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Abstract Background In recent years, ophthalmology has emerged as a new frontier in medical artificial intelligence (AI) with multi-modal AI in ophthalmology garnering significant attention across interdisciplinary research. This integration of various types and data models holds paramount importance as it enables the provision of detailed and precise information for diagnosing eye and vision diseases. By leveraging multi-modal ophthalmology AI techniques, clinicians can enhance the accuracy and efficiency of diagnoses, and thus reduce the risks associated with misdiagnosis and oversight while also enabling more precise management of eye and vision health. However, the widespread adoption of multi-modal ophthalmology poses significant challenges. Main text In this review, we first summarize comprehensively the concept of modalities in the field of ophthalmology, the forms of fusion between modalities, and the progress of multi-modal ophthalmic AI technology. Finally, we discuss the challenges of current multi-modal AI technology applications in ophthalmology and future feasible research directions. Conclusion In the field of ophthalmic AI, evidence suggests that when utilizing multi-modal data, deep learning-based multi-modal AI technology exhibits excellent diagnostic efficacy in assisting the diagnosis of various ophthalmic diseases. Particularly, in the current era marked by the proliferation of large-scale models, multi-modal techniques represent the most promising and advantageous solution for addressing the diagnosis of various ophthalmic diseases from a comprehensive perspective. However, it must be acknowledged that there are still numerous challenges associated with the application of multi-modal techniques in ophthalmic AI before they can be effectively employed in the clinical setting.

Abstract Objective To develop an AI-assisted MRI model to identify surgical target areas in pediatric hip and periarticular infections. Methods A retrospective study was conducted on the pediatric patients with hip and periarticular infections who underwent Magnetic Resonance Imaging(MRI)examinations from January 2010 to January 2023 in three hospitals in China. A total of 7970 axial Short Tau Inversion Recovery (STIR) images were selected, and the corresponding regions of osteomyelitis (label 1) and abscess (label 2) were labeled using the Labelme software. The images were randomly divided into training group, validation group, and test group at a ratio of 7:2:1. A Mask R-CNN model was constructed and optimized, and the performance of identifying label 1 and label 2 was evaluated using receiver operating characteristic (ROC) curves. Calculation of the average time it took for the model and specialists to process an image in the test group. Comparison of the accuracy of the model in the interpretation of MRI images with four orthopaedic surgeons, with statistical significance set at P

Abstract Background Enhancer RNAs (eRNAs) play a crucial role in transcriptional regulation. While significant progress has been made in understanding epigenetic regulation mediated by eRNAs, research on the construction of eRNA-mediated gene regulatory networks (eGRN) and the identification of critical network components that influence complex traits is lacking. Results Here, employing the pig as a model, we conducted a comprehensive study using H3K27ac histone ChIP-seq and RNA-seq data to construct eRNA expression profiles from multiple tissues of two distinct pig breeds, namely Enshi Black (ES) and Duroc. In addition to revealing the regulatory landscape of eRNAs at the tissue level, we developed an innovative network construction and refinement method by integrating RNA-seq, ChIP-seq, genome-wide association study (GWAS) signals and enhancer-modulating effects of single nucleotide polymorphisms (SNPs) measured by self-transcribing active regulatory region sequencing (STARR-seq) experiments. Using this approach, we unraveled eGRN that significantly influence the growth and development of muscle and fat tissues, and identified several novel genes that affect adipocyte differentiation in a cell line model. Conclusions Our work not only provides novel insights into the genetic basis of economic pig traits, but also offers a generalizable approach to elucidate the eRNA-mediated transcriptional regulation underlying a wide spectrum of complex traits for diverse organisms.

We propose a visual Simultaneous Localization and Mapping (SLAM) algorithm that integrates target detection and clustering techniques in dynamic scenarios to address the vulnerability of traditional SLAM algorithms to moving targets. The proposed algorithm integrates the target detection module into the front end of the SLAM and identifies dynamic objects within the visual range by improving the YOLOv5. Feature points associated with the dynamic objects are disregarded, and only those that correspond to static targets are utilized for frame-to-frame matching. This approach effectively addresses the camera pose estimation in dynamic environments, enhances system positioning accuracy, and optimizes the visual SLAM performance. Experiments on the TUM public dataset and comparison with the traditional ORB-SLAM3 algorithm and DS-SLAM algorithm validate that the proposed visual SLAM algorithm demonstrates an average improvement of 85.70 and 30.92% in positioning accuracy in highly dynamic scenarios. In comparison to the DynaSLAM system using MASK-RCNN, our system exhibits superior real-time performance while maintaining a comparable ATE index. These results highlight that our pro-posed SLAM algorithm effectively reduces pose estimation errors, enhances positioning accuracy, and showcases enhanced robustness compared to conventional visual SLAM algorithms.

Abstract Eusocial pollinators are crucial elements in global agriculture. The honeybees and bumblebees are associated with a simple yet host-restricted gut community, which protect the hosts against pathogen infections. Recent genome mining has led to the discovery of biosynthesis pathways of bioactive natural products mediating microbe-microbe interactions from the gut microbiota. Here, we investigate the diversity of biosynthetic gene clusters in the bee gut microbiota by analyzing 477 genomes from cultivated bacteria and metagenome-assembled genomes. We identify 744 biosynthetic gene clusters (BGCs) covering multiple chemical classes. While gene clusters for the post-translationally modified peptides are widely distributed in the bee guts, the distribution of the BGC classes varies significantly in different bee species among geographic locations, which is attributed to the strain-level variation of bee gut members in the chemical repertoire. Interestingly, we find that Gilliamella strains possessing a thiopeptide-like BGC show potent activity against the pathogenic Melissococcus plutonius. The spectrometry-guided genome mining reveals a RiPP-encoding BGC from Gilliamella with a 10 amino acid-long core peptide exhibiting antibacterial potentials. This study illustrates the widespread small-molecule-encoding BGCs in the bee gut symbionts and provides insights into the bacteria-derived natural products as potential antimicrobial agents against pathogenic infections.

Cis-regulatory elements (CREs) play a pivotal role in orchestrating interactions with trans-regulatory factors such as transcription factors, RNA-binding proteins, and noncoding RNAs. These interactions are fundamental to the molecular architecture underpinning complex and diverse biological functions in living organisms, facilitating a myriad of sophisticated and dynamic processes. The rapid advancement in the identification and characterization of these regulatory elements has been marked by initiatives such as the Encyclopedia of DNA Elements (ENCODE) project, which represents a significant milestone in the field. Concurrently, the development of CRE detection technologies, exemplified by massively parallel reporter assays, has progressed at an impressive pace, providing powerful tools for CRE discovery. The exponential growth of multimodal functional genomic data has necessitated the application of advanced analytical methods. Deep learning algorithms, particularly large language models, have emerged as invaluable tools for deconstructing the intricate nucleotide sequences governing CRE function. These advancements facilitate precise predictions of CRE activity and enable the de novo design of CREs. A deeper understanding of CRE operational dynamics is crucial for harnessing their versatile regulatory properties. Such insights are instrumental in refining gene therapy techniques, enhancing the efficacy of selective breeding programs, pushing the boundaries of genetic innovation, and opening new possibilities in microbial synthetic biology.

In addition to filtering some sediments, hydrophobic wood sponges can also absorb many organic solvents, particularly crude oil. The leakage of crude oil poses a serious threat to the marine ecosystem, and oil mixed with water also generates great danger for its use. From the perspective of low cost and high performance, wood sponges exhibit great potential for dealing with crude oil pollution. Wood sponge is a renewable material. With a highly oriented layered structure and a highly compressible three-dimensional porous frame, wood sponges are extremely hydrophobic, making them ideal for oil–water separation. Currently, the most common approach for creating wood sponge is to first destroy the wood cell wall to obtain a porous-oriented layered structure and then enhance the oil–water separation ability via superhydrophobic treatment. Wood sponge prepared using various experimental methods and different natural woods exhibits distinctive properties in regards to robustness, compressibility, fatigue resistance, and oil absorption ability. As an aerogel material, wood sponge offers multi-action (absorption, filtration) and reusable oil–water separation functions. This paper introduces the advantages of the use of wood sponge for oil–water separation. The physical and chemical properties of wood sponge and its mechanism of adsorbing crude oil are explained. The synthesis method and the properties are discussed. Finally, the use of wood sponge is summarized and prospected.

Abstract The specific energy of Li metal batteries (LMBs) can be improved by using high‐voltage cathode materials; however, achieving long‐term stable cycling performance in the corresponding system is particularly challenging for the liquid electrolyte. Herein, a novel pseudo‐oversaturated electrolyte (POSE) is prepared by introducing 1,1,2,2‐tetrafluoroethyl‐2,2,3,3‐tetrafluoropropyl ether (TTE) to adjust the coordination structure between diglyme (G2) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). Surprisingly, although TTE shows little solubility to LiTFSI, the molar ratio between LiTFSI and G2 in the POSE can be increased to 1:1, which is much higher than that of the saturation state, 1:2.8. Simulation and experimental results prove that TTE promotes closer contact of the G2 molecular with Li+ in the POSE. Moreover, it also participates in the formation of electrolyte/electrode interphases. The electrolyte shows outstanding compatibility with both the Li metal anode and typical high‐voltage cathodes. Li||Li symmetric cells show a long life of more than 2000 h at 1 mA cm−2, 1 mAh cm−2. In the meantime, Li||LiNi0.8Co0.1Mn0.1O2 (NCM811) cell with the POSE shows a high reversible capacity of 134.8 mAh g−1 after 900 cycles at 4.5 V, 1 C rate. The concept of POSE can provide new insight into the Li+ solvation structure and in the design of advanced electrolytes for LMBs.

Campylobacter jejuni (C. jejuni) is a common pathogen that often causes diarrhea, loss of appetite, and even enteritis in domestic cats, affecting their growth and development, especially in kittens under 6 months of age. Oral passive immunization with chicken yolk antibody Y has been proved effective for the treatment of gastrointestinal pathogen infections due to its high specificity. In this study, C. jejuni was isolated from diarrheal cat feces, and the specific egg yolk antibody Y against C. jejuni was demonstrated to effectively inhibit its proliferation in vitro experiments. To evaluate the effect of anti-C. jejuni IgY, the mouse C. jejuni infection model was established and it was found that IgY could alleviate C. jejuni-induced clinical symptoms. Consistent with these results, the reduction of pro-inflammatory factors and intestinal colonization by C. jejuni in the IgY-treated groups, especially in the high dose group. We then evaluated the protective effect of IgY on young Ragdoll cats infected with C. jejuni. This specific antibody reduced the rate of feline diarrhea, protected the growth of young cats, inhibited systemic inflammatory hyperactivation, and increased fecal short-chain fatty acid concentrations. Notably, IgY may have a protective role by changing intestinal amino acid metabolism and affecting C. jejuni chemotaxis. Collectively, specific IgY is a promising therapeutic strategy for C. jejuni-induced cat diarrhea.

Labeled data is widely used in various classification tasks. However, there is a huge challenge that labels are often added artificially. Wrong labels added by malicious users will affect the training effect of the model. The unreliability of labeled data has hindered the research. In order to solve the above problems, we propose a framework of Label Noise Filtering and Missing Label Supplement (LNFS). And we take location labels in Location-Based Social Networks (LBSN) as an example to implement our framework. For the problem of label noise filtering, we first use FastText to transform the restaurant's labels into vectors, and then based on the assumption that the label most similar to all other labels in the location is most representative. We use cosine similarity to judge and select the most representative label. For the problem of label missing, we use simple common word similarity to judge the similarity of users' comments, and then use the label of the similar restaurant to supplement the missing labels. To optimize the performance of the model, we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model. Finally, a case study is given to illustrate the effectiveness and reliability of LNFS.

Maintaining homeostasis within the intestinal microbiota is imperative for assessing the health status of hosts, and dysbiosis within the intestinal microbiota is closely associated with canine intestinal diseases. In recent decades, the modulation of canine intestinal health through probiotics and prebiotics has emerged as a prominent area of investigation. Evidence indicates that probiotics and prebiotics play pivotal roles in regulating intestinal health by modulating the intestinal microbiota, fortifying the epithelial barrier, and enhancing intestinal immunity. This review consolidates literature on using probiotics and prebiotics for regulating microbiota homeostasis in canines, thereby furnishing references for prospective studies and formulating evaluation criteria.

Degradation of black soil areas is a serious threat to national food security and ecological safety; nevertheless, the current lack of information on the location, size, and condition of black soil farmland productivity is a major obstacle to the development of strategies for the sustainable utilization of black soil resources. We synthesized remote sensing data and geospatial thematic data to construct a farmland productivity assessment indicator system to assess the productivity of black soil cropland at the regional scale. Furthermore, we conducted research on the spatial differentiation patterns and a spatial autocorrelation analysis of the assessment results. We found that farmland productivity within this region exhibited a decline pattern from south to north, with superior productivity in the east as opposed to the west, and the distribution follows a “spindle-shaped” pattern. Notably, the Songnen and Sanjiang typical black soil subregions centrally hosted about 46.17% of high-quality farmland and 53.51% of medium-quality farmland, while the Mondong typical black soil subregion in the west predominantly consisted of relatively low-quality farmland productivity. Additionally, farmland productivity displayed a significant positive spatial correlation and spatial clustering, with more pronounced fluctuations in the northeast–southwest direction. The developed indicator system for farmland productivity can illustrate the spatial differentiation and thereby offer a valuable reference for the sustainable management of farmland resources.

Abstract Background The genetic mechanisms that underlie phenotypic differentiation in breeding animals have important implications in evolutionary biology and agriculture. However, the contribution of cis-regulatory variants to pig phenotypes is poorly understood. Therefore, our aim was to elucidate the molecular mechanisms by which non-coding variants cause phenotypic differences in pigs by combining evolutionary biology analyses and functional genomics. Results We obtained a high-resolution phased chromosome-scale reference genome with a contig N50 of 18.03 Mb for the Luchuan pig breed (a representative eastern breed) and profiled potential selective sweeps in eastern and western pigs by resequencing the genomes of 234 pigs. Multi-tissue transcriptome and chromatin accessibility analyses of these regions suggest that tissue-specific selection pressure is mediated by promoters and distal cis-regulatory elements. Promoter variants that are associated with increased expression of the lysozyme (LYZ) gene in the small intestine might enhance the immunity of the gastrointestinal tract and roughage tolerance in pigs. In skeletal muscle, an enhancer-modulating single-nucleotide polymorphism that is associated with up-regulation of the expression of the troponin C1, slow skeletal and cardiac type (TNNC1) gene might increase the proportion of slow muscle fibers and affect meat quality. Conclusions Our work sheds light on the molecular mechanisms by which non-coding variants shape phenotypic differences in pigs and provides valuable resources and novel perspectives to dissect the role of gene regulatory evolution in animal domestication and breeding.

Edge-computing-enabled smart greenhouses are a representative application of the Internet of Things (IoT) technology, which can monitor the environmental information in real-time and employ the information to contribute to intelligent decision-making. In the process, anomaly detection for wireless sensor data plays an important role. However, the traditional anomaly detection algorithms originally designed for anomaly detection in static data do not properly consider the inherent characteristics of the data stream produced by wireless sensors such as infiniteness, correlations, and concept drift, which may pose a considerable challenge to anomaly detection based on data stream and lead to low detection accuracy and efficiency. First, the data stream is usually generated quickly, which means that the data stream is infinite and enormous. Hence, any traditional off-line anomaly detection algorithm that attempts to store the whole dataset or to scan the dataset multiple times for anomaly detection will run out of memory space. Second, there exist correlations among different data streams, and traditional algorithms hardly consider these correlations. Third, the underlying data generation process or distribution may change over time. Thus, traditional anomaly detection algorithms with no model update will lose their effects. Considering these issues, a novel method (called DLSHiForest) based on Locality-Sensitive Hashing and the time window technique is proposed to solve these problems while achieving accurate and efficient detection. Comprehensive experiments are executed using a real-world agricultural greenhouse dataset to demonstrate the feasibility of our approach. Experimental results show that our proposal is practical for addressing the challenges of traditional anomaly detection while ensuring accuracy and efficiency.

The development and growth of porcine skeletal muscle determine pork quality and yield. While genetic regulation of porcine skeletal muscle development has been extensively studied using various omics data, the role of transposable elements (TEs) in this context has been less explored. To bridge this gap, we constructed a comprehensive atlas of TE expression throughout the developmental stages of porcine skeletal muscle. This was achieved by integrating porcine TE genomic coordinates with whole-transcriptome RNA-Seq data from 27 developmental stages. We discovered that in pig skeletal muscle, active Tes are closely associated with active epigenomic marks, including low levels of DNA methylation, high levels of chromatin accessibility, and active histone modifications. Moreover, these TEs include 6074 self-expressed TEs that are significantly enriched in terms of muscle cell development and myofibril assembly. Using the TE expression data, we conducted a weighted gene co-expression network analysis (WGCNA) and identified a module that is significantly associated with muscle tissue development as well as genome-wide association studies (GWAS) of the signals of pig meat and carcass traits. Within this module, we constructed a TE-mediated gene regulatory network by adopting a unique multi-omics integration approach. This network highlighted several established candidate genes associated with muscle-relevant traits, including HES6, CHRNG, ACTC1, CHRND, MAMSTR, and PER2, as well as novel genes like ENSSSCG00000005518, ENSSSCG00000033601, and PIEZO2. These novel genes hold promise for regulating muscle-related traits in pigs. In summary, our research not only enhances the TE-centered dissection of the genetic basis underlying pork production traits, but also offers a general approach for constructing TE-mediated regulatory networks to study complex traits or diseases.

The microRNAs (miRNAs) play an important role in regulating myogenesis by targeting mRNA. However, the understanding of miRNAs in skeletal muscle development and diseases is unclear. In this study, we firstly performed the transcriptome profiling in differentiating C2C12 myoblast cells. Totally, we identified 187 miRNAs and 4260 mRNAs significantly differentially expressed that were involved in myoblast differentiation. We carried out validation of microarray data based on 5 mRNAs and 5 miRNAs differentially expressed and got a consistent result. Then we constructed and validated the significantly up- and down-regulated mRNA-miRNA interaction networks. Four interaction pairs (miR-145a-5p-Fscn1, miR-200c-5p-Tmigd1, miR-27a-5p-Sln and miR-743a-5p-Mob1b) with targeted relationships in differentiated myoblast cells were demonstrated. They are all closely related to myoblast development. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated cell cycle signals important for exploring skeletal muscle development and disease. Functionally, we discovered that miR-743a targeting gene Mps One Binder Kinase Activator-Like 1B (Mob1b) gene in differentiated C2C12. The up-regulated miR-743a can promote the differentiation of C2C12 myoblast. While the down-regulated Mob1b plays a negative role in differentiation. In addition, the expression profile of miR-743a and Mob1b are consistent with skeletal muscle recovery after Cardiotoxin (CTX) injury. Our study revealed that miR-743a-5p regulates myoblast differentiation by targeting Mob1b involved in skeletal muscle development and regeneration. Our findings made a further exploration for mechanisms in myogenesis and might provide potential possible miRNA-based target therapies for skeletal muscle regeneration and disease in the near future.

In high-dimensional data analysis, the bi-level (or the sparse group) variable selection can simultaneously conduct penalization on the group level and within groups, which has been developed for continuous, binary, and survival responses in the literature. Zhou et al. (2022) (PMID: 35766061) has further extended it under the longitudinal response by proposing a quadratic inference function-based penalization method in gene–environment interaction studies. This study introduces “springer,” an R package implementing the bi-level variable selection within the QIF framework developed in Zhou et al. (2022). In addition, R package “springer” has also implemented the generalized estimating equation-based sparse group penalization method. Alternative methods focusing only on the group level or individual level have also been provided by the package. In this study, we have systematically introduced the longitudinal penalization methods implemented in the “springer” package. We demonstrate the usage of the core and supporting functions, which is followed by the numerical examples and discussions. R package “springer” is available at https://cran.r-project.org/package=springer.

Background: Corneal fluorescein sodium staining is a valuable diagnostic method for various ocular surface diseases. However, the examination results are highly dependent on the subjective experience of ophthalmologists. Objectives: To develop an artificial intelligence system based on deep learning to provide an accurate quantitative assessment of sodium fluorescein staining score and the size of cornea epithelial patchy defect. Design: A prospective study. Methods: We proposed an artificial intelligence system for automatically evaluating corneal staining scores and accurately measuring patchy corneal epithelial defects based on corneal fluorescein sodium staining images. The design incorporates two segmentation models and a classification model to forecast and assess the stained images. Meanwhile, we compare the evaluation findings from the system with ophthalmologists with varying expertise. Results: For the segmentation task of cornea boundary and cornea epithelial patchy defect area, our proposed method can achieve the performance of dice similarity coefficient (DSC) is 0.98/0.97 and Hausdorff distance (HD) is 3.60/8.39, respectively, when compared with the manually labeled gold standard. This method significantly outperforms the four leading algorithms (Unet, Unet++, Swin-Unet, and TransUnet). For the classification task, our algorithm achieves the best performance in accuracy, recall, and F1-score, which are 91.2%, 78.6%, and 79.2%, respectively. The performance of our developed system exceeds seven different approaches (Inception, ShuffleNet, Xception, EfficientNet_B7, DenseNet, ResNet, and VIT) in classification tasks. In addition, three ophthalmologists were selected to rate corneal staining images. The results showed that the performance of our artificial intelligence system significantly outperformed the junior doctors. Conclusion: The system offers a promising automated assessment method for corneal fluorescein staining, decreasing incorrect evaluations caused by ophthalmologists’ subjective variance and limited knowledge.

This work reports a novel carrier flotation protocol for removing scaling cations from an oilfield produced water source which significantly reduces the collector consumption by employing natural minerals such as quartz, montmorillonite and talcum as the scaling cations carriers. The scaling cations uptake onto all carrier minerals exhibited homogeneous and monolayer adsorption, which was mainly dominated by physisorption. After adding oleate collector, the scaling cations removal rate was further enhanced, which was attributed to its high affinity with the scaling cations. Notably, the talcum flotation process simultaneously offered a high scaling cations removal rate (76.1%) and mineral recovery rate (98.3%), which achieved a sediment yield reduction of 72.2%. By summarizing the characterization results, the scaling cations removal mechanisms were also proposed. Moreover, high regeneration efficiencies (86.1% and 84.8% for quartz and talcum regeneration within three cycles) were achieved by the proposed regeneration protocol. This carrier flotation protocol with its low collector consumption offered technical promise for scaling cations removal from oilfield produced water. HIGHLIGHTS Novel carrier flotation protocol to remove scaling cations from produced water.; Outstanding scaling inhibition and carrier mineral recycle efficiencies simultaneously achieved.; Comprehensive removal mechanisms of scaling cations are explored.; Spent carrier minerals could be effectively regenerated.;

ObjectiveThis study aimed at establishing a new model to predict malignant thyroid nodules using machine learning algorithms.MethodsA retrospective study was performed on 274 patients with thyroid nodules who underwent fine-needle aspiration (FNA) cytology or surgery from October 2018 to 2020 in Xianyang Central Hospital. The least absolute shrinkage and selection operator (lasso) regression analysis and logistic analysis were applied to screen and identified variables. Six machine learning algorithms, including Decision Tree (DT), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Naive Bayes Classifier (NBC), Random Forest (RF), and Logistic Regression (LR), were employed and compared in constructing the predictive model, coupled with preoperative clinical characteristics and ultrasound features. Internal validation was performed by using 10-fold cross-validation. The performance of the model was measured by the area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, F1 score, Shapley additive explanations (SHAP) plot, feature importance, and correlation of features. The best cutoff value for risk stratification was identified by probability density function (PDF) and clinical utility curve (CUC).ResultsThe malignant rate of thyroid nodules in the study cohort was 53.2%. The predictive models are constructed by age, margin, shape, echogenic foci, echogenicity, and lymph nodes. The XGBoost model was significantly superior to any one of the machine learning models, with an AUC value of 0.829. According to the PDF and CUC, we recommended that 51% probability be used as a threshold for determining the risk stratification of malignant nodules, where about 85.6% of patients with malignant nodules could be detected. Meanwhile, approximately 89.8% of unnecessary biopsy procedures would be saved. Finally, an online web risk calculator has been built to estimate the personal likelihood of malignant thyroid nodules based on the best-performing ML-ed model of XGBoost.ConclusionsCombining clinical characteristics and features of ultrasound images, ML algorithms can achieve reliable prediction of malignant thyroid nodules. The online web risk calculator based on the XGBoost model can easily identify in real-time the probability of malignant thyroid nodules, which can assist clinicians to formulate individualized management strategies for patients.

Utilizing large-scale epigenomics data, deep learning tools can predict the regulatory activity of genomic sequences, annotate non-coding genetic variants, and uncover mechanisms behind complex traits. However, these tools primarily rely on human or mouse data for training, limiting their performance when applied to other species. Furthermore, the limited exploration of many species, particularly in the case of livestock, has led to a scarcity of comprehensive and high-quality epigenetic data, posing challenges in developing reliable deep learning models for decoding their non-coding genomes. The cross-species prediction of the regulatory genome can be achieved by leveraging publicly available data from extensively studied organisms and making use of the conserved DNA binding preferences of transcription factors within the same tissue. In this study, we introduced DeepSATA, a novel deep learning-based sequence analyzer that incorporates the transcription factor binding affinity for the cross-species prediction of chromatin accessibility. By applying DeepSATA to analyze the genomes of pigs, chickens, cattle, humans, and mice, we demonstrated its ability to improve the prediction accuracy of chromatin accessibility and achieve reliable cross-species predictions in animals. Additionally, we showcased its effectiveness in analyzing pig genetic variants associated with economic traits and in increasing the accuracy of genomic predictions. Overall, our study presents a valuable tool to explore the epigenomic landscape of various species and pinpoint regulatory deoxyribonucleic acid (DNA) variants associated with complex traits.

Abstract B cell response plays a critical role against SARS-CoV-2 infection. However, little is known about the diversity and frequency of the paired SARS-CoV-2 antigen-specific BCR repertoire after SARS-CoV-2 infection. Here, we performed single-cell RNA sequencing and VDJ sequencing using the memory and plasma B cells isolated from five convalescent COVID-19 patients, and analyzed the spectrum and transcriptional heterogeneity of antibody immune responses. Via linking BCR to antigen specificity through sequencing (LIBRA-seq), we identified a distinct activated memory B cell subgroup (CD11c high CD95 high ) had a higher proportion of SARS-CoV-2 antigen-labeled cells compared with memory B cells. Our results revealed the diversity of paired BCR repertoire and the non-stochastic pairing of SARS-CoV-2 antigen-specific immunoglobulin heavy and light chains after SARS-CoV-2 infection. The public antibody clonotypes were shared by distinct convalescent individuals. Moreover, several antibodies isolated by LIBRA-seq showed high binding affinity against SARS-CoV-2 receptor-binding domain (RBD) or nucleoprotein (NP) via ELISA assay. Two RBD-reactive antibodies C14646P3S and C2767P3S isolated by LIBRA-seq exhibited high neutralizing activities against both pseudotyped and authentic SARS-CoV-2 viruses in vitro. Our study provides fundamental insights into B cell response following SARS-CoV-2 infection at the single-cell level.

Microstructure engineering of electrodes is one of the efficient routes to improve rate performance of lithium-ion batteries (LIBs). Currently, there is a lack of descriptors to rationally guide the regional electrode design. Here, we propose two descriptors, the time differential of the average state of lithium (SoL) and the span of SoL in individual particles, to identify the rate performance constraints across the electrode depth. 3D microstructure-based electrochemical simulations are performed on a homogeneous electrode, and the predictability of the microstructure-based model is verified with the experimental measurement on a LiNi1/3Mn1/3Co1/3O2 electrode. At electrode level, the descriptors divide the electrode into four regions, namely, a solid-state transport (SST)-controlled region, two mixed SST and liquid-state transport (LST)-controlled regions (SST-dominant and LST-dominant, respectively), and an LST-controlled region. Based on these insights, dual-gradient electrodes are designed with smaller particles in the SST-controlled region and graded porosity increasing from current collector to the separator. Results show that the optimized dual-gradient electrode has significantly more excellent LST capability compared to the homogeneous electrode, thus improving the utilization of particles near the collector. As a result, the capacity performance of the optimized dual-gradient electrode increases by 39% at 5C without sacrificing the gravimetric energy density.

With the ever-increasing popularity of sports and health ideas, people are paying more attentions to gaining high-quality healthy life through various taking various sport items or exercises. Through observing and analyzing the past sport exercise score records, we can cluster the players into different categories, each of which share the same or similar sport preferences or performances. However, the sport exercise score records are often massive and often stored in different cloud platforms, which raise a big difficulty for time-efficient player clustering. Furthermore, the sport exercise score records are a kind of privacy for most players; therefore, it is often not rational or legal to release these sensitive data to the public for similar player clustering purpose. Considering the above two issues, we use SimHash, a kind of privacy-aware approximate neighbor search technique, for similar player clustering by analyzing the sport exercise score records distributed across different cloud platforms. Thus, we can realize privacy-aware similar player clustering through SimHash. At last, we provide a set of experiments to validate the advantages of our proposed privacy-aware similar player clustering algorithm. Reported experimental results show the effectiveness of our proposal in remedying the big data volume and privacy concerns in player clustering based on sport exercise score records.

The historical training or exam data of an athlete produced in the past sport exercise or test activities have provided a promising way to objectively and accurately evaluate the real-time sport performance of the athlete. However, the continuous generation of sport training or exam data has placed a heavy transmission and processing burden on the traditional centralized data processing paradigm (e.g., cloud platform). Considering this drawback, a decentralized blockchain-based athlete sport data transmission and utilization solution is proposed in this research work. Moreover, the available athlete sport data produced in past sport exercise or test activities is often sparse and time-related, which call for a robust and time-aware data fusion and processing solution. In this situation, HMM model is employed in this article to cope with the data sparsity and dynamics and further make accurate sports performance prediction for athletes accordingly. Finally, we design a set of experiments on a real-world dataset to validate the feasibility of our proposal in terms of effectiveness and efficiency.

Abstract The pore size distribution of the catalyst layer (CL) significantly influences the effective diffusion coefficient (EDC) of the reactant gas due to the Knudsen diffusion effect in the proton exchange membrane fuel cell (PEMFC). In this study, a novel fractal method is proposed to evaluate the Knudsen diffusion based on the pore size distribution of the CL, which is then incorporated in the calculation of the EDC of oxygen in the CL. It suggests that the EDC of oxygen obtained from the proposed method considering the pore size distribution is greatly smaller than that obtained from the conventional Bruggeman correlation, hence resulting in much smaller maximum current density of PEMFC.

MYB proto-oncogene-like 1 (MYBL1) has been reported to be a strong activator of transcription and plays an important role in the development of cancer. However, the precise biological function and molecular mechanism of MYBL1 in hepatocellular carcinoma (HCC) cells remain unclear. In the present study, we found that the expression of MYBL1 was markedly overexpressed in HCC cell lines and HCC samples, respectively. Moreover, MYBL1 expression positively correlated with tumor progression and inversely correlated with patient survival in 368 human HCC tissue samples. Overexpression of MYBL1 induced, whereas knockdown of MYBL1 reduced, HCC proliferation and metastasis both in vitro and in vivo. Furthermore, we demonstrated that HCC patients with high MYBL1 expression had significantly shorter overall and poorer disease-free survival than those with low MYBL1 expression. MYBL1 transcriptionally upregulated TWIST1 expression by directly targeting the TWIST1 promoter. More importantly, the in vitro analysis was consistent with the significant correlation between MYBL1 and TWIST1 expression observed in a large cohort of human HCC specimens. Taken together, our results demonstrate that MYBL1 plays an important role in HCC growth and metastasis and reveal a plausible mechanism for upregulation of TWIST1 in HCC.

Growth factors (GF), with the activity of stimulating cell growth, play a significant role in biology, medicine, and exercise physiology. In the process of exercise, human tissues are impacted, making cells suffer damage. Growth factor can accelerate the repair of damaged cells and regulate the synthesis of protein, so biological preparations of growth factors can be added to traditional therapies. A combination of growth factor biologics and conventional therapies may improve the efficiency of injury repair, but growth factor biologics may not produce any results. The feasibility of growth factor biologics in the treatment of motor injury was discussed. The research have shown that: 1) GF biological agent therapy is a very promising treatment for motor injury, which is based on the power of autologous growth factor (GFs) to accelerate tissue healing, promote muscle regeneration, increase angiogenesis, reduce fibrosis, and make the muscle injury rapid recovery. 2) There are various methods for delivering the higher dose of GF to the injured tissue, but most of them depend on the platelet release of GF. At the site of injury, there are several ways to deliver higher doses of GF to the injured tissue. 3) At present, the inhibition of GF is mainly through signal transduction inhibitors and inhibition of transcription factor production. 4) Pattern of GF during wound repair: GF directly regulates many key steps of normal wound repair, including inflammatory cell chemotaxis, division and proliferation of fibroblasts, keratinocytes and vascular endothelial cells, formation of new blood vessels, and synthesis and degradation of intercellular substances. 5) When GF promotes chronic wound healing, in most cases, certain GF can be used targeted only when in vivo regulation still cannot meet the need for repair.

It is easy to skid and roll by centrifugal force and wind, which affects the safety of the vehicle. At the same time, the special location of bridge and tunnel connecting segment in the canyon will accelerate the wind, and make the transverse wind play a more important role in driving vehicles. Based on the acceleration effect of canyons on wind, the vehicle model is simulated by Carsim software, and the road and wind models are established. It is studied that the vehicle has different radius of circular curve under different wind levels, corresponding to different superelevation. The lateral acceleration, lateral deflection and transverse force coefficient are selected as the running state of the vehicle at different speeds to research the linear optimization design of bridge and tunnel connecting segment in the canyon. The result shows that when the wind force exceeds the fifth grade, it is possible for the vehicle to overturn under the limit minimum radius required by the standard value. In order to ensure the stability and safety of the vehicle, this paper considers the geographical position of bridge and tunnel connecting segment in the canyon and the relationship between the radius of the superelevation and circular curve. What’s more, it puts forward the optimal limit minimum radius of the circular curve with different wind grades of 5-9 grades. At the same time, when the road alignment cannot be optimized under the condition, this paper puts forward the speed limit that the vehicle safety can be guaranteed under different wind speed conditions. The speed limit can provide reference for traffic management and safety guarantee of mountain expressway.

BackgroundIn China, the internet has become one of the most important ways to obtain information about breast cancer. However, quantitative evaluations of the quality of Chinese health websites and the breast cancer treatment information they publish are lacking. ObjectiveThis study aimed to evaluate the quality of Chinese breast cancer websites and the value, suitability, and accuracy of the breast cancer treatment information they publish. MethodsChinese breast cancer health websites were searched and manually screened according to their Alexa and Baidu search engine rankings. For each website included in the survey, which was conducted on April 8, 2019, the three most recently published papers on the website that met the inclusion criteria were included for evaluation. Three raters assessed all materials using the LIDA, DISCERN, and Suitability Assessment of Materials (SAM) tools and the Michigan Checklist. Data analysis was completed with the Statistical Package for Social Sciences (SPSS) version 20.0 and Microsoft Excel 2010. ResultsThis survey included 20 Chinese breast cancer websites and 60 papers on breast cancer treatment. The LIDA tool was used to evaluate the quality of the 20 websites. The LIDA’s scores of the websites (mean=54.85, SD 3.498; total possible score=81) were low. In terms of the layout, color scheme, search facility, browsing facility, integration of nontextual media, submission of comments, declaration of objectives, content production method, and robust method, more than half of the websites scored 0 (never) or 1 (sometimes). For the online breast cancer treatment papers, the scores were generally low. Regarding suitability, 32 (53.33%) papers were evaluated as presenting unsuitable material. Regarding accuracy, the problems were that the papers were largely not original (44/60, 73%) and lacked references (46/60, 77%). ConclusionsThe quality of Chinese breast cancer websites is poor. The color schemes, text settings, user comment submission functions, and language designs should be improved. The quality of Chinese online breast cancer treatment information is poor; the information has little value to users, and pictorial information is scarcely used. The online breast cancer treatment information is accurate but lacks originality and references. Website developers, governments, and medical professionals should play a full role in the design of health websites, the regulation of online health information, and the use of online health information.

Natural and artificial directional selections have resulted in significantly genetic and phenotypic differences across breeds in domestic animals. However, the molecular regulation of skeletal muscle diversity remains largely unknown. Here, we conducted transcriptome profiling of skeletal muscle across 27 time points, and performed whole-genome re-sequencing in Landrace (lean-type) and Tongcheng (obese-type) pigs. The transcription activity decreased with development, and the high-resolution transcriptome precisely captured the characterizations of skeletal muscle with distinct biological events in four developmental phases: Embryonic, Fetal, Neonatal, and Adult. A divergence in the developmental timing and asynchronous development between the two breeds was observed; Landrace showed a developmental lag and stronger abilities of myoblast proliferation and cell migration, whereas Tongcheng had higher ATP synthase activity in postnatal periods. The miR-24-3p driven network targeting insulin signaling pathway regulated glucose metabolism. Notably, integrated analysis suggested SATB2 and XLOC_036765 contributed to skeletal muscle diversity via regulating the myoblast migration and proliferation, respectively. Overall, our results provide insights into the molecular regulation of skeletal muscle development and diversity in mammals.

Ten-eleven translocation methylcytosine dioxygenase 1 (TET1) is involved in multiple biological functions in cell development, differentiation, and transcriptional regulation. Tet1 deficient mice display the defects of murine glucose metabolism. However, the role of TET1 in metabolic homeostasis keeps unknown. Here, our finding demonstrates that hepatic TET1 physically interacts with silent information regulator T1 (SIRT1) via its C-terminal and activates its deacetylase activity, further regulating the acetylation-dependent cellular translocalization of transcriptional factors PGC-1α and FOXO1, resulting in the activation of hepatic gluconeogenic gene expression that includes PPARGC1A, G6PC, and SLC2A4. Importantly, the hepatic gluconeogenic gene activation program induced by fasting is inhibited in Tet1 heterozygous mice livers. The adenosine 5’-monophosphate-activated protein kinase (AMPK) activators metformin or AICAR—two compounds that mimic fasting—elevate hepatic gluconeogenic gene expression dependent on in turn activation of the AMPK-TET1-SIRT1 axis. Collectively, our study identifies TET1 as a SIRT1 coactivator and demonstrates that the AMPK-TET1-SIRT1 axis represents a potential mechanism or therapeutic target for glucose metabolism or metabolic diseases.

Agricultural equipment works poorly under low illumination such as nighttime, and there is more noise in soybean plant images collected under light constraints, and the reconstructed soybean plant model cannot fully and accurately represent its growth condition. In this paper, we propose a low-illumination soybean plant reconstruction and trait perception method. Our method is based on low-illumination enhancement, using the image enhancement algorithm EnlightenGAN to adjust soybean plant images in low-illumination environments to improve the performance of the scale-invariant feature transform (SIFT) algorithm for soybean plant feature detection and matching and using the motion recovery structure (SFM) algorithm to generate the sparse point cloud of soybean plants, and the point cloud of the soybean plants is densified by the face slice-based multi-view stereo (PMVS) algorithm. We demonstrate that the reconstructed soybean plants are close to the growth conditions of real soybean plants by image enhancement in challenging low-illumination environments, expanding the application of three-dimensional reconstruction techniques for soybean plant trait perception, and our approach is aimed toward achieving the accurate perception of current crop growth conditions by agricultural equipment under low illumination.

As a key component in overhead cables, insulators play an important role. However, in the process of insulator inspection, due to background interference, small fault area, limitations of manual detection, and other factors, detection is difficult, has low accuracy, and is prone to missed detection and false detection. To detect insulator defects more accurately, the insulator defect detection algorithm based on You Only Look Once version 5 (YOLOv5) is proposed. A backbone network was built with lightweight modules to reduce network computing overhead. The small-scale network detection layer was increased to improve the network for small target detection accuracy. A receptive field module was designed to replace the original spatial pyramid pooling (SPP) module so that the network can obtain feature information and improve network performance. Finally, experiments were carried out on the insulator image dataset. The experimental results show that the average accuracy of the algorithm is 97.4%, which is 7% higher than that of the original YOLOv5 network, and the detection speed is increased by 10 fps, which improves the accuracy and speed of insulator detection.

The Bama Xiang pig (BM) is a unique pig species in Guangxi Province, China. Compared to other breeds of domestic pig, such as the Debao pig (DB), it is smaller in size, better in meat quality, resistant to rough feeding and strong in stress resistance. These unique advantages of Bama Xiang pigs make them of great edible value and scientific research value. However, the differences in muscle metabolites between Bama Xiang pigs (BM) and Debao pigs (DB) are largely unexplored. Here, we identified 214 differential metabolites between these two pig breeds by LC-MS. Forty-one such metabolites are enriched into metabolic pathways, and these metabolites correspond to 11 metabolic pathways with significant differences. In Bama pigs, the abundance of various metabolites such as creatine, citric acid, L-valine and hypoxanthine is significantly higher than in Debao pigs, while the abundance of other metabolites, such as carnosine, is significantly lower. Among these, we propose six differential metabolites: L-proline, citric acid, ribose 1-phosphate, L-valine, creatine, and L-arginine, as well as four potential differential metabolites (without the KEGG pathway), alanyl-histidine, inosine 2′-phosphate, oleoylcarnitine, and histidinyl hydroxyproline, as features for evaluating the meat quality of Bama pigs and for differentiating pork from Bama pigs and Debao pigs. This study provides a proof-of-concept example of distinguishing pork from different pig breeds at the metabolite level and sheds light on elucidating the biological processes underlying meat quality differences. Our pork metabolites data are also of great value to the genomics breeding community in meat quality improvement.

Spinal cord injury (SCI) may cause long-term physical impairment and bring a substantial burden to both the individual patient and society. Existing therapeutic approaches for SCI have proven inadequate. This is mainly owing to the incomplete understanding of the cellular and molecular events post-injury. Circular RNAs (circRNAs) represent a new class of non-coding RNAs with a covalently closed annular structure that participates in regulating the transcription of certain genes and are linked to various biological processes and diseases. Mounting evidence is indicative that circRNAs are highly expressed in the spinal cord and they play key roles in multiple processes of neurological diseases. Recently, a role for circRNAs as effectors of SCI has emerged, leading to the continuity of relevant research. In this review, we presented current studies with regards to the abnormality of circRNAs mediating SCI by affecting mechanisms of autophagy, apoptosis, inflammation, and neural regeneration. Furthermore, the potential clinical value of circRNAs as therapeutic targets of SCI was also analyzed.

To improve the positioning accuracy and reliability of autonomous navigation agricultural machinery and reduce the cost of high-precision positioning, an integrated navigation system based on Real-Time Dynamic Kinematic BeiDou Navigation Satellite System (RTK-BDS) and Inertial Navigation System (INS) is designed in this study. On the one hand, an autonomous navigation control board is designed and made in the system, which integrates BDS high-precision analysis module, Inertial Measurement Unit (IMU) module, and radio module, and realizes the integrated navigation algorithm on the control board. On the other hand, low-cost RTK technology is realized by building differential reference stations and vehicle-mounted mobile stations. Experiments are carried out on actual farm machinery under different road conditions including open road, signal-shielded road, and urban congested road. According to the angular velocity and acceleration information from INS and the position and velocity information from the BDS high-precision analysis module, the system uses Kalman filter algorithm for data fusion to calculate the precise position, velocity, and attitude information of agricultural machinery in real time. The experimental results show that the position error of the integrated navigation system on the open road is within 3 cm, the azimuth error is within 0.6°, and the inclination error is within 1°, all of which converge rapidly when encountering bad road conditions. It can be known from the experimental results that the RTK-BDS/INS integrated navigation system has high positioning accuracy, strong adaptive anti-interference ability, and low implementation cost of RTK technology, which provides a reliable way for automatic navigation control of agricultural machinery.

Finding driver genes sheds lights on the biological mechanisms propelling the development of a tumour, and can suggest therapeutic strategies. Here, the authors develop driverMAPS, a model-based approach to identify driver genes, and apply it to TCGA datasets.

Objective: The gut microbiota is an important influencing factor of metabolic health. Although dietary interventions with probiotics, prebiotics, and synbiotics can be effective means to regulate obesity and associated comorbidities, the underlying shifts in gut microbial communities, especially at the functional level, have not been characterized in great details. In this study, we sought to investigate the effects of synbiotics on the regulation of gut microbiota and the alleviation of high-fat diet (HFD)-induced metabolic disorders in mice. Methods: Specific pathogen-free (SPF) male C57BL/6J mice were fed diets with either 10% (normal diet, ND) or 60% (high-fat diet, HFD) of total calories from fat (lard). Dietary interventions in the HFD-fed mice included (i) probiotic (Bifidobacterium animalis subsp. lactis and Lactobacillus paracasei subsp. paracasei DSM 46331), (ii) prebiotic (oat β-glucan), and (iii) synbiotic (a mixture of i and ii) treatments for 12 weeks. Besides detailed characterization of host metabolic parameters, a multi-omics approach was used to systematically profile the microbial signatures at both the phylogenetic and functional levels using 16S rRNA gene sequencing, metaproteomics and targeted metabolomics analysis. Results: The synbiotic intervention significantly reduced body weight gain and alleviated features of metabolic complications. At the phylogenetic level, the synbiotic treatment significantly reversed HFD-induced changes in microbial populations, both in terms of richness and the relative abundance of specific taxa. Potentially important species such as Faecalibaculum rodentium and Alistipes putredinis that might mediate the beneficial effects of the synbiotic were identified. At the functional level, short-chain fatty acid and bile acid profiles revealed that all dietary interventions significantly restored cecal levels of acetate, propionate, and butyrate, while the synbiotic treatment reduced the bile acid pools most efficiently. Metaproteomics revealed that the effects of the synbiotic intervention might be mediated through metabolic pathways involved in carbohydrate, amino acid, and energy metabolisms. Conclusions: Our results suggested that dietary intervention using the novel synbiotic can alleviate HFD-induced weight gain and restore gut microbial ecosystem homeostasis phylogenetically and functionally. Keywords: Dietary intervention, Gut microbiota, High-fat diet, Obesity, Synbiotics

Based on a coupled finite element method (FEM) and computational fluid dynamics (CFD) model, the structural deformation and performance of a proton exchange membrane fuel cell (PEMFC) under different membrane water contents are studied. The water absorption behavior of the membrane is investigated experimentally to obtain its expansion coefficient with water content, and the Young’s modulus of the membrane and catalyst (CL) are obtained through a tensile experiment. The simulation results show that the deformation of the membrane increases with water content, and membrane swelling under the channel is larger than that under the rib, forming a surface bump under the channel. The structural changes caused by the membrane water content have little effect on the performance of PEMFC in the low-current density range; while its influence is significant in the medium- and high-current density range. A medium membrane water content value of 12 achieves the best fuel cell performance due to the balance of membrane resistance and mass transport.

Myogenesis is a central step in prenatal myofiber formation, postnatal myofiber hypertrophy, and muscle damage repair in adulthood. RNA-Seq technology has greatly helped reveal the molecular mechanism of myogenesis, but batch effects in different experiments inevitably lead to misinterpretation of differentially expressed genes (DEGs). We previously applied the robust rank aggregation (RRA) method to effectively circumvent batch effects across multiple RNA-Seq datasets from 3T3-L1 cells. Here, we also used the RRA method to integrate nine RNA-Seq datasets from C2C12 cells and obtained 3140 robust DEGs between myoblasts and myotubes, which were then validated with array expression profiles and H3K27ac signals. The upregulated robust DEGs were highly enriched in gene ontology (GO) terms related to muscle cell differentiation and development. Considering that the cooperative binding of transcription factors (TFs) to enhancers to regulate downstream gene expression is a classical epigenetic mechanism, differentially expressed TFs (DETFs) were screened, and potential novel myogenic factors (MAF, BCL6, and ESR1) with high connection degree in protein–protein interaction (PPI) network were presented. Moreover, KLF5 cooperatively binds with the three key myogenic factors (MYOD, MYOG, and MEF2D) in C2C12 cells. Motif analysis speculates that the binding of MYOD and MYOG is KLF5-independent, while MEF2D is KLF5-dependent. It was revealed that KLF5-binding sites could be exploited to filter redundant MYOD-, MYOG-, and MEF2D-binding sites to focus on key enhancers for myogenesis. Further functional annotation of KLF5-binding sites suggested that KLF5 may regulate myogenesis through the PI3K-AKt signaling pathway, Rap1 signaling pathway, and the Hippo signaling pathway. In general, our study provides a wealth of untapped candidate targets for myogenesis and contributes new insights into the core regulatory mechanisms of myogenesis relying on KLF5-binding signal.

Nonalcoholic steatohepatitis (NASH) is a liver disease defined as the dynamic condition of hepatocellular injury during the progress of nonalcoholic fatty liver disease (NAFLD). Total flavonoids from the dry and immature fruits of Citrus Paradisi cv.Changshanhuyou (accepted species name: Citrus × aurantium L) (Qu Zhi Qiao, QZQ) are purified and named TFCH. This study was purposed to investigate and analyze the effect of TFCH on NASH model through Nuclear factor erythroid 2-related factor 2 (Nrf2)- antioxidant response elements pathway in vivo and in vitro. In vivo study was performed using male C57BL/6 mice fed with high fat diet 16 weeks for NASH model. After 7-week modeling, mice in TFCH-treated group were daily treated with intragastric administration of TFCH at 25 mg/kg, 50 mg/kg, 200 mg/kg, respectively, for successive 8 weeks. Histopathological and immunohistochemical analyses were conducted for evaluating severity of NASH-mice model and the effect of TFCH treatment. In vitro experiment was performed by using human LX-2 cells and cultured with Free fatty acid (FFA) (Oleic acid: palmitic: l: 0.5 mmol/L) for 24 h and then treated with TFCH at different concentrations (0, 25, 50, 100, 200 mg/ml) for 6 h,12 h, and 24 h. Anti-apoptosis effect of TFCH on LX-2 cells cultured with FFA was revealed by the CCK-8 assay. Lipid parameters and oxidative stress markers were measured in vivo and in vitro, results showed that TFCH dose-dependently and greatly increased the antioxidant ability and reduced the oxidative damage in NASH model. The protein expression of Nrf2 and the downstream target genes in mice liver and human LX-2 cells were tested by Western blot analysis to investigate the possible molecular mechanisms of TFCH. Our results indicated that TFCH up-regulated protein expression of these genes and have the significant influence in activating the Nrf2-ARE signaling pathway. This study shows Nrf2-ARE signaling pathway may provide novel therapeutic opportunities for NASH therapy in the future.

Abstract Background Genome-wide quantification of enhancer activity in the human genome has proven to be a challenging problem. Recent efforts have led to the development of powerful tools for enhancer quantification. However, because of genome size and complexity, these tools have yet to be applied to the whole human genome. Results In the current study, we use a human prostate cancer cell line, LNCaP as a model to perform whole human genome STARR-seq (WHG-STARR-seq) to reliably obtain an assessment of enhancer activity. This approach builds upon previously developed STARR-seq in the fly genome and CapSTARR-seq techniques in targeted human genomic regions. With an improved library preparation strategy, our approach greatly increases the library complexity per unit of starting material, which makes it feasible and cost-effective to explore the landscape of regulatory activity in the much larger human genome. In addition to our ability to identify active, accessible enhancers located in open chromatin regions, we can also detect sequences with the potential for enhancer activity that are located in inaccessible, closed chromatin regions. When treated with the histone deacetylase inhibitor, Trichostatin A, genes nearby this latter class of enhancers are up-regulated, demonstrating the potential for endogenous functionality of these regulatory elements. Conclusion WHG-STARR-seq provides an improved approach to current pipelines for analysis of high complexity genomes to gain a better understanding of the intricacies of transcriptional regulation.

Abstract Background Most cancer risk-associated single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) are noncoding and it is challenging to assess their functional impacts. To systematically identify the SNPs that affect gene expression by modulating activities of distal regulatory elements, we adapt the self-transcribing active regulatory region sequencing (STARR-seq) strategy, a high-throughput technique to functionally quantify enhancer activities. Results From 10,673 SNPs linked with 996 cancer risk-associated SNPs identified in previous GWAS studies, we identify 575 SNPs in the fragments that positively regulate gene expression, and 758 SNPs in the fragments with negative regulatory activities. Among them, 70 variants are regulatory variants for which the two alleles confer different regulatory activities. We analyze in depth two regulatory variants—breast cancer risk SNP rs11055880 and leukemia risk-associated SNP rs12142375—and demonstrate their endogenous regulatory activities on expression of ATF7IP and PDE4B genes, respectively, using a CRISPR-Cas9 approach. Conclusions By identifying regulatory variants associated with cancer susceptibility and studying their molecular functions, we hope to help the interpretation of GWAS results and provide improved information for cancer risk assessment.

Background/Aims: The purpose of this study was to investigate the role of autophagy in oxygen-glucose-deprivation/reoxygenation (OGD/R) injury in rat neurons. Methods and results: Cortical neurons were isolated from Sprague-Dawley rats and identified by immunofluorescence. The cortical neurons were randomly assigned to one of four groups: control group (I), experimental group (OGD/R group, II), JNK inhibitor pretreatment group (III) and JNK inhibitor pretreatment + OGD/R group (IV). Neuronal cell viability significantly decreased after 6h and 12h of reoxygenation in Group IV (P P Conclusion: The regulation of the JNK/Bcl-2/Beclin-1 signaling pathway may be one of the mechanisms underlying the OGD/R-induced autophagic cell death of neurons.

In breast cancer, nuclear receptors (NRs) play a prominent role in governing gene expression, have prognostic utility, and are therapeutic targets. We built a regulatory map for 24 NRs, six chromatin state markers, and 14 breast-cancer-associated transcription factors (TFs) that are expressed in the breast cancer cell line MCF-7. The resulting network reveals a highly interconnected regulatory matrix where extensive crosstalk occurs among NRs and other breast -cancer-associated TFs. We show that large numbers of factors are coordinately bound to highly occupied target regions throughout the genome, and these regions are associated with active chromatin state and hormone-responsive gene expression. This network also provides a framework for stratifying and predicting patient outcomes, and we use it to show that the peroxisome proliferator-activated receptor delta binds to a set of genes also regulated by the retinoic acid receptors and whose expression is associated with poor prognosis in breast cancer.