Alguns dados sobre a Fauna entomológica da ilha das Flores - Açores

IV Expedição Científica do Departamento de Biologia - Flores 1989Com este trabalho, realizado em Julho de 1989 nas Flores - a ilha mais ocidental do Arquipélago dos Açores -, acrescentaram-se onze espécies de Lepidópteros à lista referenciada para aquela ilha, pertencendo uma à família Lycaenidae (Lampides boeticus L.), oito a familia Noctuidae (Agrotis ipsilon HFN., Brotolomia meticulosa L., Chrysodeixis chalcites ESPER., Heliothis armigera HBN., Noctua atlantica WARREN, Noctua pronuba L., Peridroma saucia HBN., Sesamia nonagrioides LEF.), uma à família Nymphalidae (Vanessa atalanta L.) e uma a família Pyralidae (Glyphodes unionalis HBN.). Entre os demais insectos, foram identificadas cerca de duas dezenas e meia de espécies, distribuídas pelas Ordens Dermaptera, Orthoptera, Dictyoptera, Heteroptera, Homoptera, Coleoptera, Neuroptera, Diptera, Hymenoptera e Collembola. Salienta-se ainda a importância, do ponto de vista agronómico, das pragas Mythimna unipuncta (HAWORTH) e Xestia c-nigrun L. naquela ilha.RÉSUMÉ: Avec ce travail, réalisé en Juillet 1989 a Flores - l'île plus occidental de l'archipel des Açores, onze espèces de Lépidoptères ont été ajoutées à la liste des espèces connus pour cette île, dont une appartient a la famille Lycaenidae (Lampides boelicus L.), huit à la famille Noctuidae (Agrotis ipsilon HFN., Brotolomia meticulosa L. Chrysodeicis chalcites ESPER., Heliothis armigera HBN., Noctua atlantica WARREN, Noctua pronuba L., Peridroma saucia HBN., Sesamia nonagrioides LEF.), une à la famille Nymphalidae (Vanessa atalanta L.) et une à la famille Pyralidae (Glyphodes unionalis HBN.). Parmi les autres insects ont été identifiés environ deux dizaines et demie d'espèces, lesquelles sont réparties par les Ordres Dermaptera, Orthoptera, Dictyoptera, Heteroptera, Homoptera, Coleoptera, Neuroptera, Diptera, Hymenoptera et Collembola. On remarque I'importance, du point de vue agronomique, des ravageurs Mythimna unipuncra (HAWORTH) et Xestia c-nigrum L. dans cette île

Effects of emotion on prospection during decision-making

In two experiments we examined the role of emotion, specifically worry, anxiety, and mood, on prospection during decision-making. Worry is a particularly relevant emotion to study in the context of prospection because high levels of worry may make individuals more aversive toward the uncertainty associated with the prospect of obtaining future improvements in rewards or states. Thus, high levels of worry might lead to reduced prospection during decision-making and enhance preference for immediate over delayed rewards. In Experiment 1 participants performed a two-choice dynamic decision-making task where they were required to choose between one option (the decreasing option) which provided larger immediate rewards but declines in future states, and another option (the increasing option) which provided smaller immediate rewards but improvements in future states, making it the optimal choice. High levels of worry were associated with poorer performance in the task. Additionally, fits of a sophisticated reinforcement-learning model that incorporated both reward-based and state-based information suggested that individuals reporting high levels of worry gave greater weight to the immediate rewards they would receive on each trial than to the degree to which each action would lead to improvements in their future state. In Experiment 2 we found that high levels of worry were associated with greater delay discounting using a standard delay discounting task. Combined, the results suggest that high levels of worry are associated with reduced prospection during decision-making. We attribute these results to high worriers' aversion toward the greater uncertainty associated with attempting to improve future rewards than to maximize immediate reward. These results have implications for researchers interested in the effects of emotion on cognition, and suggest that emotion strongly affects the focus on temporal outcomes during decision-making.The open access fee for this work was funded through the Texas A&M University Open Access to Knowledge (OAK) Fund

Zoonotic transmission of SARS-CoV.

<p>Genomic analyses provided evidence that genetic changes in the spike gene of SARS-CoV from bats (left) and civet cats (center) are essential for the animal-to-human transmission (horizontal arrows). Species-to-species genetic variation in the (thus far unidentified) viral receptor in bats and in the <i>angiotensin converting enzyme 2 (ACE2)</i> gene, encoding the SARS-CoV receptor in civet cats and humans also affects the efficiency with which the virus can enter cells (vertical arrows). The SARS-CoV that caused the epidemic evolved a high affinity for both civet (center) and human (right) ACE2 receptors (indicated by the single diagonal and the right side vertical arrow). Image credit: Bart Haagmans, Erasmus MC. Original images (left to right) by Dodoni, Paul Hilton, and Hoang Dinh Nam.</p

Zoonotic transmission of influenza A virus.

<p>The hemagglutinin of avian influenza A viruses (blue) preferentially bind to oligosaccharides that terminate in sialic acid–α-2,3-Gal (red), whereas the hemagglutinin on human influenza A viruses (green) prefer oligosaccharides that terminate in sialic acid–α-2,6-Gal (orange). Fatal viral pneumonia in humans infected with the H5N1 subtype of avian influenza A viruses is likely due to the ability of these viruses to attach to and replicate in the lower respiratory tract cells, which have sialic acid-α-2,3-Gal terminated saccharides. The horizontal arrows indicate interspecies transmission, including the transmission from an avian or porcine reservoir into the human species. Image credit: Bart Haagmans, Erasmus MC. Original images (left to right, from top to bottom) by Roman Köhler, Alvesgaspar, Anton Holmquist, Joshua Lutz, and CDC.</p

Relationship between optimal β₂/β₁ ratio and S_i,2/S_i,1 ratio in the absence of pre-existing immunity.

<p>The optimal infectivity rate in the bronchiolar compartment (β₂) is smaller than the optimal infectivity rate in the tracheo-bronchial compartment (β₁) provided that the initial number of susceptible cells in the bronchiolar compartment (S_i,2) is larger than the initial number of susceptible cells in the tracheo-bronchial compartment (S_i,1).</p

Conceptual overview of the framework.

<p>Within-host models of infection dynamics in a spatially-structured respiratory tract composed of three linearly-connected compartments are used to estimate overall virus excretion (X) and pathogenicity (P). The parameters that differ per respiratory compartment (i) are the initial number of susceptible cells (S_0,i), the viral clearance rate (χ_i), and the distribution coefficients of immunoglobulins of type A (IgA) and IgG (c_ai and c_gi, respectively). The measures of virus excretion (X) and pathogenicity (P) in turn are used to estimate population-level transmission rate (β_h), mortality rate (α_h), and recovery rate (γ_h), which define the virus reproductive number (R). See Methods for more details.</p

Optimal patterns of tissue tropism and associated morbidity and mortality burdens.

<p>Contour plots of influenza virus reproductive number (color scales) in an immunologically naïve population (R₀; <b>A</b>) and in a partially-immune population (R_e; <b>B</b>) are drawn when the infectivity rates β₂ (x axis) and β₁ (y axis) are varied. In all cases, the infectivity rate β₃ is kept constant and equals the lowest infectivity rate in the explored range (10⁻¹⁰ h⁻¹). Note that the optimal tissue tropism differs in an immunologically naïve and in a partially-immune population. <b>C.</b> The total number of cases per 10 000 individuals (light grey bars) and the number of fatal cases per 100 000 individuals (black bars) are represented for the influenza virus with optimal tissue tropism in an immunologically naïve population (year 0) and for the influenza virus with optimal tissue tropism in a partially-immune population (year 1). Their respective case-fatality rate is indicated by a dark grey diamond. <b>D.</b> The percentage reduction in pathogenicity in the bronchiolar compartment (P₂) of the influenza virus with optimal tissue tropism in a partially-immune population is shown in a naïve individual and in an individual with pre-existing immunity in year 1 compared to that of the influenza virus with optimal tissue tropism in an immunologically naïve population (year 0).</p

Optimal receptor binding affinity patterns.

<p>Contour plots of influenza virus basic reproductive number R₀ (color scales) are drawn when the affinity coefficients a_2,3 (x axis) and a_2,6 (y axis) are varied from 0 to 1. For all graphs, the initial number of susceptible cells differ per respiratory compartment to reflect the heterogeneities in abundance and distribution of epithelial cells with sialic acids with α_2,3 or α_2,6 linkage to galactose. The effect of heterogeneities in viral clearance rates (χ_i) and in the contribution of pathogenicity in each respiratory compartment (P_i) to the overall virus pathogenicity (P) on the virus R₀ is determined, when either non-linear or linear functions link within-host model output of viral excretion (X) and pathogenicity (P) to between-host model parameters. For panels <b>A</b> to <b>D</b>, χ₁> χ₂> χ₃; for panels <b>E</b> and <b>F</b>, χ₁ = χ₂ = χ₃. For panels <b>A</b>, <b>C</b> and <b>E</b>, P = ∑ P_i; for panels <b>B</b>, <b>D</b> and <b>F</b>, P = P₁+10² P₂+10³ P₃. For panels <b>A</b>, <b>B</b>, <b>E</b> and <b>F</b>, non-linear functions link within-host model output to between-host model parameters; for panels <b>C</b> and <b>D</b>, linear functions link within-host model output to between-host model parameters.</p

Range of parameter estimates of the within-host models of infection dynamics.

†<p>Rates are given per hour if not otherwise indicated.</p>‡<p>In more complex versions of the models, IgA and IgG production rates were fixed to the maximal value indicated, to reduce the number of redundant free parameters.</p

Localization and phenotype of T-cells in HSV-1 latently infected human TG.

<p>(A) Representative image of an HSV-1 latently infected TG stained by immunohistochemistry (IHC) for CD3 (red). Inset: magnification of the TG tissue showing a cluster of CD3⁺ cells in panel A. (B; left panel) Double immunofluoresence staining for CD4 (red) and CD8 (green) combined with DNA counterstaining (DAPI; blue nuclei). The white arrows signify autofluorescent cytoplasmatic granules in neurons containing lipofuscin and neuron outlines are marked with white dotted lines. (B; right panel) Consecutive TG tissue sections stained for CD8 (brown) and granzyme B (brown), CD8 (brown) and TIA-1 (brown), and CD3 (red) and CD137 (red). Sections were developed with diaminobenzidine (brown staining pattern) or 3-amino-9-ethylcarbazole (red staining pattern) and counterstained with hematoxylin (blue nuclei). Magnifications were: (A) ×20 and inset ×200, (B; left panel) ×400 and (B; right panel) ×1000. Representative images from 10 HSV-1 latently infected TG donors analyzed.</p