Smoothing regression and impact measures for accidents of traffic flows.

Traffic pattern identification and accident evaluation are essential for improving traffic planning, road safety, and traffic management. In this paper, we establish classification and regression models to characterize the relationship between traffic flows and different time points and identify different patterns of traffic flows by a negative binomial model with smoothing splines. It provides mean response curves and Bayesian credible bands for traffic flows, a single index, and the log-likelihood difference, for traffic flow pattern recognition. We further propose an impact measure for evaluating the influence of accidents on traffic flows based on the fitted negative binomial model. The proposed method has been successfully applied to real-world traffic flows, and it can be used for improving traffic management.

Clustering Gene Expressions Using the Table Invitation Prior.

A prior for Bayesian nonparametric clustering called the Table Invitation Prior (TIP) is used to cluster gene expression data. TIP uses information concerning the pairwise distances between subjects (e.g., gene expression samples) and automatically estimates the number of clusters. TIP's hyperparameters are estimated using a univariate multiple change point detection algorithm with respect to the subject distances, and thus TIP does not require an analyst's intervention for estimating hyperparameters. A Gibbs sampling algorithm is provided, and TIP is used in conjunction with a Normal-Inverse-Wishart likelihood to cluster 801 gene expression samples, each of which belongs to one of five different types of cancer.

Weight Stigma Model on Quality of Life Among Children in Hong Kong: A Cross-Sectional Modeling Study.

We proposed a model to examine the relationship among different types of weight-related stigmas and their relationship to quality of life (QoL). We recruited 430 dyads of elementary school children [mean age = 10.07 years; nboy = 241 (56.0%); noverweight = 138 (32.1%)] and their parents. Parents completed QoL instruments about their children assessing generic QoL and weight-related QoL. Children completed QoL instruments assessing generic QoL and weight-related QoL and stigma scales assessing experienced weight stigma, weight-related self-stigma, and perceived weight stigma. Experienced weight stigma was significantly associated with perceived weight stigma, and in turn, perceived weight stigma was significantly associated with weight-related self-stigma. However, experienced weight stigma was not directly associated with weight-related self-stigma. In addition, experienced stigma was negatively associated with both child-rated and parent-rated QoL. Perceived weight stigma was associated only with parent-rated weight-related QoL but not child-rated QoL. Self-stigma was associated with child-rated QoL but not parent-rated QoL. Moreover, perceived weight stigma and weight-related self-stigma were significant mediators in the association between body weight and children's QoL; experienced weight stigma was not a significant mediator. The study findings can be used to inform healthcare providers about the relationship among different types of stigmas and their influence on child-rated and parent-rated QoL and help them develop interventions to address the global trend of overweight/obesity in youth and pediatric populations.

College students' sleep difficulty during COVID-19 and correlated stressors: A large-scale cross-sessional survey study.

Objective: Sleep difficulty is one of the main concerns during the COVID-19 pandemic. This study examined factors related to vaccination and physical and psychological health conditions, and sleep difficulty in college students in China. Methods: An online, cross-sectional, anonymous survey was used to investigate college students' perceived sleep difficulty and relevant components (i.e., physical health condition, psychological distress, knowledge of vaccine, and autonomy of vaccine uptake). Hierarchical ordinal logistic regression was conducted to examine the proposed model with the control of participants' demographics (i.e., gender and age). Results: Valid data of 3,145 students from 43 universities in mainland China was collected in January 2021. The average age of participants was 20.8 years old (S. D. = 2.09). The majority were single (97.4%), and about half were male (49.8%). Results showed that participants had less psychological distress when they had more knowledge about the COVID-19 vaccine and more autonomy to decide whether to receive it. In addition, participants with better physical health experienced less sleep difficulty. In contrast, those with more psychological distress experienced more sleep difficulty. Conclusions: These findings can inform healthcare providers about the relationship between different factors and difficulty sleeping and aid them in developing interventions addressing sleep difficulties associated with the global pandemic. Health authorities also can improve vaccine uptake and reduce hesitancies in future vaccination campaigns based on the study results showing that greater vaccine knowledge and autonomy reduced psychological distress.

Discrete Wavelet Packet Transform Based Discriminant Analysis for Whole Genome Sequences.

In recent years, alignment-free methods have been widely applied in comparing genome sequences, as these methods compute efficiently and provide desirable phylogenetic analysis results. These methods have been successfully combined with hierarchical clustering methods for finding phylogenetic trees. However, it may not be suitable to apply these alignment-free methods directly to existing statistical classification methods, because an appropriate statistical classification theory for integrating with the alignment-free representation methods is still lacking. In this article, we propose a discriminant analysis method which uses the discrete wavelet packet transform to classify whole genome sequences. The proposed alignment-free representation statistics of features follow a joint normal distribution asymptotically. The data analysis results indicate that the proposed method provides satisfactory classification results in real time.

A Novel Real-Time Genome Comparison Method Using Discrete Wavelet Transform.

Real-time genome comparison is important for identifying unknown species and clustering organisms. We propose a novel method that can represent genome sequences of different lengths as a 12-dimensional numerical vector in real time for this purpose. Given a genome sequence, a binary indicator sequence of each nucleotide base location is computed, and then discrete wavelet transform is applied to these four binary indicator sequences to attain the respective power spectra. Afterward, moments of the power spectra are calculated. Consequently, the 12-dimensional numerical vectors are constructed from the first three order moments. Our experimental results on various data sets show that the proposed method is efficient and effective to cluster genes and genomes. It runs significantly faster than other alignment-free and alignment-based methods.

Clustering DNA sequences using the out-of-place measure with reduced n-grams.

The alignment-free n-gram based method with the out-of-place measures as the distance has been successfully applied to automatic text or natural languages categorization in real time. However, it is not clear about its performance and the selection of n for comparing genome sequences. Here we propose a symmetric version of the out-of-place measure and a new approach for finding the optimal range of n to construct a phylogenetic tree with the symmetric out-of-place measures. Our method is then applied to real genome sequence datasets. The resulting phylogenetic trees are matching with the standard biological classification. It shows that our proposed method is a very powerful tool for phylogenetic analysis in terms of both classification accuracy and computation efficiency.

An ensemble distance measure of k-mer and Natural Vector for the phylogenetic analysis of multiple-segmented viruses.

The Natural Vector combined with Hausdorff distance has been successfully applied for classifying and clustering multiple-segmented viruses. Additionally, k-mer methods also yield promising results for global genome comparison. It is not known whether combining these two approaches can lead to more accurate results. The author proposes a method of combining the Hausdorff distances of the 5-mer counting vectors and natural vectors which achieves the best classification without cutting off any sample. Using the proposed method to predict the taxonomic labels for the 2363 NCBI reference viral genomes dataset, the accuracy rates are 96.95%, 94.37%, 99.41% and 93.82% for the Baltimore, family, subfamily, and genus labels, respectively. We further applied the proposed method to 48 isolates of the influenza A H7N9 viruses which have eight complete segments of nucleotide sequences. The single-linkage clustering trees and the statistical hypothesis testing results all indicate that the proposed ensemble distance measure can cluster viruses well using all of their segments of genome sequences.

Comparing logistic regression, support vector machines, and permanental classification methods in predicting hypertension.

In this paper, we compare logistic regression and 2 other classification methods in predicting hypertension given the genotype information. We use logistic regression analysis in the first step to detect significant single-nucleotide polymorphisms (SNPs). In the second step, we use the significant SNPs with logistic regression, support vector machines (SVMs), and a newly developed permanental classification method for prediction purposes. We also detect rare variants and investigate their impact on prediction. Our results show that SVMs and permanental classification both outperform logistic regression, and they are comparable in predicting hypertension status.

Applications of machine learning and data mining methods to detect associations of rare and common variants with complex traits.

Machine learning methods (MLMs), designed to develop models using high-dimensional predictors, have been used to analyze genome-wide genetic and genomic data to predict risks for complex traits. We summarize the results from six contributions to our Genetic Analysis Workshop 18 working group; these investigators applied MLMs and data mining to analyses of rare and common genetic variants measured in pedigrees. To develop risk profiles, group members analyzed blood pressure traits along with single-nucleotide polymorphisms and rare variant genotypes derived from sequence and imputation analyses in large Mexican American pedigrees. Supervised MLMs included penalized regression with varying penalties, support vector machines, and permanental classification. Unsupervised MLMs included sparse principal components analysis and sparse graphical models. Entropy-based components analyses were also used to mine these data. None of the investigators fully capitalized on the genetic information provided by the complete pedigrees. Their approaches either corrected for the nonindependence of the individuals within the pedigrees or analyzed only those who were independent. Some methods allowed for covariate adjustment, whereas others did not. We evaluated these methods using a variety of metrics. Four contributors conducted primary analyses on the real data, and the other two research groups used the simulated data with and without knowledge of the underlying simulation model. One group used the answers to the simulated data to assess power and type I errors. Although the MLMs applied were substantially different, each research group concluded that MLMs have advantages over standard statistical approaches with these high-dimensional data.

Global comparison of multiple-segmented viruses in 12-dimensional genome space.

We have recently developed a computational approach in a vector space for genome-based virus classification. This approach, called the "Natural Vector (NV) representation", which is an alignment-free method, allows us to classify single-segmented viruses with high speed and accuracy. For multiple-segmented viruses, typically phylogenetic trees of each segment are reconstructed for discovering viral phylogeny. Consensus tree methods may be used to combine the phylogenetic trees based on different segments. However, consensus tree methods were not developed for instances where the viruses have different numbers of segments or where their segments do not match well. We propose a novel approach for comparing multiple-segmented viruses globally, even in cases where viruses contain different numbers of segments. Using our method, each virus is represented by a set of vectors in R(12). The Hausdorff distance is then used to compare different sets of vectors. Phylogenetic trees can be reconstructed based on this distance. The proposed method is used for predicting classification labels of viruses with n-segments (n ⩾ 1). The correctness rates of our predictions based on cross-validation are as high as 96.5%, 95.4%, 99.7%, and 95.6% for Baltimore class, family, subfamily, and genus, respectively, which are comparable to the rates for single-segmented viruses only. Our method is not affected by the number or order of segments. We also demonstrate that the natural graphical representation based on the Hausdorff distance is more reasonable than the consensus tree for a recent public health threat, the influenza A (H7N9) viruses.

Real time classification of viruses in 12 dimensions.

The International Committee on Taxonomy of Viruses authorizes and organizes the taxonomic classification of viruses. Thus far, the detailed classifications for all viruses are neither complete nor free from dispute. For example, the current missing label rates in GenBank are 12.1% for family label and 30.0% for genus label. Using the proposed Natural Vector representation, all 2,044 single-segment referenced viral genomes in GenBank can be embedded in [Formula: see text]. Unlike other approaches, this allows us to determine phylogenetic relations for all viruses at any level (e.g., Baltimore class, family, subfamily, genus, and species) in real time. Additionally, the proposed graphical representation for virus phylogeny provides a visualization of the distribution of viruses in [Formula: see text]. Unlike the commonly used tree visualization methods which suffer from uniqueness and existence problems, our representation always exists and is unique. This approach is successfully used to predict and correct viral classification information, as well as to identify viral origins; e.g. a recent public health threat, the West Nile virus, is closer to the Japanese encephalitis antigenic complex based on our visualization. Based on cross-validation results, the accuracy rates of our predictions are as high as 98.2% for Baltimore class labels, 96.6% for family labels, 99.7% for subfamily labels and 97.2% for genus labels.

The effects of inverter magnetic fields on early seed germination of mung beans.

The biological effects of extremely low frequency magnetic fields (ELF MFs) on living organisms have been explored in many studies. Most of them demonstrate the biological effects caused by 50/60 Hz magnetic fields or pulsed magnetic fields. However, as the development of power electronics flourishes, the magnetic fields induced are usually in other different waveforms. This study aims to assess the effects of magnetic fields generated by inverter systems on the early growth of plants using mung beans as an example. In the experiment, an inverter which can produce sinusoidal pulsed width modulation (SPWM) voltages was used to drive 3 specially made circular coils and an AC motor. Six SPWM voltages with different fundamental frequencies (10, 20, 30, 40, 50, and 60 Hz) set on the inverter drive the circuit to produce the specific kinds of MFs. The results indicate that the magnetic field induced by a 20 or 60 Hz SPWM voltage has an enhancing effect on the early growth of mung beans, but the magnetic fields induced by SPWM voltages of other frequencies (30, 40, and 50 Hz) have an inhibitory effect, especially at 50 Hz.

Posterior correction and fixation without anterior fusion for pseudoarthrosis with kyphotic deformity in ankylosing spondylitis.

STUDY DESIGN: Retrospective review. OBJECTIVE: To assess the effectiveness of posterior correction and fixation without anterior fusion for pseudarthrosis with kyphosis in patients with ankylosing spondylitis. SUMMARY OF BACKGROUND DATA: Anterior fusion is the current surgical treatment for pseudarthrosis with kyphosis in ankylosing spondylitis. The unique characteristic in ankylosing spondylitis is the superior ability to bridge and fuse the large anterior opening-wedge gap created by posterior osteotomy to correct the kyphosis without anterior fusion after the osteotomy site is adequately fixed. This ability may persist even if pseudarthrosis is present. METHODS: A total of 30 consecutive patients with ankylosing spondylitis (mean age 41.7 years, range 29-55) underwent posterior correction and fixation without anterior fusion to treat pseudarthrosis with kyphosis. Mean follow-up was 4.7 years (range 2.2-9.1). Radiographic and clinical results and complications were assessed. RESULTS: Local kyphosis was substantially corrected from 45.5 degrees (range 37 degrees-68 degrees) to 7.5 degrees (0 degrees-14 degrees), with a mean correction of 38 degrees. All patients had no evidence of nonunion on x-ray at the level of the pseudarthrosis at final follow-up. None had a notable loss of correction. No major complication occurred. There were 3 patients with neurologic deficits who had postoperative improvement. CONCLUSION: Posterior correction and fixation is effective for treating pseudarthrosis with kyphosis in ankylosing spondylitis. No anterior fusion procedure was necessary.