Visual Investigation on Effect of Structural Parameters and Operation Condition of Two-phase Ejector

As an important component in transcritical CO2 refrigeration cycle, complex flow in ejector have not been clearly elucidated. In this paper, CO2 flow in two-phase rectangle ejector was investigated experimentally by visualization measurement. The phase transition and the relaxation phenomena in the ejector were observed. By analyze the picture and the data collected from this experiment, we study the relationship between efficiency of ejector and the phase transition position in the ejector. Firstly, the microstructure of the flow pattern in the ejector was captured by a high speed digital video camera with a microscope to analyze the mixing process in mixing chamber. It was found that there were two stages with different characteristics in mixing process ,which were named fluid mixing section and fluid equilibrium section. When fluid get through mixing channel, the ejector realize the majority functions of entrainment in the first stage, and the ejector also homogenize the velocity of primary fluid and secondary flow by the way of flow core expand to almost all the channel in the second stage. Secondly, based on the comparison of pictures collected from different ejectors under different operating conditions, we found that phase transition position and the form of phase transition was mainly depended on the entrance condition of motive nozzle. For an ejector that keeps the suction nozzle under the same operation condition, when the phase transition point trend to exit of motive nozzle, in mixing channel ,motive flow will occupy more space meanwhile the relaxation phenomena occurred in longer region. It was worth mentioning that the phase transition point will change with different operation condition. But there exist only one best position where the ejector contributes to best efficiency. So, it is of great significance to treat phase transition point as an important sign which was easy to be recognized. Visualization research of ejector will be an important reference for theoretical study of flow pattern in the ejector. It also can provide some date to validate the results from the numerical calculation. The visualization study of ejector will also be the basis of further learn of shock waves and delayed phase transition in the ejector

A Bott periodicity theorem for ℓp\ell^p-spaces and the coarse Novikov conjecture at infinity

We formulate and prove a Bott periodicity theorem for an $\ell^p$-space ($1\leq p<\infty$). For a proper metric space $X$ with bounded geometry, we introduce a version of $K$-homology at infinity, denoted by $K_*^{\infty}(X)$, and the Roe algebra at infinity, denoted by $C^*_{\infty}(X)$. Then the coarse assembly map descents to a map from $\lim_{d\to\infty}K_*^{\infty}(P_d(X))$ to $K_*(C^*_{\infty}(X))$, called the coarse assembly map at infinity. We show that to prove the coarse Novikov conjecture, it suffices to prove the coarse assembly map at infinity is an injection. As a result, we show that the coarse Novikov conjecture holds for any metric space with bounded geometry which admits a fibred coarse embedding into an $\ell^p$-space. These include all box spaces of a residually finite hyperbolic group and a large class of warped cones of a compact space with an action by a hyperbolic group.Comment: 55 page

Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation

Few-shot Medical Image Segmentation (FSMIS) is a more promising solution for medical image segmentation tasks where high-quality annotations are naturally scarce. However, current mainstream methods primarily focus on extracting holistic representations from support images with large intra-class variations in appearance and background, and encounter difficulties in adapting to query images. In this work, we present an approach to extract multiple representative sub-regions from a given support medical image, enabling fine-grained selection over the generated image regions. Specifically, the foreground of the support image is decomposed into distinct regions, which are subsequently used to derive region-level representations via a designed Regional Prototypical Learning (RPL) module. We then introduce a novel Prototypical Representation Debiasing (PRD) module based on a two-way elimination mechanism which suppresses the disturbance of regional representations by a self-support, Multi-direction Self-debiasing (MS) block, and a support-query, Interactive Debiasing (ID) block. Finally, an Assembled Prediction (AP) module is devised to balance and integrate predictions of multiple prototypical representations learned using stacked PRD modules. Results obtained through extensive experiments on three publicly accessible medical imaging datasets demonstrate consistent improvements over the leading FSMIS methods. The source code is available at https://github.com/YazhouZhu19/PAMI

Research progress of quercetin in cardiovascular disease

Quercetin is one of the most common flavonoids. More and more studies have found that quercetin has great potential utilization value in cardiovascular diseases (CVD), such as antioxidant, antiplatelet aggregation, antibacterial, cholesterol lowering, endothelial cell protection, etc. However, the medicinal value of quercetin is mostly limited to animal models and preclinical studies. Due to the complexity of the human body and functional structure compared to animals, more research is needed to explore whether quercetin has the same mechanism of action and pharmacological value as animal experiments. In order to systematically understand the clinical application value of quercetin, this article reviews the research progress of quercetin in CVD, including preclinical and clinical studies. We will focus on the relationship between quercetin and common CVD, such as atherosclerosis, myocardial infarction, ischemia reperfusion injury, heart failure, hypertension and arrhythmia, etc. By elaborating on the pathophysiological mechanism and clinical application research progress of quercetin's protective effect on CVD, data support is provided for the transformation of quercetin from laboratory to clinical application

Investigating and Mitigating the Side Effects of Noisy Views in Multi-view Clustering in Practical Scenarios

Multi-view clustering (MvC) aims at exploring category structures among multi-view data without label supervision. Multiple views provide more information than single views and thus existing MvC methods can achieve satisfactory performance. However, their performance might seriously degenerate when the views are noisy in practical scenarios. In this paper, we first formally investigate the drawback of noisy views and then propose a theoretically grounded deep MvC method (namely MvCAN) to address this issue. Specifically, we propose a novel MvC objective that enables un-shared parameters and inconsistent clustering predictions across multiple views to reduce the side effects of noisy views. Furthermore, a non-parametric iterative process is designed to generate a robust learning target for mining multiple views' useful information. Theoretical analysis reveals that MvCAN works by achieving the multi-view consistency, complementarity, and noise robustness. Finally, experiments on extensive public datasets demonstrate that MvCAN outperforms state-of-the-art methods and is robust against the existence of noisy views

Exploring EEG Features in Cross-Subject Emotion Recognition

Recognizing cross-subject emotions based on brain imaging data, e.g., EEG, has always been difficult due to the poor generalizability of features across subjects. Thus, systematically exploring the ability of different EEG features to identify emotional information across subjects is crucial. Prior related work has explored this question based only on one or two kinds of features, and different findings and conclusions have been presented. In this work, we aim at a more comprehensive investigation on this question with a wider range of feature types, including 18 kinds of linear and non-linear EEG features. The effectiveness of these features was examined on two publicly accessible datasets, namely, the dataset for emotion analysis using physiological signals (DEAP) and the SJTU emotion EEG dataset (SEED). We adopted the support vector machine (SVM) approach and the "leave-one-subject-out" verification strategy to evaluate recognition performance. Using automatic feature selection methods, the highest mean recognition accuracy of 59.06% (AUC = 0.605) on the DEAP dataset and of 83.33% (AUC = 0.904) on the SEED dataset were reached. Furthermore, using manually operated feature selection on the SEED dataset, we explored the importance of different EEG features in cross-subject emotion recognition from multiple perspectives, including different channels, brain regions, rhythms, and feature types. For example, we found that the Hjorth parameter of mobility in the beta rhythm achieved the best mean recognition accuracy compared to the other features. Through a pilot correlation analysis, we further examined the highly correlated features, for a better understanding of the implications hidden in those features that allow for differentiating cross-subject emotions. Various remarkable observations have been made. The results of this paper validate the possibility of exploring robust EEG features in cross-subject emotion recognition

Glycolysis mediates neuron specific histone acetylation in valproic acid-induced human excitatory neuron differentiation

Pregnancy exposure of valproic acid (VPA) is widely adopted as a model of environmental factor induced autism spectrum disorder (ASD). Increase of excitatory/inhibitory synaptic transmission ratio has been proposed as the mechanism of VPA induced ASD. How this happened, particularly at the level of excitatory neuron differentiation in human neural progenitor cells (NPCs) remains largely unclear. Here, we report that VPA exposure remarkably inhibited human NPC proliferation and induced excitatory neuronal differentiation without affecting inhibitory neurons. Following VPA treatment, mitochondrial dysfunction was observed before neuronal differentiation, as showed by ultrastructural changes, respiratory complex activity, mitochondrial membrane potential and oxidation levels. Meanwhile, extracellular acidification assay revealed an elevation of glycolysis by VPA stimulation. Interestingly, inhibiting glycolysis by 2-deoxy-d-glucose-6-phosphate (2-DG) efficiently blocked the excitatory neuronal differentiation of human NPCs induced by VPA. Furthermore, 2-DG treatment significantly compromised the VPA-induced expression of H3ac and H3K9ac, and the VPA-induced binding of H3K9ac on the promoter of Ngn2 and Mash1, two key transcription factors of excitatory neuron fate determination. These data, for the first time, demonstrated that VPA biased excitatory neuron differentiation by glycolysis-mediated histone acetylation of neuron specific transcription factors

Microbicidal Phagocytosis of Nucleus Pulposus Cells Against Staphylococcus aureus via the TLR2/MAPKs Signaling Pathway

Intervertebral disc (IVD) is an immune-privileged organ that lacks immunocytes, such as macrophages or neutrophils; therefore, it is unclear how IVD immunological defense against bacterial infection occurs. Here, we demonstrated that nucleus pulposus cells (NPCs), the vital machinery for maintaining the homeostasis of IVD, exerted microbicidal activity against Staphylococcus aureus via induction of phagolysosome formation. Moreover, we found that the Toll-like receptor 2 (TLR2)/mitogen-activated protein kinases (MAPKs) signaling pathway is critical for bacterial phagocytosis and phagolysosome formation of NPCs. These findings demonstrated for the first time that NPCs could function as non-professional phagocytes against S. aureus infection, thereby enhancing antimicrobial defense against bacterial infections in IVDs