MULTIVARIATE FINITE MIXTURE GROUP-BASED TRAJECTORY MODELING WITH APPLICATION TO MENTAL HEALTH STUDIES

ABSTRACT Traditionally, two kinds of methods are applied in trajectory analysis: 1) hierarchical modeling based on a multilevel structure, or 2) latent growth curve modeling (LGCM) based on a covariance structure (Raudenbush & Bryk, 2002; Bollen & Curran, 2006). However, this thesis used a third trajectory analysis method: group-based trajectory modeling (GBTM). GBTM was an extension of the finite mixture modeling (FMM) method that has been widely used in various fields of trajectory analysis in the last 25 years (Nagin & Odgers, 2010). GBTM was able to detect unobserved subgroups based on the multinomial logit function (Nagin, 1999). As an extended form of FMM, GBTM parameters could be estimated using maximum likelihood estimation (MLE) procedures. Since FMMs had no closed-form solution to the maximum likelihood, the Expectation-Maximization (EM) algorithm would often be applied to find maximized likelihood (Schlattmann, 2009). However, GBTM used a different optimization method called the Quasi-Newton procedure to perform the maximization. This thesis studied both GBTM with a single outcome and trajectory modeling with multiple outcomes. Nagin constructed two extended trajectory models that can involve multiple outcomes. Group-based dual trajectory modeling (GBDTM) deals with two outcomes combined with comorbidity or heterotypic continuity, while group-based multi-trajectory modeling (GBMTM) could include more than two outcomes in one model with the same subgroup weights among the outcomes (Nagin, 2005; Nagin, Jones, Passos, & Tremblay, 2018; Nagin & Tremblay, 2001). The methodology was applied to the Korea health panel survey (KHPS) data, which included 3983 individuals who were 65 years old or older at the baseline. GBTM, GBDTM, and GBMTM were three approaches performed with two binary longitudinal outcomes - depression and anxiety. GBDTM was selected as the best model with this data set because it is more flexible than GBMTM when handling group membership, and unlike GBTM, GMDTM addressed the interrelationship between the outcomes based on conditional probability. Four iii depression trajectories were identified across eight years of follow-up: “low-flat” (n = 3641; 87.0%), “low-to-middle” (n = 205; 8.8%), “low-to-high” (n = 33; 1.3%) and “high-curve” (n = 104; 2.8%). Also, four anxiety trajectories were identified with: “low-flat” (n =3785; 92.5%), “low-to-middle” (n = 96; 4.7%), “high-to-low” (n =89; 2.2%) and “high-curve” (n = 13; 0.6%) trajectory groups. Female sex, the presence of more than three chronic diseases, and income-generating activity were significant risk factors for depression trajectory groups. Anxiety trajectory groups had the same risk factors except for the presence of more than three chronic diseases. To further study the GBTM, GBDTM and GBMTM approach, the simulation study was also performed based on two correlated repeatedly measured binary outcomes. Compared based on these two outcomes with different correlation levels (ρ = 0.1, 0.2, 0.4, 0.6). GBDTM was always a better model than GBTM when we were interested in the association between the two outcomes. GBMTM could be used instead of GBDTM when the correlation coefficients between two longitudinal outcomes were high

A clustering based transfer function for volume rendering using gray-gradient mode histogram

Volume rendering is an emerging technique widely used in the medical field to visualize human organs using tomography image slices. In volume rendering, sliced medical images are transformed into attributes, such as color and opacity through transfer function. Thus, the design of the transfer function directly affects the result of medical images visualization. A well-designed transfer function can improve both the image quality and visualization speed. In one of our previous paper, we designed a multi-dimensional transfer function based on region growth to determine the transparency of a voxel, where both gray threshold and gray change threshold are used to calculate the transparency. In this paper, a new approach of the transfer function is proposed based on clustering analysis of gray-gradient mode histogram, where volume data is represented in a two-dimensional histogram. Clustering analysis is carried out based on the spatial information of volume data in the histogram, and the transfer function is automatically generated by means of clustering analysis of the spatial information. The dataset of human thoracic is used in our experiment to evaluate the performance of volume rendering using the proposed transfer function. By comparing with the original transfer function implemented in two popularly used volume rendering systems, visualization toolkit (VTK) and RadiAnt DICOM Viewer, the effectiveness and performance of the proposed transfer function are demonstrated in terms of the rendering efficiency and image quality, where more accurate and clearer features are presented rather than a blur red area. Furthermore, the complex operations on the two-dimensional histogram are avoided in our proposed approach and more detailed information can be seen from our final visualized image

Adaptive Global Synchronization for a Class of Quaternion-Valued Cohen-Grossberg Neural Networks with Known or Unknown Parameters

In this paper, the adaptive synchronization problem of quaternion-valued Cohen–Grossberg neural networks (QVCGNNs), with and without known parameters, is investigated. On the basis of constructing an appropriate Lyapunov function, and utilizing parameter identification theory and decomposition methods, two effective adaptive feedback schemes are proposed, to guarantee the realization of global synchronization of CGQVNNs. The control gain of the above schemes can be obtained using the Matlab LMI toolbox. The theoretical results presented in this work enrich the literature exploring the adaptive synchronization problem of quaternion-valued neural networks (QVNNs). Finally, the reliability of the theoretical schemes derived in this work is shown in two interesting numerical examples

Transcriptome profiling of anthocyanin-related genes reveals effects of light intensity on anthocyanin biosynthesis in red leaf lettuce

Red leaf lettuce (Lactuca sativa L.) is popular due to its high anthocyanin content, but poor leaf coloring often occurs under low light intensity. In order to reveal the mechanisms of anthocyanins affected by light intensity, we compared the transcriptome of L. sativa L. var. capitata under light intensities of 40 and 100 μmol m−2 s−1. A total of 62,111 unigenes were de novo assembled with an N50 of 1,681 bp, and 48,435 unigenes were functionally annotated in public databases. A total of 3,899 differentially expressed genes (DEGs) were detected, of which 1,377 unigenes were up-regulated and 2,552 unigenes were down-regulated in the high light samples. By Kyoto Encyclopedia of Genes and Genomes enrichment analysis, the DEGs were significantly enriched in 14 pathways. Using gene annotation and phylogenetic analysis, we identified seven anthocyanin structural genes, including CHS, CHI, F3H, F3′H, DFR, ANS, and 3GT, and two anthocyanin transport genes, GST and MATE. In terms of anthocyanin regulatory genes, five MYBs and one bHLH gene were identified. An HY5 gene was discovered, which may respond to light-signaling and regulate anthocyanin structural genes. These genes showed a log2FC of 2.7–9.0 under high irradiance, and were validated using quantitative real-time-PCR. In conclusion, our results indicated transcriptome variance in red leaf lettuce under low and high light intensity, and observed a anthocyanin biosynthesis and regulation pattern. The data should further help to unravel the molecular mechanisms of anthocyanins influenced by light intensity

NSSIA: A New Self-Sovereign Identity Scheme with Accountability

Self-Sovereign Identity (SSI) is a new distributed method for identity management, commonly used to address the problem that users are lack of control over their identities. However, the excessive pursuit of self-sovereignty in the most existing SSI schemes hinders sanctions against attackers. To deal with the malicious behavior, a few SSI schemes introduce accountability mechanisms, but they sacrifice users' privacy. What's more, the digital identities (static strings or updatable chains) in the existing SSI schemes are as inputs to a third-party executable program (mobile app, smart contract, etc.) to achieve identity reading, storing and proving, users' self-sovereignty are weakened. To solve the above problems, we present a new self-sovereign identity scheme to strike a balance between privacy and accountability and get rid of the dependence on the third-party program. In our scheme, one and only individual-specific executable code is generated as a digital avatar-i for each human to interact with others in cyberspace without a third-party program, in which the embedding of biometrics enhances uniqueness and user control over their identity. In addition, a joint accountability mechanism, which is based on the shamir (t, n) threshold algorithm and a consortium blockchain, is designed to restrict the power of each regulatory authority and protect users' privacy. Finally, we analyze the security, SSI properties and conduct detailed experiments in term of the cost of computation, storage and blockchain gas. The analysis results indicate that our scheme resists the known attacks and fulfills all the six SSI properties. Compared with the state-of-the-art schemes, the extensive experiment results show that the cost is larger in server storage, blockchain storage and blockchain gas, but is still low enough for practical situations

Effect of Salt Tracer Dosages on the Mixing Process in the Water Model of a Single Snorkel Refining Furnace

The improvement in mixing conditions in a vacuum refining unit plays an important role in enhancing the purity and decarburization of molten steel. Mixing time is an important index to evaluate the operation efficiency of a metallurgical reactor. However, in water models, the effect of salt tracer dosages on the measured mixing time in a vacuum reactor is not clear. In this study, a water model of a Single Snorkel Refining Furnace (SSRF) was established to study the effect of salt solution tracer dosages on the mixing time of monitor points. The experimental results show that, in some areas at the top of the ladle, the mixing time decreases first and then increases when increasing the tracer dosage. Numerical simulation results show that, when the tracer dosage increases, the tracer flows downwards at a higher pace from the vacuum chamber to the bottom of the ladle. This may compensate for the injection time interval of large dosage cases. However, the mass fraction of the KCl tracer at the right side of the bottom is the highest, which indicates that there may be a dead zone. For the dimensionless concentration time curves and a 99% mixing time, at the top of the vacuum chamber, the curve shifts to the right side and the mixing time decreases gradually with the increase in tracer dosage. At the bottom of the ladle, with the increase in tracer dosage, the peak value of the dimensionless concentration time curve is increased slightly. The mixing time of the bottom of the ladle decreases significantly with the increase in tracer dosage. However, in the dead zone, the mixing time will increase when the tracer dosage is large. At the top of the ladle, the effect of the tracer dosage is not obvious. The mixing time of the top of the ladle decreases first and then increases when increasing the tracer dosage. In addition, the mixing time of the top of the ladle is the shortest, which means that sampling at the top of the ladle in industrial production cannot represent the entire mixing state in the ladle

Estimation of genetic parameters and their sampling variances for quantitative traits in the type 2 modified augmented design

The type 2 modified augmented design (MAD2) is an efficient unreplicated experimental design used for evaluating large numbers of lines in plant breeding and for assessing genetic variation in a population. Statistical methods and data adjustment for soil heterogeneity have been previously described for this design. In the absence of replicated test genotypes in MAD2, their total variance cannot be partitioned into genetic and error components as required to estimate heritability and genetic correlation of quantitative traits, the two conventional genetic parameters used for breeding selection. We propose a method of estimating the error variance of unreplicated genotypes that uses replicated controls, and then of estimating the genetic parameters. Using the Delta method, we also derived formulas for estimating the sampling variances of the genetic parameters. Computer simulations indicated that the proposed method for estimating genetic parameters and their sampling variances was feasible and the reliability of the estimates was positively associated with the level of heritability of the trait. A case study of estimating the genetic parameters of three quantitative traits, iodine value, oil content, and linolenic acid content, in a biparental recombinant inbred line population of flax with 243 individuals, was conducted using our statistical models. A joint analysis of data over multiple years and sites was suggested for genetic parameter estimation. A pipeline module using SAS and Perl was developed to facilitate data analysis and appended to the previously developed MAD data analysis pipeline (http://probes.pw.usda.gov/bioinformatics_ tools/MADPipeline/index.html)

CBCT evaluation of the upper airway morphological changes in growing patients of class II division 1 malocclusion with mandibular retrusion using twin block appliance: a comparative research.

OBJECTIVE: The purpose of this study was to evaluate the morphological changes of upper airway after Twin Block (TB) treatment in growing patients with Class II division 1 malocclusion and mandibular retrusion compared with untreated Class II patients by cone beam computed tomography (CBCT). MATERIALS AND METHODS: Thirty growing patients who have completed TB treatment were recruited into TB group. The control group (n = 30) was selected from the patients with the same diagnosis and without TB treatment. CBCT scans of the pre-treatment (T1) and post-treatment (T2) data of TB group and control data were collected. After three-dimensional (3D) reconstruction and registration of T1 and T2 data, the morphological changes of upper airway during TB treatment were measured. The statistical differences between T1 and T2 data of TB group as well as T2 and control data were accessed by t-test. RESULTS: During the TB treatment, the mandible moved advanced by 3.52 ± 2.14 mm in the horizontal direction and 3.77 ± 2.10 mm in the vertical direction. The hyoid bone was in a more forward and inferior place. The upper airway showed a significant enlargement in nasopharynx, oropharynx and hypopharynx. In addition, the nasopharynx turned more circular, and the oropharynx became more elliptic in transverse shape. However, the transverse shape of the hypopharynx showed no significant difference. After comparison between T2 and control data, only the horizontal movement of the hyoid bone, the volumetric expansion of the oropharynx and hypopharynx, and changes of the oropharyngeal transverse shape showed significant difference. CONCLUSION: Compared to the untreated Class II patients, the upper airway of growing patients with Class II division 1 malocclusion and mandibular retrusion showed a significant enlargement in the oropharynx and hypopharynx as well as a more elliptic transverse shape in the oropharynx, and the hyoid bone moved to an anterior position after TB treatment