Visual Responses in Mice Lacking Critical Components of All Known Retinal Phototransduction Cascades

The mammalian visual system relies upon light detection by outer-retinal rod/cone photoreceptors and melanopsin-expressing retinal ganglion cells. Gnat1(-/-); Cnga3(-/-); Opn4(-/-) mice lack critical elements of each of these photoreceptive mechanisms via targeted disruption of genes encoding rod alpha transducin (Gnat1); the cone-specific alpha 3 cyclic nucleotide gated channel subunit (Cnga3); and melanopsin (Opn4). Although assumed blind, we show here that these mice retain sufficiently widespread retinal photoreception to drive a reproducible flash electroretinogram (ERG). The threshold sensitivity of this ERG is similar to that of cone-based responses, however it is lost under light adapted conditions. Its spectral efficiency is consistent with that of rod opsin, but not cone opsins or melanopsin, indicating that it originates with light absorption by the rod pigment. The TKO light response survives intravitreal injection of U73122 (a phospholipase C antagonist), but is inhibited by a missense mutation of cone alpha transducin (Gnat2(cpfl3)), suggesting Gnat2-dependence. Visual responses in TKO mice extend beyond the retina to encompass the lateral margins of the lateral geniculate nucleus and components of the visual cortex. Our data thus suggest that a Gnat1-independent phototransduction mechanism downstream of rod opsin can support relatively widespread responses in the mammalian visual system. This anomalous rod opsin-based vision should be considered in experiments relying upon Gnat1 knockout to silence rod phototransduction

Evaluating Bayesian spatial methods for modelling species distributions with clumped and restricted occurrence data

Statistical approaches for inferring the spatial distribution of taxa (Species Distribution Models, SDMs) commonly rely on available occurrence data, which is often clumped and geographically restricted. Although available SDM methods address some of these factors, they could be more directly and accurately modelled using a spatially-explicit approach. Software to fit models with spatial autocorrelation parameters in SDMs are now widely available, but whether such approaches for inferring SDMs aid predictions compared to other methodologies is unknown. Here, within a simulated environment using 1000 generated species’ ranges, we compared the performance of two commonly used non-spatial SDM methods (Maximum Entropy Modelling, MAXENT and boosted regression trees, BRT), to a spatial Bayesian SDM method (fitted using R-INLA), when the underlying data exhibit varying combinations of clumping and geographic restriction. Finally, we tested how any recommended methodological settings designed to account for spatially non-random patterns in the data impact inference. Spatial Bayesian SDM method was the most consistently accurate method, being in the top 2 most accurate methods in 7 out of 8 data sampling scenarios. Within high-coverage sample datasets, all methods performed fairly similarly. When sampling points were randomly spread, BRT had a 1–3% greater accuracy over the other methods and when samples were clumped, the spatial Bayesian SDM method had a 4%-8% better AUC score. Alternatively, when sampling points were restricted to a small section of the true range all methods were on average 10–12% less accurate, with greater variation among the methods. Model inference under the recommended settings to account for autocorrelation was not impacted by clumping or restriction of data, except for the complexity of the spatial regression term in the spatial Bayesian model. Methods, such as those made available by R-INLA, can be successfully used to account for spatial autocorrelation in an SDM context and, by taking account of random effects, produce outputs that can better elucidate the role of covariates in predicting species occurrence. Given that it is often unclear what the drivers are behind data clumping in an empirical occurrence dataset, or indeed how geographically restricted these data are, spatially-explicit Bayesian SDMs may be the better choice when modelling the spatial distribution of target species

Pre and Post-Treatment Stratification of Cervical Cancer – A Global Perspective

O carcinoma do colo do útero é um dos tumores malignos mais frequentes a nível mundial. Para garantir a uniformização de critérios entre países com diferentes recursos, o estadiamento deste tumor permanece clínico, segundo as orientações da Federação Internacional de Ginecologia e Obstetrícia, e tem por principal objectivo a identificação das doentes que são candidatas à cirurgia. A avaliação radiológica é amplamente recomendada, quando disponível, com o objectivo de aumentar a acuidade do diagnóstico, assegurando a optimização terapêutica, sendo também recomendada no seguimento. Importa, assim, que o radiologista tenha presente não só o protocolo técnico adequado na suspeita de carcinoma do colo do útero e o respectivo espectro de apresentação radiológica, mas também algumas características da própria doença e possíveis abordagens terapêuticas, de forma a incluir no seu relatório toda a informação relevante. A ressonância magnética permanece o principal pilar na avaliação radiológica destas doentes, embora recentemente o papel da tomografia computorizada por emissão de positrões tenha vindo a ganhar relevo, sobretudo no que respeita à avaliação ganglionar e ao despiste de recidiva. Neste artigo as autoras dão uma perspectiva aprofundada da avaliação radiológica do carcinoma do colo do útero, deste o diagnóstico ao seguimento pós-terapêutico, à luz dos estudos mais recentes.info:eu-repo/semantics/publishedVersio

The Multiscale Systems Immunology project: software for cell-based immunological simulation

<p>Abstract</p> <p>Background</p> <p>Computer simulations are of increasing importance in modeling biological phenomena. Their purpose is to predict behavior and guide future experiments. The aim of this project is to model the early immune response to vaccination by an agent based immune response simulation that incorporates realistic biophysics and intracellular dynamics, and which is sufficiently flexible to accurately model the multi-scale nature and complexity of the immune system, while maintaining the high performance critical to scientific computing.</p> <p>Results</p> <p>The Multiscale Systems Immunology (MSI) simulation framework is an object-oriented, modular simulation framework written in C++ and Python. The software implements a modular design that allows for flexible configuration of components and initialization of parameters, thus allowing simulations to be run that model processes occurring over different temporal and spatial scales.</p> <p>Conclusion</p> <p>MSI addresses the need for a flexible and high-performing agent based model of the immune system.</p

Responsible modelling: Unit testing for infectious disease epidemiology

Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase the uptake of this methodology in our field

Responsible modelling: Unit testing for infectious disease epidemiology

Infectious disease epidemiology is increasingly reliant on large-scale computation and inference. Models have guided health policy for epidemics including COVID-19 and Ebola and endemic diseases including malaria and tuberculosis. Yet a coding bug may bias results, yielding incorrect conclusions and actions causing avoidable harm. We are ethically obliged to make our code as free of error as possible. Unit testing is a coding method to avoid such bugs, but it is rarely used in epidemiology. We demonstrate how unit testing can handle the particular quirks of infectious disease models and aim to increase the uptake of this methodology in our field

A 15.65 solar mass black hole in an eclipsing binary in the nearby spiral galaxy Messier 33

Stellar-mass black holes are discovered in X-ray emitting binary systems, where their mass can be determined from the dynamics of their companion stars. Models of stellar evolution have difficulty producing black holes in close binaries with masses >10 solar masses, which is consistent with the fact that the most massive stellar black holes known so all have masses within 1 sigma of 10 solar masses. Here we report a mass of 15.65 +/- 1.45 solar masses for the black hole in the recently discovered system M33 X-7, which is located in the nearby galaxy Messier 33 (M33) and is the only known black hole that is in an eclipsing binary. In order to produce such a massive black hole, the progenitor star must have retained much of its outer envelope until after helium fusion in the core was completed. On the other hand, in order for the black hole to be in its present 3.45 day orbit about its 70.0 +/- 6.9 solar mass companion, there must have been a ``common envelope'' phase of evolution in which a significant amount of mass was lost from the system. We find the common envelope phase could not have occured in M33 X-7 unless the amount of mass lost from the progenitor during its evolution was an order of magnitude less than what is usually assumed in evolutionary models of massive stars.Comment: To appear in Nature October 18, 2007. Four figures (one color figure degraded). Differs slightly from published version. Supplementary Information follows in a separate postin

Revised Lithostratigraphy of the Sonsela Member (Chinle Formation, Upper Triassic) in the Southern Part of Petrified Forest National Park, Arizona

BACKGROUND: Recent revisions to the Sonsela Member of the Chinle Formation in Petrified Forest National Park have presented a three-part lithostratigraphic model based on unconventional correlations of sandstone beds. As a vertebrate faunal transition is recorded within this stratigraphic interval, these correlations, and the purported existence of a depositional hiatus (the Tr-4 unconformity) at about the same level, must be carefully re-examined. METHODOLOGY/PRINCIPAL FINDINGS: Our investigations demonstrate the neglected necessity of walking out contacts and mapping when constructing lithostratigraphic models, and providing UTM coordinates and labeled photographs for all measured sections. We correct correlation errors within the Sonsela Member, demonstrate that there are multiple Flattops One sandstones, all of which are higher than the traditional Sonsela sandstone bed, that the Sonsela sandstone bed and Rainbow Forest Bed are equivalent, that the Rainbow Forest Bed is higher than the sandstones at the base of Blue Mesa and Agate Mesa, that strata formerly assigned to the Jim Camp Wash beds occur at two stratigraphic levels, and that there are multiple persistent silcrete horizons within the Sonsela Member. CONCLUSIONS/SIGNIFICANCE: We present a revised five-part model for the Sonsela Member. The units from lowest to highest are: the Camp Butte beds, Lot's Wife beds, Jasper Forest bed (the Sonsela sandstone)/Rainbow Forest Bed, Jim Camp Wash beds, and Martha's Butte beds (including the Flattops One sandstones). Although there are numerous degradational/aggradational cycles within the Chinle Formation, a single unconformable horizon within or at the base of the Sonsela Member that can be traced across the entire western United States (the "Tr-4 unconformity") probably does not exist. The shift from relatively humid and poorly-drained to arid and well-drained climatic conditions began during deposition of the Sonsela Member (low in the Jim Camp Wash beds), well after the Carnian-Norian transition