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Archaeological dental calculus has emerged as a rich source of ancient biomolecules, including proteins. Previous analyses of proteins extracted from ancient dental calculus revealed the presence of the dietary milk protein β-lactoglobulin, providing direct evidence of dairy consumption in the archaeological record. However, the potential for calculus to preserve other food-related proteins has not yet been systematically explored. Here we analyse shotgun metaproteomic data from 100 archaeological dental calculus samples ranging from the Iron Age to the post-medieval period (eighth century BC to nineteenth century AD) in England, as well as 14 dental calculus samples from contemporary dental patients and recently deceased individuals, to characterize the range and extent of dietary proteins preserved in dental calculus. In addition to milk proteins, we detect proteomic evidence of foodstuffs such as cereals and plant products, as well as the digestive enzyme salivary amylase. We discuss the importance of optimized protein extraction methods, data analysis approaches and authentication strategies in the identification of dietary proteins from archaeological dental calculus. This study demonstrates that proteomic approaches can robustly identify foodstuffs in the archaeological record that are typically under-represented due to their poor macroscopic preservation. Proceedings of the Royal Society B: Biological Sciences, 285 (1883) ISSN:1471-2954

Taking up the view that semi-institutional gatherings such as clubs, societies, research schools, have been instrumental in creating sheltered spaces from which many a 20th-century project-driven interdisciplinary research programme could develop and become established within the institutions of science, the paper explores the history of one such gathering from its inception in the early 1980s into the 2000s, the Helmholtz Club, which brought together scientists from such various research fields as neuroanatomy, neurophysiology, psychophysics, computer science and engineering, who all had an interest in the study of the visual system and of higher cognitive functions relying on visual perception such as visual consciousness. It argues that British molecular biologist turned South Californian neuroscientist Francis Crick had an early and lasting influence over the Helmholtz Club of which he was a founding pillar, and that from its inception, the club served as a constitutive element in his long-term plans for a neuroscience of vision and of cognition. Further, it argues that in this role, the Helmholtz Club served many purposes, the primary of which was to be a social forum for interdisciplinary discussion, where ‘discussion’ was not mere talk but was imbued with an epistemic value and as such, carefully cultivated. Finally, it questions what counts as ‘doing science’ and in turn, definitions of success and failure—and provides some material evidence towards re-appraising the successfulness of Crick’s contribution to the neurosciences. Highlights • Question what counts as doing science and reconsider standards of failure and success. • Crick’s contribution to neurosciences was significant contrary to the received view. • Crick promoted his plans for neuroscience through patronage, networking and influence. • The uses of a ‘mere’ scientific club can cut across many dimensions of science.

Bioethics emerged in a specific social and historical context. Its relationship to older traditions in medical ethics and to environmental ethics is an ongoing matter of debate. This paper analyses the social, institutional and economic factors that led to the development of bioethics in the United Kingdom (U.K.) in the 1980s, in and the course it has taken since. We show how phenomena such as globalisation, the focus on ‘ethical legal and social issues’ (ELSI) and the empirical turn have affected the methods employed, and argue that ongoing controversies about the nature and possibility of ethical expertise will affect its future.

The SSHOC project aims to build the SSH (Social Science and Humanities) part of the EOSC (European Open Science Cloud). One of the main goals of the project is to ensure that SSH will be present in EOSC and that their specifics are taken into account. In this regard, an important point is to be able to give high visibility to the research data used in Social Science and Humanities following FAIR data principles. This can be achieved by fostering "Data Citation" through providing a common mechanism to cite SSH data and build stronger links between data and publications. An expected side effect would be to enhance the reproducibility of SSH research, which is not very common nowadays. In line with these goals, this report delivers an overview of existing data-citing mechanisms intended for citing data, that are currently used in different communities with a focus on the SSH. It then provides some guidelines describing what is relevant in the SSHOC context with respect to the technology to be implemented and also about the structuring of the content.

We analyse new genomic data (0.05–2.95x) from 14 ancient individuals from Portugal distributed from the Middle Neolithic (4200–3500 BC) to the Middle Bronze Age (1740–1430 BC) and impute genomewide diploid genotypes in these together with published ancient Eurasians. While discontinuity is evident in the transition to agriculture across the region, sensitive haplotype-based analyses suggest a significant degree of local hunter-gatherer contribution to later Iberian Neolithic populations. A more subtle genetic influx is also apparent in the Bronze Age, detectable from analyses including haplotype sharing with both ancient and modern genomes, D-statistics and Y-chromosome lineages. However, the limited nature of this introgression contrasts with the major Steppe migration turnovers within third Millennium northern Europe and echoes the survival of non-Indo-European language in Iberia. Changes in genomic estimates of individual height across Europe are also associated with these major cultural transitions, and ancestral components continue to correlate with modern differences in stature. Index for VCF fileIndex for VCF filepost_imputation_Martiniano_et_al_2017_public.vcf.gz.tbiVCF file containing imputed genotype data belonging to 67 newly sequenced and publicly available ancient samples.VCF file containing imputed genotype data belonging to 67 newly sequenced and publicly available ancient samples which we analysed in Martiniano et al. (2017).post_imputation_Martiniano_et_al_2017_public.vcf.gzREADME_Martiniano_et_al_2017Description of the methods used for genotype imputation.

UK Biobank custom height association statistics on ~700k genotyped SNPsThe zip file contains six files: (1) ukb_cal_v2_height_allancestry_10pcs_assoc_linear.tsv (2) ukb_cal_v2_height_allancestry_nopcs_assoc_linear.tsv (3) ukb_cal_v2_height_britishancestry_10pcs_assoc_linear.tsv (4) ukb_cal_v2_height_britishancestry_nopcs_assoc_linear.tsv (5) ukb_cal_v2_height_sibs_perm_qfam.tsv (6) ukb_cal_v2_height_wbsibs_perm_qfam.tsv (1) - (4) are height GWAS estimates on all samples / white British samples using 10 PCs as covariates or no PCs as covariates. Sex was included as covariate in all analyses. (3) is equivalent to the UK Biobank height GWAS from the Neale lab. The remaining small differences can be explained by genotype differences in the UK Biobank imputed data and genotyped data. (5) and (6) are family based estimates from 20166 sibling pairs of any ancestry (5) and 17358 sibling pairs where both siblings are of white British ancestry (6) in the UK Biobank. Pairs of samples with IBS0 > 0.0018 and Kinship coefficient > 0.185 were identified as sibling pairs. For the analyses in Sohail, Maier et al., only the subset of ~300,000 SNPs with SDS scores was used. For a description of the columns in files (1)-(4) please see the PLINK documentation for the ‘--linear’ command. Column “A2” has been added and denotes the non-effect allele. For a description of the columns in files (5) and (6) please see the PLINK documentation for the ‘--qfam’ command. Column “A2” has been added and denotes the non-effect allele. “EMP1” and “NP” refer to permutation p-value and number of permutations, respectively. Please note: These data are derived from the UK Biobank Resource under Application Number 18597.sohail_maier_2018.zip Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population structure. More generally, our results imply that typical constructions of polygenic scores are sensitive to population structure and that population-level differences should be interpreted with caution.

In this report Mariza Dima sets out the findings of a research project examining the experiential and educational value of Tate’s ticketed exhibitions to its audiences. Exhibition planning, the contributions of small and medium-sized enterprises and the museum’s data-gathering practices are explored, taking the 2014 exhibitions Late Turner and Malevich as case studies.

Background: Deep Phenotyping is the precise and comprehensive analysis of phenotypic features in which the individual components of the phenotype are observed and described. In UK mental health clinical practice, most clinically relevant information is recorded as free text in the Electronic Health Record, and offers a granularity of information beyond what is expressed in most medical knowledge bases. The SNOMED CT nomenclature potentially offers the means to model such information at scale, yet given a sufficiently large body of clinical text collected over many years, it is difficult to identify the language that clinicians favour to express concepts. Methods: By utilising a large corpus of healthcare data, we sought to make use of semantic modelling and clustering techniques to represent the relationship between the clinical vocabulary of internationally recognised SMI symptoms and the preferred language used by clinicians within a care setting. We explore how such models can be used for discovering novel vocabulary relevant to the task of phenotyping Serious Mental Illness (SMI) with only a small amount of prior knowledge. Results: 20 403 terms were derived and curated via a two stage methodology. The list was reduced to 557 putative concepts based on eliminating redundant information content. These were then organised into 9 distinct categories pertaining to different aspects of psychiatric assessment. 235 concepts were found to be expressions of putative clinical significance. Of these, 53 were identified having novel synonymy with existing SNOMED CT concepts. 106 had no mapping to SNOMED CT. Conclusions: We demonstrate a scalable approach to discovering new concepts of SMI symptomatology based on real-world clinical observation. Such approaches may offer the opportunity to consider broader manifestations of SMI symptomatology than is typically assessed via current diagnostic frameworks, and create the potential for enhancing nomenclatures such as SNOMED CT based on real-world expressions.