The expression of Gli3, regulated by HOXD13, may play a role in idiopathic congenital talipes equinovarus

<p>Abstract</p> <p>Background</p> <p>Idiopathic congenital talipes equinovarus (ICTEV) is a congenital limb deformity. Based on extended transmission disequilibrium testing, <it>Gli-Kruppel family member 3 </it>(<it>Gli3</it>) has been identified as a candidate gene for ICTEV. Here, we verify the role of <it>Gli3 </it>in ICTEV development.</p> <p>Methods</p> <p>Using the rat ICTEV model, we analyzed the differences in <it>Gli3 </it>expression levels between model rats and normal control rats. We used luciferase reporter gene assays and ChIP/EMSA assays to analyze the regulatory elements of <it>Gli3</it>.</p> <p>Results</p> <p><it>Gli3 </it>showed higher expression levels in ICTEV model rats compared to controls (P < 0.05). We identified repressor and activator regions in the rat <it>Gli3 </it>promoter. The <it>Gli3 </it>promoter also contains two putative Hoxd13 binding sites. Using EMSA, the Hoxd13 binding site 2 was found to directly interact with Hoxd13 <it>in vitro</it>. ChIP assays of the Hoxd13-<it>Gli3 </it>promoter complex from a developing limb confirmed that endogenous Hoxd13 interacts with this region <it>in vivo</it>.</p> <p>Conclusion</p> <p>Our findings suggest that <it>HoxD13 </it>directly interacts with the promoter of <it>Gli3</it>. The increase of <it>Gli3 </it>expression in ICTEV model animal might result from the low expression of <it>HoxD13</it>.</p

Symptoms of Anxiety and Depression Among Adolescents Before vs During COVID-19-Related School Closures in China

This cohort study compares the psychological status of Chinese adolescents at school before the COVID-19 pandemic and at home during the pandemic to assess whether school attendance was associated with negative mental health outcomes

Treatment response prediction and individualized identification of first-episode drug-naive schizophrenia using brain functional connectivity

Identifying biomarkers in schizophrenia during the first episode without the confounding effects of treatment has been challenging. Leveraging these biomarkers to establish diagnosis and make individualized predictions of future treatment responses to antipsychotics would be of great value, but there has been limited progress. In this study, by using machine learning algorithms and the functional connections of the superior temporal cortex, we successfully identified the first-episode drug-naive (FEDN) schizophrenia patients (accuracy 78.6%) and predict their responses to antipsychotic treatment (accuracy 82.5%) at an individual level. The functional connections (FC) were derived using the mutual information and the correlations, between the blood-oxygen-level dependent signals of the superior temporal cortex and other cortical regions acquired with the resting-state functional magnetic resonance imaging. We also found that the mutual information and correlation FC was informative in identifying individual FEDN schizophrenia and prediction of treatment response, respectively. The methods and findings in this paper could provide a critical step toward individualized identification and treatment response prediction in first-episode drug-naive schizophrenia, which could complement other biomarkers in the development of precision medicine approaches for this severe mental disorder.</p

Association Between Hippocampal Subfields and Clinical Symptoms of First-Episode and Drug Naive Schizophrenia Patients During 12 Weeks of Risperidone Treatment

Small hippocampal size may be implicated in the pathogenesis and psychopathology of schizophrenia (SCZ). However, does the volume of hippocampal subfields in SCZ patients affect response to antipsychotic treatment? In this study, we used risperidone to treat first-episode drug naive (FEDN) SCZ patients for 12 weeks, and then explored the relationship between baseline hippocampal subfield volumes, as well as any changes in these hippocampal subfield volumes during treatment, and improvement in their psychopathological symptoms. By adopting a state-of the-art automated algorithm, the hippocampal subfields were segmented in 43 FEDN SCZ inpatients at baseline and after 12 weeks of risperidone monotherapy, as well as in 30 matched healthy controls. We adopted the Positive and Negative Syndrome Scale (PANSS) to assess psychopathological symptoms in patients at baseline and at post-treatment. Before treatment, SCZ patients had no significant differences in total or subfield hippocampal volumes compared with healthy volunteers. However, we found a significant correlation between a smaller left CA1 at baseline and a lower PANSS total score and general psychopathology sub-score at post-treatment (both p 50% improvement in PANSS total score, than in non-responders (p < 0.05). Our results suggest that smaller left CA1 volume may be a predicator for improvement in psychotic symptoms of FEDN SCZ patients

iGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control

Agriculture is the foundation of human civilization. However, the rapid increase of the global population poses a challenge on this cornerstone by demanding more food. Modern autonomous greenhouses, equipped with sensors and actuators, provide a promising solution to the problem by empowering precise control for high-efficient food production. However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task. With the advances of artificial intelligence (AI), the internet of things (IoT), and cloud computing technologies, we are hopeful to provide a solution to automate and smarten greenhouse control to address the above challenges. In this paper, we propose a smart agriculture solution named iGrow, for autonomous greenhouse control (AGC): (1) for the first time, we formulate the AGC problem as a Markov decision process (MDP) optimization problem; (2) we design a neural network-based simulator incorporated with the incremental mechanism to simulate the complete planting process of an autonomous greenhouse, which provides a testbed for the optimization of control strategies; (3) we propose a closed-loop bi-level optimization algorithm, which can dynamically re-optimize the greenhouse control strategy with newly observed data during real-world production. We not only conduct simulation experiments but also deploy iGrow in real scenarios, and experimental results demonstrate the effectiveness and superiority of iGrow in autonomous greenhouse simulation and optimal control. Particularly, compelling results from the tomato pilot project in real autonomous greenhouses show that our solution significantly increases crop yield (+10.15%) and net profit (+92.70%) with statistical significance compared to planting experts. Our solution opens up a new avenue for greenhouse production. The code is available at https://github.com/holmescao/iGrow.git

Metabolite Profiling of 14 Wuyi Rock Tea Cultivars Using UPLC-QTOF MS and UPLC-QqQ MS Combined with Chemometrics

Wuyi Rock tea, well-recognized for rich flavor and long-lasting fragrance, is a premium subcategory of oolong tea mainly produced in Wuyi Mountain and nearby regions of China. The quality of tea is mainly determined by the chemical constituents in the tea leaves. However, this remains underexplored for Wuyi Rock tea cultivars. In this study, we investigated the leaf metabolite profiles of 14 major Wuyi Rock tea cultivars grown in the same producing region using UPLC-QTOF MS and UPLC-QqQ MS with data processing via principal component analysis and cluster analysis. Relative quantitation of 49 major metabolites including flavan-3-ols, proanthocyanidins, flavonol glycosides, flavone glycosides, flavonone glycosides, phenolic acid derivatives, hydrolysable tannins, alkaloids and amino acids revealed clear variations between tea cultivars. In particular, catechins, kaempferol and quercetin derivatives were key metabolites responsible for cultivar discrimination. Information on the varietal differences in the levels of bioactive/functional metabolites, such as methylated catechins, flavonol glycosides and theanine, offers valuable insights to further explore the nutritional values and sensory qualities of Wuyi Rock tea. It also provides potential markers for tea plant fingerprinting and cultivar identification