Bis(2-methoxy­phenolato-κ2 O,O′)copper(II)

In the title compound, [Cu(C7H7O2)2], the asymmetric unit contains one and a half molecules with the central Cu(II) atoms situated on a general position and on a centre of inversion, respectively. Both Cu(II) atoms show a similar slightly distorted square-planar coordination, resulting from four O atoms of two 2-methoxyphenolate anions

Expression of soluble vascular endothelial growth factor receptor-1 and placental growth factor in fetal growth restriction cases and intervention effect of tetramethylpyrazine

AbstractObjectiveTo investigate the expression of soluble vascular endothelial growth factor receptor-1 (sFlt-1) and placental growth factor (PLGF) in the fetal growth restriction (FGR) cases and the intervention mechanism of tetramethylpyrazine.MethodsA total of 60 fetal growth restriction cases that admitted to our hospital were randomly divided into ligustrazine intervention group (group A) and nutritional support group (group B). A total of 50 healthy pregnant women were also enrolled as control group (group C). Expression level of maternal serum sFlt1, PLGF and fetal growth parameters including HC, AC, FL, BPD, EFW as well as placenta PLGF, sFlt-1 mRNA expression were recorded and compared among the three groups. A total of 15 SD rats were selected and were divided into three groups, TMP group, alcohol and tobacco group and blank control group. Three groups of rats were dissected on the twentieth day of gestation.ResultsExpression level of sFlt-1 and PLGF in group A was not significantly different from that of group C (P>0.05); but significant difference in SFlt1 and PLGF expression level was observed between group C and group B (P<0.05). Before treatment, HC, AC, FL, BPD and EFW of group A and group B were significant lower than those of group C, but after treatment, those parameters in group A were significantly improved (P<0.05). In the animal experiment there was no significant difference in sFlt-1 between treatment group and FGR group without treatment or control group (P>0.05). There was significant difference in PLGF between FGR group with treatment and FGR group without treatment or control group (P<0.01).ConclusionsPLGF level is decreased and sFlt-1 increased in patients suffered from fetal growth restriction, and FGR rats show increased sFlt-1 and decreased PLGF, thus they can be indicator of the fetal growth restriction. Ligustrazine can effectively improve sFlt-1, PLGF expression level in fetal growth restriction cases, which can be used as treatment for FGR

All-trans retinoic acid ameliorates glycemic control in diabetic mice via modulating pancreatic islet production of vascular endothelial growth factor-A.

Patients with type 1 diabetes mellitus are associated with impairment in vitamin A metabolism. This study evaluated whether treatment with retinoic acid, the biologically active metabolite of vitamin A, can ameliorate diabetes. All-trans retinoic acid (atRA) was used to treat streptozotocin (STZ)-induced diabetic mice which revealed atRA administration ameliorated blood glucose levels of diabetic mice. This hyperglycemic amelioration was accompanied by an increase in the amount of β cells co-expressed Pdx1 and insulin and by restoration of the vascular laminin expression. The atRA-induced production of vascular endothelial growth factor-A from the pancreatic islets was possibly the key factor that mediated the restoration of islet vascularity and recovery of β-cell mass. Furthermore, the combination of islet transplantation and atRA administration significantly rescued hyperglycemia in diabetic mice. These findings suggest that vitamin A derivatives can potentially be used as a supplementary treatment to improve diabetes management and glycemic control

Phase transformation-induced improvement in hardness and high-temperature wear resistance of plasma-sprayed and remelted NiCrBSi/WC coatings

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The remelting method is introduced to improve the properties of the as-sprayed NiCrBSi coatings. In this work, tungsten carbide (WC) was selected as reinforcement and the as-sprayed and remelted NiCrBSi/WC composite coatings were investigated by X-ray diffraction, scanning electron microscopy, hardness test and tribology test. After spraying, WC particles are evenly distributed in the coating. The remelting process induced the decarburizing reaction of WC, resulting in the formation of dispersed W2 C. The dispersed W2 C particles play an important role in the dispersion strengthening. Meanwhile, the pores and lamellar structures are eliminated in the remelted NiCrBSi/WC composite coating. Due to these two advantages, the hardness and the high-temperature wear resistance of the remelted NiCrBSi/WC composite coating are significantly improved compared with those with an as-sprayed NiCrBSi coating; the as-sprayed NiCrBSi coating, as-sprayed NiCrBSi/WC composite coating and remelted NiCrBSi/WC composite coating have average hardness of 673.82 HV, 785.14 HV, 1061.23 HV, and their friction coefficients are 0.3418, 0.3261, 0.2431, respectively. The wear volume of the remelted NiCrBSi/WC composite coating is only one-third of that of the as-sprayed NiCrBSi coating

Automatic Diagnosis of Late-Life Depression by 3D Convolutional Neural Networks and Cross-Sample Entropy Analysis From Resting-State fMRI

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy \u3e 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD

Predicting Suicidality in Late-Life Depression by 3D Convolutional Neural Network and Cross-Sample Entropy Analysis of Resting-State fMRI

BACKGROUND: Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD). METHODS: We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation. RESULTS: We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide. CONCLUSION: Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality

Insights into the Ecological Roles and Evolution of Methyl-Coenzyme M Reductase-Containing Hot Spring Archaea

Several recent studies have shown the presence of genes for the key enzyme associated with archaeal methane/alkane metabolism, methyl-coenzyme M reductase (Mcr), in metagenome-assembled genomes (MAGs) divergent to existing archaeal lineages. Here, we study the mcr-containing archaeal MAGs from several hot springs, which reveal further expansion in the diversity of archaeal organisms performing methane/alkane metabolism. Significantly, an MAG basal to organisms from the phylum Thaumarchaeota that contains mcr genes, but not those for ammonia oxidation or aerobic metabolism, is identified. Together, our phylogenetic analyses and ancestral state reconstructions suggest a mostly vertical evolution of mcrABG genes among methanogens and methanotrophs, along with frequent horizontal gene transfer of mcr genes between alkanotrophs. Analysis of all mcr-containing archaeal MAGs/genomes suggests a hydrothermal origin for these microorganisms based on optimal growth temperature predictions. These results also suggest methane/alkane oxidation or methanogenesis at high temperature likely existed in a common archaeal ancestor

Almost sure state estimation with H2-type performance constraints for nonlinear hybrid stochastic systems

This paper is concerned with the problem of almost sure state estimation for general nonlinear hybrid stochastic systems whose coefficients only satisfy local Lipschitz conditions. By utilizing the stopping time method combined with martingale inequalities, a theoretical framework is established for analyzing the so-called almost surely asymptotic stability of the addressed system. Within such a theoretical framework, some sufficient conditions are derived under which the estimation dynamics is almost sure asymptotically stable and the upper bound of estimation error is also determined. Furthermore, a suboptimal state estimator is obtained by solving an optimization problem in the H2 sense. According to the obtained results, for a class of special nonlinear hybrid stochastic systems, the corresponding conditions reduce to a set of matrix inequalities for the purpose of easy implementation. Finally, two numerical simulation examples are used to demonstrate the effectiveness of the results derived.This work was supported in part by the National Natural Science Foundation of China under Grants 61134009 and 61329301, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany