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5 results on '"Mendoza AM"'

1. Unraveling Amazon tree community assembly using Maximum Information Entropy: a quantitative analysis of tropical forest ecology.

Author

Pos E, de Souza Coelho L, de Andrade Lima Filho D, Salomão RP, Amaral IL, de Almeida Matos FD, Castilho CV, Phillips OL, Guevara JE, de Jesus Veiga Carim M, López DC, Magnusson WE, Wittmann F, Irume MV, Martins MP, Sabatier D, da Silva Guimarães JR, Molino JF, Bánki OS, Piedade MTF, Pitman NCA, Mendoza AM, Ramos JF, Hawes JE, Almeida EJ, Barbosa LF, Cavalheiro L, Dos Santos MCV, Luize BG, de Leão Novo EMM, Vargas PN, Silva TSF, Venticinque EM, Manzatto AG, Reis NFC, Terborgh J, Casula KR, Coronado ENH, Montero JC, Marimon BS, Marimon-Junior BH, Feldpausch TR, Duque A, Baraloto C, Arboleda NC, Engel J, Petronelli P, Zartman CE, Killeen TJ, Vasquez R, Mostacedo B, Assis RL, Schöngart J, Castellanos H, de Medeiros MB, Simon MF, Andrade A, Camargo JL, Demarchi LO, Laurance WF, Laurance SGW, de Sousa Farias E, Lopes MA, Magalhães JLL, Nascimento HEM, de Queiroz HL, Aymard GAC, Brienen R, Revilla JDC, Costa FRC, Quaresma A, Vieira ICG, Cintra BBL, Stevenson PR, Feitosa YO, Duivenvoorden JF, Mogollón HF, Ferreira LV, Comiskey JA, Draper F, de Toledo JJ, Damasco G, Dávila N, García-Villacorta R, Lopes A, Vicentini A, Noronha JC, Barbosa FR, de Sá Carpanedo R, Emilio T, Levis C, de Jesus Rodrigues D, Schietti J, Souza P, Alonso A, Dallmeier F, Gomes VHF, Lloyd J, Neill D, de Aguiar DPP, Araujo-Murakami A, Arroyo L, Carvalho FA, de Souza FC, do Amaral DD, Feeley KJ, Gribel R, Pansonato MP, Barlow J, Berenguer E, Ferreira J, Fine PVA, Guedes MC, Jimenez EM, Licona JC, Mora MCP, Peres CA, Zegarra BEV, Cerón C, Henkel TW, Maas P, Silveira M, Stropp J, Thomas-Caesar R, Baker TR, Daly D, Dexter KG, Householder JE, Huamantupa-Chuquimaco I, Pennington T, Paredes MR, Fuentes A, Pena JLM, Silman MR, Tello JS, Chave J, Valverde FC, Di Fiore A, Hilário RR, Phillips JF, Rivas-Torres G, van Andel TR, von Hildebrand P, Barbosa EM, de Matos Bonates LC, Doza HPD, Fonty É, Gómez RZ, Gonzales T, Gonzales GPG, Guillaumet JL, Hoffman B, Junqueira AB, Malhi Y, de Andrade Miranda IP, Pinto LFM, Prieto A, Rudas A, Ruschel AR, Silva N, Vela CIA, Vos VA, Zent EL, Zent S, Albuquerque BW, Cano A, Correa DF, Costa JBP, Flores BM, Holmgren M, Nascimento MT, Oliveira AA, Ramirez-Angulo H, Rocha M, Scudeller VV, Sierra R, Tirado M, Umaña MN, van der Heijden G, Torre EV, Vriesendorp C, Wang O, Young KR, Reategui MAA, Baider C, Balslev H, Cárdenas S, Casas LF, Farfan-Rios W, Ferreira C, Linares-Palomino R, Mendoza C, Mesones I, Torres-Lezama A, Giraldo LEU, Villarroel D, Zagt R, Alexiades MN, Garcia-Cabrera K, Hernandez L, Milliken W, Cuenca WP, Pansini S, Pauletto D, Arevalo FR, Sampaio AF, Sandoval EHV, Gamarra LV, Boenisch G, Kattge J, Kraft N, Levesley A, Melgaço K, Pickavance G, Poorter L, and Ter Steege H

Subjects
Entropy, Forests, Plants, Ecology, Tropical Climate, Ecosystem, Biodiversity
Abstract

In a time of rapid global change, the question of what determines patterns in species abundance distribution remains a priority for understanding the complex dynamics of ecosystems. The constrained maximization of information entropy provides a framework for the understanding of such complex systems dynamics by a quantitative analysis of important constraints via predictions using least biased probability distributions. We apply it to over two thousand hectares of Amazonian tree inventories across seven forest types and thirteen functional traits, representing major global axes of plant strategies. Results show that constraints formed by regional relative abundances of genera explain eight times more of local relative abundances than constraints based on directional selection for specific functional traits, although the latter does show clear signals of environmental dependency. These results provide a quantitative insight by inference from large-scale data using cross-disciplinary methods, furthering our understanding of ecological dynamics., (© 2023. The Author(s).)

Published
2023
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2. Mathematical model for the thermal enhancement of radiation response: thermodynamic approach.

Author

De Mendoza AM, Michlíková S, Berger J, Karschau J, Kunz-Schughart LA, and McLeod DD

Abstract

Radiotherapy can effectively kill malignant cells, but the doses required to cure cancer patients may inflict severe collateral damage to adjacent healthy tissues. Recent technological advances in the clinical application has revitalized hyperthermia treatment (HT) as an option to improve radiotherapy (RT) outcomes. Understanding the synergistic effect of simultaneous thermoradiotherapy via mathematical modelling is essential for treatment planning. We here propose a theoretical model in which the thermal enhancement ratio (TER) relates to the cell fraction being radiosensitised by the infliction of sublethal damage through HT. Further damage finally kills the cell or abrogates its proliferative capacity in a non-reversible process. We suggest the TER to be proportional to the energy invested in the sensitisation, which is modelled as a simple rate process. Assuming protein denaturation as the main driver of HT-induced sublethal damage and considering the temperature dependence of the heat capacity of cellular proteins, the sensitisation rates were found to depend exponentially on temperature; in agreement with previous empirical observations. Our findings point towards an improved definition of thermal dose in concordance with the thermodynamics of protein denaturation. Our predictions well reproduce experimental in vitro and in vivo data, explaining the thermal modulation of cellular radioresponse for simultaneous thermoradiotherapy.

Published
2021
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3. Molecular features similarities between SARS-CoV-2, SARS, MERS and key human genes could favour the viral infections and trigger collateral effects.

Author

Maldonado LL, Bertelli AM, and Kamenetzky L

Subjects
Codon genetics, Codon Usage, Computational Biology methods, Coronavirus genetics, Coronavirus Infections metabolism, Genes, Viral, Genome, Viral, Humans, Middle East Respiratory Syndrome Coronavirus genetics, Phylogeny, Severe acute respiratory syndrome-related coronavirus genetics, SARS-CoV-2 genetics, Betacoronavirus genetics, Coronavirus Infections genetics, Coronavirus Infections virology
Abstract

In December 2019, rising pneumonia cases caused by a novel β-coronavirus (SARS-CoV-2) occurred in Wuhan, China, which has rapidly spread worldwide, causing thousands of deaths. The WHO declared the SARS-CoV-2 outbreak as a public health emergency of international concern, since then several scientists are dedicated to its study. It has been observed that many human viruses have codon usage biases that match highly expressed proteins in the tissues they infect and depend on the host cell machinery for the replication and co-evolution. In this work, we analysed 91 molecular features and codon usage patterns for 339 viral genes and 463 human genes that consisted of 677,873 codon positions. Hereby, we selected the highly expressed genes from human lung tissue to perform computational studies that permit to compare their molecular features with those of SARS, SARS-CoV-2 and MERS genes. The integrated analysis of all the features revealed that certain viral genes and overexpressed human genes have similar codon usage patterns. The main pattern was the A/T bias that together with other features could propitiate the viral infection, enhanced by a host dependant specialization of the translation machinery of only some of the overexpressed genes. The envelope protein E, the membrane glycoprotein M and ORF7 could be further benefited. This could be the key for a facilitated translation and viral replication conducting to different comorbidities depending on the genetic variability of population due to the host translation machinery. This is the first codon usage approach that reveals which human genes could be potentially deregulated due to the codon usage similarities between the host and the viral genes when the virus is already inside the human cells of the lung tissues. Our work leaded to the identification of additional highly expressed human genes which are not the usual suspects but might play a role in the viral infection and settle the basis for further research in the field of human genetics associated with new viral infections. To identify the genes that could be deregulated under a viral infection is important to predict the collateral effects and determine which individuals would be more susceptible based on their genetic features and comorbidities associated.

Published
2021
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4. Rarity of monodominance in hyperdiverse Amazonian forests.

Author

Ter Steege H, Henkel TW, Helal N, Marimon BS, Marimon-Junior BH, Huth A, Groeneveld J, Sabatier D, Coelho LS, Filho DAL, Salomão RP, Amaral IL, Matos FDA, Castilho CV, Phillips OL, Guevara JE, Carim MJV, Cárdenas López D, Magnusson WE, Wittmann F, Irume MV, Martins MP, Guimarães JRDS, Molino JF, Bánki OS, Piedade MTF, Pitman NCA, Mendoza AM, Ramos JF, Luize BG, Moraes de Leão Novo EM, Núñez Vargas P, Silva TSF, Venticinque EM, Manzatto AG, Reis NFC, Terborgh J, Casula KR, Honorio Coronado EN, Montero JC, Feldpausch TR, Duque A, Costa FRC, Arboleda NC, Schöngart J, Killeen TJ, Vasquez R, Mostacedo B, Demarchi LO, Assis RL, Baraloto C, Engel J, Petronelli P, Castellanos H, de Medeiros MB, Quaresma A, Simon MF, Andrade A, Camargo JL, Laurance SGW, Laurance WF, Rincón LM, Schietti J, Sousa TR, de Sousa Farias E, Lopes MA, Magalhães JLL, Mendonça Nascimento HE, Lima de Queiroz H, Aymard C GA, Brienen R, Revilla JDC, Vieira ICG, Cintra BBL, Stevenson PR, Feitosa YO, Duivenvoorden JF, Mogollón HF, Araujo-Murakami A, Ferreira LV, Lozada JR, Comiskey JA, de Toledo JJ, Damasco G, Dávila N, Draper F, García-Villacorta R, Lopes A, Vicentini A, Alonso A, Dallmeier F, Gomes VHF, Lloyd J, Neill D, de Aguiar DPP, Arroyo L, Carvalho FA, de Souza FC, do Amaral DD, Feeley KJ, Gribel R, Pansonato MP, Barlow J, Berenguer E, Ferreira J, Fine PVA, Guedes MC, Jimenez EM, Licona JC, Peñuela Mora MC, Villa B, Cerón C, Maas P, Silveira M, Stropp J, Thomas R, Baker TR, Daly D, Dexter KG, Huamantupa-Chuquimaco I, Milliken W, Pennington T, Ríos Paredes M, Fuentes A, Klitgaard B, Pena JLM, Peres CA, Silman MR, Tello JS, Chave J, Cornejo Valverde F, Di Fiore A, Hilário RR, Phillips JF, Rivas-Torres G, van Andel TR, von Hildebrand P, Noronha JC, Barbosa EM, Barbosa FR, de Matos Bonates LC, Carpanedo RS, Dávila Doza HP, Fonty É, GómeZárate Z R, Gonzales T, Gallardo Gonzales GP, Hoffman B, Junqueira AB, Malhi Y, Miranda IPA, Pinto LFM, Prieto A, Rodrigues DJ, Rudas A, Ruschel AR, Silva N, Vela CIA, Vos VA, Zent EL, Zent S, Weiss Albuquerque B, Cano A, Carrero Márquez YA, Correa DF, Costa JBP, Flores BM, Galbraith D, Holmgren M, Kalamandeen M, Nascimento MT, Oliveira AA, Ramirez-Angulo H, Rocha M, Scudeller VV, Sierra R, Tirado M, Umaña Medina MN, van der Heijden G, Vilanova Torre E, Vriesendorp C, Wang O, Young KR, Ahuite Reategui MA, Baider C, Balslev H, Cárdenas S, Casas LF, Farfan-Rios W, Ferreira C, Linares-Palomino R, Mendoza C, Mesones I, Torres-Lezama A, Giraldo LEU, Villarroel D, Zagt R, Alexiades MN, de Oliveira EA, Garcia-Cabrera K, Hernandez L, Palacios Cuenca W, Pansini S, Pauletto D, Ramirez Arevalo F, Sampaio AF, Sandoval EHV, Valenzuela Gamarra L, Levesley A, Pickavance G, and Melgaço K

Abstract

Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such "monodominant" forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors.

Published
2019
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5. Species Distribution Modelling: Contrasting presence-only models with plot abundance data.

Author

Gomes VHF, IJff SD, Raes N, Amaral IL, Salomão RP, de Souza Coelho L, de Almeida Matos FD, Castilho CV, de Andrade Lima Filho D, López DC, Guevara JE, Magnusson WE, Phillips OL, Wittmann F, de Jesus Veiga Carim M, Martins MP, Irume MV, Sabatier D, Molino JF, Bánki OS, da Silva Guimarães JR, Pitman NCA, Piedade MTF, Mendoza AM, Luize BG, Venticinque EM, de Leão Novo EMM, Vargas PN, Silva TSF, Manzatto AG, Terborgh J, Reis NFC, Montero JC, Casula KR, Marimon BS, Marimon BH, Coronado ENH, Feldpausch TR, Duque A, Zartman CE, Arboleda NC, Killeen TJ, Mostacedo B, Vasquez R, Schöngart J, Assis RL, Medeiros MB, Simon MF, Andrade A, Laurance WF, Camargo JL, Demarchi LO, Laurance SGW, de Sousa Farias E, Nascimento HEM, Revilla JDC, Quaresma A, Costa FRC, Vieira ICG, Cintra BBL, Castellanos H, Brienen R, Stevenson PR, Feitosa Y, Duivenvoorden JF, Aymard C GA, Mogollón HF, Targhetta N, Comiskey JA, Vicentini A, Lopes A, Damasco G, Dávila N, García-Villacorta R, Levis C, Schietti J, Souza P, Emilio T, Alonso A, Neill D, Dallmeier F, Ferreira LV, Araujo-Murakami A, Praia D, do Amaral DD, Carvalho FA, de Souza FC, Feeley K, Arroyo L, Pansonato MP, Gribel R, Villa B, Licona JC, Fine PVA, Cerón C, Baraloto C, Jimenez EM, Stropp J, Engel J, Silveira M, Mora MCP, Petronelli P, Maas P, Thomas-Caesar R, Henkel TW, Daly D, Paredes MR, Baker TR, Fuentes A, Peres CA, Chave J, Pena JLM, Dexter KG, Silman MR, Jørgensen PM, Pennington T, Di Fiore A, Valverde FC, Phillips JF, Rivas-Torres G, von Hildebrand P, van Andel TR, Ruschel AR, Prieto A, Rudas A, Hoffman B, Vela CIA, Barbosa EM, Zent EL, Gonzales GPG, Doza HPD, de Andrade Miranda IP, Guillaumet JL, Pinto LFM, de Matos Bonates LC, Silva N, Gómez RZ, Zent S, Gonzales T, Vos VA, Malhi Y, Oliveira AA, Cano A, Albuquerque BW, Vriesendorp C, Correa DF, Torre EV, van der Heijden G, Ramirez-Angulo H, Ramos JF, Young KR, Rocha M, Nascimento MT, Medina MNU, Tirado M, Wang O, Sierra R, Torres-Lezama A, Mendoza C, Ferreira C, Baider C, Villarroel D, Balslev H, Mesones I, Giraldo LEU, Casas LF, Reategui MAA, Linares-Palomino R, Zagt R, Cárdenas S, Farfan-Rios W, Sampaio AF, Pauletto D, Sandoval EHV, Arevalo FR, Huamantupa-Chuquimaco I, Garcia-Cabrera K, Hernandez L, Gamarra LV, Alexiades MN, Pansini S, Cuenca WP, Milliken W, Ricardo J, Lopez-Gonzalez G, Pos E, and Ter Steege H

Subjects
Brazil, Chrysobalanaceae physiology, Fabaceae physiology, Humans, Polygonaceae physiology, Conservation of Natural Resources statistics & numerical data, Models, Statistical, Plant Dispersal physiology, Trees physiology
Abstract

Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high. Furthermore, the pipeline removed half of the NHCs records. Presence-only SDM applications should consider this limitation, especially for large biodiversity assessments projects, when they are automatically generated without subsequent checking. Our pipeline provides a conservative estimate of a species' area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments.

Published
2018
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