On the balance of drift and selection: the evolution of the Orkney vole

Demography and selection have been under the spotlight for a long time in evolutionary biology. As human activities lead to increased risk of population fragmentation and biological invasion, both of which involve drastic demographic changes, the importance of advanced knowledge about the impacts of genetic drift in the short and long term is increasing. Populations on islands are ideal models to study adaptive and non-adaptive evolutionary processes simultaneously. In isolated island populations, the efficacy of purifying selection is reduced by genetic drift, leading to accumulation of deleterious variants in homozygous state, hence reduced fitness (mutation load). On the other hand, island populations often show phenotypic differences when compared to the continental populations (island syndrome), which are considered to be related to divergent selection posed by the contrast in environmental factors. In this thesis, I used the Orkney vole (Microtus arvalis orcadensis) as my model to investigate the genomic consequences of bottlenecks and long-term isolation. The Orkney vole was introduced by Neolithic farmers from the European continent, and isolated since introduction for over 5,000 years, providing a unique opportunity to study the long-term effects of isolation in nature. In Chapter 1, I reconstructed the detailed demographic history of Orkney populations and found that Orkney voles have been through a strong bottleneck related to the introduction. I further investigated the mutation load in Orkney populations and found high fixation of potential deleterious alleles. In Chapter 2, I looked at the genomic landscape of Orkney voles and found genome-wide relaxation of purifying selection. I performed genomic tests for divergent selective sweeps, and detected signatures indicating the reduction of positive selection in Orkney voles related to their increase of body size. The research of mutation load and selection in most species has been mainly constrained to autosomes so far. Inherited along with the autosomes, the sex chromosomes undergo disparate evolutionary paths not only because of their functions but also differences in ploidy. In Chapter 3, I first assembled the sex chromosomes of the common vole. With population genomic data, I found that the autosomes, X and Y chromosomes had different levels of genetic diversity, accumulation of deleterious alleles, and genetic responses to severe bottlenecks. Such differences are likely correlated to the ploidy of the chromosomes and sex-biased mutation rates

Demographic history and genomic consequences of 10,000 generations of isolation in a wild mammal.

Increased human activities caused the isolation of populations in many species-often associated with genetic depletion and negative fitness effects. The effects of isolation are predicted by theory, but long-term data from natural populations are scarce. We show, with full genome sequences, that common voles (Microtus arvalis) in the Orkney archipelago have remained genetically isolated from conspecifics in continental Europe since their introduction by humans over 5,000 years ago. Modern Orkney vole populations are genetically highly differentiated from continental conspecifics as a result of genetic drift processes. Colonization likely started on the biggest Orkney island and vole populations on smaller islands were gradually split off, without signs of secondary admixture. Despite having large modern population sizes, Orkney voles are genetically depauperate and successive introductions to smaller islands resulted in further reduction of genetic diversity. We detected high levels of fixation of predicted deleterious variation compared with continental populations, particularly on smaller islands, yet the fitness effects realized in nature are unknown. Simulations showed that predominantly mildly deleterious mutations were fixed in populations, while highly deleterious mutations were purged early in the history of the Orkney population. Relaxation of selection overall due to benign environmental conditions on the islands and the effects of soft selection may have contributed to the repeated, successful establishment of Orkney voles despite potential fitness loss. Furthermore, the specific life history of these small mammals, resulting in relatively large population sizes, has probably been important for their long-term persistence in full isolation

Regularized Functional Regression Models with Applications to Brain Imaging.

Positron emission tomography (PET) is an imaging technique that provides useful information about brain metabolism to help clinicians in the early diagnosis of Alzheimer's disease (AD). In order to identify the brain areas that show significant signals, many statistical methods have been developed for the analysis of brain imaging data. However, most of them neglect accounting for spatial information in imaging data. One way to address this problem is to treat each image as a realization of a functional predictor. This dissertation includes three research projects concerning regularized functional regression models via Haar wavelets for the analysis of brain imaging data, particularly PET images. The first project develops a lasso penalized 3D functional linear regression model by viewing PET image as a 3D functional predictor and cognitive impairment as the response variable, aiming to identify the most predictive voxels with the underlying assumption that only a few brain areas are truly predictive. The PET images are obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The second project concerns a lasso penalized 3D functional logistic regression model for classification of PET images from ADNI database. ADNI participants were classified into three groups during their initial visits: AD, Mild Cognitive Impairment (MCI) and Normal Control (NC). The model is applied to all the pairwise classifications using baseline PET images. The third project develops a regularized 3D multiple functional logistic regression model that can account for the group structure among voxels. Cerebral cortex can be partitioned into multiple regions. Treating each region as a group, within-group and groupwise regularization is imposed into the estimation to identify the most predictive voxels. This model is applied to the prediction of MCI-to-AD conversion using ADNI MCI subjects’ baseline PET images. All proposed models are evaluated through extensive simulation studies which are based on simulated data and slices extracted from ADNI PET images. Comparisons with existing methods for the prediction performance are also conducted using ADNI data. The results suggest that the proposed models are able to not only identify the predictive voxels, but also achieve higher prediction accuracy than existing methods in general.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99975/1/xuejwang_1.pd

GENHOP: An Image Generation Method Based on Successive Subspace Learning

Being different from deep-learning-based (DL-based) image generation methods, a new image generative model built upon successive subspace learning principle is proposed and named GenHop (an acronym of Generative PixelHop) in this work. GenHop consists of three modules: 1) high-to-low dimension reduction, 2) seed image generation, and 3) low-to-high dimension expansion. In the first module, it builds a sequence of high-to-low dimensional subspaces through a sequence of whitening processes, each of which contains samples of joint-spatial-spectral representation. In the second module, it generates samples in the lowest dimensional subspace. In the third module, it finds a proper high-dimensional sample for a seed image by adding details back via locally linear embedding (LLE) and a sequence of coloring processes. Experiments show that GenHop can generate visually pleasant images whose FID scores are comparable or even better than those of DL-based generative models for MNIST, Fashion-MNIST and CelebA datasets.Comment: 10 pages, 5 figures, accepted by ISCAS 202

Homography Estimation Based on Order-Preserving Constraint and Similarity Measurement

Copyright 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.Homography is an important concept that has been extensively applied in many computer vision applications. However, accurate estimation of the homography is still a challenging problem. The classical approaches for robust estimation of the homography are all based on the iterative RANSAC framework. In this paper, we explore the problem from a new perspective by finding four point correspondences between two images given a set of point correspondences. The approach is achieved by means of an order-preserving constraint and a similarity measurement of the quadrilateral formed by the four points. The proposed method is computationally efficient as it requires much less iterations than the RANSAC algorithm. But this method is designed for small camera motions between consecutive frames in video sequences. Extensive evaluations on both synthetic data and real images have been performed to validate the effectiveness and accuracy of the proposed approach. In the synthetic experiments, we investigated and compared the accuracy of three types of methods and the influence of the proportion of outliers and the level of noise for homography estimation. We also analyzed the computational cost of the proposed method and compared our method with the state-of-the-art approaches in real image experiments. The experimental results show that the proposed method is more robust than the RANSAC algorithm

The impacts of air pollution on human and natural capital in China: A look from a provincial perspective

Abstract Air quality has a significant impact on human health and natural systems worldwide. China, as one of the largest developing countries, faces very much serious air pollution and requires much attention. While the influences of air pollution on human or nature have been extensively investigated, few scholars considered the two effects of air pollution on human health and nature simultaneously based on the same framework. Indeed, human and nature coexist in the same biosphere on which they depend for their development and the impacts of air pollution on human health and nature occur at the same time with different and synergic effects. Only by considering both impacts we can develop a more comprehensive understanding of air pollution impacts, in particular including SO2, NO2, CO, PM10 and PM2.5. Impacts can be looked at from the point of view of damage provided and damage repair (health recovery, replacement cost). Therefore, considering the different pollutants and sectors, the influences of air pollution on human health and nature are accounted for in this study by applying the Emergy Accounting and Life Cycle Assessment Eco-indicator 99 methods under a unified framework in 31 provinces of China taken as case study. While LCA provides an accurate assessment of the direct consequences of pollution on human and natural capital (human health and biodiversity losses), the Emergy Accounting approach quantifies the biosphere work associated to repair or replace such losses over time. Furthermore, the spatial agglomeration characteristics of emissions, human and natural capital losses analyzed by means of Moran's I index. Results show that: (1) Concerning human capital losses, the amount of emissions of PM10 and PM2.5 only account for 10% of total impacts, compared to SO2, NO2, and CO emissions, but in some provinces cause more than 70% of human capital losses. And more than 80% of PM2.5 and PM10 that cause human capital losses come from the industrial and civil sectors. (2) As far as natural capital losses are concerned, compared with SO2, the losses caused by NO2 account for 80% in most provinces. And the power, industrial and transportation sectors are the three major sources of NO2 causing natural capital losses. (3) The spatial agglomeration characteristics, such as high-high cluster, high-low cluster, low-low cluster and low–high cluster, are different for air pollution emissions, human and natural capital losses. A comprehensive and detailed understanding of the impacts of air pollution is crucial for policy makers to take informed decisions

CVD growth and properties of boron phosphide on 3C-SiC

Citation: CVD growth and properties of boron phosphide on 3C-SiC, B. Padavala, C.D.Frye, X. Wang, B. Raghothamachar, and J.H. Edgar, Journal of Crystal Growth, volume 449 pp. 15-21 (2016).Improving the crystalline quality of boron phosphide (BP) is essential for realizing its full potential in semiconductor device applications. In this study, 3C-SiC was tested as a substrate for BP epitaxy. BP films were grown on 3C-SiC(100)/Si, 3C-SiC(111)/Si, and 3C-SiC(111)/4H-SiC(0001) substrates in a horizontal chemical vapor deposition (CVD) system. Films were produced with good crystalline orientation and morphological features in the temperature range of 1000–1200 °C using a PH3+B2H6+H2 mixture. Rotational twinning was absent in the BP due to the crystal symmetry-matching with 3C-SiC. Confocal 3D Raman imaging of BP films revealed primarily uniform peak shift and peak widths across the scanned area, except at defects on the surface. Synchrotron white beam X-ray topography showed the epitaxial relationship between BP and 3C-SiC was (100)(100)〈011〉〈011〉BP||(100)(100)〈011〉〈011〉3C-SiC and (111)(111)View the MathML source〈112̅〉BP||(111)(111)View the MathML source〈112̅〉3C-SiC. Scanning electron microscopy, Raman spectroscopy and X-ray diffraction analysis indicated residual tensile strain in the films and improved crystalline quality at temperatures below 1200 °C. These results indicated that BP properties could be further enhanced by employing high quality bulk 3C-SiC or 3C-SiC epilayers on 4H-SiC substrates

Protective effect of astragalus injection against myocardial injury in septic young rats via inhibition of JAK/STAT signal pathway and regulation of inflammation

Purpose: To investigate the protective effect of astragalus injection against myocardial injury in septic young rats, and the underlying mechanism of action. Methods: Seventy-two healthy Sprague Dawley (SD) rats were randomly selected and used to establish a young rat model of sepsis. The young rats were randomly divided into 3 groups: sham, model and astragalus injection groups. Each group had 24 young rats. Serum cardiac troponin I (cTnI), IL-10, IL-6, JAK2 and STAT3 were measured after op. Results: Compared with sham group, serum cTnI level in the model group was significantly higher, while serum cTnI level of the drug group was significantly lower than that of the model group (p < 0.05). Compared with model group, the level of IL-10 in the myocardial tissue of the drug group was significantly elevated, while IL-6 level was lower (p < 0.05). Relative to sham rats, myocardial JAK2 and STAT3 protein levels in model rats were high. However, myocardial JAK2 and STAT3 proteins in the drug-treated rats were significantly downregulated, relative to model rats (p < 0.05). Conclusion: Astragalus injection upregulates IL-10 and IL-6 in rats by inhibiting the activation of JAK/STAT signal pathway, and via maintenance of pro-inflammation/anti-inflammation balance. Thus, astragalus exerts protective effect against myocardial injury in sepsis, and can potentially be developed for use as such in clinical practice. Keywords: Astragalus injection, JAK/STAT signal pathway, Pro-inflammatory/anti-inflammatory imbalance, Sepsis, Myocardial injur

An efficient decision support system for flood inundation management using intermittent remote-sensing data

Abstract: Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model’s performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions