Performance-based earthquake engineering (PBEE) has emerged as a powerful method of analysis and design philosophy in earthquake engineering and is leading the way to a new generation of seismic design guidelines. PBEE requires a comprehensive understanding of the earthquake response of Soil-Foundation-Structure-Interaction (SFSI) systems when damage occurs in the structural system during the earthquake. In the context of PBEE, this research combines finite element (FE) modeling and seismic response analysis of SFSI systems with state-of-the-art methods in response sensitivity and reliability analysis. New analytical and numerical methods are developed and existing algorithms adopted for studying the propagation of uncertainties in nonlinear static and dynamic analyses of SFSI systems and for probabilistic performance assessment of these systems. This research makes several contributions to reliability analysis of structural and SFSI systems. For the purpose of accurately and efficiently computing the response gradients, an 'exact' FE response sensitivity computation algorithm based on the Direct Differentiation Method (DDM) and available in the widely used FE analysis software framework OpenSees is further extended to various types of material models, finite elements and multi-point constraint equations used in modeling large-scale realistic SFSI systems. As a main contribution to this research, this sensitivity algorithm is extended to a multi-yield surface J₂ plasticity model used extensively to model clay soil materials in seismic response analysis. Related to response sensitivity analysis of SFSI systems, several issues are studied, such as discontinuities in response sensitivities, the relative importance of various soil and structural material parameters in regards to a specified aspect of the system response (i.e., response parameters). As contributions to the reliability analysis of structural and SFSI systems, several existing solution tools such as first-order reliability method (FORM), second-order reliability method (SORM), and various sampling techniques, such as importance sampling (IS) and orthogonal plane sampling (OPS), are implemented in OpenSees and/or further improved to solving reliability analysis problems of structural and SFSI systems. A powerful general-purpose optimization toolbox SNOPT, developed by Professor Philip Gill at UCSD, is integrated into the reliability analysis framework in OpenSees and customized for efficiently finding the design point(s) of structural and SFSI systems. For time variant reliability analysis, an existing mean upcrossing rate analysis algorithm is implemented in OpenSees and improved. It is found that the FORM approximation for mean upcrossing rate is significantly inaccurate, especially in cases of highly nonlinear response behavior of the system analyzed. In such case, the OPS method based on the design point(s) of the reliability problem significantly improves the FORM approximation of the mean upcrossing rate and therefore of the upper bound of the failure probability. In order to study the topology of limit-state surfaces (LSS) for reliability problems, a new visualization method called Multi-dimensional Visualization in Principal Plane (MVPP) is developed and implemented in OpenSees. The geometrical insight gained from the MVPP has led to the development of a new hybrid computational reliability method, called the DP-RS-Sim method, which combines the design point (DP) search, the response surface methodology (RS), and simulation techniques (Sim). This method is applied for the time invariant reliability analysis of a realistic nonlinear structural system. Several other closely related topics are studied. A simplified probabilistic response analysis method is developed taking advantage of DDM-based response sensitivity analysis. This method is then applied to a nonlinear structural and SFSI system. It is much more efficient than the crude Monte Carlo Simulation method and provides, at low computational cost, good estimates of the mean and standard deviation of the response for low to moderate level of material nonlinearity in the response. A general-purpose OpenSees- SNOPT based optimization framework was developed and applied to soil model updating problems using numerically simulated data. It is found that the optimization process is significantly more efficient when using the DDM-based over the FDM-based sensitivities. Additionally, nonlinear FE model updating is performed for an actual site, the Lotung downhole array in Taiwan, and based on data recorded during a 1986 earthquake

Strains of Francisella spp. were isolated from cooling water from an air conditioning system in Guangzhou, China. These strains are Gram negative, coccobacilli, non-motile, oxidase negative, catalase negative, esterase and lipid esterase positive. In addition, these bacteria grow on cysteine-supplemented media at 20 °C to 40 °C with an optimal growth temperature of 30 °C. Analysis of 16S rRNA gene sequences revealed that these strains belong to the genus Francisella. Biochemical tests and phylogenetic and BLAST analyses of 16S rRNA, rpoB and sdhA genes indicated that one strain was very similar to Francisella philomiragia and that the other strains were identical or highly similar to the Francisella guangzhouensis sp. nov. strain 08HL01032 we previously described. Biochemical and molecular characteristics of these strains demonstrated that multiple Francisella species exist in air conditioning systems.

Brain radiomics can reflect the characteristics of brain pathophysiology. However, the value of T1-weighted images, quantitative susceptibility mapping, and R2* mapping in the diagnosis of Parkinson's disease (PD) was underestimated in previous studies. In this prospective study to establish a model for PD diagnosis based on brain imaging information, we collected high-resolution T1-weighted images, R2* mapping, and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August 2019. According to the inclusion time, 123 PD patients and 121 healthy controls were assigned to train the diagnostic model, while the remaining 106 subjects were assigned to the external validation dataset. We extracted 1408 radiomics features, and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training dataset. The informative features so identified were then used to construct a diagnostic model for PD. The constructed model contained 36 informative radiomics features, mainly representing abnormal subcortical iron distribution (especially in the substantia nigra), structural disorganization (e.g., in the inferior temporal, paracentral, precuneus, insula, and precentral gyri), and texture misalignment in the subcortical nuclei (e.g., caudate, globus pallidus, and thalamus). The predictive accuracy of the established model was 81.1 ± 8.0% in the training dataset. On the external validation dataset, the established model showed predictive accuracy of 78.5 ± 2.1%. In the tests of identifying early and drug-naïve PD patients from healthy controls, the accuracies of the model constructed on the same 36 informative features were 80.3 ± 7.1% and 79.1 ± 6.5%, respectively, while the accuracies were 80.4 ± 6.3% and 82.9 ± 5.8% for diagnosing middle-to-late PD and those receiving drug management, respectively. The accuracies for predicting tremor-dominant and non-tremor-dominant PD were 79.8 ± 6.9% and 79.1 ± 6.5%, respectively. In conclusion, the multiple-tissue-specific brain radiomics model constructed from magnetic resonance imaging has the ability to discriminate PD and exhibits the advantages for improving PD diagnosis.

RNA viruses are a major human health threat. The life cycles of many highly pathogenic RNA viruses like influenza A virus (IAV) and Lassa virus depends on host mRNA, because viral polymerases cleave 5'-m7G-capped host transcripts to prime viral mRNA synthesis ("cap-snatching"). We hypothesized that start codons within cap-snatched host transcripts could generate chimeric human-viral mRNAs with coding potential. We report the existence of this mechanism of gene origination, which we named "start-snatching." Depending on the reading frame, start-snatching allows the translation of host and viral "untranslated regions" (UTRs) to create N-terminally extended viral proteins or entirely novel polypeptides by genetic overprinting. We show that both types of chimeric proteins are made in IAV-infected cells, generate T cell responses, and contribute to virulence. Our results indicate that during infection with IAV, and likely a multitude of other human, animal and plant viruses, a host-dependent mechanism allows the genesis of hybrid genes.