Significant correlation between leaf vein length per unit area and stomatal density: evidence from Red Tip and Chinese photinias.

The vascular veins in photosynthetic leaves play an important role in transporting water and sugars throughout the plant body, and their venation pattern and vein density determine the hydraulic efficiency of the leaf. Likewise, stomatal density (SD) can influence photosynthetic gas exchange. However, the correlation between leaf vein density and SD is seldom reported. Herein, we examined 16 leaves from the hybrid Photinia × fraseri and 16 leaves from one of its parents, P. serratifolia, to explore the correlation between leaf vein density and SD. For each leaf, equidistant lamina quadrats were excised along two longitudinal transects (one along the midrib and another along the leaf margin). For each quadrat, micrographs of 1.2 mm × 0.9 mm stomatal imprints, and 2.51 mm × 1.88 mm micrographs of leaf veins were used to measure total vein area per leaf unit area (VAA) and total vein length per unit area (VLA), as indicators of leaf vein density, to determine the correlation between SD and leaf vein density. For each taxon, there was no significant correlation between SD and VAA, but there was a significant correlation between SD and VLA. The data indicate that SD is not positively correlated with VAA but positively correlated with VLA for both the hybrid and the parent species. This study indicates that future work should focus on the relationships between SD and total vein length per unit area rather than on total leaf vein area per unit area within and across species.

Mertk Reduces Blood-Spinal Cord Barrier Permeability Through the Rhoa/Rock1/P-MLC Pathway After Spinal Cord Injury.

Disruption of the blood-spinal cord barrier (BSCB) is a critical event in the secondary injury following spinal cord injury (SCI). Mertk has been reported to play an important role in regulating inflammation and cytoskeletal dynamics. However, the specific involvement of Mertk in BSCB remains elusive. Here, we demonstrated a distinct role of Mertk in the repair of BSCB. Mertk expression is decreased in endothelial cells following SCI. Overexpression of Mertk upregulated tight junction proteins (TJs), reducing BSCB permeability and subsequently inhibiting inflammation and apoptosis. Ultimately, this led to enhanced neural regeneration and functional recovery. Further experiments revealed that the RhoA/Rock1/P-MLC pathway plays a key role in the effects of Mertk. These findings highlight the role of Mertk in promoting SCI recovery through its ability to mitigate BSCB permeability and may provide potential targets for SCI repair.

Addressing Challenges in Ion-Selectivity Characterization in Nanopores.

Ion selectivity is the basis for designing smart nanopore/channel-based devices, e.g., ion separators and biosensors. Quantitative characterization of ion selectivities in nanopores often employs the Nernst or Goldman-Hodgkin-Katz (GHK) equation to interpret transmembrane potentials. However, the direction of the measured transmembrane potential drop is not specified in these equations, and selectivity values calculated using absolute values of transmembrane potentials do not directly reveal the ion for which the membrane is selective. Moreover, researchers arbitrarily choose whether to use the Nernst or GHK equation and overlook the significant differences between them, leading to ineffective quantitative comparisons between studies. This work addresses these challenges through (a) specifying the transmembrane potential (sign) and salt concentrations in terms of working and reference electrodes and the solutions in which they reside when using the Nernst and GHK equations, (b) reporting of both Nernst-selectivity and GHK-selectivity along with solution compositions and transmembrane potentials when comparing different nanopores/channels, and (c) performing simulations to define an ideal selectivity for nanochannels. Experimental and modeling studies provide significant insight into these fundamental equations and guidelines for the development of nanopore/channel-based devices.

Precise Regulation in Chain-Edge Structural Microenvironments of 1D Covalent Organic Frameworks for Photocatalysis.

Covalent organic frameworks (COFs) constitute a promising research topic for photocatalytic reactions, but the rules and conformational relationships of 1D COFs are poorly defined. Herein, the chain edge structure is designed by precise modulation at the atomic level, and the 1D COFs bonded by C, O, and S elements is directionally prepared for oxygen-tolerant photoinduced electron transfer-atom transfer radical polymerization (PET-ATRP) reactions. It is demonstrated that heteroatom-type chain edge structures (─O─, ─S─) lead to a decrease in intra-plane conjugation, which restricts the effective transport of photogenerated electrons along the direction of the 1D strip. In contrast, the all-carbon type chain edge structure (─C─) with higher intra-plane conjugation not only reduces the energy loss of photoexcited electrons but also enhances the carrier density, which exhibits the optimal photopolymerization performance. This work offers valuable guidance in the exploitation of 1D COFs for high photocatalytic performance. This work offers valuable guidance in the exploitation of 1D COFs for high photocatalytic performance.

HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.

Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.

Controllable π-π coupling of intramolecular dimer models in aggregated states.

π-π coupling as a common interaction plays a key role in emissions, transport and mechanical properties of organic materials. However, the precise control of π-π coupling is still challenging owing to the possible interference from other intermolecular interactions in the aggregated state, usually resulting in uncontrollable emission properties. Herein, with the rational construction of intramolecular dimer models and crystal engineering, π-π coupling can be subtly modulated by conformation variation with balanced π-π and π-solvent interactions and visualized by green-to-blue emission switching. Moreover, it can rapidly respond to temperature, pressure and mechanical force, affording a facile way to modulate π-π coupling in situ. This work contributes to a deeper understanding of the internal mechanism of molecular motions in aggregated states.

Multi-omics comprehensive analysis reveals the predictive value of N6-methyladenosine- related genes in prognosis and immune escape of bladder cancer.

BACKGROUND: N6-methyladenosine (m6A) is the most frequent RNA modification in mammals, and its role in bladder cancer (BC) remains rarely revealed. OBJECTIVE: To predict the value of m6A-related genes in prognosis and immunity in BC. METHODS: We performed multiple omics analysis of 618 TCGA and GEO patients and used principal component analysis (PCA) to calculate the m6A score for BC patients. RESULTS: We described the multiple omics status of 23 m6A methylation-related genes (MRGs), and four m6A clusters were identified, which showed significant differences in immune infiltration and biological pathways. Next, we intersected the differential genes among m6A clusters, and 11 survival-related genes were identified, which were used to calculate the m6A score for the patients. We found that the high-score (HS) group showed lower tumor mutation burden (TMB) and TP53 mutations and better prognosis than the low-score (LS) group. Lower immune infiltration, higher expression of PD-L1, PD-1, and CTLA4, and higher immune dysfunction and immune exclusion scores were identified in the LS group, suggesting a higher possibility of immune escape. Finally, the experimental verification shows that the m6A related genes, such as IGFBP1, plays an important role in the growth and metastasis of bladder cancer. CONCLUSIONS: These findings revealed the important roles of m6A MRGs in predicting prognosis, TMB status, TP53 mutation, immune functions and immunotherapeutic response in BC.

Multidimensional pan-cancer analysis of HSPA5 and its validation in the prognostic value of bladder cancer.

Endoplasmic reticulum (ER) stress-related genes are closely related to the occurrence, development, and immunotherapy response of tumors. This study provides a comprehensive assessment of HSPA5 from a pan-cancer perspective using multi-omics data. We analyzed the function of HSPA5 in multiple tumor types using multiple databases. Finally, immunohistochemistry was used to examine the relationship between HSPA5 expression in tissue microarrays from 100 patients with bladder cancer and the prognosis of patients with bladder cancer. Using the TCGA database, we were able to determine that HSPA5 is significantly elevated in a number of common malignancies and is linked with a bad prognosis. Cox regression analysis showed that the high expression of HSPA5 was correlated with OS, progression free survival (PFS), disease free survival (DFS), and disease special survival (DSS) of adrenocortical carcinoma (ACC). In addition, we discovered significant disparities in HSPA5 methylation and phosphorylation levels between various malignancies and normal tissues. HSPA5 expression was significantly correlated with the levels of infiltrating cells and immune checkpoint genes. HSPA5 is highly expressed in bladder cancer and patients with high HSPA5 expression have a poor prognosis. Our study provides a basis for further understanding of the role of ER stress-related gene HSPA5 in different tumor genesis and development. HSPA5 has also been shown to be a prognostic biomarker for bladder cancer patients.

Constructions of quorum sensing signaling network for activated sludge microbial community.

In wastewater treatment systems, the interactions among various microbes based on chemical signals, namely quorum sensing (QS), play critical roles in influencing microbial structure and function. However, it is challenging to understand the QS-controlled behaviors and the underlying mechanisms in complex microbial communities. In this study, we constructed a QS signaling network, providing insights into the intra- and interspecies interactions of activated sludge microbial communities based on diverse QS signal molecules. Our research underscores the role of diffusible signal factors in both intra- and interspecies communication among activated sludge microorganisms, and signal molecules commonly considered to mediate intraspecies communication may also participate in interspecies communication. QS signaling molecules play an important role as communal resources among the entire microbial group. The communication network within the microbial community is highly redundant, significantly contributing to the stability of natural microbial systems. This work contributes to the establishment of QS signaling network for activated sludge microbial communities, which may complement metabolic exchanges in explaining activated sludge microbial community structure and may help with a variety of future applications, such as making the dynamics and resilience of highly complex ecosystems more predictable.

Circular RNA MKLN1 promotes epithelial-mesenchymal transition in pulmonary fibrosis by regulating the miR-26a/b-5p/CDK8 axis in human alveolar epithelial cells and mice models.

Pulmonary fibrosis involves destruction of the lung parenchyma and extracellular matrix deposition. Effective treatments for pulmonary fibrosis are lacking and its pathogenesis is still unclear. Studies have found that epithelial-mesenchymal transition (EMT) of alveolar epithelial cells (AECs) plays an important role in progression of pulmonary fibrosis. Thus, an in-depth exploration of its mechanism might identify new therapeutic targets. In this study, we revealed that a novel circular RNA, MKLN1 (circMKLN1), was significantly elevated in two pulmonary fibrosis models (intraperitoneally with PQ, 50 mg/kg for 7 days, and intratracheally with BLM, 5 mg/kg for 28 days). Additionally, circMKLN1 was positively correlated with the severity of pulmonary fibrosis. Inhibition of circMKLN1 expression significantly reduced collagen deposition and inhibited EMT in AECs. EMT was aggravated after circMKLN1 overexpression in AECs. MiR-26a-5p/miR-26b-5p (miR-26a/b), the targets of circMKLN1, were confirmed by luciferase reporter assays. CircMKLN1 inhibition elevated miR-26a/b expression. Significantly decreased expression of CDK8 (one of the miR-26a/b targets) was observed after inhibition of circMKLN1. EMT was exacerbated again, and CDK8 expression was significantly increased after circMKLN1 inhibition and cotransfection of miR-26a/b inhibitors in AECs. Our research indicated that circMKLN1 promoted CDK8 expression through sponge adsorption of miR-26a/b, which regulates EMT and pulmonary fibrosis. This study provides a theoretical basis for finding new targets or biomarkers in pulmonary fibrosis.

Identified S100A9 as a target for diagnosis and treatment of ulcerative colitis by bioinformatics analysis.

Ulcerative colitis (UC) is a chronic, recurrent inflammatory bowel disease. UC confronts with severe challenges including the unclear pathogenesis and lack of specific diagnostic markers, demanding for identifying predictive biomarkers for UC diagnosis and treatment. We perform immune infiltration and weighted gene co-expression network analysis on gene expression profiles of active UC, inactive UC, and normal controls to identify UC related immune cell and hub genes. Neutrophils, M1 macrophages, activated dendritic cells, and activated mast cells are significantly enriched in active UC. MMP-9, CHI3L1, CXCL9, CXCL10, CXCR2 and S100A9 are identified as hub genes in active UC. Specifically, S100A9 is significantly overexpressed in mice with colitis. The receiver operating characteristic curve demonstrates the excellent performance of S100A9 expression in diagnosing active UC. Inhibition of S100A9 expression reduces DSS-induced colonic inflammation. These identified biomarkers associated with activity in UC patients enlighten the new insights of UC diagnosis and treatment.

Bacterial isolation and genome analysis of a novel Klebsiella quasipneumoniae phage in southwest China's karst area.

BACKGROUND: Southwest China is one of the largest karst regions in the world. Karst environment is relatively fragile and vulnerable to human activities. Due to the discharge of sewage and domestic garbage, the karst system may be polluted by pathogenic bacteria. The detection of bacterial distribution and identification of phage capable of infecting them is an important approach for environmental assessment and resource acquisition. METHODS: Bacteria and phages were isolated from karst water in southwest China using the plate scribing and double plate method, respectively. Isolated phage was defined by transmission electron microscopy, one-step growth curve and optimal multiplicity of infection (MOI). Genomic sequencing, phylogenetic analysis, comparative genomic and proteomic analysis were performed. RESULTS: A Klebsiella quasipneumoniae phage was isolated from 32 isolates and named KL01. KL01 is morphologically identified as Caudoviricetes with an optimal MOI of 0.1, an incubation period of 10 min, and a lysis period of 60 min. The genome length of KL01 is about 45 kb, the GC content is 42.5%, and it contains 59 open reading frames. The highest average nucleotide similarity between KL01 and a known Klebsiella phage 6939 was 83.04%. CONCLUSIONS: KL01 is a novel phage, belonging to the Autophagoviridae, which has strong lytic ability. This study indicates that there were not only some potential potentially pathogenic bacteria in the karst environment, but also phage resources for exploration and application.

Abnormal energy metabolism, oxidative stress, and polyunsaturated fatty acid metabolism in depressed adolescents associated with childhood maltreatment: A targeted metabolite analysis.

The purpose of this study was to explore the metabolomic differences between Major depressive disorder (MDD) and healthy individuals among adolescents and the association between childhood maltreatment (CM) and differentially abundant metabolites. The exploratory study included 40 first-episode drug-naïve adolescents with MDD and 20 healthy volunteers. We used the Beck Depression Inventory (BDI-13) to assess the severity of depression and the Childhood Trauma Questionnaire (CTQ) to assess the presence of childhood maltreatment. The plasma samples from all participants were collected for targeted metabolomics analysis using ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC‒MS/MS) methods. Spearman correlation was applied to analyse the correlations between clinical variables and metabolites. We found 11 increased metabolites and 37 decreased metabolites that differed between adolescents with MDD and healthy individuals. Pathway enrichment analysis of differentially abundant metabolites showed abnormalities in energy metabolism and oxidative stress in MDD. Importantly, we found that creatine, valine, isoleucine, glutamic acid and pyroglutamic acid were negatively correlated with the BDI-13, while isocitric acid, fatty acid and acylcarnitine were negatively associated with CTQ, and 4-hydroxyproline was positively related to CTQ in adolescents with MDD. These studies provide new ideas for the pathogenesis and potential treatment of adolescents with MDD.

Unveiling the role of YARS1 in bladder cancer: A prognostic biomarker and therapeutic target.

YARS is responsible for catalysing the binding of tyrosine to its cognate tRNA and plays a crucial role in basic biosynthesis. However, its biological functions in bladder cancer remains to be proven. We analysed variations in YARS1 expression and survival in bladder cancer using multiple data sets, including TCGA-BLCA, GSE13507 and bladder cancer-specific tissue microarrays. Furthermore, we explored the biological functions of YARS1 using transcriptome data. Our findings revealed a noteworthy correlation between YARS1 and immune infiltration in bladder cancer, as determined using the XCELL algorithm and single-cell analysis. In addition, we employed the TIDE algorithm to evaluate the responsiveness of different cohorts to immune checkpoint therapy. We investigated the regulatory associations between YARS1 and various aspects of bladder cancer, including senescence, ferroptosis and stemness. Finally, we established a ceRNA network that is directly linked to the overall prognosis, YARS1 can serve as a prognostic biomarker for bladder cancer; its interaction with MYC has implications for bladder cancer cell senescence, ferroptosis and stemness. Moreover, the identified ceRNA network has potential as a therapeutic target in bladder cancer.

Comparison of four performance models in quantifying the inequality of leaf and fruit size distribution.

The inequality in leaf and fruit size distribution per plant can be quantified using the Gini index, which is linked to the Lorenz curve depicting the cumulative proportion of leaf (or fruit) size against the cumulative proportion of the number of leaves (or fruits). Prior researches have predominantly employed empirical models-specifically the original performance equation (PE-1) and its generalized counterpart (GPE-1)-to fit rotated and right-shifted Lorenz curves. Notably, another potential performance equation (PE-2), capable of generating similar curves to PE-1, has been overlooked and not systematically compared with PE-1 and GPE-1. Furthermore, PE-2 has been extended into a generalized version (GPE-2). In the present study, we conducted a comparative analysis of these four performance equations, evaluating their applicability in describing Lorenz curves related to plant organ (leaf and fruit) size. Leaf area was measured on 240 culms of dwarf bamboo (Shibataea chinensis Nakai), and fruit volume was measured on 31 field muskmelon plants (Cucumis melo L. var. agrestis Naud.). Across both datasets, the root-mean-square errors of all four performance models were consistently smaller than 0.05. Paired t-tests indicated that GPE-1 exhibited the lowest root-mean-square error and Akaike information criterion value among the four performance equations. However, PE-2 gave the best close-to-linear behavior based on relative curvature measures. This study presents a valuable tool for assessing the inequality of plant organ size distribution.

JAM3: A prognostic biomarker for bladder cancer via epithelial­mesenchymal transition regulation

Understanding the intricate relationship between prognosis, immune function, and molecular markers in bladder cancer (BC) demands sophisticated analytical methods. To identify novel biomarkers for predicting prognosis and immune function in BC patients, we combined weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression analysis. This was conducted using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Ultimately, we screened the junctional adhesion molecule 3 (JAM3) as an independent risk factor in BC. High levels of JAM3 were linked to adverse clinical parameters, such as higher T and N stages. Additionally, a JAM3-based nomogram model accurately predicted 1-, 3- and 5-year survival rates of BC patients, indicating potential clinical utility. Functional enrichment analysis revealed that high JAM3 expression activated the calcium signaling pathway, the extracellular matrix (ECM)-receptor interaction, and the PI3K-Akt signaling pathway, and was positively correlated with genes associated with epithelial­mesenchymal transition (EMT). Subsequently, we found that overexpression of JAM3 promoted the migration and invasion abilities in BC cells, regulating the expression levels of N-Cadherin, matrix metallopeptidase 2 (MMP2), and Claudin-1 thereby promoting EMT levels. Additionally, we showed that JAM3 was negatively correlated with anti-tumor immune cells such as CD8+T cells, while positively correlated with pro-tumor immune cells such as M2 macrophages, suggesting its involvement in immune cell infiltration. The immune checkpoint CD200 also showed a positive correlation with JAM3. Our findings revealed that elevated JAM3 levels are predictive of poor prognosis and immune cell infiltration in BC patients by regulating the EMT process.

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning.

Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. We chose four standard views in pediatric cardiac ultrasound to identify atrial septal defects; the four standard views were as follows: subcostal sagittal view of the atrium septum (subSAS), apical four-chamber view (A4C), the low parasternal four-chamber view (LPS4C), and parasternal short-axis view of large artery (PSAX). We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). In our model, we present a block random selection, maximal agreement decision, and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. We validate our model using our private dataset by five cross-validation. For ASD detection, we achieve [Formula: see text] AUC, [Formula: see text] accuracy, [Formula: see text] sensitivity, [Formula: see text] specificity, and [Formula: see text] F1 score. The proposed model is a multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors.

A raster-based spatial clustering method with robustness to spatial outliers.

Spatial clustering is an essential method for the comprehensive understanding of a region. Spatial clustering divides all spatial units into different clusters. The attributes of each cluster of the spatial units are similar, and simultaneously, they are as continuous as spatially possible. In spatial clustering, the handling of spatial outliers is important. It is necessary to improve spatial integration so that each cluster is connected as much as possible, while protecting spatial outliers can help avoid the excessive masking of attribute differences This paper proposes a new spatial clustering method for raster data robust to spatial outliers. The method employs a sliding window to scan the entire region to determine spatial outliers. Additionally, a mechanism based on the range and standard deviation of the spatial units in each window is designed to judge whether the spatial integration should be further improved or the spatial outliers should be protected. To demonstrate the usefulness of the proposed method, we applied it in two case study areas, namely, Changping District and Pinggu District in Beijing. The results show that the proposed method can retain the spatial outliers while ensuring that the clusters are roughly contiguous. This method can be used as a simple but powerful and easy-to-interpret alternative to existing geographical spatial clustering methods.

Rapid authentication of characteristic milk powders by recombinase polymerase amplification assays.

The authentication of dairy species has great significance for food safety. This study focused on a more rapid method for identifying major dairy species, and specific recombinase polymerase amplification (RPA)-based assays for cattle, goat, sheep, camel and donkey were developed. Through the developed RPA-based assays, goats and sheep could be simultaneously identified and bovine families could be differentiated. The performances of the RPA assays were validated using 37 milk powder samples, of which 16.2% (6/37) were suspected of being adulterated and 24.3% (9/37) were potentially at risk of being wrongly identified as adulteration. The effectiveness of the developed assays for crude DNA detection was also validated by a rapid nucleic acid extraction kit, and results showed that the presence of large amounts of protein and fat did not affect the qualitative results. Therefore, these assays could combine with the rapid nucleic acids extraction methods for being used in field detection.