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PurposeTo construct and compare machine learning models for predicting the risk of gestational diabetes mellitus (GDM).MethodThe clinical data of 2048 pregnant women who gave birth at Shunde Women’s and Children’s Hospital of Guangdong Medical University between June 2019 and June 2021 were retrospectively collected. Logistic regression, backpropagation neural networks, random forests, and support vector machines were constructed with the R studio and Python software programs. The logistic regression and random forest models were used to identify significant influencing factors. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive performance and discriminative ability of the models, and the Hosmer-Lemeshow test was used to determine goodness of fit.ResultsAge, glycated hemoglobin, fasting blood glucose, white blood cell count, hemoglobin, and activated partial prothrombin time were identified as significant factors associated with GDM. The random forest model had the best prediction effect (accuracy, 78.07%; Youden index, 1.56). In all four models, AUC was greater than 78%. The Hosmer–Lemeshow fit test showed that all four models were a good fit.ConclusionIt was concluded that age, GHB, FBG, WBC, HB, and APTT are the more important related influencing factors or early predictors of gestational diabetes. Among the tested models, random forest was the best one for predicting the risk of GDM in early pregnancy.

Objective: To study the pregnancy and its prognosis and explore influence factors on pregnancy after Uterine fibroids (UFs) treatment by microwave ablation (MWA). Design：A prospective observational study. Setting: Department of Interventional Ultrasound in a single centre in China. Population: Patient of UFs with fertility desiring. Methods: Each patient with fertility desiring underwent MWA for UFs from August 2010 to September 2020 was interviewed regularly. Main Outcome Measures: Pregnancy situation and outcomes, multiple factors affecting pregnancy. Results:81 women were enrolled in the study. Among them, there were 45 patients with 50 pregnancies. The pregnancy rate was 55.56% (45/81), and the median conception time was 18 months (1–60 months). Natural and artificially assisted pregnancy rates were 48.15% (39/81) and 7.41% (6/81), respectively. The number of pregnancies that ended in spontaneous and induced abortion were 11 and 9, respectively. And 25 pregnancies resulted in deliveries, with 1 of patients having two deliveries. Cesarean sections were performed by 76% (19/25) of the gravida, and 24% (6/25) opted for a vaginal delivery. All the 25 newborns survived and were in good health at the end of the follow-up. 1 case had placental abruptions at 26 weeks gestation, while the others had no serious complications during the perinatal period. For UFs patients with fertility requirements, the patient’s age, history of spontaneous abortion and fibroid types are relevant factors affecting pregnancy after MWA. Conclusions: MWA could be considered a promising clinical treatment for women with UFs and plans for future pregnancy.

Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.

The emergence of coronavirus disease 2019 (COVID-19) demonstrates the importance of research on understanding and accurately modeling the transmission and spread of pandemic. In this paper, we consider a susceptible-exposed-infected-recovered-deceased (SEIRD) system of differential equations to describe relationship among the number of susceptible individuals, the number of exposed individuals who are transmitting the virus, the number of infected individuals among the exposed people, the number of recovered individuals from those infected, and the number of deaths from those infected in a town, state or country. Based on the empirical results of transmission process of COVID-19 in the United States from April 16th to June 30th, 2020, we consider a few cases of contact rate, incidence rate, recovery rate, and mortality rate to model the transmission and dynamics of the virus. Numerical analysis and analytical method are used to explore the dynamics and prediction of the pandemic.

In this paper, we develop functional ordinal logistic regression (FOLR) models to perform gene-based analysis of ordinal traits. In the proposed FOLR models, genetic variant data are viewed as stochastic functions of physical positions and the genetic effects are treated as a function of physical positions. The FOLR models are built upon functional data analysis which can be revised to analyze the ordinal traits and high dimension genetic data. The proposed methods are capable of dealing with dense genotype data which is usually encountered in analyzing the next-generation sequencing data. The methods are flexible and can analyze three types of genetic data: (1) rare variants only, (2) common variants only, and (3) a combination of rare and common variants. Simulation studies show that the likelihood ratio test statistics of the FOLR models control type I errors well and have good power performance. The proposed methods achieve the goals of analyzing ordinal traits directly, reducing high dimensionality of dense genetic variants, being computationally manageable, facilitating model convergence, properly controlling type I errors, and maintaining high power levels. The FOLR models are applied to analyze Age-Related Eye Disease Study data, in which two genes are found to strongly associate with four ordinal traits.

Electrowetting display (EWD) is one of the latest and most promising reflective displays. However, some defects are easily caused in a driving process. For example, the aperture ratio of pixels can be reduced due to oil splitting, and the grayscale cannot be stabilized due to charge trapping. These defects can be effectively solved by designing driving waveforms for EWDs. So, a power function driving waveform was proposed in this paper, which consisted of an oil splitting suppression stage, a direct current (DC) driving stage and an oil stabilization stage. Firstly, the relationships among luminance values, power constants and driving time were measured. An optimal oil splitting suppression stage was obtained, which could effectively inhibit oil splitting. Then, the response time could be reduced by a DC voltage in the DC driving stage. Finally, a voltage slope was tested during the oil stabilization stage, which was used to counteract voltage created by the charge trapping. The experimental results showed that compared with a linear function waveform, the response time could be shortened by 16.1%, and the luminance value could be increased by 3.8%. The aperture ratio and oil stability of EWD can be effectively improved by these findings, thereby increasing its potential application in the display field.

The global SARS-CoV-2 coronavirus pandemic continues to be devastating in many areas. Treatment options have been limited and convalescent donor plasma has been used by many centers to transfer passive neutralizing antibodies to patients with respiratory involvement. The results often vary by institution and are complicated by the nature and quality of the donor plasma itself, the timing of administration and the clinical aspects of the recipients. SARS-CoV-2 infection is known to be associated with an increase in the blood concentrations of several inflammatory cytokines/chemokines, as part of the overall immune response to the virus and consequential to mediated lung pathology. Some of these correlates contribute to the cytokine storm syndrome and acute respiratory distress syndrome, often resulting in fatality. A Phase IIa clinical trial at our institution using high neutralizing titer convalescent plasma transfer gave us the unique opportunity to study the elevations of correlates in the first 10 days after infusion. Plasma recipients were divided into hospitalized COVID-19 pneumonia patients who did not (Track 2) or did (Track 3) require mechanical ventilation. Several cytokines were elevated in the patients of each Track and some continued to rise through Day 10, while others initially increased and then subsided. Furthermore, elevations in MIP-1α, MIP-1β and CRP correlated with disease progression of Track 2 recipients. Overall, our observations serve as a foundation for further study of these correlates and the identification of potential biomarkers to improve upon convalescent plasma therapy and to drive more successful patient outcomes., Author summary COVID-19, the disease caused by the SARS-CoV-2 virus, has a varied clinical course with limited treatment options. While some patients mount a productive immune response leading to recovery, others progress to rapid respiratory deterioration that may require hospitalization and mechanical ventilation. Our institution conducted a clinical trial to evaluate the efficacy of convalescent plasma therapy (CPT) to treat patients hospitalized with COVID-19 pneumonia. In this arm of the study, we sought to examine immune analytes in donor plasma as well as evaluate the recipients’ plasma before CPT infusion, and at Day 3 and Day 10 post-CPT infusion. We found some analytes to be elevated in plasma donors, compared to healthy controls, even after recovery. Plasma composition in CPT recipients prior to infusion showed elevations in several analytes associated with immune activation. Some significant differences were seen in plasma composition in patients in our Track 2 cohort (hospitalized without mechanical ventilation) compared to the Track 3 cohort (hospitalized with mechanical ventilation). In addition, we obtained plasma samples for hospitalized COVID-19 patients that did not receive CPT and noted several differences in the course of immune analyte production over time compared to the CPT-treated patients.

Railway freight demand is a part of the railway freight market, so the understanding of this demand is the basis for the market-oriented reform of railway freight prices. The price elasticity of railway freight demand is one of the important tools to understand this demand. Therefore, based on a more reasonable freight rate calculation method and commodity demand indicator, this paper estimates the price elasticity of railway freight demand for some goods according to the commodities category of railway freight rate rules. Estimates show that the freight demand for coal and coke is inelastic while the non-metallic ore, cement and grain is elastic, which is mainly caused by the difference in transportation distance. Based on the estimated elasticity, this paper provides suggestions for the market-oriented reform of railway freight rate.

Construction of a new media interaction design system based on the HCI model and data mining is presented in this manuscript. The proposed model has the following novelties. (1) The scheduling algorithm based on round-robin and GPS model is a rate-based scheduling algorithm that guarantees delay guarantee by providing a certain rate guarantee for each queue. The delay-based algorithm directly takes the queuing time as a parameter and aims to provide delay guarantee. (2) For the actual network topology, the accuracy of algorithm in obtaining the quality of feasible solutions in the sparse and dense patterns of group members is very high, and it can even be considered that the optimal solution can always be found. (3) The rate at which the network transmits binary information is throughput, also known as bit rate or the bandwidth. Networks that support different applications need to meet different throughput needs. The biggest feature of multimedia information transmission is the continuous transmission of large amounts of data. Besides this, the HCI model is combined for the further design suggestions. The simulation results prove that the design accuracy and also the effectiveness are jointly promoted.

Formation and general characteristics of multi-dimensional tibetan traditional motion are discussed in a data mining perspective in this paper. In the proposed data analytic framework, the following aspects are considered. (1) Data with core characteristics do not have core characteristics of large sample data and we cannot use the existing methods to draw meaningful conclusions. (2) The multidimensional IRT observation score equivalent is obtained by converting the node value of each dimension capability in multidimensional test to the one-dimensional capability node value. (3) The decision algorithm accurately completely describes inference relationship between the condition attribute and the decision attribute in decision table. With these mentioned novelties, we conduct the analysis on the tibetan traditional motion feature analysis and the classifications. The experimental results prove that we can obtain a better performance using the proposed paradigm.

Background Some young people may become addicted to indoor tanning in a manner similar to other forms of addiction, but research on genetic associations with indoor tanning addiction remains limited. Purpose To examine if liabilities in genetic addiction reward pathways and psychiatric comorbidity influence the risk of indoor tanning addiction. Methods This was a cross-sectional study with a community sample of 292 non-Hispanic white young adult women aged 18–30 years who reported indoor tanning in the past year. Self-report measures included indoor tanning frequency, appearance orientation, depressive symptoms, and two screeners of tanning addiction. DNA samples were analyzed for 34 single nucleotide polymorphisms (SNPs) in candidate genes in addiction reward pathways. Results No SNPs were significantly associated with tanning addiction in univariate analyses after multiplicity adjustment. In multivariable analyses adjusting for indoor tanning frequency, appearance orientation, and depressive symptoms, variant genotypes (CC or CT) in two DRD2 dopamine receptor gene SNPs were associated with increased odds of indoor tanning addiction (rs4436578, odds ratio [OR]: 2.30, 95% confidence interval [CI]: 1.11–4.77; rs4648318, OR: 1.95, 95% CI: 1.02–3.72). Variant SNP genotypes interacted with depressive symptoms to increase the risk of indoor tanning addiction: OR: 10.79, 95% CI: 3.25, 35.80, OR: 13.60, 95% CI: 4.13, 44.78, respectively. Conclusions This study provides preliminary evidence that DRD2 dopamine receptor gene SNPs are associated with indoor tanning addiction and young women with variant genotypes and elevated depressive symptoms may be at higher risk. These preliminary results support a reward-based model for indoor tanning addiction and warrant further investigation.

The importance to integrate survival analysis into genetics and genomics is widely recognized, but only a small number of statisticians have produced relevant work toward this research direction. For unrelated population data, functional regression models have been developed to test for association between a quantitative/dichotomous/survival trait and genetic variants in a gene region. In major gene association analysis, these models have higher power than sequence kernel association tests (SKAT). In this paper, we extend this approach to analyze censored traits for family data or related samples using functional regression based mixed effect Cox models (FamCoxME). The FamCoxME model the effect of major gene as fixed mean via functional data analysis techniques, the local gene or polygene variations or both as random, and the correlation of pedigree members by kinship coefficients or genetic relationship matrix (GRM) or both. The association between the censored trait and the major gene is tested by likelihood ratio tests (FamCoxME FR LRT). Simulation results indicate that the LRT control the type I error rates accurately/conservatively and have good power levels when both local gene or polygene variations are modeled. The proposed methods were applied to analyze a breast cancer data set from the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA). The FamCoxME provides a new tool for gene-based analysis of family-based studies or related samples.

We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

The Tibetan Plateau is of fundamental ecological significance to China, Asia, and the world. In recent years, Tibetan grasslands have suffered from severe degradation due to climate change and anthropogenic disturbance. In this study, nitrogen (N) and phosphate were applied to a moderately degraded Kobresia pygmaea meadow and Stipa purpurea steppe in the arid alpine northern Tibetan Plateau. The results showed that with increasing nitrogenous fertilizer, the height, coverage, biomass, and importance value of the K. pygmaea population decreased whereas the population of S. purpurea exhibited the opposite trend. Application of a mixed fertilizer with the same amount of N and phosphorus (P) (5 g each per m2) doubled the biomass of the K. pygmaea meadow and increased the aboveground biomass of the S. purpurea steppe by 72.3%. The nitrogenous fertilizer increased the total biomass and belowground biomass of the S. purpurea steppe, whereas the mixed fertilizer was beneficial to aboveground grass recovery. Application of 10 g N + 5 g P m−2 fertilizer increased aboveground biomass by 164.8%, whereas the belowground biomass was less than the control by 4.7%. The N and P fertilizer did not affect soil pH, except for some changes in soil N and P contents.