The family Orobanchaceae including autotrophic, hemiparasitic, and holoparasitic species, is becoming a key taxa to study the evolution of chloroplast genomes in different lifestyles. But the early evolutionary trajectory in the transit from autotrophism to hemiparasitism still maintains unclear for the inadequate sampling. In this study, we compared 50 complete chloroplast genomes in Orobanchaceae, containing four newly sequenced plastomes from hemiparasitic Pedicularis, to elucidate the sequence variation patterns in the evolution of plastomes. Contrasted to the sequence and structural hypervariabilities in holoparasites, hemiparasitic plastomes exhibited high similarity to those of autotrophs in gene and GC contents. They are generally characterized with functional or physical loss of ndh/tRNA genes and the inverted small-single-copy region. Gene losses in Orobanchaceae were lineage-specific and convergent, possibly related to structural reconfiguration and expansion/contraction of the inverted region. Pseudogenization of ndh genes was unique in hemiparasites. At least in Pedicularis, the ndhF gene might be most sensitive to the environmental factors and easily pseudogenized when autotrophs transit to hemiparasites. And the changes in gene contents and structural variation potentially deeply rely on the feeding type. Selective pressure, together with mutational bias, was the dominant factor of shaping the codon usage patterns. The relaxed selective constraint, potentially with genome-based GC conversion (gBGC) and preferential codon usage, drive the fluctuation of GC contents among taxa with different lifestyles. Phylogenetic analysis in Orobanchaceae supported that parasitic species were single-originated while holoparasites were multiple-originated. Overall, the comparison of plastomes provided a good opportunity to understand the evolution process in Orobanchaceae with different lifestyles.Sexual Zika virus (ZIKV) transmission from men to women occurs less frequently than the often-detected high viral loads in semen would suggest, but worries that this transmission route predisposes to fetal damage in pregnant women remain. To better understand sexual ZIKV pathogenesis, we studied the permissiveness of the human female genital tract to infection and the effect of semen on this process. ZIKV replicates in vaginal tissues and primary epithelial cells from the vagina, ectocervix, and endocervix and induces an innate immune response, but also continues to replicate without cytopathic effect. Infection of genital cells and tissues is strongly inhibited by extracellular vesicles (EV) in semen at physiological vesicle-to-virus ratios. Liposomes with the same composition as semen EVs also impair infection, indicating that the EV's lipid fraction, rather than their protein or RNA cargo, is responsible for this anti-viral effect. Thus, EVs in semen potently restrict ZIKV transmission, but the virus propagates well once infection in the recipient mucosa has been established.When, why, and how does interpersonal forgiveness occur? These questions guided recent research that compared the relative abilities of empathy versus motivated reasoning models to account for the influence of relationship closeness on interpersonal forgiveness. Consistent support was provided for the Model of Motivated Interpersonal Forgiveness. This model hypothesizes that, following relationship transgressions, relationship closeness leads to a desire to maintain a relationship. Desire to maintain a relationship leads to motivated reasoning. And motivated reasoning fosters interpersonal forgiveness. The goal of the present research was to examine two concerns that emerged from the initial support for the Model of Motivated Interpersonal Forgiveness. First, were the measures of motivated reasoning and interpersonal forgiveness conflated, thus reducing the potential for empathy to account for interpersonal forgiveness? Second, did the analytic estimation used reduce the power to detect the mediational role of empathy? The present research examined these questions. When motivated reasoning was measured by thought listings (in addition to the original questionnaire items) and when the analytic estimation provided greater power, the Model of Motivated Interpersonal Forgiveness was replicated.Hypoxia (Hx) is a component of multiple disorders, including stroke and sleep-disordered breathing, which often precede or are comorbid with neurodegenerative diseases. However, little is known about how hypoxia affects the ability of microglia, resident CNS macrophages, to respond to subsequent inflammatory challenges that are often present during neurodegenerative processes. We, therefore, tested the hypothesis that hypoxia would enhance or "prime" microglial pro-inflammatory gene expression in response to a later inflammatory challenge without programmatically increasing basal levels of pro-inflammatory cytokine expression. To test this, we pre-exposed immortalized N9 and primary microglia to hypoxia (1% O2) for 16 h and then challenged them with pro-inflammatory lipopolysaccharide (LPS) either immediately or 3-6 days following hypoxic exposure.  https://www.selleckchem.com/products/Acadesine.html  We used RNA sequencing coupled with chromatin immunoprecipitation sequencing to analyze primed microglial inflammatory gene expression and modifications to histone H3 lysine 4 trimethylation (H3K4me3) at the promoters of primed genes. We found that microglia exhibited enhanced responses to LPS 3 days and 6 days post-hypoxia. Surprisingly, however, the majority of primed genes were not enriched for H3K4me3 acutely following hypoxia exposure. Using the bioinformatics tool MAGICTRICKS and reversible pharmacological inhibition, we found that primed genes required the transcriptional activities of NF-κB. These findings provide evidence that hypoxia pre-exposure could lead to persistent and aberrant inflammatory responses in the context of CNS disorders.We propose in this work a graph-based approach for automatic public health analysis using social media. In our approach, graphs are created to model the interactions between features and between tweets in social media. We investigated different graph properties and methods in constructing graph-based representations for population health analysis. The proposed approach is applied in two case studies (1) estimating health indices, and (2) classifying health situation of counties in the US. We evaluate our approach on a dataset including more than one billion tweets collected in three years 2014, 2015, and 2016, and the health surveys from the Behavioral Risk Factor Surveillance System. We conducted realistic and large-scale experiments on various textual features and graph-based representations. Experimental results verified the robustness of the proposed approach and its superiority over existing ones in both case studies, confirming the potential of graph-based approach for modeling interactions in social networks for population health analysis.