Dispositional Awe and Self-Worth in Chinese Undergraduates: The Suppressing Effects of Self-Concept Clarity and Small Self.

BACKGROUND: The positive role of dispositional awe has been seen in personality and in health. However, its impact on self-worth and internal mechanisms have been unclear. PURPOSES: This study explored the relationship between dispositional awe and self-worth and the roles of self-concept clarity and the small self in this association. METHODS: With a cluster sampling, a cross-sectional sample of 1888 Chinese undergraduates were recruited from Fuzhou, a southeast coastal city in the P.R.C. All the data were analyzed with Pearson's correlations and the structural equation model (SEM) based on SPSS 25.0 and Mplus 8.1. RESULTS: Dispositional awe was positively correlated with both personal-oriented and social-oriented self-worth (rs = 0.12, 0.27) and was also positively correlated with small self (r = 0.33) but negatively correlated with self-concept clarity (r = -0.18); in the full model, the direct effect of dispositional awe on society-oriented self-worth was 0.36 (75%); the indirect effects of small self and self-concept clarity were -0.09 (18.8%) and -0.01 (2.1%), respectively; and the chain indirect effect was -0.02 (4.2%). Similarly, the direct effect of dispositional awe on person-oriented self-worth was 0.50 (83.3%); the indirect effects of small self and self-concept clarity were -0.07 (11.7%) and -0.01 (1.7%), respectively; and the chain indirect effect was -0.02 (3.3%); all the indirect effects were suppressing effects, for they were contrary to the direct effects. CONCLUSION: This study suggested that dispositional awe could help people better understand themselves and enhance their sense of self-worth.

Privacy-Preserving Multi-Class Support Vector Machine Model on Medical Diagnosis.

With the rapid development of machine learning in the medical cloud system, cloud-assisted medical computing provides a concrete platform for remote rapid medical diagnosis services. Support vector machine (SVM), as one of the important algorithms of machine learning, has been widely used in the field of medical diagnosis for its high classification accuracy and efficiency. In some existing schemes, healthcare providers train diagnostic models with SVM algorithms and provide online diagnostic services to doctors. Doctors send the patient's case report to the diagnostic models to obtain the results and assist in clinical diagnosis. However, case report involves patients' privacy, and patients do not want their sensitive information to be leaked. Therefore, the protection of patient's privacy has become an important research direction in the field of online medical diagnosis. In this paper, we propose a privacy-preserving medical diagnosis scheme based on multi-class SVMs. The scheme is based on the distributed two trapdoors public key cryptosystem (DT-PKC) and Boneh-Goh-Nissim (BGN) cryptosystem. We design a secure computing protocol to compute the core process of the SVM classification algorithm. Our scheme can deal with both linearly separable data and nonlinear data while protecting the privacy of user data and support vectors. The results show that our scheme is secure, reliable, scalable with high accuracy.

Proteome-centric cross-omics characterization and integrated network analyses of triple-negative breast cancer.

We report a comprehensive proteomic study of a 90-case cohort of paired samples of triple-negative breast cancer (TNBC) in quantification, phosphorylation, and DNA-binding capacity. Four integrative subtypes (iP-1-4) are stratified on the basis of global proteome and phosphoproteome, each of which exhibits distinct molecular and pathway features. Scaffold and co-expression network analyses of three proteomic datasets, integrated with those from genome and transcriptome of the same cohort, reveal key pathways and master regulators that, characteristic of TNBC subtypes, play important regulatory roles within and between scaffold sub-structures and co-expression communities. We find that NAE1 is a potential drug target for subtype iP-1, and a series of key molecules in fatty acid metabolism, such as AKT1/FASN, are plausible targets for subtype iP-2. Libraries of proteins, pathways and networks of TNBC provide a valuable molecular infrastructure for further clinical exploration and in-depth studies of the molecular mechanisms of the disease.

Updatable privacy-preserving K -nearest neighbor query in location-based s-ervice.

The K -nearest neighbor ( K -NN) query is an important query in location-based service (LBS), which can query the nearest k points to a given point, and provide some convenient services such as interest recommendations. Hence the privacy protection issue of K -NN query has been a popular research area, protecting the information of queries and the queried results, especially in the information era. However, most of existing schemes fail to consider the privacy protection of location points already stored on servers. Or some schemes support no update of location points. In this paper, we present an updatable and privacy-preserving K -NN query scheme to address the above two issues. Concretely, our scheme utilizes the K D-tree ( K -Dimensional tree) to store the location points of data owners in location service provider and encrypts the points with a distributed double-trapdoor public-key cryptosystem. Then, based on the Ciphertext Comparison Protocol and Ciphertext Euclidean Distance Calculation Protocol, our scheme can protect the privacy of location and query contents. Experimental analyses show our proposal supports some new location points for a fixed location service provider. Moreover, the queried results show a high accuracy of more than 95%.

Graph embedding and extensions: a general framework for dimensionality reduction.

Over the past few decades, a large family of algorithms - supervised or unsupervised; stemming from statistics or geometry theory - has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper a general formulation known as graph embedding to unify them within a common framework. In graph embedding, each algorithm can be considered as the direct graph embedding or its linear/kernel/tensor extension of a specific intrinsic graph that describes certain desired statistical or geometric properties of a data set, with constraints from scale normalization or a penalty graph that characterizes a statistical or geometric property that should be avoided. Furthermore, the graph embedding framework can be used as a general platform for developing new dimensionality reduction algorithms. By utilizing this framework as a tool, we propose a new supervised dimensionality reduction algorithm called Marginal Fisher Analysis in which the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring points of the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show the superiority of our proposed MFA in comparison to LDA, also for corresponding kernel and tensor extensions.