UM ESQUEMA DE VOLUMES FINITOS PARA A SIMULAÇÃO DE ESCOAMENTOS EM RESERVATÓRIOS NATURALMENTE FRATURADOS EM 2-D UTILIZANDO UM MODELO DE FRATURAS COM DIMENSÃO REDUZIDA

O problemado escoamentobifásico em reservatórios de petróleo, heterogêneos e anisotrópicos, pode ser descrito por um sistema de equações diferenciais parciais nãolineares. A modelagem deste problema representa um grande desafio, devido à complexidade dos ambientes deposicionais, incluindo camadas inclinadas e fraturas, que dificultam a construção de malhas estruturadas adequadas. No presente artigo, utilizou-se o Método de Volume Finitos com Aproximação do Fluxo por Múltiplos Pontos (MPFA-O) centrado na célula, o qual é capaz de lidar com tensores de permeabilidade completos e malhas poligonais arbitrárias, acoplado com um Modelo de Fraturas com Dimensão Reduzida (Lower-Dimensional Fracture Model - LDFM). O LDFM utiliza uma equação adicional associada à fratura que é tratada como uma entidade geométrica com dimensão inferior à do problema original, ou seja, para problemas em 2-D, a fratura tem apenas uma dimensão no espaço. Isso reduz consideravelmente o número de graus de liberdade do sistema. É importante observar que o campo de velocidades nas superfícies de controle que coincidem com as fraturas é dependente, tanto das pressões nas fraturas, quanto das pressões nos volumes de controle que representam a rocha matriz. A acurácia da formulação proposta foi verificada através da resolução de alguns problemas envolvendo uma matriz fraturada

Safety issues of raw milk: evaluation of bacteriological and physicochemical characteristics of human milk from a bank in a teaching hospital, focusing on Staphylococcus species

Many infants are nurtured with milk supplied by human banks, whose bacteriological and physical-chemical profiles are a major issue. We investigated the bacteriological and physical-chemical characteristics, as well as genotypic and phenotypic and profiles of Staphylococcus species isolated from 240 samples of breast milk from a bank in a teaching hospital. Dornic acidity of milk revealed that 95.4% (229/240) had acceptable limits (< 8.0 oD). Caloric intake showed a wide variation in cream content (4%), fat (4%) and energy values (559.81 Kcal/L). Staphylococcus (105/186 or 56.5%) and Enterobacter (25/186 or 13.4%) were the most prevalent genera, although other microorganisms were identified, including Klebsiella pneumoniae and Pseudomonas aeruginosa. Amoxicillin/clavulanic acid (125/157 or 79.6%), vancomycin (115/157 or 73.2%), and cephalexin (112/157 or 71.3%) were the most effective antimicrobials. High resistance rates of isolates were found to penicillin G (141/157 or 89.8%), ampicillin (135/157 or 86%), and oxacillin (118/157 or 75.2%). Multidrug resistance to ≥ 3 antimicrobials occurred in 66.2% (123/186) of the isolates. Residues of microbial multiplication inhibitory substances were found in 85% (204/240) of samples. Among the coagulase-positive-CPS and negative-CoNS staphylococci, the mecA gene was detected in 53.3% (8/15) and 75% (30/40), respectively. Genes sea, seb and sec were detected in 20% (3/15) of CPS, while tsst-1 was detected in 13.34% (2/15). In addition, 13.3% (2/15) of S. aureus were toxin-producers. Genes sea, seb and sec were detected in 90% (36/40), 5% (2/40) and 15% (6/40) CoNS, respectively. Enterotoxin production was identified in 5% (2/40) of CoNS. The identification of multidrug-resistant bacteria, staphylococci species toxin-producers harboring methicillin-resistance genes, and residues of microbial multiplication inhibitory substances reinforce the need for a continuous vigilance of milk quality offered to infant consumption by human banks

Alvarado’s Criteria for Diagnosis of Children’s Acute Apendicitis

Acute abdomen in children is a condition that causes great distress to parents, and appendicitis is its most common cause, being more frequent at school age. This pathology is the cause of numerous visits to public and private hospitals around the world, and brings several complications. It is important that the health team is aware of the possibility of appendicitis in children, due to its high incidence and difficulty in establishing its diagnosis, because the symptoms are nonspecific and there are different clinical presentations. Objective: to explain the importance of the Alvarado criterion for the diagnosis of acute childhood appendicitis. Methodology: This is an integrative bibliographic review, in articles published in the PubMed, Virtual Health Library and Google Scholar databases. For the search for data, the descriptors “Acute abdomen”, “Appendicitis”, “Children” and the keyword “Alvarado score” were used. Data were collected in December 2020. Results: 16 articles were selected as the final sample for analysis of the review, six in English, nine in Portuguese and one in Spanish. Conclusion: Through this review it can be concluded that the use of the Alvarado Score for the diagnosis of acute appendicitis in children is useful and effective, avoiding the use of imaging tests in patients with a score above 7 on this scale

Main Electroclinic Syndromes of The Infant: A Literature Review

An electroclinic syndrome (also known as epileptic syndrome) consists of a set of characteristics that associate with seizures, characteristics of electroencephalography and imaging that tend to occur together. Among them is a group that affects infants, usually having a genetic etiology. Objective: to carry out a literature review on the most common types of infant electroclinical syndromes, describing their main characteristics. Methodology: This is a narrative review of the literature of articles published between 2005 and 2020, in Portuguese and English. The research was carried out in December 2020. The following descriptors were used to search for the articles: Electroclinical syndromes. Epilepsy. Infant. Results: Through the research carried out, 12 scientific articles were selected, nine of them in English and three in Portuguese. Conclusion: The study identified 7 common electroclinical syndromes in infants and made it possible to conclude that knowledge of the clinical profile, types of crises and characteristics of the electroencephalography of the disease phenotype associated with the specific mutation can help improve the accuracy and management of the diagnosis of electroclinical syndromes

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

ATLANTIC EPIPHYTES: a data set of vascular and non-vascular epiphyte plants and lichens from the Atlantic Forest

Epiphytes are hyper-diverse and one of the frequently undervalued life forms in plant surveys and biodiversity inventories. Epiphytes of the Atlantic Forest, one of the most endangered ecosystems in the world, have high endemism and radiated recently in the Pliocene. We aimed to (1) compile an extensive Atlantic Forest data set on vascular, non-vascular plants (including hemiepiphytes), and lichen epiphyte species occurrence and abundance; (2) describe the epiphyte distribution in the Atlantic Forest, in order to indicate future sampling efforts. Our work presents the first epiphyte data set with information on abundance and occurrence of epiphyte phorophyte species. All data compiled here come from three main sources provided by the authors: published sources (comprising peer-reviewed articles, books, and theses), unpublished data, and herbarium data. We compiled a data set composed of 2,095 species, from 89,270 holo/hemiepiphyte records, in the Atlantic Forest of Brazil, Argentina, Paraguay, and Uruguay, recorded from 1824 to early 2018. Most of the records were from qualitative data (occurrence only, 88%), well distributed throughout the Atlantic Forest. For quantitative records, the most common sampling method was individual trees (71%), followed by plot sampling (19%), and transect sampling (10%). Angiosperms (81%) were the most frequently registered group, and Bromeliaceae and Orchidaceae were the families with the greatest number of records (27,272 and 21,945, respectively). Ferns and Lycophytes presented fewer records than Angiosperms, and Polypodiaceae were the most recorded family, and more concentrated in the Southern and Southeastern regions. Data on non-vascular plants and lichens were scarce, with a few disjunct records concentrated in the Northeastern region of the Atlantic Forest. For all non-vascular plant records, Lejeuneaceae, a family of liverworts, was the most recorded family. We hope that our effort to organize scattered epiphyte data help advance the knowledge of epiphyte ecology, as well as our understanding of macroecological and biogeographical patterns in the Atlantic Forest. No copyright restrictions are associated with the data set. Please cite this Ecology Data Paper if the data are used in publication and teaching events. © 2019 The Authors. Ecology © 2019 The Ecological Society of Americ

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