Extratos de Piper marginatum e Azadirachta indica no controle de Colletotrichum scovillei em pimentão

The objective of this work was to evaluate the effect of extracts of capeba (Piper marginatum) and neem (Azadirachta indica) on the fungus Colletotrichum scovillei, and evaluate the most active compound on the post‑harvest control of anthracnose in bell pepper. Methanolic extracts activity of leaves (P. marginatum and A. indica) and of seeds (A. indica) at 0, 125, 250, 500, 1,000, and 2,000 ppm, for in vitro inhibition of mycelial growth of C. scovillei, were evaluated. The methanolic extract of P. marginatum leaves was the most active and, consequently, it was subjected to a bioassay‑guided fractionation. This processing produced ten major compounds, obtained by preparative HPLC, from which the fraction at 1.5 ppm concentration was capable of inhibiting mycelial growth and was more effective than the fungicide mancozeb.O objetivo deste trabalho foi avaliar o efeito de extratos de capeba (Piper marginatum) e nim (Azadirachta indica) sobre o fungo Colletotrichum scovillei e determinar o componente mais ativo no controle pós‑colheita da antracnose em pimentão. A atividade de extratos metanólicos das folhas (P. marginatum e A. indica) e das sementes (A. indica) a 0, 125, 250, 500, 1.000 e 2.000 ppm, na inibição do crescimento micelial de C. scovillei in vitro foram avaliadas. O extrato metanólico de folhas de P. marginatum foi o mais ativo e, consequentemente, foi submetido ao fracionamento biomonitorado ("bioassay‑guided fractionation"). Esse processo rendeu 10 frações majoritárias, obtidas por cromatografia líquida de alta performance, das quais a fração à concentração de 1,5 ppm inibiu o desenvolvimento de C. scovillei de forma mais eficiente do que o fungicida mancozeb

Use of light-emitting diode (LED) in the physiology of cultivated plants – review

The objective of this study was to demonstrate the effects of the use of LED bulbs in the physiology of cultivated plants. Light-emitting diodes (LEDs) have been proposed as a light source for controlled environments or in plant growth chambers, since they have desirable characteristics for plant development. The development and physiology of plants are strongly influenced by the environment light spectrum provided by the LEDs, among which we can mention the blue, red, green, and their combinations. The blue light, for example, is responsible for a number of important photomorphogenetic features in plants, including stomatal control. Studies indicate that the red wavelength is efficiently absorbed by the pigments present in the plants, since its wavelength is very close to the absorption peak of chlorophyll. The relevance of green light responses is based on the assumption that there are situations in nature that the plant may find conditions of increased green radiation in its growing environment. LED bulbs offer many advantages as radiation sources for plants, but there are difficulties that delay their implementation for applications in the plant cultivation. With the advancement of technologies, the characteristics and performance of LED bulbs, as a source of radiation for plants, tend to be improved. However, this efficiency varies among the light spectra and the species. Thus, the advancement in the use of LEDs in the protected cultivation of cultivated plants is linked to specific physiological responses of each species

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

Taking the pulse of Earth's tropical forests using networks of highly distributed plots

Tropical forests are the most diverse and productive ecosystems on Earth. While better understanding of these forests is critical for our collective future, until quite recently efforts to measure and monitor them have been largely disconnected. Networking is essential to discover the answers to questions that transcend borders and the horizons of funding agencies. Here we show how a global community is responding to the challenges of tropical ecosystem research with diverse teams measuring forests tree-by-tree in thousands of long-term plots. We review the major scientific discoveries of this work and show how this process is changing tropical forest science. Our core approach involves linking long-term grassroots initiatives with standardized protocols and data management to generate robust scaled-up results. By connecting tropical researchers and elevating their status, our Social Research Network model recognises the key role of the data originator in scientific discovery. Conceived in 1999 with RAINFOR (South America), our permanent plot networks have been adapted to Africa (AfriTRON) and Southeast Asia (T-FORCES) and widely emulated worldwide. Now these multiple initiatives are integrated via ForestPlots.net cyber-infrastructure, linking colleagues from 54 countries across 24 plot networks. Collectively these are transforming understanding of tropical forests and their biospheric role. Together we have discovered how, where and why forest carbon and biodiversity are responding to climate change, and how they feedback on it. This long-term pan-tropical collaboration has revealed a large long-term carbon sink and its trends, as well as making clear which drivers are most important, which forest processes are affected, where they are changing, what the lags are, and the likely future responses of tropical forests as the climate continues to change. By leveraging a remarkably old technology, plot networks are sparking a very modern revolution in tropical forest science. In the future, humanity can benefit greatly by nurturing the grassroots communities now collectively capable of generating unique, long-term understanding of Earth's most precious forests.Additional co-authors: Susan Laurance, William Laurance, Francoise Yoko Ishida, Andrew Marshall, Catherine Waite, Hannsjoerg Woell, Jean-Francois Bastin, Marijn Bauters, Hans Beeckman, Pfascal Boeckx, Jan Bogaert, Charles De Canniere, Thales de Haulleville, Jean-Louis Doucet, Olivier Hardy, Wannes Hubau, Elizabeth Kearsley, Hans Verbeeck, Jason Vleminckx, Steven W. Brewer, Alfredo Alarcón, Alejandro Araujo-Murakami, Eric Arets, Luzmila Arroyo, Ezequiel Chavez, Todd Fredericksen, René Guillén Villaroel, Gloria Gutierrez Sibauty, Timothy Killeen, Juan Carlos Licona, John Lleigue, Casimiro Mendoza, Samaria Murakami, Alexander Parada Gutierrez, Guido Pardo, Marielos Peña-Claros, Lourens Poorter, Marisol Toledo, Jeanneth Villalobos Cayo, Laura Jessica Viscarra, Vincent Vos, Jorge Ahumada, Everton Almeida, Jarcilene Almeida, Edmar Almeida de Oliveira, Wesley Alves da Cruz, Atila Alves de Oliveira, Fabrício Alvim Carvalho, Flávio Amorim Obermuller, Ana Andrade, Fernanda Antunes Carvalho, Simone Aparecida Vieira, Ana Carla Aquino, Luiz Aragão, Ana Claudia Araújo, Marco Antonio Assis, Jose Ataliba Mantelli Aboin Gomes, Fabrício Baccaro, Plínio Barbosa de Camargo, Paulo Barni, Jorcely Barroso, Luis Carlos Bernacci, Kauane Bordin, Marcelo Brilhante de Medeiros, Igor Broggio, José Luís Camargo, Domingos Cardoso, Maria Antonia Carniello, Andre Luis Casarin Rochelle, Carolina Castilho, Antonio Alberto Jorge Farias Castro, Wendeson Castro, Sabina Cerruto Ribeiro, Flávia Costa, Rodrigo Costa de Oliveira, Italo Coutinho, John Cunha, Lola da Costa, Lucia da Costa Ferreira, Richarlly da Costa Silva, Marta da Graça Zacarias Simbine, Vitor de Andrade Kamimura, Haroldo Cavalcante de Lima, Lia de Oliveira Melo, Luciano de Queiroz, José Romualdo de Sousa Lima, Mário do Espírito Santo, Tomas Domingues, Nayane Cristina dos Santos Prestes, Steffan Eduardo Silva Carneiro, Fernando Elias, Gabriel Eliseu, Thaise Emilio, Camila Laís Farrapo, Letícia Fernandes, Gustavo Ferreira, Joice Ferreira, Leandro Ferreira, Socorro Ferreira, Marcelo Fragomeni Simon, Maria Aparecida Freitas, Queila S. García, Angelo Gilberto Manzatto, Paulo Graça, Frederico Guilherme, Eduardo Hase, Niro Higuchi, Mariana Iguatemy, Reinaldo Imbrozio Barbosa, Margarita Jaramillo, Carlos Joly, Joice Klipel, Iêda Leão do Amaral, Carolina Levis, Antonio S. Lima, Maurício Lima Dan, Aline Lopes, Herison Madeiros, William E. Magnusson, Rubens Manoel dos Santos, Beatriz Marimon, Ben Hur Marimon Junior, Roberta Marotti Martelletti Grillo, Luiz Martinelli, Simone Matias Reis, Salomão Medeiros, Milton Meira-Junior, Thiago Metzker, Paulo Morandi, Natanael Moreira do Nascimento, Magna Moura, Sandra Cristina Müller, Laszlo Nagy, Henrique Nascimento, Marcelo Nascimento, Adriano Nogueira Lima, Raimunda Oliveira de Araújo, Jhonathan Oliveira Silva, Marcelo Pansonato, Gabriel Pavan Sabino, Karla Maria Pedra de Abreu, Pablo José Francisco Pena Rodrigues, Maria Piedade, Domingos Rodrigues, José Roberto Rodrigues Pinto, Carlos Quesada, Eliana Ramos, Rafael Ramos, Priscyla Rodrigues, Thaiane Rodrigues de Sousa, Rafael Salomão, Flávia Santana, Marcos Scaranello, Rodrigo Scarton Bergamin, Juliana Schietti, Jochen Schöngart, Gustavo Schwartz, Natalino Silva, Marcos Silveira, Cristiana Simão Seixas, Marta Simbine, Ana Claudia Souza, Priscila Souza, Rodolfo Souza, Tereza Sposito, Edson Stefani Junior, Julio Daniel do Vale, Ima Célia Guimarães Vieira, Dora Villela, Marcos Vital, Haron Xaud, Katia Zanini, Charles Eugene Zartman, Nur Khalish Hafizhah Ideris, Faizah binti Hj Metali, Kamariah Abu Salim, Muhd Shahruney Saparudin, Rafizah Mat Serudin, Rahayu Sukmaria Sukri, Serge Begne, George Chuyong, Marie Noel Djuikouo, Christelle Gonmadje, Murielle Simo-Droissart, Bonaventure Sonké, Hermann Taedoumg, Lise Zemagho, Sean Thomas, Fidèle Baya, Gustavo Saiz, Javier Silva Espejo, Dexiang Chen, Alan Hamilton, Yide Li, Tushou Luo, Shukui Niu, Han Xu, Zhang Zhou, Esteban Álvarez-Dávila, Juan Carlos Andrés Escobar, Henry Arellano-Peña, Jaime Cabezas Duarte, Jhon Calderón, Lina Maria Corrales Bravo, Borish Cuadrado, Hermes Cuadros, Alvaro Duque, Luisa Fernanda Duque, Sandra Milena Espinosa, Rebeca Franke-Ante, Hernando García, Alejandro Gómez, Roy González-M., Álvaro Idárraga-Piedrahíta, Eliana Jimenez, Rubén Jurado, Wilmar López Oviedo, René López-Camacho, Omar Aurelio Melo Cruz, Irina Mendoza Polo, Edwin Paky, Karen Pérez, Angel Pijachi, Camila Pizano, Adriana Prieto, Laura Ramos, Zorayda Restrepo Correa, James Richardson, Elkin Rodríguez, Gina M. Rodriguez M., Agustín Rudas, Pablo Stevenson, Markéta Chudomelová, Martin Dancak, Radim Hédl, Stanislav Lhota, Martin Svatek, Jacques Mukinzi, Corneille Ewango, Terese Hart, Emmanuel Kasongo Yakusu, Janvier Lisingo, Jean-Remy Makana, Faustin Mbayu, Benjamin Toirambe, John Tshibamba Mukendi, Lars Kvist, Gustav Nebel, Selene Báez, Carlos Céron, Daniel M. Griffith, Juan Ernesto Guevara Andino, David Neill, Walter Palacios, Maria Cristina Peñuela-Mora, Gonzalo Rivas-Torres, Gorky Villa, Sheleme Demissie, Tadesse Gole, Techane Gonfa, Kalle Ruokolainen, Michel Baisie, Fabrice Bénédet, Wemo Betian, Vincent Bezard, Damien Bonal, Jerôme Chave, Vincent Droissart, Sylvie Gourlet-Fleury, Annette Hladik, Nicolas Labrière, Pétrus Naisso, Maxime Réjou-Méchain, Plinio Sist, Lilian Blanc, Benoit Burban, Géraldine Derroire, Aurélie Dourdain, Clement Stahl, Natacha Nssi Bengone, Eric Chezeaux, Fidèle Evouna Ondo, Vincent Medjibe, Vianet Mihindou, Lee White, Heike Culmsee, Cristabel Durán Rangel, Viviana Horna, Florian Wittmann, Stephen Adu-Bredu, Kofi Affum-Baffoe, Ernest Foli, Michael Balinga, Anand Roopsind, James Singh, Raquel Thomas, Roderick Zagt, Indu K. Murthy, Kuswata Kartawinata, Edi Mirmanto, Hari Priyadi, Ismayadi Samsoedin, Terry Sunderland, Ishak Yassir, Francesco Rovero, Barbara Vinceti, Bruno Hérault, Shin-Ichiro Aiba, Kanehiro Kitayama, Armandu Daniels, Darlington Tuagben, John T. Woods, Muhammad Fitriadi, Alexander Karolus, Kho Lip Khoon, Noreen Majalap, Colin Maycock, Reuben Nilus, Sylvester Tan, Almeida Sitoe, Indiana Coronado G., Lucas Ojo, Rafael de Assis, Axel Dalberg Poulsen, Douglas Sheil, Karen Arévalo Pezo, Hans Buttgenbach Verde, Victor Chama Moscoso, Jimmy Cesar Cordova Oroche, Fernando Cornejo Valverde, Massiel Corrales Medina, Nallaret Davila Cardozo, Jano de Rutte Corzo, Jhon del Aguila Pasquel, Gerardo Flores Llampazo, Luis Freitas, Darcy Galiano Cabrera, Roosevelt García Villacorta, Karina Garcia Cabrera, Diego García Soria, Leticia Gatica Saboya, Julio Miguel Grandez Rios, Gabriel Hidalgo Pizango, Eurídice Honorio Coronado, Isau Huamantupa-Chuquimaco, Walter Huaraca Huasco, Yuri Tomas Huillca Aedo, Jose Luis Marcelo Peña, Abel Monteagudo Mendoza, Vanesa Moreano Rodriguez, Percy Núñez Vargas, Sonia Cesarina Palacios Ramos, Nadir Pallqui Camacho, Antonio Peña Cruz, Freddy Ramirez Arevalo, José Reyna Huaymacari, Carlos Reynel Rodriguez, Marcos Antonio Ríos Paredes, Lily Rodriguez Bayona, Rocio del Pilar Rojas Gonzales, Maria Elena Rojas Peña, Norma Salinas Revilla, Yahn Carlos Soto Shareva, Raul Tupayachi Trujillo, Luis Valenzuela Gamarra, Rodolfo Vasquez Martinez, Jim Vega Arenas, Christian Amani, Suspense Averti Ifo, Yannick Bocko, Patrick Boundja, Romeo Ekoungoulou, Mireille Hockemba, Donatien Nzala, Alusine Fofanah, David Taylor, Guillermo Bañares-de Dios, Luis Cayuela, Íñigo Granzow-de la Cerda, Manuel Macía, Juliana Stropp, Maureen Playfair, Verginia Wortel, Toby Gardner, Robert Muscarella, Hari Priyadi, Ervan Rutishauser, Kuo-Jung Chao, Pantaleo Munishi, Olaf Bánki, Frans Bongers, Rene Boot, Gabriella Fredriksson, Jan Reitsma, Hans ter Steege, Tinde van Andel, Peter van de Meer, Peter van der Hout, Mark van Nieuwstadt, Bert van Ulft, Elmar Veenendaal, Ronald Vernimmen, Pieter Zuidema, Joeri Zwerts, Perpetra Akite, Robert Bitariho, Colin Chapman, Eilu Gerald, Miguel Leal, Patrick Mucunguzi, Miguel Alexiades, Timothy R. Baker, Karina Banda, Lindsay Banin, Jos Barlow, Amy Bennett, Erika Berenguer, Nicholas Berry, Neil M. Bird, George A. Blackburn, Francis Brearley, Roel Brienen, David Burslem, Lidiany Carvalho, Percival Cho, Fernanda Coelho, Murray Collins, David Coomes, Aida Cuni-Sanchez, Greta Dargie, Kyle Dexter, Mat Disney, Freddie Draper, Muying Duan, Adriane Esquivel-Muelbert, Robert Ewers, Belen Fadrique, Sophie Fauset, Ted R. Feldpausch, Filipe França, David Galbraith, Martin Gilpin, Emanuel Gloor, John Grace, Keith Hamer, David Harris, Tommaso Jucker, Michelle Kalamandeen, Bente Klitgaard, Aurora Levesley, Simon L. Lewis, Jeremy Lindsell, Gabriela Lopez-Gonzalez, Jon Lovett, Yadvinder Malhi, Toby Marthews, Emma McIntosh, Karina Melgaço, William Milliken, Edward Mitchard, Peter Moonlight, Sam Moore, Alexandra Morel, Julie Peacock, Kelvin Peh, Colin Pendry, R. Toby Pennington, Luciana de Oliveira Pereira, Carlos Peres, Oliver L. Phillips, Georgia Pickavance, Thomas Pugh, Lan Qie, Terhi Riutta, Katherine Roucoux, Casey Ryan, Tiina Sarkinen, Camila Silva Valeria, Dominick Spracklen, Suzanne Stas, Martin Sullivan, Michael Swaine, Joey Talbot, James Taplin, Geertje van der Heijden, Laura Vedovato, Simon Willcock, Mathew Williams, Luciana Alves, Patricia Alvarez Loayza, Gabriel Arellano, Cheryl Asa, Peter Ashton, Gregory Asner, Terry Brncic, Foster Brown, Robyn Burnham, Connie Clark, James Comiskey, Gabriel Damasco, Stuart Davies, Tony Di Fiore, Terry Erwin, William Farfan-Rios, Jefferson Hall, David Kenfack, Thomas Lovejoy, Roberta Martin, Olga Martha Montiel, John Pipoly, Nigel Pitman, John Poulsen, Richard Primack, Miles Silman, Marc Steininger, Varun Swamy, John Terborgh, Duncan Thomas, Peter Umunay, Maria Uriarte, Emilio Vilanova Torre, Ophelia Wang, Kenneth Young, Gerardo A. Aymard C., Lionel Hernández, Rafael Herrera Fernández, Hirma Ramírez-Angulo, Pedro Salcedo, Elio Sanoja, Julio Serrano, Armando Torres-Lezama, Tinh Cong Le, Trai Trong Le, Hieu Dang Tra

ESTIMATIVA DA ÁREA FOLIAR DE Myrcia variabilis Mart. ex DC. (MYRTACEAE)

The specie Myrcia variabilis (Myrtaceae) is native and endemic to Brazil and can be found in open environments such as the Cerrado, Campo Rupestre and in fragments of the Atlantic Forest. The plant is popularly known as "goiabinha-do-campo-roxa" and is used by traditional communities to treat wounds and diarrhea. Considering the medicinal potential of the species, there is a need for basic research involving aspects related to propagation, growth and development, such as the study of the leaf area. However, it is difficult to perform this measurement due to the need to acquire high-value equipment and destructive techniques. Non-destructive methods allow you to track plant growth and provide data accuracy while preserving integrity. The aim of this study was to estimate the leaf area of M. variabilis and establish mathematical models using the linear dimensional parameters length and width. 200 leaves of 10 specimens from a population of M. variabilis present in an area of altered cerrado, in the municipality of Lavras-MG, were collected. After collection, the leaves were scanned and analyzed to determine linear measurements. It was observed that the product of the linear measurements length and width are enough to estimate the leaf area of the species. The present study contributes to the study of the M. variabilis species, by eliminating the destructive methods to estimate leaf area. Furthermore, the linear model showed a good fit, being the ideal tool to estimate the leaf area of this species, especially in studies that investigate its growth and development.El Cerrado es el segundo bioma brasileño más grande y cubre 16 estados brasileños. Su flora es típica, conteniendo plantas endémicas y raras. La especie Myrcia variabilis (Myrtaceae Juss) se encuentra en ambientes abiertos, como el Cerrado. La planta se conoce como "edifium de campo púrpura" o "marmelinho de campo púrpura". Tradicionalmente se utiliza en el tratamiento de heridas y diarrea. Teniendo en cuenta el potencial medicinal de la especie, existe la necesidad de una investigación básica que involucre aspectos relacionados con la propagación, el crecimiento y el desarrollo, como el estudio del área foliar. Sin embargo, es difícil realizar esta medición debido a la necesidad de la adquisición de equipos de alto valor y técnicas destructivas.  Los métodos no destructivos le permiten rastrear el crecimiento de las plantas y proporcionar precisión de datos al tiempo que preservan la integridad. El objetivo de este estudio fue estimar el área foliar de Myrcia variabilis y establecer modelos matemáticos utilizando los parámetros lineales dimensionales de longitud y anchura. Se recolectaron un total de 200 hojas de una población de especímenes de Myrcia variabilis presentes en un área alterada del cerrado en el municipio de Lavras - MG. Después de la recolección, las hojas fueron escaneadas y analizadas para determinar mediciones lineales. Se observó que el producto de las mediciones lineales de longitud y anchura son suficientes para estimar el área foliar de la especie. A espécie Myrcia variabilis (Myrtaceae) é nativa e endêmica do Brasil e pode ser encontrada em ambientes abertos, como o Cerrado, Campo Rupestre e em fragmentos da Mata Atlântica. A planta é conhecida popularmente como “goiabinha-do-campo-roxa” e é utilizada pelas comunidades tradicionais no tratamento de feridas e diarreias. Considerando-se o potencial medicinal da espécie, existe a necessidade de pesquisas básicas envolvendo aspectos relacionados à propagação, crescimento e desenvolvimento, tais como o estudo da área foliar. No entanto, há dificuldade para realizar esta mensuração devido a necessidade da aquisição de equipamentos de valor elevado e técnicas destrutivas.  Métodos não destrutivos permitem acompanhar o crescimento da planta e oferecem precisão dos dados, preservando a integridade. O objetivo desse estudo foi estimar a área foliar de M. variabilis e estabelecer modelos matemáticos utilizando os parâmetros lineares dimensionais comprimento e largura. Foram coletadas 200 folhas de 10 espécimes de uma população de M. variabilis presentes em uma área de cerrado alterado, no Município de Lavras – MG. Após a coleta, as folhas foram escaneadas e analisadas para determinação das medidas lineares. Foi observado que o produto das medidas lineares comprimento e largura são suficientes para estimar a área foliar da espécie. O presente estudo contribui para o estudo da espécie M.  variabilis, por eliminar os métodos destrutivos para estimar a área foliar. Além disso, o modelo linear demonstrou bom ajuste, sendo a ferramenta ideal para estimar a área foliar desta espécie, principalmente em estudos que investiguem o seu crescimento e desenvolvimento

Solvent Mixture Optimization in the Extraction of Bioactive Compounds and Antioxidant Activities from Garlic (Allium sativum L.)

Garlic is a health promoter that has important bioactive compounds. The bioactive extraction is an important step in the analysis of constituents present in plant preparations. The purpose of this study is to optimize the extraction with the best proportion of solvents to obtain total phenolic compounds (TPC) and thiosulfinates (TS) from dried garlic powder, and evaluate the antioxidant activities of the optimized extracts. A statistical mixture simplex axial design was used to evaluate the effect of solvents (water, ethanol, and acetone), as well as mixtures of these solvents, after two ultrasound extraction cycles of 15 min. Results showed that solvent mixtures with a high portion of water and pure water were efficient for TPC and TS recovery through this extraction procedure. According to the regression model computed, the most significant solvent mixtures to obtain high TPC and TS recovery from dried garlic powder are, respectively, the binary mixture with 75% water and 25% acetone and pure water. These optimized extracts presented oxygen radical absorbance capacity. Pure water was better for total antioxidant capacity, and the binary mixture of water–acetone (75:25) was better for DPPH scavenging activity. These optimized extracts can be used for industrial and research applications