Bioinformatics Identification and Experimental Validation of a Prognostic Model for the Survival of Lung Squamous Cell Carcinoma Patients.

Lung squamous cell carcinoma (LUSC) kills more than four million people yearly. Creating more trustworthy tumor molecular markers for LUSC early detection, diagnosis, prognosis, and customized treatment is essential. Cuproptosis, a novel form of cell death, opened up a new field of study for searching for trustworthy tumor indicators. Our goal was to build a risk model to assess drug sensitivity, monitor immune function, and predict prognosis in LUSC patients. The 19 cuproptosis-related genes were found in the literature, and patient genomic and clinical information was collected using the Cancer Genomic Atlas (TCGA) database. The LUSC patients were grouped using unsupervised clustering techniques, and 7626 differentially expressed genes were identified. Using univariate COX analysis, LASSO regression analysis, and multivariate COX analysis, a prognostic model for LUSC patients was developed. The tumor immune escape was evaluated using the Tumor Immune Dysfunction and Exclusion (TIDE) method. The R packages 'pRRophetic,' 'ggpubr,' and 'ggplot2' were utilized to examine drug sensitivity. For modeling, a 6-cuproptosis-based gene signature was found. Patients with high-risk LUSC had significantly worse survival rates than those with low-risk conditions. The possibility of tumor immunological escape was increased in patients with higher risk scores due to more immune cell inactivation. For patients with high-risk LUSC, we discovered seven potent potential drugs (AZD6482, CHIR.99021, CMK, Embelin, FTI.277, Imatinib, and Pazopanib). In conclusion, the cuproptosis-based genes predictive risk model can be utilized to predict outcomes, track immune function, and evaluate medication sensitivity in LUSC patients.

Bayesian decision based fusion algorithm for remote sensing images.

Remote sensing image fusion is dedicated to obtain a high-resolution multispectral (HRMS) image without spatial or spectral distortion compared to the single source image. In this paper, a novel fusion algorithm based on Bayesian estimation for remote sensing images is proposed from the new perspective of risk decisions. In this study, an observation model based on Bayesian estimation for remote sensing image fusion is constructed. Three categories of probabilities including prior, conditional and posterior probabilities are calculated after an intensity-hue-saturation (IHS) transformation is applied to the original low-resolution MS image. To obtain the desired HRMS image, with the corrected posterior probability, a fusion rule based on Bayesian decisions is designed to estimate which pixels to select from the panchromatic (PAN) image and the intensity component of the MS image. The selected pixels constitute a new component that will participate in an IHS inverse transformation to yield the fused image. Extensive experiments were performed on the Pleiades, WorldView-3, and IKONOS datasets, and the results demonstrate the effectiveness of the proposed method.

Dexmedetomidine alleviates acute postoperative anxiety in rats by suppressing the NF-ĸB pathway.

Evidence suggests that surgical procedures can effect the central nervous system and lead to changes in mood and behavior, rarely understood about the role of acute inflammation in promoting acute anxiety postoperatively. This study was designed to explore the possible mechanism of dexmedetomidine (DEX, a2-adrenergic receptor agonist) for reducing acute postoperative anxiety, which may be related to the activation of nuclear factor kappa B (NF-κB) and downstream signal pathway in the hippocampus. Experiments were conducted with rat, the elevated plus-maze and open field test were performed to evaluate anxiety-like behavior. Inhibit DEX with Atipamezole (AT, α2-adrenergic receptor antagonist) and inhibit NF-κB with Pyrrolidinedithiocarbamate (PDTC, inhibit phosphorylation of IκB, prevent the activation of NF-κB), the level of interleukin-6 (IL-6), IL-1ß, IL-10 and Tumor necrosis factor-α (TNF-α); the nuclear translocation of NF-κB in the hippocampus and anxiety-like behavior were measured. Rats exhibited anxiety-like behavior at 6h and 12h after surgery. Preoperative administration of DEX significantly alleviated postoperative anxiety-like behavior. DEX premedication inhibited the nuclear translocation of NF-κB alleviate acute postoperative anxiety. These findings are the first to show that acute postoperative anxiety may be related to NF-κB nuclear translocation in the hippocampus in rats, which can be alleviated by DEX premedication.

Efficient incorporation of highly migratory thallium into struvite structure: Unraveling the stabilization mechanisms from a mineralogical perspective.

The remediation of high-concentration thallium (Tl+) contaminated wastewater is a critical environmental concern. Current research emphasizes the effectiveness of adsorption and oxidation methods for Tl+ treatment, yet challenges persist in enhancing their performance. This study explores the feasibility of emergency Tl+ wastewater treatment and elucidates the mechanisms of Tl+ incorporation into mineral structures, with a focus on the struvite mineral as a framework for Tl+ integration via NH4+ ion exchange. To assess the efficacy and mechanisms of Tl+ immobilization, we utilized comprehensive analytical techniques, including X-ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), Fourier-Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS), Thermogravimetric Analysis (TG), and Density Functional Theory (DFT) calculations. The findings reveal that struvite adsorbs Tl+ onto its surface, followed by an ion exchange process between monovalent cations (NH4+/K+) within the structure and Tl+. Ultimately, Tl+ is incorporated in the form of a (NH4,Tl)MgPO4 solid solution within the structure, achieving a remarkable maximum incorporation capacity of 320.56 mg/g, which significantly surpasses the capacity of typical adsorbents. The findings demonstrate significant Tl+ incorporation, validating the approach for emergency wastewater treatment and suggesting the potential of mineralogy in environmental remediation. This research contributes to advancing heavy metal wastewater treatment strategies, offering a foundation for further investigation.

MiR-1976/NCAPH/P65 axis inhibits the malignant phenotypes of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a malignancy with an abysmal survival rate. High metastasis is the leading cause of the low survival rate of LUAD. NCAPH, an oncogene, is involved in the carcinogenesis of LUAD. However, the regulation of NCAPH in LUAD remains controversial. In this work, we identified an up-regulation of NCAPH in LUAD tissues. Patients who expressed more NCAPH had shorter overall survival (OS). Furthermore, NCAPH overexpression promoted LUAD cell migration while inhibiting apoptosis. MiR-1976 and miR-133b were predicted to target NCAPH expression by searching TargetScan and linkedomics databases. Following that, we confirmed that miR-1976 suppressed NCAPH by directly targeting a 7-bp region of NCAPH 3' untranslated regions (UTR). In addition, increased expression of miR-1976 decreased the proliferation & migration and promoted apoptosis of LUAD cells, and the re-introduction of NCAPH reversed these influences. Furthermore, the xenograft and metastasis mouse models also confirmed that miR-1976 inhibited tumor growth and metastasis in vivo by targeting NCAPH. Finally, we found that MiR-1976 targeting NCAPH blocked the activation of NF-κB. In conclusion, miR-1976 inhibits NCAPH activity in LUAD and acts as a tumor suppressor. The miR-1976/NCAPH/NF-κB axis may, in the future, represent crucial diagnostic and prognostic biomarkers and promising therapeutic options.

Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis.

PURPOSE: To develop an artificial intelligence (AI) tool with automated pancreas segmentation and measurement of pancreatic morphological information on CT images to assist improved and faster diagnosis in acute pancreatitis. METHODS: This study retrospectively contained 1124 patients suspected for AP and received non-contrast and enhanced abdominal CT examination between September 2013 to September 2022. Patients were divided into training (N = 688), validation (N = 145), testing dataset (N = 291, N = 104 for normal pancreas, N = 98 for AP, N = 89 for AP complicated with PDAC (AP&PDAC)). A model based on convolutional neural network (MSAnet) was developed. The pancreas segmentation and measurement were performed via eight open-source models and MSAnet based tools, and the efficacy was evaluated using Dice similarity coefficient (DSC) and Intersection over union (IoU). The DSC and IoU for patients with different ages were also compared. The outline of tumor and edema in the AP and were segmented by clustering. The diagnostic efficacy for radiologists with or without the assistance of MSAnet tool in AP and AP&PDAC was evaluated using receiver operation curve and confusion matrix. RESULTS: Among all models, MSAnet based tool showed best performance on the training and validation dataset, and had high efficacy on testing dataset. The performance was age-affected. With assistance of the AI tool, the diagnosis time was significantly shortened by 26.8% and 32.7% for junior and senior radiologists, respectively. The area under curve in diagnosis of AP was improved from 0.91 to 0.96 for junior radiologist and 0.98 to 0.99 for senior radiologist. In AP&PDAC diagnosis, AUC was increased from 0.85 to 0.92 for junior and 0.97 to 0.99 for senior. CONCLUSION: MSAnet based tools showed good pancreas segmentation and measurement performance, which help radiologists improve diagnosis efficacy and workflow in both AP and AP with PDAC conditions. ADVANCES IN KNOWLEDGE: This study developed an AI tool with automated pancreas segmentation and measurement and provided evidence for AI tool assistance in improving the workflow and accuracy of AP diagnosis.

Rapid Antidepressant-Like Potential of Chaihu Shugan San Depends on Suppressing Glutamate Neurotransmission and Activating Synaptic Proteins in Hippocampus of Female Mice.

OBJECTIVE: To explore the rapid antidepressant potential and the underlying mechanism of Chaihu Shugan San (CSS) in female mice. METHODS: Liquid chromatography mass spectrometry (LC-MS)/MS was used to determine the content of main components in CSS to determine its stability. Female C57BL/6J mice were randomly divided into 4 groups, including control (saline), vehicle (saline), CSS (4 g/kg) and ketamine (30 mg/kg) groups. Mice were subjected to irregular stress stimulation for 4 weeks to establish the chronic mild stress (CMS) model, then received a single administration of drugs. Two hours later, the behavioral tests were performed, including open field test, tail suspension test (TST), forced swimming test (FST), novelty suppression feeding test (NSF), and sucrose preference test (SPT). Western blot analysis was used to detect the expression levels of N-methyl-D-aspartate receptor (NMDA) subtypes [N-methyl-D-aspartate receptor 1 (NR1), NR2A, NR2B], synaptic proteins [synapsin1 and post synaptic density protein 95 (PSD95)], and brain-derived neurotrophic factor (BDNF). Moreover, the rapid antidepressant effect of CSS was tested by pharmacological technologies and optogenetic interventions that activated glutamate receptors, NMDA. RESULTS: Compared with the vehicle group, a single administration of CSS (4 g/kg) reversed all behavioral defects in TST, FST, SPT and NSF caused by CMS (P<0.05 or P<0.01). CSS also significantly decreased the expressions of NMDA subtypes (NR1, NR2A, NR2B) at 2 h in hippocampus of mice (all P<0.01). In addition, similar to ketamine, CSS increased levels of synaptic proteins and BDNF (P<0.05 or P<0.01). Furthermore, the rapid antidepressant effects of CSS were blocked by transient activation of NMDA receptors in the hippocampus (all P<0.01). CONCLUSION: Rapid antidepressant effects of CSS by improving behavioral deficits in female CMS mice depended on rapid suppression of NMDA receptors and activation of synaptic proteins.

Controllable Fabrication of Highly Ordered Spherical Microcavity Arrays by Replica Molding of In Situ Self-Emulsified Droplets.

Ordered spherical hollow micro- and nanostructures hold great appeal in the fields of cell biology and optics. However, it is extremely challenging for standard lithography techniques to achieve spherical micro-/nanocavities. In this paper, we describe a simple, cost-effective, and scalable approach to fabricate highly ordered spherical microcavity arrays by replica molding of in situ self-emulsified droplets. The in situ self-emulsion involves a two-step process: discontinuous dewetting-induced liquid partition and interfacial tension-driven liquid spherical transformation. Subsequent replica molding of the droplets creates spherical microcavity arrays. The shapes and sizes of the microcavities can be easily modulated by varying the compositions of the droplet templates or utilizing an osmotically driven water permeation. To demonstrate the utility of this method, we employed it to create a spherical microwell array for the mass production of embryoid bodies with high viability and minimal loss. In addition, we also demonstrated the optical functions of the generated spherical microcavities by using them as microlenses. We believe that our proposed method will open exciting avenues in fields ranging from regenerative medicine and microchemistry to optical applications.

Stem cell therapies: a new era in the treatment of multiple sclerosis.

Multiple Sclerosis (MS) is an immune-mediated condition that persistently harms the central nervous system. While existing treatments can slow its course, a cure remains elusive. Stem cell therapy has gained attention as a promising approach, offering new perspectives with its regenerative and immunomodulatory properties. This article reviews the application of stem cells in MS, encompassing various stem cell types, therapeutic potential mechanisms, preclinical explorations, clinical research advancements, safety profiles of clinical applications, as well as limitations and challenges, aiming to provide new insights into the treatment research for MS.

Carcinoembryonic antigen potentiates non-small cell lung cancer progression via PKA-PGC-1ɑ axis.

Carcinoembryonic antigen (CEA) is a tumor-associated antigen primarily produced by tumor cells. It has been implicated in various biological processes such as cell adhesion, proliferation, differentiation, and metastasis. Despite this, the precise molecular mechanisms through which CEA enhances tumor cell proliferation remain largely unclear. Our study demonstrates that CEA enhances the proliferation and migration of non-small cell lung cancer (NSCLC) while also inhibiting cisplatin-induced apoptosis in NSCLC cells. Treatment with CEA led to an increase in mitochondrial numbers and accumulation of lipid droplets in A549 and H1299 cells. Additionally, our findings indicate that CEA plays a role in regulating the fatty acid metabolism of NSCLC cells. Inhibiting fatty acid metabolism significantly reduced the CEA-mediated proliferation and migration of NSCLC cells. CEA influences fatty acid metabolism and the proliferation of NSCLC cells by activating the PGC-1α signaling pathway. This regulatory mechanism involves CEA increasing intracellular cAMP levels, which in turn activates PKA and upregulates PGC-1α. In NSCLC, inhibiting the PKA-PGC-1α signaling pathway reduces both fatty acid metabolism and the proliferation and migration induced by CEA, both in vitro and in vivo. These results suggest that CEA contributes to the promotion of proliferation and migration by modulating fatty acid metabolism. Targeting CEA or the PKA-PGC-1ɑ signaling pathway may offer a promising therapeutic approach for treating NSCLC.

Upregulated lncRNA LINC01128 in colorectal cancer accelerates cell growth and predicts malignant prognosis through sponging miR-363-3p.

PURPOSE: Colorectal cancer (CRC) refers to high-mortality tumors arising in the colon or rectum with a high rate of recurrence. The involvement of long non-coding RNAs (lncRNAs) contributes to the treatment and prognosis evaluation of CRC, and brings a new direction for the radical cure of patients. To identify the pathological mechanism and regulation of lncRNA LINC01128 (LINC01128) on CRC cells, and analyze its potential prognostic value. METHODS: LINC01128 level in tissue and cell specimens from 122 CRC patients was evaluated by RT-qPCR. The clinical significance and prognostic value of LINC01128 in CRC were analyzed via Kaplan-Meier and Cox analysis. CCK8 and Transwell assays were used to study the function of LINC01128 in vitro. The relationship between LINC01128 and miR-363-3p was confirmed by luciferase reporter gene assay. RESULTS: The overexpression of LINC01128 is associated with TNM stage and lymph node metastasis in CRC patients. Silencing LINC01128 inhibited the proliferation and metastasis of CRC cells. In addition, LINC01128 directly targeted and negatively regulated the miR-363-3p expression, while miR-363-3p inhibitor restored the inhibitory function of LINC01128. CONCLUSION: As an independent prognostic factor of CRC, upregulation of LINC01128 predicts poor prognosis and accelerates tumor deterioration through miR-363-3p.

Thermal decomposition study of 4-methyloxybenzyl-glycoside by TG/DTA and on-line pyrolysis-photoionization mass spectrometry.

A flavor precursor of 4-methyloxybenzyl-2, 3, 4, 6-tetra-O-acetyl-ß-D-glucopyranoside (MBGL) was synthesized via a modified Koenigs-Knorr reaction. The thermal decomposition behaviour and pyrolysis intermediate products of the glycoside were studied by simultaneous thermogravimetric/differential thermal analysis (TG/DTA) and synchrotron vacuum ultraviolet (VUV) photoionization mass spectrometry (PIMS). TG/DTA results showed that the largest mass loss rate appeared at a Tp of 246.7 °C. PIMS was used to identify the pyrolysis products of MBGL at 300 °C, 500 °C and 700 °C, respectively. The experimental apparatus had some advantages in real-time analysis and fewer secondary reactions. Some important pyrolysis intermediates, such as the ions of the 4-methyloxybenzyl group at m/z 121 and the glycone moiety at m/z 347, were detected by PIMS. The results indicate that the MBGL was probably showed a different pyrolysis way compared with the other glycosides. This work reports a useful application of synchrotron VUV PIMS in a thermal decomposition study of glycoside flavor precursors.

A MXene Hydrogel-Based Versatile Microrobot for Controllable Water Pollution Management.

The urgent demand for addressing dye contaminants in water necessitates the development of microrobots that exhibit remote navigation, rapid removal, and molecular identification capabilities. The progress of microrobot development is currently hindered by the scarcity of multifunctional materials. In this study, a plasmonic MXene hydrogel (PM-Gel) is synthesized by combining bimetallic nanocubes and Ti3C2Tx MXene through the rapid gelation of degradable alginate. The hydrogel can efficiently adsorb over 60% of dye contaminants within 2 min, ultimately achieving a removal rate of >90%. Meanwhile, the hydrogel exhibits excellent sensitivity in surface enhanced Raman scattering (SERS) detection, with a limit of detection (LOD) as low as 3.76 am. The properties of the plasmonic hydrogel can be further adjusted for various applications. As a proof-of-concept experiment, thermosensitive polymers and superparamagnetic particles are successfully integrated into this hydrogel to construct a versatile, light-responsive microrobot for dye contaminants. With magnetic and optical actuation, the robot can remotely sample, identify, and remove pollutants in maze-like channels. Moreover, light-driven hydrophilic-hydrophobic switch of the microrobots through photothermal effect can further enhance the adsorption capacity and reduced the dye residue by up to 58%. These findings indicate of a broad application potential in complex real-world environments.

Predictive Value of Plasma PCSK9 Levels for Degree of Atherosclerosis and Major Adverse Cardiovascular and Cerebrovascular Events in Older Adult Patients with Non-Alcoholic Fatty Liver Disease.

Purpose: This study investigated the influence of plasma proprotein convertase subtilisin/kexin 9 (PCSK9) levels on the degree of atherosclerosis and major adverse cardiovascular and cerebrovascular events (MACCE) in older adults with non-alcoholic fatty liver disease. Methods: The degree of atherosclerosis severity was assessed by the standard Gensini score quartile method. According to the degree of atherosclerosis, patients were divided into mild (0-24 points; n=84), moderate (25-53 points; n=86), and severe groups (≥54 points; n=84) and then categorized as MACCE (n=30) or non-MACCE (n=224) according to 6-month follow-up data. The patients' age, sex, smoking history, medical history, and early morning fasting venous blood, for measuring biochemical indexes, were collected. Clinical data were compared between groups and the relationship between Gensini scores and PCSK9 was evaluated. Results: Compared with the mild group, the moderate and severe groups had higher high-sensitivity C-reactive protein(hs-CRP), PCSK9, triglycerides(TG), low-density lipoprotein cholesterol (LDL-C), and lipoprotein(a)[Lp(a)] levels and lower high-density lipoprotein cholesterol(HDL-C) levels (all P<0.05). Moreover, PCSK9 positively correlated with Gensini scores (r=0.657, P<0.01). The MACCE and non-MACCE groups had significantly different ages, statin use, Gensini scores, PCSK9, and LDL-C (all P<0.05). Multi-factorial Cox risk regression analysis showed the Gensini score (HR=1.018, 95% CI: 1.006~1.029) and PCSK9 (HR=1.147, 95% CI: 1.038~1.287) were independent risk factors for MACCE. Conclusion: The Gensini score and PCSK9 levels can be used as predictive indicators for the degree of illness and occurrence of MACCE in older NAFLD patients.

Comparison of inflammatory markers, coagulation indicators and outcomes between influenza and COVID-19 infection amongst children: A systematic review and meta-analysis.

Background: Influenza and COVID-19 patients share similar features and outcomes amongst adults. However, the difference between these diseases is not explored in paediatric age group especially in terms of inflammatory markers, coagulation profile and outcomes. Hence, we did this review to compare the inflammatory, coagulation features and outcomes between influenza and COVID-19 infected children. Methods: Literature search was done in PubMed Central, Scopus, EMBASE, CINAHL, Cochrane library, Google Scholar & ScienceDirect from November 2019 to May 2022. Risk of bias assessment was done through Newcastle Ottawa scale. Meta-analysis was done using random-effects model and the final pooled estimate was reported as pooled odds ratio (OR) or standardized mean difference (SMD) along with 95 % confidence interval (CI) depending on the type of outcome. Results: About 16 studies were included with most studies having higher risk of bias. Influenza paediatric patients had significantly higher erythrocyte sedimentation rate (ESR) (pooled SMD = 0.60; 95%CI: 0.30-0.91; I2 = 0 %), lactate dehydrogenase (LDH) (pooled SMD = 2.01; 95%CI: 0.37-3.66; I2 = 98.4 %) and prothrombin time (PT) (pooled SMD = 2.12; 95%CI: 0.44-3.80; I2 = 98.3 %) when compared to paediatric COVID-19 patients. There was no significant difference in terms of features like CRP, procalcitonin, serum albumin, aPTT, mortality and need for mechanical ventilation. Conclusion: Inflammatory markers like ESR, LDH and PT was significantly higher in influenza patients when compared to COVID-19 in children, while rest of the markers and adverse clinical outcomes were similar between both the groups. Identification of these biomarkers has helped in understanding the distinctness of COVID-19 and influenza virus and develop better management strategies.

A Distortion Correction Method Based on Actual Camera Imaging Principles.

In the human-robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results.

Identification and classification of glioma subtypes based on RNA-binding proteins.

BACKGROUND: Glioma is a common and aggressive primary malignant cancer known for its high morbidity, mortality, and recurrence rates. Despite this, treatment options for glioma are currently restricted. The dysregulation of RBPs has been linked to the advancement of several types of cancer, but their precise role in glioma evolution is still not fully understood. This study sought to investigate how RBPs may impact the development and prognosis of glioma, with potential implications for prognosis and therapy. METHODS: RNA-seq profiles of glioma and corresponding clinical data from the CGGA database were initially collected for analysis. Unsupervised clustering was utilized to identify crucial tumor subtypes in glioma development. Subsequent time-series analysis and MS model were employed to track the progression of these identified subtypes. RBPs playing a significant role in glioma progression were then pinpointed using WGCNA and Lasso Cox regression models. Functional analysis of these key RBP-related genes was conducted through GSEA. Additionally, the CIBERSORT algorithm was utilized to estimate immune infiltrating cells, while the STRING database was consulted to uncover potential mechanisms of the identified biomarkers. RESULTS: Six tumor subgroups were identified and found to be highly homogeneous within each subgroup. The progression stages of these tumor subgroups were determined using time-series analysis and a MS model. Through WGCNA, Lasso Cox, and multivariate Cox regression analysis, it was confirmed that BCLAF1 is correlated with survival in glioma patients and is closely linked to glioma progression. Functional annotation suggests that BCLAF1 may impact glioma progression by influencing RNA splicing, which in turn affects the cell cycle, Wnt signaling pathway, and other cancer development pathways. CONCLUSIONS: The study initially identified six subtypes of glioma progression and assessed their malignancy ranking. Furthermore, it was determined that BCLAF1 could serve as an RBP-related prognostic marker, offering significant implications for the clinical diagnosis and personalized treatment of glioma.

WAV E3 ubiquitin ligases mediate degradation of IAA32/34 in the TMK1-mediated auxin signaling pathway during apical hook development.

Auxin regulates plant growth and development through downstream signaling pathways, including the best-known SCFTIR1/AFB-Aux/IAA-ARF pathway and several other less characterized "noncanonical" pathways. Recently, one SCFTIR1/AFB-independent noncanonical pathway, mediated by Transmembrane Kinase 1 (TMK1), was discovered through the analyses of its functions in Arabidopsis apical hook development. Asymmetric accumulation of auxin on the concave side of the apical hook triggers DAR1-catalyzed release of the C-terminal of TMK1, which migrates into the nucleus, where it phosphorylates and stabilizes IAA32/34 to inhibit cell elongation, which is essential for full apical hook formation. However, the molecular factors mediating IAA32/34 degradation have not been identified. Here, we show that proteins in the CYTOKININ INDUCED ROOT WAVING 1 (CKRW1)/WAVY GROWTH 3 (WAV3) subfamily act as E3 ubiquitin ligases to target IAA32/34 for ubiquitination and degradation, which is inhibited by TMK1c-mediated phosphorylation. This antagonistic interaction between TMK1c and CKRW1/WAV3 subfamily E3 ubiquitin ligases regulates IAA32/34 levels to control differential cell elongation along opposite sides of the apical hook.

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.