N6-methyladenosine enhances the expression of TGF-ß-SMAD signaling family to inhibit cell growth and promote cell metastasis.

TGF-ß-SMAD signaling pathway plays an important role in the progression of various cancers. However, posttranscriptional regulation such as N6-methyladenosine (m6A) of TGF-ß-SMAD signaling axis remains incompletely understood. Here, we reveal that insulin like growth factor 2 mRNA binding protein 2 (IGF2BP2) is low expression as well as associated with poor prognosis in clear cell renal cell carcinoma (ccRCC) patients and inhibits proliferation as well as promotes metastasis of ccRCC cells. Mechanistically, IGF2BP2 systematically regulates TGF-ß-SMAD signaling family, including TGF-ß1/2, TGF-ßR1/2 and SMAD2/3/4, through mediating their mRNA stability in an m6A-dependent manner. Furthermore, the functional effects of IGF2BP2 on ccRCC cells is mediated by TGF-ß-SMAD signaling downstream effector SMAD4, which is identified three m6A sites in 5'UTR and CDS. Our study establishes IGF2BP2-TGF-ß-SMAD axis as a new regulatory effector in ccRCC, providing new insights for developing novel therapeutic strategies.

Barnacle-inspired and polyphenol-assisted modification of bacterial cellulose-based wound dressings for promoting infectious wound healing.

Bacterial cellulose (BC) is an ideal candidate for wound dressings due to its natural origin, exceptional water-holding capacity, pliability, biocompatibility, and high absorption capability. However, the lack of essential antimicrobial activity limits its biomedical applications. This study reported BC-based wound dressings containing silk fibroin protein (SF), offering the potential for biomimetic properties, and (-)-epigallocatechin-3-gallate (EGCG) for polyphenol-assisted surface modification to promote infectious wound healing. Glycerol was used as the carbon source to promote the formation of an adhesive layer by facilitating the ß-sheet folding of SF, and different concentrations of EGCG were employed to interact with SF through strong hydrogen bonding facilitated by the polyphenolic groups. The functionalized membrane exhibited outstanding water-holding capacity, swelling ratio, and degradation properties, along with enhanced hydrophilicity, adhesiveness, and a remarkable free radical scavenging ability. Both in vitro and in vivo experiments confirmed its potent bacteriostatic activity. The composite membrane displayed excellent biocompatibility, including cellular and hemocompatibility. Importantly, it effectively promoted wound healing in murine back infections. These findings suggest the significant feasibility of the innovative modification approach, and that functionalized membranes have great potential as wound-dressing materials for infection management in future clinical applications.

Study on the Design and Mechanical Properties of a Novel Hexagonal Cell Body Topology.

The honeycomb structure is a topological structure with excellent performance that stems from the properties of the basic units of the structure. Different structural features of basic units may lead to different mechanical characteristics in the whole part. In this study, a novel hexagonal cell body topology structure (NH) was designed and manufactured by the fused deposition modeling (FDM) technique to explore the effects on mechanical properties. The tensile and impact performance of the NH structure were compared with the regular hexagonal honeycomb structure (HH), and the influence of different unit single-cell sizes on the impact performance of the NH structure was investigated. The force transmission of the basic units of the NH structure was revealed through finite element analysis. The results indicate that both the tensile and impact performances of the NH structure have been improved compared to the HH structure. The improvement is due to the better force transmission capability of the basic units of the NH structure, leading to a more uniform stress distribution. Moreover, excessively large or small single-cell sizes of the NH structure will reduce the overall structure's impact resistance. The overall structure achieves optimal impact resistance when the single-cell size is around 1.2 mm.

Interferon regulatory factor 1 mediated inhibition of Treg cell differentiation induces maternal-fetal immune imbalance in preeclampsia.

The establishment and maintenance of a successful pregnancy rely heavily on maternal-fetal immune tolerance. Inflammatory and immune mechanisms during pregnancy bear a resemblance to those observed in tumor progression, with Treg cells exhibiting similar immunoregulatory functions in both contexts. Interferon regulatory factor 1 (IRF1) is implicated in modulating the immune milieu within tumors and influencing regulatory T (Treg) cell differentiation. However, the precise association between IRF1 and the onset of preeclampsia (PE) remains unclear. In our investigation, we identified trophoblasts as a significant source of IRF1 expression at the maternal-fetal interface through immunofluorescence analysis. Moreover, heightened levels of IRF1 expression were detected in both placental tissues and peripheral blood samples obtained from PE patients, concomitant with an imbalance in the Th17/Treg ratio. In the peripheral circulation, a notable inverse correlation was observed between IRF1 mRNA levels and Foxp3 mRNA, a transcription factor specific to Treg cells. IRF1 mRNA expression showed a positive association with systolic blood pressure and a negative association with serum albumin levels. Furthermore, co-culturing naïve T cells with supernatants from HTR-8/SV neo cells overexpressing IRF1 resulted in diminished differentiation of T cells into Treg cells. In summary, our study indicates elevated IRF1 expression in the peripheral blood and trophoblast cells of PE patients. Elevated IRF1 in trophoblast cells hinders the differentiation of maternal Treg cells, disrupting maternal-fetal immune tolerance and contributing to PE pathogenesis. Additionally, IRF1 expression correlates with disease severity, suggesting its potential as a novel sensitive target in PE.

Multifunctional bacterial cellulose-bentonite@polyethylenimine composite membranes for enhanced water treatment: Sustainable dyes and metal ions adsorption and antibacterial properties.

Developing multifunctional materials for water treatment remains a significant challenge. Bacterial cellulose (BC) holds immense potential as an adsorbent with high pollutant-binding capacity, hydrophilicity, and biosafety. In this study, N-acetylglucosamine was used as a carbon source to ferment BC, incorporating amide bonds in situ. Bentonite, renowned for its adsorption properties, was added to the culture medium, resulting in BC-bentonite composite membranes via a one-step fermentation process. Polyethyleneimine (PEI) was crosslinked with amide bonds on the membrane via glutaraldehyde through Schiff base reactions to enhance the performance of the composite membrane. The obtained membrane exhibited increased hydrophilicity, enhanced active adsorption sites, and enlarged specific surface area. It not only physically adsorbed contaminants through its unique structure but also effectively captured dye molecules (Congo red, Methylene blue, Malachite green) via electrostatic interactions. Additionally, it formed stable complexes with metal ions (Cd²âº, Pb²âº, Cu²âº) through coordination and effectively adsorbed their mixtures. Moreover, the composite membrane demonstrated the broad-spectrum antibacterial activity, effectively inhibiting the growth of tested bacteria. This study introduces an innovative method for fabricating composite membranes as adsorbents for complex water pollutants, showing significant potential for long-term wastewater treatment of organic dyes, heavy metal ions, and pathogens.

Effect of different treatment protocols on in vitro fertilisation/intracytoplasmic sperm injection (IVF/ICSI) outcomes in adenomyosis women: a systematic review and meta-analysis.

OBJECTIVES: Pregnancy outcomes of different ovarian stimulation protocols for in vitro fertilisation/intracytoplasmic sperm injection (IVF/ICSI) in patients with adenomyosis are not explicit. This meta-analysis aimed to systematically evaluate the effects of different IVF/ICSI protocols on pregnancy outcomes. DESIGN: Meta-analysis. DATA SOURCES: PubMed, Web of Science and Cochrane library were searched up to October 2023. ELIGIBILITY CRITERIA: Comparative studies on IVF/ICSI outcomes in the adenomyosis population were eligible. Studies on preimplantation genetic testing, reviews, case reports and animal experiments were excluded. DATA EXTRACTION AND SYNTHESIS: Valid information was extracted by two independent authors according to a standard data format. All analyses were conducted using Review Manager (RevMan, V.5.3). RESULTS: Compared with the non-adenomyosis population, adenomyosis was responsible for a 26% reduction in clinical pregnancy rate (CPR; 42.47% vs 55.89%, OR: 0.74, 95% CI: 0.66 to 0.82, p<0.00001), a 35% reduction in live birth rate (LBR; 30.72% vs 47.77%, OR: 0.65, 95% CI: 0.58 to 0.73, p<0.00001) and a 1.9-fold increase in miscarriage rate (MR; 27.82% vs 13.9%, OR: 1.90, 95% CI: 1.56 to 2.31, p<0.00001). Subgroup analysis suggested that, in fresh embryo transfer (ET) cycles, the CPR (34.4% vs 58.25%) in the long/short/antagonist protocol group was poorer than that in the ultralong protocol group. In frozen ET (FET) cycles, there were no statistical differences in CPR ((GnRHa+FET) AM(adenomyosis) vs non-AM: 51.32% vs 43.48%, p=0.31; (non-GnRHa+FET) AM vs non-AM: 50.25% vs 60.10%, p=0.82), MR ((GnRHa+FET) AM vs non-AM:12.82% vs 12.50%, p=0.97; (non-GnRHa+FET) AM vs non-AM: 30.5% vs 15.54%, p=0.15) and LBR ((GnRHa+FET) AM vs non-AM:44.74% vs 36.96%, p=0.31; (non-GnRHa+FET) AM vs non-AM: 34.42% vs 50.25%, p=0.28). The MR in the adenomyosis group was high in the fresh ET and FET cycles. CONCLUSIONS: FET might be a better choice for women with adenomyosis, especially those pretreated with GnRHa. In fresh ET cycles, pregnancy outcomes of the long/short/antagonist protocols were poorer than those of the ultralong protocol. TRIAL REGISTRATION NUMBER: CRD42022340743.

An auto-segmented multi-time window dual-scale neural network for brain-computer interfaces based on event-related potentials.

Objective.Event-related potentials (ERPs) are cerebral responses to cognitive processes, also referred to as cognitive potentials. Accurately decoding ERPs can help to advance research on brain-computer interfaces (BCIs). The spatial pattern of ERP varies with time. In recent years, convolutional neural networks (CNNs) have shown promising results in electroencephalography (EEG) classification, specifically for ERP-based BCIs.Approach.This study proposes an auto-segmented multi-time window dual-scale neural network (AWDSNet). The combination of a multi-window design and a lightweight base network gives AWDSNet good performance at an acceptable cost of computing. For each individual, we create a time window set by calculating the correlation of signedR-squared values, which enables us to determine the length and number of windows automatically. The signal data are segmented based on the obtained window sets in sub-plus-global mode. Then, the multi-window data are fed into a dual-scale CNN model, where the sizes of the convolution kernels are determined by the window sizes. The use of dual-scale spatiotemporal convolution focuses on feature details while also having a large enough receptive length, and the grouping parallelism undermines the increase in the number of parameters that come with dual scaling.Main results.We evaluated the performance of AWDSNet on a public dataset and a self-collected dataset. A comparison was made with four popular methods including EEGNet, DeepConvNet, EEG-Inception, and PPNN. The experimental results show that AWDSNet has excellent classification performance with acceptable computational complexity.Significance.These results indicate that AWDSNet has great potential for applications in ERP decoding.

Comparative transcriptomic and metabolomic profiles reveal fruit peel color variation in two red pomegranate cultivars.

Pomegranate (Punica granatum L.) which belongs to family Lythraceae, is one of the most important fruit crops of many tropical and subtropical regions. A high variability in fruit color is observed among different pomegranate accessions, which arises from the qualitative and quantitative differences in anthocyanins. However, the mechanism of fruit color variation is still not fully elucidated. In the present study, we investigated the red color mutation between a red-skinned pomegranate 'Hongbaoshi' and a purple-red-skinned cultivar 'Moshiliu', by using transcriptomic and metabolomic approaches. A total of 51 anthocyanins were identified from fruit peels, among which 3-glucoside and 3,5-diglucoside of cyanidin (Cy), delphinidin (Dp), and pelargonidin (Pg) were dominant. High proportion of Pg in early stages of 'Hongbaoshi' but high Dp in late stages of 'Moshiliu' were characterized. The unique high levels of Cy and Dp anthocyanins accumulating from early developmental stages accounted for the purple-red phenotype of 'Moshiliu'. Transcriptomic analysis revealed an early down-regulated and late up-regulated of anthocyanin-related structure genes in 'Moshiliu' compared with 'Hongbaoshi'. Alao, ANR was specially expressed in 'Hongbaoshi', with extremely low expression levels in 'Moshiliu'. For transcription factors R2R3-MYB, the profiles demonstrated a much higher transcription levels of three subgroup (SG) 5 MYBs and a sharp decrease in expression of SG6 MYB LOC116202527 in high-anthocyanin 'Moshiliu'. SG4 MYBs exhibited two entirely different patterns, LOC116203744 and LOC116212505 were down-regulated whereas LOC116205515 and LOC116212778 were up-regulated in 'Moshiliu' pomegranate. The results indicate that specific SG members of the MYB family might promote the peel coloration in different manners and play important roles in color mutation in pomegranate.

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces.

Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response to external events and their associated underlying complex spatiotemporal feature information is governed by ongoing oscillatory activity within the brain. Deep learning methods have been increasingly adopted for ERP-based brain-computer interfaces (BCIs) due to their excellent feature representation abilities, which allow for deep analysis of oscillatory activity within the brain. Features with higher spatiotemporal frequencies usually represent detailed and localized information, while features with lower spatiotemporal frequencies usually represent global structures. Mining EEG features from multiple spatiotemporal frequencies is conducive to obtaining more discriminative information. A multiscale feature fusion octave convolution neural network (MOCNN) is proposed in this article. MOCNN divides the ERP signals into high-, medium- and low-frequency components corresponding to different resolutions and processes them in different branches. By adding mid- and low-frequency components, the feature information used by MOCNN can be enriched, and the required amount of calculations can be reduced. After successive feature mapping using temporal and spatial convolutions, MOCNN realizes interactive learning among different components through the exchange of feature information among branches. Classification is accomplished by feeding the fused deep spatiotemporal features from various components into a fully connected layer. The results, obtained on two public datasets and a self-collected ERP dataset, show that MOCNN can achieve state-of-the-art ERP classification performance. In this study, the generalized concept of octave convolution is introduced into the field of ERP-BCI research, which allows effective spatiotemporal features to be extracted from multiscale networks through branch width optimization and information interaction at various scales.

DRAIC mediates hnRNPA2B1 stability and m6A-modified IGF1R instability to inhibit tumor progression.

Type 1 insulin-like growth factor receptor (IGF1R) plays an important role in cancer, however, posttranscriptional regulation such as N6-methyladenosine (m6A) of IGF1R remains unclear. Here, we reveal a role for a lncRNA Downregulated RNA in Cancer (DRAIC) suppress tumor growth and metastasis in clear cell Renal Carcinoma (ccRCC). Mechanistically, DRAIC physically interacts with heterogeneous nuclear ribonucleoprotein A2B1 (hnRNPA2B1) and enhances its protein stability by blocking E3 ligase F-box protein 11 (FBXO11)-mediated ubiquitination and proteasome-dependent degradation. Subsequently, hnRNPA2B1 destabilizes m6A modified-IGF1R, leading to inhibition of ccRCC progression. Moreover, four m6A modification sites are identified to be responsible for the mRNA degradation of IGF1R. Collectively, our findings reveal that DRAIC/hnRNPA2B1 axis regulates IGF1R mRNA stability in an m6A-dependent manner and highlights an important mechanism of IGF1R fate. These findings shed light on DRAIC/hnRNPA2B1/FBXO11/IGF1R axis as potential therapeutic targets in ccRCC and build a link of molecular fate between m6A-modified RNA and ubiquitin-modified protein.

Risk factors for preeclampsia in patients with chronic kidney disease primarily focused on stage 1 CKD. Are referred and registered patients alike?

Limited research exists on identifying risk factors for preeclampsia (PE) in the chronic kidney disease (CKD) population, especially across different patient sources. This study aimed to address this gap by analyzing clinical data from CKD pregnant women admitted to Peking University Third Hospital from January 2012 to December 2022. Logistic regression analysis identified independent risk factors for PE in the CKD population and assessed variations among patients from different sources. Additionally, a predictive model for PE was established using data from the registered group. The study included 524 CKD patients. Hypertension, proteinuria, fibrinogen >4 g/L, serum albumin ≤30 g/L, and uric acid >260 µmol/L were independent risk factors for PE in the overall CKD population. Subgroup analysis revealed that hypertension, serum albumin ≤30 g/L, and uric acid >260 µmol/L were independent risk factors in the referred group, while hypertension, uric acid >260 µmol/L, and fibrinogen >4 g/L were independent risk factors in the registered group. The prediction model based on registered group risk factors showed good predictive efficiency, with the area under the curve of 0.774 in the training set and 0.714 in the validation set. In conclusion, this study revealed that hypertension and elevated uric acid are independent risk factors for PE in CKD patients regardless of patient source, while serum albumin and fibrinogen levels are associated with PE risk in specific patient subgroups. Our predictive model enables clinicians to quickly identify the risk of PE in CKD patients, and early intervention treatment to improve pregnancy outcomes.

Study on the matching degree of major groups and industrial groups in higher vocational colleges.

Major groups matching industrial groups in higher vocational colleges is the requirement of integration of industry and education. It is a talent cultivation mode based on the demand of regional advantageous industrial groups for talent quality. It is significant to improve the rationality of professional settings in colleges and promote the development of the economy. This study takes the Yi-Jing-Jing Metropolitan area as a case and uses the coupling coordination model to measure the matching degree of the industrial groups and the major groups. The conclusion is as follows: major groups and industrial groups in this region do not match well, and pay insufficient attention to the development of emerging advantageous industries. Some majors have not adapted to the characteristics of the current era and cannot meet the future development of the industry. According to the research results, it is proposed to strengthen policy guidance, based on changes in demand, and adjust the course content and other aspects to promote the matching of major groups and regional advantageous industrial groups.

Integrated transcriptomic, metabolomic, and functional analyses unravel the mechanism of bagging delaying fruit cracking of pomegranate (Punica granatum L.).

The economic impact of fruit cracking in pomegranate products is substantial. In this study, we present the inaugural comprehensive analysis of transcriptome and metabolome in the outermost pericarp of pomegranate fruit in bagging conditions. Our investigation revealed a notable upregulation of differentially expressed genes (DEGs) associated with the calcium signaling pathway (76.92%) and xyloglucan endotransglucosylase/hydrolase (XTH) genes (87.50%) in the fruit peel of non-cracking fruit under bagging. Metabolomic analysis revealed that multiple phenolics, flavonoids, and tannins were identified in pomegranate. Among these, calmodulin-like 23 (PgCML23) exhibited a significant correlation with triterpenoids and demonstrated a marked upregulation under bagging treatment. The transgenic tomatoes overexpressing PgCML23 exhibited significantly higher cellulose content and xyloglucan endotransglucosylase (XET) enzyme activity in the pericarp at the red ripening stage compared to the wild type. Conversely, water-soluble pectin content, polygalacturonase (PG), and ß-galactosidase (ß-GAL) enzyme activities were significantly lower in the transgenic tomatoes. Importantly, the heterologous expression of PgCML23 led to a substantial reduction in the fruit cracking rate in tomatoes. Our findings highlight the reduction of fruit cracking in bagging conditions through the manipulation of PgCML23 expression.

Characterization of Therapeutic Antibody Charge Heterogeneity Under Stress Conditions by Microfluidic Capillary Electrophoresis Coupled with Mass Spectrometry.

Therapeutic antibodies are a major class of biopharmaceutics that are applied in disease treatment because of their many advantages, including high specificity and high affinity to molecular targets. Between their production and administration, therapeutic antibodies are exposed to multiple stress conditions. Forced degradation and stress stability studies are conducted to simulate the risk of degradation and the effects of these stresses, thereby enhancing understanding of the drug product to support strategies to mitigate the impact from stressed conditions. These types of studies are also routinely conducted to evaluate product comparability when major process changes are implemented during the production. Charge variant analysis helps understand the changes in the electrostatic environment of biotherapeutics and can uncover underlying molecular level alterations associated with charge variants. Herein, we used ZipChip native capillary electrophoresis-mass spectrometry (nCE-MS) to elucidate the changes in charge variant profiles at the molecular level. In two case studies under thermal stress conditions, we observed that charge variants arose from both post-translational modifications (including deamidation, oxidation, and pyroglutamate formation) and sequence truncations at the hinge regions. Under oxidative stress conditions, oxidation was found to be the major contributor to the changes in the charge variant profiles. Under pH stress conditions, the changes in the charge variant profile were due to increased levels of deamidation, oxidation, and pyroglutamate formation. ZipChip nCE-MS analysis enables identification of charge variant species under various stress conditions, thus supporting process and formulation development of biotherapeutics.

Enhancing Wound Healing and Bactericidal Efficacy: A Hydrogel Membrane of Bacterial Cellulose and Sanxan Gel for Accelerating the Healing of Infected Wounds.

Bacterial cellulose is an extracellular polysaccharide produced by microorganisms, offering advantages such as high water-holding capacity, flexibility, and biocompatibility. However, its lack of bactericidal activity hampers its wide application. Usnic acid, a secondary metabolite derived from lichens of the Usnea genus, is recognized for its antibacterial and anti-biofilm efficiency, coupled with anti-inflammatory properties. Its water insolubility presents challenges for wide utilization and stable release. Sanxan gel, a novel polysaccharide, exhibits exceptional freeze-thaw stability, suspension properties, and high elasticity, rendering it effective as a suspending agent to improve the bioavailability of water-insoluble drugs. In this study, a hydrogel membrane is designed by combining bacterial cellulose and usnic acid suspended in sanxan gel through a simple in situ microorganism fermentation. The obtained membranes demonstrate excellent ability for sustained drug release, strong eradication capability against tested bacteria in both in vitro and in vivo experiments, effective inhibition of biofilm formation, and excellent hemocompatibility and cytocompatibility. Additionally, the composite membranes promote wound healing with reduced inflammation and bacterial infection in a full-thickness wound infection model in mice. This study provides innovative insights and strategies for the development of functional dressings for infected wounds in future clinical applications.

Investigation of the transport and metabolic patterns of oil-displacing bacterium FY-07-G in the microcosm model using X-CT technology.

AIMS: Microbial enhanced oil recovery (MEOR) is dedicated to enhancing oil recovery by harnessing microbial metabolic activities and their byproducts within reservoir rocks and fluids. Therefore, the investigation of microbial mobility and their extensive distribution within crude oil is of paramount importance in MEOR. While microscale models have been valuable for studying bacterial strain behavior in reservoirs, they are typically limited to 2D representations of porous media, making them inadequate for simulating actual reservoir conditions. Consequently, there is a critical need for 3D models and dependable visualization methods to observe bacterial transport and metabolism within these complex reservoir environments. METHODS AND RESULTS: Bacterial cellulose (bc) is a water-insoluble polysaccharide produced by bacteria that exhibits biocompatibility and biodegradability. It holds significant potential for applications in the field of MEOR as an effective means for selective plugging and spill prevention during oil displacement processes. Conditionally cellulose-producing strain, FY-07-G, with green fluorescent labeling, was engineered for enhanced oil recovery. 3D micro-visualization model was constructed to directly observe the metabolic activities of the target bacterial strain within porous media and to assess the plugging interactions between cellulose and the medium. Additionally, X-ray computed tomography (X-CT) technology was employed for a comprehensive analysis of the transport patterns of the target strain in oil reservoirs with varying permeabilities. The results indicated that FY-07-G, as a microorganism employing biopolymer-based plugging principles to enhance oil recovery, selectively targets and seals regions characterized by lower permeability and smaller pore spaces. CONCLUSIONS: This work provided valuable insights into the transport and metabolic behavior of MEOR strains and tackled the limitation of 2D models in faithfully replicating oil reservoir conditions, offering essential theoretical guidance and insights for the further application of oil-displacing bacterial strains in MEOR processes.

The Genetic Diversity of White-Backed Planthoppers (Sogatella furcifera) between Myanmar and Yunnan Province of China.

In order to clarify the migration route and the source of white-backed planthopper (WBPH) (Sogatella furcifera) between Myanmar and Yunnan Province, China, we collected six populations throughout Myanmar and five populations around the border areas in Yunnan Province, China. A total of 790 base pairs in the mtDNA COI genes from 416 individuals were obtained. A total of 43 haplotypes were identified, among which 37 were unique haplotypes, and the remaining 6 were shared among different populations. Two common shared haplotypes (H_1 and H_2) had a widespread distribution in all populations and accounted for 88.8% of the total haplotype frequency, suggesting a high-level gene flow among the Myanmar and Yunnan populations. Bayesian skyline plot (BSP) analysis results indicated that the effective population size of WBPH expanded between about 10,000 and 7000 years ago, and S. furcifera might follow the post-LGM (Last Glacial Maximum) expansion pattern. Based on the total migrant (Nem) value, it can be deduced that north and northeast Myanmar were the primary migration sources for WBPH populations in the southwest and south Yunnan regions. This study aims to contribute to the sustainable regional management of this important rice pest and provide new insights into the genetic diversity of WBPH in Southeast Asia.

Association of IL-17 and IL-27 polymorphisms with susceptibility to recurrent pregnancy loss and pre-eclampsia: A systematic review and meta-analysis.

OBJECTIVE: Recurrent pregnancy loss (RPL) and pre-eclampsia (PE) are immune-related pregnancy complications that have been linked to CD4+ T cells and their cytokines, which can be influenced by genetic polymorphisms. This meta-analysis aimed to investigate the relationship between interleukin (IL)-17 and -27 polymorphisms and the susceptibility to RPL and PE. METHODS: All eligible case-control studies published up to February 2023 were identified by searching PubMed, EMBASE, Cochrane, Web of Science, and Google Scholar. The risk of recurrent pregnancy loss and PE associated with the IL-17 rs2275913, IL-17 rs763780, IL-27 rs153109, and IL-27 rs17855750 polymorphisms were estimated for each study. RESULTS: The meta-analysis incorporated a total of 13 studies. The overall analysis indicated that IL-17 rs2275913, IL-17 rs763780, IL-27 rs153109, and IL-27 rs17855750 polymorphisms were not significantly associated with immune-related pregnancy complications, including RPL and PE. However, when the analysis was stratified by disease type, the IL-17 rs2275913 polymorphism was found to be associated with an increased risk of RPL (recessive model AA/GA + GG: OR = 1.68, 95% confidence interval [CI]: 1.13-2.49, p = .01). CONCLUSIONS: The IL-17 rs763780, IL-27 rs153109, and IL-27 rs17855750 polymorphisms were not significantly associated with RPL and PE, whereas the IL-17 rs2275913 polymorphism was associated with the susceptibility to recurrent miscarriage.

Effects of nutrient injection on the Xinjiang oil field microbial community studied in a long core flooding simulation device.

Microbial Enhanced Oil Recovery (MEOR) is an option for recovering oil from depleted reservoirs. Numerous field trials of MEOR have confirmed distinct microbial community structure in diverse production wells within the same block. The variance in the reservoir microbial communities, however, remains ambiguously documented. In this study, an 8 m long core microbial flooding simulation device was built on a laboratory scale to study the dynamic changes of the indigenous microbial community structure in the Qizhong Block, Xinjiang oil field. During the MEOR, there was an approximate 34% upswing in oil extraction. Based on the 16S rRNA gene high-throughput sequencing, our results indicated that nutrition was one of the factors affecting the microbial communities in oil reservoirs. After the introduction of nutrients, hydrocarbon oxidizing bacteria became active, followed by the sequential activation of facultative anaerobes and anaerobic fermenting bacteria. This was consistent with the hypothesized succession of a microbial ecological "food chain" in the reservoir, which preliminarily supported the two-step activation theory for reservoir microbes transitioning from aerobic to anaerobic states. Furthermore, metagenomic results indicated that reservoir microorganisms had potential functions of hydrocarbon degradation, gas production and surfactant production. Understanding reservoir microbial communities and improving oil recovery are both aided by this work.

Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems.

Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.