Natural and value-added approaches for pathogen control

Infectious diseases caused by established foodborne pathogens, multidrug-resistant bacteria and opportunistic fungal pathogens pose serious threats to both public health and the economy. The aim of this work was to provide new approaches based on natural or value-added agricultural materials to address these important threats. Chapter 1 of this thesis provides a general introduction and description of how the work is organized. Chapter 2 reviews the literature related to our topics of research and Chapter 8 provides general conclusions. The remaining chapters report on the various projects that were conducted on the topics of natural and value-added approaches for pathogen control. Specifically, the cationic vegetable oil-based polyurethane dispersions and coatings described in Chapters 3, 4 and 5 of this work displayed promising antimicrobial properties against multiple human pathogens of both clinical and foodborne concern. The antimicrobial properties of these coatings were determined using a variety of cultural and instrumental approaches. Cultural methods included disk diffusion tests, time course plating and broth microdilution assay using a Bioscreen C Microbiological Reader. Instrumental approaches included flow cytometry for determination of cell membrane permeability, fluorescence and light microscopies for determining changes in cell and colony morphology and spectrophotometry for measuring the release of intracellular compounds. The use of multiple antimicrobial testing strategies allowed us to more fully characterize the cellular effects of polymer treatment and to gain clearer insight into polymer mode(s) of action (MOA). An understanding of MOA may help identify potential benefits and limitations of the practical use of these polymers in medical or food-related environments. Over the last three decades, Escherichia coli O157:H7, Salmonella Typhimurium and Listeria monocytogenes have emerged as the three most prevalent bacterial pathogens causing outbreaks in fruits and vegetables. Natural antimicrobial systems capable of inactivating these pathogens could provide attractive clean-label solutions for enhancing produce safety. In Chapters 6 and 7, antimicrobial hurdle systems comprised of GRAS natural antimicrobials having complementary or interactive modes of action were developed and examined. Systems containing Grape Seed Extract, long-chain sodium polyphosphate (polyP) and various organic acids were found to be effective against E. coli O157:H7 and S. Typhimurium in both broth culture and in lettuce extract, a simulant for the plant-based organic materials expected in produce processing waters and which may challenge the efficacy of applied antimicrobials. Data gathered using various methods demonstrated that use of polyP resulted in enhancement of GSE against L. innocua or L. monocytogenes. Lastly, pronounced differences in the antimicrobial efficacy of two commercial GSE’s were observed, suggesting that intrinsic biological or preparative variations resulting in different levels of antimicrobially-active polyphenolic components might be important for practical use of this ingredient. A greater understanding of the biochemical basis of these variations, coupled with quality control and standardization is needed before GSE can be widely adopted by the fresh produce industry. Together, the studies presented in this thesis provide natural and value-added approaches for pathogen control that may be useful in both medical and food production applications

Parallel numerical simulation of impact crater with perfect matched layers

Impact craters are the primary geomorphic features on the surfaces of celestial bodies such as the Moon, and their formation has significant implications for the evolutionary history of the celestial body. The study of the impact crater formation process relies mainly on numerical simulation methods, with two-dimensional simulations capable of reproducing general patterns of impact processes while conserving computational resources. However, to mitigate the artificial reflections of shock waves at numerical boundaries, a common approach involves expanding the computational domain, greatly reducing the efficiency of numerical simulations. In this study, we developed a novel two-dimensional code SALEc-2D that employs the perfect matched layer (PML) method to suppress artificial reflections at numerical boundaries. This method enhances computational efficiency while ensuring reliable results. Additionally, we implemented MPI parallel algorithms in the new code to further improve computational efficiency. Simulations that would take over ten hours using the conventional iSALE-2D code can now be completed in less than half an hour using our code, SALEc-2D, on a standard computer. We anticipate that our code will find widespread application in numerical simulations of impact craters in the future.Comment: 17 pages, 8 figure

Measuring the Configuration of Street Networks: The Spatial Profiles of 118 Urban Areas in the 12 Most Populated Metropolitan Regions in the US

In this paper we report an analysis of 118 urban areas sampled from the 12 largest metropolitan regions in the US. We deal with familiar measures of block size, street density, intersection density and distance between intersections. We also introduce two new variables, Reach and Directional Distance. Reach is the aggregate street length that can be accessed from the midpoint of each road segment subject to a limitation of distance. Directional distance is the average number of direction changes needed in order to access all the spaces within reach. We provide parametric definitions of these variables and implement their computation using new software which runs on standard GIS representations of street center lines

Robustness-Inspired Defense Against Backdoor Attacks on Graph Neural Networks

Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable to backdoor attacks, posing a significant threat to their real-world adoption. Despite initial efforts to defend against specific graph backdoor attacks, there is no work on defending against various types of backdoor attacks where generated triggers have different properties. Hence, we first empirically verify that prediction variance under edge dropping is a crucial indicator for identifying poisoned nodes. With this observation, we propose using random edge dropping to detect backdoors and theoretically show that it can efficiently distinguish poisoned nodes from clean ones. Furthermore, we introduce a novel robust training strategy to efficiently counteract the impact of the triggers. Extensive experiments on real-world datasets show that our framework can effectively identify poisoned nodes, significantly degrade the attack success rate, and maintain clean accuracy when defending against various types of graph backdoor attacks with different properties

Nonlinear robust control of tail-sitter aircrafts in flight mode transitions

© 2018 Elsevier Masson SAS In this paper, a nonlinear robust controller is proposed to deal with the flight mode transition control problem of tail-sitter aircrafts. During the mode transitions, the control problem is challenging due to the high nonlinearities and strong couplings. The tail-sitter aircraft model can be considered as a nominal part with uncertainties including nonlinear terms, parametric uncertainties, and external disturbances. The proposed controller consists of a nominal H∞controller and a nonlinear disturbance observer. The nominal H∞controller based on the nominal model is designed to achieve the desired trajectory tracking performance. The uncertainties are regarded as equivalent disturbances to restrain their influences by the nonlinear disturbance observer. Theoretical analysis and simulation results are given to show advantages of the proposed control method, compared with the standard H∞control approach

Exploring the relationship between distress rumination, resilience, depression, and self-injurious behaviors among Chinese college athletes infected with COVID-19: a cross-sectional study

ObjectivesDistress rumination is a cause of suicidality and self-injurious behavior (SSIB) among individuals. Although previous studies have shown that distress rumination, SSIB, resilience, and depression are significantly related, the interaction mechanism remains unclear. This study aimed to evaluate resilience and depression as mediators of the relationship between distress rumination and SSIB among Chinese college athletes infected with COVID-19.MethodsConvenience sampling was used to recruit participants from the National College Football Championship in Guangxi City, China from January to February 2023. Participants completed the Ruminative Responses Scale (RRS), a subscale of the Health-Risk Behavior Inventory (HBI), the Mental Toughness Index (MTI) and the Patient Health Questionnaire (PHQ-9). We used the PROCESS macro for SPSS to determine the mediating effect of resilience and depression between distress rumination and SSIB.ResultsA total of 350 Chinese college athletes participated in this study and completed the questionnaire survey. 289 (81.7% boys; Mage = 20.31 years, SD = 1.60) of them have been infected with COVID-19. 59.9% (n = 173) participants were from urban areas and 15.6% (n = 45) of participants have engaged in self-injurious behaviors or suicidal ideation. College athletes’ distress rumination was significantly negatively correlated with resilience (r = − 0.28, p < 0.01), and was significantly positively correlated with depression (r = 0.49, p < 0.01) and SSIB (r = − 0.18, p < 0.01). Resilience was significantly negatively correlated with depression (r = − 0.35, p < 0.01) and SSIB (r = − 0.30, p < 0.01). Finally, depression was significantly positively correlated with SSIB (r = − 0.38, p < 0.01). Resilience and depression played a mediating role of the total effects of distress rumination and SSIB, respectively. Meanwhile, the chain mediating effect of resilience and depression was also significant.ConclusionThis study found that distress rumination can directly predict SSIB, and indirectly predict SSIB through the mediating effect of resilience and depression, and the chain mediating effect of resilience-depression. Therefore, reducing the degree of distress rumination of college athletes infected by COVID-19 and improving their resilience, as well as reducing their depression may help prevent SSIB

Region Normalization for Image Inpainting

Feature Normalization (FN) is an important technique to help neural network training, which typically normalizes features across spatial dimensions. Most previous image inpainting methods apply FN in their networks without considering the impact of the corrupted regions of the input image on normalization, e.g. mean and variance shifts. In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation. RN divides spatial pixels into different regions according to the input mask, and computes the mean and variance in each region for normalization. We develop two kinds of RN for our image inpainting network: (1) Basic RN (RN-B), which normalizes pixels from the corrupted and uncorrupted regions separately based on the original inpainting mask to solve the mean and variance shift problem; (2) Learnable RN (RN-L), which automatically detects potentially corrupted and uncorrupted regions for separate normalization, and performs global affine transformation to enhance their fusion. We apply RN-B in the early layers and RN-L in the latter layers of the network respectively. Experiments show that our method outperforms current state-of-the-art methods quantitatively and qualitatively. We further generalize RN to other inpainting networks and achieve consistent performance improvements.Comment: Accepted by AAAI-202