15 research outputs found
Flow: the Axiom of Choice is independent from the Partition Principle
We introduce a general theory of functions called Flow. We prove ZF, non-well
founded ZF and ZFC can be immersed within Flow as a natural consequence from
our framework. The existence of strongly inaccessible cardinals is entailed
from our axioms. And our first important application is the introduction of a
model of Zermelo-Fraenkel set theory where the Partition Principle (PP) holds
but not the Axiom of Choice (AC). So, Flow allows us to answer to the oldest
open problem in set theory: if PP entails AC.Comment: 37 pages, 4 Figure
A list of land plants of Parque Nacional do Caparaó, Brazil, highlights the presence of sampling gaps within this protected area
Brazilian protected areas are essential for plant conservation in the Atlantic Forest domain, one of the 36 global biodiversity hotspots. A major challenge for improving conservation actions is to know the plant richness, protected by these areas. Online databases offer an accessible way to build plant species lists and to provide relevant information about biodiversity. A list of land plants of “Parque Nacional do Caparaó” (PNC) was previously built using online databases and published on the website "Catálogo de Plantas das Unidades de Conservação do Brasil." Here, we provide and discuss additional information about plant species richness, endemism and conservation in the PNC that could not be included in the List. We documented 1,791 species of land plants as occurring in PNC, of which 63 are cited as threatened (CR, EN or VU) by the Brazilian National Red List, seven as data deficient (DD) and five as priorities for conservation. Fifity-one species were possible new ocurrences for ES and MG states
Tratamento endovascular da síndrome de compressão da veia ilíaca (May-Thurner): relato de caso
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
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
Estudo do comportamento de RSU em uma célula experimental e suas correlações com aspectos microbiológicos, físicos e químicos
Reflexões sobre poder e controle nas Organizações da Economia Solidária (OES): um olhar à luz dos bichos de Orwell
Qualitative and anatomical characteristics of tree-shrub legumes in the Forest Zone in Pernambuco state, Brazil
The objective of this study was to characterize the nutritional value of forage legumes Mimosa caesalpiniifolia (Benth.), Bauhinia cheilantha (Bong.) and Leucaena leucocephala (Lan.) and evaluate the anatomy of plants incubated and not incubated in the rumen. The experiment was conducted from September 2007 to November 2008. The experimental plot consisted of three useful plants, totaling three plots per block, and four repetitions. Plants were cut at 1 m height at intervals of 70 days; samples of leaf plus stem with a diameter smaller than 4 mm were collected for determination of dry matter, crude protein, neutral detergent fiber, acid detergent fiber, insoluble protein bound to acid detergent fiber and in vitro dry matter digestibility. The anatomical characterization occurred through the analysis of the proportion of plant tissue nonincubated and incubated in the rumen for a period of 48 hours. The legumes had high crude protein, with an average of 26.1% to Leucena, 22.4% to Sabiá and 18.5% to Mororó, and low levels of in vitro digestibility of dry matter, with an average of 47.3% to Leucena, 42.8% to Mororó and 37.2% to Sabiá. In the leaf blade of Sabiá plants, much lignified tissues that differed from plants of Leucena and Mororó were observed. The degradation process was more visible in the leaves of the Leucena, Sabiá and Mororó plants. The degradation was more pronounced in the spongy parenchyma, leaving the incubated material intact. The average proportion of the epidermis in the incubated and not incubated leaves was 15.8 and 16.4% in Leucena, 16.8 and 19.2% in Mororó and 27.2 and 25.5% in Sabiá, respectively. There are differences in the digestion and nutritional value of leaf tissues of the evaluated legumes