Assembly dynamics of microtubules at molecular resolution

Microtubules are highly dynamic protein polymers that form a crucial part of the cytoskeleton in all eukaryotic cells. Although microtubules are known to self-assemble from tubulin dimers, information on the assembly dynamics of microtubules has been limited, both in vitro and in vivo, to measurements of average growth and shrinkage rates over several thousands of tubulin subunits. As a result there is a lack of information on the sequence of molecular events that leads to the growth and shrinkage of microtubule ends. Here we use optical tweezers to observe the assembly dynamics of individual microtubules at molecular resolution. We find that microtubules can increase their overall length almost instantaneously by amounts exceeding the size of individual dimers (8 nm). When the microtubule-associated protein XMAP215 (ref. 6) is added, this effect is markedly enhanced and fast increases in length of about 40-60 nm are observed. These observations suggest that small tubulin oligomers are able to add directly to growing microtubules and that XMAP215 speeds up microtubule growth by facilitating the addition of long oligomers. The achievement of molecular resolution on the microtubule assembly process opens the way to direct studies of the molecular mechanism by which the many recently discovered microtubule end-binding proteins regulate microtubule dynamics in living cells

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