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Abstract Advances in mass spectrometry accelerates the characterization of HLA ligandome, necessitating the development of efficient methods for immunopeptidomics analysis and (neo)antigen prediction. We develop ImmuneApp, an interpretable deep learning framework trained on extensive HLA ligand datasets, which improves the prediction of HLA-I epitopes, prioritizes neoepitopes, and enhances immunopeptidomics deconvolution. ImmuneApp extracts informative embeddings and identifies key residues for pHLA binding. We also present a more accurate model-based deconvolution approach and systematically analyzed 216 multi-allelic immunopeptidomics samples, identifying 835,551 ligands restricted to over 100 HLA-I alleles. Our investigation reveals the effectiveness of the composite model, denoted as ImmuneApp-MA, which integrates mono- and multi-allelic data to enhance predictive performance. Leveraging ImmuneApp-MA as a pre-trained model, we built ImmuneApp-Neo, an immunogenicity predictor that outperforms existing methods for prioritizing immunogenic neoepitope. ImmuneApp demonstrates its utility across various immunopeptidomics datasets, which will promote the discovery of novel neoantigens and the development of new immunotherapies.

Markets for technology have undergone remarkable changes and received considerable attention, but few studies focus on the markets of a developing country from an evolutionary perspective. Using a unique dataset of patent trades from the National Intellectual Property Administration, PRC (CNIPA), this study seeks to uncover the landscape of China’s markets for technology (CMFT) from an evolutionary view. The research findings show that (1) patent trades in the CMFT have experienced a notable increase, with an average growth rate of 22.3%; (2) the average age and geographical distance of traded patents are 6.3 years and 71.97 km; (3) spatial autocorrelation analysis shows that patent trades in the western region present increasingly stronger correlation; (4) social network analysis indicates that the patent trading network has gotten growing dense over time. We expect this descriptive study to present some basic evidence for theoretical development and consequent research about the markets for technology.

Jingdong 120-meter radio telescope (JRT) is poised to become the world's largest single-aperture fully steerable medium-low frequency radio telescope. However, like other large-aperture radio telescopes, the JRT is vulnerable to wind loads, which can cause structural deformation and pointing errors. Addressing this challenge requires the ability to predict dynamic winds in real-time. This study developed a wind pressure preprocessing and prediction model using sensor data collected from the Kunming 40-meter radio telescope (KRT), enabling real-time prediction of wind pressure on the telescope. The model employs adaptive noise and Variational Mode Decomposition (VMD) techniques to eliminate random noise from the original wind pressure data. Subsequently, wind pressure predictions are made using a Bidirectional Long Short-term Memory (BiLSTM) model. By conducting predictions under various stabilization conditions and conducting a thorough analysis of measurement data from five sensors, the study has achieved impressive results in predicting wind pressure on the KRT reflector surface. The proposed model demonstrates the lowest MAE, RMSE, and MAPE, while achieving the highest R2 across various data sets. Where the average R2 of the proposed model is 0.9392 at 45° pitch angle attitude and the RMSE, MAE and MAPE values are 1.4923, 1.2377 and 1.82% respectively. This model helps wind load monitoring of real-time wind pressure monitoring of the telescope surface, to study the effects of wind load on pointing accuracy. By adjusting the control parameters to reduce wind load interference, to ensure the high-precision work of a large radio telescope, such as JRT.

Summary: Emerging evidence highlights the regulatory role of paired-like (PRD-like) homeobox transcription factors (TFs) in embryonic genome activation (EGA). However, the majority of PRD-like genes are lost in rodents, thus prompting an investigation into PRD-like TFs in other mammals. Here, we showed that PRD-like TFs were transiently expressed during EGA in human, monkey, and porcine fertilized embryos, yet they exhibited inadequate expression in their cloned embryos. This study, using pig as the research model, identified LEUTX as a key PRD-like activator of porcine EGA through genomic profiling and found that LEUTX overexpression restored EGA failure and improved preimplantation development and cloning efficiency in porcine cloned embryos. Mechanistically, LEUTX opened EGA-related genomic regions and established histone acetylation via recruiting acetyltransferases p300 and KAT2A. These findings reveal the regulatory mechanism of LEUTX to govern EGA in pigs, which may provide valuable insights into the study of early embryo development for other non-rodent mammals.

Considering its Venturi effect, long corridor terrain is considered as one of the outstanding wind-energy resources, but has not been developed due to unclear inflow turbulence effects on wind turbines. To advance the wind-power technology in long corridors and reveal the physics behind it, we conducted a series of data-fusion analyses combining field measurements in the Hexi Corridor, Gansu, China, and numerical simulations and summarize the effort here. In this study, the classical spectrum for the atmospheric boundary layer for wind-energy research, the IEC Kaimal spectrum (also known as IECKwind, International Elector technical Commission 61400-1) cannot describe the wind profile in corridors. Here, we proposed a new spectrum to describe the wind in the Hexi Corridor by modifying the IEC Kaimal spectrum, and we call it the IECK-HX spectrum. The results demonstrate that it preferentially resides at lower frequencies (larger spatial scales) compared with IECKwind. Large-scale motions (LSMs) and very large-scale motions (VLSMs) coexist in the Hexi Corridor atmospheric boundary layer, which can be well described by the IECK-HX spectrum, and these two dominate the large fluctuations in the power, thrust, and blade root flapwise moment of wind turbines, while the high-frequency small-scale coherent structures mainly cause high-frequency fluctuations in wind-turbine load. Furthermore, IEC Kaimal spectrum can not describe the VLSMs, and the generated flow presents significant fluctuations and thereby reduces the power output in high wind-speed regions. This study provides fundamental support for wind-energy scientists and engineers who are interested in wind energy in corridor terrains.

Purpose – Owing to increased energy demands, China has become the world’s top CO2 emitter, with electricity generation accounting for the majority of emissions. Therefore, the Chinese Government aspires to achieve a low-carbon transformation of the electric industry by enhancing its green innovation capacity. However, little attention has been paid to the green development of electric technology. Thus, this paper aims to uncover the spatiotemporal evolution of electric technology in the context of China’s low-carbon transformation through patent analysis. Design/methodology/approach – Using granted green invention patent data for China’s electric industry between 2000 and 2021, this paper conducted an exploratory, spatial autocorrelation and time-varying difference-in-differences (DID) analysis to reveal the landscape of electric technology. Findings – Exploratory analysis shows that the average growth rate of electric technology is 8.1%, with spatial heterogeneity, as there is slower growth in the north and west and faster growth in the south and east. In addition, electric technology shows spatial clustering in local areas. Finally, the time-varying DID analysis provides positive evidence that low-carbon policies improve the green innovation capacity of electric technology. Research limitations/implications – The different effects of the low-carbon pilot policy (LCPC) on R&D subjects and the LCPC’s effectiveness in enhancing the value of patented technology were not revealed. Originality/value – This paper reveals the spatiotemporal evolutionary characteristics of electric technology in mainland China. The results can help the Chinese Government clarify how to carry out innovative development in the electric industry as part of the low-carbon transformation and provide a theoretical basis and research direction for newcomers in this field.

The dysregulation of the m6A epitranscriptomic networks have been reported to contribute to the development of gliomas. Here, the authors utilize induced pluripotent stem cell-derived astrocytes with a p53 mutation and demonstrate that mutant p53 upregulates the m6A reader YTHDF2, resulting in the initiation of gliomas.

Abstract Background The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes. Results scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants. Conclusions We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.

Unmanned aerial vehicles (UAVs) have emerged as a highly efficient means of monitoring landslide-prone regions, given the growing concern for urban safety and the increasing occurrence of landslides. Designing optimal UAV flight routes is crucial for effective landslide monitoring. However, in real-world scenarios, the testing and validating of flight path planning algorithms incur high cost and safety concerns, making overall flight operations challenging. Therefore, this paper proposes the use of the Unreal Engine simulation framework to design UAV flight path planning specifically for landslide monitoring. It aims to validate the authenticity of the simulated flight paths and the correctness of the algorithms. Under the proposed simulation framework, we then test a novel flight path planning algorithm. The simulation results demonstrate that the model reconstruction obtained using the novel flight path algorithm exhibits more detailed textures, with a 3D model simulation accuracy ranging from 10 to 14 cm. Among them, the RMSE value of the novel flight route algorithm falls within the range of 10 to 11 cm, exhibiting a 2 to 3 cm improvement in accuracy compared to the traditional flight path algorithm. Additionally, it effectively reduces the flight duration by 9.3% under the same flight path compared to conventional methods. The results confirm that the simulation framework developed in this paper meets the requirements for landslide damage monitoring and validates the feasibility and correctness of the UAV flight path planning algorithm.

Abstract The effects of sampling frequency on the efficacy of the POD technique for reconstructing the flow field of a wind turbine wake are studied in a neutral atmospheric boundary layer (ABL) using large eddy simulations (LES) coupled with an actuator disk model. The objective of this paper is to study how the sampling frequency over a fixed period of time affects the POD modes. The results showed that sampling frequency has little influence on the convergence of POD modes and the extraction of large‐scale coherent structures. Meanwhile, sampling frequency has negligible influence on the kinetic energy contained in each mode of the first 25 modes. When the mode number is greater than 25, it affects the distributed energy in each mode and the accumulative energy in a given number of modes, and the accuracy of the reconstructed flow field. In present study, the flow field reconstructed using the smallest sampling frequency can be as accurate as that with the largest sampling frequency, while the former can save about 86% of the computational cost. For a given number of POD modes, a smaller sampling frequency may lead to more accurate flow field reconstruction. The sampling frequency mainly affects the small‐scale structures.

Computer-assisted diagnostic systems have been developed to aid doctors in diagnosing thyroid-related abnormalities. The aim of this research is to improve the diagnosis accuracy of thyroid abnormality detection models that can be utilized to alleviate undue pressure on healthcare professionals. In this research, we proposed deep learning, metaheuristics, and a MCDM algorithms-based framework to detect thyroid-related abnormalities from ultrasound and histopathological images. The proposed method uses three recently developed deep learning techniques (DeiT, Swin Transformer, and Mixer-MLP) to extract features from the thyroid image datasets. The feature extraction techniques are based on the Image Transformer and MLP models. There is a large number of redundant features that can overfit the classifiers and reduce the generalization capabilities of the classifiers. In order to avoid the overfitting problem, six feature transformation techniques (PCA, TSVD, FastICA, ISOMAP, LLE, and UMP) are analyzed to reduce the dimensionality of the data. There are five different classifiers (LR, NB, SVC, KNN, and RF) evaluated using the 5-fold stratified cross-validation technique on the transformed dataset. Both datasets exhibit large class imbalances and hence, the stratified cross-validation technique is used to evaluate the performance. The MEREC-TOPSIS MCDM technique is used for ranking the evaluated models at different analysis stages. In the first stage, the best feature extraction and classification techniques are chosen, whereas, in the second stage, the best dimensionality reduction method is evaluated in wrapper feature selection mode. Two best-ranked models are further selected for the weighted average ensemble learning and features selection using the recently proposed meta-heuristics FOX-optimization algorithm. The PCA+FOX optimization-based feature selection + random forest model achieved the highest TOPSIS score and performed exceptionally well with an accuracy of 99.13%, F2-score of 98.82%, and AUC-ROC score of 99.13% on the ultrasound dataset. Similarly, the model achieved an accuracy score of 90.65%, an F2-score of 92.01%, and an AUC-ROC score of 95.48% on the histopathological dataset. This study exploits the combination novelty of different algorithms in order to improve the thyroid cancer diagnosis capabilities. This proposed framework outperforms the current state-of-the-art diagnostic methods for thyroid-related abnormalities in ultrasound and histopathological datasets and can significantly aid medical professionals by reducing the excessive burden on the medical fraternity.

Abstract White matter (WM) abnormalities in patients with cocaine use disorder (CUD) have been studied; however, the reported effects on the human brain are heterogenous and most results have been obtained from male participants. In addition, biological data supporting the imaging findings and revealing possible mechanisms underlying the neurotoxic effects of chronic cocaine use (CU) on WM are largely restricted to animal studies. To evaluate the neurotoxic effects of CU in the WM, we performed an in vivo diffusion tensor imaging assessment of male and female cocaine users (n = 75) and healthy controls (HC) (n = 58). Moreover, we performed an ex vivo large-scale proteomic analysis using liquid chromatography-tandem mass spectrometry in postmortem brains of patients with CUD (n = 8) and HC (n = 12). Compared with the HC, the CUD group showed significant reductions in global fractional anisotropy (FA) (p

Abstract Background Aberrant changes in epigenetic mechanisms such as histone modifications play an important role in cancer progression. PRMT1 which triggers asymmetric dimethylation of histone H4 on arginine 3 (H4R3me2a) is upregulated in human colorectal cancer (CRC) and is essential for cell proliferation. However, how this dysregulated modification might contribute to malignant transitions of CRC remains poorly understood. Methods In this study, we integrated biochemical assays including protein interaction studies and chromatin immunoprecipitation (ChIP), cellular analysis including cell viability, proliferation, colony formation, and migration assays, clinical sample analysis, microarray experiments, and ChIP-Seq data to investigate the potential genomic recognition pattern of H4R3me2s in CRC cells and its effect on CRC progression. Results We show that PRMT1 and SMARCA4, an ATPase subunit of the SWI/SNF chromatin remodeling complex, act cooperatively to promote colorectal cancer (CRC) progression. We find that SMARCA4 is a novel effector molecule of PRMT1-mediated H4R3me2a. Mechanistically, we show that H4R3me2a directly recruited SMARCA4 to promote the proliferative, colony-formative, and migratory abilities of CRC cells by enhancing EGFR signaling. We found that EGFR and TNS4 were major direct downstream transcriptional targets of PRMT1 and SMARCA4 in colon cells, and acted in a PRMT1 methyltransferase activity-dependent manner to promote CRC cell proliferation. In vivo, knockdown or inhibition of PRMT1 profoundly attenuated the growth of CRC cells in the C57BL/6 J-ApcMin/+ CRC mice model. Importantly, elevated expression of PRMT1 or SMARCA4 in CRC patients were positively correlated with expression of EGFR and TNS4, and CRC patients had shorter overall survival. These findings reveal a critical interplay between epigenetic and transcriptional control during CRC progression, suggesting that SMARCA4 is a novel key epigenetic modulator of CRC. Our findings thus highlight PRMT1/SMARCA4 inhibition as a potential therapeutic intervention strategy for CRC. Conclusion PRMT1-mediated H4R3me2a recruits SMARCA4, which promotes colorectal cancer progression by enhancing EGFR signaling.

Drug response in cancer patients vary dramatically due to inter- and intra-tumor heterogeneity and transcriptome context plays a significant role in shaping the actual treatment outcome. Here, the authors develop a deep variational autoencoder model to compress gene signatures into latent vectors and accurately impute drug response.

Abstract Early embryos undergo extensive epigenetic reprogramming to achieve gamete‐to‐embryo transition, which involves the loading and removal of histone variant H2A.Z on chromatin. However, how does H2A.Z regulate gene expression and histone modifications during preimplantation development remains unrevealed. Here, by using ultra‐low‐input native chromatin immunoprecipitation and sequencing, the genome‐wide distribution of H2A.Z is delineated in mouse oocytes and early embryos. These landscapes indicate that paternal H2A.Z is removed upon fertilization, followed by unbiased accumulation on parental genomes during zygotic genome activation (ZGA). Remarkably, H2A.Z exhibits hierarchical accumulation as different peak types at promoters: promoters with double H2A.Z peaks are colocalized with H3K4me3 and indicate transcriptional activation; promoters with a single H2A.Z peak are more likely to occupy bivalent marks (H3K4me3+H3K27me3) and indicate development gene suppression; promoters with no H2A.Z accumulation exhibit persisting gene silencing in early embryos. Moreover, H2A.Z depletion changes the enrichment of histone modifications and RNA polymerase II binding at promoters, resulting in abnormal gene expression and developmental arrest during lineage commitment. Furthermore, similar transcription and accumulation patterns between mouse and porcine embryos indicate that a dual role of H2A.Z in regulating the epigenome required for proper gene expression is conserved during mammalian preimplantation development.

Since its launch, open innovation has been one of the hottest research topics in the field of innovation management. However, most of the current literature focuses on open innovation participants, severing the link between actors engaging in open innovation activities and social third-party firms, i.e., lacks discussions of the social attributes of open innovation. Therefore, via the perspective of the evolution of open innovation participants, this paper reviews current research from the aspects of unilateral subjects, bilateral subjects, and third-party subjects. Integrating the perspective with a sociological view, this paper theoretically constructs a framework for the open innovation information spillover effect. Moreover, taking Huawei Technologies Co., Ltd., as an example, this paper verifies the practical existence of the information spillover effect among firms’ open innovation activities. This paper aims to extend the research boundary of open innovation, thereby providing a theoretical reference for further studies.

Mass spectrometry (MS)-based lipidomic has become a powerful tool for studying lipids in biological systems. However, lipidome analysis at the single-cell level remains a challenge. Here, we report a highly sensitive lipidomic workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS)-MS. This approach enables the high-coverage identification of lipidome landscape at the single-oocyte level. By using the proposed method, comprehensive lipid changes in porcine oocytes during their maturation were revealed. The results provide valuable insights into the structural changes of lipid molecules during porcine oocyte maturation, highlighting the significance of sphingolipids and glycerophospholipids. This study offers a new approach to the single-cell lipidomic.

Fully resolved direct numerical simulations are conducted to study the effects of fluctuating freestream flow on the drag force and wake characteristics of a stationary spherical particle. Sinusoidal fluctuation around a mean value is adopted as the freestream velocity. The interaction between the particle and fluctuating flow is computed by the direct-forcing immersed boundary method. We principally consider the relative difference between the computed mean drag and the drag law of a uniform flow past the particle and the properties of drag fluctuation in different freestream fluctuation directions. For the influence of streamwise fluctuating inflow, the relative mean drag difference increases with the particle Reynolds number. At small or intermediate particle scale ratios, the relative mean drag difference is very close to zero, indicating that the classical drag law can be used in these cases, while a large particle scale ratio can induce a notable increase in the relative mean drag difference at a large particle Reynolds number and high fluctuation intensity. For the transverse fluctuating inflow, generally, there is an evident increase in the mean drag coefficient when the particle scale ratio is small. Compared with the streamwise fluctuation case, the drag fluctuation intensity is a little smaller with the transverse fluctuating inflow. An explicit empirical drag fluctuation law is obtained by fitting the data for streamwise fluctuating inflow. The wake characteristics are also analyzed, and they are found to be strongly dependent on the direction of inflow fluctuation.

Fluctuating wall shear stress in turbulent channel flows is decomposed into small-scale and large-scale components. The large-scale fluctuating wall shear stress is computed as the footprints of the outer turbulent motions, and the small-scale one is obtained by subtracting the large-scale one from the total, which fully remove the outer influences. We show that the statistics of the small-scale fluctuating wall shear stress is Reynolds number independent at the friction Reynolds number larger than 1000, while which is Reynolds number dependent or the low-Reynolds-number effect exists at the friction Reynolds number smaller than 1000. Therefore, a critical Reynolds number that defines the emergence of universal small-scale fluctuating wall shear stress is proposed to be 1000. The total and large-scale fluctuating wall shear stress intensities approximately follow logarithmic-linear relationships with Reynolds number, and empirical fitting expressions are given in this work.

Platelets are implicated in the pathophysiology of breast and other cancers through their role in exchanging biomolecules with tumor cells in the tumor microenvironment. Such exchange results in tumor‐educated platelets with altered RNA expression profiles. Multiple lines of evidence indicate that platelet RNA profiles may be suitable as diagnostic biomarkers for cancer‐related biological processes. In this study, we characterized the gene expression signatures of platelets in breast cancer (BC) by high‐throughput sequencing and quantitative real‐time RT‐PCR. Our results indicate that the expression of TPM3 (tropomyosin 3) mRNA is significantly elevated in platelets from patients with BC compared with age‐matched healthy control subjects. Furthermore, up‐regulation of TPM3 mRNA in platelets was found to be significantly correlated with metastasis in patients with BC. Finally, we report that platelet TPM3 mRNA is delivered into BC cells through microvesicles and leads to enhanced migrative phenotype of BC cells. In summary, our findings suggest that the transfer of platelet TPM3 mRNA into cancer cells via microvesicles promotes cancer cell migration, and thus platelet‐derived TPM3 mRNA may be a suitable biomarker for early diagnosis of metastatic BC.

Lung cancer is the leading cause of cancer‐related morbidity and mortality worldwide, with lung adenocarcinoma (LUAD) being the most common histological subtype (approximately 40%). In the absence of reliable screening biomarkers for early diagnosis, most patients with LUAD are inevitably diagnosed at an advanced stage. MicroRNAs (miRNAs) encapsulated within plasma‐derived extracellular vesicles (EVs) may be suitable for use as noninvasive diagnostic biomarkers for aggressive malignancies, including LUAD. In this study, we first investigated the miRNA profiles of plasma‐derived EVs from LUAD patients and healthy donors, and then systematically evaluated the expression patterns of selected plasma‐derived EV miRNAs in a large cohort of patients with LUAD and healthy controls. Notably, we observed that miR‐451a, miR‐194‐5p, and miR‐486‐5p were significantly increased in EVs from LUAD patients, compared to healthy controls. The area under the curve values for the three miRNAs were 0.9040 (95% confidence interval [CI], 0.8633–0.9447) for miR‐451a, 0.7492 (95% CI, 0.6992–0.7992) for miR‐194‐5p, and 0.9574 (95% CI, 0.9378–0.9769) for miR‐486‐5p, while the AUC of the combination of these three miRNAs was 0.9650. Thus, these results suggest that these EV miRNAs may be promising candidates for the development of highly effective, noninvasive biomarkers for early LUAD diagnosis.

Microfluidic flow generation plays a fundamental role in microfluidic systems and shows potential for applications in basic biology and clinical medicine. In this study, an enabling technology is proposed to quantitatively generate microfluid flow through the automatic movement of a microsphere in liquid by using optical tweezers. A closed-loop control strategy with visual servoing feedback is introduced to achieve high precision and robustness. The theoretical solution of the generated microfluid is obtained on the basis of Stokes equations. An experimental method is proposed, and experiments are performed to verify the effectiveness of our approach. This method does not impose any dedicated fabrication of microtool, and the microfluidic flow can be dexterously adjusted by controlling the direction, speed, and distance of the microsphere from a target location. To the best of our knowledge, this is the first demonstration of optically actuating liquids through the translational movement of microspheres with closed-loop control. The proposed method will be useful in various biomedical applications needing quantitative, precise and controllable localized microfluid.

Abstract Rivaroxaban is an oral direct factor Xa inhibitor approved for the treatment of stroke and systemic thromboembolism in patients with non-valvular atrial fibrillation. Despite its efficacy, rivaroxaban therapy results in adverse effects and complications, such as bleeding. Angiotensin II (AngII) is implicated in many cardiovascular conditions, such as hypertension and heart failure. In this study, we investigate whether AngII influences anticoagulant effects of rivaroxaban by using an experimental mouse model with type 2 diabetes mellitus and advanced glycation end product (AGE)-exposed human umbilical vein endothelial cells (HUVECs). We found that AngII promoted the anticoagulant effects of rivaroxaban in KKAy mice. The combination of rivaroxaban and AngII enhanced in vivo tissue factor pathway inhibitor (TFPI) activity and induced TFPI expression and activity in AGE-exposed HUVECs. Angiotensin type 2 receptor (AT2R) and Mas antagonists attenuated the AngII-enhanced anticoagulant action of rivaroxaban in vivo, and abolished the increased endothelial TFPI expression and activity. However, angiotensin type 1 receptor (AT1R) antagonist exerted no effects. Additionally, combination of rivaroxaban and AngII induced aortic AT2R and Mas expression. Our data suggest that the anticoagulant effects of rivaroxaban are promoted by AngII via AT2R and Mas signaling. These findings are significant for the clinical administration of rivaroxaban.

ABSTRACT IMPACT: This project will use human neuron models and bioinformatics techniques to elucidate mechanisms of cocaine neurotoxicity, which will allow treatments to be developed for minimizing or preventing neurological damage caused by cocaine abuse and overdose. OBJECTIVES/GOALS: The goals of this project are to identify genes and gene networks altered by cocaine exposure in neurons (short term), and to use these pathways to understand mechanisms of cocaine neurotoxicity for the establishment of therapeutic targets (long term). METHODS/STUDY POPULATION: To study the molecular effects of cocaine, we generated preliminary proteomics and next-generation RNA sequencing (RNAseq) data from human postmortem prefrontal cortex (Broadmann area 9 or BA9) of 12 cocaine overdose subjects and 17 controls. Future directions for this project include RNAseq analysis of neuronal nuclei sorted from human postmortem BA9 and a human induced pluripotent stem cell-derived neuron (hiPSN) model of cocaine exposure from the same postmortem subjects from whom we have brain samples. RESULTS/ANTICIPATED RESULTS: We found alterations in neuronal synaptic protein levels and gene expression, including the serotonin transporter SLC6A4, and synaptic proteins SNAP25, SYN2, SYNGR3. Pathway analysis of our results revealed alterations in specific pathways involved with neuronal function including voltage-gated calcium channels, and GABA receptor signaling. In the future, we expect to see an enhancement in neuron-specific gene expression signatures in our sorted neuronal nuclei and our hiPSN model of cocaine exposure. The hiPSN model will help elucidate which effects are due to acute versus chronic exposure of cocaine. DISCUSSION/SIGNIFICANCE OF FINDINGS: Transcriptomic signatures found with this analysis can help us understand mechanisms of cocaine neurotoxicity in human neurons. With this work and future proposed studies, we can discover targetable molecular pathways to develop drugs that can reduce or reverse cocaine-related impairment.

OBJECTIVES/GOALS: The goal of this project is to identify new therapeutic targets and biomarkers to treat or prevent cocaine toxicity by investigating proteomic, transcriptomic and epigenetic signatures of cocaine exposure in human subjects. METHODS/STUDY POPULATION: Cocaine is a highly addictive neurotoxic substance, and it is estimated that 1.9 million Americans are current users of cocaine. To study the molecular effects of cocaine, we generated preliminary proteomics and next-generation RNA sequencing (RNAseq) data from human postmortem dorsolateral prefrontal cortex (Broadmann area 9 or BA9) of 12 cocaine-exposed subjects and 17 controls. Future directions for this project include RNAseq and DNA methylation analysis of neuronal nuclei sorted from human postmortem BA9 and a human induced pluripotent stem cell-derived neuron (hiPSN) model of cocaine exposure from the same postmortem subjects from whom we have brain samples. RESULTS/ANTICIPATED RESULTS: We found alterations in neuronal synaptic protein levels and gene expression, including the serotonin transporter SLC6A4, and synaptic proteins SNAP25, SYN2, SYNGR3. Pathway analysis of our results revealed alterations in specific pathways involved with neuronal function including voltage-gated calcium channels, and GABA receptor signaling. In the future, we expect to see an enhancement in neuron-specific gene expression signatures in our sorted neuronal nuclei and our hiPSN model of cocaine exposure. The hiPSN model will help elucidate which effects are due to acute versus chronic exposure of cocaine. DISCUSSION/SIGNIFICANCE OF IMPACT: Neuronal signatures found with this analysis can help us understand mechanisms of cognitive decline in long-term cocaine users as well as the acute effects on the brain of cocaine taken in overdose. With this work and future proposed studies, we can discover novel clinical biomarkers for cocaine neurotoxicity in patients with cocaine use disorder and determine readouts for future therapeutic development on cocaine addiction and overdose.

Salvianolic acid B (SalB) and ginsenoside Re (Re) protect endotheliocytes against apoptosis through different mechanisms. However, whether both compounds could synergistically protect endothelial cells against oxidized low-density lipoprotein (Ox-LDL)-induced apoptosis is unclear. This study aimed to assess the protective effect of combined SalB and Re (SR) treatment on Ox-LDL-induced endothelial apoptosis and to explore the mechanism underlying this effect. Results showed that SalB, Re, or SR could protect against Ox-LDL-induced endothelial apoptosis. Furthermore, the composition of SR was optimized through central composite design with response surface methodology. SR with a composition of 60 μg/mL of SalB and 120 μg/mL of Re exerted the optimal protective effect. Network pharmacology research revealed that SalB and Re in SR synergistically protect against Ox-LDL-induced endothelial apoptosis by regulating oxidative stress and phlogistic pathways. In vitro experiments confirmed these results. Compared with the same dose of SalB or Re alone, SR significantly decreased the contents of inflammatory mediators and increased the activities of antioxidant enzymes. SR could synergistically restore the balanced redox state of the cells and inhibit the activation of nuclear transcription factor kappa B and the caspase cascade by activating the phosphatidylinositol 3 kinase/protein kinase B pathway and inhibiting the phosphorylation of p38 mitogen-activated protein kinase. These pathways are regulated by down-regulating the expression of lectin-like Ox-LDL receptor-1 and NADPH oxidase and up-regulating the expression of estrogen receptor alpha. Therefore, SR effectively prevents Ox-LDL-induced endothelial apoptosis through antioxidative and antiinflammatory mechanisms.

In the present work, a highly efficient incompressible flow solver with a semi-implicit time advancement on a fully staggered grid using a high-order compact difference scheme is developed firstly in the framework of approximate factorization. The fourth-order compact difference scheme is adopted for approximations of derivatives and interpolations in the incompressible Navier–Stokes equations. The pressure Poisson equation is efficiently solved by the fast Fourier transform (FFT). The framework of approximate factorization significantly simplifies the implementation of the semi-implicit time advancing with a high-order compact scheme. Benchmark tests demonstrate the high accuracy of the proposed numerical method. Secondly, by applying the proposed numerical method, we compute turbulent channel flows at low and moderate Reynolds numbers by direct numerical simulation (DNS) and large eddy simulation (LES). It is found that the predictions of turbulence statistics and especially energy spectra can be obviously improved by adopting the high-order scheme rather than the traditional second-order central difference scheme.

Complex diseases seriously threaten human health. Drug discovery approaches based on "single genes, single drugs, and single targets" are limited in targeting complex diseases. The development of new multicomponent drugs for complex diseases is imperative, and the establishment of a suitable solution for drug group-target protein network analysis is a key scientific problem that must be addressed. Herbal medicines have formed the basis of sophisticated systems of traditional medicine and have given rise to some key drugs that remain in use today. The search for new molecules is currently taking a different route, whereby scientific principles of ethnobotany and ethnopharmacognosy are being used by chemists in the discovery of different sources and classes of compounds.In this study, we developed TarNet, a manually curated database and platform of traditional medicinal plants with natural compounds that includes potential bio-target information. We gathered information on proteins that are related to or affected by medicinal plant ingredients and data on protein-protein interactions (PPIs). TarNet includes in-depth information on both plant-compound-protein relationships and PPIs. Additionally, TarNet can provide researchers with network construction analyses of biological pathways and protein-protein interactions (PPIs) associated with specific diseases. Researchers can upload a gene or protein list mapped to our PPI database that has been manually curated to generate relevant networks. Multiple functions are accessible for network topological calculations, subnetwork analyses, pathway analyses, and compound-protein relationships.TarNet will serve as a useful analytical tool that will provide information on medicinal plant compound-affected proteins (potential targets) and system-level analyses for systems biology and network pharmacology researchers. TarNet is freely available at http://www.herbbol.org:8001/tarnet, and detailed tutorials on the program are also available.

Unexpected performance degradation occurs in wind turbine blades due to leading edge defect when suffering from continuous impacts with rain drops, hails, insects, or solid particles during its operation life. To assess this issue, this paper numerically investigates the steady and dynamic stall characteristics of an S809 airfoil with various leading edge defects. More leading edge defect sizes and much closer to practical parameters are investigated in the paper.Numerical computation is conducted using the SST k-ω turbulence model, and the method has been validated by comparison with existed published data. In order to ensure the calculation convergence, the residuals for the continuity equation are set to be less than 10-7 and 10-6 in steady state and dynamic stall cases. The simulations are conducted with the software ANSYS Fluent 13.0.It is found that the characteristics of aerodynamic coefficients and flow fields are sensitive to leading edge defect both in steady and dynamic conditions. For airfoils with the defect thickness of 6%tc, leading edge defect has a relative small influence on the aerodynamics of S809 airfoil. For other investigated defect thicknesses, leading edge defect has much greater influence on the flow field structures, pressure coefficients and aerodynamic characteristics of airfoil at relative small defect lengths. For example, the lift coefficients decrease and drag coefficients increase sharply after the appearance of leading edge defect. However, the aerodynamic characteristics could reach a constant value when the defect length is large enough. The flow field, pressure coefficient distribution and aerodynamic coefficients do not change a lot when the defect lengths reach to 0.5%c,1%c, 2%c and 3%c with defect thicknesses of 6%tc, 12%tc,18%tc and 25%tc, respectively. In addition, the results also show that the critical defect length/thickness ratio is 0.5, beyond which the aerodynamic characteristics nearly remain unchanged. In dynamic stall, leading edge defect imposes a greater influence on the aerodynamic characteristics of airfoil than steady conditions. By increasing in defect length, it is found that the separated area becomes more intense and moves forward along the suction surface.Leading edge defect has significant influence on the aerodynamic and flow characteristics of the airfoil, which will reach a stable status with enough large defect size. The leading edge separation bubble, circulation in the defect cavity and intense tailing edge vortex are the main features of flow around defective airfoils.