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Summary: Bidirectional communication between the developing conceptus and endometrium is essential for pregnancy recognition and establishment in ruminants. We dissect the transcriptomic dynamics of sheep conceptus and corresponding endometrium at pre- and peri-implantation stages using single-cell RNA sequencing. Spherical blastocysts contain five cell types, with 68.62% trophectoderm cells. Strikingly, elongated conceptuses differentiate into 17 cell types, indicating dramatic cell fate specifications. Cell-type-specific gene expression delineates the features of distinctive trophectoderm lineages and indicates that the transition from polar trophectoderm to trophoblast increases interferon-tau expression and likely drives elongation initiation. We identify 13 endometrium-derived cell types and elucidate their molecular responses to conceptus development. Integrated analyses uncover multiple paired transcripts mediating the dialogues between extraembryonic membrane and endometrium, including IGF2-IGF1R, FGF19-FGFR1, NPY-NPY1R, PROS1-AXL, and ADGRE5-CD55. These data provide insight into the molecular regulation of conceptus elongation and represent a valuable resource for functional investigations of pre- and peri-implantation ruminant development.

BackgroundIt is unclear how cardiac autonomic function, as indicated by heart rate (HR), heart rate variability (HRV), HR increase during exercise, and HR recovery after exercise, is related to blood pressure (BP). We aimed to examine the observational and genetic evidence for a potential causal effect of these HR(V) traits on BP.MethodsWe performed multivariable adjusted linear regression using Lifelines and UK Biobank cohorts to investigate the relationship between HR(V) traits and BP. Linkage disequilibrium score regression was conducted to examine genetic correlations. We used two-sample Mendelian randomization (2SMR) to examine potential causal relations between HR(V) traits and BP.ResultsObservational analyses showed negative associations of all HR(V) traits with BP, except for HR, which was positively associated. Genetic correlations were directionally consistent with the observational associations, but most significant genetic correlations between HR(V) traits and BP were limited to diastolic blood pressure (DBP). 2SMR analyses suggested a potentially causal relationship between HR(V) traits and DBP but not systolic blood pressure (SBP). No reverse effect of BP on HR(V) traits was found. One standard deviation (SD) unit increase in HR was associated with a 1.82 mmHg elevation of DBP. In contrast, one ln(ms) unit increase of the root mean square of the successive differences (RMSSD) and corrected RMSSD (RMSSDc), decreased DBP by 1.79 and 1.83 mmHg, respectively. For HR increase and HR recovery at 50 s, every additional SD increase was associated with a lower DBP by 2.05 and 1.47 mmHg, respectively. Results of secondary analyses with pulse pressure as outcome were inconsistent between observational and 2SMR analyses, as well as between HR(V) traits, and therefore inconclusive.ConclusionBoth observational and genetic evidence show strong associations between indices of cardiac autonomic function and DBP, suggesting that a larger relative contribution of the sympathetic versus the parasympathetic nervous system to cardiac function may cause elevated DBP.

This paper introduces ‘SkeletonCLIP++’, an extension of our prior work in human action recognition, emphasizing the use of semantic information beyond traditional label-based methods. The innovation, ‘Weighted Frame Integration’ (WFI), shifts video feature computation from averaging to a weighted frame approach, enabling a more nuanced representation of human movements in line with semantic relevance. Another key development, ‘Contrastive Sample Identification’ (CSI), introduces a novel discriminative task within the model. This task involves identifying the most similar negative sample among positive ones, enhancing the model’s ability to distinguish between closely related actions. Incorporating the ‘BERT Text Encoder Integration’ (BTEI) leverages the pre-trained BERT model as our text encoder to refine the model’s performance. Empirical evaluations on HMDB-51, UCF-101, and NTU RGB+D 60 datasets illustrate positive improvements, especially in smaller datasets. ‘SkeletonCLIP++’ thus offers a refined approach to human action recognition, ensuring semantic integrity and detailed differentiation in video data analysis.

Virus infections cause devastative economic losses for various plant species, and early diagnosis and prevention are the most effective strategies to avoid the losses. Exploring virus genomic evolution and constructing virus infectious cDNA clones is essential to achieve a deeper understanding of the interaction between host plant and virus. Therefore, this work aims to guide people to better prevent, control, and utilize the youcai mosaic virus (YoMV). Here, the YoMV was found to infect the Solanum nigrum under natural conditions. Then, an infectious cDNA clone of YoMV was successfully constructed using triple-shuttling vector-based yeast recombination. Furthermore, we established phylogenetic trees based on the complete genomic sequences, the replicase gene, movement protein gene, and coat protein gene using the corresponding deposited sequences in NCBI. Simultaneously, the evolutionary relationship of the YoMV discovered on S. nigrum to others was determined and analyzed. Moreover, the constructed cDNA infectious clone of YoMV from S. nigrum could systematically infect the Nicotiana benthamiana and S. nigrum by agrobacterium-mediated infiltration. Our investigation supplied a reverse genetic tool for YoMV study, which will also contribute to in-depth study and profound understanding of the interaction between YoMV and host plant.

To ensure global food security, crop breeders conduct extensive trials across various locations to discover new crop varieties that grow more robustly, have higher yields, and are resilient to local stress factors. These trials consist of thousands of plots, each containing a unique crop variety monitored at intervals during the growing season, requiring considerable manual effort. In this study, we combined satellite imagery and deep learning techniques to automatically collect plot-level phenotypes from plant breeding trials in South Australia and Sonora, Mexico. We implemented two novel methods, utilising state-of-the-art computer vision architectures, to predict plot-level phenotypes: flowering, canopy cover, greenness, height, biomass, and normalised difference vegetation index (NDVI). The first approach uses a classification model to predict for just the centred plot. The second approach predicts per-pixel and then aggregates predictions to determine a value per-plot. Using a modified ResNet18 model to predict the centred plot was found to be the most effective method. These results highlight the exciting potential for improving crop trials with remote sensing and machine learning.

Clustering analysis for single-cell RNA sequencing (scRNA-seq) data is essential for characterizing cellular heterogeneity. However, batch information caused by batch effects is often confused with the intrinsic biological information in scRNA-seq data, which makes accurate clustering quite challenging. A Deep Adaptive Clustering with Adversarial Learning method (DACAL) is proposed here. DACAL jointly optimizes the batch correcting and clustering processes to remove batch effects while retaining biological information. DACAL achieves batch correction and adaptive clustering without requiring manually specified cell types or resolution parameters. DACAL is compared with other widely used batch correction and clustering methods on human pancreas datasets from different sequencing platforms and mouse mammary datasets from different laboratories. The results demonstrate that DACAL can correct batch effects efficiently and adaptively find accurate cell types, outperforming competing methods. Moreover, it can obtain cell subtypes with biological meanings.

An analytical study was carried out on an anchored circular pit with a submerged free surface in layered soil. The seepage field around the anchor circular pit was divided into three zones. Separate variable method was used to obtain the graded solution forms of head distribution in the column coordinate system for each of the three regions. Combined with the continuity condition between the regions the Bessel function orthogonality was used to obtain the explicit analytical solution of the seepage field in each region, and the infiltration line was determined. Comparison with the calculation results of Plaxis 2D 8.5 software verified the correctness of the analytical solution. Based on the analytical solution, the influence of the radius of the pit and the distance of the retaining wall from the top surface of the impermeable layer on the total head distribution on both sides of the retaining wall was analyzed. And the variation in the infiltration line was determined with the above parameters. The results show that as the pit radius, r, decreased, the total head on both sides of the retaining wall gradually increased, and the height of the submerged surface drop also increased. As the distance, a, between the retaining wall and the impermeable boundary at the bottom increased, the hydraulic head on the outer side of the retaining wall decreased and the head on the inner side increased. The height of the submerged surface drop increased with decreasing depth of insertion of the retaining wall. The depth of insertion of the retaining wall had a greater influence on the degree of diving surface drop than the pit radius.

Energy crisis and climate changes have brought great challenges to traditional power generation and transmission. Nowadays, the power transmission system of VSC-HVDC attracts more and more attention. The reactive and active power can be separately controlled, which greatly improves the flexibility of power transmission. Moreover, the harmonic content of this system is extremely low. As an important topology of HVDC system, modular multilevel converters (MMC) have significant advantages. However, there is a problem of excessive circulating current between phases in MMC, which easily causes the fluctuation amplitude of the capacitor voltage in each sub-module to be extremely large, or even unstable. An improved method to suppress the circulating current is proposed in this paper. In order to balance the voltage of each capacitor, the average capacitor voltage control of the outer loop is used. Meanwhile, an independent controller of voltage balance and a controller of arm voltage balance are applied. In order to eliminate the circulating harmonics of currents, a controller of quasi-PR is adopted for the inner control loop. Then, a simulating model of MMC system was established to prove the effectiveness of the proposed method. Finally, an experimental prototype was built and the corresponding tested results are given. It is proved that the proposed method has obvious advantage on suppressing harmonic circulating current and capacitor voltage balance control.

Abstract In a modular multilevel converter‐based high‐voltage direct current (MMC‐HVDC) system, the dc fault ride through (FRT) control is an effective way to deal with a dc‐side pole‐to‐ground (PTG) fault. However, the setting of FRT duration brings potential hazards: 1) MMC will face the risk of “secondary short circuit” if FRT duration is short; 2) ac grid may have power angle stability issue if FRT duration is long. To avoid these hazards and provide theoretical guidance for the FRT duration setting, transient behaviour of dc fault current during the PTG FRT stage is explored in this work. Firstly, challenges of the transient analysis are summarised as non‐linearity and high‐order issues. In light of this, numerical and Hilbert‐Huang Transformation methods are introduced to evaluate the non‐linearity issue. It is found that the path of MMC from dc current to dc internal voltage is weakly non‐linear. Hence, a linear transient model is built to analyse the dc fault current. By participation factor analysis, the order of the proposed model is further reduced, so that an analytical expression of the dc fault current is approximately derived. Based on the analytical expression, regularities and mechanism of dc fault current are fully revealed. Application of the transient analysis to the setting of FRT duration is elaborated in detail. Finally, the PSCAD/EMTDC simulation verifies the validity of the proposed model and analytical expression.

Abstract Mesenteric adipose tissue (MAT) in Crohn's disease (CD) is associated with transmural inflammation. Extended mesenteric excision can reduce surgical recurrence and improve long‐term outcomes, indicating that MAT plays an important role in the pathogenesis of CD. Bacterial translocation has been reported to occur in the MAT of patients with CD (CD‐MAT), but the mechanisms by which translocated bacteria lead to intestinal colitis remain unclear. Here it is shown that members of Enterobacteriaceae are highly enriched in CD‐MAT compared with non‐CD controls. Viable Klebsiella variicola in Enterobacteriaceae is isolated exclusively in CD‐MAT and can induce a pro‐inflammatory response in vitro and exacerbates colitis both in dextran sulfate sodium (DSS)‐induced colitis mice model and IL‐10−/− spontaneous colitis mice model. Mechanistically, active type VI secretion system (T6SS) is identified in the genome of K. variicola, which can impair the intestinal barrier by inhibiting the zonula occludens (ZO‐1) expression. Dysfunction of T6SS by CRISPR interference system alleviates the inhibitory effect of K. variicola on ZO‐1 expression and attenuated colitis in mice. Overall, these findings demonstrate that a novel colitis‐promoting bacteria exist in the mesenteric adipose tissue of CD, opening a new therapeutic avenue for colitis management.

Epidermal growth factor receptor (EGFR) mutations are common driver genes in nonsmall-cell lung cancer and have different sensitivities to EGFR-tyrosine kinase inhibitors (EGFR-TKIs). EGFR is divided into classic mutations and rare mutations. Classic mutations are well known, but the understanding of rare mutations is not sufficient. In this article, we summarize the clinical research and treatment progress of rare mutations for different EGFR-TKIs and provide a basis for clinical treatment decisions.

Abstract Platinum-based chemotherapy was previously the first-choice treatment for lung cancer. The discovery of epidermal growth factor receptor (EGFR) gene mutations and the development of EGFR tyrosine kinase inhibitors (TKIs) marked the beginning of the targeted therapy era for non-small-cell lung cancer (NSCLC). Thirty percent of NSCLC patients carry EGFR gene mutations. For these advanced NSCLC patients, EGFR-TKIs are currently preferred for their superior activity and survival benefits over platinum-based chemotherapy. However, therapeutic efficacy is quite different in patients with EGFR exon 20 insertion (ex20ins) mutations versus common mutations. Patients with ex20ins mutations are insensitive to EGFR-TKIs and have poor prognosis. Some drugs targeting EGFR ex20ins mutations have been approved. Here, we systematically reviewed the recent clinical research of and treatments used for EGFR ex20ins mutations, summarized the latest data on emerging therapies, and discussed future prospects and treatments.

During recent decades, many new emerging or re-emerging RNA viruses have been found in plants through the development of deep-sequencing technology and big data analysis. These findings largely changed our understanding of the origin, evolution and host range of plant RNA viruses. There is evidence that their genetic composition originates from viruses, and host populations play a key role in the evolution and host adaptability of plant RNA viruses. In this mini-review, we describe the state of our understanding of the evolution of plant RNA viruses in view of compositional biases and explore how they adapt to the host. It appears that adenine rich (A-rich) coding sequences, low CpG and UpA dinucleotide frequencies and lower codon usage patterns were found in the vast majority of plant RNA viruses. The codon usage pattern of plant RNA viruses was influenced by both natural selection and mutation pressure, and natural selection mostly from hosts was the dominant factor. The codon adaptation analyses support that plant RNA viruses probably evolved a dynamic balance between codon adaptation and deoptimization to maintain efficient replication cycles in multiple hosts with various codon usage patterns. In the future, additional combinations of computational and experimental analyses of the nucleotide composition and codon usage of plant RNA viruses should be addressed.

With the development of information technology construction in schools, predicting student grades has become a hot area of application in current educational research. Using data mining to analyze the influencing factors of students' performance and predict their grades can help students identify their shortcomings, optimize teachers' teaching methods and enable parents to guide their children's progress. However, there are no models that can achieve satisfactory predictions for education-related public datasets, and most of these weakly correlated factors in the datasets can still adversely affect the predictive effect of the model. To solve this issue and provide effective policy recommendations for the modernization of education, this paper seeks to find the best grade prediction model based on data mining. Firstly, the study uses the Factor Analyze (FA) model to extract features from the original data and achieve dimension reduction. Then, the Bidirectional Gate Recurrent Unit (BiGRU) model and attention mechanism are utilized to predict grades. Lastly, Comparing the prediction results of ablation experiments and other single models, such as linear regression (LR), back propagation neural network (BP), random forest (RF), and Gate Recurrent Unit (GRU), the FA-BiGRU-attention model achieves the best prediction effect and performs equally well in different multi-step predictions. Previously, problems with students' grades were only detected when they had already appeared. However, the methods presented in this paper enable the prediction of students' learning in advance and the identification of factors affecting their grades. Therefore, this study has great potential to provide data support for the improvement of educational programs, transform the traditional education industry, and ensure the sustainable development of national talents.

Abstract Background Metabolic reprogramming is a hallmark of cancer. Metabolic rate‐limiting enzymes and oncogenic c‐Myc (Myc) play critical roles in metabolic reprogramming to affect tumourigenesis. However, a systematic assessment of metabolic rate‐limiting enzymes and their relationship with Myc in human cancers is lacking. Methods Multiple Pan‐cancer datasets were used to develop the transcriptome, genomic alterations, clinical outcomes and Myc correlation landscapes of 168 metabolic rate‐limiting enzymes across 20 cancers. Real‐time quantitative PCR and immunoblotting were, respectively, used to examine the mRNA and protein of inosine monophosphate dehydrogenase 1 (IMPDH1) in human colorectal cancer (CRC), azoxymethane/dextran sulphate sodium‐induced mouse CRC and spontaneous intestinal tumours from APCMin/+ mice. Clone formation, CCK‐8 and subcutaneous xenograft model were applied to investigate the possible mechanisms connecting IMPDH1 to CRC growth. Co‐immunoprecipitation and protein half‐life assay were used to explore the mechanisms underlying the regulation of IMPDH1. Results We explored the global expression patterns, dysregulation profiles, genomic alterations and clinical relevance of 168 metabolic rate‐limiting enzymes across human cancers. Importantly, a series of enzymes were associated with Myc, especially top three upregulated enzymes (TK1, RRM2 and IMPDH1) were positively correlated with Myc in multiple cancers. As a proof‐of‐concept exemplification, we demonstrated that IMPDH1, a rate‐limiting enzyme in GTP biosynthesis, is highly upregulated in CRC and promotes CRC growth in vitro and in vivo. Mechanistically, IMPDH2 stabilizes IMPDH1 by decreasing the polyubiquitination levels of IMPDH1, and Myc promotes the de novo GTP biosynthesis by the transcriptional activation of IMPDH1/2. Finally, we confirmed that the Myc–IMPDH1/2 axis is dysregulated across human cancers. Conclusions Our study highlights the essential roles of metabolic rate‐limiting enzymes in tumourigenesis and their crosstalk with Myc, and the Myc–IMPDH1/2 axis promotes tumourigenesis by altering GTP metabolic reprogramming. Our results propose the inhibition of IMPDH1 as a viable option for cancer treatment.

Objective: To investigate the incidence and risk factors of subclinical atherosclerosis (SA) in psoriatic arthritis (PsA). Methods: This study enrolled 53 PsA patients and 158 normal subjects between April 2017 to December 2019. Carotid ultrasound examination was conducted on these patients to determine the presence of carotid plaque and assess the IMT for each individual. The propensity score matching (PSM) was performed to decrease the possible bias between the PsA group and control group. Then we analyze the risk factors of SA by multiple logistic regression and multiple linear regression, respectively. Results: After PSM matching, the confounding factors had no significant differences between the 50 pairs of patients. The PsA group had higher incidence of carotid plaques and thicker carotid IMT (all P

Abstract Background Mesenteric adipose tissue (mAT) hyperplasia, known as creeping fat is a pathologic characteristic of Crohn’s disease (CD). The reserve of creeping fat in surgery is associated with poor prognosis of CD patients, but the mechanism remains unknown. Methods Mesenteric microbiome, metabolome, and host transcriptome were characterized using a cohort of 48 patients with CD and 16 non-CD controls. Multidimensional data including 16S ribosomal RNA gene sequencing (16S rRNA), host RNA sequencing, and metabolome were integrated to reveal network interaction. Mesenteric resident bacteria were isolated from mAT and functionally investigated both in the dextran sulfate sodium (DSS) model and in the Il10 gene-deficient (Il10 −/−) mouse colitis model to validate their pro-inflammatory roles. Results Mesenteric microbiota contributed to aberrant metabolites production and transcripts in mATs from patients with CD. The presence of mAT resident microbiota was associated with the development of CD. Achromobacter pulmonis (A. pulmonis) isolated from CD mAT could translocate to mAT and exacerbate both DSS-induced and Il10 gene-deficient (Il10 −/−) spontaneous colitis in mice. The levels of A. pulmonis in both mAT and mucous layer from CD patients were higher compared to those from the non-CD group. Conclusions This study suggests that the mesenteric microbiota from patients with CD sculpt a detrimental microenvironment and promote intestinal inflammation. Video abstract

Abstract Background The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can accommodate input from different spectral ranges would potentially make such models extensible to more affordable sensors. Here we compare the accuracy of prediction of PLSR with various deep learning approaches and an ensemble model, each trained and tested using previously published data sets. Results We demonstrate that the accuracy of PLSR to predict photosynthetic and related leaf traits in wheat can be improved with deep learning-based and ensemble models without overfitting. Additionally, these models can be flexibly applied across spectral ranges without significantly compromising accuracy. Conclusion The method reported provides an improved prediction of wheat leaf and photosynthetic traits from leaf hyperspectral reflectance and do not require a full range, high cost leaf spectrometer. We provide a web service for deploying these algorithms to predict physiological traits in wheat from a variety of spectral data sets, with important implications for wheat yield prediction and crop breeding.

Microbial electrochemical system (MES) technology has been widely investigated for organic degradation. However, the removal of recalcitrant organic contaminants containing halogen-, nitro-, or azo-groups remains a great challenge. Integrating the cathodic and anodic processes in an MES is able to improve or complete the mineralization of the target halogen-, nitro- and azo-organics via a sequential reductive and oxidative process. In this way, a cathode is used to reduce the toxic target organics, while an anode is to oxidize the residual organics from the reduction process and at the same time generate electrons to support the reduction process. This paper has provided a concise review about the sequential cathode-anode contaminant degradation in an MES and its specific mechanisms. Potential strategies to improve the MES degradation performance were discussed, mainly including the application and development of the biocatalyzed cathode as well as the optimization of the anodic operating condition and the improvement of anodic bacteria and electrode material. Perspectives on future directions were proposed and the key challenges were identified as the competitive or inhibitive influence of other compounds that could coexist in real wastewater on the target contaminants.

BackgroundHepatitis B surface antigen (HBsAg) loss is considered a functional cure for chronic hepatitis B (CHB), however, several factors influence HBsAg loss.Methods29 CHB patients who had achieved HBsAg loss, were selected and 58 CHB patients with persistent HBsAg were matched, according to gender and age (+/- 3 years). Logistic regression and restricted cubic spline (RCS) modelling were performed.ResultsMultivariate-adjusted logistic regression, based on stepwise selection, showed that baseline HBsAg levels negatively correlated with HBsAg loss (odds ratio [OR] = 0.99, 95% confidence interval [CI] = 0.98-0.99). Interferon treatment positively related with HBsAg loss (OR = 7.99, 95%CI = 1.62-44.88). After adjusting for age, HBsAg level, ALT level, HBeAg status and interferon treatment, MMP-1 (OR = 0.66, 95%CI = 0.44-0.97), CXCL9 (OR = 0.96, 95%CI = 0.93-0.99) and TNF-R1 (OR = 0.97, 95%CI = 0.94-0.99) baseline levels all negatively correlated with HBsAg loss. Our multivariate-adjusted RCS model showed that baseline CXCL10 was associated with HBsAg loss although the relationship was “U-shaped”.ConclusionsCytokines such as MMP-1, CXCL9, CXCL10 and TNF-R1 are important factors which influence HBsAg loss. It may be possible to develop a nomogram which intercalates these factors; however, further research should consider immune processes involved in HBsAg loss.

Background and aimChronic hepatitis B (CHB) can be divided into immune tolerance (IT), immune clearance (IC), hepatitis B e antigen (HBeAg)-negative inactive/quiescent carrier (ENQ), and HBeAg-negative hepatitis (ENH) phases. The conventional biomarkers used to distinguish these phases have limitations. We examined the clinical significance of hepatitis B virus (HBV) RNA and hepatitis B core-related antigen (HBcrAg) as novel biomarkers.MethodsOne hundred eighty-nine patients without treatment currently were categorized by CHB phase (IT = 46, IC = 45, ENQ = 49, ENH = 49). The associations of HBV RNA and HBcrAg with HBV DNA and alanine transaminase (ALT) were analyzed. The decision tree model was used to distinguish the four phases in the natural course of CHB.ResultsThe concentrations of HBV RNA and HBcrAg were highest in the IT and IC phases (P < 0.01). Serum HBV RNA was similar to HBcrAg in treatment-naïve patients. HBV RNA and HBcrAg correlated with HBV DNA in the HBeAg+ and HBeAg− status (HBV RNA: e+ r = 0.51, e− r = 0.62; HBcrAg: e+ r = 0.51, e− r = 0.71), but their association with HBV DNA differed among phases. The accuracy, sensitivity, and specificity of HBcrAg with ALT in distinguishing the CHB phases were 95.65%, 95.83%, and 95.55%, respectively.ConclusionSerum HBV RNA and HBcrAg may be useful to monitor CHB progression.

The theory of cultural capital has long been applied to explain academic achievement. This qualitative study investigated first-year students in higher vocational colleges and, taking this low cultural capital group as an example, explored how cultural capital has affected their past teacher–student interactions. Participants described their experience of interacting with their teachers. We found that these students primarily experienced authoritative and laissez-faire teacher–student interactions. Students’ embodied and institutionalized cultural capital profoundly affected different aspects of the teacher–student interaction. In modern China, vocational and academic education are of the same status, so teachers should modify their evaluations based solely on academic achievement. When interacting with students, teachers should be more understanding and interact in a more student-centred way. Teacher training programmes should also be reformed, and training for teachers in general and vocational schools should be distinguished.

In recent years, skeleton-based human action recognition has garnered significant research attention, with proposed recognition or segmentation methods typically validated on large-scale coarse-grained action datasets. However, there remains a lack of research on the recognition of small-scale fine-grained human actions using deep learning methods, which have greater practical significance. To address this gap, we propose a novel approach based on heatmap-based pseudo videos and a unified, general model applicable to all modality datasets. Leveraging anthropometric kinematics as prior information, we extract common human motion features among datasets through an ad hoc pre-trained model. To overcome joint mismatch issues, we partition the human skeleton into five parts, a simple yet effective technique for information sharing. Our approach is evaluated on two datasets, including the public Nursing Activities and our self-built Tai Chi Action dataset. Results from linear evaluation protocol and fine-tuned evaluation demonstrate that our pre-trained model effectively captures common motion features among human actions and achieves steady and precise accuracy across all training settings, while mitigating network overfitting. Notably, our model outperforms state-of-the-art models in recognition accuracy when fusing joint and limb modality features along the channel dimension.

Abstract Background N 6 -methyladenosine (m6A) is the most common RNA modification in eukaryotes and has been implicated as a novel epigenetic marker that is involved in various biological processes. The pattern and functional dissection of m6A in the regulation of several major human viral diseases have already been reported. However, the patterns and functions of m6A distribution in plant disease bursting remain largely unknown. Results We analyse the high-quality m6A methylomes in rice plants infected with two devastating viruses. We find that the m6A methylation is mainly associated with genes that are not actively expressed in virus-infected rice plants. We also detect different m6A peak distributions on the same gene, which may contribute to different antiviral modes between rice stripe virus or rice black-stripe dwarf virus infection. Interestingly, we observe increased levels of m6A methylation in rice plant response to virus infection. Several antiviral pathway-related genes, such as RNA silencing-, resistance-, and fundamental antiviral phytohormone metabolic-related genes, are also m6A methylated. The level of m6A methylation is tightly associated with its relative expression levels. Conclusions We revealed the dynamics of m6A modification during the interaction between rice and viruses, which may act as a main regulatory strategy in gene expression. Our investigations highlight the significance of m6A modifications in interactions between plant and viruses, especially in regulating the expression of genes involved in key pathways.

Phosphorylation is one of the most extensively investigated post-translational modifications that orchestrate a variety of cellular signal transduction processes. The phosphorylation of virus-encoded proteins plays an important regulatory role in the infection cycle of such viruses in plants. In recent years, molecular mechanisms underlying the phosphorylation of plant viral proteins have been widely studied. Based on recent publications, our study summarizes the phosphorylation analyses of plant viral proteins and categorizes their effects on biological functions according to the viral life cycle. This review provides a theoretical basis for elucidating the molecular mechanisms of viral infection. Furthermore, it deepens our understanding of the biological functions of phosphorylation in the interactions between plants and viruses.

A facile and universal route has been developed to synthesize yolk-shelled metal oxides@N doped C spheres with controllable void size. Based on this approach, a yolk-shelled SnO2@N dopped C (yolk-shelled SnO2@NxC) composite has been successfully synthesized for lithium-ion battery electrodes. The reported yolk-shelled SnO2@NxC, with SnO2 as inner shell and core, can remarkably increase the volumetric energy density of electrodes. The void between shell and core could accommodate the volume expansion during lithiation. More importantly, the NxC outer layer as a framework maintains the structure's integrity and shortens the transmission distance of charges. The results confirm the much-improved electrochemical performance. The yolk-shelled SnO2@NxC electrode delivers excellent cycling stability of 740 mAh g−1 at a current density of 0.25C after 750 cycles and a superior rate performance of 500 mAh g−1 at 5C (1C = 800 mA g−1). In addition, some other yolk-shelled metal oxides such as yolk-shelled TiO2 and yolk-shelled Fe2O3 have also been directly synthesized through the method in this paper for their proper applications.

BackgroundTranscription factor (TF) regulates the transcription of DNA to messenger RNA by binding to upstream sequence motifs. Identifying the locations of known motifs in whole genomes is computationally intensive.Methodology/principal findingsThis study presents a computational tool, named "Grit", for screening TF-binding sites (TFBS) by coordinating transcription factors to their promoter sequences in orthologous genes. This tool employs a newly developed mixed Student's t-test statistical method that detects high-scoring binding sites utilizing conservation information among species. The program performs sequence scanning at a rate of 3.2 Mbp/s on a quad-core Amazon server and has been benchmarked by the well-established ChIP-Seq datasets, putting Grit amongst the top-ranked TFBS predictors. It significantly outperforms the well-known transcription factor motif scanning tools, Pscan (4.8%) and FIMO (17.8%), in analyzing well-documented ChIP-Atlas human genome Chip-Seq datasets.SignificanceGrit is a good alternative to current available motif scanning tools.

Since the beginning of year 2020, when the whole world were undergoing the COVID-19 epidemic, all schools were lockout and classes were suspended until e-learning was rushed to be online for students to continue their learning, including the students in China. Although many studies had discussed the effectiveness of online learning from many different points of views, it still remained many uncertainties on the qualities of distance learning, especially when under the circumstances of rush and students’ involuntary learning. This manuscript attempted to determine whether students’ learning expectation reach the qualities of distance learning or not. In this manuscript, the snowball sampling method was adopted to have 356 students who studied at vocational-technical teachers’ college in China. Based on the expectation confirmation theory and its model, five hypotheses were proposed to construct a research model to determine relationship between student’s expectancy belief, course satisfaction, learning effectiveness, and continuous learning intention when facing the dilemma of classes suspended but learning continues, learning online during this ongoing pandemic. The results of this study showed that: (1) The expectancy value belief were positively related with theoretical course satisfaction, but negatively related with practical course satisfaction; (2) Theoretical course satisfaction and practical course satisfaction were positively related with learning effectiveness; and (3) Learning effectiveness was positively related with continuance to learn. In addition, three factors influencing the most on the qualities of theoretical course were environmental interference such as surrounding noises, poor internet connection, and poor absorption of learning contents, respectively, while three factors influencing the most on the qualities of practical course were inaccessible to practice, poor absorption of learning content, and lack of practical materials, respectively. Based on the results analyzed, this study suggested schools and teachers focused on how to improve the qualities and to reduce or prevent any disturbances to the class given to fulfill students’ class expectation first and then to ensure students’ learning effectiveness and intention to continuous learning.

As short video addiction has gradually become an emerging Internet behavioral addiction, its negative impacts on the student population have been noticed and cannot be ignored. Based on a literature review, this study referred to the person-process-content framework and drew on the ecosystem theory to define the relationship between short video addiction, learning avoidance motivation, learning commitment, and silent classroom behavior by using structural equation modeling. This study recruited 1000 participants from Chinese vocational colleges to fill out questionnaires. With an effective recovery rate of 94.6%, there were 946 effective study participants comprising 445 males (47%) and 501 females (53%). After it was confirmed that the data passed the reliability and validity tests, structural equation model analysis was carried out. The study results showed that short video addiction was positively correlated with learning avoidance motivation, but negatively correlated with learning commitment; on the other hand, study avoidance motivation was negatively correlated with learning commitment, but positively correlated with silent classroom behavior. Meanwhile, there was a negative correlation between learning commitment and silent classroom behavior. As a result, the negative correlation between short video addiction and learning behavior needs attention from parents and teachers.

Pulmonary arterial hypertension (PAH) is a group of severe, progressive, and debilitating diseases with limited therapeutic options. This study aimed to explore novel therapeutic targets in PAH through bioinformatics and experiments. Weighted gene co-expression network analysis (WGCNA) was applied to detect gene modules related to PAH, based on the GSE15197, GSE113439, and GSE117261. GSE53408 was applied as validation set. Subsequently, the validated most differentially regulated hub gene was selected for further ex vivo and in vitro assays. PARM1, TSHZ2, and CCDC80 were analyzed as potential intervention targets for PAH. Consistently with the bioinformatic results, our ex vivo and in vitro data indicated that PARM1 expression increased significantly in the lung tissue and/or pulmonary artery of the MCT-induced PAH rats and hypoxia-induced PAH mice in comparison with the respective controls. Besides, a similar expression pattern of PARM1 was found in the hypoxia- and PDGF--treated isolated rat primary pulmonary arterial smooth muscle cells (PASMCs). In addition, hypoxia/PDGF--induced PARM1 protein expression could promote the elevation of phosphorylation of AKT, phosphorylation of FOXO3A and PCNA, and finally the proliferation of PASMCs in vitro, whereas PARM1 siRNA treatment inhibited it. Mechanistically, PARM1 promoted PAH via AKT/FOXO3A/PCNA signaling pathway-induced PASMC proliferation.

Saro_0803 is a transcriptional factor modulating the transcription of the stilbene-degrading enzyme gene nov1 in Novosphingobium aromaticivorans DSM 12444. Reportedly, Saro_0803 undergoes resveratrol-mediated dissociation from the nov1 promotor and distinguishes resveratrol from its precursors, p-coumaric acid and trans-cinnamic acid, enabling the transcriptional factor to serve as a biosensor component for regulating resveratrol biosynthesis. However, little is known about the molecular mechanisms underlying the Saro_0803 interactions with either the nov1 promotor gene or resveratrol, which undermines the potential for Saro_0803 to be further modified for improved biosynthetic performance and other applications. Here, we report the discovery of the 22 bp A/T-rich Saro_0803 binding site near the −10 box of the nov1 promotor (named nov1p22bp). As validated by molecular docking-guided mutagenesis and binding affinity assays, the Saro_0803 binding of its target DNA sequence relies on charge-predominating interactions between several typical positively charged residues and nucleic acid. Furthermore, we semi-quantified the influence of resveratrol presence on Saro_0803–nov1p22bp interaction and identified a bilateral hydrophobic pocket within Saro_0803 comprising four aromatic residues that are crucial to maintaining the resveratrol binding capability of the transcriptional factor. Our data are beneficial to understanding saro_0803′s structural and functional properties, and could provide theoretical clues for future adaptations of this transcriptional factor.

Abstract Aims/Introduction Despite increasing interest in growth differentiation factor 11 (GDF11) based on its involvement in age‐related disorders, clinical implications – especially for metabolic diseases – remain unclear. Therefore, we assessed the association between serum GDF11 levels and metabolic disturbance in the Chinese population. Materials and Methods A total of 381 individuals from the Shanghai Nicheng Cohort Study were included. In addition to anthropometry, laboratory and ultrasonography measurements, serum concentrations of GDF11 were measured by enzyme‐linked immunosorbent assay. Results Circulating GDF11 concentrations were unchanged with age (r = –0.064, P = 0.210), but showed an inverse relationship to body mass index, waist circumference and fat‐free mass index (all P

Lotus is one of the most important aquatic vegetables in China. Previously, we detected sweet potato latent virus from lotus (SPLV-lotus) and found that it has highly significant sequence diversity with SPLV-sweet potato isolates (SPLV-sp). Here, we developed serological methods for the detection of SPLV-lotus in Chinese lotus cultivation areas. Based on the high sensitivity of SPLV-lotus coat protein antiserum, rapid, sensitive and large-scale diagnosis methods of enzyme-linked immunosorbent assay (ELISA) and dot blot in lotus planting area were developed. The established ELISA and dot blot diagnostic methods can be used to detect SPLVlotus from samples successfully. And our results also showed that the SPLV-lotus and sweet potato isolates appeared clearly distinction in serology. Our study provides a high-throughput, sensitive, and rapid diagnostic method based on serology that can detect SPLV on lotus, which is suggested to be included in viral disease management approach due to its good detection level.

Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-level complexity is far from being well characterized up to date. As the indicator of strain-level complexity, metagenomic single-nucleotide polymorphisms (SNPs) have been utilized to disentangle conspecific strains. Lots of SNP-based tools have been developed to identify strains in metagenomes. However, the sufficient sequencing depth for SNP and strain-level analysis remains unclear. We conducted ultra-deep sequencing of the human gut microbiome and constructed an unbiased framework to perform reliable SNP analysis. SNP profiles of the human gut metagenome by ultra-deep sequencing were obtained. SNPs identified from conventional and ultra-deep sequencing data were thoroughly compared and the relationship between SNP identification and sequencing depth were investigated. The results show that the commonly used shallow-depth sequencing is incapable to support a systematic metagenomic SNP discovery. In contrast, ultra-deep sequencing could detect more functionally important SNPs, which leads to reliable downstream analyses and novel discoveries. We also constructed a machine learning model to provide guidance for researchers to determine the optimal sequencing depth for their projects (SNPsnp, https://github.com/labomics/SNPsnp). To conclude, the SNP profiles based on ultra-deep sequencing data extend current knowledge on metagenomics and highlights the importance of evaluating sequencing depth before starting SNP analysis. This study provides new ideas and references for future strain-level investigations.

Teachers and parents are key participants in the growth of secondary vocational students, and can jointly support them. This study aimed to explore how the support of teachers and parents affects the academic performance of secondary vocational students and to reveal the ‘black box’ mechanism for their interaction and relationship. We adapted a Chinese version of a scale for secondary vocational students’ perception of teacher and parent support, drawing on self-determination theory. A survey was conducted through the Chinese questionnaire platform wjx.cn, and respondents were fully informed about the research. Data were collected from 710 secondary vocational students in Shanghai and analyzed using SPSS and AMOS. Our hypothesis model was verified, with results indicating that the support of autonomy, emotion, and ability provided by parents and teachers has a significant impact on students’ academic performance. The degree of learning engagement plays a mediating role in the relationship between support and achievement. The achievement goal orientation of students can adjust individual learning engagement, and further, affect the influence of teacher and parent support on academic performance. Teacher and parent support interactively influence learning engagement. The study findings suggest that an optimization strategy is needed to promote the academic improvement of secondary vocational students to meet students’ basic psychological needs and promote cooperation between family and school. Parents and teachers should also consider the impact of learning engagement and the learning process, and offer guidance in developing an appropriate achievement goal orientation and a positive learning concept.

BackgroundProphylactic granulocyte-colony stimulating factor (G-CSF) has been shown to effectively prevent febrile neutropenia (FN) and grade 3/4 neutropenia during myelosuppressive treatment. The present study reports the clinical efficacy and safety of the prophylactic use of G-CSF with a half dose for cancer patients with an intermediate risk of FN combined with ≥1 patient-specific risk during multiple chemotherapy.MethodsThis multicenter, one-arm, and open-label clinical study involved 151 patients [median age, 54 years old (range, 46.0–62.5); 38.4% female] with malignant tumors, including >20 different cancers. These patients underwent a total of 604 cycles of chemotherapy and received a half dose of PEG-rhG-CSF administration prior to each cycle.ResultsThe incidence rate of FN was 3.3% for this cohort during chemotherapy. Chemotherapy delay occurred in 6 (4.0%) patients for 12 (2.0%) cycles. Early termination of cancer treatment occurred in 14 (9.3%) patients. In this cohort, 23 (15.2%) patients required antibiotic use during courses of chemotherapy. A total of 28 (18.5%) patients experienced clear adverse effects during cancer treatment.ConclusionThe prophylactic PEG-rhG-CSF with a half dose can both efficaciously and safely prevent neutropenia for patients of diverse cancers with an intermediate risk of FN combined with ≥1 patient-specific risk during chemotherapy.

Technology innovation has led to an increase in floating wind turbine size aimed at releasing the pressure on capital cost and increasing its capacity factor. Large-size turbines pose high challenges regarding design with essential structure reliability. The dynamic performance of an integrated 15-megawatt (MW) wind turbine under extreme sea loads is investigated in this paper. Platform motions, mooring system positioning forces, sizeable blades, and tower behaviors are all studied under the targeted typhoon condition and extreme coherent gust with direction change (ECD) wind condition. Potential flow theory is used to analyze the first-order wave load, mean-drift wave load, and second-order difference-frequency wave load on the substructure of the ultra-large 15 MW floating offshore wind turbine (FOWT). The blade element momentum (BEM) theory is adopted for the calculation of the aerodynamic loads on the floating wind turbine, and the finite element method (FEM) is applied to analyze the mooring lines of the floating wind turbine. The results show that the effect of quadratic transfer function (QTF) will significantly increase the dynamic response of FOWT under the typhoon sea state. The ECD wind condition has an influential impact on the motion responses, the axial force of the mooring lines, and structural responses under the normal operating state.

Accurate and timely crop mapping is crucial for environment assessment, food security and agricultural production. However, for the areas with high landscape heterogeneity and frequent cloudy and rainy weather, the insufficient high-quality satellite images limit the accuracy of crop classification. The recently launched Chinese GF-6 wide field-of-view camera (WFV) with a revisit cycle of 4-day and spatial resolution of 16-meter shows great potential for agricultural monitoring. In this study, Qianjiang City characterized by complex agricultural landscapes was selected as the research area to assess the potential of GF-6 data in identifying crop types. Firstly, the pairwise and global separability were calculated to analyze the effect of different spectral-temporal features of GF-6 images on crop classification. A total of 255 spectral-temporal features derived from 15 GF-6 tiles were then used to perform random forest classification. Furthermore, the classification results were evaluated based on 671 field samples and then compared the accuracy between GF-6 data and Sentinel-2 or Landsat-8 data. In addition, the earliest identifiable time of crop types was also determined by iteratively using all available GF-6 data during each time period. The results suggested that the overall accuracy (OA) of all available GF-6 images was 91.55%, which was significantly higher than that of Landsat-8 data (OA = 85.97%) and was slightly lower than that of Sentinel-2 data (OA = 93.10%). The newly added red-edge bands (0.69 ∼ 0.73 μm, 0.73 ∼ 0.77 μm) and their derivative vegetation indices were important spectral features, and the period from mid-March to early-April was the best temporal window for crop identification in our research area. Moreover, late July was the earliest crop identifiable time with overall accuracy of 90% for the first time of the year. These results indicated the great potential of GF-6 images for classifying crop types in the areas with complex cropping system and fragmented agricultural landscapes, particularly when integrating other satellite data with comparable spatial resolution (e.g. Chinese GF-1 data and Sentinel-2 data).

Lotus (Nelumbo nucifera Gaertn.) is an economically important perennial aquatic herb plant in China. Presently, viral diseases caused by Cucumber mosaic virus (CMV) and Dasheen mosaic virus (DsMV) are detected in lotus. In this study, the putatively novel badnaviruses from lotus plants (LBVs) were identified using next-generation sequencing of siRNAs and conventional Sanger sequencing based on the RT/RNase H coding region sequences. Lotus plants infected by LBVs harbored virus quasispecies. A unique recombination event of LBVs was found in a single lotus plant. Sap inoculation showed that LBVs could actively replicate in the lotus plants, but without a wide host range. The field survey of 43 lotus plants in Jiangsu Province showed a prevalence of 62.8% for LBVs, confirming that it is widely distributed in Jiangsu Province of China.

Rechargeable magnesium ion batteries are potential candidates to replace the lithium ion batteries due to their high volumetric energy density, dendrite free cycling, and low costs. In present work, we have critically reviewed the recent advances made in the field of cathode materials development to achieve the high reversible capacities and working potentials. In first part, carbon-based cathodes such as fluorine-doped graphene nanosheets and graphite fluoride (CF0.8) are discussed in terms of compatibilities of positive electrode materials and electrolyte solutions for rechargeable magnesium-ion batteries. Whereas, the second part of this review focuses on crystal structure of vanadium oxide and its capability to accommodate the Mg2+ ions. Likewise, electrochemical performance of selected vanadium oxide based cathodes including VO2 (B), FeVO4.0.9H2O, Mo2.5+yVO9+δ, RFC/V2O5 and V2O5/Graphene composite, are discussed at different temperatures. To support the future research on magnesium ion batteries, particularly positive electrode material developments, several innovative research directions are proposed.

Abstract Background Repeat-induced point (RIP) mutation in Neurospora crassa degrades transposable elements by targeting repeats with C→T mutations. Whether RIP affects core genomic sequence in important ways is unknown. Results By parent-offspring whole genome sequencing, we estimate a mutation rate (3.38 × 10−6 per bp per generation) that is two orders of magnitude higher than reported for any non-viral organism, with 93–98% of mutations being RIP-associated. RIP mutations are, however, relatively rare in coding sequence, in part because RIP preferentially attacks GC-poor long duplicates that interact in three dimensional space, while coding sequence duplicates are rare, GC-rich, short, and tend not to interact. Despite this, with over 5 coding sequence mutations per genome per generation, the mutational burden is an order of magnitude higher than the previously highest observed. Unexpectedly, the majority of these coding sequence mutations appear not to be the direct product of RIP being mostly in non-duplicate sequence and predominantly not C→T mutations. Nonetheless, RIP-deficient strains have over an order of magnitude fewer coding sequence mutations outside of duplicated domains than RIP-proficient strains. Conclusions Neurospora crassa has the highest mutation rate and mutational burden of any non-viral life. While the high rate is largely due to the action of RIP, the mutational burden appears to be RIP-associated but not directly caused by RIP.

Abstract Background Non-invasive diagnostic markers are of great importance for early screening nonalcoholic fatty liver disease (NAFLD). MicroRNAs (miRNAs) play significant roles in many metabolic disease, including NAFLD. Therefore, this study focusd on a Chinese population to explore the possible correlation between circulating miR-132 and NAFLD. Results Serum miR-132 was positively associated with NAFLD in non-type 2 diabetes mellitus (T2DM) groups by logistic regression (OR = 3.082 [1.057, 8.988], P = 0.039) after adjusting age, sex, and body mass index (BMI). Additionally, in non-T2DM subgroup, after adjusting age, sex, bmi, serum miR-132 was significantly associated with ALT (β ± SE = 0.005 ± 0.002, P = 0.018), TG (β ± SE = 0.072 ± 0.029, P = 0.015), FPG (β ± SE = 0.123 ± 0.058, P = 0.036), γ-GT (β ± SE = 0.002 ± 0.001, P = 0.047), apoE (β ± SE = 0.038 ± 0.002, P = 0.017) . Conclusions Serum miR-132 was found to be associated with NAFLD risk in a Chinese cross-section study. This finding provides a prospective research direction for early screening and diagnosing NAFLD.

Modern smartphone operating systems (e.g., Android 6.0 and later versions) employ an ask-on-first-use policy to regulate app permissions. To assist users in policy decisions, relevant efforts have been focusing on leveraging contexts to capture users' privacy preferences. However, these techniques have various limitations, such as heavily relying on users' historical decisions on granting permissions, ignoring the fact that users are not experts on privacy protection, and hard to determine whether a permission shall be granted. To address this problem, we propose an autonomous permission recommendation system, AutoPer+, to automatically recommend users the permission decisions at runtime. The main insight of our proposed system is that the natural language description of an app reflects its functionality and its similarity to other apps, and thus can be used to analyze whether a permission is needed indeed by it, and the apps similar to it. First, we introduce a multi-topic model into app functionality mining, and design a topic-permission mapper for the proposed recommendation system. Then we propose a deep semi-supervised machine using Long Short-Term Memory (LSTM) neural networks to identify similar apps, by which we can explore privacy permission usage in a cluster of apps. Finally, we capture a trade-off between privacy and utility to present a systematic recommendation. In addition to the permission decision (“Allow” or “Deny”), the permission explanations are also provided for users to make decisions (called “Ask”). We evaluate the proposed system via extensive comparison experiments on 31,023 Android apps. The results show that our approach achieves an accuracy of 84.1%. Moreover, we conduct user studies via installing AutoPer+ in the participants' own Android devices. We receive positive responses from the participants, which implies AutoPer+ is potentially for real-world deployment for enhancing current permission recommendation.

Fast light which demonstrates negative group velocity, is achieved by the anomalous dispersion or photon tunneling. However, many applications based on the fast light are limited due to the disadvantages of inferior tunability or nonlinear dispersion relationship of the fast light-carrying medium. In this paper, we propose the graphene plasmonic crystal waveguides whose guiding and claddings are composed of the graphene plasmonic metamaterials, where the backward propagating plasmonic modes corresponding to negative group velocity are observed. The dispersion relation and the group velocity of three types of graphene plasmonic crystal waveguides are investigated by varying the materials and the geometrical parameters of the graphene plasmonic crystal waveguides. Numerical experiments are designed to verify the authenticity of a fast plasmon in the graphene plasmonic crystal waveguides. Our proposed graphene plasmonic crystal waveguides might find significant applications in the fields of nanophotonics, on-chip electromagnetic field manipulation in deep nanoscale, and the technique of high density plasmonic integrated plasmonic circuit in the future.

Pole-to-ground (PTG) fault analysis is of vital importance for high-voltage direct current (HVDC) grid. However, many factors are not considered in the existing studies such as the asymmetrical property of PTG fault, the coupling issue between DC transmission lines and the complexity of the structure of DC grid. This paper presents a PTG fault analysis method, which is based on common- and differential-mode (CDM) transformation. Similar to the symmetrical component method in AC system, the transformation decomposes the HVDC grid into CDM networks, which is balanced and decoupled. Then, a transfer impedance is defined and calculated based on the impedance matrices of the CDM networks. With the transfer impedance, analytical expressions of fault characteristics that vary with space and time are obtained. The proposed PTG fault analysis method is applicable to arbitrary HVDC grid topologies, and provides a new perspective to understand the fault mechanism. Moreover, the analytical expressions offer theoretical guidance for PTG fault protection. The validity of the proposed PTG fault analysis method is verified in comparison with the simulation results in PSCAD/EMTDC.

The characteristic of a flexible system with weak damping is that the amplitude characteristics show a large change, and the situation on the Nyquist chart is extremely complicated. Due to the existence of weak damping, the perturbation range of the flexible modal parameters allowed by the system is greatly compressed, and the robust stability is reduced, which brings challenges to the control design. To deal with this problem, the traditional H-inf loop shaping method often leads to the controller instability and poor robustness. In order to improve the stability margin of the system, increase the robustness and stability, and solve the problem of the instability of H-infcontrollers, this paper proposes a robust comprehensive design method based on phase control. In this method, by using the closed-loop pole configuration corresponding to the flexible mode at low frequency, the phase control is realized. Through the phase control, the apex of the Nyquist curve of the weakly damped mode of the system is limited to the Right half plane. The H-inf weighting function relaxes the pole configuration requirements, reduces the conservativeness of strict positive real, and combines the phase control with the H-inf optimization solution to obtain a robust and self-stable H-inf controller. A specific design example is given. The simulation results show that this control method has extremely high robustness. This design example can provide a certain degree of robust design for other weakly damp.

Biomineralization is a typical interdisciplinary subject attracting biologists, chemists, and geologists to figure out its potential mechanism. A mounting number of studies have revealed that the classical nucleation theory is not suitable for all nucleation process of biominerals, and phase-separated structures such as polymer-induced liquid precursors (PILPs) play essential roles in the non-classical nucleation processes. These structures are able to play diverse roles biologically or pathologically, and could also give inspiring clues to bionic applications. However, a lot of confusion and dispute occurred due to the intricacy and interdisciplinary nature of liquid precursors. Researchers in different fields may have different opinions because the terminology and current state of understanding is not common knowledge. As a result, our team reviewed the most recent articles focusing on the nucleation processes of various biominerals to clarify the state-of-the-art understanding of some essential concepts and guide the newcomers to enter this intricate but charming field.