Proteomic Studies of the Influenza Virus-human Cell Interactions: The Responses of Host Cell Protein Expression to Viral Infection and the Novel Host Proteins that Interact with Virus Protein NS1

Influenza A viruses (IAVs) continue to be a threat to human health. Despite extensive studies, the mechanisms underlying the IAVs-host interactions during IAV infection remain elusive. We employed quantitative proteomic methods to systematically explore the host cell protein expression responses to IAV infection and examine the function of a critical IAV protein called NS1 by identifying its host binding partners. Specifically, we used a 2-dimentional gel electrophoresis (2-DE) based proteomic method to screen host proteins whose expression was substantially altered by IAV. One critical protein named IκB kinase-gamma (IKKγ) was found to be significantly down-regulated during IAV infection. Functional studies indicated that IKKγ and IAVs were mutually inhibitory and IKKγ might be the target for virus to inhibit IFN production. IAV protein NS1 is known to play critical roles in viral pathogenesis and host immune responses. Through 2-DE proteomic approach and mass spectrometry, we identified several novel host cellular proteins that were associated with NS1. First, we found that heterogeneous nuclear ribonucleoprotein A2/B1 (hnRNP A2/B1) interacted with NS1, affected replication, transcription, expression and nucleo-cytoplasmic translocation of NS1 mRNA, and the eventual whole virus replication. Second, two ATPase proteins, RUVBL1 and RUVBL2, were identified to associate with NS1 for regulation of cell apoptosis in the absence of IFNs. Third, based on previous finding of the interaction between a DEAD family protein designated as DDX100 and NS1 through a more sensitive proteomic approach called SILAC (stable isotope labeling with amino acids in cell culture), we found this interaction promoted virus replication through enhancing viral NS1 gene replication, transcription, and dsRNA unwinding. In summary, through quantitative proteomic, molecular and cell biology studies, we generated the global picture of host cell protein expression responses to IAV infection. For IAV NS1, several host cellular proteins were found to interact with NS1 to regulate the host cell action and virus proliferation

PHYSIOLOGICAL CHARACTERIZATION ON SEED AGING OF SIX NATIVE SHRUB SPECIES

Vegetation reclamation in oil-sands requires a consistent and adequate supply of seeds of native shrubs. However, annual seed production is erratic and seeds are usually short lived and insufficient for the reclamation projects. Seeds of six native shrub species including: Prunus virginiana, Prunus pensylvanica, Arctostaphylos uva-ursi, Shepherdia canadensis, Cornus sericea, and Viburnum edule were used to analyze physiological changes during storage and artificial aging processes. The shrub seeds were studied for one year during storage under eight different combinations of temperature (-20, 4, 22.5 °C), atmosphere (Air / N2) and relative humidity (RH; 7-8 % / 3-4 %). No significant differences were detected among the storage parameters after one year; however, sub-zero and N2 environments showed a potential in maintaining a higher seed vigour during storage. In the artificial aging experiment, seeds were subjected to 45 oC, 60 % RH for 5-25 d. For most shrub species, the seed viability decreased significantly after 10-15 d artificial aging and was down to 0 % after 20 d. The germination percentage declined already after 5 d; therefore, there was a delay in detecting viability loss using the tetrazolium test. Non-aged seeds and aged seeds of most collections showed significantly different seedling lengths, which indicated a negative effect of accelerated aging process on the seedling growth. The electrolyte conductivity, as well as seed dehydrin protein expression, is strongly correlated with the seed vigour, which can be used as seed quality assessment methods in seed longevity predicting. A loss of membrane integrity occurred during the accelerated seed aging processes, as indicated by an increased electrolyte conductivity that was negatively correlated with the seed viability and germination. During artificial aging process, heat stress of Prunus virginiana induced expression of dehydrins with a molecular mass of 27 kDa, which reached a detectable level after 5 d. The storage protocol developed in this study would benefit the adequate supply of viable shrub seeds for reclamation. With species-specific parameters taken into consideration, the artificial aging technique to predict seed longevity can be further expanded to other non-crop species used in reclamation of lands after oil extraction

Learning Robust Object Recognition Using Composed Scenes from Generative Models

Recurrent feedback connections in the mammalian visual system have been hypothesized to play a role in synthesizing input in the theoretical framework of analysis by synthesis. The comparison of internally synthesized representation with that of the input provides a validation mechanism during perceptual inference and learning. Inspired by these ideas, we proposed that the synthesis machinery can compose new, unobserved images by imagination to train the network itself so as to increase the robustness of the system in novel scenarios. As a proof of concept, we investigated whether images composed by imagination could help an object recognition system to deal with occlusion, which is challenging for the current state-of-the-art deep convolutional neural networks. We fine-tuned a network on images containing objects in various occlusion scenarios, that are imagined or self-generated through a deep generator network. Trained on imagined occluded scenarios under the object persistence constraint, our network discovered more subtle and localized image features that were neglected by the original network for object classification, obtaining better separability of different object classes in the feature space. This leads to significant improvement of object recognition under occlusion for our network relative to the original network trained only on un-occluded images. In addition to providing practical benefits in object recognition under occlusion, this work demonstrates the use of self-generated composition of visual scenes through the synthesis loop, combined with the object persistence constraint, can provide opportunities for neural networks to discover new relevant patterns in the data, and become more flexible in dealing with novel situations.Comment: Accepted by 14th Conference on Computer and Robot Visio

Reorganization of functional hubs in sleep and in epilepsy

Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is a non-invasive brain imaging technique that measures brain activity non-invasively. Functional connectivity (FC) quantifies how Blood-Oxygen-Level-Dependent (BOLD) signal of remote brain regions correlates with each other temporally. Using variety of methodologies such as Independent Component Analysis (ICA) or sparse dictionary learning, Resting-State Networks (RSNs) are consistently found in human brain connectome. Functional hubs denote the brain regions that exhibit connections denser than others, whereas connector hubs especially participate in inter-network communication. My Master thesis is based on a previously published methodology called Sparsity-based analysis of reliable k-hubness (SPARK), which estimates the functional hubs by counting the number of RSNs connected to each brain voxels. By acquiring simultaneous electroencephalogram (EEG)-fMRI, functional connectivity (FC) during sleep can also be investigated. In addition, functional connectivity has been commonly applied to find potential biomarkers for neurological disease, such as epilepsy. Therefore, in the first study of this thesis, we investigated functional segregation during a recovery nap after total sleep deprivation and its association with cognitive performance. We applied an algorithm called Hierarchical Segregation Index (HSI) based on the hubness estimated by SPARK. As a result, we found significant correlation between functional segregation during sleep and working memory performance after sleep. In the second study of this thesis, we investigated the different patterns of functional hub reorganization in temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE). By applying similar methods used in the first study, we found significant and exclusive functional hub alteration both in TLE and FLE. To conclude, in sleep, functional segregation during a whole night sleep and its association between cognitive performance can be further investigated. In TLE and FLE, further research of the hub alterations in subcortical structures will be of interest, and might serve as potential biomarkers for post-surgical outcomes

Assessment of topsoil removal as an effective method for vegetation restoration in farmed peatlands

Peatland areas have dramatically declined in the past century because of the demand for agriculture. Therefore, it is necessary to develop suitable techniques to preserve these unique ecosystems. We studied the effects of topsoil removal on vegetation restoration in silt- and sand-amended peatlands in Changbai Mountain, China. We observed that topsoil removal effectively improved soil nutrient levels and water holding capacity in the silt-amended peatland but exhibited no significant effect on the sand-amended peatland. Topsoil removal decreased the species richness in both silt- and sand-amended peatlands but did not have any effect on the plant cover and biomass in the sand-amended peatland. The coverage, density, and aboveground biomass of dominant species, namely, Carex schmidtii, significantly increased after topsoil removal in the silt-amended peatland. The target Carex species was absent from the sand-amended peatland. Redundancy analysis identified that the soil water content, soil organic carbon, total nitrogen, and total phosphorus explained the most variance in vegetation composition in the silt-amended peatland. Our results demonstrated that topsoil removal is necessary to reduce the weed seeds and promote the recolonization of peatland species, particularly the tussock-forming Carex, in the silt-amended peatland during restoration

Attosecond Entangled Photons from Two-Photon Decay of Metastable Atoms: A Source for Attosecond Experiments and Beyond

We propose the generation of attosecond entangled bi-photons in the extreme-ultraviolet regime by two-photon decay of a metastable atomic state as a source similar to spontaneous parametric down-conversion photons. The 1s2s $^1S_0$ metastable state in helium decays to the ground state by emission of two energy-time entangled photons with a photon bandwidth equal to the total energy spacing of 20.62 eV. This results in a pair correlation time in the attosecond regime making these entangled photons a highly suitable source for attosecond pump-probe experiments. The bi-photon generation rate from a direct four photon excitation of helium at 240 nm is calculated and used to assess some feasible schemes to generate these bi-photons. Possible applications of entangled bi-photons in attosecond time scale experiments, and a discussion of their potential to reach the zeptosecond regime are presented.Comment: 6 pages, 3 figures, and supplementary materia

Machine learning-based quantitative trading strategies across different time intervals in the American market

Stocks are the most common financial investment products and attract many investors around the world. However, stock price volatility is usually uncontrollable and unpredictable for the individual investor. This research aims to apply different machine learning models to capture the stock price trends from the perspective of individual investors. We consider six traditional machine learning models for prediction: decision tree, support vector machine, bootstrap aggregating, random forest, adaptive boosting, and categorical boosting. Moreover, we propose a framework that uses regression models to obtain predicted values of different moving average changes and converts them into classification problems to generate final predictive results. With this method, we achieve the best average accuracy of 0.9031 from the 20-day change of moving average based on the support vector machine model. Furthermore, we conduct simulation trading experiments to evaluate the performance of this predictive framework and obtain the highest average annualized rate of return of 29.57%

Distributed Estimation and Inference for Spatial Autoregression Model with Large Scale Networks

The rapid growth of online network platforms generates large-scale network data and it poses great challenges for statistical analysis using the spatial autoregression (SAR) model. In this work, we develop a novel distributed estimation and statistical inference framework for the SAR model on a distributed system. We first propose a distributed network least squares approximation (DNLSA) method. This enables us to obtain a one-step estimator by taking a weighted average of local estimators on each worker. Afterwards, a refined two-step estimation is designed to further reduce the estimation bias. For statistical inference, we utilize a random projection method to reduce the expensive communication cost. Theoretically, we show the consistency and asymptotic normality of both the one-step and two-step estimators. In addition, we provide theoretical guarantee of the distributed statistical inference procedure. The theoretical findings and computational advantages are validated by several numerical simulations implemented on the Spark system. Lastly, an experiment on the Yelp dataset further illustrates the usefulness of the proposed methodology