Activity

003), having trust in healthcare providers (p = 0.000), and physical activity for gravida women with fear of childbirth (p = 0.000). CONCLUSION From the findings, special attention on the identified predictors of fear of childbirth during prenatal sessions would help in managing fear of childbirth before they give birth. Adaptive CD8+ T cells were observed to contribute to the initiation and progression of obesity-induced visceral adipose tissue (VAT) chronic inflammation that is critically linked to metabolic disorders. Numerous peptides presented by the major histocompatibility complex (MHC) class I molecules at the cell surface are collectively termed as MHC I-associated immunopeptidome (MIP) for the interaction with CD8+ T cells. We conducted the in-depth mapping of MIP of VAT from lean and obese mice using large-scale high-resolution mass spectrometry and observed that obesity significantly alters the landscape of VAT MIPs. Additionally, the obese VAT-exclusive MIP source proteome reflected a distinct obesity-associated signature. A peptide derived from lactate dehydrogenase A (LDHA) or B chain, named LDHA237-244, was identified as an obese VAT-exclusive immunogenic peptide that was capable of eliciting pro-inflammatory CD8+ T cells responses. Our findings suggest that certain immunogenic peptides generated by obesity may trigger CD8+ T cell-mediated VAT inflammation. Cancer treatment has known a revolution with the emergence of immune checkpoint inhibitors. However, accurate theranostic biomarkers are lacking. In this review, we discuss different types of biomarkers currently under investigation. First, we focus on tissue biomarkers including PD-L1 expression by immunohistochemistry-the first Food and Drug Administration-approved biomarker-despite conflicting results. In addition, we report on novel biomarkers, including protein-based, molecular (tumor mutational load, immune signature…), circulating (neutrophil-to-lymphocyte ratio, serum cytokines…), and imaging-based biomarkers (radiomic signatures and positron-emission tomography using radiolabeled antibodies). We highlight the limitations of each candidate biomarker and finally discuss combinatorial approaches for their use and the opportunity to switch from a predictive strategy of biomarker research to an adaptive one in the field of cancer immunotherapy. Worry has been experimentally linked to a range of cognitive consequences, including impairments in working memory, inhibition, and cognitive control. However, findings are mixed, and the effects of worry on other phenomenologically-relevant constructs, such as sustained attention, have received less attention. Potential confounds such as speed-accuracy tradeoffs have also received little attention, as have psychometric and related design considerations, and potential moderators beyond trait worry. The present study investigated the effects of experimentally-induced worry versus a neutral control condition on speed-accuracy tradeoff-corrected performance on a validated measure of sustained attention (88 participants; within-subjects). selleck chemicals Moderation by trait worry and trait mindfulness was probed in confirmatory and exploratory analyses, respectively. Worry led to faster and less accurate responding relative to the neutral comparison condition. There was no main effect of condition or trait worry on sustained attention after accounting for speed-accuracy tradeoffs. In exploratory analyses, higher trait mindfulness was robustly related to better post-worry performance, including after controlling for trait worry, general distress, and post-neutral performance, and correction for multiple comparisons. Follow-up analyses exploring dissociable mindfulness facets found a robust relationship between present-moment attention and post-worry performance. Future research should experimentally manipulate mindfulness facets to probe causality and inform treatment development. Complex network is a general model to represent the interactions within technological, social, information, and biological interaction. Often, the direct detection of the interaction relationship is costly. Thus, network structure reconstruction, the inverse problem in complex networked systems, is of utmost importance for understanding many complex systems with unknown interaction structures. In addition, the data collected from real network system is often contaminated by noise, which makes the network structure inference task much more challenging. In this paper, we develop a new framework for the game dynamics network structure reconstruction based on deep learning method. In contrast to the compressive sensing methods that employ computationally complex convex/greedy algorithms to solve the network reconstruction task, we introduce a deep learning framework that can learn a structured representation from nodes data and efficiently reconstruct the game dynamics network structure with few observation data. Specifically, we propose the denoising autoencoders (DAEs) as the unsupervised feature learner to capture statistical dependencies between different nodes. Compared to the compressive sensing based method, the proposed method is a global network structure inference method, which can not only get the state-of-art performance, but also obtain the structure of network directly. Besides, the proposed method is robust to noise in the observation data. Moreover, the proposed method is also effective for the network which is not exactly sparse. Accordingly, the proposed method can extend to a wide scope of network reconstruction task in practice. Cross-modal retrieval has recently attracted much interest along with the rapid development of multimodal data, and effectively utilizing the complementary relationship of different modal data and eliminating the heterogeneous gap as much as possible are the two key challenges. In this paper, we present a novel network model termed cross-modal Dual Subspace learning with Adversarial Network (DSAN). The main contributions are as follows (1) Dual subspaces (visual subspace and textual subspace) are proposed, which can better mine the underlying structure information of different modalities as well as modality-specific information. (2) An improved quadruplet loss is proposed, which takes into account the relative distance and absolute distance between positive and negative samples, together with the introduction of the idea of hard sample mining. (3) Intra-modal constrained loss is proposed to maximize the distance of the most similar cross-modal negative samples and their corresponding cross-modal positive samples.