Tensor Generalized Estimating Equations for Longitudinal Imaging Analysis

Longitudinal neuroimaging studies are becoming increasingly prevalent, where brain images are collected on multiple subjects at multiple time points. Analyses of such data are scientifically important, but also challenging. Brain images are in the form of multidimensional arrays, or tensors, which are characterized by both ultrahigh dimensionality and a complex structure. Longitudinally repeated images and induced temporal correlations add a further layer of complexity. Despite some recent efforts, there exist very few solutions for longitudinal imaging analyses. In response to the increasing need to analyze longitudinal imaging data, we propose several tensor generalized estimating equations (GEEs). The proposed GEE approach accounts for intra-subject correlation, and an imposed low-rank structure on the coefficient tensor effectively reduces the dimensionality. We also propose a scalable estimation algorithm, establish the asymptotic properties of the solution to the tensor GEEs, and investigate sparsity regularization for the purpose of region selection. We demonstrate the proposed method using simulations and by analyzing a real data set from the Alzheimer's Disease Neuroimaging Initiative

TENSOR GENERALIZED ESTIMATING EQUATIONS FOR LONGITUDINAL IMAGING ANALYSIS.

Longitudinal neuroimaging studies are becoming increasingly prevalent, where brain images are collected on multiple subjects at multiple time points. Analyses of such data are scientifically important, but also challenging. Brain images are in the form of multidimensional arrays, or tensors, which are characterized by both ultrahigh dimensionality and a complex structure. Longitudinally repeated images and induced temporal correlations add a further layer of complexity. Despite some recent efforts, there exist very few solutions for longitudinal imaging analyses. In response to the increasing need to analyze longitudinal imaging data, we propose several tensor generalized estimating equations (GEEs). The proposed GEE approach accounts for intra-subject correlation, and an imposed low-rank structure on the coefficient tensor effectively reduces the dimensionality. We also propose a scalable estimation algorithm, establish the asymptotic properties of the solution to the tensor GEEs, and investigate sparsity regularization for the purpose of region selection. We demonstrate the proposed method using simulations and by analyzing a real data set from the Alzheimer's Disease Neuroimaging Initiative

Seed Germination and Growth Improvement for Early Maturing Pear Breeding

Breeding early maturing cultivars is one of the most important objectives in pear breeding. Very early maturing pears provide an excellent parental material for crossing, but the immature embryo and low seed germination of their hybrid progenies often limit the selection and breeding of new early maturing pear cultivars. In this study, we choose a very early maturing pear cultivar ‘Pearl Pear’ as the study object and investigate the effects of cold stratification, the culture medium, and the seed coat on the germination and growth of early maturing pear seeds. Our results show that cold stratification (4 °C) treatment could significantly improve the germination rates of early maturing pear seeds. A total of 100 days of cold-temperature treatment in 4 °C and in vitro germination on White medium increased the germination rate to 84.54%. We also observed that seed coat removal improved the germination of early maturing pear seeds, with middle seed coat removal representing the optimal method, with a high germination rate and low contamination. The results of our study led to the establishment of an improved protocol for the germination of early maturing pear, which will greatly facilitate the breeding of new very early maturing pear cultivars

Nomogram Based on Dual-Layer Spectral Detector CTA Parameter for the Prediction of Infarct Core in Patients with Acute Ischemic Stroke

(1) Background: Acute ischemic stroke (AIS) is time-sensitive. The accurate identification of the infarct core and penumbra areas in AIS patients is an important basis for formulating treatment plans, and is the key to dual-layer spectral detector computed tomography angiography (DLCTA), a safer and more accurate diagnostic method for AIS that will replace computed tomography perfusion (CTP) in the future. Thus, this study aimed to investigate the value of DLCTA in differentiating infarct core from penumbra in patients with AIS to establish a nomogram combined with spectral computed tomography (CT) parameters for predicting the infarct core and performing multi-angle evaluation. (2) Methods: Data for 102 patients with AIS were retrospectively collected. All patients underwent DLCTA and CTP. The patients were divided into the non-infarct core group and the infarct core group, using CTP as the reference. Multivariate logistic regression analysis was used to screen predictors related to the infarct core and establish a nomogram model. The receiver operating characteristic (ROC) curve, the calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive efficacy, accuracy, and clinical practicability of the model, respectively. (3) Results: Multivariate logistic analysis identified three independent predictors: iodine density (OR: 0.022, 95% CI: 0.003–0.170, p p = 0.006), and triglycerides (OR: 0.255, 95% CI: 0.109–0.594, p = 0.002). The AUC–ROC of the nomogram was 0.913. Calibration was good. Decision curve analysis was clinically useful. (4) Conclusions: The spectral CT parameters, specifically iodine density values, effectively differentiate between the infarct core and penumbra areas in patients with AIS. The nomogram, based on iodine density values, showed strong predictive power, discrimination, and clinical utility to accurately predict infarct core in AIS patients

Semirational Approach for Ultrahigh Poly(3-hydroxybutyrate) Accumulation in <i>Escherichia coli</i> by Combining One-Step Library Construction and High-Throughput Screening

As a product of a multistep enzymatic reaction, accumulation of poly­(3-hydroxybutyrate) (PHB) in <i>Escherichia coli</i> (<i>E. coli</i>) can be achieved by overexpression of the PHB synthesis pathway from a native producer involving three genes <i>phbC</i>, <i>phbA</i>, and <i>phbB</i>. Pathway optimization by adjusting expression levels of the three genes can influence properties of the final product. Here, we reported a semirational approach for highly efficient PHB pathway optimization in <i>E. coli</i> based on a <i>phbCAB</i> operon cloned from the native producer <i>Ralstonia entropha</i> (<i>R. entropha</i>). Rationally designed ribosomal binding site (RBS) libraries with defined strengths for each of the three genes were constructed based on high or low copy number plasmids in a one-pot reaction by an oligo-linker mediated assembly (OLMA) method. Strains with desired properties were evaluated and selected by three different methodologies, including visual selection, high-throughput screening, and detailed in-depth analysis. Applying this approach, strains accumulating 0%–92% PHB contents in cell dry weight (CDW) were achieved. PHB with various weight-average molecular weights (<i>M</i>_<i>w</i>) of 2.7–6.8 × 10⁶ were also efficiently produced in relatively high contents. These results suggest that the semirational approach combining library design, construction, and proper screening is an efficient way to optimize PHB and other multienzyme pathways

The occurrence and risk factors associated with post‐traumatic stress disorder among discharged COVID‐19 patients in Tianjin, China

Background Post‐traumatic stress disorder (PTSD) is a serious mental health condition that is triggered by a terrifying event. We aimed to investigate the occurrence and risk factors of PTSD among discharged COVID‐19 patients. Methods This study included 144 discharged COVID‐19 patients. PTSD was assessed by using validated cut‐offs of the impact of event scale‐revised (IES‐R, score ≥25). All patients completed a detailed questionnaire survey, and clinical parameters were routinely measured in the hospital. Binary logistic regression models were applied to identify factors associated with PTSD. Results Of the 144 participants with laboratory‐confirmed COVID‐19, the occurrence of PTSD was 16.0%. In multivariable analyses, age above 40 years (adjusted OR [95% CI], 5.19 [2.17–12.32]), female sex (adjusted OR [95% CI], 7.82 [3.18–18.21]), current smoker (adjusted OR [95% CI], 6.72 [3.23–15.26]), and ≥3 involved pulmonary lobes (adjusted OR [95% CI], 5.76 [1.19–15.71]) were significantly associated with a higher risk of PTSD. Conversely, history of hypertension and serum hemoglobin levels were significantly associated with a lower risk of PTSD with adjusted ORs (95% CI) of 0.37 (0.12–0.87) and 0.91 (0.82–0.96), respectively. Conclusion Old age, gender (being female), current smoking, bacterial pneumonia, and ≥3 involved pulmonary lobes were associated with an increased occurrence of PTSD among discharged COVID‐19 patients

Electroacupuncture Alleviates Anxiety-Like Behaviors Induced by Chronic Neuropathic Pain via Regulating Different Dopamine Receptors of the Basolateral Amygdala

Chronic pain, such as neuropathic pain, causes anxiety and other negative emotions, which aggravates the pain sensation and increases the risk of chronic pain over time. Dopamine receptor D1 (DRD1) and dopamine receptor D2 (DRD2) in the basolateral amygdala (BLA) have been implicated in mediating anxiety-related behaviors, but their potential roles in the BLA in neuropathic pain-induced anxiety have not been examined. Electroacupuncture (EA) is commonly used to treat chronic pain and emotional disorders, but it is still unclear whether EA plays a role in analgesia and anxiety relief through DRD1 and DRD2 in the BLA. Here, we used western blotting to examine the expression of DRD1 and DRD2 and pharmacological regulation combined with behavioral testing to detect anxiety-like behaviors. We observed that injection of the DRD1 antagonist SCH23390 or the DRD2 agonist quinpirole into the BLA contributed to anxiety-like behaviors in naive mice. EA also activated DRD1 or inhibited DRD2 in the BLA to alleviate anxiety-like behaviors. To further demonstrate the role of DRD1 and DRD2 in the BLA in spared nerve injury (SNI) model-induced anxiety-like behaviors, we injected the DRD1 agonist SKF38393 or the DRD2 antagonist sulpiride into the BLA. We found that both activation of DRD1 and inhibition of DRD2 could alleviate SNI-induced anxiety-like behaviors, and EA had a similar effect of alleviating anxiety. Additionally, neither DRD1 nor DRD2 in the BLA affected SNI-induced mechanical allodynia, but EA did. Overall, our work provides new insights into the mechanisms of neuropathic pain-induced anxiety and a possible explanation for the effect of EA treatment on anxiety caused by chronic pain