Citron binds to PSD-95 at glutamatergic synapses on inhibitory neurons in the hippocampus

Synaptic NMDA-type glutamate receptors are anchored to the second of three PDZ (PSD-95/Discs large/ZO-1) domains in the postsynaptic density (PSD) protein PSD-95. Here, we report that citron, a protein target for the activated form of the small GTP-binding protein Rho, preferentially binds the third PDZ domain of PSD-95. In GABAergic neurons from the hippocampus, citron forms a complex with PSD-95 and is concentrated at the postsynaptic side of glutamatergic synapses. Citron is expressed only at low levels in glutamatergic neurons in the hippocampus and is not detectable at synapses onto these neurons. In contrast to citron, p135 SynGAP, an abundant synaptic Ras GTPase-activating protein that can bind to all three PDZ domains of PSD-95, and Ca2+/calmodulin-dependent protein kinase II (CaM kinase II) are concentrated postsynaptically at glutamatergic synapses on glutamatergic neurons. CaM kinase II is not expressed and p135 SynGAP is expressed in less than half of hippocampal GABAergic neurons. Segregation of citron into inhibitory neurons does not occur in other brain regions. For example, citron is expressed at high levels in most thalamic neurons, which are primarily glutamatergic and contain CaM kinase II. In several other brain regions, citron is present in a subset of neurons that can be either GABAergic or glutamatergic and can sometimes express CaM kinase II. Thus, in the hippocampus, signal transduction complexes associated with postsynaptic NMDA receptors are different in glutamatergic and GABAergic neurons and are specialized in a way that is specific to the hippocampus

Numerical Simulation of Oscillating Multiphase Heat Transfer in Parallel plates using Pseudopotential Multiple-Relaxation-Time Lattice Boltzmann Method

Multiphase flows frequently occur in many important engineering and scientific applications, but modeling of such flows is a rather challenging task due to complex interfacial dynamics between different phases, let alone if the flow is oscillating in the porous media. Using humid air as the working fluid in the thermoacoustic refrigerator is one of the research focus to improve the thermoacoustic performance, but the corresponding effect is the condensation of humid air in the thermal stack. Due to the small sized spacing of thermal stack and the need to explore the detailed condensation process in oscillating flow, a mesoscale numerical approach need to be developed. Over the decades, several types of Lattice Boltzmann (LB) models for multiphase flows have been developed under different physical pictures, for example the color-gradient model, the Shan-Chen model, the nonideal pressure tensor model and the HSD model. In the current study, a pseudopotential Multiple-Relaxation-Time (MRT) LBM simulation was utilized to simulate the incompressible oscillating flow and condensation in parallel plates. In the initial stage of condensation, the oscillating flow benefits to accumulate the saturated vapor at the exit regions, and the velocity vector of saturated vapor clearly showed the flow over the droplets. It was also concluded that if the condensate can be removed out from the parallel plates, the oscillating flow and condensation will continuously feed the cold surface to form more water droplets. The effect of wettability to the condensation was discussed, and it turned out that by increasing the wettability, the saturated water vapor was easier to condense on the cold walls, and the distance between each pair of droplets was also strongly affected by the wettability

ADVANCED REPRESENTATION LEARNING STRATEGIES FOR BIG DATA ANALYSIS

With the fast technological advancement in data storage and machine learning, big data analytics has become a core component of various practical applications ranging from industrial automation to medical diagnosis and from cyber-security to space exploration. Recent studies show that every day, more than 1.8 billion photos/images are posted on social media, and 720 thousand hours of videos are uploaded to YouTube. Thus, to handle this large amount of visual data efficiently, image/video classification, object detection/recognition, and segmentation tasks have gathered a lot of attention since the decade. Consequently, the researchers in this domain has proposed various feature extraction, feature learning, and feature encoding algorithms for improving the generalization performance of the aforesaid tasks. For example, the generalization performance of the image classification models mainly depends on the choice of data representation. These models aim at building comprehensive representation learning (RL) strategies to encode the relationship among the input and output attributes from the raw big data. Existing RL strategies can be divided into three general categories: statistic approaches (e.g. probabilistic-based analysis, and correlation-based measures), unsupervised learning (e.g., autoencoders), and supervised learning (e.g., deep convolutional neural network (DCNN)). Among these categories, the unsupervised and supervised learning strategies using artificial neural networks (ANNs) have been widely adopted. In this direction, several auxiliary ideas have been proposed over the past decade, to improve the learning capability of the ANNs. For instance, Moore-Penrose (MP) inverse is exploited to refine the parameters (weights and biases) of a trained network. However, the existing MP inverse-based RL methods have an important limitation. The representations learned through the MP inverse-based strategies suffer from loosely-connected feature coding, resulting into a poor representation of the objects having lack of discriminative power. To address this issue, this dissertation proposes a set of eight novel MP inverse-based RL algorithms. The first part of this dissertation from Chapter 4 to Chapter 7 is dedicated to proposing novel width-growth models based on subnet neural network (SNN) for representation learning and image classification. In this part, a novel feature learning algorithm, coined Wi-HSNN is proposed, followed by an improved batch-by-batch learning algorithm, called OS-HSNN. Then, two novel SNNs are introduced to detect extreme outliers for one-class classification (OCC). Finally, a semi-supervised SNN, named SS-HSNN is introduced to extend the strategy from the supervised learning domain to the semi-supervised learning domain. The second part of this thesis, subsuming Chapter 8 and Chapter 9, focuses on improving the performance of the existing multilayer neural networks through harnessing the MP inverse. Here, a novel weight optimization strategy is proposed to improve the performance of multilayer extreme learning machines (ELMs), where the MP inverse is used to feedback the classification imprecision information from the output layer to the hidden layers. Then, a novel fast retraining framework is proposed to enhance the efficiency of transfer learning of DCNNs. The effectiveness of the proposed subnet- and retraining-based algorithms have been evaluated on several widely used image classification datasets, such as ImageNet and Places-365. Furthermore, we validated the performance of the proposed strategies in some extended domains, such as ship-target detection, food image classification, camera model identification and misinformation identification. The experimental results illustrate the superiority of the proposed algorithms

LBL: Logarithmic Barrier Loss Function for One-class Classification

One-class classification (OCC) aims to train a classifier only with the target class data and attracts great attention for its strong applicability in real-world application. Despite a lot of advances have been made in OCC, it still lacks the effective OCC loss functions for deep learning. In this paper, a novel logarithmic barrier function based OCC loss (LBL) that assigns large gradients to the margin samples and thus derives more compact hypersphere, is first proposed by approximating the OCC objective smoothly. But the optimization of LBL may be instability especially when samples lie on the boundary leading to the infinity loss. To address this issue, then, a unilateral relaxation Sigmoid function is introduced into LBL and a novel OCC loss named LBLSig is proposed. The LBLSig can be seen as the fusion of the mean square error (MSE) and the cross entropy (CE) and the optimization of LBLSig is smoother owing to the unilateral relaxation Sigmoid function. The effectiveness of the proposed LBL and LBLSig is experimentally demonstrated in comparisons with several state-of-the-art OCC algorithms on different network structures. The source code can be found at https://github.com/ML-HDU/LBL_LBLSig

Simulation of droplet impacting a square solid obstacle in microchannel with different wettability by using high density ratio pseudopotential multiplerelaxation- time (MRT) lattice Boltzmann Method (LBM)

In this paper, a pseudopotential high density ratio (DR) lattice Boltzmann Model was developed by incorporating multi-relaxation-time (MRT) collision matrix, large DR external force term, surface tension adjustment external force term and solid-liquid pseudopotential force. It was found that the improved model can precisely capture the two-phase interface at high DR. Besides, the effects of initial Reynolds number, Weber number, solid wall contact angle (CA), ratio of obstacle size to droplet diameter ( 1 χ ), ratio of channel width to droplet diameter ( 2 χ ) on the deformation and breakup of droplet when impacting on a square obstacle were investigated. The results showed that with the Reynolds number increasing, the droplet will fall along the obstacle and then spread along both sides of the obstacle. Besides, by increasing Weber number, the breakup of the liquid film will be delayed and the liquid film will be stretched to form an elongated ligament. With decreasing of the wettability of solid particle (CA→ 180°), the droplet will surround the obstacle and then detach from the obstacle. When 1 χ is greater than 0.5, the droplet will spread along both sides of the obstacle quickly; otherwise, the droplet will be ruptured earlier. Furthermore, when 2 χ decreases, the droplet will spread earlier and then fall along the wall more quickly; otherwise, the droplet will expand along both sides of the obstacle. Moreover, increasing the hydrophilicity of the microchannel, the droplet will impact the channel more rapidly and infiltrate the wall along the upstream and downstream simultaneously; on the contrary, the droplet will wet downstream only

Hybrid phase-change Lattice Boltzmann simulation of vapor condensation on vertical subcooled walls

Saturated vapor condensation on homogenous and heterogeneous subcooled walls is presented in this study by adopting a hybrid phase-change multiple-relaxation-time Lattice Boltzmann model. The effects of wall wettability on the condensation process, including droplets’ growth, coalescence and falling, and the influence of vapor flow to condensation are investigated. The results demonstrate that the heat fluxes around the triple-phase contact lines are higher than that in other cold areas in homogeneous subcooled walls, which actually indicates the fact that filmwise condensation is preventing the continuous condensation process. Furthermore, the dropwise condensation can be formed more easily on the heterogeneous surface with a mixed surface wettability. At last, the dynamic process of condensation of continuous vapor flow is also investigated by considering the homogenous and heterogeneous subcooled surfaces. The results show that the heterogeneous surface with mixed wettability doesn’t contribute to the formation, growth of droplets, when compared to the homogeneous surface. It is expected that this study can bring more attentions to simulate condensation using multiphase LBM for complex geometries in heat transfer community

Heat transfer performance of a device integrating thermosyphon with form-stable phase change materials

peer reviewedPhase change materials (PCM) are applied worldwide as a thermal energy storage technology to reduce energy demands in buildings and solve environmental contamination issues. Form-stable phase change materials (FSPCM), as one branch of PCMs, can be improved by embedding them with thermosyphon, resulting in a better thermal performance. In this paper, a novel thermoplastic elastomer-based FSPCM was developed and tested. A device integrating FSPCM with thermosyphon was created, and the heat transfer mechanism of the unit was studied. The numerical model was established, and experiments were conducted accordingly. The average relative error between the experimental data and the model predictions was <3 %. Furthermore, a parameter study was conducted to investigate the effects of several essential factors. As a result, the evaporator length and PCM thermal conductivity were found to significantly influence the unit's heat transfer rate and overall thermal performance. However, the impact of the latent heat of the FSPCM on the heat transfer rate was negligible, for which it takes 1170 s, 1230 s, and 1394 s to finish changing phase with 136 kJ/kg, 160 kJ/kg, and 200 kJ/kg, respectively. This paper provides insights on the performance of thermosyphon integrated form-stable phase change materials and discusses its relevance for thermal energy storage applications.11. Sustainable cities and communitie