Adaptweb: um ambiente para ensinoaprendizagem adaptativo na Web

Este artigo, com ênfase nos aspectos técnicos, apresenta os resultados obtidos no âmbito dos projetos Electra e Adaptweb, voltados para a autoria e apresentação adaptativa de disciplinas integrantes de cursos EAD na Web. O objetivo é permitir a adequação de táticas e formas de apresentação de conteúdos para alunos de diferentes cursos de graduação e com diferentes estilos de aprendizagem, possibilitando diferentes formas de apresentação de cada conteúdo, de forma adequada a cada curso e às preferências individuais dos alunos participantes. Esta proposta foi validada através de um estudo de caso real, envolvendo a disciplina de Métodos Numéricos da Universidade Estadual de Londrina (UEL). Esta disciplina é oferecida para três cursos universitários: Matemática, Engenharia e Ciência da Computação. Em cada curso, o professor-autor configura uma seqüência de conceitos, com seus respectivos níveis de profundidade, bem como exemplos, exercícios e materiais complementares apropriados. Os alunos podem utilizar diferentes estilos de aprendizagem através da seleção do modo de navegação e dos estilos de apresentação. Os aspectos de adaptabilidade navegacional incluem navegação em modo tutorial ou livre, oferecendo diferentes alternativas de táticas de ensino. O ambiente completo para autoria e apresentação de cursos encontra-se atualmente disponível na fase de protótipo operacional. Este protótipo está sendo incorporado na plataforma de EAD em software livre da Universidade Federal do Rio Grande do Sul (UFRGS). O ambiente está baseado em um conjunto de componentes que suportam as fases de pré-autoria, adaptação de conteúdo e navegação. A implementação utiliza primordialmente a linguagem de programação PHP, o gerenciador de banco de dados MySQL e a linguagem XML

Method to obtain platelet-rich plasma from rabbits (Oryctolagus cuniculus )

Abstract: Platelet-rich plasma (PRP) is a product easy and inxpesnsive, and stands out to for its growth factors in tissue repair. To obtain PRP, centrifugation of whole blood is made with specific time and gravitational forces. Thus, the present work aimed to study a method of double centrifugation to obtain PRP in order to evaluate the effective increase of platelet concentration in the final product, the preparation of PRP gel, and to optimize preparation time of the final sample. Fifteen female White New Zealand rabbits underwent blood sampling for the preparation of PRP. Samples were separated in two sterile tubes containing sodium citrate. Tubes were submitted to the double centrifugation protocol, with lid closed and 1600 revolutions per minute (rpm) for 10 minutes, resulting in the separation of red blood cells, plasma with platelets and leucocytes. After were opened and plasma was pipetted and transferred into another sterile tube. Plasma was centrifuged again at 2000rpm for 10 minutes; as a result it was split into two parts: on the top, consisting of platelet-poor plasma (PPP) and at the bottom of the platelet button. Part of the PPP was discarded so that only 1ml remained in the tube along with the platelet button. This material was gently agitated to promote platelets resuspension and activated when added 0.3ml of calcium gluconate, resulting in PRP gel. Double centrifugation protocol was able to make platelet concentration 3 times higher in relation to the initial blood sample. The volume of calcium gluconate used for platelet activation was 0.3ml, and was sufficient to coagulate the sample. Coagulation time ranged from 8 to 20 minutes, with an average of 17.6 minutes. Therefore, time of blood centrifugation until to obtain PRP gel took only 40 minutes. It was concluded that PRP was successfully obtained by double centrifugation protocol, which is able to increase the platelet concentration in the sample compared with whole blood, allowing its use in surgical procedures. Furthermore, the preparation time is appropriate to obtain PRP in just 40 minutes, and calcium gluconate is able to promote the activation of platelets

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,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

Gonadotropin subunits of the characiform Astyanax altiparanae : Molecular characterization, spatiotemporal expression and their possible role on female reproductive dysfunction in captivity

To better understand the endocrine control of reproduction in Characiformes and the reproductive dysfunctions that commonly occur in migratory fish of this order when kept in captivity, we chose Astyanax altiparanae, which has asynchronous ovarian development and multiple spawning events, as model species. From A. altiparanae pituitary total RNA, we cloned the full-length cDNAs coding for the follicle-stimulating hormone β subunit (fshb), the luteinizing hormone β subunit (lhb), and the common gonadotropin α subunit (gpha). All three sequences showed the highest degree of amino acid identity with other homologous sequences from Siluriformes and Cypriniformes. Real-time, quantitative PCR analysis showed that gpha, fshb and lhb mRNAs were restricted to the pituitary gland. In situ hybridization and immunofluorescence, using specific-developed and characterized polyclonal antibodies, revealed that both gonadotropin β subunits mRNAs/proteins are expressed by distinct populations of gonadotropic cells in the proximal pars distalis. No marked variations for lhb transcripts levels were detected during the reproductive cycle, and 17α,20β-dihydroxy-4-pregnen-3-one plasma levels were also constant, suggesting that the reproductive dysfunction seen in A. altiparanae females in captivity are probably due to a lack of increase of Lh synthesis during spawning season. In contrast, fshb transcripts changed significantly during the reproductive cycle, although estradiol-17β (E2) levels remained constant during the experiment, possibly due to a differential regulation of E2 synthesis. Taken together, these data demonstrate the putative involvement of gonadotropin signaling on the impairment of the reproductive function in a migratory species when kept in captivity. Future experimental studies must be carried to clarify this hypothesis. All these data open the possibility for further basic and applied studies related to reproduction in this fish model