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Fragaria × ananassa mannose-binding lectin gene, FaMBL1, is a member of strawberry G-type lectin family involved in strawberry defense. Genome-wide identification of G-type lectin gene family was carried out in F. vesca, where 133 G-lectin genes were found. Their expression profile was retrieved from available databases showing that many G-lectin genes are actively expressed during strawberry development or interaction with pathogens. Hence, stable transgenic strawberry plants overexpressing the FaMBL1 gene were generated to better investigate the role of this gene in defense. Transformed strawberry plants were selected and evaluated for their disease-related phytohormones content and reaction to biotic stresses revealing that jasmonic acid (JA) content decreased in overexpressing lines compared to wild type (WT). Petioles of overexpressing lines inoculated with C. fioriniae had lower disease incidence than WT, and leaves of overexpressing lines challenged by B. cinerea showed remarkably smaller lesion diameters compared to WT and higher chitinase 2-1 (FaChi2-1) gene expression. Our results indicate that FaMBL1 gene plays an important role in strawberry response to fungal diseases caused by C. fioriniae and B. cinerea.

Background. Diabetes is one of the most common diseases and major public health burdens worldwide. Type 2 diabetes mellitus (T2DM) is associated with chronic hepatitis C virus (HCV) infection, and lncRNAs play an important role in HCV-induced T2DM. We aimed to explore the effect of lncRNA AC040162.3 on HCV-induced T2DM. Methods. HCV was used to infect MIN6 cells to establish an in vitro model. HCV copy number and miRNA expression were detected by Real Time Quantitative PCR (RT-qPCR). Enzyme-Linked Immunosorbent Assay (ELISA) was used to detect the secretion of insulin, and methyl thiazolyl tetrazolium (MTT) was applied to analyze cell viability. Apoptosis was analyzed by Western blotting and flow cytometry. In addition, Western blotting and TdT-mediated dUTP Nick End Labeling (TUNEL) were used to analyze pyroptosis. Luciferase reporter assays were used to investigate the targeting relationship. Results. The expression of LncRNA AC040162.3 and NLRP3 was markedly increased in HCV–T2DM, while the expression of miR-223-3p was remarkably inhibited. In vitro experiments demonstrated that lncRNA AC040162.3 silencing or miR-223-3p overexpression remarkably alleviated HCV-induced T2DM deterioration by inhibiting cell apoptosis and pyroptosis and enhancing cell viability. We then demonstrated that silencing lncRNA AC040162.3 promoted the expression of miR-223-3p and that miR-223-3p bound to lncRNA AC040162.3 and the NLRP3 binding site. In addition, the protective effects of LncRNA AC040162.3 silencing in HCV-infected MIN6 cells were reversed by overexpression of NLRP3 or silencing of miR-223-3p. Conclusion. Silencing of lncRNA AC040162.3 alleviates the process of HCV-induced T2DM by governing the miR-223-3p/NLRP3 axis.

The current study used a longitudinal design to investigate how the four types of expected relational self-change affect future relationship satisfaction and commitment. Results indicated that participants had higher expected self-changes than their actual self-changes after three months, but their expected self-changes and actual self-changes were correlated. At Time 1 of their relationship, participants’ relationship satisfaction and commitment were related to their expected self-changes. However, controlling for Time 1 relationship satisfaction and commitment, expected self-changes predicted commitment, but not relationship satisfaction, three months later.

The purpose of this study is to explore how people’s self-disclosure to their romantic partners affects their health outcomes.

Strawberry (Fragaria × ananassa) is an important soft fruit but easily to be infected by pathogens. Anthracnose and gray mold are two of the most destructive diseases of strawberry which lead to serious fruit rot. The first chapter introduced strawberry anthracnose caused by Colletotrichum acutatum. The infection strategy, disease cycle and management of C. acutatum on strawberry were reported. Likewise, the second chapter summarized the infection strategy of Botrytis cinerea and the defense responses of strawberry. As we already know white unripe strawberry fruits are more resistant to C. acutatum than red ripe fruits. During the interaction between strawberry white/red fruit and C. acutaum, a mannose binding lectin gene, FaMBL1, was found to be the most up-regulated gene and induced exclusively in white fruit. FaMBL1 belongs to the G-type lectin family which has important roles in plant development and defense process. To get insight into the role of FaMBL1, genome-wide identification was carried out on G-type lectin gene family in Fragaria vesca and the results were showed in chapter 3. G-type lectin genes make up a large family in F. vesca. Active expression upon biotic/abiotic stresses suggested a potential role of G-lectin genes in strawberry defenses. Hence, stable transgenic strawberry plants with FaMBL1 gene overexpressed were generated. Transformed strawberry plants were screened and identified. The results were showed in chapter 4, content of disease-related phytohormone, jasmonic acid, was found decreased in overexpressing lines compared with wild type (WT). Petioles inoculated by C. fioriniae of overexpressing lines had lower disease incidence than WT. Leaves of overexpressing lines challenged by B. cinerea showed remarkably smaller lesion diameters compared with WT. The chitinase 2-1 (FaChi2-1) showed higher expression in overexpressing lines than in WT during the interaction with B. cinerea, which could be related with the lower susceptibility of overexpressing lines.

In time series analysis, it is essential to check whether the observations are obtained by one or by several different mechanisms of data generation. In recent years, this problem, which is known as a change-point or break-point detection problem, has become a question of renewed interest for many researchers. Problems of this type arise in a wide range of applications, including financial time series analysis (e.g. changing volatility), signal processing (e.g.structural analysis of EEG signals), geology data analysis (e.g. analysis of volcanic eruption series) and environmental applications (e.g. detecting changes in ecological systems due to climatic conditions crossing some critical thresholds). This thesis focuses on detecting the changes in the mean level of autoregressive processes. We develop the Cross Entropy method for estimating the locations of change-points as well as parameters of the process in each segment. In order to identify the number of change-points, we use the Minimum Description Length information criterion. We apply the proposed method to simulated and real data to illustrate the usefulness of the approach.

The change-point detection method is helpful in autoregressive time series modeling. The instability in the underlying model parameters may affect the forecasting accuracy and lead to biased data analysis. This thesis is trying to answer two questions: (a) whether the given correlated time series data is generated from a stationary stochastic process or different processes; (b) if the time series sequence is generated from different mechanisms, where are the starting points of each new mechanism. We have developed three algorithms to seek the solution, which can be categorized into two groups: the global segmentation group, including CE.AR algorithm and local segmentation group, including MCP2 method (Multiple Comparisons Procedure for Multiple Change Point) and HarmonicCPT algorithm. In Chapter 3, we develop the CE.AR method to estimate the unknown number and the locations of change-points in autoregressive time series. In order to find the optimal value of a performance function based on the Minimum Description Length principle, we modified the Cross-Entropy algorithm for the combinatorial optimization problem. Our numerical experiments show that the proposed approach is very efficient in detecting multiple change-points when the underlying process has moderate to substantial variations in the mean and the autocorrelation coefficient. However, due to the heavy computational cost, CE.AR method is not suitable for long sequence time series data. Therefore, we move to the approximate segmentation direction and develop the MCP2 method, which can be found in Chapter 4. The first step is to use a likelihood ratio scan based estimation technique to identify the potential change points to segment the time series. Next, modified parametric spectral discrimination tests are used to validate the proposed segments. The numerical study shows that our method performs not very well under stationary AR(1) scenarios. Therefore, in order to further improve the detection power, we shift our focus on testing if there is no change point in the sequence. Hence, in Chapter 5, we introduced the Harmonic mean p-value technique to combine all the p-values. The numerical study is conducted to demonstrate the performance of the proposed method across various scenarios and compared with other methods. The review of previous research on non-stationary time series segmentation methods is provided at the beginning, in Chapter 2.

The catalysts of hexaaluminate (BaMn x Al12−x O19−δ , x = 1.0, 2.0, 3.0, 4.0) to be used in methane combustion have been successfully synthesized by co-precipitation method and supercritical drying. The crystalline structure and surface area of catalyst were characterized by X-ray diffraction (XRD) and nitrogen adsorption analysis of BET method. BET analysis revealed that the preparing and drying method proposed here provides stable materials with higher surface area of 51.4 m2/g in comparison to materials prepared using conventional ambient drying method for BaMn x Al12−x O19−δ calcined at 1200° under oxygen. XRD analysis indicated that formation of a pure single phase BaMn x Al12−x O19−δ occurred up to x = 3 in the case of Mn-substituted barium hexaaluminates. Incorporation of Mn in excess leads to BaAl2O4 phase formation. As far as the valence state of Manganese ions was concerned, the introduced Mn ions were either divalent or trivalent. The first Mn ions were introduced in the matrix essentially as Mn2+ and only for BaMn3Al9O19−δ does manganese exist exclusively as Mn3+; the higher the Mn concentration, the higher the proportion of Mn3+. Catalytic activity for methane combustion has been measured for Mn-substituted barium hexaaluminates, light-off temperature was observed in the 512−624°C range. The highest activity was obtained for catalysts containing 3 Mn ions per unit cell, which reveals that the BaMn x Al12−x O19−δ catalyst was a promising methane combustion catalyst with high activity and good thermal stability. Temperature programmed reduction (TPR) under hydrogen has been used to correlate the catalytic activity with the amount of easily reducible species.

The catalysts of copper oxide supported on cerium dioxide were prepared by different methods for methane catalytic combustion. The effects of copper content in the catalysts and calcination temperatures of the catalysts on the catalytic activity are investigated. Results show that the complex oxide catalyst exhibits high catalytic activity for methane combustion due to the synergistic effect of CuO and CeO2. The catalyst prepared by impregnation is more active than that prepared by controlled coprecipitation even if CuO content is the same. When W (CuO)