Cultivation of Pleurotus mushrooms in substrates obtained by short composting and steam pasteurization

This paper presents results of two experiments for cultivation of Pleurotus ostreatus, Pleurotus pulmonarius and Pleurotus eryngii grown with different formulations of grass and straw mixtures derived from agro-industrial residues. Cultivation was prepared through a number of approaches, such as short composting/pasteurization and axenic culture. In the first experiment, P. pulmonarius was grown on two formulations of different grasses, with no significant differences observed for either productivity or biological efficiency, with values close to 20 and 60%, respectively. The second experiment revealed similar productivity and biological efficiency between P. pulmonarius and P. ostreatus for both forms of substrate treatment (short composting/pasteurization vs. axenic culture), with similar values to those observed in the first experiment. P. eryngii did not produce mushrooms in the composting treatment and showed lower productivity (17.5%) than the other two species (20.5 and 20.8%, respectively) when the substrates were autoclaved (axenic culture). The preparation for short composting and steam pasteurization was described in illustrative figures in order to provide expertise to small producers who wish to initiate economic and sustainable mushroom cultivation making use of regional lignocellulosic residues.Keywords: Steam pasteurization, lignocellulosic biomass, straw mixtures, mushroom

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

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Doctors' awareness concerning primary immunodeficiencies in Brazil

Background: PIDs are a heterogeneous group of genetic illnesses, and delay in their diagnosis is thought to be caused by a lack of awareness among physicians concerning PIDs. the latter is what we aimed to evaluate in Brazil.Methods: Physicians working at general hospitals all over the country were asked to complete a 14-item questionnaire. One of the questions described 25 clinical situations that could be associated with PIDs and a score was created based on percentages of appropriate answers.Results: A total of 4026 physicians participated in the study: 1628 paediatricians (40.4%), 1436 clinicians (35.7%), and 962 surgeons (23.9%). About 67% of the physicians had learned about PIDs in medical school or residency training, 84.6% evaluated patients who frequently took antibiotics, but only 40.3% of them participated in the immunological evaluation of these patients. Seventy-seven percent of the participating physicians were not familiar with the warning signs for PIDs. the mean score of correct answers for the 25 clinical situations was 48.08% (+/- 16.06). Only 18.3% of the paediatricians, 7.4% of the clinicians, and 5.8% of the surgeons answered at least 2/3 of these situations appropriately.Conclusions: There is a lack of medical awareness concerning PIDs, even among paediatricians, who have been targeted with PID educational programmes in recent years in Brazil. An increase in awareness with regard to these disorders within the medical community is an important step towards improving recognition and treatment of PIDs. (C) 2014 SEICAP. Published by Elsevier Espana, S.L.U. All rights reserved.Jeffrey Modell FoundationBrazilian Jeffrey Modell CentreUniversidade Federal de São Paulo, São Paulo, BrazilUniv Fed Pernambuco, Recife, PE, BrazilUniv Hosp, Brasilia, DF, BrazilChildrens Hosp, Brasilia, DF, BrazilAlbert Sabin Childrens Hosp, Fortaleza, Ceara, BrazilUniv Estadual Montes Claros, Montes Claros, BrazilUniv Fed Parana, BR-80060000 Curitiba, Parana, BrazilUniv São Paulo, Fac Med Ribeirao Preto, BR-14049 Ribeirao Preto, BrazilFac Med Sao Jose Rio Preto, Sao Jose Do Rio Preto, BrazilUniv Hosp Sao Vicente Paulo, Passo Fundo, BrazilJoana Gusmao Childrens Hosp, Florianopolis, SC, BrazilUniv Hosp Muller, Fac Med, Cuiaba, BrazilUniv Fed Mato Grosso, Cuiaba, BrazilNipo Brasileiro Hosp, São Paulo, BrazilHosp lsraelita Albert Einstein, São Paulo, BrazilUniv Fed Rio Grande do Norte, Natal, RN, BrazilUniv Fed Uberlandia, BR-38400 Uberlandia, MG, BrazilUniv Fed Bahia, Complexo Hosp Univ Prof Edgar Santos, Salvador, BA, BrazilUniv Estadual Piaui, Teresina, BrazilABC, Fac Med, Santo Andre, BrazilUniv Fed Minas Gerais, Belo Horizonte, MG, BrazilUniv Ctr Para, Belem, Para, BrazilMed Course Lusiada Univ Ctr UNILUS, Dept Pediat, Santos, BrazilUniv Fed Sergipe, Aracaju, BrazilHosp Servidor Publ Municipal, São Paulo, BrazilUniv São Paulo, Inst Biomed Sci, BR-05508 São Paulo, BrazilUniv Estadual Campinas, Sch Med, Dept Pediat, Campinas, BrazilPrivate Off, Macapa, BrazilHosp Ministro Costa Cavalcanti, Foz Do Iguacu, BrazilChildrens Hosp Cosme & Damitio, Rondonia, BrazilUniv Fed Rio de Janeiro, Rio de Janeiro, BrazilUniversidade Federal de São Paulo, São Paulo, BrazilWeb of Scienc