Trait Variation in Yeast Is Defined by Population History

A fundamental goal in biology is to achieve a mechanistic understanding of how and to what extent ecological variation imposes selection for distinct traits and favors the fixation of specific genetic variants. Key to such an understanding is the detailed mapping of the natural genomic and phenomic space and a bridging of the gap that separates these worlds. Here we chart a high-resolution map of natural trait variation in one of the most important genetic model organisms, the budding yeast Saccharomyces cerevisiae, and its closest wild relatives and trace the genetic basis and timing of major phenotype changing events in its recent history. We show that natural trait variation in S. cerevisiae exceeds that of its relatives, despite limited genetic variation, and follows the population history rather than the source environment. In particular, the West African population is phenotypically unique, with an extreme abundance of low-performance alleles, notably a premature translational termination signal in GAL3 that cause inability to utilize galactose. Our observations suggest that many S. cerevisiae traits may be the consequence of genetic drift rather than selection, in line with the assumption that natural yeast lineages are remnants of recent population bottlenecks. Disconcertingly, the universal type strain S288C was found to be highly atypical, highlighting the danger of extrapolating gene-trait connections obtained in mosaic, lab-domesticated lineages to the species as a whole. Overall, this study represents a step towards an in-depth understanding of the causal relationship between co-variation in ecology, selection pressure, natural traits, molecular mechanism, and alleles in a key model organism

Alpha-fetoprotein as a biomarker for recessive ataxias

Universidade Federal de São Paulo (UNIFESP) Department of Neurology and NeurosurgeryUNIFESP, Department of Neurology and NeurosurgerySciEL

Diagnóstico de tumores do ângulo ponto-cerebelar com o auxílio de técnicas de inteligência artificial A diagnostic model for cerebellum-pontine angle tumors using artificial intelligence techniques

Trata-se de estudo multidisciplinar, cujo objetivo é a obtenção de modelo discriminatório entre diagnóstico de tumores do ângulo ponto-cerebelar (APC) e de distúrbios otorrinolaringológicos. Presentemente, a realização de um acurado exame neurológico e/ou otorrinolaringológico é incapaz de firmar diagnóstico de tumor do APC, sem valer-se de exames radiológicos de alto custo (tomografia computadorizada, ressonância magnética). O modelo proposto foi obtido através da utilização de técnicas de inteligência artificial e apresentou bom nível de acurácia (88,4%) no teste de novos casos, considerando-se apenas o exame clínico e sem o auxílio de exames radiológicos.<br>We are concerned in this paper with learning classification procedures from known cases. More precisely, we provide a diagnostic model that discriminate between cerebellum-pontine angle (CPA) tumors and otorhinolaryngological (ENT) disorders. Usually, in order to distinguish between CPA tumors and ENT disorders one must perform clinical-neurological examination together with expensive radiological imagery (CT and MRI). The proposed model was obtained through artificial intelligence methods and presented a good accuracy level (88.4%) when tested against new cases, considering only clinical examination without radiological imagery results