ZFHX3 variants cause childhood partial epilepsy and infantile spasms with favourable outcomes.

BACKGROUND: The ZFHX3 gene plays vital roles in embryonic development, cell proliferation, neuronal differentiation and neuronal death. This study aims to explore the relationship between ZFHX3 variants and epilepsy. METHODS: Whole-exome sequencing was performed in a cohort of 378 patients with partial (focal) epilepsy. A Drosophila Zfh2 knockdown model was used to validate the association between ZFHX3 and epilepsy. RESULTS: Compound heterozygous ZFHX3 variants were identified in eight unrelated cases. The burden of ZFHX3 variants was significantly higher in the case cohort, shown by multiple/specific statistical analyses. In Zfh2 knockdown flies, the incidence and duration of seizure-like behaviour were significantly greater than those in the controls. The Zfh2 knockdown flies exhibited more firing in excitatory neurons. All patients presented partial seizures. The five patients with variants in the C-terminus/N-terminus presented mild partial epilepsy. The other three patients included one who experienced frequent non-convulsive status epilepticus and two who had early spasms. These three patients had also neurodevelopmental abnormalities and were diagnosed as developmental epileptic encephalopathy (DEE), but achieved seizure-free after antiepileptic-drug treatment without adrenocorticotropic-hormone/steroids. The analyses of temporal expression (genetic dependent stages) indicated that ZFHX3 orthologous were highly expressed in the embryonic stage and decreased dramatically after birth. CONCLUSION: ZFHX3 is a novel causative gene of childhood partial epilepsy and DEE. The patients of infantile spasms achieved seizure-free after treatment without adrenocorticotropic-hormone/steroids implies a significance of genetic diagnosis in precise treatment. The genetic dependent stage provided an insight into the underlying mechanism of the evolutional course of illness.

Soluble expression of recombinant human interleukin-2 in Escherichia coli and its facile production.

Recombinant human interleukin-2 (rhIL-2) represents one of the most difficult-to-produce cytokines in E. coli due to its extreme hydrophobicity and high tendency to formation of inclusion bodies. Refolding of rhIL-2 inclusion bodies always represents cumbersome downstream processes and low production efficiency. Herein, we disclosed a fusion strategy for efficiently soluble expression and facile production of rhIL-2 in E. coli Origami B (DE3) host. A two-tandem SUMO fusion partner (His-2SUMO) with a unique SUMO protease cleavage site at C-terminus was devised to fuse with the N-terminus of rhIL-2 and the fusion protein (His-2SUMO-rhIL-2) was almost completely expressed in a soluble from. The fusion partner could be efficiently removed by Ulp1 cleavage and the rhIL-2 was simply produced by a two-step Ni-NTA affinity chromatography with a considerable purity and whole recovery. The eventually obtained rhIL-2 was well-characterized and the results showed that the purified rhIL-2 exhibits a compact and ordered structure. Although the finally obtained rhIL-2 exists in a soluble aggregates form and the aggregation probably has been occurred during expression stage, the soluble rhIL-2 aggregates remain exhibit comparable bioactivity with the commercially available rhIL-2 drug formulation.

Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model.

BACKGROUND: Infectious diarrhea remains a major public health problem worldwide. This study used stacking ensemble to developed a predictive model for the incidence of infectious diarrhea, aiming to achieve better prediction performance. METHODS: Based on the surveillance data of infectious diarrhea cases, relevant symptoms and meteorological factors of Guangzhou from 2016 to 2021, we developed four base prediction models using artificial neural networks (ANN), Long Short-Term Memory networks (LSTM), support vector regression (SVR) and extreme gradient boosting regression trees (XGBoost), which were then ensembled using stacking to obtain the final prediction model. All the models were evaluated with three metrics: mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE). RESULTS: Base models that incorporated symptom surveillance data and weekly number of infectious diarrhea cases were able to achieve lower RMSEs, MAEs, and MAPEs than models that added meteorological data and weekly number of infectious diarrhea cases. The LSTM had the best prediction performance among the four base models, and its RMSE, MAE, and MAPE were: 84.85, 57.50 and 15.92%, respectively. The stacking ensembled model outperformed the four base models, whose RMSE, MAE, and MAPE were 75.82, 55.93, and 15.70%, respectively. CONCLUSIONS: The incorporation of symptom surveillance data could improve the predictive accuracy of infectious diarrhea prediction models, and symptom surveillance data was more effective than meteorological data in enhancing model performance. Using stacking to combine multiple prediction models were able to alleviate the difficulty in selecting the optimal model, and could obtain a model with better performance than base models.

Method for Estimating Road Impulse Ahead of Vehicles in Urban Environment with Microelectromechanical System Three-Dimensional Sensor.

Most automated vehicles (AVs) are equipped with abundant sensors, which enable AVs to improve ride comfort by sensing road elevation, such as speed bumps. This paper proposes a method for estimating the road impulse features ahead of vehicles in urban environments with microelectromechanical system (MEMS) light detection and ranging (LiDAR). The proposed method deploys a real-time estimation of the vehicle pose to solve the problem of sparse sampling of the LiDAR. Considering the LiDAR error model, the proposed method builds the grid height measurement model by maximum likelihood estimation. Moreover, it incorporates height measurements with the LiDAR error model by the Kalman filter and introduces motion uncertainty to form an elevation weight method by confidence eclipse. In addition, a gate strategy based on the Mahalanobis distance is integrated to handle the sharp changes in elevation. The proposed method is tested in the urban environment. The results demonstrate the effectiveness of our method.

Dried fruit intake can lower the risk of ulcerative colitis: evidence from a Mendelian randomization study.

BACKGROUND AND OBJECTIVES: This study aims to examine the causal relationship between dietary factors and ulcerative colitis (UC). METHODS AND STUDY DESIGN: The analysis utilized data from genome-wide association studies (GWAS). Dried fruit, vegetables, processed meat, fresh fruit, and cereal intake were examined as exposure factors. UC was considered the outcome. Two-sample Mendelian randomization (TSMR) analysis was performed using methods. Heterogeneity and horizontal pleiotropy assessments were conducted to ensure the robustness of our findings. Additionally, we applied False Discovery Rate (FDR) corrections for multiple tests. RESULTS: The analysis revealed a significant inverse causal relationship between dried fruit intake and UC risk (odds ratio [OR]: 0.488, 95% confidence interval [CI]: 0.261 to 0.915, p = 0.025). No significant association was observed between vegetable intake (OR: 1.742, 95% CI: 0.561 to 5.415, p = 0.337), processed meat intake (OR: 1.136, 95% CI: 0.552 to 2.339, p = 0.729), fresh fruit intake (OR: 0.977, 95% CI: 0.465 to 2.054, p = 0.952), cereal intake (OR: 1.195, 95% CI: 0.669 to 2.134, p = 0.547). The low heterogeneity observed across analyses and the confirmation of stability through leave-one-out analysis reinforce the reliability of these results. Moreover, after adjusting for multiple tests, none of the dietary factors reached a p-value below the conventional significance threshold of 0.05. CONCLUSIONS: This study provides evidence of a potential association between dried fruit intake and a reduced risk of UC. Further MR studies incorporating larger GWAS datasets are needed to confirm these findings.

A 4D-Printable Photocurable Resin Derived from Waste Cooking Oil with Enhanced Tensile Strength.

In pursuit of enhancing the mechanical properties, especially the tensile strength, of 4D-printable consumables derived from waste cooking oil (WCO), we initiated the production of acrylate-modified WCO, which encompasses epoxy waste oil methacrylate (EWOMA) and epoxy waste oil acrylate (EWOA). Subsequently, a series of WCO-based 4D-printable photocurable resins were obtained by introducing a suitable diacrylate molecule as the second monomer, coupled with a composite photoinitiator system comprising Irgacure 819 and p-dimethylaminobenzaldehyde (DMAB). These materials were amenable to molding using an LCD light-curing 3D printer. Our findings underscored the pivotal role of triethylene glycol dimethacrylate (TEGDMA) among the array of diacrylate molecules in enhancing the mechanical properties of WCO-based 4D-printable resins. Notably, the 4D-printable material, composed of EWOA and TEGDMA in an equal mass ratio, exhibited nice mechanical strength comparable to that of mainstream petroleum-based 4D-printable materials, boasting a tensile strength of 9.17 MPa and an elongation at break of 15.39%. These figures significantly outperformed the mechanical characteristics of pure EWOA or TEGDMA resins. Furthermore, the EWOA-TEGDMA resin demonstrated impressive thermally induced shape memory performance, enabling deformation and recovery at room temperature and retaining its shape at -60 °C. This resin also demonstrated favorable biodegradability, with an 8.34% weight loss after 45 days of soil degradation. As a result, this 4D-printable photocurable resin derived from WCO holds immense potential for the creation of a wide spectrum of high-performance intelligent devices, brackets, mold, folding structures, and personalized products.

Detection of diabetic patients in people with normal fasting glucose using machine learning.

BACKGROUND: Diabetes mellitus (DM) is a chronic metabolic disease that could produce severe complications threatening life. Its early detection is thus quite important for the timely prevention and treatment. Normally, fasting blood glucose (FBG) by physical examination is used for large-scale screening of DM; however, some people with normal fasting glucose (NFG) actually have suffered from diabetes but are missed by the examination. This study aimed to investigate whether common physical examination indexes for diabetes can be used to identify the diabetes individuals from the populations with NFG. METHODS: The physical examination data from over 60,000 individuals with NFG in three Chinese cohorts were used. The diabetes patients were defined by HbA1c ≥ 48 mmol/mol (6.5%). We constructed the models using multiple machine learning methods, including logistic regression, random forest, deep neural network, and support vector machine, and selected the optimal one on the validation set. A framework using permutation feature importance algorithm was devised to discover the personalized risk factors. RESULTS: The prediction model constructed by logistic regression achieved the best performance with an AUC, sensitivity, and specificity of 0.899, 85.0%, and 81.1% on the validation set and 0.872, 77.9%, and 81.0% on the test set, respectively. Following feature selection, the final classifier only requiring 13 features, named as DRING (diabetes risk of individuals with normal fasting glucose), exhibited reliable performance on two newly recruited independent datasets, with the AUC of 0.964 and 0.899, the balanced accuracy of 84.2% and 81.1%, the sensitivity of 100% and 76.2%, and the specificity of 68.3% and 86.0%, respectively. The feature importance ranking analysis revealed that BMI, age, sex, absolute lymphocyte count, and mean corpuscular volume are important factors for the risk stratification of diabetes. With a case, the framework for identifying personalized risk factors revealed FBG, age, and BMI as significant hazard factors that contribute to an increased incidence of diabetes. DRING webserver is available for ease of application ( http://www.cuilab.cn/dring ). CONCLUSIONS: DRING was demonstrated to perform well on identifying the diabetes individuals among populations with NFG, which could aid in early diagnosis and interventions for those individuals who are most likely missed.

A 3D-Printed Cuttlefish Bone Elastomeric Sponge Rapidly Controlling Noncompressible Hemorrhage.

Developing a self-expanding hemostatic sponge with high blood absorption and rapid shape recovery for noncompressible hemorrhage remains a challenge. In this study, a 3D-printed cuttlefish bone elastomeric sponge (CBES) is fabricated, which combined ordered channels and porous structures, presented tunable mechanical strength, and shape memory potentials. The incorporation of cuttlefish bone powder (CBp) plays key roles in concentrating blood components, promoting aggregation of red blood cells and platelets, and activating platelets, which makes CBES show enhanced hemostatic performance compared with commercial gelatin sponges in vivo. Moreover, CBES promotes more histiocytic infiltration and neovascularization in the early stage of degradation than gelatin sponges, which is conducive to the regeneration and repair of injured tissue. To conclude, CBp loaded 3D-printed elastomeric sponges can promote coagulation, present the potential to guide tissue healing, and broaden the hemostatic application of traditional Chinese medicine.

Vascular Electrical Stimulation with Wireless, Battery-Free, and Fully Implantable Features Reduces Atherosclerotic Plaque Formation Through Sirt1-Mediated Autophagy.

Electrical stimulation (ES) is a safe and effective procedure in clinical rehabilitation with few adverse effects. However, studies on ES for atherosclerosis (AS) are scarce because ES does not provide a long-term intervention for chronic disease processes. Battery-free implants and surgically mounted them in the abdominal aorta of high-fat-fed Apolipoprotein E (ApoE-/- ) mice are used, which are electrically stimulated for four weeks using a wireless ES device to observe changes in atherosclerotic plaques. Results showed that there is almost no growth of atherosclerotic plaque at the stimulated site in AopE-/- mice after ES. RNA-sequencing (RNA-seq) analysis of Thp-1 macrophages reveal that the transcriptional activity of autophagy-related genes increase substantially after ES. Additionally, ES reduces lipid accumulation in macrophages by restoring ABCA1- and ABCG1-mediated cholesterol efflux. Mechanistically, it is demonstrated that ES reduced lipid accumulation through Sirtuin 1 (Sirt1)/Autophagy related 5 (Atg5) pathway-mediated autophagy. Furthermore, ES reverse autophagic dysfunction in macrophages of AopE-/- mouse plaques by restoring Sirt1, blunting P62 accumulation, and inhibiting the secretion of interleukin (IL)-6, resulting in the alleviation of atherosclerotic lesion formation. Here, a novel approach is shown in which ES can be used as a promising therapeutic strategy for AS treatment through Sirt1/Atg5 pathway-mediated autophagy.

Distributed receiving system with local digitization and combination for SNR enhancement.

We demonstrate an X-band distributed receiving system with 4 remote ends for signal-to-noise ratio (SNR) enhancement. The X-band analog signal received by 4 remote ends is first transmitted to the local end through optical fiber links and is then down-converted with a photonic method for digitization and further coherent combination. Finally, a combined signal with a higher SNR can be obtained. In the proposed system, a frequency-tunable single-tone signal is stably transmitted to the remote end for both down-converting the received signal and for generating a dithered sample clock to eliminate the transmission delay jitter with an unlimited compensation range. Experimentally, X-band binary phase shift keying signals are used for system performance evaluation. After 20 to 25 km transmission, the relative timing drifts between different links are at the order of picoseconds, and a near-theoretical SNR enhancement is achieved. The proposed scheme has a simple remote structure with no need for time synchronization, increasing its signal combining precision, flexibility, and scalability, making it an ideal candidate for long-distance weak signal detection.

Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes.

Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.

Spatio-temporal pattern, matching level and prediction of ageing and medical resources in China.

OBJECTIVE: Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR). METHODS: Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011-2020). We employed spatial autocorrelation to examine the spatial-temporal distribution trends and analyzed the spatio-temporal interaction using a Bayesian spatio-temporal effect model. The IAR, an improved evaluation indicator, was used to measure the matching level of medical resources to population ageing with kernel density analysis for visualization. Finally, an ETS-DNN model was used to forecast the trends in population ageing, medical resources, and their matching level over the next decade. RESULTS: The study found that China's ageing population and medical resources are growing annually, yet distribution is uneven across districts. There is a spatio-temporal interaction effect between ageing and medical resources, with higher levels of both in Eastern China and lower levels in Western China. The IAR is relatively high in Northwest, North China, and the Yangtze River Delta, but showed a declining trend in North China and the Yangtze River Delta. The hybrid model (ETS-DNN) gained an R2 of 0.9719, and the predicted median IAR for 2030 (0.99) across 31 regions was higher than the median IAR for 2020 (0.93). CONCLUSION: This study analyzes the relationship between population ageing and medical resources, revealing a spatio-temporal interaction between them. The IAR evaluation indicator highlights the need to address ageing population challenges and cultivate a competent health workforce. The ETS-DNN forecasts indicate higher concentrations of both medical resources and ageing populations in eastern China, emphasizing the need for region-specific ageing security systems and health service industries. The findings provide valuable policy insights for addressing a hyper-aged society in the future.

Modal wavenumber estimation by combining physical informed neural network.

Estimation of modal wavenumbers is important for inference of geoacoustic properties and data-driven matched field processing in shallow water waveguides. This paper introduces a deep neural network called combining physical informed neural network (CPINN) for modal wavenumber estimation using a vertical linear array (VLA). Note that the sound field recorded by a VLA can be expressed as a linear superposition of finite modal depth functions, and the differential equations satisfied by the modal depth functions are related to the corresponding modal wavenumbers. CPINN can estimate the modal wavenumbers by introducing the proxies of the modal depth functions and constraining them to satisfy the corresponding differential equations. The performance of the CPINN is evaluated by simulated data in a noisy shallow water environment. Numerical results show that, when compared with the previous methods, CPINN does not need to know the exact horizontal distance between the sound source and the VLA. Moreover, CPINN can estimate the modal wavenumbers at the VLA position in the case where the range segment traversed by the source, i.e., the aperture in the range direction, is smaller than the maximum modal cycle distance and in a range-dependent waveguide.

Review of Energy Management Methods for Fuel Cell Vehicles: From the Perspective of Driving Cycle Information.

Energy management methods (EMMs) utilizing sensing, communication, and networking technologies appear to be one of the most promising directions for energy saving and environmental protection of fuel cell vehicles (FCVs). In real-world driving situations, EMMs based on driving cycle information are critical for FCVs and have been extensively studied. The collection and processing of driving cycle information is a fundamental and critical work that cannot be separated from sensors, global positioning system (GPS), vehicle-to-vehicle (V2V), vehicle-to-everything (V2X), intelligent transportation system (ITS) and some processing algorithms. However, no reviews have comprehensively summarized the EMMs for FCVs from the perspective of driving cycle information. Motivated by the literature gap, this paper provides a state-of-the-art understanding of EMMs for FCVs from the perspective of driving cycle information, including a detailed description for driving cycle information analysis, and a comprehensive summary of the latest EMMs for FCVs, with a focus on EMMs based on driving pattern recognition (DPR) and driving characteristic prediction (DCP). Based on the above analysis, an in-depth presentation of the highlights and prospects is provided for the realization of high-performance EMMs for FCVs in real-world driving situations. This paper aims at helping the relevant researchers develop suitable and efficient EMMs for FCVs using driving cycle information.

Drug Discovery Based on Fluorine-Containing Glycomimetics.

Glycomimetics, which are synthetic molecules designed to mimic the structures and functions of natural carbohydrates, have been developed to overcome the limitations associated with natural carbohydrates. The fluorination of carbohydrates has emerged as a promising solution to dramatically enhance the metabolic stability, bioavailability, and protein-binding affinity of natural carbohydrates. In this review, the fluorination methods used to prepare the fluorinated carbohydrates, the effects of fluorination on the physical, chemical, and biological characteristics of natural sugars, and the biological activities of fluorinated sugars are presented.

Molecular-level insights into the surface-induced assembly of functional bacterial amyloid.

Protein coating material is important in many technological fields. The interaction between carbon nanomaterial and protein is especially interesting since it makes the development of novel hybrid materials possible. Functional bacterial amyloid (FuBA) is promising as a coating material because of its desirable features, such as well-defined molecular structure, robustness against harsh conditions, and easily engineerable functionality. Here, we report the systematic assembly of the functional amyloid protein, CsgA, from Escherichia coli (E. coli) on graphite. We characterize the assemblies using scanning tunneling microscopy (STM) and show that CsgA forms assemblies according to systematic patterns, dictated by the graphite lattice. In addition, we show that graphite flakes induce the fibrillization of CsgA, in vitro, suggesting a surface-induced conformational change of CsgA facilitated by the graphite lattice. Using coarse-grained molecular dynamics simulations, we model the adhesion and lamellar formation of a CsgA-derived peptide and conclude that peptides are adsorbed both as monomers and smaller aggregates leading initially to unordered graphite-bound aggregates, which are followed by rearrangement into lamellar structures. Finally, we show that CsgA-derived peptides can be immobilized in very systematic assemblies and their molecular orientation can be tuned using a small chaperone-like molecule. Our findings have implications for the development of FuBA-based biosensors, catalysts, and other technologies requiring well-defined protein assemblies on graphite.

Principles of Amino-Acid-Nucleotide Interactions Revealed by Binding Affinities between Homogeneous Oligopeptides and Single-Stranded DNA Molecules.

We have determined the binding strengths between nucleotides of adenine, thymine, guanine and cytosine in homogeneous single stranded DNAs and homo-octapeptides consisting of 20 common amino acids. We use a bead-based fluorescence assay for these measurements in which octapeptides are immobilized on the bead surface and ssDNAs are in solutions. Comparative analyses of the distribution of the binding energies reveal unique binding strength patterns assignable to each DNA nucleotide and amino acid originating from the chemical structures. Pronounced favorable (such as Arg-G, etc.) and unfavorable (such as Ile-T, etc.) binding interactions can be identified in selected groups of amino acid and nucleotide pairs that could provide basis to elucidate energetics of amino-acid-nucleotide interactions. Such interaction selectivity, specificity and polymorphism establish the contributions from DNA backbone, DNA bases, as well as main chain and side chain of the amino acids.

Using the yeast three-hybrid system for the identification of small molecule-protein interactions with the example of ethinylestradiol.

Since the first report on a yeast three-hybrid system, several approaches have successfully utilized different setups for discovering targets of small molecule drugs. Compared to broadly applied MS based target identification approaches, the yeast three-hybrid system represents a complementary method that allows for the straightforward identification of direct protein binders of selected small molecules. One major drawback of this system, however, is that the drug has to be taken up by the yeast cells in sufficient concentrations. Here, we report the establishment of a yeast three-hybrid screen in the deletion strain ABC9Δ, which is characterized by being highly permeable to small molecules. We used this system to screen for protein binding partners of ethinylestradiol, a widely used drug mainly for contraception and hormone replacement therapy. We identified procollagen-lysine 2-oxoglutarate 5-dioxygenase 2 (PLOD2 or lysyl hydroxylase, LH2) as a novel direct target and were able to confirm the interaction identified with the yeast three-hybrid system by a complementary method, affinity chromatography, to prove the validity of the hit. Furthermore, we provide evidence for an interaction between the drug and PLOD2 in vitro and in cellulo.

The AREB1 Transcription Factor Influences Histone Acetylation to Regulate Drought Responses and Tolerance in Populus trichocarpa.

Plants develop tolerance to drought by activating genes with altered levels of epigenetic modifications. Specific transcription factors are involved in this activation, but the molecular connections within the regulatory system are unclear. Here, we analyzed genome-wide acetylated lysine residue 9 of histone H3 (H3K9ac) enrichment and examined its association with transcriptomes in Populus trichocarpa under drought stress. We revealed that abscisic acid-Responsive Element (ABRE) motifs in promoters of the drought-responsive genes PtrNAC006, PtrNAC007, and PtrNAC120 are involved in H3K9ac enhancement and activation of these genes. Overexpressing these PtrNAC genes in P trichocarpa resulted in strong drought-tolerance phenotypes. We showed that the ABRE binding protein PtrAREB1-2 binds to ABRE motifs associated with these PtrNAC genes and recruits the histone acetyltransferase unit ADA2b-GCN5, forming AREB1-ADA2b-GCN5 ternary protein complexes. Moreover, this recruitment enables GCN5-mediated histone acetylation to enhance H3K9ac and enrich RNA polymerase II specifically at these PtrNAC genes for the development of drought tolerance. CRISPR editing or RNA interference-mediated downregulation of any of the ternary members results in highly drought-sensitive P trichocarpa Thus, the combinatorial function of the ternary proteins establishes a coordinated histone acetylation and transcription factor-mediated gene activation for drought response and tolerance in Populus species.

Study on potential differentially expressed genes in stroke by bioinformatics analysis.

Stroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine - cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.