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Background: The enhancer–promoter interaction (EPI) is a critical component of gene regulatory networks, playing a significant role in understanding the complexity of gene expression. Traditional EPI prediction methods focus on one-to-one interactions, neglecting more complex one-to-many and many-to-many patterns. To address this gap, we utilize graph neural networks to comprehensively explore all interaction patterns between enhancers and promoters, capturing complex regulatory relationships for more accurate predictions. Methods: In this study, we introduce a novel EPI prediction framework, GATv2EPI, based on dynamic graph attention neural networks. GATv2EPI leverages epigenetic information from enhancers, promoters, and their surrounding regions and organizes interactions into a network to comprehensively explore complex EPI regulatory patterns, including one-to-one, one-to-many, and many-to-many relationships. To avoid overfitting and ensure diverse data representation, we implemented a connectivity-based sampling method for dataset partitioning, which constructs graphs for each chromosome and assigns entire connected subgraphs to training or test sets, thereby preventing information leakage and ensuring comprehensive chromosomal representation. Results: In experiments conducted on four cell lines—NHEK, IMR90, HMEC, and K562—GATv2EPI demonstrated superior EPI recognition accuracy compared to existing similar methods, with a training time improvement of 95.29% over TransEPI. Conclusions: GATv2EPI enhances EPI prediction accuracy by capturing complex topological structure information from gene regulatory networks through graph neural networks. Additionally, our results emphasize the importance of epigenetic features surrounding enhancers and promoters in EPI prediction. [ABSTRACT FROM AUTHOR]

Motivation: Chest X-ray (CXR) is a routine diagnostic X-ray examination for checking and screening various diseases. Automatically localizing and classifying diseases from CXR as a detection task is of much significance for subsequent diagnosis and treatment. Due to the fact that samples of some diseases are difficult to acquire, CXR detection datasets often present a long-tail distribution over different diseases. Objective: The detection performance of tail classes is very poor due to the limited number and diversity of samples in the training dataset and should be improved. Method: In this paper, motivated by a correspondence-based tracking system, we build a pipeline named RaTrack, leveraging a diffusion model to alleviate the tail class degradation problem by aligning the generation process of the tail to the head class. Then, the samples of rare classes are generated to extend the number and diversity of rare samples. In addition, we propose a filtering strategy to control the quality of the generated samples. Results: Extensive experiments on public datasets, Vindr-CXR and RSNA, demonstrate the effectiveness of the proposed method, especially for rare diseases. [ABSTRACT FROM AUTHOR]

An auxiliary structure can significantly improve the wind-trapping capacity of the Savonius wind turbine. In this study, a novel auxiliary structure called a wind energy gathering structure (WEGS) is proposed, and its five parameters, namely the lengths of the shrinkage and diffusion tubes, the length of the centerboard, the length of the throat, the length of the wind board, and the shrinkage and diffusion angles, are investigated using computational fluid dynamics (CFD) and Taguchi's method. Meanwhile, Taguchi's method and ANOVA reveal that among the studied parameters, the shrinkage and diffusion angles, the length of the centerboard, and the lengths of the shrinkage and diffusion tubes have a more significant influence on the performance of the WEGS. At a tip speed ratio (TSR) value of 1 and a wind speed of 7 m/s, the optimized combination of the WEGS parameters obtained by Taguchi's method improves the mean torque coefficient of the turbine by 42.1%. Moreover, at other TSRs (0.6–1.2), the turbine with the WEGS also outperforms an open turbine in terms of aerodynamic (increases of 20.1–53%) and lifetime performance. [ABSTRACT FROM AUTHOR]

With the widespread adoption of next-generation sequencing technologies, the speed and convenience of genome sequencing have significantly improved, and many biological genomes have been sequenced. However, during the assembly of small genomes, we still face a series of challenges, including repetitive fragments, inverted repeats, low sequencing coverage, and the limitations of sequencing technologies. These challenges lead to unknown gaps in small genomes, hindering complete genome assembly. Although there are many existing assembly software options, they do not fully utilize the potential of artificial intelligence technologies, resulting in limited improvement in gap filling. Here, we propose a novel method, DLGapCloser, based on deep learning, aimed at assisting traditional tools in further filling gaps in small genomes. Firstly, we created four datasets based on the original genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla. To further extract effective information from the gene sequences, we also added homologous genomes to enrich the datasets. Secondly, we proposed the DGCNet model, which effectively extracts features and learns context from sequences flanking gaps. Addressing issues with early pruning and high memory usage in the Beam Search algorithm, we developed a new prediction algorithm, Wave-Beam Search. This algorithm alternates between expansion and contraction phases, enhancing efficiency and accuracy. Experimental results showed that the Wave-Beam Search algorithm improved the gap-filling performance of assembly tools by 7.35%, 28.57%, 42.85%, and 8.33% on the original results. Finally, we established new gap-filling standards and created and implemented a novel evaluation method. Validation on the genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla showed that DLGapCloser increased the number of filled gaps by 8.05%, 15.3%, 1.4%, and 7% compared to traditional assembly tools. [ABSTRACT FROM AUTHOR]

Changes in land use types in alpine meadow areas have significant impacts on the ecological environment in alpine areas. Exploring land use change is crucial for land use management and optimization in alpine regions. Thus, it is necessary to analyze land use evolution and its drivers in alpine meadow regions from a production–living–ecology space (PLES) perspective by using remote sensing data. We first constructed the PLES evaluation system for Gannan. Then, we analyzed the spatial and temporal evolution characteristics and coupling degree of PLES in the study area. Finally, the driving factors affecting PLES were explored with geodetector. The conclusions of the study reveal that the distribution of productive and ecological spaces is large and concentrated, while the distribution of living spaces is more decentralized. The PLES was mainly concentrated in the area above 2500 m but below 4000 m and with a slope of 40° or less. During the study period, the area of production space showed a decreasing trend, while the areas of living and ecological space both showed increasing trends, primarily occurring at the expense of production space. DEM and GDP were the main factors affecting the distribution of PLES. The coupling level and the degree of coupling coordination were relatively stable in general, showing a pattern of "high in the east and low in the west". The study provides technical support and a theoretical basis for the future planning of land space and ecological environment optimization in the alpine meadow regions. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell's representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data's underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback–Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods. [ABSTRACT FROM AUTHOR]

The heterogeneity of tumors poses a challenge for understanding cell interactions and constructing complex ecosystems within cancer tissues. Current research strategies integrate spatial transcriptomics (ST) and single-cell sequencing (scRNA-seq) data to thoroughly analyze this intricate system. However, traditional deep learning methods using scRNA-seq data tend to filter differentially expressed genes through statistical methods. In the context of cancer tissues, where cancer cells exhibit significant differences in gene expression compared to normal cells, this heterogeneity renders traditional analysis methods incapable of accurately capturing differences between cell types. Therefore, we propose a graph-based deep learning method, GTADC, which utilizes Silhouette scores to precisely capture genes with significant expression differences within each cell type, enhancing the accuracy of gene selection. Compared to traditional methods, GTADC not only considers the expression similarity of genes within their respective clusters but also comprehensively leverages information from the overall clustering structure. The introduction of graph structure effectively captures spatial relationships and topological structures between the two types of data, enabling GTADC to more accurately and comprehensively resolve the spatial composition of different cell types within tissues. This refinement allows GTADC to intricately reconstruct the cellular spatial composition, offering a precise solution for inferring cell spatial composition. This method allows for early detection of potential cancer cell regions within tissues, assessing their quantity and spatial information in cell populations. We aim to achieve a preliminary estimation of cancer occurrence and development, contributing to a deeper understanding of early-stage cancer and providing potential support for early cancer diagnosis. [ABSTRACT FROM AUTHOR]

We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data on sea surface temperature (SST) to construct a deep convolutional embedded clustering (DCEC) model and extract the monthly SST features ( F M) based on an optimized number of clusters determined by the Davies–Bouldi index (DBI). We use the extracted F M to construct a series of Generalized Additive Models (GAM) to forecast the catch per unit effort (CPUE) of jumbo flying squid within a spatial resolution of 0.5° × 0.5°. Our results demonstrate the following findings: (1) The SST feature clusters obtained through the DCEC model could capture the SST monthly variations; (2) The GAM models with F M outperform the models with the traditional monthly average SST in terms of predictive accuracy; (3) Using both F M and average SST together can further improve model performance. This study demonstrates the effectiveness of the DCEC combined with DBI in extracting marine environmental features and highlights the ocean environment feature extraction method to enhance the precision and reliability of fishery forecasting models. [ABSTRACT FROM AUTHOR]

Severe combined immunodeficient (SCID) mice serve as a critical model for human xenotransplantation studies, yet they often suffer from low engraftment rates and susceptibility to graft-versus-host disease (GVHD). Moreover, certain SCID strains demonstrate 'immune leakage', underscoring the need for novel model development. Here, we introduce an SCID mouse model with a targeted disruption of the dclre1c gene, encoding Artemis, which is essential for V(D)J recombination and DNA repair during T cell receptor (TCR) and B cell receptor (BCR) assembly. Artemis deficiency precipitates a profound immunodeficiency syndrome, marked by radiosensitivity and compromised T and B lymphocyte functionality. Utilizing CRISPR/Cas9-mediated gene editing, we generated dclre1c-deficient mice with an NOD genetic background. These mice exhibited a radiosensitive SCID phenotype, with pronounced DNA damage and defective thymic, splenic and lymph node development, culminating in reduced T and B lymphocyte populations. Notably, both cell lines and patient-derived tumor xenografts were successfully engrafted into these mice. Furthermore, the human immune system was effectively rebuilt following peripheral blood mononuclear cells (PBMCs) transplantation. The dclre1c-knockout NOD mice described herein represent a promising addition to the armamentarium of models for xenotransplantation, offering a valuable platform for advancing human immunobiological research. [ABSTRACT FROM AUTHOR]

A growing number of studies have shown that aberrant microRNA (miRNA) expression is closely associated with the evolution and development of various complex human diseases. These key biomarkers' identification and observation are significant for gaining a deeper understanding of disease pathogenesis and therapeutic mechanisms. Consequently, pinpointing potential miRNA–disease associations (MDA) has become a prominent bioinformatics subject, encouraging several new computational methods given the advances in graph neural networks (GNN). Nevertheless, these existing methods commonly fail to exploit the network nodes' global feature information, leaving the generation of high-quality embedding representations using graph properties as a critical unsolved issue. Addressing these challenges, we introduce the DAEMDA, a computational method designed to optimize the current models' efficacy. First, we construct similarity and heterogeneous networks involving miRNAs and diseases, relying on experimentally corroborated miRNA–disease association data and analogous information. Then, a newly-fashioned parallel dual-channel feature encoder, designed to better comprehend the global information within the heterogeneous network and generate varying embedding representations, follows this. Ultimately, employing a neural network classifier, we merge the dual-channel embedding representations and undertake association predictions between miRNA and disease nodes. The experimental results of five-fold cross-validation and case studies of major diseases based on the HMDD v3.2 database show that this method can generate high-quality embedded representations and effectively improve the accuracy of MDA prediction. [ABSTRACT FROM AUTHOR]

We sought to investigate age-related differences in stepping reactions to a sudden balance perturbation, focusing on muscle activity and cortical activation. A total of 18 older healthy adults (older group, OG) and 16 young healthy adults (young group, YG) were recruited into this study. A cable-pull instrument was used to induce a forward perturbation at the waist level among participants, who were required to take the right step to maintain their postural balance. The seven right lower-limb muscle activities during periods of compensatory postural adjustments (CPAs) were recorded by surface electromyography. At the same time, the signals of channels located in the prefrontal, temporal and parietal lobes were recorded by functional near-infrared spectroscopy (fNIRS) during the whole process. Integral electromyograms of the right peroneus muscle, gluteus medius, and lateral gastrocnemius muscles showed greater activity for the OG in the CPA periods. Two channels belonging to the right pre-frontal (PFC) and pre-motor cortex (PMC) revealed lower activation in the OG compared with the YG. These findings can help us to better understand the differences at the peripheral and central levels and may provide some suggestions for future neuromodulation techniques and other clinical treatments. [ABSTRACT FROM AUTHOR]

Highlights: What are the main findings? An established method for trace, simple, rapid, cheap, sensitive, accurate, robust, and high-throughput for methylmalonic acid quantification. Good chromatographic separation of MMA and its intrinsic isomer and good signals of MMA were achieved using a simple isocratic elution strategy. Materials and reagents that are complex or not always accessible and procedures in previous methods such as derivatization, multistep SPE, incubations, evaporations, drying, or reconstitutions are not required in this MMA quantification method. What is the implication of the main finding? This method is suitable for large-scale MMA testing. Background: Methylmalonic acid (MMA) is an essential indicator of vitamin B12 (VB12) deficiency and inherited metabolic disorders (IMDs). The increasing number of requests for MMA testing call for higher requirements for convenient MMA testing methods. This study aims to develop a convenient quantification method for serum MMA. Methods: The method was established based on the stable isotope-dilution liquid chromatography–tandem mass spectroscopy (ID-LC-MS/MS) technique. The LC-MS/MS parameters and sample preparation were optimized. Specificity, sensitivity, robustness, accuracy, and clinical applicability were validated according to CLSI C62-A guidelines. MMA levels in VB12-sufficient subjects and VB12-deficient subjects were measured. Results: MMA and its intrinsic isomer, i.e., succinic acid (SA), were completely separated. The average slope, intercept, and correlation relationship (R) with 95% confidence intervals, during the two months, were 0.992 (0.926–1.059), −0.004 (−0.012–0.004), and 0.997 (0.995–0.999), respectively. The limit of detection and quantification were <0.058 μmol/L and 0.085 μmol/L, respectively. Intra-run, inter-run, and total imprecisions were 1.42–2.69%, 3.09–5.27%, and 3.22–5.47%, respectively. The mean spiked recoveries at the three levels were 101.51%, 92.40%, and 105.95%, respectively. The IS-corrected matrix effects were small. The VB12-deficient subjects showed higher MMA levels than VB12-sufficient subjects. Conclusions: A convenient LC-MS/MS method for serum MMA measurement was developed and validated, which could be suitable for large-scale MMA testing and evaluating MMA levels in VB12-deficient patients. [ABSTRACT FROM AUTHOR]

It has been reported that fulvic acid (FA) application improves soil structure and nutrient availability. However, the effects of combined application of urea (U) and FA solution on the photosynthesis and nitrogen metabolism in maize (Zea mays L.) have rarely been reported. In this study, pot experiments were conducted in 2017 and 2018, and the effects of combined application of urea and FA solution (U+FA) on soil available nutrient contents, maize endogenous hormone concentrations, carbon and nitrogen metabolism-related enzyme concentrations, maize yield, and nitrogen use efficiency (NUE) were researched. Compared with the U treatment, the maize yield and NUE in the U+FA treatment were significantly increased by 8.31% and 17.09 percentage points in 2017 and by 16.90% and 24.31 percentage points in 2018. At the jointing and 12-leaf (V12) stages of maize, soil NH4+ content increased by 139.32% and 12.08%, separately, in the U+FA treatment. At the V12 stage, the auxin, nitrate reductase, nitrite reductase, and glutamine synthetase concentrations in maize root were increased by 42.31%, 74.17%, 16.61%, and 45.60%, respectively, and the concentrations of pyruvate phosphate dikinase and phosphoenolpyruvate carboxylase in maize leave were increased by 29.40% and 42.96%, respectively, in the U+FA treatment. The combined application of urea and FA solution significantly improved soil nutrient availability, increased the concentrations of endogenous hormones in maize, stimulated the activities of enzymes related to nitrogen metabolism, promoted the photosynthetic carbon assimilation efficiency, and ultimately improved crop yield and NUE. [ABSTRACT FROM AUTHOR]

As the second source of water for cities, reclaimed water (RW) has become an effective solution to the problem of water scarcity in modern agriculture. However, the formation of biofilm in an RW distribution system seriously affects the performance of the system and has become a technical challenge in RW utilization. In this study, we first showed that several water quality parameters, including five-day biochemical oxygen demand (BOD5), total bacteria count (TB), total nitrogen (TN), and Cl− were the main factors affecting biofilm accumulation in the drip irrigation system (DIS), with the correlation coefficient averaging above 0.85. Second, after 392 to 490 h of system operation, the total biomass and extracellular polymer (EPS) accumulation rate of biofilms increased to a maximum of 0.72 g/m2·h and 0.027g/m2·h, respectively, making this time point a critical point for controlling biofilm accumulation and clogging of the system. Third, we examined changes in biofilm microbial composition over time on Illumina's MiSeq platform. High throughput sequencing data showed that bacterial community structure and microbial network interaction and modularity changed significantly between 392 and 490 h, resulting in maximum microbial diversity and community richness at 490 h. Spearman correlation analyses between genera revealed that Sphingomonas and Rhodococcus promote biofilm formation due to their hydrophobicity, while Bacillus, Mariniradius, and Arthronema may inhibit biofilm formation due to their antagonistic effects on other genera. In conclusion, this work has clarified the accumulation process and compositional changes of biofilms in agriculture DIS under different RW conditions, which provides a basis for improving RW utilization efficiency and reducing system maintenance costs. [ABSTRACT FROM AUTHOR]

In this work, we consider interference performance under direct data transmission in a heterogeneous network. The heterogeneous network consists of K-tier base stations and users, whose locations follow independent Poisson point processes (PPPs). Packet arrivals of users follow independent Bernoulli processes. Two different scheduling policies, round-robin (RR) and random scheduling (RS), are employed to all the Base Stations (BS). The universal frequency reuse mode is adopted to reveal actual spectrum reuse. By leveraging stochastic geometry and queueing theory, the interference interactions of the proposed network are accurately modelled. Accurate expressions for the mean packet throughputs of the network under universal frequency reuse mode are derived. The simulation results explore the optical bias factors in heterogeneous networks to maximize the mean packet throughput. Under a given user density, by changing BS densities, we achieved a certain mean packet throughput level. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the performance analysis of device-to-device (D2D) underlay communication in cellular networks. First, we develop a spatiotemporal traffic model to model a retransmission mechanism for D2D underlay communication. The D2D users in backlogged statuses are modeled as a thinned Poisson point process (PPP). To capture the characteristics of sporadic wireless data generation and limited buffer, we adopt queuing theory to analyze the performance of dynamic traffic. Furthermore, a feedback queuing model is adopted to analyze the performance with retransmission strategy. With the consideration of interference and channel fading, the service probability of the queue departure process is determined by the received signal-to-interference-plus-noise ratio (SINR). Then, the embedded Markov chain is employed to depict the queuing status in the D2D user buffer. We compute its steady-state distribution and derive the closed-form expressions of performance metrics, namely the average queue length, average throughput, average delay, and dropping probability. Simulation results show the validity and rationality of the theoretical analysis with different channel parameters and D2D densities. In addition, the simulation explores the dropping probability of a D2D user with and without the retransmission strategy for different D2D links in the system. When the arrival rate is comparatively high, the optimal throughput is reached after fewer retransmission attempts as a result of the limited buffer. [ABSTRACT FROM AUTHOR]

Piwi interacting RNAs (piRNAs) are small non-coding single-stranded RNA species 20–31 nucleotides in size generated from distinct loci. In germline tissues, piRNAs are amplified via a "ping-pong cycle" to produce secondary piRNAs, which act in transposon silencing. In contrast, the role of somatic-derived piRNAs remains obscure. Here, we investigated the identity and distribution of piRNAs in human somatic tissues to determine their function and potential role in Parkinson's disease (PD). Human datasets were curated from the Gene Expression Omnibus (GEO) database and a workflow was developed to identify piRNAs, which revealed 902 somatic piRNAs of which 527 were expressed in the brain. These were mainly derived from chromosomes 1, 11, and 19 compared to the germline tissues, which were from 15 and 19. Approximately 20% of somatic piRNAs mapped to transposon 3′ untranslated regions (UTRs), but a large proportion were sensed to the transcript in contrast to germline piRNAs. Gene set enrichment analysis suggested that somatic piRNAs function in neurodegenerative disease. piRNAs undergo dysregulation in different PD subtypes (PD and Parkinson's disease dementia (PDD)) and stages (premotor and motor). piR-has-92056, piR-hsa-150797, piR-hsa-347751, piR-hsa-1909905, piR-hsa-2476630, and piR-hsa-2834636 from blood small extracellular vesicles were identified as novel biomarkers for PD diagnosis using a sparse partial least square discriminant analysis (sPLS-DA) (accuracy: 92%, AUC = 0.89). This study highlights a role for piRNAs in PD and provides tools for novel biomarker development. [ABSTRACT FROM AUTHOR]

Color is an essential agronomic trait and the consumption of high anthocyanin containing vegetables in daily diet does provide benefits to human health, but the mechanisms on anthocyanin accumulation in tender pods of okra (Abelmoschus esculentus L.) were totally unknown. In this study, a wide characterization and quantitation of anthocyanins and flavonols in tender pods of 15 okra varieties were performed by UHPLC-Q-Orbitrap HRMS for the first time. Two major anthocyanins (delphinidin 3-O-sambubioside and cyanidin 3-O-sambubioside) and six kinds of flavonol glycosides (most are quercetin-based) were identified and quantified. The coloration of the purple okra pod mainly arises from the accumulation of both delphinidin 3-O-sambubioside and cyanidin 3-O-sambubioside in most of purple varieties (Hong Yu, Bowling Red and Burgundy), except Jing Orange. The significant differences in the compositions and contents of anthocyanins are responsible for the pod color ranging from brick-red to purplish-red among the various okra cultivars. Furthermore, four representative okra cultivars exhibiting obvious differences in anthocyanin accumulation were further analyzed with transcriptome and more than 4000 conserved differentially expressed genes were identified across the three compared groups (B vs. BR, B vs. HY and B vs. JO). Based on the comprehensive analysis of transcriptomic data, it was indicated that MBW complex consisting of AeMYB114, AeTT8, and AeTTG1 and other transcriptional factors coordinately regulate the accumulation of anthocyanins via the transcriptional regulation of structural genes. Moreover, four independent working models explaining the diversities of anthocyanin pigmentation in okra pods were also proposed. Altogether, these results improved our understanding on anthocyanin accumulation in okra pods, and provided strong supports for the development of okra pod as a functional food in the future. [ABSTRACT FROM AUTHOR]

The media access control (MAC) protocol is a key element in the design of vehicular ad hoc networks (VANETs) that directly affects the network performance. The backoff schemes of existing MAC protocols apply the single backoff process and therefore are not suitable for multi-class data transmission. Additionally, they cannot satisfy the delay requirements of emergency data in the case of varying number of vehicles, causing an adverse effect to the intelligent transportation system (ITS). This paper presents a priority-based adaptive backoff scheme that can enhance the binary exponential backoff (BEB) algorithm as well as the polynomial backoff (QB) algorithm. This system distinguishes priority data with different delay requirements first and designs different backoff schemes for each type of data later. The two-dimensional Markov Chain is used to analyze the backoff scheme and determine the expressions for throughput and delay. The simulation results show that the backoff scheme provided by this paper can reduce the average data delay and regulate each kind of data delay adaptively, according to the varying number of vehicles and different delay requirements. [ABSTRACT FROM AUTHOR]

Ischemia/reperfusion injury (IRI) occurs inevitably in liver transplantations and frequently during major resections, and can lead to liver dysfunction as well as systemic disorders. High-mobility group box 1 (HMGB1) plays a pathogenic role in hepatic IRI. In the normal liver, HMGB1 is located in the nucleus of hepatocytes; after ischemia reperfusion, it translocates to the cytoplasm and it is further released to the extracellular space. Unlike the well-explored functions of nuclear and extracellular HMGB1, the role of cytoplasmic HMGB1 in hepatic IRI remains elusive. We hypothesized that cytoplasmic HMGB1 interacts with binding proteins involved in the hepatocellular response to IRI. In this study, binding proteins of cytoplasmic HMGB1 during hepatic IRI were identified. Liver tissues from rats with warm ischemia reperfusion (WI/R) injury and from normal rats were subjected to cytoplasmic protein extraction. Co-immunoprecipitation using these protein extracts was performed to enrich HMGB1-protein complexes. To separate and identify the immunoprecipitated proteins in eluates, 2-dimensional electrophoresis and subsequent mass spectrometry detection were performed. Two of the identified proteins were verified using Western blotting: betaine-homocysteine S -methyltransferase 1 (BHMT) and cystathionine γ-lyase (CTH). Therefore, our results revealed the binding of HMGB1 to BHMT and CTH in cytoplasm during hepatic WI/R. This finding may help to better understand the cellular response to IRI in the liver and to identify novel molecular targets for reducing ischemic injury.