Weakly holomorphic modular forms in prime power levels of genus zero

Let $M_k^\sharp(N)$ be the space of weight $k$, level $N$ weakly holomorphic modular forms with poles only at the cusp at $\infty$. We explicitly construct a canonical basis for $M_k^\sharp(N)$ for $N\in\{8,9,16,25\}$, and show that many of the Fourier coefficients of the basis elements in $M_0^\sharp(N)$ are divisible by high powers of the prime dividing the level $N$. Additionally, we show that these basis elements satisfy a Zagier duality property, and extend Griffin's results on congruences in level 1 to levels 2, 3, 4, 5, 7, 8, 9, 16, and 25

Interpersonal and intrapersonal differences among adolescent nonsmokers, ex-smokers, and smokers

Background: A large body of research has traced tobacco dependence among adolescents to a series of intrapersonal and interpersonal factors. However, there are remaining questions regarding the differences on these factors related to tobacco use. Objectives: We sought to investigate intrapersonal and interpersonal differences among adolescent nonsmokers, ex-smokers, and smokers. Methods: We used data from a 3-year project designed to investigate and address tobacco dependence among 1071 students (Mage = 15.76, SD = 1.52; girls = 51.54%) who were recruited from 11 high schools. Participants, filling out a survey, provided information on tobacco use (nonsmoker, ex-smoker, and smoker), tobacco-related experiences (smoking-related risk perception, parental smoking, number of friends who smoke, resisting peer pressure to smoke), cognitive variables (metacognitive skills), and personality traits (disinhibition and impulsivity). Results: Results from a discriminant function analysis showed that smokers and ex-smokers reported more disinhibition, impulsivity, number of friends who smoke and less self-control under peer pressure to smoke compared to nonsmokers. Ex-smokers reported less metacognitive processes, more smoking-related risk perception and were less likely to have parents who smoke. Conclusions/Importance: Interventions and campaigns aimed to persuade adolescents to stop smoking should work to develop adaptive metacognitive skills and an accurate risk perception of tobacco use. © 2017 Taylor & Francis Group, LL

Binge Drinking and Internalised Sexual Stigma among Italian Lesbian, Gay and Bisexual Young Adults

BACKGROUND - Literature has studied the relation between youth alcohol consumption and sexual orientation, showing that lesbian, gay and bisexual (LGB) young people are at increased risk to develop alcohol-related problems compared to heterosexuals. AIM - Our study aims to describe alcohol use patterns in relation to alcohol expectancies, internalised sexual stigma and sensation seeking and to highlight the specific risk factors that sexual minority subgroups face. DESIGN - A survey was administered in order to examine drinking habits, background information and levels of internalised sexual stigma and of sensation seeking in 468 LGB youths. According to the alcohol consumption, the sample was divided into three groups: social, binge and heavy drinkers. RESULTS - Data showed that bisexual youths were at most risk of heavy drinking compared to lesbian and gay participants. Moreover, LGB heavy drinkers reported higher levels of sensation seeking, earlier age of first alcohol consumption and more positive drinking expectancies compared to binge and social drinkers. Bisexual male heavy drinkers also showed more social confidence alcohol expectancies while bisexual female heavy drinkers showed more sensation seeking. CONCLUSION - Bisexual youths are at most risk of alcohol abuse. It is conceivable that these findings are related to the peculiar discrimination to which bisexual people are subject. Practical implications for the present study are discussed. \ua9 2016 Valera Verrastro et al., published by De Gruyter Open 2016

Manifestations of infection by the novel influenza A (H1N1) virus at chest computed tomography

OBJECTIVE: The objective of this study was to describe chest computed tomography findings in confirmed cases of infection by the novel influenza A (H1N1) virus. MATERIALS AND METHODS: Computed tomography studies of nine patients with laboratory-confirmed infection by the novel influenza A (H1N1) virus were consensually evaluated by three observers. The sample of the present study included five male and four female patients with ages ranging from 14 to 64 years (mean, 40 years). Four of the patients were previously healthy, four were kidney transplant recipients and one was pregnant at the time of diagnosis. Presence, extent and distribution of the following findings were evaluated: a) ground-glass opacities; b) centrilobular nodules; c) consolidation; d) interlobular septa thickening; e) pleural effusion; f) lymphadenopathy. RESULTS: The most frequent findings were ground-glass opacities, centrilobular nodules and consolidations, present in nine (100%), five (55%) and four (44%) of cases, respectively. Pleural effusions and lymphadenopathy were less common findings, occurring in only two (22%) of the cases. CONCLUSION: Ground-glass opacities, centrilobular nodules and consolidation were the most frequent findings in cases of infection by the novel influenza A (H1N1) virus. These changes are not typical or unique to this agent and may also occur in other viral or bacterial infections.OBJETIVO: Descrever as alterações na tomografia computadorizada de tórax de casos comprovados de infecção pelo novo vírus influenza A (H1N1). MATERIAIS E MÉTODOS: Três observadores avaliaram, em consenso, nove tomografias computadorizadas de pacientes com infecção pelo vírus influenza A (H1N1) comprovada laboratorialmente. A idade dos pacientes variou de 14 a 64 anos (média de 40 anos), sendo cinco do sexo masculino e quatro do sexo feminino. Quatro pacientes eram previamente hígidos, quatro eram transplantados renais e uma era gestante à época do diagnóstico. Foram avaliadas a presença, a extensão e a distribuição de: a) opacidades em vidro fosco; b) nódulos centrolobulares; c) consolidações; d) espessamento de septos interlobulares; e) derrame pleural; f) linfonodomegalias. RESULTADOS: As alterações mais frequentemente encontradas foram opacidades em vidro fosco, nódulos centrolobulares e consolidações, presentes em nove (100%), cinco (55%) e quatro (44%) dos casos, respectivamente. Derrames pleurais e linfonodomegalias foram menos comuns, ocorrendo em apenas dois (22%) dos casos estudados. CONCLUSÃO: Os achados mais comuns nos casos de infecção pelo novo vírus influenza A (H1N1) foram opacidades em vidro fosco, nódulos centrolobulares e consolidações. Estas alterações não são típicas ou únicas a este agente, podendo ocorrer também em outras infecções virais ou bacterianas.Universidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Departamento de Diagnóstico por ImagemHospital e Maternidade São Luiz Serviço de Diagnóstico por ImagemUniversidade Federal de São Paulo (UNIFESP) Escola Paulista de Medicina Hospital do Rim e Hipertensão - Órgão Suplementar Centro de Diagnóstico por ImagemUNIFESP, EPM, Depto. de Diagnóstico por ImagemUNIFESP, EPM, Hospital do Rim e Hipertensão - Órgão Suplementar Centro de Diagnóstico por ImagemSciEL

Coalescent-based species delimitation in the sand lizards of the Liolaemus wiegmannii complex (Squamata: Liolaemidae)

Coalescent-based algorithms coupled with the access to genome-wide data have become powerful tools forassessing questions on recent or rapid diversification, as well as delineating species boundaries in the absence of reciprocal monophyly. In southern South America, the diversification of Liolaemus lizards during the Pleistocene is well documented and has been attributed to the climatic changes that characterized this recent period of time. Past climatic changes had harsh effects at extreme latitudes, including Patagonia, but habitat changes at intermediate latitudes of South America have also been recorded, including expansion of sand fields over northern Patagonia and Pampas). In this work, we apply a coalescent-based approach to study the diversification of the Liolaemus wiegmannii species complex, a morphologically conservative clade that inhabits sandy soils across northwest and south-central Argentina, and the south shores of Uruguay. Using four standard sequence markers (mitochondrial DNA and three nuclear loci) along with ddRADseq data we inferred species limits and a time calibrated species tree for the L. wiegmannii complex in order to evaluate the influence of Quaternary sand expansion/retraction cycles on diversification. We also evaluated the evolutionary independence of the recently described L. gardeli and inferred its phylogenetic position relative to L. wiegmannii. We find strong evidence for six allopatric candidate species within L. wiegmannii, which diversified during the Pleistocene. The Great Patagonian Glaciation (∼1 million years before present) likely split the species complex into two main groups: one composed of lineages associated with sub-Andean sedimentary formations, and the other mostly related to sand fields in the Pampas and northern Patagonia. We hypothesize that early speciation within L. wiegmannii was influenced by the expansion of sand dunes throughout central Argentina and Pampas. Finally, L. gardeli is supported as a distinct lineage nested within the L. wiegmannii complex.Fil: Villamil, Joaquín. Universidad de la República. Facultad de Ciencias; UruguayFil: Avila, Luciano Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico para el Estudio de los Ecosistemas Continentales; ArgentinaFil: Morando, Mariana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico. Instituto Patagónico para el Estudio de los Ecosistemas Continentales; ArgentinaFil: Sites, Jack W.. University Brigham Young; Estados UnidosFil: Leaché, Adam D.. University of Washington; Estados UnidosFil: Maneyro, Raúl. Universidad de la República. Facultad de Ciencias; UruguayFil: Camargo Bentaberry, Arley. Universidad de la República; Urugua

Anura, Hylidae, <i>Dendropsophus nahdereri</i> (Lutz and Bokermann, 1963): Distribution extension and new state record.

Dendropsophus nahdereri is included in the Dendropsophus marmoratus group. Its distribution is known fromthe Brazilian states of Paraná and Santa Catarina. Here we report new records of this species and briefly describe the habitatof calling males. We found new localities of occurrence of D. nahdereri in Brazilian states of Santa Catarina and Rio Grandedo Sul. We collected calling males in temporary lentic water bodies surrounded by arboreal vegetation, inside and on theborder of native forest, and inside Pinus plantations near native forest

Semi-Automated Stereo Image Patches Generation and Labeling Method Based on Perspective Transformations

In computer vision, Wide Baseline Stereo (WxBS) refers to Vision System configurations on which their images come from cameras with non parallel and widely separated views. One common task in reconstruction algorithms of WxBS consists of subvididing the stereo images in multiple image patches and then associate homologous patches between homologous images. Multiple approaches can be used to associate homologous patches. To train and test supervised learning algorithms for this tasks, a labeled dataset is required. In this work, a semi-automated method to generate patches and their labels from WxBS images is presented. It allows to calculate thousands of positive and negative pairs of patches with a score of correspondence between a pair of potentially homologous image patches. This method largely solves the problems of traditional approach, which requires a lot of hand labeled work and time. To apply the method, images from different viewpoints of objects with planar faces and their corner locations are required.In computer vision, Wide Baseline Stereo (WxBS) refers to Vision System configurations on which their images come from cameras with non parallel and widely separated views. One common task in reconstruction algorithms of WxBS consists of subvididing the stereo images in multiple image patches and then associate homologous patches between homologous images. Multiple approaches can be used to associate homologous patches. To train and test supervised learning algorithms for this tasks, a labeled dataset is required. In this work, a semi-automated method to generate patches and their labels from WxBS images is presented. It allows to calculate thousands of positive and negative pairs of patches with a score of correspondence between a pair of potentially homologous image patches. This method largely solves the problems of traditional approach, which requires a lot of hand labeled work and time. To apply the method, images from different viewpoints of objects with planar faces and their corner locations are required

Modelagem de abundância com drones : detectabilidade, desenho amostral e revisão automática de imagens em um estudo com cervos-do-pantanal

Entender como a abundância de uma espécie se distribui no espaço e/ou no tempo é uma questão fundamental em ecologia e conservação e ajuda, por exemplo, a elucidar relações entre a heterogeneidade de paisagens e populações ou compreender influência de predação na distribuição de indivíduos. Informações de tamanho populacional também são essenciais para avaliar risco de extinção, monitorar populações ameaçadas e planejar ações de conservação. Modelar a abundância de cervos-do-pantanal (Blastocerus dichotomus), sendo um grande herbívoro da América do Sul, pode ser importante para entender relações da espécie com a variação espacial da produtividade primária, das áreas úmidas que a espécie ocupa e do seu principal predador, a onça- pintada. Além disso, por estar ameaçado de extinção, estimar a abundância de cervos pode contribuir para avaliar populações relictuais da espécie, assim como monitorar populações após grandes eventos, como os incêndios de 2020 no Pantanal. Porém, acessar estimativas de abundância confiáveis de maneira eficiente requer métodos robustos que levem em conta os possíveis erros nas contagens e que forneçam as estimativas em tempo hábil, além de um desenho amostral otimizado para aproveitar os recursos geralmente escassos. Os drones tem aparecido como uma ferramenta versátil e custo-efetiva para amostragem de populações animais e vêm sendo aplicados para várias espécies diferentes nos mais variados contextos ecológicos. Como um método emergente, o uso de drones na ecologia fornece oportunidades para explorar novas possibilidades de amostragem e análise de dados, ao mesmo tempo em que pode apresentar novos desafios. Nesta tese, i) exploro oportunidade e desafios na utilização de drones para modelagem de abundância de animais, abordando questões de erros de detecção, desenho amostral e como lidar com os grandes bancos de imagens gerados; e ii) aplico os métodos desenvolvidos para estudar a variação na abundância de cervo-do- pantanal, assim como estabelecer uma abordagem para monitoramento robusto e efetivo dessa espécie. Assim, no primeiro capítulo, conduzo uma revisão na literatura descrevendo os potenciais erros de detecção que podem enviesar estimativas de abundância com drones, buscando soluções atuais para lidar com esses erros e identificando lacunas que precisam de desenvolvimento. Nessa revisão, destaco o potencial dos modelos hierárquicos para estimar abundância em amostragens com drone. No segundo capítulo, aplico amostragens espaço-temporalmente replicadas com drone, analisadas com modelos hierárquicos N-mixture, para entender o efeito de processos topo-base (distribuição de onças-pintadas) e base-topo (disponibilidade de forragem de qualidade e corpos d’água) na distribuição da abundância de cervos-do- pantanal. Nesse estudo, encontrei que, na época seca, os cervos se concentram em áreas de alta qualidade (maior disponibilidade de forragem e próximas a corpos d’água), mesmo sendo a região em que é esperado maior efeito da predação. No capítulo 3, em um estudo com simulações, avalio o desempenho de modelos N-mixture para estimativas de abundância a partir de amostragens espaço-temporalmente replicadas, explorando otimização de esforço amostral e o impacto de um protocolo com observadores duplos na acurácia das estimativas. No capítulo 4, desenvolvo uma abordagem para estimar abundância com drone usando observadores múltiplos na revisão das imagens, sendo um dos observadores baseado em um processamento semiautomático usando algoritmos de inteligência artificial. Nesse estudo, exploro técnicas de aprendizado profundo de máquina, com redes neurais convolucionais, acessíveis para ecólogos, treinando algoritmos para detectar cervos nas imagens de drone. Além de ajudar a elucidar questões sobre as relações do cervo-do-pantanal com aspectos diferentes da paisagem do Pantanal, as abordagens exploradas e desenvolvidas aqui têm um grande potencial de aplicação, ajudando a estabelecer os drones como uma ferramenta eficiente para modelagem e monitoramento populacional de diversas espécies animais, e particularmente de cervos.Understanding how abundance distributes in space and/or time is a fundamental question in ecology and conservation, and it helps, for example, to elucidate relationships between landscape heterogeneity or predation and populations. Information on the population size also is essential to evaluate extinction risk, monitor threatened species and plan conservation actions. Abundance modeling of marsh deer (Blastocerus dichotomus), as a large herbivore of South America, may be important to understand the relationships of this species with spatial variation in primary productivity, in the availability of wetlands that the species inhabits, and in the distribution of its main predator, the jaguar. Moreover, since marsh deer threatened to extinction, estimating its abundance can be contribute in assessments of relictual populations, as well as in monitoring the species after big events, such as the Pantanal 2020 megafires. However, efficiently assessing reliable abundance estimates require robust methods that account for possible sources of error in counts while providing the estimates timely. An optimized sampling design is also important, in order to make the best use of the usual scarce resources. Drones have raised as a versatile and cost- effective tool for sampling animal populations, and they have been applied for several species in a wide variety of ecological contexts. As being an emergent method, the use of drones in ecology provides opportunities to explore novel possibilities of sampling and analyzing data, while potentially presenting new challenges. In this thesis I: i) explore opportunities and challenges in the use of drones for animal abundance modeling, approaching issues about detection errors, sampling design and how to deal with the huge image sets generated from drone flights; and ii) apply the developed methods to study the spatial variation in marsh deer abundance and to establish an approach to monitor this species robustly and efficiently. Thus, in the first chapter, I carry on a literature review describing potential sources of errors that may bias abundance estimation with drones and the current solutions to address them, identifying gaps that need development. In this review, I highlight the potential of hierarchical models for abundance estimations from drone-based surveys. In the second chapter, I apply spatiotemporally replicated drone surveys, analyzed with N-mixture models, to understand the influence of bottom-up (forage and water) and top-down (jaguar density) variables on the spatial variation of marsh deer local abundance. In such study, I found that, in the dry season, the deer concentrate in high quality areas (high-quality forage available and close to water bodies), even these regions being expected to present higher predation risks. In chapter 3, in a simulation study, I evaluate the performance of N- mixture models for abundance estimation from spatiotemporally replicated surveys, exploring optimization of sampling effort and the impact of a double-observer protocol on estimation accuracy. In chapter 4, I develop a pipeline to estimate abundance from drone-based surveys using a multiple-observer protocol in which one of the observer is a semiautomated procedure based on deep learning algorithms. In such study, I explore deep learning techniques with convolutional neural networks that are accessible for ecologists, and train algorithms to detect marsh deer in drone imagery. Besides helping to elucidate questions about the relationships of marsh deer with landscape variables in Pantanal, the approaches explored and developed here have a great potential of application in order to establish drones as an efficient technique for population modeling and monitoring of several wildlife species, and particularly the marsh deer