Transcriptional up-regulation of relaxin-3 by Nur77 attenuates β-adrenergic agonist-induced apoptosis in cardiomyocytes.

The relaxin family peptides have been shown to exert several beneficial effects on the heart, including anti-apoptosis, anti-fibrosis, and anti-hypertrophy activity. Understanding their regulation might provide new opportunities for therapeutic interventions, but the molecular mechanism(s) coordinating relaxin expression in the heart remain largely obscured. Previous work demonstrated a role for the orphan nuclear receptor Nur77 in regulating cardiomyocyte apoptosis. We therefore investigated Nur77 in the hopes of identifying novel relaxin regulators. Quantitative real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) data indicated that ectopic expression of orphan nuclear receptor Nur77 markedly increased the expression of latexin-3 (RLN3), but not relaxin-1 (RLN1), in neonatal rat ventricular cardiomyocytes (NRVMs). Furthermore, we found that the -adrenergic agonist isoproterenol (ISO) markedly stimulated RLN3 expression, and this stimulation was significantly attenuated in Nur77 knockdown cardiomyocytes and Nur77 knockout hearts. We showed that Nur77 significantly increased RLN3 promoter activity via specific binding to the RLN3 promoter, as demonstrated by electrophoretic mobility shift assay (EMSA) and chromatin immuno-precipitation (ChIP) assays. Furthermore, we found that Nur77 overexpression potently inhibited ISO-induced cardiomyocyte apoptosis, whereas this protective effect was significantly attenuated in RLN3 knockdown cardiomyocytes, suggesting that Nur77-induced RLN3 expression is an important mediator for the suppression of cardiomyocyte apoptosis. These findings show that Nur77 regulates RLN3 expression, therefore suppressing apoptosis in the heart, and suggest that activation of Nur77 may represent a useful therapeutic strategy for inhibition of cardiac fibrosis and heart failure. © 2018 You et al

An Improved Deep Embedding Learning Method for Short Duration Speaker Verification

This paper presents an improved deep embedding learning method based on convolutional neural networks (CNN) for short-duration speaker verification (SV). Existing deep learning-based SV methods generally extract frontend embeddings from a feed-forward deep neural network, in which the long-term speaker characteristics are captured via a pooling operation over the input speech. The extracted embeddings are then scored via a backend model, such as Probabilistic Linear Discriminative Analysis (PLDA). Two improvements are proposed for frontend embedding learning based on the CNN structure: (1) Motivated by the WaveNet for speech synthesis, dilated filters are designed to achieve a tradeoff between computational efficiency and receptive-filter size; and (2) A novel cross-convolutional-layer pooling method is exploited to capture $1^{st}$-order statistics for modelling long-term speaker characteristics. Specifically, the activations of one convolutional layer are aggregated with the guidance of the feature maps from the successive layer. To evaluate the effectiveness of our proposed methods, extensive experiments are conducted on the modified female portion of NIST SRE 2010 evaluations, with conditions ranging from 10s-10s to 5s-4s. Excellent performance has been achieved on each evaluation condition, significantly outperforming existing SV systems using i-vector and d-vector embeddings

The Orbitofrontal Cortex Gray Matter Is Associated With the Interaction Between Insomnia and Depression

Insomnia and depression are highly comorbid symptoms in both primary insomnia (PI) and major depressive disorder (MDD). In the current study, we aimed at exploring both the homogeneous and heterogeneous brain structure alteration in PI and MDD patients. Sixty-five MDD patients and 67 matched PI patients were recruited and underwent a structural MRI scan. The subjects were sub-divided into four groups, namely MDD patients with higher or lower insomnia, and PI patients with higher or lower severe depression. A general linear model was employed to explore the changes in cortical thickness and volume as a result of depression or insomnia, and their interaction. In addition, partial correlation analysis was conducted to detect the clinical significance of the altered brain structural regions. A main effect of depression on cortical thickness was seen in the superior parietal lobe, middle cingulate cortex, and parahippocampal gyrus, while a main effect of insomnia on cortical thickness was found in the posterior cingulate cortex. Importantly, the interaction between depression and insomnia was associated with decreased gray matter volume in the right orbitofrontal cortex, i.e., patients with co-occurring depression and insomnia showed smaller brain volume in the right orbitofrontal cortex when compared to patients with lower insomnia/depression. These findings highlighted the role of the orbitofrontal cortex in the neuropathology of the comorbidity of insomnia and depression. Our findings provide new insights into the understanding of the brain mechanism underlying comorbidity of insomnia and depression

PIMT is a Novel and Potent Suppressor of Endothelial Activation

Proinflammatory agonists provoke the expression of cell surface adhesion molecules on endothelium in order to facilitate leukocyte infiltration into tissues. Rigorous control over this process is important to prevent unwanted inflammation and organ damage. Protein L-isoaspartyl O-methyltransferase (PIMT) converts isoaspartyl residues to conventional methylated forms in cells undergoing stress-induced protein damage. The purpose of this study was to determine the role of PIMT in vascular homeostasis. PIMT is abundantly expressed in mouse lung endothelium and PIMT deficiency in mice exacerbated pulmonary inflammation and vascular leakage to LPS(lipopolysaccharide). Furthermore, we found that PIMT inhibited LPS-induced toll-like receptor signaling through its interaction with TNF receptor-associated factor 6 (TRAF6) and its ability to methylate asparagine residues in the coiled-coil domain. This interaction was found to inhibit TRAF6 oligomerization and autoubiquitination, which prevented NF-κB transactivation and subsequent expression of endothelial adhesion molecules. Separately, PIMT also suppressed ICAM-1 expression by inhibiting its N-glycosylation, causing effects on protein stability that ultimately translated into reduced EC(endothelial cell)-leukocyte interactions. Our study has identified PIMT as a novel and potent suppressor of endothelial activation. Taken together, these findings suggest that therapeutic targeting of PIMT may be effective in limiting organ injury in inflammatory vascular diseases

Climate change : strategies for mitigation and adaptation

The sustainability of life on Earth is under increasing threat due to humaninduced climate change. This perilous change in the Earth's climate is caused by increases in carbon dioxide and other greenhouse gases in the atmosphere, primarily due to emissions associated with burning fossil fuels. Over the next two to three decades, the effects of climate change, such as heatwaves, wildfires, droughts, storms, and floods, are expected to worsen, posing greater risks to human health and global stability. These trends call for the implementation of mitigation and adaptation strategies. Pollution and environmental degradation exacerbate existing problems and make people and nature more susceptible to the effects of climate change. In this review, we examine the current state of global climate change from different perspectives. We summarize evidence of climate change in Earth’s spheres, discuss emission pathways and drivers of climate change, and analyze the impact of climate change on environmental and human health. We also explore strategies for climate change mitigation and adaptation and highlight key challenges for reversing and adapting to global climate change

Identifying potential biological processes and key targets in COVID-19-associated heart failure

Novel coronavirus pneumonia (COVID-19) is a new type of viral pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has spread rapidly and become a global pandemic. Heart failure (HF) is the ultimate period of the development of various cardiovascular diseases. There are several research have found that SARS-CoV-2 infection may induce cardiac complications including enhanced cardiac stress biomarkers and heart failure. Our research aims at identifying underlying biological processes and key targets in COVID-19-associated heart failure via bioinformatics analysis. A total of three heart failure datasets and three COVID-19 datasets were obtained using the Gene Expression Omnibus (GEO) database. Batch effects cross each sample were eliminated with surrogate variable analysis algorithm. Then, we identified key modules of COVID-19 datasets and heart failure datasets through weighted gene co-expression network analysis. HF-associated as well as COVID-19-associated key modules were intersected for determining the shared genes of COVID-19-associated heart failure. The pivotal genes associated with COVID-19-related heart failure were determined by intersecting the shared genes with the HF-associated hub genes selected through WGCNA. Furthermore, we conducted GO as well as KEGG enrichment analysis on shared genes of COVID-19-associated heart failure. Two COVID-19-associated key modules as well as three HF-associated key modules were determined. In addition, eleven shared genes for COVID-19-associated heart failure were determined. In conclusion, our work screened two critical genes, namely PYGM and BLM, which may be possible intervention targets for COVID-19-associated heart failure. According to functional enrichment results, the shared genes of COVID-19-associated heart failure showed high enrichment in starch and sucrose metabolism, homologous recombination, Fanconi anemia pathway, and insulin resistance indicate the probably biological processes linked to COVID-19-associated heart failure. These results provided further insights in possible interventional and therapeutic targets of COVID-19-associated heart failure

An Experimental Study on Pulsed Spray Cooling with Refrigerant R-404a in Laser Surgery

With a low boiling point (−45.5 °C at 1 atm) and high volatility, cryogen R-404a has the potential to replace current R-134a (−26.1 °C at 1 atm) for improved therapeutic outcome of dark skins in cutaneous laser treatment. This paper presents an experimental study on pulse spray cooling with cryogen R-404a including the spray characteristics and the resulting dynamic cooling of a solid surface. The spray system includes a special designed pressure nozzle (with the tube diameter less than 1 mm) that is connected to a fast response electric valve which can open or close within 5 ms. A high-speed video camera is used to obtain images of the spray pattern. The velocity and the diameter of the liquid droplets in spray are measured by the phase Doppler particle analyzer (PDPA). A thin film thermocouple of 2 μ in thickness is directly deposited on the epoxy resin substrate to monitor rapid drop of the surface temperature under the pulsed sprays. The Duhamel’s theorem is then solved to obtain the time-varying surface heat flux and heat transfer coefficient of the substrate surface. It is found that the large droplet size together with fairly high-speed in the early jet-like spray leads to highly efficient surface cooling

An Experimental Study on the Spray and Thermal Characteristics of R134a Two-Phase Flashing Spray

Flashing spray of volatile liquids is a common phenomenon observed in many industrial applications such as fuel injection of engines, accidental release of flammable and toxic pressure-liquefied gases, failure of a vessel or pipe in the form of a small hole in chemical industry, and cryogenic spray cooling in laser dermatology, etc. In flashing spray, the volatile liquid is depressurized rapidly at the exit of a nozzle (or a hole in a vessel) and becomes superheated. Such superheated liquid (in the form of either a jet or droplets) will lead to explosive atomization with fine droplet and a short spray distance. This paper presents an experimental investigation to the spray and thermal characteristics of flashing spray using cryogen R134a. A photographic study of the spray is firstly conducted to visualize the spray formation and the dynamic characteristics of the spray. Afterwards, the spray characteristics are measured by the phase Doppler Particle Analyzer (PDPA). The distributions of the diameter reveals the dramatic dynamic variation of the liquid droplets due to explosive atomization of large droplets in the region near the exit of nozzle, while the self-similar velocity profiles are fitted by two empirical correlations to describe the non-dimensional axial and radial velocities, respectively. The temperature field within the spray is measured by a small thermocouple. The temperature measurements provide detailed quantitative information of both radial and axial temperature distributions of droplets within the spray. These experimental results provide deep understanding into the whole characteristics of two-phase flashing spray of volatile liquids

Evaluation of Evaporation Models for Single Moving Droplet with a High Evaporation Rate

Evaporation of a moving liquid droplet in atmosphere is a common phenomenon with wide engineering applications. Theoretical models that consider mass, momentum, and energy transfer between the droplet and the surrounding gas have been well documented in the literature to predict the variation of the velocity, the size, and the temperature of a moving droplet. Carefully designed experiments of a single evaporating droplet subject to gas flow have also been conducted to provide the first hand data for model comparison. The objective of this paper is to evaluate existing evaporation models with high evaporation rate. A literature review is present firstly to examine the origins of various evaporation models, from classical Spalding diffusion model to those with sophisticated treatment of gas and liquid flows. Particular attention is paid to the treatment of surface blowing effect due to high evaporation rate. The evaporation models obtained from either experiments or theoretical analyses are summarized and carefully examined. The validity of these models is then evaluated by comparing the model predictions with three sets of existing data coming from careful experiments of single droplet evaporation in the literature. It is found that all of the models perform nearly identical for cases with a low evaporation rate, while significant deviations among the model predictions emerge when the evaporation rate is increased. Based on these comparisons, a simplified evaporation model is identified and recommended to cases with high evaporation rates. The comparison also reveals a lack of reliable experimental temperature data for droplet evaporation, particularly in the cases of high evaporation conditions