Comparison of techniques to reconstruct palaeoclimates of China

Investigating the past climate is helpful to distinguish humankind’s contribution on presentclimate change, and also helpful to understand the historic or geological evolution of global environmental conditions. A number of different types of records provide information about past climate changes, including petrology, sedimentology, glaciology, dendrochronology, palynology and others. Various different techniques can be used to make quantitative reconstructions from palaeodata. All of these methods have their shortcomings and advantages. In this thesis, I examine two reconstruction methods, which have been widely applied to make reconstructions of climate during pre-Quaternary times, specifically the coexistence approach and leaf traits analysis. The coexistence approach (CoA) assumes that the climate of a fossil assemblage can be defined from the overlap between the climate ranges of the individual taxa, where the climate range of each taxon is defined by the climate range under which it grows today. For taxa that are no longer extant, the range is defined as that of the nearest living relative (NLR). The method assumes that it is possible to define the modern climate tolerance accurately and also that taxa were physically present at the site. In Chapter 2, I test the impact of these two assumptions on CoA reconstructions of mean annual precipitation (MAP), mean annual temperature (MAT), mean temperature of the warmest month (MTWA) and mean temperature of the coldest month (MTCO) using modern pollen data from the Qinghai-Tibetan Plateau. I find that the data quality of NLRs and the exotic pollen seriously affect the reconstructed MAP, MAT, MTWA and MTCO. The uncertainties are also relatively large, especially for those three temperature parameters, even those two factors considered. Thus the complementary method should be explored. Methods based on the correlation between leaf physiognomy and climate variables provide an alternative approach to reconstructing past climate. The two most widely used methods are like leaf margin analysis (LMA) and the climate leaf analysis multivariate program (CLAMP). The disadvantages of these methods are identified in various studies, such as using the limited leaf traits and the problem of correlation within traits. Hence, we are exploring our leaf traits-climate model method. I first establish the modern relationships between leaf traits and climates (Chapter 3) based on a large data set of modern trait observations from China. I used logistic regression techniques to investigate the relationships between summer temperature (measured by the accumulated temperature sum above 0°C), plant-available moisture (measured by the ratio of actual to equilibrium transpiration) and seasonality (measured by the daily mean growing season temperature when temperatures are >5°C) and the frequencies of 25 leaf morphometric traits, collected from 98 sites sampling the range of climate and vegetation types found in China. Results show that these morphometric traits vary along climate gradients in a predictable and understandable way. Different traits combination can outline the specific climate space. Leaf traits responding to one or more climate variables indicate that traits could play multiple functions on the adaptation for the moisture and temperature. Many specific relationships between traits and bioclimate variables are conservative across all woody life forms. These findings lay a stronger foundation for using morphometric traits to reconstruct past climates. In Chapter 4, I apply the independent relationships between specific leaf traits and individual climate variables to build predictive models for estimating the length of the growing season (GDD0: growing degree days above a baseline of 0°C) and plant-available moisture (α: the ratio of actual to equilibrium evapotranspiration). I then apply these models to predict the paleoclimate of four fossil leaf floras: from the Fushun Basin (Eocene), from Shanwang Basin (Middle Miocene), from Xiaolongtan Basin (Late Miocene), and from Shengzhou (Pliocene) in China. These geological times are examples of climate intervals when CO₂ was higher than today, and as such provide opportunities to examine how the climate system has responded to enhanced greenhouse gas concentrations. Results show that our models have relatively small reconstruction biases under modern conditions. The reconstructed paleoclimates by the modified models are comparable with previous reconstructions, but our results show more constraints rather than large uncertainties like previous reconstructions. Paleoclimate changes for these four sites are consistent with the evolution of climates in this region and compatible with enhanced monsoon conditions during these high CO₂ intervals. In summary, my thesis makes three important contributions to the field of palaeoclimate reconstruction. Firstly, by quantifying the impact of extra-local pollen on climate reconstructions for the Tibetan Plateau, I demonstrate the unreliability of the coexistence approach when applied in open vegetation. This work suggests that the coexistence approach should only be used when pollen samples can be combined with e.g. macrofossil evidence that would demonstrate the local presence of individual species. Secondly, through applying generalised linear modelling (GLM) technique to establish the independent relationships between multiple climate variables and leaf morphometric traits, after removing the influence of interactions between these variables, I have shown why univariate correlations as used in standard methods such as leaf margin analysis (LMA) and the climate leaf analysis multivariate program (CLAMP) are noisy and unreliable. The GLM methodology provides a better way to use leaf traits to reconstruct climate. Finally, I have developed a new multi-model technique based on these independent trait-climate relationships to reconstruct palaeoclimates in China from fossil leaf floras, and demonstrated that this provides well-constrained estimates of temperature and moisture variables

Polymorphism of the Epidermal Growth Factor Receptor Extracellular Ligand Binding Domain: The Dimer Interface Depends on Domain Stabilization

Epidermal growth factor receptors (EGFRs) and their cytoplasmic tyrosine kinases play important roles in cell proliferation and signaling. The EGFR extracellular domain (sEGFR) forms a dimer upon the binding of ligands, such as epidermal growth factor (EGF) and transforming growth factor α (TGFα). In this study, multiple molecular dynamics (MD) simulations of the 2:2 EGF·sEGFR3−512 dimer and the 2:2 TGFα·sEGFR3−512 dimer were performed in solvent and crystal environments. The simulations of systems comprising up to half a million atoms reveal part of the structural dynamics of which sEGFR dimers are capable. The solvent simulations consistently exhibited a prominent conformational relaxation from the initial crystal structures on the nanosecond time scale, leading to symmetry breaking and more extensive contacts between the two sEGFR monomers. In the crystal control simulation, this symmetry breaking and compaction was largely suppressed by crystal packing contacts. The simulations also provided evidence that the disordered domain IV of sEGFR may act as a stabilizing spacer in the dimer. Thus, the simulations suggest that the sEGFR dimer can take diverse configurations in solvent environments. These biologically relevant conformations of the EGFR signal transduction network can be controlled by contacts among the structural domains of sEGFR and its ligands

Simulating Large-Scale Conformational Changes of Proteins by Accelerating Collective Motions Obtained from Principal Component Analysis

Enhanced sampling methods remain of continuing interest over the past decades because they are able to explore conformational space of proteins much more extensively than conventional molecular dynamics (MD) simulations. In this paper, we report a new sampling method that utilizes a few collective modes obtained from principal component analysis (PCA) to guide the MD simulations. Two multidomain proteins, bacteriophage T4 lysozyme and human vinculin, are studied to test the method. By updating the PCA modes with a proper frequency, our method can sample large-amplitude conformational changes of the proteins much more efficiently than standard MD. Since those PCA modes are calculated from structural ensembles generated by all-atom simulations, the method may overcome an inherent limitation called “tip effect” that would possibly appear in those sampling techniques based on coarse-grained elastic network models. The algorithm proposed here is potentially very useful in developing tools for flexible fitting of protein structures integrating cryo-electron microscope or small-angle X-ray scattering data

Table_2_Aggresome–Autophagy Associated Gene HDAC6 Is a Potential Biomarker in Pan-Cancer, Especially in Colon Adenocarcinoma.doc

BackgroundHistone deacetylase 6 (HDAC6) regulates cytoplasmic signaling networks through the deacetylation of various cytoplasmic substrates. Recent studies have identified the role of HDAC6 in tumor development and immune metabolism, but its specific function remains unclear.MethodsThe current study determined the role of HDAC6 in tumor metabolism and tumor immunity through a multi-database pan-cancer analysis. The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) datasets were used to determine the expression levels, prognosis, tumor progression, immune checkpoints, and immune metabolism of HDAC6 in 33 tumors. Pathways, immune checkpoints, immune neoantigens, immune microenvironment, tumor mutational burden (TMB), microsatellite instability (MSI), DNA mismatch repair (MMR), and the value of methyltransferases. The R package was used for quantitative analysis and panoramic description.ResultsIn the present study, we determined that HDAC6 is differentially expressed in pan carcinomas, and by survival, we found that HDAC6 was generally associated with the prognosis of pancreatic adenocarcinoma, Thymoma, and uveal melanoma, where low expression of HDAC6 had a significantly worse prognosis. Secondly, through this experiment, we confirmed that HDAC6 expression level was associated with tumor immune infiltration and tumor microenvironment, especially in PAAD. Finally, HDAC6 was associated with immune neoantigen and immune checkpoint gene expression profiles in all cancers in addition to TMB and MSI in pan-cancers.ConclusionHDAC6 is differentially expressed in pan-cancers and plays an essential role in tumor metabolism and immunity. HDAC6 holds promise as a tumor potential prognostic marker, especially in colon cancer.</p

Bayesian Inference of Dynamic Mediation Models for Longitudinal Data

Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time, which overlooks the dynamic nature of mediation effect. To address this issue, we propose dynamic mediation models that can capture the dynamic nature of the mediation effect. Specifically, we model the path parameters of mediation models as auto-regressive (AR) processes of time that can vary over time. Additionally, we define the mediation effect under the potential outcome framework, and examine its identification and causal interpretation. Bayesian methods utilizing Gibbs sampling are adopted to estimate unknown parameters in the proposed dynamic mediation models. We further evaluate our proposed models and methods through extensive simulations and illustrate their application through a real data application.</p

Fit for a Bayesian: An Evaluation of PPP and DIC for Structural Equation Modeling

Despite its importance to structural equation modeling, model evaluation remains underdeveloped in the Bayesian SEM framework. Posterior predictive p-values (PPP) and deviance information criteria (DIC) are now available in popular software for Bayesian model evaluation, but they remain underutilized. This is largely due to the lack of recommendations for their use. To address this problem, PPP and DIC were evaluated in a series of Monte Carlo simulation studies. The results show that both PPP and DIC are influenced by severity of model misspecification, sample size, model size, and choice of prior. The cutoffs PPP  7 work best in the conditions and models tested here to maintain low false detection rates and misspecified model selection rates, respectively. The recommendations provided in this study will help researchers evaluate their models in a Bayesian SEM analysis and set the stage for future development and evaluation of Bayesian SEM fit indices.</p

A multi-period home-based therapist routing and scheduling problem considering center-based rehabilitation using a hybrid discrete differential evolution algorithm

This is the experimental data of the manuscript entitled "A multi-period home-based therapist routing and scheduling problem considering center-based rehabilitation using a hybrid discrete differential evolution algorithm". All data are generated based on the benchmark instance from Solomon instances (Solomon, 1987) and Gehring and Homberger instances (Gehring & Homberger, 1999) and saved in the file with pkl format. 8 C-type instances, 8 R-type instances, and 4 RC-type instances from Solomon instances are selected and expanded into 40 small-scale and 40 medium-scale instances, and 5 Gehring and Homberger instances are adapted to generate 10 large-scale instances. These instances are distinguished by the number of patients. For example, instance C102-30 represents instance C102 is a small scale instance containing 30 patients.</p

Table_1_Aggresome–Autophagy Associated Gene HDAC6 Is a Potential Biomarker in Pan-Cancer, Especially in Colon Adenocarcinoma.doc

BackgroundHistone deacetylase 6 (HDAC6) regulates cytoplasmic signaling networks through the deacetylation of various cytoplasmic substrates. Recent studies have identified the role of HDAC6 in tumor development and immune metabolism, but its specific function remains unclear.MethodsThe current study determined the role of HDAC6 in tumor metabolism and tumor immunity through a multi-database pan-cancer analysis. The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) datasets were used to determine the expression levels, prognosis, tumor progression, immune checkpoints, and immune metabolism of HDAC6 in 33 tumors. Pathways, immune checkpoints, immune neoantigens, immune microenvironment, tumor mutational burden (TMB), microsatellite instability (MSI), DNA mismatch repair (MMR), and the value of methyltransferases. The R package was used for quantitative analysis and panoramic description.ResultsIn the present study, we determined that HDAC6 is differentially expressed in pan carcinomas, and by survival, we found that HDAC6 was generally associated with the prognosis of pancreatic adenocarcinoma, Thymoma, and uveal melanoma, where low expression of HDAC6 had a significantly worse prognosis. Secondly, through this experiment, we confirmed that HDAC6 expression level was associated with tumor immune infiltration and tumor microenvironment, especially in PAAD. Finally, HDAC6 was associated with immune neoantigen and immune checkpoint gene expression profiles in all cancers in addition to TMB and MSI in pan-cancers.ConclusionHDAC6 is differentially expressed in pan-cancers and plays an essential role in tumor metabolism and immunity. HDAC6 holds promise as a tumor potential prognostic marker, especially in colon cancer.</p

Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables

<p>Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent variables, structural equation modeling (SEM) and manifest regression analysis (MRA). Main findings included: (1) ESEM in general provided the least biased estimation of the regression coefficients; SEM was more biased than MRA given large cross-factor loadings. (2) MRA produced the most precise estimation, followed by ESEM and then SEM. (3) SEM was the least powerful in the significance tests; statistical power was lower for ESEM than MRA with relatively small target-factor loadings, but higher for ESEM than MRA with relatively large target-factor loadings. (4) ESEM showed difficulties in convergence and occasionally created an inflated type I error rate under some conditions. ESEM is recommended when non-ignorable cross-factor loadings exist.</p

The periodic therapist routing and scheduling problem with center-based rehabilitation

This is the experimental data of the manuscript entitled "A multi-period home-based therapist routing and scheduling problem considering center-based rehabilitation using a hybrid discrete differential evolution algorithm". All data are generated based on the benchmark instance from Solomon instances (Solomon, 1987) and Gehring and Homberger instances (Gehring & Homberger, 1999) and saved in the file with pkl format. 8 C-type instances, 8 R-type instances, and 4 RC-type instances from Solomon instances are selected and expanded into 40 small-scale and 40 medium-scale instances, and 5 Gehring and Homberger instances are adapted to generate 10 large-scale instances. These instances are distinguished by the number of patients. For example, instance C102-30 represents instance C102 is a small scale instance containing 30 patients.</p