THE ECOLOGICAL IMPORTANCE AND EVOLUTIONARY POTENTIAL OF PHENOTYPIC PLASTICITY IN NOVEL ENVIRONMENTS

Phenotypic plasticity refers to a genotype’s ability to produce different phenotypes in response to different environments. How organisms respond to environments through phenotypic plasticity can impact the fitness of individuals and thus the demography and even evolution of a population. Having environmentally relevant phenotypic responses could be especially important when a population encounters novel environments, where extinction risks are high such as at the edge of geographic ranges or when there are sudden environmental shifts. Although plasticity has been shown to facilitate the production of novel phenotypes in novel environments, it is less clear whether this leads to increased population survival. The first chapter of this dissertation addresses this question by investigating variation in phenotypic plasticity in a functional trait, alcohol dehydrogenase, and its effect on larval survival of Drosophila melanogaster in a novel alcohol environment. After a population colonizes a novel environment, the population often adapts to this new environment, and phenotypic plasticity has been proposed to facilitate trait evolution. I tested whether phenotypic plasticity could lead to increased fitness when organisms encounter a novel environment. The second chapter examines the genetic architecture of the functional trait alcohol dehydrogenase and its plasticity. Understanding the genetic architecture is important because it can influence the evolutionary response. Specifically, if the functional trait and its plasticity have shared genetic control, their evolution would be tightly linked, which could speed up the rates of evolution if selection on both the trait and plasticity was synergistic or constrain evolution if the direction of selection were divergent. Alternatively, if the trait and its plasticity had different genetic control, plasticity can evolve independently from the functional trait. I used quantitative trait loci mapping with the lines from the Drosophila Synthetic Population Resources to examine genetic architecture in historical and novel alcohol environments. The first two chapters focused on plastic responses to abiotic environments, about which we have a wealth of theoretical and empirical understanding. Natural populations, however, almost never exist alone without interacting with other organisms. Biotic interactions are important drivers of species distributions and trait evolution and new interactions are analogous to novel environments. Biotic interactions are predicted to influence plasticity evolution, but this has been challenging to test and has received little empirical attention. The third chapter explores how biotic interactions may influence trait and plasticity evolution using synthetic yeast (Saccharomyces cerevisiae) communities. I chose to use yeast as a study system because yeast has a short generation time and can be used to form relatively simple replicate communities to isolate the effects of the interaction types. Specifically, I compared competition and mutualism, because they have very different effects on resource dynamics, and I expected them to influence trait and plasticity evolution very differently. I used experimental evolution with communities engaged in either no interspecific interaction, exploitative competition, and resource exchange mutualism. Taken together, this dissertation examines the evolutionary importance of phenotypic plasticity in novel abiotic and biotic conditions and demonstrates that plasticity can be important for both population survival and subsequent evolution in novel environments

Fire retardancy of bis[2-(methacryloyloxy)ethyl] phosphate modified poly(methyl methacrylate) nanocomposites containing layered double hydroxide and montmorillonite

Copolymer nanocomposites were prepared by suspension copolymerization of bis[2-(methacryloyloxy)ethyl] phosphate and methyl methacrylate, together with bis(2-ethylhexyl) phosphate layered double hydroxide and a montmorillonite, Cloisite 93A. X-ray diffraction and transmission electron microscopy were used to characterize the morphology of nanocomposites and the dispersion of additives in the polymer. The thermal stability of the nanocomposites has been assessed by thermogravimetric analysis and cone calorimetry has been used to study the fire properties. Bis[2-(methacryloyloxy)ethyl] phosphate not only copolymerized with MMA, but also aids in the dispersion of additives in PMMA. The copolymer nanocomposites have better dispersion and higher degradation temperature and more char mass than the corresponding PMMA nanocomposites. The largest peak reduction in the heat release rate of the copolymer nanocomposites are 52 and 65% for LDH and MMT additives, respectively

Additional XPS Studies on the Degradation of Poly(Methyl Methacryalte) and Polystyrene Nanocomposites

XPS studies have been undertaken on exfoliated nanocomposites of polystyrene and poly(methyl methacrylate). One can clearly see that carbon is lost and that oxygen, silicon and aluminum accumulate at the surface of the degrading polymer. The concentration of aluminum at the surface is very low at the beginning of the experiment but makes a large jump at the same temperature at which carbon is lost and oxygen begins to accumulate at the surface. It appears that the ratio of silicon to aluminum changes as the polymer is lost. A brief discussion is given to explain the origin of oxygen at the surface

Variation of anions in layered double hydroxides: Effects on dispersion and fire properties

Layered double hydroxides (LDHs) are interesting materials for nanocomposite formation because one can vary the identity of the metals, the anions and the stoichiometry to see the effect of these on the ability of the nano-material to disperse in a polymer and to see what effect dispersion has on the properties of the polymer. In this study, the anions 2-ethylhexyl sulfate (SEHS), bis(2-ethylhexyl) phosphate (HDEHP) and dodecyl benzenesulfonate (SDBS) have been utilized as the charge balancing anions to synthesize organo-LDHs. Nanocomposites of poly(methyl methacrylate) (PMMA) and polystyrene (PS) with organo-LDHs were prepared both by melt blending and bulk polymerization. X-ray diffraction and transmission electron microscopy were used to characterize the morphology of the nanocomposites while the thermal stability and fire properties of nanocomposites were studied by thermogravimetric analysis and cone calorimetry; the mechanical properties are also investigated. In general, it is easier to disperse these organo-LDHs in PMMA than in PS, but the sulfate cannot be dispersed at the nanometer level in either material. The addition of these organo-LDHs does not affect the mechanical properties. The best fire properties are obtained with the sulfonate LDH, SDBS; the reduction in the peak heat release rate is almost 50% for both polymers

Multi-view Multi-label Fine-grained Emotion Decoding from Human Brain Activity

Decoding emotional states from human brain activity plays an important role in brain-computer interfaces. Existing emotion decoding methods still have two main limitations: one is only decoding a single emotion category from a brain activity pattern and the decoded emotion categories are coarse-grained, which is inconsistent with the complex emotional expression of human; the other is ignoring the discrepancy of emotion expression between the left and right hemispheres of human brain. In this paper, we propose a novel multi-view multi-label hybrid model for fine-grained emotion decoding (up to 80 emotion categories) which can learn the expressive neural representations and predicting multiple emotional states simultaneously. Specifically, the generative component of our hybrid model is parametrized by a multi-view variational auto-encoder, in which we regard the brain activity of left and right hemispheres and their difference as three distinct views, and use the product of expert mechanism in its inference network. The discriminative component of our hybrid model is implemented by a multi-label classification network with an asymmetric focal loss. For more accurate emotion decoding, we first adopt a label-aware module for emotion-specific neural representations learning and then model the dependency of emotional states by a masked self-attention mechanism. Extensive experiments on two visually evoked emotional datasets show the superiority of our method.Comment: Accepted by IEEE Transactions on Neural Networks and Learning System

Effective noninvasive zygosity determination by maternal plasma target region sequencing

Background: Currently very few noninvasive molecular genetic approaches are available to determine zygosity for twin pregnancies in clinical laboratories. This study aimed to develop a novel method to determine zygosity by using maternal plasma target region sequencing. Methods: We constructed a statistic model to calculate the possibility of each zygosity type using likelihood ratios (Li) and empirical dynamic thresholds targeting at 4,524 single nucleotide polymorphisms (SNPs) loci on 22 autosomes. Then two dizygotic (DZ) twin pregnancies, two monozygotic (MZ) twin pregnancies and two singletons were recruited to evaluate the performance of our novel method. Finally we estimated the sensitivity and specificity of the model in silico under different cell-free fetal DNA (cff-DNA) concentration and sequence depth. Results/Conclusions: We obtained 8.90 Gbp sequencing data on average for six clinical samples. Two samples were classified as DZ with L values of 1.891 and 1.554, higher than the dynamic DZ cut-off values of 1.162 and 1.172, respectively. Another two samples were judged as MZ with 0.763 and 0.784 of L values, lower than the MZ cut-off values of 0.903 and 0.918. And the rest two singleton samples were regarded as MZ twins, with L values of 0.639 and 0.757, lower than the MZ cut-off values of 0.921 and 0.799. In silico, the estimated sensitivity of our noninvasive zygosity determination was 99.90% under 10% total cff-DNA concentration with 2 Gbp sequence data. As the cff-DNA concentration increased to 15%, the specificity was as high as 97% with 3.50 Gbp sequence data, much higher than 80% with 10% cff-DNA concentration. Significance: This study presents the feasibility to noninvasively determine zygosity of twin pregnancy using target region sequencing, and illustrates the sensitivity and specificity under various detecting condition. Our method can act as an alternative approach for zygosity determination of twin pregnancies in clinical practice.Multidisciplinary SciencesSCI(E)2ARTICLE6null

Prenatal Detection of Aneuploidy and Imbalanced Chromosomal Arrangements by Massively Parallel Sequencing

Fetal chromosomal abnormalities are the most common reasons for invasive prenatal testing. Currently, G-band karyotyping and several molecular genetic methods have been established for diagnosis of chromosomal abnormalities. Although these testing methods are highly reliable, the major limitation remains restricted resolutions or can only achieve limited coverage on the human genome at one time. The massively parallel sequencing (MPS) technologies which can reach single base pair resolution allows detection of genome-wide intragenic deletions and duplication challenging karyotyping and microarrays as the tool for prenatal diagnosis. Here we reported a novel and robust MPS-based method to detect aneuploidy and imbalanced chromosomal arrangements in amniotic fluid (AF) samples. We sequenced 62 AF samples on Illumina GAIIx platform and with averagely 0.01× whole genome sequencing data we detected 13 samples with numerical chromosomal abnormalities by z-test. With up to 2× whole genome sequencing data we were able to detect microdeletion/microduplication (ranged from 1.4 Mb to 37.3 Mb of 5 samples from chorionic villus sampling (CVS) using SeqSeq algorithm. Our work demonstrated MPS is a robust and accurate approach to detect aneuploidy and imbalanced chromosomal arrangements in prenatal samples