CHITINASE LIKE1 Regulates Root Development of Dark-Grown Seedlings by Modulating Ethylene Biosynthesis in Arabidopsis thaliana

The plant hormone ethylene plays a regulatory role in development in light- and dark-grown seedlings. We previously isolated a group of small-molecule compounds with a quinazolinone backbone, which were named acsinones (for ACC synthase inhibitor quinazolinones), that act as uncompetitive inhibitors of 1-aminocyclopropane-1-carboxylic acid (ACC) synthase (ACS). Thus, the triple response phenotype, which consists of shortened hypocotyls and roots, radial swelling of hypocotyls and exaggerated curvature of apical hooks, was suppressed by acsinones in dark-grown (etiolated) ethylene overproducer (eto) seedlings. Here, we describe our isolation and characterization of an Arabidopsis revert to eto1 9 (ret9) mutant, which showed reduced sensitivity to acsinones in etiolated eto1 seedlings. Map-based cloning of RET9 revealed an amino acid substitution in CHITINASE LIKE1 (CTL1), which is required for cell wall biogenesis and stress resistance in Arabidopsis. Etiolated seedlings of ctl1ret9 showed short hypocotyls and roots, which were augmented in combination with eto1-4. Consistently, ctl1ret9 seedlings showed enhanced sensitivity to exogenous ACC to suppress primary root elongation as compared with the wild type. After introducing ctl1ret9 to mutants completely insensitive to ethylene, genetic analysis indicated that an intact ethylene response pathway is essential for the alterations in root and apical hook but not hypocotyl in etiolated ctl1ret9 seedlings. Furthermore, a mild yet significantly increased ethylene level in ctl1 mutants was related to elevated mRNA level and activity of ACC oxidase (ACO). Moreover, genes associated with ethylene biosynthesis (ACO1 and ACO2) and response (ERF1 and EDF1) were upregulated in etiolated ctl1ret9 seedlings. By characterizing a new recessive allele of CTL1, we reveal that CTL1 negatively regulates ACO activity and the ethylene response, which thus contributes to understanding a role for ethylene in root elongation in response to perturbed cell wall integrity

Epithelial Heat Shock Proteins Mediate the Protective Effects of Limosilactobacillus reuteri in Dextran Sulfate Sodium-Induced Colitis

Defects in gut barrier function are implicated in gastrointestinal (GI) disorders like inflammatory bowel disease (IBD), as well as in systemic inflammation. With the increasing incidence of IBD worldwide, more attention should be paid to dietary interventions and therapeutics with the potential to boost the natural defense mechanisms of gut epithelial cells. The current study aimed to investigate the protective effects of Limosilactobacillus reuteri ATCC PTA 4659 in a colitis mouse model and delineate the mechanisms behind it. Wild-type mice were allocated to the control group; or given 3% dextran sulfate sodium (DSS) in drinking water for 7 days to induce colitis; or administered L. reuteri for 7 days as pretreatment; or for 14 days starting 7 days before subjecting to the DSS. Peroral treatment with L. reuteri improved colitis severity clinically and morphologically and reduced the colonic levels of Tumor necrosis factor-alpha (TNF-alpha) (Tnf), Interleukin 1-beta (Il1 beta), and nterferon-gamma (Ifng), the crucial pro-inflammatory cytokines in colitis onset. It also prevented the CD11b(+)Ly6G(+) neutrophil recruitment and the skewed immune responses in mesenteric lymph nodes (MLNs) of CD11b(+)CD11c(+) dendritic cell (DC) expansion and Foxp3(+)CD4(+) T-cell reduction. Using 16S rRNA gene amplicon sequencing and RT-qPCR, we demonstrated a colitis-driven bacterial translocation to MLNs and gut microbiota dysbiosis that were in part counterbalanced by L. reuteri treatment. Moreover, the expression of barrier-preserving tight junction (TJ) proteins and cytoprotective heat shock protein (HSP) 70 and HSP25 was reduced by colitis but boosted by L. reuteri treatment. A shift in expression pattern was also observed with HSP70 in response to the pretreatment and with HSP25 in response to L. reuteri-DSS. In addition, the changes of HSPs were found to be correlated to bacterial load and epithelial cell proliferation. In conclusion, our results demonstrate that the human-derived L. reuteri strain 4659 confers protection in experimental colitis in young mice, while intestinal HSPs may mediate the probiotic effects by providing a supportive protein-protein network for the epithelium in health and colitis

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Imaging Findings of Follicular Dendritic Cell Sarcoma: Report of Four Cases

Follicular dendritic cell sarcoma is a rare malignant neoplasm and little is known about its radiological features. We present here four cases of follicular dendritic cell sarcomas and we provide the image characteristics of these tumors to help radiologists recognize this entity when making a diagnosis

Polyethyleneimine-coated MXene quantum dots improve cotton tolerance to Verticillium dahliae by maintaining ROS homeostasis

Verticillium dahliae is a soil-borne hemibiotrophic fungal pathogen that threatens cotton production worldwide. In this study, we assemble the genomes of two V. dahliae isolates: the more virulence and defoliating isolate V991 and nondefoliating isolate 1cd3-2. Transcriptome and comparative genomics analyses show that genes associated with pathogen virulence are mostly induced at the late stage of infection (Stage II), accompanied by a burst of reactive oxygen species (ROS), with upregulation of more genes involved in defense response in cotton. We identify the V991-specific virulence gene SP3 that is highly expressed during the infection Stage II. V. dahliae SP3 knock-out strain shows attenuated virulence and triggers less ROS production in cotton plants. To control the disease, we employ polyethyleneimine-coated MXene quantum dots (PEI-MQDs) that possess the ability to remove ROS. Cotton seedlings treated with PEI-MQDs are capable of maintaining ROS homeostasis with enhanced peroxidase, catalase, and glutathione peroxidase activities and exhibit improved tolerance to V. dahliae. These results suggest that V. dahliae trigger ROS production to promote infection and scavenging ROS is an effective way to manage this disease. This study reveals a virulence mechanism of V. dahliae and provides a means for V. dahliae resistance that benefits cotton production

The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram

ObjectiveTraumatic brain injury (TBI) leads to death and disability. This study developed an effective prognostic nomogram for assessing the risk factors for TBI mortality.MethodData were extracted from an online database called “Multiparameter Intelligent Monitoring in Intensive Care IV” (MIMIC IV). The ICD code obtained data from 2,551 TBI persons (first ICU stay, >18 years old) from this database. R divided samples into 7:3 training and testing cohorts. The univariate analysis determined whether the two cohorts differed statistically in baseline data. This research used forward stepwise logistic regression after independent prognostic factors for these TBI patients. The optimal variables were selected for the model by the optimal subset method. The optimal feature subsets in pattern recognition improved the model prediction, and the minimum BIC forest of the high-dimensional mixed graph model achieved a better prediction effect. A nomogram-labeled TBI-IHM model containing these risk factors was made by nomology in State software. Least Squares OLS was used to build linear models, and then the Receiver Operating Characteristic (ROC) curve was plotted. The TBI-IHM nomogram model's validity was determined by receiver operating characteristic curves (AUCs), correction curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision-curve analysis (DCA).ResultThe eight features with a minimal BIC model were mannitol use, mechanical ventilation, vasopressor use, international normalized ratio, urea nitrogen, respiratory rate, and cerebrovascular disease. The proposed nomogram (TBI-IHM model) was the best mortality prediction model, with better discrimination and superior model fitting for severely ill TBI patients staying in ICU. The model's receiver operating characteristic curve (ROC) was the best compared to the seven other models. It might be clinically helpful for doctors to make clinical decisions.ConclusionThe proposed nomogram (TBI-IHM model) has significant potential as a clinical utility in predicting mortality in TBI patients

Pathological demand avoidance: my thoughts on looping effects and commodification of autism

Hacking suggests autism is a human kind, and has used autism to discuss their evolution over time. Looping effects caused the autism human kind to evolve since 1995, with people identifying with the autism human kind, and the commodification of the autism human kind by the autism industry. Pathological demand avoidance (PDA) was created from the looping effects controlled by the autism industry. This has undermined autism self-advocacy by supporting the medical paradigm of the autism human kind. By refusing to engage with PDA, people of the autism human kind limit the commodification of autism; creating greater emancipation