Caracterização fenotípica de milho pipoca conservado in situ- on farm no Extremo Oeste de Santa Catarina

Popcorn is a special type of corn and approximately a thousand of landraces are conserved in situ in rural properties (on farm) from the Western Santa Catarina (WSC). The characterization is fundamental to promote their conservation, valorization and commercial use. This study aimed at characterizing ten popcorn landraces from this region. Thus, the varieties were evaluated in Florianópolis-SC, in a complete randomized block design with four repetitions and useful plot of 4m². Morphological characteristics of plants, ears, kernels and cycle of the varieties were evaluated according to descriptors of Zea mays L. The popcorn varieties from WSC showed diversity for phenological, morphological and agronomic characteristics. Two varieties were classified as extra early, three were early, one intermediate and four were late. The varieties 880A, 977A, 574A, 2312A and 2489D were highlighted as genetic sources for important characteristics in the development of new cultivars, such as plant height, productive potential and circularity index.A pipoca é um tipo especial de milho e aproximadamente mil variedades locais são conservadas in situ, em propriedades rurais do Extremo Oeste de Santa Catarina (EOSC). A caracterização destas variedades é fundamental para a promoção da sua conservação, valorização e uso comercial. O objetivo deste estudo foi caracterizar a diversidade fenotípica de dez variedades locais de milho pipoca desta região. Para tanto, as variedades foram avaliadas em Florianópolis/SC, em blocos completos casualizados com quatro repetições e parcela útil de 4m². As características morfológicas de planta, espiga e grão e o ciclo das variedades foram avaliados de acordo com os descritores de Zea mays L.  As variedades de milho pipoca do EOSC apresentaram diversidade para caracteres fenológicos, morfológicos e agronômicos. Duas variedades foram classificadas como hiperprecoces, três precoces, uma intermediárias e quatro tardias. As variedades 880A, 977A, 574A, 2312A e 2489D se destacaram como fontes genéticas de características importantes para o desenvolvimento de novos cultivares, tais como altura de planta, potencial produtivo e índice de circularidade

Identificação molecular de espécies de vírus e reação fenotípica de famílias de melancia a um isolado do vírus da mancha anelar do mamoeiro, estirpe melancia (Pappaya ringspot virus – strain watermelon - PRSV-W)

The curcubit, including watermelon, are affected by various diseases caused by viruses. In Brazil, has predominated species of the Potyvirus. This study aimed was molecular identification of the virus species from four producer regions of watermelon the state of Tocantins and test through artificial inoculation, the reaction of the progenies selected for phenotypic resistance to one of those characterized as isolated from PRSV-W. Of the nine isolates tested, six presented bands for PRSV-W. One of the isolates presented bands for PRSV-W and SqMV which can be a mixed infection. In phenotypic analysis, access PI 595201 showed a high level of resistance. The progenies had different behavior, and was identify within each progenies plants with similar resistance to the resistant parent.As curcubitaceas, incluindo a melancia, estão sujeitas a várias doenças causadas por vírus. No Brasil, tem predominado espécies de Potyvirus. O objetivo do trabalho foi efetuar a identificação molecular de espécies de vírus provenientes de quatro regiões produtoras de melancia do estado do Tocantins e verificar através da inoculação artificial, a reação fenotípica de famílias selecionadas para resistência a um desses isolado caracterizado como sendo de PRSV-W. Dos nove isolados utilizados, seis apresentaram bandas para PRSV-W. Um dos isolados apresentou bandas para PRSV-W e SqMV que pode ser uma infecção mista. Na avaliação fenotípica, o acesso PI 595201 apresentou alto nível de resistência. As famílias apresentaram comportamento diferenciado, sendo possível identificar plantas dentro de cada família com resistência semelhante ao genitor resistente

Repeatability analysis of guava fruit and leaf characteristics

Psidium guajava L. (guava) is an important species that presents high genetic variability due to its mixed reproductive system, which is desired in breeding programs. Repeatability is an important tool for the selection of genotypes in pre-breeding studies. When genetic variability is present, the knowledge regarding the number of samples to be used in repeatability studies is indispensable. This study aims to determine the number of necessary measures while optimizing resources and maintaining the reliability of the results for the variables evaluated in P. guajava. The experiment was carried out with genotypes from three Brazilian States: Espírito Santo, São Paulo, and Minas Gerais, and a total of 79 P. guajava genotypes were collected. The following characteristics were evaluated: young leaf length and width; developed leaf length and width; fruit length; fruit diameter and fruit cavity diameter; and fruit weight and pulp weight. For the evaluated characteristics, deviance, permanent phenotypic and temporary environment variance, coefficients of repeatability and determination, accuracy and the number of estimated measurements required were determined. We established that the number of measurements required in repeatability analysis for a coefficient of repeatability with a reliability of 80% is four, for the measurements of developed leaf width, pulp weight, fruit diameter, and fruit cavity diameter

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

A detailed study is presented of the expected performance of the ATLAS detector. The reconstruction of tracks, leptons, photons, missing energy and jets is investigated, together with the performance of b-tagging and the trigger. The physics potential for a variety of interesting physics processes, within the Standard Model and beyond, is examined. The study comprises a series of notes based on simulations of the detector and physics processes, with particular emphasis given to the data expected from the first years of operation of the LHC at CERN

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost