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The increasing intensity, duration, and frequency of heat waves due to human-caused climate change puts historically underserved populations in a heightened state of precarity, as studies observe that vulnerable communities—especially those within urban areas in the United States—are disproportionately exposed to extreme heat. Lacking, however, are insights into fundamental questions about the role of historical housing policies in cauterizing current exposure to climate inequities like intra-urban heat. Here, we explore the relationship between “redlining”, or the historical practice of refusing home loans or insurance to whole neighborhoods based on a racially motivated perception of safety for investment, with present-day summertime intra-urban land surface temperature anomalies. Through a spatial analysis of 108 urban areas in the United States, we ask two questions: (1) how do historically redlined neighborhoods relate to current patterns of intra-urban heat? and (2) do these patterns vary by US Census Bureau region? Our results reveal that 94% of studied areas display consistent city-scale patterns of elevated land surface temperatures in formerly redlined areas relative to their non-redlined neighbors by as much as 7 °C. Regionally, Southeast and Western cities display the greatest differences while Midwest cities display the least. Nationally, land surface temperatures in redlined areas are approximately 2.6 °C warmer than in non-redlined areas. While these trends are partly attributable to the relative preponderance of impervious land cover to tree canopy in these areas, which we also examine, other factors may also be driving these differences. This study reveals that historical housing policies may, in fact, be directly responsible for disproportionate exposure to current heat events.

The principal aim of this thesis is to investigate the origins of the research university in Britain, focussing in particular on University College London in the period 1890-1914. This account demonstrates that this period witnessed a series of pioneering attempts to establish research schools in various departments of the College, and the emergence of an institutional commitment to both teaching and research. It is argued that - contrary to the assumptions implicit within much of the existing literature on this subject - government money and initiatives were of secondary importance, and the role of the state was merely to consolidate the effects of various developments that were taking place within the College. This thesis therefore highlights the significance of the role played by a handful of pioneering professors in certain key departments of the College - notably Karl Pearson (Applied Mathematics and Statistics), Sir William Ramsay (Chemistry) and A.F. Pollard (History). These remarkable individuals managed to pursue successful research careers and pioneered the development of research training for students. Moreover, through their efforts to secure the necessary financial support for their work (principally from private sources) and involvement in the campaign to reform the University of London, the College Council and University authorities were slowly forced to accept an expanded conception of the role and function of the College in this period, and in 1905 UCL was formally reconstituted as 'a place of teaching and research'. In the absence of a satisfactory secondary literature on Britain's universities and the rise of research, it is hoped that this thesis will act as a pilot study, suggesting a possible strategy for investigating the origins of the research university in this country based on local studies of individual universities, colleges and departments.

Global ageing poses a substantial economic burden on health and social care costs. Enabling a greater proportion of older people to stay healthy for longer is key to the future sustainability of health, social and economic policy. Frailty and associated decrease in resilience plays a central role in poor health in later life. In this study, we present a population level assessment of the metabolic phenotype associated with frailty. Analysis of serum from 1191 older individuals (aged between 56 and 84 years old) and subsequent longitudinal validation (on 786 subjects) was carried out using liquid and gas chromatography-mass spectrometry metabolomics and stratified across a frailty index designed to quantitatively summarize vulnerability. Through multivariate regression and network modelling and mROC modeling we identified 12 significant metabolites (including three tocotrienols and six carnitines) that differentiate frail and non-frail phenotypes. Our study provides evidence that the dysregulation of carnitine shuttle and vitamin E pathways play a role in the risk of frailty. Risk of age-related chronic disorders and decrease in resilience is associated with ageing. Here the authors analyse the human blood metabolome and identify metabolites associated with frailty.

As climate trends accelerate, ecosystems will be pushed rapidly into new states, reducing the potential efficacy of conservation strategies based on historical patterns. In the Gulf of Maine, climate-driven changes have restructured the ecosystem rapidly over the past decade. Changes in the Atlantic meridional overturning circulation have altered deepwater dynamics, driving warming rates twice as high as the fastest surface rates. This has had implications for the copepod Calanus finmarchicus, a critical food supply for the endangered North Atlantic right whale (Eubalaena glacialis). The oceanographic changes have driven a deviation in the seasonal foraging patterns of E. glacialis upon which conservation strategies depend, making the whales more vulnerable to ship strikes and gear entanglements. The effects of rapid climate-driven changes on a species at risk undermine current management approaches.

The Gulf of Maine has recently experienced its warmest 5-year period (2015–2020) in the instrumental record. This warming was associated with a decline in the signature subarctic zooplankton species, Calanus finmarchicus. The temperature changes have also led to impacts on commercial species such as Atlantic cod (Gadus morhua) and American lobster (Homarus americanus) and protected species including Atlantic puffins (Fratercula arctica) and northern right whales (Eubalaena glacialis). The recent period also saw a decline in Atlantic herring (Clupea harengus) recruitment and an increase in novel harmful algal species, although these have not been attributed to the recent warming. Here, we use an ensemble of numerical ocean models to characterize expected ocean conditions in the middle of this century. Under the high CO2 emissions scenario (RCP8.5), the average temperature in the Gulf of Maine is expected to increase 1.1°C to 2.4°C relative to the 1976–2005 average. Surface salinity is expected to decrease, leading to enhanced water column stratification. These physical changes are likely to lead to additional declines in subarctic species including C. finmarchicus, American lobster, and Atlantic cod and an increase in temperate species. The ecosystem changes have already impacted human communities through altered delivery of ecosystem services derived from the marine environment. Continued warming is expected to lead to a loss of heritage, changes in culture, and the necessity for adaptation.

Amyloid-beta (Aβ) deposition is common in cognitively unimpaired (CU) elderly >85 years. This study investigated amyloid distribution and evaluated three published in vivo amyloid-PET staging schemes from a cognitively unimpaired (CU) cohort aged 84.9 ± 4.3 years (n = 75). SUV-based principal component analysis (PCA) was applied to 18F-flutemetamol PET data to determine an unbiased regional covariance pattern of tracer uptake across grey matter regions. PET staging schemes were applied to the data and compared to the PCA output. Concentration of p-tau181 was measured in blood plasma. The PCA revealed three distinct components accounting for 91.2% of total SUV variance. PC1 driven by the large common variance of uptake in neocortical and striatal regions was significantly positively correlated with global SUVRs, APOE4 status and p-tau181 concentration. PC2 represented mainly non-specific uptake in typical amyloid-PET reference regions, and PC3 the occipital lobe. Application of the staging schemes demonstrated that the majority of the CU cohort (up to 93%) were classified as having pathological amount and distribution of Aβ. Good correspondence existed between binary (+/−) classification and later amyloid stages, however, substantial differences existed between schemes for low stages with 8–17% of individuals being unstageable, i.e., not following the sequential progression of Aβ deposition. In spite of the difference in staging outcomes there was broad spatial overlap between earlier stages and PC1, most prominently in default mode network regions. This study critically evaluated the utility of in vivo amyloid staging from a single PET scan in CU elderly and found that early amyloid stages could not be consistently classified. The majority of the cohort had pathological Aβ, thus, it remains an open topic what constitutes abnormal brain Aβ in the oldest-old and what is the best method to determine that.

Abstract BACKGROUND Clinicoradiologic differentiation between benign and malignant peripheral nerve sheath tumors (PNSTs) has important management implications. OBJECTIVE To develop and evaluate machine-learning approaches to differentiate benign from malignant PNSTs. METHODS We identified PNSTs treated at 3 institutions and extracted high-dimensional radiomics features from gadolinium-enhanced, T1-weighted magnetic resonance imaging (MRI) sequences. Training and test sets were selected randomly in a 70:30 ratio. A total of 900 image features were automatically extracted using the PyRadiomics package from Quantitative Imaging Feature Pipeline. Clinical data including age, sex, neurogenetic syndrome presence, spontaneous pain, and motor deficit were also incorporated. Features were selected using sparse regression analysis and retained features were further refined by gradient boost modeling to optimize the area under the curve (AUC) for diagnosis. We evaluated the performance of radiomics-based classifiers with and without clinical features and compared performance against human readers. RESULTS A total of 95 malignant and 171 benign PNSTs were included. The final classifier model included 21 imaging and clinical features. Sensitivity, specificity, and AUC of 0.676, 0.882, and 0.845, respectively, were achieved on the test set. Using imaging and clinical features, human experts collectively achieved sensitivity, specificity, and AUC of 0.786, 0.431, and 0.624, respectively. The AUC of the classifier was statistically better than expert humans (P = .002). Expert humans were not statistically better than the no-information rate, whereas the classifier was (P = .001). CONCLUSION Radiomics-based machine learning using routine MRI sequences and clinical features can aid in evaluation of PNSTs. Further improvement may be achieved by incorporating additional imaging sequences and clinical variables into future models.

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association. The Structural Variation Analysis Group of The 1000 Genomes Project reports an integrated structural variation map based on discovery and genotyping of eight major structural variation classes in 2,504 unrelated individuals from across 26 populations; structural variation is compared within and between populations and its functional impact is quantified. Structural variation mapped in over 2,500 human genomes The Structural Variation Analysis Group of The 1000 Genomes Project reports an integrated structural variation map based on discovery and genotyping of eight major structural variation classes in genomes for 2,504 unrelated individuals from across 26 populations. They characterize structural variation within and between populations and quantify its functional effect. The authors further create a phased reference panel that will be valuable for population genetic and disease association studies.