Phosphorylation of Dentin Matrix Protein 1 and Phosphophoryn

Biomineralization, one of the most widespread processes in nature, uses polyanionic proteins to direct oriented crystal growth. In bone and dentin, this process is under precise control of the collagen template and the noncollagenous acidic phosphoproteins. These phosphoproteins function differently depending on their sizes and level of phosphorylation.The goal of this research is to investigate the in vitro phosphorylation as well as the phosphorylation in mammalian cells of two highly phosphorylated bone/dentin extracellular matrix proteins: dentin phosphophoryn (DPP) and dentin matrix protein 1 (DMP1). This data will be important to the general hypothesis, that the phosphorylation of non-collagenous proteins play a significant role in matrix mediated mineralization. Our data shows that the in vitro phosphorylation of DPP and DMP1 could be optimized by adjusting the phosphorylation reaction time, calcium concentration, and protein modification by assessing various forms (with or without the C or N terminal end). Following the in vitro phosphorylation, mass spectrometry analysis was used to identify the sites of phoshorylation. In addition, to identify the kinases involved in phosphorylating DMP1, cell lysates from cells that have (MC3T3) and do not have (NIH3T3) the ability to mineralize their matrix and were isolated and analyzed by zymogram. Casein kinase II catalytic subunit was identified in addition to potential novel kinases responsible for DMP1 phosphorylation.The second goal of this research is to assess if cells that have the ability to form a mineralized matrix will possess specialized kinases that can phosporylate these highly phosphorylated and acidic proteins. To achieve this goal we over-expressed and purified DMP1 from two cell types: 1) cells that have the ability to mineralize their matrix and 2) cells that do not possess the ability to mineralize their matrix. The purified proteins were then analyzed by SDS-PAGE and mass spectrometry to quantify and determine the sites of phoshorylation. This study has expanded our knowledge on the mechanisms involved in the phosphorylation of DPP and DMP1 and provided the parameters to start assessing the role of phosphorylation on tissue mineralization

Feature Extraction and Fusion Using Deep Convolutional Neural Networks for Face Detection

This paper proposes a method that uses feature fusion to represent images better for face detection after feature extraction by deep convolutional neural network (DCNN). First, with Clarifai net and VGG Net-D (16 layers), we learn features from data, respectively; then we fuse features extracted from the two nets. To obtain more compact feature representation and mitigate computation complexity, we reduce the dimension of the fused features by PCA. Finally, we conduct face classification by SVM classifier for binary classification. In particular, we exploit offset max-pooling to extract features with sliding window densely, which leads to better matches of faces and detection windows; thus the detection result is more accurate. Experimental results show that our method can detect faces with severe occlusion and large variations in pose and scale. In particular, our method achieves 89.24% recall rate on FDDB and 97.19% average precision on AFW

The psychometric properties of the quick inventory of depressive symptomatology-self-report (QIDS-SR) in patients with HBV-related liver disease

Background: Comorbid depression in Hepatitis B virus (HBV) is common. Developing accurate and time efficient tools to measure depressive symptoms in HBV is important for research and clinical practice in China. Aims: This study tested the psychometric properties of the Chinese version of the 16-item Quick Inventory of Depressive Symptomatology (QIDS-SR) in HBV patients. Methods: The study recruited 245 depressed patients with HBV and related liver disease. The severity of depressive symptoms was assessed with the Montgomery-Asberg Depression Rating Scale (MADRS) and the QIDS-SR. Results: Internal consistency (Cronbach’s alpha) was 0.796 for QIDS-SR. The QIDS-SR total score was significantly correlated with the MADRS total score (r=0.698, p. Conclusions: The QIDS-SR (Chinese version) has good psychometric properties in HBV patients and appears to be useful in assessing depression in clinical settings

The relationship between childhood trauma and Internet gaming disorder among college students: A structural equation model

open access journalBackground The aim of this study was to investigate the mechanisms of Internet gaming disorder (IGD) and the associated interaction effects of childhood trauma, depression and anxiety in college students. Methods Participants were enrolled full-time as freshmen at a University in the Hunan province, China. All participants reported their socio-demographic characteristics and undertook a standardized assessment on childhood trauma, anxiety, depression and IGD. The effect of childhood trauma on university students' internet gaming behaviour mediated by anxiety and depression was analysed using structural equation modelling (SEM) using R 3.6.1. Results In total, 922 freshmen participated in the study, with an approximately even male-to-female ratio. A mediation model with anxiety and depression as the mediators between childhood trauma and internet gaming behaviour allowing anxiety and depression to be correlated was tested using SEM. The SEM analysis revealed that a standardised total effect of childhood trauma on Internet gaming was 0.18, (Z = 5.60, 95% CI [0.02, 0.05], P < 0.001), with the direct effects of childhood trauma on Internet gaming being 0.11 (Z = 3.41, 95% CI [0.01, 0.03], P = 0.001), and the indirect effects being 0.02 (Z = 2.32, 95% CI [0.00, 0.01], P = 0.020) in the pathway of childhood trauma-depression-internet gaming; and 0.05 (Z = 3.67, 95% CI [0.00, 0.02], P < 0.001) in the pathway of childhood trauma-anxiety-Internet gaming. In addition, the two mediators anxiety and depression were significantly correlated (r = 0.50, Z = 13.54, 95% CI [3.50, 5.05], P < 0.001). Conclusions The study revealed that childhood trauma had a significant impact on adolescents' Internet gaming behaviours among college students. Anxiety and depression both significantly mediated the relationship between childhood trauma and internet gaming and augmented its negative influence. Discussion of the need to understand the subtypes of childhood traumatic experience in relationship to addictive behaviours is included

M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning

Inspired by the successful application of contrastive learning on graphs, researchers attempt to impose graph contrastive learning approaches on heterogeneous information networks. Orthogonal to homogeneous graphs, the types of nodes and edges in heterogeneous graphs are diverse so that specialized graph contrastive learning methods are required. Most existing methods for heterogeneous graph contrastive learning are implemented by transforming heterogeneous graphs into homogeneous graphs, which may lead to ramifications that the valuable information carried by non-target nodes is undermined thereby exacerbating the performance of contrastive learning models. Additionally, current heterogeneous graph contrastive learning methods are mainly based on initial meta-paths given by the dataset, yet according to our deep-going exploration, we derive empirical conclusions: only initial meta-paths cannot contain sufficiently discriminative information; and various types of meta-paths can effectively promote the performance of heterogeneous graph contrastive learning methods. To this end, we propose a new multi-scale meta-path integrated heterogeneous graph contrastive learning (M2HGCL) model, which discards the conventional heterogeneity-homogeneity transformation and performs the graph contrastive learning in a joint manner. Specifically, we expand the meta-paths and jointly aggregate the direct neighbor information, the initial meta-path neighbor information and the expanded meta-path neighbor information to sufficiently capture discriminative information. A specific positive sampling strategy is further imposed to remedy the intrinsic deficiency of contrastive learning, i.e., the hard negative sample sampling issue. Through extensive experiments on three real-world datasets, we demonstrate that M2HGCL outperforms the current state-of-the-art baseline models.Comment: Accepted to the conference of ADMA2023 as an Oral presentatio

RLPlanner: Reinforcement Learning based Floorplanning for Chiplets with Fast Thermal Analysis

Chiplet-based systems have gained significant attention in recent years due to their low cost and competitive performance. As the complexity and compactness of a chiplet-based system increase, careful consideration must be given to microbump assignments, interconnect delays, and thermal limitations during the floorplanning stage. This paper introduces RLPlanner, an efficient early-stage floorplanning tool for chiplet-based systems with a novel fast thermal evaluation method. RLPlanner employs advanced reinforcement learning to jointly minimize total wirelength and temperature. To alleviate the time-consuming thermal calculations, RLPlanner incorporates the developed fast thermal evaluation method to expedite the iterations and optimizations. Comprehensive experiments demonstrate that our proposed fast thermal evaluation method achieves a mean absolute error (MAE) of 0.25 K and delivers over 120x speed-up compared to the open-source thermal solver HotSpot. When integrated with our fast thermal evaluation method, RLPlanner achieves an average improvement of 20.28\% in minimizing the target objective (a combination of wirelength and temperature), within a similar running time, compared to the classic simulated annealing method with HotSpot

Photodegradation modeling based on laboratory accelerated test data and predictions under outdoor weathering for polymeric materials

Photodegradation, driven primarily by ultraviolet (UV) radiation, is the primary cause of failure for organic paints and coatings, as well as many other products made from polymeric materials exposed to sunlight. Traditional methods of service life prediction involve the use of outdoor exposure in harsh UV environments (e.g., Florida and Arizona). Such tests, however, require too much time (generally many years) to do an evaluation. To overcome the shortcomings of traditional methods, scientists at the U.S. National Institute of Standards and Technology (NIST) conducted a multiyear research program to collect necessary data via scientifically-based laboratory accelerated tests. This paper presents the statistical modeling and analysis of the photodegradation data collected at NIST, and predictions of degradation for outdoor specimens that are subjected to weathering. The analysis involves identifying a physics/chemistry-motivated model that will adequately describe photodegradation paths. The model incorporates the effects of explanatory variables which are UV spectrum, UV intensity, temperature, and relative humidity. We use a nonlinear mixed-effects model to describe the sample paths. We extend the model to allow for dynamic covariates and compare predictions with specimens that were exposed in an outdoor environment where the explanatory variables are uncontrolled but recorded. We also discuss the findings from the analysis of the NIST data and some areas for future research