Making Connections:John Darwin and his Histories of Empire

John Darwin and his Histories of Empir

(S)-(−)-Fluorenylethylchloroformate (FLEC) ; preparation using asymmetric transfer hydrogenation and application to the analysis and resolution of amines

Fluorenylethylchoroformate (FLEC) is a valuable chiral derivatisation reagent that is used for the resolution of a wide variety of chiral amines. Herein, we describe an improved preparation of (S)-(−)-FLEC using an efficient asymmetric catalytic transfer hydrogenation as the key step. We also demonstrate the application of FLEC as a chiral Fmoc equivalent for chiral resolution, with facile deprotection, of tetrahydroquinaldines, and its capacity for inducing regioselective outcomes in nitration reactions

Optically pure, structural and fluorescent analogues of a dimeric Y4 receptor agonist derived by an olefin metathesis approach

The dimeric peptide 1 (BVD-74D, as a diastereomeric mixture) is a potent and selective Neuropeptide Y Y4 receptor agonist. It represents a valuable candidate in developing traceable ligands for pharmacological studies of Y4 receptors, and as a lead compound for anti-obesity drugs. Its optically pure stereoisomers along with analogues and fluorescently labelled variants were prepared by exploiting alkene metathesis reactions. The (2R,7R)- diaminosuberoyl containing peptide, (R,R)-1 had markedly higher affinity and agonist efficacy than its (S,S)-counterpart. Furthermore the sulfo-Cy5 labelled (R,R)-14 retained high agonist potency as a novel fluorescent ligand for imaging Y4 receptors

Identification of a Cyanine-dye labeled peptidic ligand for Y₁R and Y₄R, based upon the Neuropeptide Y C-terminal analogue, BVD-15

Traceable truncated Neuropeptide Y (NPY) analogues with Y₁ receptor (Y₁R) affinity and selectivity are highly desirable tools in studying receptor location, regulation, and biological functions. A range of fluorescently labeled analogues of a reported Y₁R/Y₄R preferring ligand BVD-15 have been prepared and evaluated using high content imaging techniques. One peptide, [Lys²(sCy5), Arg⁴]BVD-15, was characterized as an Y₁R antagonist with a pKD of 7.2 measured by saturation analysis using fluorescent imaging. The peptide showed 8-fold lower affinity for Y₄R (pKD = 6.2) and was a partial agonist at this receptor. The suitability of [Lys²(sCy5), Arg⁴]BVD-15 for Y₁R and Y₄R competition binding experiments was also demonstrated in intact cells. The nature of the label was shown to be critical with replacement of sCy5 by the more hydrophobic Cy5.5 resulting in a switch from Y₁R antagonist to Y₁R partial agonist

Heterodimeric Analogues of the Potent Y1R Antagonist 1229U91, Lacking One of the Pharmacophoric C-Terminal Structures, Retain Potent Y1R Affinity and Show Improved Selectivity over Y4R

The cyclic dimeric peptide 1229U91 (GR231118) has an unusual structure and displays potent, insurmountable antagonism of the Y1 receptor. To probe the structural basis for this activity, we have prepared ring size variants and heterodimeric compounds, identifying the specific residues underpinning the mechanism of 1229U91 binding. The homodimeric structure was shown to be dispensible, with analogues lacking key pharmacophoric residues in one dimer arm retaining high antagonist affinity. Compounds 11d-h also showed enhanced Y1R selectivity over Y4R compared to 1229U91

Losing Our Minds? New Research Directions on Skilled Migration and Development

This paper critiques the last decade of research on the effects of high-skill emigration from developing countries, and proposes six new directions for fruitful research. The study singles out a core assumption underlying much of the recent literature, calling it the Lump of Learning model of human capital and development, and describes five ways that research has come to challenge that assumption. It assesses the usefulness of the Lump of Learning model in the face of accumulating evidence. The axioms of the Lump of Learning model have shaped research priorities in this literature, but many of those axioms do not have a clear empirical basis. Future research proceeding from established facts would set different priorities, and would devote more attention to measuring the effects of migration on skilled-migrant households, rigorously estimating human capital externalities, gathering microdata beyond censuses, and carefully considering optimal policy among others. The recent literature has pursued a series of extensions to the Lump of Learning model. This study urges discarding the Lump of Learning model, pointing toward a new paradigm for research on skilled migration and development

Modelling biodiversity distribution in agricultural landscapes to support ecological network planning

Strategic approaches to biodiversity conservation increasingly emphasise the restoration of ecological connectivity at landscape scales. However, understanding where these connecting elements should be placed in the landscape is critical if they are to provide both value for money and for biodiversity. For such planning to be effective, it is necessary to have information of the distributions of multiple taxa, however, this is of poor quality for many taxa. We show that sparse, non-systematically collected biological records can be modelled using readily available environmental variables to meaningfully predict potential biodiversity richness, including rare and threatened species, across a landscape. Using a large database of ad-hoc biological records (50 501 records of 502 species) we modelled the richness of wetland biodiversity across the Fens, a formerly extensive wetland, now agricultural landscape in eastern England. We used these models to predict those parts of the agricultural ditch network of greatest potential conservation value and compared this to current strategic network planning. Odonata distribution differed to that of other groups, indicating that single taxon groups may not be effective proxies for other priority biodiversity. Our results challenged previous assumptions that river channels should comprise the main connecting elements in the Fens region. Rather, areas of high ditch density close to a main river are likely to be of greater value and should be targeted for enhancement. This approach can be adopted elsewhere in order to improve the evidence-base for strategic networks plans, increasing their value for money

The worldwide trend to high participation higher education: dynamics of social stratification in inclusive systems

Worldwide participation in higher education now includes one-third of the age cohort and is growing at an unprecedented rate. The tendency to rapid growth, leading towards high participation systems (HPS), has spread to most middle-income and some low-income countries. Though expansion of higher education requires threshold development of the state and the middle class, it is primarily powered not by economic growth but by the ambitions of families to advance or maintain social position. However, expansion is mostly not accompanied by more equal social access to elite institutions. The quality of mass higher education is often problematic. Societies vary in the extent of upward social mobility from low-socio-economic-status backgrounds. The paper explores the intersection between stratified social backgrounds and the stratifying structures in HPS. These differentiating structures include public/private distinctions in schooling and higher education, different fields of study, binary systems and tiered hierarchies of institutions, the vertical ‘stretching’ of stratification in competitive HPS, and the unequalising effects of tuition. Larger social inequalities set limits on what education can achieve. Countries with high mobility sustain a consensus about social equality, and value rigorous and autonomous systems of learning, assessment and selection in education

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.peer-reviewe