Flow structure transition in thermal vibrational convection

This study investigates the effect of vibration on the flow structure transitions in thermal vibrational convection (TVC) systems, which occur when a fluid layer with a temperature gradient is excited by vibration. Direct numerical simulations of TVC in a two-dimensional enclosed square box were performed over a range of dimensionless vibration amplitudes $0.001 \le a \le 0.3$ and angular frequencies $10^{2} \le \omega \le 10^{7}$, with a fixed Prandtl number of 4.38. The flow visualisation shows the transition behaviour of flow structure upon the varying frequency, characterising three distinct regimes, which are the periodic-circulation regime, columnar regime and columnar-broken regime. Different statistical properties are distinguished from the temperature and velocity fluctuations at the boundary layer and mid-height. Upon transition into the columnar regime, columnar thermal coherent structures are formed, in contrast to the periodic oscillating circulation. These columns are contributed by merging of thermal plumes near the boundary layer, and the resultant thermal updrafts remain at almost fixed lateral position, leading to a decrease in fluctuations. We further find that the critical point of this transition can be described nicely by the vibrational Rayleigh number $Ra_\mathrm{vib}$. As the frequency continues to increase, entering the so-called columnar-broken regime, the columnar structures are broken, and eventually the flow state becomes a large-scale circulation, characterised by a sudden increase in fluctuations. Finally, a phase diagram is constructed to summarise the flow structure transition over a wide range of vibration amplitude and frequency parameters.Comment: 14 pages, 9 figure

Global attractive set of neural networks with neutral item

This paper investigates the global attractive set of neural networks with neutral item. To better deal with the neutral terms, different types of activation functions are considered. Based on matrix measures, inequality techniques, and Lyapunov theory, three new types of Lyapunov functions are designed to find the global attractive set of the system. We give out a simulation example to verify the validity of theory results. The result is very inclusive, whether the system has equilibrium or not. As long as the system is stable, we can find its global attractive set

Comparison between emerging adults and adults in terms of contamination fear, post-COVID-19 PTSD and psychiatric comorbidity

The present study compared Chinese emerging adults and adults regarding the association between contamination fear, posttraumatic stress disorder post-COVID-19 and psychiatric comorbidity after controlling for demographic and trauma exposure variables. 1089 Chinese civilians (M = 382; F = 707) with a mean age of 26 years (M = 26.36, SD = 8.58) were recruited from different provinces in China via an online survey posted on mainstream Chinese social networking platforms. They completed a demographic page with questions on trauma exposure, the Vancouver Obsessional Compulsive Inventory, the Posttraumatic Stress Disorder Checklist for DSM-5 and the General Health Questionnaire-28. Results showed that 12.7%, 68.7% and 18.6% met criteria for full, partial and no PTSD, respectively. Emerging adults reported significantly lower levels of symptoms of re-experiencing, avoidance, somatic problems, anxiety and fear of contamination than adults. In both emerging adults and adults, contamination fear was correlated with PTSD and psychiatric comorbidity. High educational attainment was significantly correlated with psychiatric comorbidity in emerging adults, but with PTSD in adults. Length of quarantine was correlated with psychiatric comorbidity only in adults. In conclusion, both emerging adults and adults developed varying levels of contamination fear, posttraumatic stress and general psychological symptoms following the outbreak of COVID-19. Emerging adults were more resilient than adults in coping with distress

Autophagy in the HTR-8/SVneo Cell Oxidative Stress Model Is Associated with the NLRP1 Inflammasome

We investigated whether there was activation of NLRP1 inflammasomes and excessive autophagy in oxidative stress damage. And we further demonstrate whether there is a cascade relationship between the activation of NLRP1 inflammasomes and the phenomenon of excessive autophagy. To observe the expression level of the NLRP1 inflammasome group in the pathological process of trophoblast cell oxidative stress, western blot, immunofluorescence, and qRT-PCR were performed. Autophagy in trophoblast cells after the action of H2O2 was detected by using normal trophoblast cells’ NLRP1-specific activator (MDP) as a positive control. The presence of excessive autophagy was determined by comparing it with the autophagy-related proteins in normal trophoblast cells. Through siRNA-NLRP1, we investigated the role of oxidative stress and the NLRP1 inflammasome in autophagy in cells. 100 μmol MDP for 24 hours can be used as the optimal concentration of the NLRP1 activator. In human placental trophoblast oxidative stress, the model group significantly increased the expression level of inflammasome IL-1β, CASP1, and NLRP1, compared with the control group NLRP3, and LC3-II, Beclin-1, ATG5, ATG7, and p62 overactivated the autophagy ability of cells. After the activation of NLRP1, the expression of these inflammasomes increased, accompanied by the decrease in autophagy. After the expression of NLRP1 was silenced by RNAi, the expression of inflammasome IL-1β, CASP1, and NLRP3 was also decreased. Still, the autophagy level was increased, which was manifested by the high expression of LC3-II, Beclin-1, ATG5, and ATG7 and the decrease in p62. Trophoblast cells showed the expression of NLRP1 protein and excessive autophagy under oxidative stress. Simultaneously, the NLRP1 inflammasome of trophoblast cells in the state of oxidative stress was correlated with autophagy. Inflammasome activation and autophagy were shown to be linked and to influence each other mutually. These may also provide new therapeutic targets in a pathological pregnancy

Incorporating uric acid into the CHA2DS2-VASc score improves the prediction of new-onset atrial fibrillation in patients with acute myocardial infarction

Abstract Background New-onset atrial fibrillation (NOAF) is a common cardiac arrhythmia observed in patients with acute myocardial infarction (AMI) and is associated with worse outcomes. While uric acid has been proposed as a potential biomarker for predicting atrial fibrillation, its association with NOAF in patients with AMI and its incremental discriminative ability when added to the CHA2DS2-VASc score are not well established. Methods We conducted a retrospective analysis of 1000 consecutive patients with AMI without a history of atrial fibrillation between January 2018 and December 2020. Continuous electrocardiographic monitoring was performed during the patients’ hospital stay to detect NOAF. We assessed the predictive ability of the different scoring models using receiver operating characteristic (ROC) curves. In addition, we employed the area under the curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI) analyses to assess the incremental discriminative ability of uric acid when added to the CHA2DS2-VASc score. Results Ninety-three patients (9.3%) developed NOAF during hospitalisation. In multivariate regression analyses, the adjusted odds ratio (OR) for NOAF was 1.439 per one standard deviation increase in uric acid level (95% confidence intervals (CI):1.182–1.753, p < 0.001). The ROC curve analysis revealed that the AUC for uric acid was 0.667 (95% CI:0.601–0.719), while the AUC for the CHA2DS2-VASc score was 0.678 (95% CI:0.623–0.734). After integrating the uric acid variable into the CHA2DS2-VASc score, the combined score yielded an improved AUC of 0.737 (95% CI:0.709–0.764, p = 0.009). Furthermore, there was a significant improvement in both IDI and NRI, indicating an incremental improvement in discriminative ability (IDI = 0.041, p < 0.001; NRI = 0.627, p < 0.001). Conclusion Our study suggests that uric acid level is an independent risk factor for the development of NOAF after AMI. Furthermore, the incorporation of uric acid into the CHA2DS2-VASc score significantly improves the discriminative ability of the score in identifying patients at high risk for NOAF

Artemisinin ameliorated proteinuria in rats with adriamycin-induced nephropathy through regulating nephrin and podocin expressions

AbstractObjectiveTo investigate the effects of artemisinin against proteinuria and glomerular filtration barrier damage in rats with adriamycin-induced nephropathy, and the potential mechanism underpinned the action.MethodsForty adriamycin rats were randomly divided into two groups with the ratio of 1 : 3 the small-number group served as control group (n= 10), and the rats in the large-number group were treated with adriamycin to induce nephropathy; then they were further randomly assigned into 3 subgroups: benazepril group (n=10), artemisinin group (n=10), and adriamycin group (n=10). The benazepril group and artemisinin group were treated with benazepril suspl (5.0 mg/kg daily) and artemisinin suspl (150 mg/kg daily) respectively after being modeled; those in the control group and adriamycin group were intragastrically administered an equivalent volume of distilled water every day. The treatment after model establishment lasted for a total of 4 weeks. The 24 h uric protein, blood biochemicals, renal pathological changes, renal ultrastrutural changes, Nephrin and Podocin proteins and gene expressions were measured by Coomassie brilliant blue assay, completely automatic biochemical analyzer, light microscope, electron microscopy, Western blot and reverse transcription polymerase chain reaction, respectively.ResultsThe rats in adriamycin group showed a significant increase in 24 h uric protein excretion, serum total cholesterol (TC), triglyceride (TG), blood urea nitrogen (BUN), serum creatinine (Scr) and decrease in albumin (Alb) (P<0.05 or P<0.01). Compared with adriamycin group, artemisinin could reduce uric protein excretion, decrease the serum TC, TG elevation, increase the serum Alb level, up-regulate the expressions of Nephrin and Podocin (P<0.05 or P<0.01), but no statistical significance effects on the levels of BUN, Scr in artemisinin group (P>0.05). The renal pathological and ultrastrutural observation indicate that artemisinin could attenuate the severity of foot process effacement and fusion in the nephropathic rats.ConclusionArtemisinin might have an effect on the nephropathy in rats caused by adriamycin, which may be at least partly correlated with attenuation of the severity of foot process effacement and fusion, up-regulation of the expressions of Nephrin and Podocin in the glomeruli in the rats

Metabolic Mechanism of Plant Defense against Rice Blast Induced by Probenazole

The probenazole fungicide is used for controlling rice blast (Magnaporthe grisea) primarily by inducing disease resistance of the plant. To investigate the mechanism of induced plant defense, rice seedlings were treated with probenazole at 15 days post emergence, and non-treated plants were used for the control. The plants were infected with M. grisea 5 days after chemical treatment and incubated in a greenhouse. After 7 days, rice seedlings were sampled. The metabolome of rice seedlings was chemically extracted and analyzed using gas chromatography and mass spectrum (GC-MS). The GC-MS data were processed using analysis of variance (ANOVA), principal component analysis (PCA) and metabolic pathway elucidation. Results showed that probenazole application significantly affected the metabolic profile of rice seedlings, and the effect was proportionally leveraged with the increase of probenazole concentration. Probenazole resulted in a change of 54 metabolites. Salicylic acid, γ-aminobutyrate, shikimate and several other primary metabolites related to plant resistance were significantly up-regulated and some metabolites such as phenylalanine, valine and proline were down-regulated in probenazole-treated seedlings. These results revealed a metabolic pathway of rice seedlings induced by probenazole treatment regarding the resistance to M. grisea infection

CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction

This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a Closed-loop Transcriptionbetween a multi-class, multi-dimensional data distribution and a Linear discriminative representation (CTRL) in the feature space that consists of multiple independent multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as a two-player minimax game between the encoder and decoderfor the learned representation. A natural utility function for this game is the so-called rate reduction, a simple information-theoretic measure for distances between mixtures of subspace-like Gaussians in the feature space. Our formulation draws inspiration from closed-loop error feedback from control systems and avoids expensive evaluating and minimizing of approximated distances between arbitrary distributions in either the data space or the feature space. To a large extent, this new formulation unifies the concepts and benefits of Auto-Encoding and GAN and naturally extends them to the settings of learning a both discriminative and generative representation for multi-class and multi-dimensional real-world data. Our extensive experiments on many benchmark imagery datasets demonstrate tremendous potential of this new closed-loop formulation: under fair comparison, visual quality of the learned decoder and classification performance of the encoder is competitive and arguably better than existing methods based on GAN, VAE, or a combination of both. Unlike existing generative models, the so-learned features of the multiple classes are structured instead of hidden: different classes are explicitly mapped onto corresponding independent principal subspaces in the feature space, and diverse visual attributes within each class are modeled by the independent principal components within each subspace