The scientific structure of competitive intelligence

A estrutura científica de uma área é a base para o seu reconhecimento como disciplina científica. Sendo a Inteligência Competitiva uma área recente do conhecimento, objetiva-se com esta pesquisa a proposição de sua estrutura científica e sistema de investigação nos níveis epistemológico, científico e aplicado. Busca-se também identificar o paradigma da Inteligência Competitiva, enumerar as teorias e modelos que a fundamentam e listar as soluções de problemas da vida real propostos por essa área. Trata-se de pesquisa descritiva cujo método utilizado foi o levantamento e análise estatística do comportamento de variáveis de um modelo sistêmico de definição de objeto científico em uma amostra formada por artigos científicos publicados em língua inglesa e portuguesa. Os resultados mostram a existência de estudo nos três níveis da estrutura científica. Há três paradigmas que regem a pesquisa nessa área. Apresenta corpo teórico que apoia estudos desse campo e que é utilizado para auxiliar na solução de problemas de ordem prática. Há pesquisas em nível aplicado, destacando-se as relacionadas à melhoria do processo de Inteligência Competitiva e à contribuição desta área a outros processos organizacionais. Conclui-se que a proposta explicitada de estrutura científica e de sistema de investigação, nos níveis epistemológico, científico e aplicado, para a Inteligência Competitiva é adequada à compreensão desta disciplina.The scientific framework of a new field of knowledge is the foundation for its recognition as a scientific discipline. Considering that competitive intelligence is a recent field of knowledge, the main purpose of this study is to propose a scientific framework for competitive intelligence and a hierarchical research system with practical, scientific and epistemological perspectives levels. Other purposes include identifying the paradigm of competitive intelligence, enumerating its theories and models, and listing the solutions for real world problems proposed by this field. This is a descriptive research that uses the survey method and statistical analysis of variable behavior from a systemic model for defining scientific objects, using a sample of scientific articles published both in English and Portuguese. The results showed the existence of three scientific levels of research in this field. The following three paradigms support research in this field: a theoretical framework that supports studies in the field that are used to help solving practical problems; applied studies, particularly those related to improving the process of competitive intelligence; and those that contribute to other organizational processes. It may be concluded that the explicit scientific framework and a hierarchical research system proposed in the epistemological, scientific and practical levels are suitable for understanding this discipline

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

Quantification of interferon-tau during the maternal recognition of pregnancy in Bos taurus indicus cows

Durante o período crítico do reconhecimento materno, compreendido entre o 15° e 19° dias da gestação, o concepto deve sintetizar competentemente moléculas capazes de bloquear a síntese de prostaglandina F2a (pGF2a) e a luteólise. Em bovinos, a principal macromolécula protéica envolvida em tal bloqueio é o interferon-tau (IFN-r). Durante o período crítico, falhas neste reconhecimento determinam à mortalidade embrionária em até 40% das fêmeas inseminadas. Informações sobre o IFN-r em animais Bos taurus indicus, ainda são restritas. Este estudo objetivou uma avaliação quantitativa do IFN-r durante o período crítico do reconhecimento materno, em lavados uterinos obtidos por sonda de Foley (dias 14, 16 e 18 pósestro) oupost-mortem (dia 18 pós-estro). Para tanto, foram utilizadas fêmeas multíparas azebuadas (Bos taurus indicus), cíclicas ou prenhes, nos dias 14, 16 e 18 pós-estro. Para a obtenção dos lavados, os úteros foram infundidos com solução de Ringer Simples. Os lavados foram concentrados por ultra-f1ltração e liofilizados. As macromoléculas protéicas foram separadas por Eletroforese Unidimensional SDSPAGE, em gel com 15% de poliacrilamida. A quantificação do IFN-r nos lavados uterinos foi realizada por Western-Blotting e densitometria. Tanto nos lavados obtidos por sonda de Foley quanto nos post-mortem foi possível observar bandas de proteínas que apresentaram reação cruzada com os anticorpos utilizados no WesternBlotting. O IFN-T foi detectado apenas nos lavados uterinos post-mortem de vacas prenhes (P<0,05). A densidade óptica não foi afetada pelo dia do período crítico, estado (cíclico ou prenhe) ou interação dia x estado. Nos lavados post-mortem não houve efeito de peso do concepto ou concentração de progesterona plasmática no dia do lavado na densidade da banda protéica referente ao IFN-T . Concluiu-se que a detecção e quantificação do IFN-T no ambiente uterino de vacas azebuadas, nestas condições ) condições experimentais, é possível apenas em lavados uterinos obtidos post-mortemDuring the critical period of the maternal recognition, which occurs between days 15 and 19 of pregnancy, the conceptus must competendy synthesize molecules capable of blocking the synthesis of prostaglandin F2a (pGF2a) and luteolysis. In canle, the major macromolecule involved in suck blockage is the protein interferontau (IFN-i). During the critical period, failures in the recognition of pregnancy determine embryonic mortality on up to 40% of inseminated cows. Data about IFN-i in Bos taurus indicus are still scarce. Objective of this studywas to quantitatively evaluate the presence of IFN-i during the critical period for maternal recognition of pregnancy in uterine flushings obtained in vivo by Foley catheter (Days 14, 16 and 18 post estrus) or post-mortem (Day 18 post estrus). Multiparous, cyclic or pregnant zebu cows (Bos taurus indicus) on days 14,16 and 18 post estrus were used for in vivo or post mortem uterine flushing collection. In both cases, a Ringer solution was used to wash the uterus of cows. Uterine flushings were concentrated by ultra@tration and lyophilized. Prateins were separated by one-dimensional electrophoresis (SDS-PAGE) in a 15% polyacrilamide geL Interferontau quantification in uterine flushings was performed by western blotting and densitometry. Non-specific protein bands were observed in both in vivo and post mortem uterine flushings. Interferon-tau was detected only in uterine flushings obtained fram pregnant cows post-mortem (p<0.05). Optical density of protein bands was not affected by the day of the critical period, state (cyclic ar pregnant) or interaction day x state. There was no effect of the conceptus weight or progesterone concentration on the day of uterine flushing collection in the optical density of the IFN-i pratein bando It was concluded that the detection and quantification of IFN-i in the uterine environment of zebu cows, in these experimental? conditions, is only possible in uterine flushings obtained post-mortemFAPES