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The fault characteristics of large-capacity wind farms affect the sensitivity of transmission-line protection, resulting in protection devices refusing to operate or mal-operate. A phase-to-ground fault-locating method with single-end transient information for wind farm transmission lines was proposed in this study. The proposed method employs a time-domain transient quantity integration equation matrix with the transmission line equivalent resistance, inductance, and fault path resistance to solve the fault location. The effectiveness of the scheme was evaluated using Real-Time Lab (RTLAB). The impact of factors such as the fault resistance, location, and distributed capacitance is considered. Under different fault locations, the distance measurement error of the proposed algorithm is within 3%, and there is no rejection or mal-operation. The results demonstrate that the proposed algorithm is characterized by high speed and reliability.

Due to the distinctive fault characteristics of wind power converters in large-capacity wind farms (WFs) with power converter-based systems, protective relays for the connected transmission lines can malfunction, which poses a serious challenge to the stable and reliable power transmission. This article focuses on addressing this issue by proposing a phase-to-phase fault protection method that utilizes single-end transient information for wind farm transmission lines. Conventional solutions often encounter difficulties such as the negative influence of fault resistance and the uncertainty of remote parameters in single-ended data locations. The proposed method employs time-domain differential equations to accurately solve the fault location problem. It takes into account the equivalent resistance and inductance of the transmission line, as well as the fault path resistance. This helps minimize the adverse effects of fault resistance and parameter uncertainties, making the method insensitive to fault impedance and system parameters. The effectiveness of the proposed scheme is comprehensively evaluated using the Real-Time Lab (RTLAB). Various factors like fault resistances, locations, and distributed capacitance are considered. The results clearly demonstrate that the proposed algorithm not only operates at high speed but also exhibits remarkable reliability, offering a promising solution for enhancing the protection of wind farm transmission lines.

Abstract Background The decline in the quantity and quality of mitochondria are closely associated with infertility, particularly in advanced maternal age. Transferring autologous mitochondria into the oocytes of infertile females represents an innovative and viable strategy for treating infertility, with no concerns regarding ethical considerations. As the donor cells of mitochondria, stem cells have biological advantages but research and evidence in this area are quite scarce. Methods To screen out suitable human autologous ooplasmic mitochondrial donor cells, we performed comprehensive assessment of mitochondrial physiology, function and metabolic capacity on a varity of autologous adipose, marrow, and urine-derived mesenchymal stromal cells (ADSC, BMSC and USC) and ovarian germline granulosa cells (GC). Further, to explore the biosafety, effect and mechanism of stem cell-derived mitochondria transfer on human early embryo development, randomized in-vitro basic studies were performed in both of the young and aged oocytes from infertile females. Results Compared with other types of mesenchymal stromal cells, USC demonstrated a non-fused spherical mitochondrial morphology and low oxidative stress status which resembled the oocyte stage. Moreover, USC mitochondrial content, activity and function were all higher than other cell types and less affected by age, and it also exhibited a biphasic metabolic pattern similar to the pre-implantation stage of embryonic development. After the biosafety identification of the USC mitochondrial genome, early embryos after USC mitochondrial transfer showed improvements in mitochondrial content, activity, and cytoplasmic Ca2+ levels. Further, aging embryos also showed improvements in embryonic morphological indicators, euploidy rates, and oxidative stress status. Conclusion Autologous non-invasively derived USC mitochondria transfer may be an effective strategy to improve embryonic development and metabolism, especially in infertile females with advanced age or repeated pregnancy failure. It provides evidence and possibility for the autologous treatment of infertile females without invasive and ethical concerns.

【Objective】The trial explores the effect of compound plant essential oil from the nutmeg and basill on the performance and intestinal microflora of yellow-feathered broilers.【Method】3 942 1-day-old Jiangcun yellow broilers were divided into three treatments groups (low-dose compound essential oil group, high-dose compound essential oil group and blank control group). The experiment lasted for 60 days. At the age of 60 days, all broilers were weighed, and the number of survival chickens, the number of dead and unqualified chickens, feed consumption of each replicates were recorded, and the percentage of survival chickens, weight gain and feed-gain ratio were calculated. At the same time, 5 chickens were randomly selected from each group and killed with neck cut. The contents of jejunum and cecum were respectively extracted, and bacterial nucleic acid was extracted, the 16S rDNA V3-V4 variable region was high-throughput sequencing to analyze the intestinal microflora of jejunum and cecum of broilers.【Result】The feed-gain ratio of the high dose group and the low dose group was 2.40, which was significantly lower than that of the blank control group (2.71, P < 0.01), and the feed conversion rate was increased by 11.4%. The results of jejunum microflora analysis showed that Firmicutes were the dominant bacteria at phylum level followed by Actinobacteriota, and Lactobacillus was the dominant bacteria at genus level. The relative abundance of Verrucomicrobiota in jejunum in high-dose and low-dose groups was significantly lower than that in blank control group (P < 0.05). The relative abundance of Aeriscardovia, Bifidobacterium and Weissella in jejunum of compound essential oil low-dose group was significantly higher than that of blank control group (P < 0.05). The results of cecal microflora analysis showed that Firmicutes and Bacteroidetes were dominant bacteria at phylum level. The relative abundance of Clostridia_vadinBB60_group and Alistipes in high-dose and low-dose compound essential oil groups was significantly lower than that in blank control group (P < 0.05), and that of Clostridia_UCG-014 in high-dose compound essential oil group was significantly lower than that in blank control group (P < 0.05). The relative abundance of Bacteroides in high dose compound essential oil group was significantly higher than that in blank control group and low dose compound essential oil group (P < 0.05), and the relative abundance of Ruminococcus_torques_group in low dose compound essential oil group was significantly higher than that in blank control group (P < 0.05).【Conclusion】Feeding nutmeg and basil compound essential oil can promote the growth of beneficial bacteria in jejunum and cecum of yellow-feathered broilers, inhibit the proliferation of harmful bacteria, maintain the balance of bacteria, and promote intestinal health, and obviously improve the performance of yellow-feathered broilers.

With the prevalence of renewable energy sources such as wind power in the power system, analyzing the fault characteristics of systems composed of DFIGs is becoming increasingly important. Therefore, this article analyzes, at first theoretically, the fault characteristics of a doubly fed induction generator (DFIG) during fault periods. It was found that the fault current of the DFIG exhibited the frequency offset phenomenon, which is affected by the depth of voltage dips and can negatively impact traditional distance protection. Furthermore, a method using a dynamic voltage restorer (DVR) based on superconducting magnetic energy storage (SMES) was adopted to compensate for the fault voltage of DFIG, which can mitigate the voltage dips of the DFIG. This method can not only achieve the fault ride through for DFIG but also significantly improve the frequency offset of the fault current during fault periods. Finally, a model composed of a 2.5 MW DFIG-based wind turbine and a 2.5 MW DVR-based SMES was built using a real-time digital simulator (RTDS) platform, and the simulation results showed that the fault stator voltage of DFIG can be compensated at a rated value of 0.69 kV, and the frequency of fault current can be maintained at 50 Hz These results validate the excellent performance of the method in achieving the fault ride through of DFIG and improving the frequency offset of the fault current by comparing multiple type faults while employing different protection methods.

Objective The study aimed to develop a machine learning (ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications (MCs).Methods We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4 (training cohort, n = 428; independent testing cohort, n = 35) in the First Affiliated Hospital of Nanjing Medical University between September 2010 and January 2019. Subsequently, 837 textures and 9 shape features were subsequently extracted from each view and finally selected by an XGBoost-embedded recursive feature elimination technique (RFE), followed by four machine learning-based classifiers to build the radiomics signature.Results Ten radiomic features constituted a malignancy-related signature for breast MCs as logistic regression (LR) and support vector machine (SVM) yielded better positive predictive value (PPV)/sensitivity (SE), 0.904 (95% CI, 0.865–0.949)/0.946 (95% CI, 0.929–0.977) and 0.891 (95% CI, 0.822–0.939)/0.939 (95% CI, 0.907–0.973) respectively, outperforming their negative predictive value (NPV)/specificity (SP) from 10-fold cross-validation (10FCV) of the training cohort. The optimal prognostic model was obtained by SVM with an area under the curve (AUC) of 0.906 (95% CI, 0.834–0.969) and accuracy (ACC) 0.787 (95% CI, 0.680–0.855) from 10FCV against AUC 0.810 (95% CI, 0.760–0.960) and ACC 0.800 from the testing cohort.Conclusion The proposed radiomics signature dependens on a set of ML-based advanced computational algorithms and is expected to identify pathologically cancerous cases from mammographically undecipherable MCs and thus offer prospective clinical diagnostic guidance.

The high morbidity of patients with coronavirus disease 2019 (COVID-19) brings on a panic around the world. COVID-19 is associated with sex bias, immune system, and preexisting chronic diseases. We analyzed the gene expression in patients with COVID-19 and in their microbiota in order to identify potential biomarkers to aid in disease management. A total of 129 RNA samples from nasopharyngeal, oropharyngeal, and anal swabs were collected and sequenced in a high-throughput manner. Several microbial strains differed in abundance between patients with mild or severe COVID-19. Microbial genera were more abundant in oropharyngeal swabs than in nasopharyngeal or anal swabs. Oropharyngeal swabs allowed more sensitive detection of the causative SARS-CoV-2. Microbial and human transcriptomes in swabs from patients with mild disease showed enrichment of genes involved in amino acid metabolism, or protein modification via small protein removal, and antibacterial defense responses, respectively, whereas swabs from patients with severe disease showed enrichment of genes involved in drug metabolism, or negative regulation of apoptosis execution, spermatogenesis, and immune system, respectively. Microbial abundance and diversity did not differ significantly between males and females. The expression of several host genes on the X chromosome correlated negatively with disease severity. In this way, our analyses identify host genes whose differential expression could aid in the diagnosis of COVID-19 and prediction of its severity via non-invasive assay.

The aim of this study was to investigate the association between expressions of five periodontal pathogens (Actinobacillus actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), Tannerella forsythensis (Tf), Treponema denticola (Td) and Prevotella intermedia (Pi) and the periodontal pocket depth (PD) in people suffering from periodontitis. A study of 1164 plaque samples from individuals with periodontitis were conducted by multiplex real-time quantitative fluorescence-polymerase chain reaction to evaluate the PD and loads of five periodontal pathogens. The total bacterial number did not differ significantly between people with different subtypes of periodontitis. However, expression of specific pathogens (Pg, Td, Tf, Pi) showed a significant increase with the development of periodontitis. Expression of Pg, Td and Tf showed close correlation between them as well as with PD. Analyses of receiver operating characteristic curves showed that expression of Pg, Td, Tf and Pi had good potential for grading periodontitis severity. We found a close correlation between the relative expression of Pg, Td and Tf and PD. Measuring expression of target bacteria in the oral cavity could facilitate the understanding of the role of bacteria in periodontitis. Taking the appropriate steps to eliminate these pathogens might aid the prevention of periodontitis development.

Determining appropriate master images, reducing radiometric error accumulation, and eliminating outliers from the cloud, water, and land changes, are three main issues in radiometric normalization of multitemporal high-resolution satellite images (HRSI) during mosaicking. However, these three issues have not been simultaneously considered by the existing methods. This article presents a comprehensive radiometric normalization method for multitemporal HRSI using a radiometric block adjustment without master images. Pseudoinvariant features (PIFs) extracted from image pairs using the iteratively reweighted multivariate alteration detection are used as the corresponding pixel observations and organized to form radiometric tie points according to the corresponding horizontal space coordinates. Radiometric error equations are subsequently constructed, and the linear radiometric transformation parameters are solved by a global adjustment. The time-invariant PIFs generally represent the true corresponding features and naturally avoid the cloud, water, and land changes, which can eliminate the effects of outliers. Furthermore, the pixel values of tie points calculated from the weighted average of the corresponding pixel observations are used as virtual radiometric control points to eliminate the dependency on master images. Moreover, a global optimum can be achieved by the global adjustment, effectively overcoming the error accumulation, which is severe in large datasets. Four groups of HRSI datasets from various satellites are used to validate the performance of the proposed method. Experimental results demonstrate that the proposed method outperforms two state-of-the-art methods and has good applicability and stability, considering both visual effects and quantitative performance.

Over the past decade, airborne digital sensor series linear array scanners, among the most important imaging sensors in photogrammetry practices, have been used around the world to measure and interpret the terrain. They have attracted much interest from researchers in image quality and data processing and application. This paper analyzes the factors affecting the quality of airborne digital sensor images and proposes suggestions for data acquisition and flight plan design. Factors affecting image geometric quality are first analyzed theoretically, including the velocity and attitude variations of aircraft, and terrain elevation changes and slope, and formulas are given to express the quantitative relationship. Then, a real data set is used to verify the theoretical analysis and evaluate the practical influence, and according to the formulas, advice on image data acquisition control and flight plan design is provided, so as to improve image quality and facilitate subsequent works.

Georeferencing satellite imagery is the basis for mapping and other subsequent processing in photogrammetry and remote sensing. Traditional georeferencing methods, which require adequate and well-distributed ground control points (GCPs), are time-consuming, costly, constantly cumbersome and inefficient. This study proposes a method of adaptive re-weighted block adjustment without GCPs for satellite stereo images. Furthermore, an approach of using multi-coverage images to improve the direct georeferencing accuracy is also investigated. The bias-compensated rational function model with affine transformation parameters is used as the adjustment mathematical model. The “weighted virtual control points” are introduced as constraints in the adjustment. Space intersection and resection are executed iteratively to solve the accurate affine transformation parameters. A total of 722 three-line array stereo scenes of Chinese Mapping Satellite-1 images captured from December 2010 to March 2016, which cover the entire Shandong Province of China (about 158,000 km2) nearly 10 times in space, are used as experimental data to evaluate the performance of the proposed method. Experiment results demonstrate that the proposed method can effectively eliminate the systematic errors of images. Moreover, the additional comparison with experiments of three groups of mono-coverage images indicates that the redundant observations from multi-coverage images can significantly improve the direct georeferencing accuracy, more specifically, from 13.69-16.02 m to 11.09 m in the horizontal direction, and from 16.39-29.27 m to 8.47 m in the vertical direction.

Currently, deep-learning-based methods have been widely used in fault diagnosis to improve the diagnosis efficiency and intelligence. However, most schemes require a great deal of labeled data and many iterations for training parameters. They suffer from low accuracy and over fitting under the few-shot scenario. In addition, a large number of parameters in the model consumes high computing resources, which is far from practical. In this paper, a multi-scale and lightweight Siamese network architecture is proposed for the fault diagnosis with few samples. The architecture proposed contains two main modules. The first part implements the feature vector extraction of sample pairs. It is composed of two lightweight convolutional networks with shared weights symmetrically. Multi-scale convolutional kernels and dimensionality reduction are used in these two symmetric networks to improve feature extraction and reduce the total number of model parameters. The second part takes charge of calculating the similarity of two feature vectors to achieve fault classification. The proposed network is validated by multiple datasets with different loads and speeds. The results show that the model has better accuracy, fewer model parameters and a scale compared to the baseline approach through our experiments. Furthermore, the model is also proven to have good generalization capability.

In this paper, we report the development of a label-free biosensor, based on surface plasmon resonance, for real-time monitoring of captured human CD4+T-cells, and their dynamic cytokine production upon specific antigen stimulation. Microarrays of CD4+T-cells, and interferon gamma (IFN-γ) specific antibody (Ab) spots were printed side by side onto the poly(OEGMA-co-HEMA) matrix. The placement of CD4+ T-cells near the anti IFN-γ Ab-coated spots ensured a high local concentration of secreted IFN-γ for detection. We have demonstrated that this approach enables the detection of tuberculosis (TB) infection in clinical samples, with an overall sensitivity of 85.5% and an overall specificity of 97.7%.

Abstract Background More than a decade after the outbreak of human coronaviruses (HCoVs) SARS in Guangdong province and Hong Kong SAR of China in 2002, there is still no reoccurrence, but the evolution and recombination of the coronaviruses in this region are still unknown. Therefore, surveillance on the prevalence and the virus variation of HCoVs circulation in this region is conducted. Methods A total of 3298 nasopharyngeal swabs samples were collected from cross-border children ( 37.5 °C), from 2014 May to 2015 Dec. Viral nucleic acids were analyzed and sequenced to study the prevalence and genetic diversity of the four human coronaviruses. The statistical significance of the data was evaluated with Fisher chi-square test. Results 78 (2.37%; 95%CI 1.8-2.8%) out of 3298 nasopharyngeal swabs specimens were found to be positive for OC43 (36;1.09%), HKU1 (34; 1.03%), NL63 (6; 0.18%) and 229E (2;0.01%). None of SARS or MERS was detected. The HCoVs predominant circulating season was in transition of winter to spring, especially January and February and NL63 detected only in summer and fall. Complex population with an abundant genetic diversity of coronaviruses was circulating and they shared homology with the published strains (99-100%). Besides, phylogenetic evolutionary analysis indicated that OC43 coronaviruses were clustered into three clades (B,D,E), HKU1 clustered into two clades(A,B) and NL63 clustered into two clades(A,B). Moreover, several novel mutations including nucleotides substitution and the insertion of spike of the glycoprotein on the viral surface were discovered. Conclusions The detection rate and epidemic trend of coronaviruses were stable and no obvious fluctuations were found. The detected coronaviruses shared a conserved gene sequences in S and RdRp. However, mutants of the epidemic strains were detected, suggesting continuous monitoring of the human coronaviruses is in need among cross-border children, who are more likely to get infected and transmit the viruses across the border easily, in addition to the general public.

Background/Aims: Hypoxia leads to the development of neovascularization in solid tumor by regulating VEGF expression. Aromatic hydrocarbon receptor (AHR), a receptor for dioxin-like compounds, functions as a transcription factor through dimerization with hypoxia-inducible factors 1β (HIF-1β) and inhibits the secretion of vascular endothelial growth factor (VEGF). The purpose of this study was to explore whether AHR can suppress hypoxia-induced VEGF production in prostate bone metastasis cells and repress neovascularization in endothelial progenitor cells (EPCs), and, if so, through what mechanisms. Methods: PC-3 or LNCaP cells induced angiogenesis was detected by Matrigel-based tube formation assay, mRNA expression levels was measured by qRT-PCR, VEGF secretion level was determined by ELISA assay, respectively. Results: AHR activation inhibits hypoxia-induced adhesiveness and vasculogenesis of EPCs induced by PC-3 or LNCaP cells under hypoxia. Moreover, AHR activation suppressed hypoxia-induced VEGF production in PC-3 and LNCaP cells (48 ± 14% in PC-3, p = 0.000; 41 ± 14% in LNCaP, p = 0.000) by attenuating HIF-1α and HIF-1β level that in turn diminished the angiogenic ability of EPCs in vitro. Furthermore, we found the mRNA level of hypoxia-inducible factors 1α (HIF-1α) (1.54 ± 0.13 fold in PC-3, p = 0.002, 1.62 ± 0.12 fold in LNCaP, p = 0.001) and HIF-1β (1.67 ± 0.23 fold in PC-3, p = 0.007; 1.75 ± 0.26 fold in LNCaP, p=0.008) were upregulated in prostate cancer bone metastasis PC-3 and LNCaP cell lines in response to hypoxia, and revealed that the regulation of VEGF by HIF-1α and HIF-1β was possibly mediated by the activation of phosphatidylinositol 3-kinase pathway. Conclusion: By providing a mechanistic insight into the modulation of neovascularization by AHR ligand, we suggest that AHR ligand has a strong potential of being a new therapeutic agent with applications in the field of bone metastatic prostate cancer.

Environmental factors that drive carbon storage are often used as an explanation for alpine treeline formation. However, different tree species respond differently to environmental changes, which challenges our understanding of treeline formation and shifts. Therefore, we selected Picea jezoensis and Betula ermanii, the two treeline species naturally occurring in Changbai Mountain in China, and measured the concentration of non-structural carbohydrates (NSC), soluble sugars and starch in one-year-old leaves, shoots, stems and fine roots at different elevations. We found that compared with P. jezoensis, the NSC and soluble sugars concentrations of leaves and shoots of B. ermanii were higher than those of P. jezoensis, while the starch concentration of all the tissues were lower. Moreover, the concentration of NSC, soluble sugars and starch in the leaves of B. ermanii decreased with elevation. In addition, the starch concentration of B. ermanii shoots, stems and fine roots remained at a high level regardless of whether the soluble sugars concentration decreased. Whereas the concentrations of soluble sugars and starch in one-year-old leaves, shoots and stems of P. jezoensis responded similarly changes with elevation. These findings demonstrate that compared with P. jezoensis, B. ermanii has a higher soluble sugars/starch ratio, and its shoots, stems and fine roots actively store NSC to adapt to the harsh environment, which is one of the reasons that B. ermanii can be distributed at higher altitudes.

It is well established that estrogens regulate female reproduction through estrogen receptors (ERs) in the ovary. However, the precise physiological role of estrogen/ER signaling in reproduction processes remains poorly defined in zebrafish. In this study, we successfully generated an ERα (esr1) mutant line in zebrafish via transcription activator-like effectors nucleases (TALENs). It was found in the mutant females that the fertility was enhanced and the ovarian histology was normal at 90 days post-fertilization (dpf). However, the number of fertile females decreased with age. By 180 dpf, esr1 mutant females were infertile with degenerated ovaries, while the age-matched wild-type females were still fertile. Additionally, few large vitellogenic granules can be found in full grown (FG) follicles at 90 dpf and the expression of vtg genes were down-regulated at both 90 and 180 dpf in esr1 mutant zebrafish. Moreover, steroidogenesis pathway and mTOR signaling pathway were over-activated at 90 dpf, but declined prematurely in esr1 mutant zebrafish by 180 dpf. Collectively, the present study provides evidence that esr1 is fundamental for ovarian maintenance in zebrafish.

A fast surface plasmon resonance (SPR) imaging biosensor system based on wavelength interrogation using an acousto-optic tunable filter (AOTF) and a white light laser is presented. The system combines the merits of a wide-dynamic detection range and high sensitivity offered by the spectral approach with multiplexed high-throughput data collection and a two-dimensional (2D) biosensor array. The key feature is the use of AOTF to realize wavelength scan from a white laser source and thus to achieve fast tracking of the SPR dip movement caused by target molecules binding to the sensor surface. Experimental results show that the system is capable of completing a SPR dip measurement within 0.35 s. To the best of our knowledge, this is the fastest time ever reported in the literature for imaging spectral interrogation. Based on a spectral window with a width of approximately 100 nm, a dynamic detection range and resolution of 4.63 × 10−2 refractive index unit (RIU) and 1.27 × 10−6 RIU achieved in a 2D-array sensor is reported here. The spectral SPR imaging sensor scheme has the capability of performing fast high-throughput detection of biomolecular interactions from 2D sensor arrays. The design has no mechanical moving parts, thus making the scheme completely solid-state.