guiding the synthesis of new and valuable semiconductor materials.

Accurate crop distribution mapping is required for crop yield prediction and field management. Due to rapid progress in remote sensing technology, fine spatial resolution (FSR) remotely sensed imagery now offers great opportunities for mapping crop types in great detail. However, within-class variance can hamper attempts to discriminate crop classes at fine resolutions. Multi-temporal FSR remotely sensed imagery provides a means of increasing crop classification from FSR imagery, although current methods do not exploit the available information fully. In this research, a novel Temporal Sequence Object-based Convolutional Neural Network (TS-OCNN) was proposed to classify agricultural crop type from FSR image time-series. An object-based CNN (OCNN) model was adopted in the TS-OCNN to classify images at the object level (i.e., segmented objects or crop parcels), thus, maintaining the precise boundary information of crop parcels. The combination of image time-series was first utilized as the input to the OCNN model to produce an ‘original’ or baseline classification. Then the single-date images were fed automatically into the deep learning model scene-by-scene in order of image acquisition date to increase successively the crop classification accuracy. By doing so, the joint information in the FSR multi-temporal observations and the unique individual information from the single-date images were exploited comprehensively for crop classification. The effectiveness of the proposed approach was investigated using multitemporal SAR and optical imagery, respectively, over two heterogeneous agricultural areas. The experimental results demonstrated that the newly proposed TS-OCNN approach consistently increased crop classification accuracy, and achieved the greatest accuracies (82.68% and 87.40%) in comparison with state-of-the-art benchmark methods, including the object-based CNN (OCNN) (81.63% and 85.88%), object-based image analysis (OBIA) (78.21% and 84.83%), and standard pixel-wise CNN (79.18% and 82.90%). The proposed approach is the first known attempt to explore simultaneously the joint information from image time-series with the unique information from single-date images for crop classification using a deep learning framework. The TS-OCNN, therefore, represents a new approach for agricultural landscape classification from multi-temporal FSR imagery. Besides, it is readily generalizable to other landscapes (e.g., forest landscapes), with a wide application prospect.

A multi-energy (ME) system can coordinate local energy sources and users and optimize the supply of various energy forms, to maximize the comprehensive utilization efficiency of energy. As the scale of the multi-energy system becomes more extensive and the physical connections of different energy forms become diversified, the coupling of various forms of energy becomes closer. Such development has brought significant challenges to safe operations and effective regulation of the system and equipment. However, no efficient and easy-to-use dynamic simulation method is available for multi-energy systems. It has become an urgent problem how to fully use the existing rich models and algorithms of conventional energy systems to construct the dynamic simulations of multi-energy systems. Based on the multi-energy system’s structure, components, and model characteristics, this paper studies the mechanism of cross-energy-form dynamic coupling, proposes the critical techniques of multi-energy hybrid simulation and verifies the effectiveness and accuracy of the methods through case tests.

Sucrose synthase (SUS) and sucrose phosphate synthase (SPS) are essential in plant sucrose metabolism. The potato is an important crop worldwide, but systematic analyses of the StSUS and StSPS gene families in potatoes are still lacking. Ten sucrose metabolism-related genes were identified in this study. The SUSs and SPSs could each be split into three subgroups through phylogenetic analysis. StSUSIc was the most highly expressed gene in different developmental tissues. Ka/Ks analysis showed that StSUSIb and StSUSIc were subjected to more-significant homozygous selection pressure. Our cis-acting element analysis of the StSUS and StSPS promoter sequences showed four elements: defense- and stress-responsive, hormone-responsive, light-responsive, and transcription factor elements. The expression of StSUS and StSPS genes was found to be regulated by circadian rhythm. In the treatments of 1% to 5% sucrose, glucose, and fructose, the expression of StSUS and StSPS family genes was enhanced by sucrose, but inhibited at high-glucose and fructose concentrations. This study identified six StSUS and four StSPS genes and analyzed their gene structure, conserved motifs, chromosome position, promoter elements, phylogenetic tree, and tissue-specific expression patterns. Our results will motivate more research into the biological process underlying the genes of sucrose metabolism in potatoes.

Axial piston pumps are critical components of hydraulic systems due to their compact design and high volumetric efficiency, making them widely used. However, they are prone to failure in harsh environments characterized by high pressure and heavy loads over extended periods. Therefore, detecting abnormal behavior in axial piston pumps is of significant importance. Traditional detection methods often rely on vibration signals from the pump casings; however, these signals are susceptible to external environmental interference. In contrast, pressure signals exhibit greater stability. In this study, we propose a novel anomaly detection method for axial piston pumps, referred to as DTW-RCK-IF, which combines dynamic time warping (DTW) for data segmentation, a random convolutional kernel (RCK) for feature extraction, and isolation forest (IF) for anomaly detection using pressure signals. The model is trained using normal operating data to enable the effective detection of abnormal states. First, the DTW algorithm is employed to segment the raw data, ensuring a high degree of similarity between the segmented data. Next, the random convolutional kernel approach is used in a convolutional neural network for feature extraction, resulting in features that are representative of normal operating conditions. Finally, the isolation forest algorithm calculates the anomaly scores for anomaly detection. Experimental simulations on axial piston pumps demonstrate that, compared with vibration signals, the DTW-RCK-IF approach using pressure signals yields superior results in detecting abnormal data, with an average F1 score of 98.79% and a good fault warning effect. Validation using the publicly available CWRU-bearing and XJTU-SY-bearing full-life datasets further confirms the effectiveness of this method, with average F1 scores of 99.35% and 99.73%, respectively, highlighting its broad applicability and potential for widespread use.

Spectrum-based fault localization (SBFL), a widely recognized technique in automated fault localization, has limited effectiveness due to its disregard for the internal information of the program under test suites. To overcome this limitation, a novel TrustRank-based fault localization (TRFL) technique is introduced. TRFL enhances traditional SBFL by incorporating internal data dependencies of the program under the test suite, thereby providing a more comprehensive analysis. It constructs a node-weighted program execution network and employs the TrustRank algorithm to analyze network centrality and re-rank program entities based on their suspiciousness. Furthermore, a bidirectional TrustRank algorithm (Bi-TRFL) is extended that takes into account the influence relationship between network nodes for more accurate fault localization. When applied to large-scale datasets with real faults, such as Defects4J, TRFL, and Bi-TRFL, it significantly outperforms traditional SBFL methods in fault localization. They demonstrate up to 40% and 13% improvement in Top-1 and Top-5 rankings, respectively, proving their robustness and efficiency with minimal sensitivity to related parameters.

This paper develops a high-precision time synchronization control method based on IEEE 1588 protocol. A novel control technique based on the hybridization of the improved wolf colony algorithm and cuckoo search algorithm (hybrid IWCA-CS) is used to dynamically adjust the fuzzy PID controller parameters to reduce the error of the master-slave clock. To make better performance of the conventional wolf colony algorithm (WCA), it is improved by using the Bloch coordinate encoding to determine the initial population and adding inertial weights to balance the search ability of the algorithm. The proposed hybrid IWCA-CS combines the best attributes of WCA’s global search and local search of cuckoo search (CS). In optimizing benchmark function experiments, the proposed hybrid IWCA-CS presents a superior performance compared to other soft computing methods. Furthermore, the hybrid IWCA-CS optimized controllers with different structures are presented to demonstrate its dynamic response characteristics. The results show that IWCA-CS tuned fuzzy PID controller shows the best dynamic performance as compared to PID/PI controllers. And the IWCA-CS based fuzzy PID controller is less sensitive to step load disturbance and parameter changes. Finally, the effectiveness of the proposed time synchronization control method in this paper over published methods is further confirmed by employing in power information collection system (PICS).

The highly dynamic nature of agro-ecosystems in space and time usually leads to high intra-class variance and low inter-class separability in the fine spatial resolution (FSR) remotely sensed imagery. This makes traditional classifiers essentially relying on spectral information for crop mapping from FSR imagery an extremely challenging task. To mine effectively the rich spectral and spatial information in FSR imagery, this paper proposed a Scale Sequence Object-based Convolutional Neural Network (SS-OCNN) that classifies images at the object level by taking segmented objects (crop parcels) as basic units of analysis, thus, ensuring that the boundaries between crop parcels are delineated precisely. These segmented objects were subsequently classified using a CNN model integrated with an automatically generated scale sequence of input patch sizes. This scale sequence can fuse effectively the features learned at different scales by transforming progressively the information extracted at small scales to larger scales. The effectiveness of the SS-OCNN was investigated using two heterogeneous agricultural areas with FSR SAR and optical imagery, respectively. Experimental results revealed that the SS-OCNN consistently achieved the most accurate classification results. The SS-OCNN, thus, provides a new paradigm for crop classification over heterogeneous areas using FSR imagery, and has a wide application prospect.

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, and senescence. The main goal of this study was to estimate maize GMC at maturity through CCC retrieved from multi-spectral UAV images using a PROSAIL model inversion and compare its performance with GMC estimation through simple vegetation indices (VIs) approaches. This study was conducted in two separate maize fields of 50.3 and 56 ha located in Hailun County, Heilongjiang Province, China. Each of the fields was cultivated with two maize varieties. One field was used as reference data for constructing the model, and the other field was applied to validate. The leaf chlorophyll content (LCC) and leaf area index (LAI) of maize were collected at three critical stages of crop growth, and meanwhile, the GMC of maize at maturity was also obtained. During the collection of field data, a UAV flight campaign was performed to obtain multi-spectral images from two fields at three main crop growth stages. In order to calibrate and evaluate the PROSAIL model for obtaining maize CCC, crop canopy spectral reflectance was simulated using crop-specific parameters. In addition, various VIs were computed from multi-spectral images to estimate maize GMC at maturity and compare the results with CCC estimations. When the CCC-retrieved results were compared to measured data, the R2 value was 0.704, the RMSE was 34.58 μg/cm2, and the MAE was 26.27 μg/cm2. The estimation accuracy of the maize GMC based on the normalized red edge index (NDRE) was demonstrated to be the greatest among the selected VIs in both fields, with R2 values of 0.6 and 0.619, respectively. Although the VIs of UAV inversion GMC accuracy are lower than those of CCC, their rapid acquisition, high spatial and temporal resolution, suitability for empirical models, and capture of growth differences within the field are still helpful techniques for field-scale crop monitoring. We found that maize varieties are the main reason for the maturity variation of maize under the same geographical and environmental conditions. The method described in this article enables precision agriculture based on UAV remote sensing by giving growers a spatial reference for crop maturity at the field scale.

This study delves into the intricate usage and interpretation issues of the Chinese term “體” (tǐ) in Xuanzang’s translation of the Abhidharmakośa (AKBh[X]) by providing a Sanskrit-Chinese comparative investigation. Xuanzang’s translations are pivotal in understanding certain Abhidharma scriptures, as some of them are the sole complete versions available. This study focuses on the term “體” in AKBh[X], evaluating its usage in relation to 16 corresponding Sanskrit equivalents and the instances where Xuanzang introduced “體” without a Sanskrit equivalent. The analysis uncovers translation errors, potential misinterpretations, and the lack of clarity in certain contexts, emphasizing the need for readers to be cautious and consult additional sources for a comprehensive understanding of his translations.

The vibration signal of a pump is often used for analysis in the study of hydraulic-pump fault diagnosis methods. In this study, for the analysis, sound signals were used, which can be used to acquire data in a non-contact manner to expand the use scenarios of hydraulic-pump fault-diagnosis methods. First, the original data are denoised using complete ensemble empirical mode decomposition with adaptive noise and the minimum redundancy maximum relevance algorithm. Second, the noise-reduced data are plotted as mean spectrogram bar graphs, and the datasets are divided. Third, the training set graphs are input into the ResNet-50 network to train the base model for fault diagnosis. Fourth, all the layers of the base model are frozen, except for the fully connected and softmax layers, and the support set graphs are used to train the base model through transfer learning. Finally, a fault diagnosis model is obtained. The model is tested using data from two test pumps, resulting in accuracies of 86.1% and 90.8% and providing evidence for the effectiveness of the proposed method for diagnosing faults in hydraulic plunger pumps.

Swarm intelligence algorithms have been widely used in the dimensional reduction of hyperspectral remote sensing imagery. The ant colony algorithm (ACA), the clone selection algorithm (CSA), particle swarm optimization (PSO), and the genetic algorithm (GA) are the most representative swarm intelligence algorithms and have often been used as subset generation procedures in the selection of optimal band subsets. However, studies on their comparative performance for band selection have been rare. For this paper, we employed ACA, CSA, PSO, GA, and a typical greedy algorithm (namely, sequential floating forward selection (SFFS)) as subset generation procedures and used the average Jeffreys–Matusita distance (JM) as the objective function. In this way, the band selection algorithm based on ACA (BS-ACA), band selection algorithm based on CSA (BS-CSA), band selection algorithm based on PSO (BS-PSO), band selection algorithm based on GA (BS-GA), and band selection algorithm based on SFFS (BS-SFFS) were tested and evaluated using two public datasets (the Indian Pines and Pavia University datasets). To evaluate the algorithms’ performance, the overall classification accuracy of maximum likelihood classifier and the average runtimes were calculated for band subsets of different sizes and were compared. The results show that the band subset selected by BS-PSO provides higher overall classification accuracy than the others and that its runtime is approximately equal to BS-GA’s, higher than those of BS-ACA, BS-CSA, and BS-SFFS. However, the premature characteristic of BS-ACA makes it unacceptable, and its average JM is lower than those of other algorithms. Furthermore, BS-PSO converged in 500 generations, whereas the other three swarm-intelligence based algorithms either ran into local optima or took more than 500 generations to converge. BS-PSO was thus proved to be an excellent band selection method for a hyperspectral image.

The integrated energy system (IES) includes various energy forms, complex operation modes, abundant control equipment and coupling links, which brings challenges to its steady-state modeling and steady-state power flow calculation. In order to obtain the steady-state characteristics of the thermal subsystem in the IES, the topological structure of the typical IES was provided; the steady-state models of the power subsystem, the thermal subsystem, the cold subsystem and the distributed energy station were established respectively, and then the hybrid power flow model was established, and the Newton-Raphson algorithm was used to solve the hybrid power flow; the influence of the key technical parameters (including the water supply temperature of the source node, the water outlet temperature of the load node, the length and diameter of the heat network pipe, and the heat load power) on the steady-state power flow of the IES was analyzed. The analysis results can support the planning, design and optimal operation of the IES.

Plant mitochondrial phosphate transporters regulate phosphate transport and ATP synthesis. Determining whether they function in abiotic stress response process would shed light on their response to salt stress. We used the CRISPR/Cas9 gene-editing system to mutagenize two mitochondrial phosphate transporters, OsMPT3;1 and OsMPT3;2, to investigate their regulatory roles under salt stress. Two cas9 (CRISPR-associated protein 9)-free homozygous mutants, mpt33 and mpt30, were confirmed to be stable. Both OsMPT3;1 and OsMPT3;2 were markedly induced by salt stress, and their mutagenesis strongly inhibited growth and development, especially under salt stress. Mutagenesis sharply reduced the accumulation of ATP, phosphate, calcium, soluble sugar, and proline and increased osmotic potential, malondialdehyde, and Na+/K+ ratio under salt stress. Both mutants demonstrate normal growth and development in the presence of ATP, revealing high sensitivity to exogenous ATP under salt stress. The mutants showed lowered rates of Na+ efflux but also of K+ and Ca2+ influx under salt stress. Mutagenesis of OsMPT3;2 altered the enrichment profiles of differentially expressed genes involved mainly in synthesis of secondary metabolites, metabolism of glycolysis, pyruvate, tricarboxylic acid cycle, in response to salt stress. The mutant displayed significant accumulation differences in 14 metabolites involved in 17 metabolic pathways, and strongly up-regulated the accumulation of glutamine, a precursor in proline synthesis, under salt stress. These findings suggest that the OsMPT3 gene modulates phosphate transport and energy supply for ATP synthesis and triggers changes in accumulation of ions and metabolites participating in osmotic regulation in rice under salt stress, thus increasing rice salt tolerance. This study demonstrates the effective application of CRISPR/Cas9 gene-editing to the investigation of plant functional genes.

Circular RNA (circRNA) is a newly discovered noncoding RNA that regulates gene transcription, binds to RNA-related proteins, and encodes protein microRNAs (miRNAs). The development of molecular biomarkers such as circRNAs holds great promise in the diagnosis and prognosis of clinical disorders. Importantly, circRNA-mediated maternal-fetus risk factors including environmental (high altitude), maternal (preeclampsia, smoking, and chorioamnionitis), placental, and fetal (preterm birth and low birth weight) factors are the early origins and likely to contribute to the occurrence and progression of developmental and pediatric cardiopulmonary disorders. Although studies of circRNAs in normal cardiopulmonary development and developmental diseases have just begun, some studies have revealed their expression patterns. Here, we provide an overview of circRNAs’ biogenesis and biological functions. Furthermore, this review aims to emphasize the importance of circRNAs in maternal-fetus risk factors. Likewise, the potential biomarker and therapeutic target of circRNAs in developmental and pediatric lung diseases are explored.

Large field circular scanning system obtains remote sensing images by rotating scanning, which is one of the technical ways to effectively expand the field of view of geostationary orbit optical remote sensing imaging system. However, due to its special rotation scanning imaging mechanism, the on-orbit imaging process of this system is affected by the coupling of detector rotation sampling, satellite platform vibration, optical system aberration and other factors, so the image degradation characteristics are very complex. In this paper, the on-orbit imaging characteristics of large field circular scanning system are first analyzed to provide a theoretical basis for the subsequent research of image restoration method. Secondly, aiming at the problems of annular geometric distortion and spatial blur of the system image, the image restoration method is proposed based on ring block and regularization methodology. Experimental results show that under the condition of detector rotation speed ω

The agricultural landscape can be interpreted at different semantic levels, such as fine low-level crop (LLC) classes (e.g., Wheat, Almond, and Alfalfa) and broad high-level crop (HLC) classes (e.g., Winter crops, Tree crops, and Forage). The LLC and HLC are hierarchically correlated with each other, but such intrinsically hierarchical relationships have been overlooked in previous crop classification studies in remote sensing. In this research, a novel Iterative Deep Learning (IDL) framework was proposed for the classification of complex agricultural landscapes using remotely sensed imagery. The IDL adopts an object-based convolutional neural network (OCNN) as the basic classifier for both the LLC and HLC classifications, which has the advantage of maintaining precise crop parcel boundaries. In IDL, the HLC classification implemented by the OCNN is conditional upon the LLC classification probabilities, whereas the HLC probabilities combined with the original imagery are, in turn, re-used as inputs to the OCNN to enhance the LLC classification. Such an iterative updating procedure forms a Markov process, where both the LLC and HLC classifications are refined and evolve collaboratively. The effectiveness of the IDL was tested on two heterogeneous agricultural fields using fine spatial resolution (FSR) SAR and optical imagery. The experimental results demonstrate that the iterative process of IDL helps to resolve contradictions within the class hierarchies. The new proposed IDL consistently increased the accuracies of both the LLC and HLC classifications with iteration, and achieved the highest accuracies for each at four iterations. The average overall accuracies were 88.4% for LLC and 91.2% for HLC, for both study sites, far greater than the accuracies of the state-of-the-art benchmarks, including the pixel-wise CNN (81.7% and 85.9%), object-based image analysis (OBIA) (84.0% and 85.8%), and OCNN (84.0% and 88.4%). To the best of our knowledge, the proposed model is the first to identify and use the relationship between the class levels in an ontological hierarchy in a remote sensing classification process. It is applied here to increase progressively the accuracy of classification at two levels for a complex agricultural landscape. As such IDL represents an entirely new paradigm for remote sensing image classification. Moreover, the promising results demonstrate the great potential of the proposed IDL with wide application prospect.

The ant colony algorithm (ACA) has been widely used for reducing the dimensionality of hyperspectral remote sensing imagery. However, the ACA suffers from problems of slow convergence and of local optima (caused by loss of population diversity). This paper proposes an improved ant colony algorithm (IMACA) based band selection algorithm (IMACA-BS), to overcome the two shortcomings of the standard ACA. For the former problem, a pre-filter is applied to improve the heuristic desirability of the ant colony system; the Pearson's similarity measurement of the degree of redundancy among the selected bands is taken as one of the terms in the heuristic function, and this further accelerates the convergence of the IMACA-BS. For the latter problem, a pseudo-random rule and an adaptive information update strategy are, respectively, introduced to increase the population diversity of the ant colony system. The effectiveness of the proposed algorithm was evaluated on three public datasets (Indian Pines, Pavia University and Botswana datasets), and compared with a series of benchmarks. Experimental results demonstrated that the IMACA-BS consistently achieved the highest overall classification accuracies and significantly outperformed other benchmarks over all of the three experiments. The proposed IMACA-BS is, therefore, recommended as an effective alternative for band selection of hyperspectral imagery.

The medium and long-term electricity trading approach considering uncertain renewable energy participation is established based on the bi-level model in this paper. The upper-level is designed to maximize the profitability of power generation companies, while the lower-level aims to maximize the social welfare. Afterwards, a hybrid algorithm combining the discrete and continuous particle swarm optimization is proposed to solve the upper-level model, and the nonlinear programming method is used to address the lower-level model. Finally, verification is performed on the improved IEEE 39-bus system, and simulation results demonstrate that the proposed approach could indeed improve the profit of renewable energy as well as increase the social welfare of power generation enterprises.

Hydraulic systems have been frequently used in deep-sea equipment because of their renowned long service life, stable and reliable performance. However, impurities contained in hydraulic oil, such as solid particles, gas and water, would jeopardize work efficiency of deep-sea hydraulic systems, and in some extreme cases even cause failure of the whole system, resulting in breakdown of crucial deep-sea equipment. Therefore, this paper aims to present a device, multifunctional oil-injection equipment (MOIE), expected to be able to purify hydraulic oil in deep-sea hydraulic systems. Its major functions are to absorb oil, remove impurities and fill oil, among which the removal of water and gas, i.e. dewatering and degassing, is of significance. Theoretical analysis shows that the paper oil filter is able to remove the solid particles in hydraulic oil, and degassing and dewatering of hydraulic oil could be achieved through vacuum filtration and vacuum heating in high-temperature and low-pressure environment. The prototype of the MOIE is designed and manufactured. The results from laboratory experiments indicate that this equipment can effectively remove solid particles, gas and water from hydraulic oil. This study ensures the stability, safety and reliability of the deep-sea hydraulic system by improving the quality of hydraulic oil.

Abstract Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in high-frequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

With the advantage of integrating distributed energy, storage and DC load with high efficiency, the DC distribution network recently attracted wide attention in the field of nearly zero energy buildings. Considering the large number of buildings and the enormous energy-saving potential, the distribution form and the revenue distribution are key factors affecting energy efficiency and operational economy. Aiming at the characteristics of AC and DC distribution topologies in nearly zero energy buildings, an energy efficiency evaluation method is proposed based on time-sequential power flow simulation, where the piecewise linear efficiency function of converters and dynamic line power loss model are established. Based on a DC distribution demonstration project, an AC power distribution system with the same boundary conditions is designed. The results show that the efficiency of DC distribution topology is about 6% higher than the AC system, while the efficiency advantage in office, hotel, commercial, educational and residential buildings in Beijing, Shenzhen and Shanghai varies from 0.6–4.96%, which shows a high correlation with the proportion of a high-power load, with Pearson correlation coefficient of 0.794. With the increase of DC load ratio and distributed power supply access capacity, the efficiency of the system will be improved accordingly. On the basis of this, a method to equalize DC distribution income in the nearly zero energy buildings power market is proposed. In addition, 15 typical scenarios of different cities were selected to evaluate efficiency and influencing factors. Finally, assessing the multi-stakeholder impact of DC retrofitting to balance the revenue generated by the DC distribution model suggests that transport costs should be raised by 8.2–13.1% to balance revenue with the equipment prices decreased by about 0.1 yuan/W.

As a hydraulic pump is the power source of a hydraulic system, predicting its remaining useful life (RUL) can effectively improve the operating efficiency of the hydraulic system and reduce the incidence of failure. This paper presents a scheme for predicting the RUL of a hydraulic pump (gear pump) through a combination of a deep convolutional autoencoder (DCAE) and a bidirectional long short-term memory (Bi-LSTM) network. The vibration data were characterized by the DCAE, and a health indicator (HI) was constructed and modeled to determine the degradation state of the gear pump. The DCAE is a typical symmetric neural network, which can effectively extract characteristics from the data by using the symmetry of the encoding network and decoding network. After processing the original vibration data segment, health indicators were entered as a label into the RUL prediction model based on the Bi-LSTM network, and model training was carried out to achieve the RUL prediction of the gear pump. To verify the validity of the methodology, a gear pump accelerated life experiment was carried out, and whole life cycle data were obtained for method validation. The results show that the constructed HI can effectively characterize the degenerative state of the gear pump, and the proposed RUL prediction method can effectively predict the degeneration trend of the gear pump.

Chronic non-communicable diseases are one of the major human health challenges in the 21st century. Fermented apple juice rich in polyphenols has a beneficial effect on the prevention of non-communicable diseases. This review illustrates that the fermentation process involved in biotransformation significantly changes the composition and content of polyphenols in cloudy apple juice related to apple varieties, fermentation types, and fermentation strains. The enzymatic transformation reaction converts polyphenols into small molecules phenolic compounds with higher biologically activity. These small molecular weight active substances could help prevent cardiovascular disease and type 2 diabetes mellitus by mediating the AMPK pathway, the Nrf2 signaling pathway, the polyol pathway, and the NF-κB pathway. Moreover, this review is intended to provide a systematic theoretical basis for researchers to further study the bioactive function of fermented apple juice for the first time.

An axial piston pump fault diagnosis algorithm based on empirical wavelet transform (EWT) and one-dimensional convolutional neural network (1D-CNN) is presented. The fault vibration signals and pressure signals of axial piston pump are taken as the analysis objects. Firstly, the original signals are decomposed by EWT, and each signal component is screened and reconstructed according to the energy characteristics. Then, the time-domain features and the frequency-domain features of the denoised signal are extracted, and features of time domain and frequency domain are fused. Finally, the 1D-CNN model was deployed to the WISE-Platform as a Service (WISE-PaaS) cloud platform to realize the real-time fault diagnosis of axial piston pump based on the cloud platform. Compared with ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD), the results show that the axial piston pump fault diagnosis algorithm based on EWT and 1D-CNN has higher fault identification accuracy.

Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the Random Forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in structural characteristics between most crops were the largest. At the same time, the images in spring (April and May) and autumn (October) also contributed to the crop classification since they respectively provided unique information for discriminating fruit crops (almond and walnut) as well as summer crops (corn, sunflower, and tomato). As a result, the combined use of only four acquisitions (dated May, July, August, and October for 2011 and April, June, August, and October for 2014) was adequate to achieve a nearly-optimal overall accuracy. In light of the promising classification accuracies demonstrated in this research, it becomes increasingly viable to provide accurate and up-to-date crops inventories over large areas based solely on multitemporal polarimetric SAR.

The demand side management (DSM) measures are designed to process a large number of controllable loads of different types in the multi-energy system, which is helpful to increase the sustainability of the overall energy system. This paper proposes a novel model for the optimal planning of integrated electricity-gas system (IEGS) with DSM that considers the impact of DSM measures. The proposed model minimizes the total cost of the system which includes investment cost, operation cost, carbon emission cost, and DSM compensation. In order to secure the normal operation of IEGS, the electricity and natural gas system normal operation constraints are included in the proposed model. By linearizing nonlinear equations, the formulation is transformed into a mixed-integer linear programming problem to enhance computational tractability. Finally, the combined IEEE 39-bus power system and 12-node natural gas system is adopted to verify the effectiveness of the proposed model for ensuring the system security.

The rapidly emerging human health crisis associated with the Zika virus (ZIKV) epidemic and its link to severe complications highlights the growing need to identify the mechanisms by which ZIKV accesses hosts. Interferon response protects host cells against viral infection, while the cellular factors that mediate this defense are the products of interferon-stimulated genes (ISGs). Although hundreds of ISGs have been identified, only a few have been characterized for their antiviral potential, target specificity and mechanisms of action. In this work, we focused our investigation on the possible antiviral effect of a novel ISG, C19orf66 in response to ZIKV infection and the associated mechanisms. We found that ZIKV infection could induce C19orf66 expression in ZIKV-permissive cells, and such an overexpression of C19orf66 remarkably suppressed ZIKV replication. Conversely, the depletion of C19orf66 led to a significant increase in viral replication. Furthermore, C19orf66 was found to interact and co-localize with ZIKV nonstructural protein 3 (NS3), thus inducing NS3 degradation via a lysosome-dependent pathway. Taken together, this study identified C19orf66 as a novel ISG that exerts antiviral effects against ZIKV by specifically degrading a viral nonstructural protein. These findings uncovered an intriguing mechanism of C19orf66 that targeting NS3 protein of ZIKV, providing clues for understanding the actions of innate immunity, and affording the possible availability of new drug targets that can be used for therapeutic intervention.

Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the rolling bearings. The HI serves as the label of the original vibration data, and the original data with such label is input into the prediction model of the RUL based on a one-dimensional convolutional neural network (1D-CNN). The model was trained for predicting the RUL of a rolling bearing. The bearing degradation dataset was evaluated to verify the method’s effectiveness. The results demonstrate that the constructed HI can characterize the bearing degradation state effectively and that the method of predicting the RUL can accurately predict the bearing degradation trend.

Hydraulic pumps are commonly used; however, it is difficult to predict their remaining useful life (RUL) effectively. A new method based on kernel principal component analysis (KPCA) and the just in time learning (JITL) method was proposed to solve this problem. First, as the research object, the non-substitute time tac-tail life experiment pressure signals of gear pumps were collected. Following the removal and denoising of the DC component of the pressure signals by the wavelet packet method, multiple characteristic indices were extracted. Subsequently, the KPCA method was used to calculate the weighted fusion of the selected feature indices. Then the state evaluation indices were extracted to characterize the performance degradation of the gear pumps. Finally, an RUL prediction method based on the k-vector nearest neighbor (k-VNN) and JITL methods was proposed. The k-VNN method refers to both the Euclidean distance and angle relationship between two vectors as the basis for modeling. The prediction results verified the feasibility and effectiveness of the proposed method. Compared to the traditional JITL RUL prediction method based on the k-nearest neighbor algorithm, the proposed prediction model of the RUL of a gear pump presents a higher prediction accuracy. The method proposed in this paper is expected to be applied to the RUL prediction and condition monitoring and has broad application prospects and wide applicability.