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Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general‐purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long‐term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder‐brains to reader‐brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core.

Ancestry information at the individual level can be a valuable resource for personalized medicine, medical, demographical and history research, as well as for tracing back personal history. We report a new method for quantitatively determining personal genetic ancestry based on genome-wide data. Numerical ancestry component scores are assigned to individuals based on comparisons with reference populations. These comparisons are conducted with an existing analytical pipeline making use of genotype phasing, similarity matrix computation and our addition-multidimensional best fitting by MixFit. The method is demonstrated by studying Estonian and Finnish populations in geographical context. We show the main differences in the genetic composition of these otherwise close European populations and how they have influenced each other. The components of our analytical pipeline are freely available computer programs and scripts one of which was developed in house. Peer reviewed

BackgroundIn migration and health research, the healthy migrant effect has been a common finding, but it usually pertains to specific contexts only. Existing findings are inconsistent and inconclusive regarding the cognitive functioning of the (aging) foreign-origin population relative to the populations of their host and sending countries. Moreover, this comparison is an understudied design setting.ObjectiveWe analyze the outcomes and associations of cognitive functioning outcomes of the non-institutionalized middle-aged and older population, comparing the Russian-origin population in Estonia with Estonians in Estonia and Russians in Russia in a cross-sectional design. We aim to estimate the (long-term) effects of migration on cognitive functioning in later life, contextualizing the findings in previous research on the healthy migrant effect.Data and methodsWe use data from face-to-face interviews conducted within the SHARE Estonia (2010–2011) and SAGE Russia (2007–2010) surveys. Respondents aged 50+ living in urban areas were grouped by self-identified ethnicity, including 2,365 Estonians, 1,373 Russians in Estonia, and 2,339 Russians in Russia (total N = 6,077). Cognitive functioning was measured using a 25-percentile cut-off threshold for the results of two cognition outcomes - immediate recall and verbal fluency - and the odds of impairment were estimated using binary logistic regression.ResultsRussian men and women living in Estonia have significantly higher odds of impairment in immediate recall than Estonian men and women, though they do not differ from Russians in Russia in the final adjusted models. The differences between all groups are non-significant if age at migration is considered. There are no significant differences between the groups in verbal fluency.ConclusionContrary to the commonly found healthy migrant effect, the middle-aged and older foreign-origin population in Estonia fares initially worse than the native population in the immediate recall outcome, but does not differ from their sending country population, possibly due to Russia’s higher mortality rate and therefore the selective survival of healthier people. Different results depending on the cognitive functioning outcome suggest that migration may affect temporary memory more than crystallized knowledge. However, there are no differences between the groups if defined based on age at migration, which suggests that the age profile differences explain most of the groups’ differences in cognitive functioning.

J. Kr., A.M. and C.P. are grateful for support from DFG grant number KR 4015/1-1, the Carl-Zeiss Foundation and the Baden Württemberg Foundation. S. P., G. R., Q. F., C. F., K. P., S.C. and J.Ke. acknowledge support from the Presidential Innovation Fund of the Max Planck Society. G.R. was supported by an NSERC fellowship. J.G.S. acknowledges use of the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by NSF grant number OCI-1053575. E.B. and O.B. were supported by RFBR grants 13-06-00670, 13-04-01711, 13-04-90420. B.M. was supported by grants OTKA 73430 and 103983. A.Saj. was supported by a Finnish Professor Pool (Paulo Foundation) Grant. The Lithuanian sampling was supported by the LITGEN project (VP1-3.1-ŠMM-07-K-01-013), funded by the European Social Fund under the Global Grant Measure. A.S. was supported by Spanish grants SAF2011-26983 and EM 2012/045. O.U. was supported by Ukrainian SFFS grant F53.4/071. S.A.T. was supported by NIH Pioneer Award 8DP1ES022577-04 and NSF HOMINID award BCS-0827436. K.T. was supported by an Indian CSIR Network Project (GENESIS: BSC0121). L.S. was supported by an Indian CSIR Bhatnagar Fellowship. R.V., M.M., J.P. and E.M. were supported by the European Union Regional Development Fund through the Centre of Excellence in Genomics to the Estonian Biocentre and University of Tartu and by an Estonian Basic Research grant SF0270177As08. M.M. was additionally supported by Estonian Science Foundation grant number 8973. J.G.S. and M.S. were supported by NIH grant GM40282. P.H.S. and E.E.E. were supported by NIH grants HG004120 and HG002385. D.R. and N.P. were supported by NSF HOMINID award BCS-1032255 and NIH grant GM100233. This project has been funded in part with federal funds from the National Cancer Institute, National Institutes of Health, under contract HHSN26120080001E. This Research was supported in part by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research We sequenced the genomes of a ∼7,000-year-old farmer from Germany and eight ∼8,000-year-old hunter-gatherers from Luxembourg and Sweden. We analysed these and other ancient genomes with 2,345 contemporary humans to show that most present-day Europeans derive from at least three highly differentiated populations: west European hunter-gatherers, who contributed ancestry to all Europeans but not to Near Easterners; ancient north Eurasians related to Upper Palaeolithic Siberians, who contributed to both Europeans and Near Easterners; and early European farmers, who were mainly of Near Eastern origin but also harboured west European hunter-gatherer related ancestry. We model these populations' deep relationships and show that early European farmers had ∼44% ancestry from a 'basal Eurasian' population that split before the diversification of other non-African lineages.

While the series of events that shaped the transition between foraging societies and food producers are well described for Central and Southern Europe, genetic evidence from Northern Europe surrounding the Baltic Sea is still sparse. Here, we report genome-wide DNA data from 38 ancient North Europeans ranging from similar to 9500 to 2200 years before present. Our analysis provides genetic evidence that hunter-gatherers settled Scandinavia via two routes. We reveal that the first Scandinavian farmers derive their ancestry from Anatolia 1000 years earlier than previously demonstrated. The range of Mesolithic Western hunter-gatherers extended to the east of the Baltic Sea, where these populations persisted without gene-flow from Central European farmers during the Early and Middle Neolithic. The arrival of steppe pastoralists in the Late Neolithic introduced a major shift in economy and mediated the spread of a new ancestry associated with the Corded Ware Complex in Northern Europe. Peer reviewed