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Alexander L. George was one of the most productive and respected political scientists of the late twentieth century. He and his wife, Juliette George, wrote one of the first psychobiographies, and Professor George went on to write seminal articles and books focusing on political psychology, the operational code, foreign policy decisionmaking,case study methodology, deterrence, coercive diplomacy, policy legitimacy, and bridging the gap between the academic and policymaking communities. This book is the first and only one to contain examples of the works across these fields written by Alexander George and several of his collaborators.

Background While there is increasing interest in identifying pregnancies at risk for adverse outcome, existing prediction models have not adequately assessed population-based risks, and have been based on conventional regression methods. The objective of the current study was to identify predictors of fetal growth abnormalities using logistic regression and machine learning methods, and compare diagnostic properties in a population-based sample of infants. Methods Data for 30,705 singleton infants born between 2009 and 2014 to mothers resident in Nova Scotia, Canada was obtained from the Nova Scotia Atlee Perinatal Database. Primary outcomes were small (SGA) and large for gestational age (LGA). Maternal characteristics pre-pregnancy and at 26 weeks were studied as predictors. Logistic regression and select machine learning methods were used to build the models, stratified by parity. Area under the curve was used to compare the models; relative importance of predictors was compared qualitatively. Results 7.9% and 13.5% of infants were SGA and LGA, respectively; 48.6% of births were to primiparous women and 51.4% were to multiparous women. Prediction of SGA and LGA was poor to fair (area under the curve 60–75%) and improved with increasing parity and pregnancy information. Smoking, previous low birthweight infant, and gestational weight gain were important predictors for SGA; pre-pregnancy body mass index, gestational weight gain, and previous macrosomic infant were the strongest predictors for LGA. Conclusions The machine learning methods used in this study did not offer any advantage over logistic regression in the prediction of fetal growth abnormalities. Prediction accuracy for SGA and LGA based on maternal information is poor for primiparous women and fair for multiparous women.

\"In celebration of the 50th anniversary of the Apollo 11 landing, the endlessly-mysterious moon is explored in this reprint short science fiction anthology from award-winning editor and anthologist Neil Clarke ... On July 20, 1969, mankind made what had only years earlier seemed like an impossible leap forward: when Apollo 11 became the first manned mission to land on the moon, and Neil Armstrong the first person to step foot on the lunar surface. While there have only been a handful of new missions since, the fascination with our planet's satellite continues, and generations of writers and artists have imagined the endless possibilities of lunar life. From adventures in the vast gulf of space between the earth and the moon, to journeys across the light face to the dark side, to the establishment of permanent residences on its surface, science fiction has for decades given readers bold and forward-thinking ideas about our nearest interstellar neighbor and what it might mean to humankind, both now and in our future. [This book] collects the best stories written in the fifty years since mankind first stepped foot on the lunar surface, serving as a shining reminder that the moon is and always has been our most visible and constant example of all the infinite possibility of the wider universe\"-- Provided by publisher.

PurposeUsing narratives from leading international academics and commentators, the authors chart four, possible, “universities of the future” models and discuss how current university management issues can enable or hinder them.Design/methodology/approachDeploying a Gioia methodology analysis of “University of the Future” narratives, the authors derive 12 categories of institutional properties and, ultimately, four distinct models.FindingsThe authors identify how current, classic and polytechnic institutions can adapt their operations and service delivery in order to transition into future-ready business models.Originality/valueThe authors interpret the opinions and predictions from world-leading experts in the higher education field in order to present the first, to our knowledge, typology of aspirational university models.

This Letter proposes a place-based approach to circular innovation. The original concept, as discussed by Cherrington et al. (2023), views ‘circular innovation’ as a strategy for sustainable development, focusing on resource efficiency and the regeneration of natural systems. However, we argue that it overlooks the significance of ‘place.’ This Letter argues that local conditions and contexts are crucial for effectively implementing circular innovations and maximizing their benefits. It advocates for tailoring circular strategies to local dynamics, leveraging local resources, and fostering community involvement. We identify five ‘loops’ that define a place-based approach to circular innovation, namely resource loops, social loops, economic loops, ecological loops, and policy loops. We argue that such a place-based approach supports the creation of localized, circular economies, emphasizing the importance of understanding and integrating the unique attributes of different locales into circular economy practices and policies.

In this randomized trial comparing delivery strategies in women with twin gestation, planned cesarean section did not significantly increase or decrease the risk of fetal or neonatal death or serious neonatal morbidity, as compared with planned vaginal delivery. Because of assisted reproductive technologies, twin pregnancy occurs more frequently now than in the past, and it complicates 2 to 3% of all births. 1 , 2 Twins are at higher risk for an adverse perinatal outcome than singletons. 3 , 4 Planned cesarean section, as compared with planned vaginal delivery, may reduce this risk. 5 Although a small, randomized, controlled trial did not show better perinatal outcomes with planned cesarean section than with planned vaginal delivery, 6 several cohort studies have shown a reduced risk of adverse perinatal outcomes for both twins, or for the second twin, when twins at or near term were delivered . . .

We conducted a linked database cohort study of infants born between 1990 and 2002 in Nova Scotia, Canada. Diagnoses of autism were identified from administrative databases with relevant diagnostic information to 2005. A factor representing genetic susceptibility was defined as having an affected sibling or a mother with a history of a psychiatric or neurologic condition. Among 129,733 children, there were 924 children with an autism diagnosis. The results suggest that among those with low genetic susceptibility, some maternal and obstetric factors may have an independent role in autism etiology whereas among genetically susceptible children, these factors appear to play a lesser role. The role of pre-pregnancy obesity and excessive weight gain during pregnancy on autism risk require further investigation.

As genome-editing nucleases move toward broader clinical applications, the need to define the limits of their specificity and efficiency increases. A variety of approaches for nuclease cleavage detection have been developed, allowing a full-genome survey of the targeting landscape and the detection of a variety of repair outcomes for nuclease-induced double-strand breaks. Each approach has advantages and disadvantages relating to the means of target-site capture, target enrichment mechanism, cellular environment, false discovery, and validation of bona fide off-target cleavage sites in cells. This review examines the strengths, limitations, and origins of the different classes of off-target cleavage detection systems including anchored primer enrichment (GUIDE-seq), in situ detection (BLISS), in vitro selection libraries (CIRCLE-seq), chromatin immunoprecipitation (ChIP) (DISCOVER-Seq), translocation sequencing (LAM PCR HTGTS), and in vitro genomic DNA digestion (Digenome-seq and SITE-Seq). Emphasis is placed on the specific modifications that give rise to the enhanced performance of contemporary techniques over their predecessors and the comparative performance of techniques for different applications. The clinical relevance of these techniques is discussed in the context of assessing the safety of novel CRISPR/Cas9 HIV-1 curative strategies. With the recent success of HIV-1 and SIV-1 viral suppression in humanized mice and non-human primates, respectively, using CRISPR/Cas9, rigorous exploration of potential off-target effects is of critical importance. Such analyses would benefit from the application of the techniques discussed in this review.

Inefficient knock-in of transgene cargos limits the potential of cell-based medicines. In this study, we used a CRISPR nuclease that targets a site within an exon of an essential gene and designed a cargo template so that correct knock-in would retain essential gene function while also integrating the transgene(s) of interest. Cells with non-productive insertions and deletions would undergo negative selection. This technology, called SLEEK (SeLection by Essential-gene Exon Knock-in), achieved knock-in efficiencies of more than 90% in clinically relevant cell types without impacting long-term viability or expansion. SLEEK knock-in rates in T cells are more efficient than state-of-the-art TRAC knock-in with AAV6 and surpass more than 90% efficiency even with non-viral DNA cargos. As a clinical application, natural killer cells generated from induced pluripotent stem cells containing SLEEK knock-in of CD16 and mbIL-15 show substantially improved tumor killing and persistence in vivo. Transgene knock-ins into housekeeping genes lead to high efficiency of cell selection.

Beverages, especially sugar-sweetened beverages (SSBs), have been increasingly subject to policies aimed at reducing their consumption as part of measures to tackle obesity. However, precision targeting of policies is difficult as information on what types of consumers they might affect, and to what degree, is missing. We fill this gap by creating a typology of beverage consumers in Great Britain (GB) based on observed beverage purchasing behaviour to determine what distinct types of beverage consumers exist, and what their socio-demographic (household) characteristics, dietary behaviours, and weight status are. We used cross-sectional latent class analysis to characterise patterns of beverage purchases. We used data from the 2016 GB Kantar Fast-Moving Consumer Goods (FMCG) panel, a large representative household purchase panel of food and beverages brought home, and restricted our analyses to consumers who purchase beverages regularly (i.e., >52 l per household member annually) (n = 8,675). Six categories of beverages were used to classify households into latent classes: SSBs; diet beverages; fruit juices and milk-based beverages; beer and cider; wine; and bottled water. Multinomial logistic regression and linear regression were used to relate class membership to household characteristics, self-reported weight status, and other dietary behaviours, derived from GB Kantar FMCG. Seven latent classes were identified, characterised primarily by higher purchases of 1 or 2 categories of beverages: 'SSB' (18% of the sample; median SSB volume = 49.4 l/household member/year; median diet beverage volume = 38.0 l), 'Diet' (16%; median diet beverage volume = 94.4 l), 'Fruit & Milk' (6%; median fruit juice/milk-based beverage volume = 30.0 l), 'Beer & Cider' (7%; median beer and cider volume = 36.3 l; median diet beverage volume = 55.6 l), 'Wine' (18%; median wine volume = 25.5 l; median diet beverage volume = 34.3 l), 'Water' (4%; median water volume = 46.9 l), and 'Diverse' (30%; diversity of purchases, including median SSB volume = 22.4 l). Income was positively associated with being classified in the Diverse class, whereas low social grade was more likely for households in the classes SSB, Diet, and Beer & Cider. Obesity (BMI > 30 kg/m2) was more prevalent in the class Diet (41.2%, 95% CI 37.7%-44.7%) despite households obtaining little energy from beverages in that class (17.9 kcal/household member/day, 95% CI 16.2-19.7). Overweight/obesity (BMI > 25 kg/m2) was above average in the class SSB (66.8%, 95% CI 63.7%-69.9%). When looking at all groceries, households from the class SSB had higher total energy purchases (1,943.6 kcal/household member/day, 95% CI 1,901.7-1,985.6), a smaller proportion of energy from fruits and vegetables (6.0%, 95% CI 5.8%-6.3%), and a greater proportion of energy from less healthy food and beverages (54.6%, 95% CI 54.0%-55.1%) than other classes. A greater proportion of energy from sweet snacks was observed for households in the classes SSB (18.5%, 95% CI 18.1%-19.0%) and Diet (18.8%, 95% CI 18.3%-19.3%). The main limitation of our analyses, in common with other studies, is that our data do not include information on food and beverage purchases that are consumed outside the home. Amongst households that regularly purchase beverages, those that mainly purchased high volumes of SSBs or diet beverages were at greater risk of obesity and tended to purchase less healthy foods, including a high proportion of energy from sweet snacks. These households might additionally benefit from policies targeting unhealthy foods, such as sweet snacks, as a way of reducing excess energy intake.