Targeted genome editing is a continually evolving technology employing programmable nucleases to specifically change, insert, or remove a genomic sequence of interest. These advanced molecular tools include meganucleases, zinc finger nucleases, transcription activator-like effector nucleases and RNA-guided engineered nucleases (RGENs), which create double-strand breaks at specific target sites in the genome, and repair DNA either by homologous recombination in the presence of donor DNA or via the error-prone non-homologous end-joining mechanism. A recently discovered group of RGENs known as CRISPR/Cas9 gene-editing systems allowed precise genome manipulation revealing a causal association between disease genotype and phenotype, without the need for the reengineering of the specific enzyme when targeting different sequences. CRISPR/Cas9 has been successfully employed as an ex vivo gene-editing tool in embryonic stem cells and patient-derived stem cells to understand pancreatic beta-cell development and function. RNA-guided nucleases also open the way for the generation of novel animal models for diabetes and allow testing the efficiency of various therapeutic approaches in diabetes, as summarized and exemplified in this manuscript.Bayesian optimal interval (BOIN) design is a model-assisted phase I dose-finding design to find the maximum tolerated dose (MTD). The hallmark of the BOIN design is its concise decision rule - making the decision of dose escalation and de-escalation by simply comparing the observed dose-limiting toxicity (DLT) rate at the current dose with a pair of optimal dose escalation and de-escalation boundaries. The interval 3+3 (i3+3) design is a recently proposed algorithm-based dose-finding design based on a similar decision rule with some modifications. The similarity in the appearance of the two designs has caused confusions among practitioners. In this article, we demystify the i3+3 design by elucidating its links with the BOIN design and compare their similarities and differences, as well as pros and cons. We perform comprehensive simulation studies to compare the operating characteristics of the two designs. Our results show that, compared to the algorithm-based i3+3 design, which are characterized by ad hoc and often scientifically and logically incoherent decision rules, the mode-assisted BOIN design is not only simpler, but also statistically more rigorous with better operating characteristics, thus providing a better design choice for phase I oncology trials.Social capital is widely recognized as health bolstering and more recently as playing a central role in family and community disaster response and recovery. Community social institutions may be considered a critical mechanism for the development of social capital, as they provide opportunities for community members to interact to build the networks and relationships that are necessary for taking collective action. In particular, social institutions may have a pivotal role to play in supporting children's health and welfare postdisaster. Community social institutions such as membership, civic, and religious organizations are community resources that stimulate learning and foster healthy child development. This study explores communities impacted by Hurricane Katrina and the Deepwater Horizon Oil Spill (DWHOS). Social institutions data were paired with household interviews from the Women and Their Children's Health Study (n = 521) to explore whether the density and type of community social institutions in the community were associated with child mental health outcomes. Multilevel logistic regression models examining the role of social institutions, household characteristics, maternal characteristics, and child-specific factors in child mental health showed that for each additional prosocial institution established in the community during recovery from Hurricane Katrina, respondents were 21% less likely to report a child mental health diagnosis (odds ratio 0.79; 95% confidence interval 0.63-0.98). These findings highlight the potential of investment in social institutions in communities to bolster resilience and foster meaningful recovery.Differences in population-level climate change beliefs have been identified, which are often attributable to coastline proximity, urban-rural classifications, race, ethnicity, political affiliation, gender, education, socioeconomic status, and age. This study assessed the impact of spatial, experiential, and demographic-related characteristics on climate change beliefs among a population of Hurricane Katrina survivors. Participants from the Gulf Coast Child and Family Health Study who answered climate change belief questions were included in this analysis. Race was found to be the most critical contributor to climate change belief, where the adjusted odds of white individuals believing in climate change were 0.2 times the odds of Black individuals believing in climate change (confidence interval 0.1-0.4). Other sociodemographic factors, such as age, gender, income, and education, were not found to be significant. https://www.selleckchem.com/products/icg-001.html Several theoretical perspectives were considered to explain the variation in climate change beliefs, including social vulnerability, environmental deprivation, and political ideology. Future research as to why these racial differences exist should be conducted. By doing so, climate change communication, education, and mitigation and adaptation strategies may be improved.[This corrects the article DOI 10.1093/aesa/saaa057.][This corrects the article DOI 10.1093/aesa/saaa057.].Despite the critical role that contact between hosts and vectors, through vector bites, plays in driving vector-borne disease (VBD) transmission, transmission risk is primarily studied through the lens of vector density and overlooks host-vector contact dynamics. This review article synthesizes current knowledge of host-vector contact with an emphasis on mosquito bites. It provides a framework including biological and mathematical definitions of host-mosquito contact rate, blood-feeding rate, and per capita biting rates. We describe how contact rates vary and how this variation is influenced by mosquito and vertebrate factors. Our framework challenges a classic assumption that mosquitoes bite at a fixed rate determined by the duration of their gonotrophic cycle. We explore alternative ecological assumptions based on the functional response, blood index, forage ratio, and ideal free distribution within a mechanistic host-vector contact model. We highlight that host-vector contact is a critical parameter that integrates many factors driving disease transmission.