The relationship between self-consciousness and depression in college students: the chain mediating effect of meaning life and self-efficacy, with the moderating effect of social support.

BACKGROUND: This study aimed to investigate the impact of self-consciousness on depression of college students, and mainly focus on confirming the mediator role of life meaningful and self-efficacy, as well as the moderator role of social support. METHODS: In the present study, convenient sampling method was adopted, 583 college students were recruited from Harbin city and Wenzhou city in China. All students were assessed using self-assessment scales, including self-consciousness scale, life meaningful scale, self-efficacy scale, social support scale, and self-rating depression scale. Descriptive statistical analysis and correlation analysis, structural equation model analysis were conducted by SPSS 25.0 and M-plus. RESULTS: Results showed that self-consciousness was negatively related to depression, life meaningful and self-efficacy partially mediated the relation between self-consciousness and depression. Moderated mediation analysis further indicated that the relation between self-efficacy and depression were moderated bu social support. Compare with college students who had high social support, depression in those with low social support was more susceptible to the effect of self-efficacy. CONCLUSION: These findings imply that college students with low levels of self-consciousness are more easy to be depressive, enhancing their sense of life meaning and self-efficacy can effectively alleviate depression, and college student with high social support can benefit more from self-efficacy. Therefore we should pay more attention to the mental health problems of low levels self-consciousness college students in university.

ShinyMetID: An R shiny package for metabolite identification by mass spectral matching.

We present metabolite identification software in the form of R Shiny. Metabolite identification by mass spectral matching in gas chromatography (GC-MS)-based untargeted metabolomics can be done by using the easy-to-use software. Various similarity measures are given and toy example using graphical user interface is presented.

A strategy for healthy eating habits of daily fruits revisited: A metabolomics study.

Many people peel fruits, commonly persimmon, grape, apple, and peach, before eating as table fruits. Differences of bioactive compounds between peels and pulps of daily fruits are widely known but limited to individual compound because understanding of differences in their global metabolites is lack. We employed 1H NMR-based metabolomics to explore the global metabolite differences between their peels and pulps from the fruits, which included changes of diverse metabolites in persimmon after harvest ripening. Of diverse metabolites observed among the fruits tested, various health-beneficial metabolites were present in the peels rather than the pulps and their classes were dependent on the type of fruit: gallocatechin, epicatechin and epigallocatehin only in persimmon, apple, and peach, respectively; quercetin only in persimmon and apple; kaempferol only in persimmon; chlorogenic acid only in grape and peach; neochlorogenic acid only in apple and peach; p-coumaric acid only in grape; phloridzin and catechin only in apple. These metabolites in the peels of each fruits were strongly correlated with free radical-scavenging activity and delay of carbohydrate digestion. Therefore, intake of whole fruits, rather than removal of their peels, were recommended for potential improvement of healthy lifespan and human wellness. This study highlights the critical role of metabolomic studies in simultaneous determinations of diverse and intrinsic metabolites in different types of fruits and thus providing a strategy for healthy eating habits of daily fruits.

The Chain-Mediation Pathway of Social Avoidance to Depression in College Students Is Regulated by Self-Esteem.

Objective: Here, we investigated the relationship between social avoidance and depression in college students, explored the mediating roles of loneliness and trust, and the regulatory role of self-esteem, to provide a theoretical intervention approach based on internal mechanisms. Methods: Using a simple random overall sampling method, 1,021 college students were investigated using self-rating depression, social avoidance and distress, loneliness, interpersonal trust and self-esteem scales. Results: There was a significant positive correlation between social avoidance and depression. Loneliness and interpersonal trust played chain-mediating roles between social avoidance and depression. The influence of social avoidance on interpersonal trust was regulated by self-esteem. Specifically, the social avoidance level of the low self-esteem group was more likely to be affected by interpersonal trust issues. Conclusion: Social avoidance not only directly affects college students' depression, it also has indirect effects through interpersonal trust and loneliness. Thus, interpersonal trust and loneliness have chain-mediating effects between social avoidance and depression in college students, and self-esteem regulates the mediation process.

The relationship between depression and social avoidance of college students: A moderated mediation model.

BACKGROUND: The fact that depression and anxiety are highly prevalent and often co-occur has been well documented. The present study hypothesized that loneliness and interpersonal trust mediate the relationship between depression and social anxiety, with self-esteem playing a moderating role. METHODS: 1021 college students completed the interpersonal trust scale (ITS), self-rating depression scale (SDS), UCLA loneliness scale, self-esteem scale (SES), and social avoidance and distress (SAD) scale. And descriptive statistical analysis and correlation analysis, structural equation model analysis were conducted. RESULTS: 1) The correlations between depression, loneliness, interpersonal trust, self-esteem and social avoidance were all statistically significant. 2) Loneliness and interpersonal trust mediated the relationship between depression and social avoidance. 3) Self-esteem moderated the relationship between interpersonal trust and social avoidance. Specifically, compared with individuals who had high self-esteem, social avoidance in those with low self-esteem individuals was more susceptible to the effects of interpersonal trust. LIMITATIONS: First, the questionnaire data may be influenced by social approval. Second, most of the participants were college students. Finally, the causal relationship between the variables could not be inferred. CONCLUSIONS: The results indicated that loneliness and interpersonal trust played mediating roles between depression and social avoidance, and the relationship between interpersonal trust and social avoidance was moderated by self-esteem. It provides a new way to explain the mechanism of depression, and a new perspective for the clinical intervention of depression, that is, from the perspective of their self-experience and self-esteem.

Comprehensive Comparative Analysis of Local False Discovery Rate Control Methods.

Due to the advance in technology, the type of data is getting more complicated and large-scale. To analyze such complex data, more advanced technique is required. In case of omics data from two different groups, it is interesting to find significant biomarkers between two groups while controlling error rate such as false discovery rate (FDR). Over the last few decades, a lot of methods that control local false discovery rate have been developed, ranging from one-dimensional to k-dimensional FDR procedure. For comparison study, we select three of them, which have unique and significant properties: Efron et al. (2001), Ploner et al. (2006), and Kim et al. (2018) in chronological order. The first approach is one-dimensional approach while the other two are two-dimensional ones. Furthermore, we consider two more variants of Ploner's approach. We compare the performance of those methods on both simulated and real data.

Metabolomic understanding of intrinsic physiology in Panax ginseng during whole growing seasons.

BACKGROUND: Panax ginseng Meyer has widely been used as a traditional herbal medicine because of its diverse health benefits. Amounts of ginseng compounds, mainly ginsenosides, vary according to seasons, varieties, geographical regions, and age of ginseng plants. However, no study has comprehensively determined perturbations of various metabolites in ginseng plants including roots and leaves as they grow. METHODS: Nuclear magnetic resonance (1H NMR)-based metabolomics was applied to better understand the metabolic physiology of ginseng plants and their association with climate through global profiling of ginseng metabolites in roots and leaves during whole growing periods. RESULTS: The results revealed that all metabolites including carbohydrates, amino acids, organic acids, and ginsenosides in ginseng roots and leaves were clearly dependent on growing seasons from March to October. In particular, ginsenosides, arginine, sterols, fatty acids, and uracil diphosphate glucose-sugars were markedly synthesized from March until May, together with accelerated sucrose catabolism, possibly associated with climatic changes such as sun exposure time and rainfall. CONCLUSION: This study highlights the intrinsic metabolic characteristics of ginseng plants and their associations with climate changes during their growth. It provides important information not only for better understanding of the metabolic phenotype of ginseng but also for quality improvement of ginseng through modification of cultivation.

MetPC: Metabolite Pipeline Consisting of Metabolite Identification and Biomarker Discovery Under the Control of Two-Dimensional FDR.

Due to the complex features of metabolomics data, the development of a unified platform, which covers preprocessing steps to data analysis, has been in high demand over the last few decades. Thus, we developed a new bioinformatics tool that includes a few of preprocessing steps and biomarker discovery procedure. For metabolite identification, we considered a hierarchical statistical model coupled with an Expectation-Maximization (EM) algorithm to take care of latent variables. For biomarker metabolite discovery, our procedure controls two-dimensional false discovery rate (fdr2d) when testing for multiple hypotheses simultaneously.

Numerical analysis of intracellular amino acid profiles of breast cancer cells with K-Ras or PI3K mutation in response to kinase inhibitors.

BACKGROUND: Various efforts to understand the relationship between biological information and disease have been done using many different types of highthroughput data such as genomics and metabolomics. However, information obtained from previous studies was not satisfactory, implying that new direction of studies is in need. Thus, we have tried profiling intracellular free amino acids in normal and cancerous cells to extract some information about such relationship by way of the change in IFAA levels in response to the treatment of three kinase inhibitors. We define two measures such as relative susceptibility (RS) and relative efficacy (RE) to numerically quantify susceptibility of cell line to treatment and efficacy of treatment on cell line, respectively. METHODS: We applied principal component analysis (PCA) to the intracellular free amino acids (IFAAs) of isogenic breast cells with oncogenic mutation in K-Ras or PI3K genes to investigate the change in IFAA levels in response to the treatment of three kinase inhibitors. Two-dimensional plot, which was graphically represented by using the first two principal components (PCs), enabled us to evaluate the treatment efficacy in cancerous cells in terms of the quantitative distance of two IFAA profiles from cancerous and normal cells with the same treatment condition. RESULTS: The biggest change in metabolic states in K-Ras mutant cell was caused by REGO for both treatment time (RS=2.31 (24 h) and 1.64 (48 h)). Regarding RE, REGO was the most effective on K-Ras/PI3K mutant cell line for treatment time 24h (RE=1.28) while PI3K inhibitor had good effect on K-Ras mutant cell line for 48h (RE=1.1). CONCLUSIONS: Numerical study on the link between amino acid profile and cancer has been done in two different dimensions. We then summarized such link in terms of two new metrics such as RS and RE, which we first define in this work. Although our study based on those metrics seems to work, we think that the usefulness of the metrics in cancer study of this kind need to be further investigated.

Metabotyping of rice (Oryza sativa L.) for understanding its intrinsic physiology and potential eating quality.

Rice (Oryza sativa L.), the major staple food in many countries, has genetic diversity adapted to different environmental conditions. However, metabolic traits about diverse rice plants are rarely discovered. In the present study, rice leaves and grains were collected at whole growth stages from late (LMC) and early (EMC) maturing cultivars. Metabolic dependences of rice plants on both growth and cultivar were investigated in their leaves and grains through NMR-based metabolomics approach. Rice leaf metabolome were differently regulated between two rice cultivars, thereby affecting variations of rice grain metabolome. Sucrose levels in leaves of EMC were markedly decreased compared to those in LMC, and more accumulations of sucrose, amino acids and free fatty acids were found in grains of EMC. These distinct metabolisms between EMC and LMC rice cultivars were associated with temperature during their growing seasons and might affect the eating quality of rice. The current study highlights that metabolomic approach of rice leaves and grains could lead to better understanding of the relationship between their distinct metabolisms and environmental conditions, and provide novel insights to metabolic qualities of rice grains.

A (1)H HR-MAS NMR-Based Metabolomic Study for Metabolic Characterization of Rice Grain from Various Oryza sativa L. Cultivars.

Rice grain metabolites are important for better understanding of the plant physiology of various rice cultivars and thus for developing rice cultivars aimed at providing diverse processed products. However, the variation of global metabolites in rice grains has rarely been explored. Here, we report the identification of intra- or intercellular metabolites in rice (Oryza sativa L.) grain powder using a (1)H high-resolution magic angle spinning (HR-MAS) NMR-based metabolomic approach. Compared with nonwaxy rice cultivars, marked accumulation of lipid metabolites such as fatty acids, phospholipids, and glycerophosphocholine in the grains of waxy rice cultivars demonstrated the distinct metabolic regulation and adaptation of each cultivar for effective growth during future germination, which may be reflected by high levels of glutamate, aspartate, asparagine, alanine, and sucrose. Therefore, this study provides important insights into the metabolic variations of diverse rice cultivars and their associations with environmental conditions and genetic backgrounds, with the aim of facilitating efficient development and the improvement of rice grain quality through inbreeding with genetic or chemical modification and mutation.

Estimation of treatment effect in a subpopulation: An empirical Bayes approach.

It is well recognized that the benefit of a medical intervention may not be distributed evenly in the target population due to patient heterogeneity, and conclusions based on conventional randomized clinical trials may not apply to every person. Given the increasing cost of randomized trials and difficulties in recruiting patients, there is a strong need to develop analytical approaches to estimate treatment effect in subpopulations. In particular, due to limited sample size for subpopulations and the need for multiple comparisons, standard analysis tends to yield wide confidence intervals of the treatment effect that are often noninformative. We propose an empirical Bayes approach to combine both information embedded in a target subpopulation and information from other subjects to construct confidence intervals of the treatment effect. The method is appealing in its simplicity and tangibility in characterizing the uncertainty about the true treatment effect. Simulation studies and a real data analysis are presented.

A NEW METHOD OF PEAK DETECTION FOR ANALYSIS OF COMPREHENSIVE TWO-DIMENSIONAL GAS CHROMATOGRAPHY MASS SPECTROMETRY DATA.

We develop a novel peak detection algorithm for the analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS) data using normal-exponential-Bernoulli (NEB) and mixture probability models. The algorithm first performs baseline correction and denoising simultaneously using the NEB model, which also defines peak regions. Peaks are then picked using a mixture of probability distribution to deal with the co-eluting peaks. Peak merging is further carried out based on the mass spectral similarities among the peaks within the same peak group. The algorithm is evaluated using experimental data to study the effect of different cut-offs of the conditional Bayes factors and the effect of different mixture models including Poisson, truncated Gaussian, Gaussian, Gamma, and exponentially modified Gaussian (EMG) distributions, and the optimal version is introduced using a trial-and-error approach. We then compare the new algorithm with two existing algorithms in terms of compound identification. Data analysis shows that the developed algorithm can detect the peaks with lower false discovery rates than the existing algorithms, and a less complicated peak picking model is a promising alternative to the more complicated and widely used EMG mixture models.

Selective inhibition of pancreatic ductal adenocarcinoma cell growth by the mitotic MPS1 kinase inhibitor NMS-P715.

Most solid tumors, including pancreatic ductal adenocarcinoma (PDAC), exhibit structural and numerical chromosome instability (CIN). Although often implicated as a driver of tumor progression and drug resistance, CIN also reduces cell fitness and poses a vulnerability that can be exploited therapeutically. The spindle assembly checkpoint (SAC) ensures correct chromosome-microtubule attachment, thereby minimizing chromosome segregation errors. Many tumors exhibit upregulation of SAC components such as MPS1, which may help contain CIN within survivable limits. Prior studies showed that MPS1 inhibition with the small molecule NMS-P715 limits tumor growth in xenograft models. In cancer cell lines, NMS-P715 causes cell death associated with impaired SAC function and increased chromosome missegregation. Although normal cells appeared more resistant, effects on stem cells, which are the dose-limiting toxicity of most chemotherapeutics, were not examined. Elevated expression of 70 genes (CIN70), including MPS1, provides a surrogate measure of CIN and predicts poor patient survival in multiple tumor types. Our new findings show that the degree of CIN70 upregulation varies considerably among PDAC tumors, with higher CIN70 gene expression predictive of poor outcome. We identified a 25 gene subset (PDAC CIN25) whose overexpression was most strongly correlated with poor survival and included MPS1. In vitro, growth of human and murine PDAC cells is inhibited by NMS-P715 treatment, whereas adipose-derived human mesenchymal stem cells are relatively resistant and maintain chromosome stability upon exposure to NMS-P715. These studies suggest that NMS-P715 could have a favorable therapeutic index and warrant further investigation of MPS1 inhibition as a new PDAC treatment strategy.

Treatment benefit and treatment harm rate to characterize heterogeneity in treatment effect.

It is well recognized that the conventional summary of treatment effect by averaging across individual patients has its limitation in ignoring the heterogeneous responses to the treatment in the target population. However, there are few alternative metrics in the literature that are designed to capture such heterogeneity. We propose the concept of treatment benefit rate (TBR) and treatment harm rate (THR) that characterize both the overall treatment effect and the magnitude of heterogeneity. We discuss a method to estimate TBR and THR that easily incorporates a sensitivity analysis scheme, and illustrate the idea through analysis of a randomized trial that evaluates the implantable cardioverter-defibrillator (ICD) in reducing mortality. A simulation study is presented to assess the performance of the proposed method.

An efficient post-hoc integration method improving peak alignment of metabolomics data from GCxGC/TOF-MS.

BACKGROUND: Since peak alignment in metabolomics has a huge effect on the subsequent statistical analysis, it is considered a key preprocessing step and many peak alignment methods have been developed. However, existing peak alignment methods do not produce satisfactory results. Indeed, the lack of accuracy results from the fact that peak alignment is done separately from another preprocessing step such as identification. Therefore, a post-hoc approach, which integrates both identification and alignment results, is in urgent need for the purpose of increasing the accuracy of peak alignment. RESULTS: The proposed post-hoc method was validated with three datasets such as a mixture of compound standards, metabolite extract from mouse liver, and metabolite extract from wheat. Compared to the existing methods, the proposed approach improved peak alignment in terms of various performance measures. Also, post-hoc approach was verified to improve peak alignment by manual inspection. CONCLUSIONS: The proposed approach, which combines the information of metabolite identification and alignment, clearly improves the accuracy of peak alignment in terms of several performance measures. R package and examples using a dataset are available at http://mrr.sourceforge.net/download.html.

A wavelet-based Bayesian approach to regression models with long memory errors and its application to FMRI data.

This article considers linear regression models with long memory errors. These models have been proven useful for application in many areas, such as medical imaging, signal processing, and econometrics. Wavelets, being self-similar, have a strong connection to long memory data. Here we employ discrete wavelet transforms as whitening filters to simplify the dense variance-covariance matrix of the data. We then adopt a Bayesian approach for the estimation of the model parameters. Our inferential procedure uses exact wavelet coefficients variances and leads to accurate estimates of the model parameters. We explore performances on simulated data and present an application to an fMRI data set. In the application we produce posterior probability maps (PPMs) that aid interpretation by identifying voxels that are likely activated with a given confidence.

Compound identification using partial and semipartial correlations for gas chromatography-mass spectrometry data.

Compound identification is a key component of data analysis in the applications of gas chromatography-mass spectrometry (GC-MS). Currently, the most widely used compound identification is mass spectrum matching, in which the dot product and its composite version are employed as spectral similarity measures. Several forms of transformations for fragment ion intensities have also been proposed to increase the accuracy of compound identification. In this study, we introduced partial and semipartial correlations as mass spectral similarity measures and applied them to identify compounds along with different transformations of peak intensity. The mixture versions of the proposed method were also developed to further improve the accuracy of compound identification. To demonstrate the performance of the proposed spectral similarity measures, the National Institute of Standards and Technology (NIST) mass spectral library and replicate spectral library were used as the reference library and the query spectra, respectively. Identification results showed that the mixture partial and semipartial correlations always outperform both the dot product and its composite measure. The mixture similarity with semipartial correlation has the highest accuracy of 84.6% in compound identification with a transformation of (0.53,1.3) for fragment ion intensity and m/z value, respectively.

Model-based peak alignment of metabolomic profiling from comprehensive two-dimensional gas chromatography mass spectrometry.

BACKGROUND: Comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS) has been used for metabolite profiling in metabolomics. However, there is still much experimental variation to be controlled including both within-experiment and between-experiment variation. For efficient analysis, an ideal peak alignment method to deal with such variations is in great need. RESULTS: Using experimental data of a mixture of metabolite standards, we demonstrated that our method has better performance than other existing method which is not model-based. We then applied our method to the data generated from the plasma of a rat, which also demonstrates good performance of our model. CONCLUSIONS: We developed a model-based peak alignment method to process both homogeneous and heterogeneous experimental data. The unique feature of our method is the only model-based peak alignment method coupled with metabolite identification in an unified framework. Through the comparison with other existing method, we demonstrated that our method has better performance. Data are available at http://stage.louisville.edu/faculty/x0zhan17/software/software-development/mspa. The R source codes are available at http://www.biostat.iupui.edu/~ChangyuShen/CodesPeakAlignment.zip. TRIAL REGISTRATION: 2136949528613691.

An empirical Bayes model using a competition score for metabolite identification in gas chromatography mass spectrometry.

BACKGROUND: Mass spectrometry (MS) based metabolite profiling has been increasingly popular for scientific and biomedical studies, primarily due to recent technological development such as comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC/TOF-MS). Nevertheless, the identifications of metabolites from complex samples are subject to errors. Statistical/computational approaches to improve the accuracy of the identifications and false positive estimate are in great need. We propose an empirical Bayes model which accounts for a competing score in addition to the similarity score to tackle this problem. The competition score characterizes the propensity of a candidate metabolite of being matched to some spectrum based on the metabolite's similarity score with other spectra in the library searched against. The competition score allows the model to properly assess the evidence on the presence/absence status of a metabolite based on whether or not the metabolite is matched to some sample spectrum. RESULTS: With a mixture of metabolite standards, we demonstrated that our method has better identification accuracy than other four existing methods. Moreover, our method has reliable false discovery rate estimate. We also applied our method to the data collected from the plasma of a rat and identified some metabolites from the plasma under the control of false discovery rate. CONCLUSIONS: We developed an empirical Bayes model for metabolite identification and validated the method through a mixture of metabolite standards and rat plasma. The results show that our hierarchical model improves identification accuracy as compared with methods that do not structurally model the involved variables. The improvement in identification accuracy is likely to facilitate downstream analysis such as peak alignment and biomarker identification. Raw data and result matrices can be found at http://www.biostat.iupui.edu/~ChangyuShen/index.htm. TRIAL REGISTRATION: 2123938128573429.