315 research outputs found
The Effect of Trait Mindfulness on Teachers’ Emotional Exhaustion: The Chain Mediating Role of Psychological Capital and Job Engagement
No full textEmotional exhaustion has become an important occupational health problem faced by teachers, and it has seriously affected their mental health. It is necessary to pay attention to the factors that affect emotional exhaustion. In this study, 815 frontline university faculty were selected as subjects to explore the relationship between trait mindfulness and emotional exhaustion and the role of psychological capital and work engagement in this relation, using the trait mindfulness, psychological capital, work engagement, and emotional exhaustion scales. It was found that trait mindfulness and emotional exhaustion are negatively correlated; the mediating role of psychological capital between trait mindfulness and emotional exhaustion is not significant; the mediating role of work engagement between trait mindfulness and emotional exhaustion is significant; the chain mediation effect of psychological capital and work engagement between trait mindfulness and emotional exhaustion are significant
Notice of Retraction: The research on training the original creative ability of the students majoring in economy and management of agricultural and forestry
No full textEvaluate the Driving Safety of the User Based on the Trajectory Collected: User-Specific Behavior for Safety Analysis
No full textThe author has granted permission for their work to be available to the general public.This study aims to evaluate the driving ability of users by collecting data from the CARLA simulator. Various types of data, such as collisions, line crossings, and traffic rule violations, are ordered to analyze the user's driving behavior. Initially, the User will provide information, such as age and driving experience, to evaluate their impact on driving ability. Now, The collected data will analyzed using Maximum Entropy Inverse Reinforcement Learning. The results provide insights into the user's driving behavior, including their driving preferences and patterns. Using information from can improve driving skills and reduce accidents in the real world if they follow the program interaction. Moreover, the study contributes to developing autonomous driving systems by identifying common driving patterns and behaviors. The study can help to design more personalized autonomous driving systems that mimic the user's driving style. In summary, this study provides insights into driver behavior analysis and the driving ability of users based on data collected during the CARLA simulation. The findings can improve driving safety by identifying areas where users need to improve their driving. Additionally, this project can use reinforcement learning to develop autonomous driving systems by providing an understanding of user behavior and preferences.Electrical and Computer Engineerin
Uncovering gene regulatory network using sparse Bayesian factor model
No full textThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.Response of cells to changing endogenous or exogenous conditions is governed by intricate networks of gene regulations including those by, most notably, transcription factors (TFs) and microRNAs (miRNAs). Due to technical limitations, the protein level expressions of TFs are difficult to measure, making computational reconstruction of transcriptional network usually a difficult task.
In this dissertation, the author proposes a novel Bayesian factor model for uncovering transcriptional networks regulated by TFs from microarray data, where the TF activities are modeled as the unknown factors. To enable accurate estimation of the TF activities and model the sparsity of TF regulation, the prior knowledge of the TF regulation is integrated based on the Bayesian framework and modeled by a spike-slab prior. Three particular aspects of the TF regulation are investigated in this dissertation. First, the author investigates correlated TF activates, where the correlation of non-negative TF activities are modeled by the Dirichlet mixture of rectified Gaussian distribution (DMrG). Second, the author investigates the modeling of the clustering effect among biological samples, which are due to, for instance, samples of patients with the same cancer subtype. To this end, a DMrG prior is introduced to data samples. Third, the author investigates the modeling of cooperative transcription regulations between TF and miRNAs, where a hybrid Bayesian factor model is proposed. For all three investigations, the author is able to develop the respective Gibbs sampling solutions for model parameter inference. The validity and effectiveness of the proposed Gibbs sampling solutions are demonstrated through simulated systems. The developed models are applied to the cancer expression profiling data.
The novelty and significance of this work lies in that: Firstly, a modeling framework of a Bayesian sparse factor model is proposed to model TF mediated regulation with the absence of knowledge on TF activities. Secondly, based on the framework, three different aspects of TF regulations are investigated including the correlated behavior of transcription factors, the clustering effect among biological samples, and the cooperative transcriptional regulations by both transcription factors and miRNAs. These problems are timely and open questions in molecular biology. Third, the application of the proposed models in cancer profiling data is new and suggests an alternative method for personalized cancer prognosis and diagnosis.Electrical and Computer Engineerin
LC/MS peptide alignment and identification approach based on replicate spectral data
No full textThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.Liquid Chromatography/Mass Spectrometry (LC-MS) is becoming a widely-used approach for quantifying the protein composition of complex samples. The LC-MS spectra show the intensity of a peptide feature with a specific mass-charge ratio (m/z) and retention time. This technology has been used to compare complex biological samples from multiple LC-MS experiments. One challenge for comparison is to match corresponding peptide features from different LC-MS experiments. Alignment corrects for experimental variations in the chromatography, which is an important technology for the comparison of LC-MS experiments.
The corresponding feature pair is two features that are generated exactly by the same peptide in replicates. There are two key steps for corresponding feature identification: alignment and identification. Alignment gives the corresponding and non-corresponding feature pairs together and the identification step can choose the corresponding feature out of the total pairs.
Before the alignment and identification steps, it is needed to perform LC peak detection accurately. Instead of checking MS templates at the base position, the author checks the consistency of isotope patterns on the premises that peptides produce consistent isotope patterns on scans within their elution periods. After accurate elution peak detection, the author obtains the candidate elution profiles for the peptides. The author verifies the interval detection method on SILAC data. The dissertation compared several quantification method based on the accurate interval detection. The performance of H/L ratio is much better than the result from Maxquant.
Common alignment methods use warping functions to correct elution time shifts between two different LC-MS datasets to identify corresponding features (LC peaks registered by the same peptide). Although a warping function can correct the mean difference of elution time shifts, it alone cannot resolve the ambiguity in alignment completely because elution time shifts are random. Instead the author explored the R-statistic to measure the similarity in LC peak shapes between corresponding feature pairs for alignment, which means the correlation between two elution profiles.
In Super-SILAC labeled data, based on MS/MS identifications, considered that the LC peak shape is an important factor for alignment, the author proposed a Statistical Corresponding Feature Identification Algorithm (SCFIA) based on both time shifts and the similarity of LC peak shapes between corresponding features. The author tested SCFIA on publicly available datasets and compared its performance with that of warping function based methods. The accuracy and the number of detected corresponding features are improved significantly.
In 18O labeled data, as the author mentioned above, warping functions are commonly used to correct elution time shifts, which cannot resolve the ambiguity completely because elution time shifts are unpredicted. So the author takes peak shape, labeling efficiency, peptide isotope pattern and peptide predicted elution time into consideration.
The author compared the algorithm, which is not only based on elution time shift but also many other parameters, to the other software. The result shows a great improvement.Electrical and Computer Engineerin
Supplementary file for "Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism"
No full textSupplemental MaterialTitle: Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolismAuthors: Yanan Hou, Huajie Dai, Na Chen, Zhiyun Zhao, Qi Wang, Tianzhichao Hou, Jie Zheng, Tiange Wang, Mian Li, Hong Lin, Shuangyuan Wang, Ruizhi Zheng, Jieli Lu, Yu Xu, Yuhong Chen, Ruixin Liu, Guang Ning, Weiqing Wang, Yufang Bi, Jiqiu Wang, Min XuSupporting informationTable S1. Clinical characteristics of study participants.Table S2. Weights of 144 gene transcripts included in wb-TRS, ordered from most negative to most positive associated with nonobese type 2 diabetes.Table S3. Association of z-score normalized wb-TRS with cardiometabolic traits in initial cohort.Table S4. Receiver operator characteristic curves for prediction of nonobese type 2 diabetes.Table S5. Top30 Reactome pathways associated with genes in wb-TRS.Fig S1. Flowchart of study participants selection.Fig S2. Cross-validation results for least absolute shrinkage and selection operator (LASSO) logistics-model for nonobese type 2 diabetes in training dataset.Fig S3. Violin plot of wb-TRS and gene transcripts most negatively (FUOM) and positively (RPF1) associated with nonobese type 2 diabetes.Fig S4. Stratified analysis. Data are odds ratio (OR) and 95% confidence interval (CI) calculated using multivariable logistic regression models. The results were adjusted for age, sex, body mass index, family history of diabetes, waist circumference, smoking and drinking status, education level, physical activity, systolic and diastolic blood pressure, total cholesterol, triglycerides, high-density and low-density lipoprotein cholesterol.</p
Supplementary file for "Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolism"
No full textTitle: Whole blood-based transcriptional risk score for nonobese type 2 diabetes predicts dynamic changes in glucose metabolismAuthors: Yanan Hou, Huajie Dai, Na Chen, Zhiyun Zhao, Qi Wang, Tianzhichao Hou, Jie Zheng, Tiange Wang, Mian Li, Hong Lin, Shuangyuan Wang, Ruizhi Zheng, Jieli Lu, Yu Xu, Yuhong Chen, Ruixin Liu, Guang Ning, Weiqing Wang, Yufang Bi, Jiqiu Wang, Min XuSupporting informationTable S1. Clinical characteristics of study participants.Table S2. Weights of 144 gene transcripts included in wb-TRS, ordered from most negative to most positive associated with nonobese type 2 diabetes.Table S3. Association of z-score normalized wb-TRS with cardiometabolic traits in initial cohort.Table S4. Receiver operator characteristic curves for prediction of nonobese type 2 diabetes.Table S5. Top30 Reactome pathways associated with genes in wb-TRS.Fig S1. Flowchart of study participants selection.Fig S2. Cross-validation results for least absolute shrinkage and selection operator (LASSO) logistics-model for nonobese type 2 diabetes in training dataset.Fig S3. Violin plot of wb-TRS and gene transcripts most negatively (FUOM) and positively (RPF1) associated with nonobese type 2 diabetes.Fig S4. Stratified analysis. Data are odds ratio (OR) and 95% confidence interval (CI) calculated using multivariable logistic regression models. The results were adjusted for age, sex, body mass index, family history of diabetes, waist circumference, smoking and drinking status, education level, physical activity, systolic and diastolic blood pressure, total cholesterol, triglycerides, high-density and low-density lipoprotein cholesterol.</p
Modeling and output feedback control of a floating ball inside a tube
No full textThe author has granted permission for their work to be available to the general public.This thesis presents modeling, simulation, and real-time output feedback control of a floating ball system that contains a noisy sensor feedback. It concentrates on the control of the vertical position of an object inside a tube pushed upward by an air flow. The procedures of this thesis are to obtain a dynamic model, introduce an effective filtering method, and implement the filtering method on the system in real-time.
The improvement of the floating object's convergence to a desired vertical position is demonstrated by comparing two control techniques with and without the filtering method. First, the results of the convergence of the system output are shown by using a Fuzzy Logic Controller. Then, the new convergence results are presented by using the Fuzzy Logic Controller with the Median Filter. Second, the convergence results of the system output are shown using the tracking controller. Then, again, the new convergence results are shown using the tracking controller with the Median Filter.
Removing the noise of the feedback in a floating ball system leads to less of the unwanted oscillatory response from the system, faster settling time of the floating object to a desired height, and disturbance rejection. This work may further improve an object's floating stability at a desired altitude of a floating object system containing a very noisy sensor feedback.Electrical and Computer Engineerin
3D simulation of scar formation post myocardial infarction using CT images
No full textThe author has granted permission for their work to be available to the general public.BACKGROUND: Myocardial infarction is a major contributor to death and disability worldwide. The key to reducing the devastating effects of myocardial infarction is to better understand the effects of the scar formations in the anterior left ventricle wall of the heart, and to one day restrict scar tissue growth. This study develops a method for simulating scar tissue growth post myocardial infarction on computed tomography (CT) heart images.
METHODS: In order to simulate scar tissue formations, I used CT images of mouse hearts and imported them into Slicer, a C++ coded, NIH-funded software designed for processing medical images in research studies. Canny edge detection was performed to segment the left ventricular wall and measure its thickness. A module was designed inside the Slicer frame work that outputs a series of images displaying scar tissue growth on the left ventricle based on user input of scar location and maximum size. These images were then displayed in a time simulation based on a nonlinear growth rate function derived from experimental data.
RESULTS: Binary threshold. Outside threshold, and Canny edge detection filters successfully segmented the wall of the left ventricle and measured its length. The module developed in Slicer demonstrated the ability to generate 3D images of scar tissue growth in the left ventricle. The scar growth was measured and found to be comparable to the input value for scar sizes smaller than thickness of the wall of the left ventricle. These images were displayed in a time simulation that matched an experimentally derived function for scar tissue growth rate.Electrical and Computer Engineerin
Toxic responses of cytochrome P450 sub-enzyme activities to heavy metals exposure in soil and correlation with their bioaccumulation in Eisenia fetida
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