Probiotics in Dairy Fermented Products

Interest in the role of probiotics for human health began as early as 1908 when Metchnikoff associated the intake of fermented milk with prolonged life (Lourens-Hattingh and Vilijoen, 2001b). However, the relationship between intestinal microbiota and good health and nutrition has only recently been investigated. Therefore, it was not until the 1960’s that health benefit claims began appearing on foods labels. In recent years,there has been an increasing interest in probiotic foods, which has stimulated innovation and fueled the development of new products around the world. Probiotic bacteria have increasinglybeen incorporated into foods in order to improve gut health by maintaining the microbial gastrointestinal balance. The most popular probioticfoodsare produced in the dairy industry because fermented dairy products have been shown to be the most efficient delivery vehicle for live probiotics to date. In this chapter, we will discuss the application of probiotic microorganisms in fermented dairy products, particularly cheeses. In addition, we will also discuss the benefits of probiotic fermented foods on human health

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

Growth, viability, and post-acidification of Lactobacillus plantarum in bovine transition milk

In this study, four milk substrates were analyzed to evaluate bovine Lactobacillus plantarum strain viability after 24 and 48 h of fermentation. In addition, cell viability, and post-acidification in transition milk fermented by these bacteria were assessed over a 60-d storage period at 4 and 25 °C. Significant reduction (30.9 %) of cell viability after 48 h of fermentation was observed for the formulation with whole milk. However, in fermented transition milk stored at 4 °C, cell viability and acidity were maintained at acceptable levels throughout the 60-d storage period. The viability of L. plantarum in fermented transition milk stored at 25 °C remained acceptable up to 50 d and minimum pH values were analyzed after 38 d of storage and maximum acidity levels after 56 d. Considering these results, transition milk may be preserved by L. plantarum fermentation as a substitute for milk in the artificial feed for calves as functional food

Influência do tipo de leite nos parâmetros de textura e estabilidade de sorvete

The aim of this work was evaluate the features of ice cream produced from goat's milk of two different breeds and compare them to ice cream produced with cow's milk. Samples of cow's and goat's milk were analyzed for composition, physicochemical properties (density and cryoscopy) and foam ability. The partial molar volume of sorbitan monostearate (SM) in the milk samples was determined. It was prepared similar formulations of chocolate ice cream, varying only the type of the milk. The fat content and melting profile of ice creams was determined. The texture profile analysis (TPA) and acceptance sensory analysis of ice creams were also evaluated. There was no difference (p>0.05) between constituents and properties of different milk, except for cryoscopy and foam ability. The partial molar volume of SM was positive for all samples of milk. There was no difference (p>0.05) in fat content of ice cream samples. In relation to TPA, hardness and gumminess were different in cow's milk. There was no difference (p>0.05) in the initial time and speed of melting of goat's milk ice cream compared to cow's milk ice cream. Ice cream produced with goat's milk showed good acceptance and did not differ from cow's milk ice cream. The results point out the potential of goat's milk as an alternative to cow's milk in order to produce ice cream.O objetivo deste trabalho foi avaliar as características do leite de cabra de duas raças para a produção de sorvete e comparar com o sorvete produzido a partir de leite de vaca. Amostras de leite de vaca e de cabra foram analisadas em relação à composição e às propriedades físico-químicas (densidade, crioscopia) e capacidade espumante. O volume molar parcial do monoestearato de sorbitana (MS) nas amostras de leite foi determinado. Foram preparadas formulações idênticas de sorvete de chocolate, variando apenas o tipo de leite. Foram analisados o teor de gordura e o perfil de derretimento dos sorvetes. Realizaram-se também o Perfil de Textura (TPA) e a análise sensorial de aceitação dos sorvetes. Não houve diferença (p>0,05) entre os constituintes e as propriedades dos tipos de leite, exceto para crioscopia e capacidadeespumante. O volume molar parcial de MS foi positivo para todos os leites. Não houve diferença (p>0,05) nos teores de gordura dos sorvetes. Em relação à TPA, a dureza e a gomosidade foram diferentes para o leite de vaca. Não houve diferença (p>0,05) no tempo inicial de derretimento e na velocidade de derretimento do leite de cabra comparado ao de vaca. Os sorvetes produzidos apresentaram boa aceitação sensorial, não havendo diferença (p>0,05) entre eles. Os resultados apontam o potencial do leite de cabra como alternativa ao leite de vaca na produção de sorvete