Modified-half-normal distribution and different methods to estimate average treatment effect.

This dissertation consists of three projects related to Modified-Half-Normal distribution and causal inference. In my first project, a new distribution called Modified-Half-Normal distribution was introduced. I explored a few of its distributional properties, the procedures for generating random samples based on Bayesian approaches, and the parameter estimation based on the method of moments. The second project deals with the problem of selection bias of average treatment effect (ATE) if we use the observational data. I combined the propensity score based inverse probability of treatment weighting (IPTW) method and the directed acyclic graph (DAG) to solve this problem. The third project solves the problem of bias ATE using observational data by combining the doubly robust methods with the super learner algorithm

Differential repression of Otx2 underlies the capacity of NANOG and ESRRB to induce germline entry

Primordial germ cells (PGCs) arise from cells of the post-implantation epiblast in response to cytokine signaling. PGC development can be recapitulated in vitro by differentiating epiblast-like cells (EpiLCs) into PGC-like cells (PGCLCs) through cytokine exposure. Interestingly, the cytokine requirement for PGCLC induction can be bypassed by enforced expression of the transcription factor (TF) NANOG. However, the underlying mechanisms are not fully elucidated. Here, we show that NANOG mediates Otx2 downregulation in the absence of cytokines and that this is essential for PGCLC induction by NANOG. Moreover, the direct NANOG target gene Esrrb, which can substitute for several NANOG functions, does not downregulate Otx2 when overexpressed in EpiLCs and cannot promote PGCLC specification. However, expression of ESRRB in Otx2(+/−) EpiLCs rescues emergence of PGCLCs. This study illuminates the interplay of TFs occurring at the earliest stages of PGC specification

Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer

One of two-dimensional transition metal dichalcogenide materials, tungsten disulfide (WS2), has aroused much research interest, and its mechanical properties play an important role in a practical application. Here the mechanical properties of h-WS2 and t-WS2 monolayers in the armchair and zigzag directions are evaluated by utilizing the molecular dynamics (MD) simulations and machine learning (ML) technique. We mainly focus on the effects of chirality, system size, temperature, strain rate, and random vacancy defect on mechanical properties, including fracture strain, fracture strength, and Young’s modulus. We find that the mechanical properties of h-WS2 surpass those of t-WS2 due to the different coordination spheres of the transition metal atoms. It can also be observed that the fracture strain, fracture strength, and Young’s modulus decrease when temperature and vacancy defect ratio are enhanced. The random forest (RF) supervised ML algorithm is employed to model the correlations between different impact factors and target outputs. A total number of 3600 MD simulations are performed to generate the training and testing dataset for the ML model. The mechanical properties of WS2 (i.e., target outputs) can be predicted using the trained model with the knowledge of different input features, such as WS2 type, chirality, temperature, strain rate, and defect ratio. The mean square errors of ML predictions for the mechanical properties are orders of magnitude smaller than the actual values of each property, indicating good training results of the RF model

A preliminary evaluation of targeted nanopore sequencing technology for the detection of Mycobacterium tuberculosis in bronchoalveolar lavage fluid specimens

ObjectiveTo evaluate the efficacy of targeted nanopore sequencing technology for the detection of Mycobacterium tuberculosis(M.tb.) in bronchoalveolar lavage fluid(BALF) specimens.MethodsA prospective study was used to select 58 patients with suspected pulmonary tuberculosis(PTB) at Henan Chest Hospital from January to October 2022 for bronchoscopy, and BALF specimens were subjected to acid-fast bacilli(AFB) smear, Mycobacterium tuberculosis MGIT960 liquid culture, Gene Xpert MTB/RIF (Xpert MTB/RIF) and targeted nanopore sequencing (TNS) for the detection of M.tb., comparing the differences in the positive rates of the four methods for the detection of patients with different classifications.ResultsAmong 58 patients with suspected pulmonary tuberculosis, there were 48 patients with a final diagnosis of pulmonary tuberculosis. Using the clinical composite diagnosis as the reference gold standard, the sensitivity of AFB smear were 27.1% (95% CI: 15.3-41.8); for M.tb culture were 39.6% (95% CI: 25.8-54.7); for Xpert MTB/RIF were 56.2% (95% CI: 41.2-70.5); for TNS were 89.6% (95% CI: 77.3-96.5). Using BALF specimens Xpert MTB/RIF and/or M.tb. culture as the reference standard, TNS showed 100% (30/30) sensitivity. The sensitivity of NGS for pulmonary tuberculosis diagnosis was significantly higher than Xpert MTB/RIF, M.tb. culture, and AFB smear. Besides, P values of <0.05 were considered statistically significant.ConclusionUsing a clinical composite reference standard as a reference gold standard, TNS has the highest sensitivity and consistency with clinical diagnosis, and can rapidly and efficiently detect PTB in BALF specimens, which can aid to improve the early diagnosis of suspected tuberculosis patients

Influences of resolvin D1 and D2 on the risk of type 2 diabetes mellitus: a Chinese community-based cohort study

BackgroundAlthough cellular and animal studies have reported that resolvin D1 (RvD1) and resolvin D2 (RvD2) are mechanisms involved in the development of type 2 diabetes mellitus (T2DM), the impact of RvD1 and RvD2 on the risk of T2DM at a population level remains unclear.MethodsWe included 2755 non-diabetic adults from a community-based cohort in China and followed them for seven years. Cox proportional hazards model was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of RvD1 and RvD2 with T2DM probability. Time-dependent receiver operator characteristics (ROC) curve was used to evaluate the predictive performance of RvD1 and RvD2 for the risk of T2DM based on the Chinese CDC T2DM prediction model (CDRS).ResultsA total of 172 incident T2DM cases were identified. Multivariate-adjusted HRs (95% CI) for T2DM across quartiles of RvD1 levels (Q1, Q2, Q3 and Q4) were 1.00, 1.64 (1.03-2.63), 1.80 (1.13-2.86) and 1.61 (1.01-2.57), respectively. Additionally, body mass index (BMI) showed a significant effect modification in the association of RvD1 with incident T2DM (Pinteraction = 0.026). After multivariate adjustment, the HR (95% CI) for T2DM in the fourth compared with the first quartile of RvD2 was 1.94 (95% CI: 1.24-3.03). Time-dependent ROC analysis showed that the area under time-dependent ROC curves of the “CDRS+RvD1+RvD2” model for the 3-, 5- and 7-year risk of T2DM were 0.842, 0.835 and 0.828, respectively.ConclusionsHigher RvD1 and RvD2 levels are associated with a higher risk of T2DM at the population level