Resilience As A Mediator Between Social Support And Mental Health Among Children Affected By Hiv/aids In China

RESILIENCE AS A MEDIATOR BETWEEN SOCIAL SUPPORT AND MENTAL HEALTH AMONG CHILDREN AFFECTED BY HIV/AIDS IN CHINA by CHENGUANG DU THESIS Submitted to the Graduate School of Wayne State University, Detroit, Michigan in partial fulfillment of the requirements for the degree of MASTER OF EDUCATION 2016 MAJOR: EDUCATION EVALUATION and RESEARCH Approved By: Advisor Dat

DICER1 regulated let-7 expression levels in p53-induced cancer repression requires cyclin D1.

Let-7 miRNAs act as tumour suppressors by directly binding to the 3\u27UTRs of downstream gene products. The regulatory role of let-7 in downstream gene expression has gained much interest in the cancer research community, as it controls multiple biological functions and determines cell fates. For example, one target of the let-7 family is cyclin D1, which promotes G0/S cell cycle progression and oncogenesis, was correlated with endoribonuclease DICER1, another target of let-7. Down-regulated let-7 has been identified in many types of tumours, suggesting a feedback loop may exist between let-7 and cyclin D1. A potential player in the proposed feedback relationship is Dicer, a central regulator of miRNA expression through sequence-specific silencing. We first identified that DICER1 is the key downstream gene for cyclin D1-induced let-7 expression. In addition, we found that let-7 miRNAs expression decreased because of the p53-induced cell death response, with deregulated cyclin D1. Our results also showed that cyclin D1 is required for Nutlin-3 and TAX-induced let-7 expression in cancer repression and the cell death response. For the first time, we provide evidence that let-7 and cyclin D1 form a feedback loop in regulating therapy response of cancer cells and cancer stem cells, and importantly, that alteration of let-7 expression, mainly caused by cyclin D1, is a sensitive indicator for better chemotherapies response

Exploring the Confounding Factors of Academic Career Success: An Empirical Study with Deep Predictive Modeling

Understanding determinants of success in academic careers is critically important to both scholars and their employing organizations. While considerable research efforts have been made in this direction, there is still a lack of a quantitative approach to modeling the academic careers of scholars due to the massive confounding factors. To this end, in this paper, we propose to explore the determinants of academic career success through an empirical and predictive modeling perspective, with a focus on two typical academic honors, i.e., IEEE Fellow and ACM Fellow. We analyze the importance of different factors quantitatively, and obtain some insightful findings. Specifically, we analyze the co-author network and find that potential scholars work closely with influential scholars early on and more closely as they grow. Then we compare the academic performance of male and female Fellows. After comparison, we find that to be elected, females need to put in more effort than males. In addition, we also find that being a Fellow could not bring the improvements of citations and productivity growth. We hope these derived factors and findings can help scholars to improve their competitiveness and develop well in their academic careers

Dual light-responsive shape transformations of a nanocomposite hydrogel sheet enabled by in situ etching shaped plasmonic nanoparticles

We report here on dual shape transformations of the same thermo-responsive hybrid hydrogel sheet under irradiation of a laser with two different wavelengths (808 nm and 450 nm). By in situ etching the silver nanoprisms in the sheet to silver nanodiscs by using chloride ions (Cl-), two areas with distinct light extinction properties are integrated in a single sheet. The conversion of photon energy to thermal energy in local areas by the silver nanoprisms or nanodiscs under laser irradiation with an appropriate wavelength heats up the sheet locally and causes a local volumetric shrinkage, and hence a volumetric mismatch in different areas in the sheet. The sheet then transforms in a specific way to accommodate this volumetric mismatch. Different patterns of silver nanoprisms and nanodiscs in the sheet are achieved by controlling the delivery patterns of the etchant Cl-. We demonstrate that by designing the distribution pattern of silver nanoprisms and nanodiscs in the sheet, the same hybrid sheet transforms either to a saddle or to a helical twisted shape, while with another type of distribution pattern, it transforms either to a hoof-like or to a boat-like shape under the irradiation of a laser with a wavelength of either 808 nm or 450 nm. We point out that to program arbitrary curvature of the transformed shape of our sheet, the pattern size of silver nanoprisms and nanodiscs (i.e. actuation area) in the sheet needs to be largely reduced, which however limits the heat generated and hence the shape transformations. Factors that affect the sheet's shape transformations are discussed and solutions are suggested to enhance its performance. The hybrid sheet may find applications in soft robotics, for example, as a robotic finger

Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph

Recent years have witnessed the rapid development of heterogeneous graph neural networks (HGNNs) in information retrieval (IR) applications. Many existing HGNNs design a variety of tailor-made graph convolutions to capture structural and semantic information in heterogeneous graphs. However, existing HGNNs usually represent each node as a single vector in the multi-layer graph convolution calculation, which makes the high-level graph convolution layer fail to distinguish information from different relations and different orders, resulting in the information loss in the message passing. %insufficient mining of information. To this end, we propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN. To avoid the information loss caused by the single vector node representation, we first design a sequential node representation learning mechanism to represent each node as a sequence of meta-path representations during the node message passing. Then we propose a heterogeneous representation fusion module, empowering Seq-HGNN to identify important meta-paths and aggregate their representations into a compact one. We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB). Experimental results show that our proposed method outperforms state-of-the-art baselines in both accuracy and efficiency. The source code is available at https://github.com/nobrowning/SEQ_HGNN.Comment: SIGIR 202

RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale Graphs

Heterogeneous graph neural networks (HGNNs) have been widely applied in heterogeneous information network tasks, while most HGNNs suffer from poor scalability or weak representation when they are applied to large-scale heterogeneous graphs. To address these problems, we propose a novel Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning (RHCO) for large-scale heterogeneous graph representation learning. Unlike traditional heterogeneous graph neural networks, we adopt the contrastive learning mechanism to deal with the complex heterogeneity of large-scale heterogeneous graphs. We first learn relation-aware node embeddings under the network schema view. Then we propose a novel positive sample selection strategy to choose meaningful positive samples. After learning node embeddings under the positive sample graph view, we perform a cross-view contrastive learning to obtain the final node representations. Moreover, we adopt the label smoothing technique to boost the performance of RHCO. Extensive experiments on three large-scale academic heterogeneous graph datasets show that RHCO achieves best performance over the state-of-the-art models

Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction

Accurate citation count prediction of newly published papers could help editors and readers rapidly figure out the influential papers in the future. Though many approaches are proposed to predict a paper's future citation, most ignore the dynamic heterogeneous graph structure or node importance in academic networks. To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers. First, a dynamic heterogeneous network embedding module is provided to capture the dynamic evolutionary trends of the whole academic network. Then, a node importance embedding module is proposed to capture the global consistency relationship to figure out each paper's node importance. Finally, the dynamic evolutionary trend embeddings and node importance embeddings calculated above are combined to jointly predict the future citation counts of each paper, by a log-normal distribution model according to multi-faced paper node representations. Extensive experiments on two large-scale datasets demonstrate that our model significantly improves all indicators compared to the SOTA models.Comment: Accepted by CIKM'202

Ultra-broadband near-field Josephson microwave microscopy

Advanced microwave technologies constitute the foundation of a wide range of modern sciences, including quantum computing, microwave photonics, spintronics, etc. To facilitate the design of chip-based microwave devices, there is an increasing demand for state-of-the-art microscopic techniques capable of characterizing the near-field microwave distribution and performance. In this work, we integrate Josephson junctions onto a nano-sized quartz tip, forming a highly sensitive microwave mixer on-tip. This allows us to conduct spectroscopic imaging of near-field microwave distributions with high spatial resolution. Leveraging its microwave-sensitive characteristics, our Josephson microscope achieves a broad detecting bandwidth of up to 200 GHz with remarkable frequency and intensity sensitivities. Our work emphasizes the benefits of utilizing the Josephson microscope as a real-time, non-destructive technique to advance integrated microwave electronics

Petrogenesis of early Paleozoic I-type granitoids in the Longshoushan and implications for the tectonic affinity and evolution of the southwestern Alxa Block

In the Paleozoic, the Alxa Block was situated between the Central Asian Orogenic Belt and the North Qilian Orogenic Belt, and it experienced intense magmatic activity. Thus, the Alxa Block is an important area for understanding the tectonic framework and evolution of these two orogenic belts. However, there has long been debate regarding the tectonic affinity and tectonic evolution of the Longshoushan, located in the southwestern margin of the Alxa Block, during the Paleozoic. In this study, we present zircon U-Pb ages, whole-rock major and trace elements, and Hf isotopic data for the granitoids from the east of the Longshoushan to investigate these issues. Bulk-rock analyses show that these granitoids are weakly peraluminous, with high SiO2 and K2O but low MgO, TFe2O3, and P2O5. They are also characterized by enrichment in LREE and LILE, depletion in HREE and HFSE, and a large range of ϵHf(t) values (monzogranite: -0.3 to -16.2; K-feldspar granite: 3.5 to -7.7). These geochemical features indicate that these granitoids are highly fractionated I-type granites, which were formed by crust- and mantle-derived magma mixing. LA-ICP-MS zircon U-Pb dating constrains the monzogranite and K-feldspar granite formed at 440.8 ± 2.1 Ma and 439.4 ± 2.0 Ma, respectively. Combining these results with previous chronological data, the geochronology framework of Paleozoic magmatic events in the Longshoushan is consistent with the North Qilian Orogenic Belt to the south but significantly differs from other parts of the Alxa Block and the Central Asian Orogenic Belt to the north. This result indicates that the Longshoushan was primarily influenced by the North Qilian Orogenic Belt during the early Paleozoic. Integrated with previous studies, a three-stage tectonic model is proposed of early Paleozoic accretion and arc magmatism leading to collision in the Longshoushan: (1) arc magmatism on an active continental margin with the northward subduction of the North Qilian back-arc basins (NQ bab; 460-445 Ma); (2) magmatic rocks, dominated by I-type granites, forming in a continent-continent collision setting, with significant crustal thickening interpreted as resulting from compressional stress and/or magmatic additions (445-435 Ma); (3) the development of abundant A-type granites and mafic dikes in response to extension, supported by a change in trace element chemistry indicating crustal thinning at this stage (435-410 Ma). This sequence of events and their timings is similar to other parts of the Central China Orogenic Belt and requires either a coincidence of several oceanic plates closing at the same time or an along-strike repetition of the same system

Shexiang Tongxin Dripping Pills regulates SOD/TNF-α/IL-6 pathway to inhibit inflammation and oxidative stress to improve myocardial ischemia-reperfusion injury in mice

IntroductionShexiang Tongxin Dropping Pills (STDP), a traditional Chinese medicine (TCM), is clinically used for cardiovascular diseases like myocardial ischemia. Myocardial ischemia-reperfusion injury (MIRI), worsened by oxidative stress and inflammation, remains a significant problem, and the mechanisms underlying STDP's cardioprotection are incompletely understood. This study aimed to investigate STDP's effects on the SOD/TNF-α/IL-6 pathway and its impact on inflammation and oxidative stress in MIRI.MethodsA mouse model of MIRI was employed to evaluate the cardioprotective effects and mechanisms of STDP in vivo. Pretreatment with STDP was administered prior to MIRI induction. Assessments included serum SOD activity, cardiac tissue ROS levels, cardiomyocyte apoptosis rates (TUNEL assay), mRNA and protein expression of IL-1β, TNF-α, and IL-6 (qPCR, Western blot), histopathological evaluation of myocardial tissue morphology and inflammatory infiltration (H&E staining), myocardial infarction size (TTC staining), and cardiac function parameters (contractility, diastolic function).ResultsSTDP pretreatment significantly enhanced serum SOD activity and reduced cardiac ROS levels and cardiomyocyte apoptosis. It effectively downregulated mRNA and protein expression of IL-1β, TNF-α, and IL-6. Histopathology revealed reduced inflammatory cell infiltration and more intact cardiomyocyte morphology in STDP-treated groups. TTC staining confirmed a reduction in myocardial infarction size. Cardiac function assessments showed STDP improved both contractility and diastolic function post-MIRI and reduced arrhythmia incidence.DiscussionSTDP ameliorates MIRI in mice by inhibiting inflammatory responses and oxidative stress, primarily through modulation of the SOD/TNF-α/IL-6 pathway. Its cardioprotective effects include reducing apoptosis, inflammation, ROS, infarction size, and arrhythmias, while improving cardiac function and tissue repair. These findings elucidate a key mechanism for STDP and provide empirical support for its clinical use in MIRI, offering innovative perspectives for managing cardiovascular disorders with TCM and facilitating the integration of traditional and modern medicine