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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha : |
24/04/2024 |
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Actualizado : |
24/04/2024 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Autor : |
PARODI, P.; BAZZANO, V.; ARMÚA-FERNÁNDEZ, M.T.; FÉLIX, M.L.; CARVALHO, L.A.; FREIRE, J.; VENZAL, J.M. |
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Afiliación : |
PABLO ANDRÉS PARODI TEXEIRA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; VALENTIN BAZZANO, Laboratorio de Vectores y Enfermedades Transmitidas, Departamento de Ciencias Biológicas, CENUR Litoral Norte, Universidad de la República, Salto, Uruguay; MARÍA T. ARMÚA-FERNÁNDEZ, Unidad de Parasitología Veterinaria, Departamento de Patobiología, Facultad de Veterinaria, Universidad de la República, Montevideo, Uruguay; MARÍA L. FÉLIX, Laboratorio de Vectores y Enfermedades Transmitidas, Departamento de Ciencias Biológicas, CENUR Litoral Norte, Universidad de la República, Salto, Uruguay; LUIS A. CARVALHO, AgResearch, Grasslands Research Centre, Palmerston North, New Zealand; JORGE FREIRE, Laclivet, Laboratorio Clínico Veterinario, Montevideo, Uruguay; JOSÉ M. VENZAL, Laboratorio de Vectores y Enfermedades Transmitidas, Departamento de Ciencias Biológicas, CENUR Litoral Norte, Universidad de la República, Salto, Uruguay. |
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Título : |
Molecular survey of Piroplasmida, Hepatozoon spp. and Anaplasmataceae in anemic and thrombocytopenic dogs from Uruguay. |
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Complemento del título : |
Original article. |
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Fecha de publicación : |
2024 |
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Fuente / Imprenta : |
Veterinary Parasitology: Regional Studies and Reports. 2024, Volume 51, 101027. https://doi.org/10.1016/j.vprsr.2024.101027 |
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ISSN : |
2405-9390 |
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DOI : |
10.1016/j.vprsr.2024.101027 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 28 February 2024, Revised 8 April 2024, Accepted 17 April 2024, Available online 21 April 2024, Version of Record 23 April 2024. -- Corresponding author at: Rivera 1350, CP 50000 Salto, Uruguay. E-mail address: jvenzal@unorte.edu.uy (J.M. Venzal). -- |
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Contenido : |
ABSTRACT.- Canine tick-borne diseases, such as babesiosis, rangeliosis, hepatozoonosis, anaplasmosis and ehrlichiosis, are of veterinarian relevance, causing mild or severe clinical cases that can lead to the death of the dog. The aim of this study was detecting tick-borne protozoan and rickettsial infections in dogs with anemia and/or thrombocytopenia in Uruguay. A total of 803 domestic dogs were evaluated, and 10% were found positive (detected by PCR) at least for one hemoparasite. Sequence analysis confirmed the presence of four hemoprotozoan species: Rangelia vitalii, Babesia vogeli, Hepatozoon canis and Hepatozoon americanum, and the rickettsial Anaplasma platys. The most detected hemoparasite was R. vitalii, followed by H. canis and A. platys. This is the first report of B. vogeli in Uruguay and the second report of H. americanum in dogs from South America. The results highlight the importance for veterinarians to include hemoparasitic diseases in their differential diagnosis of agents causing anemia and thrombocytopenia. © 2024 Elsevier B.V. All rights reserved. |
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Palabras claves : |
Anemia; Canine hemoparasites; PLATAFORMA DE INVESTIGACIÓN EN SALUD ANIMAL - INIA; Thrombocytopenia; Tick-borne diseases; Uruguay. |
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Asunto categoría : |
L73 Enfermedades de los animales |
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Marc : |
LEADER 02345naa a2200301 a 4500
001 1064614
005 2024-04-24
008 2024 bl uuuu u00u1 u #d
022 $a2405-9390
024 7 $a10.1016/j.vprsr.2024.101027$2DOI
100 1 $aPARODI, P.
245 $aMolecular survey of Piroplasmida, Hepatozoon spp. and Anaplasmataceae in anemic and thrombocytopenic dogs from Uruguay.$h[electronic resource]
260 $c2024
500 $aArticle history: Received 28 February 2024, Revised 8 April 2024, Accepted 17 April 2024, Available online 21 April 2024, Version of Record 23 April 2024. -- Corresponding author at: Rivera 1350, CP 50000 Salto, Uruguay. E-mail address: jvenzal@unorte.edu.uy (J.M. Venzal). --
520 $aABSTRACT.- Canine tick-borne diseases, such as babesiosis, rangeliosis, hepatozoonosis, anaplasmosis and ehrlichiosis, are of veterinarian relevance, causing mild or severe clinical cases that can lead to the death of the dog. The aim of this study was detecting tick-borne protozoan and rickettsial infections in dogs with anemia and/or thrombocytopenia in Uruguay. A total of 803 domestic dogs were evaluated, and 10% were found positive (detected by PCR) at least for one hemoparasite. Sequence analysis confirmed the presence of four hemoprotozoan species: Rangelia vitalii, Babesia vogeli, Hepatozoon canis and Hepatozoon americanum, and the rickettsial Anaplasma platys. The most detected hemoparasite was R. vitalii, followed by H. canis and A. platys. This is the first report of B. vogeli in Uruguay and the second report of H. americanum in dogs from South America. The results highlight the importance for veterinarians to include hemoparasitic diseases in their differential diagnosis of agents causing anemia and thrombocytopenia. © 2024 Elsevier B.V. All rights reserved.
653 $aAnemia
653 $aCanine hemoparasites
653 $aPLATAFORMA DE INVESTIGACIÓN EN SALUD ANIMAL - INIA
653 $aThrombocytopenia
653 $aTick-borne diseases
653 $aUruguay
700 1 $aBAZZANO, V.
700 1 $aARMÚA-FERNÁNDEZ, M.T.
700 1 $aFÉLIX, M.L.
700 1 $aCARVALHO, L.A.
700 1 $aFREIRE, J.
700 1 $aVENZAL, J.M.
773 $tVeterinary Parasitology: Regional Studies and Reports. 2024, Volume 51, 101027. https://doi.org/10.1016/j.vprsr.2024.101027
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
23/02/2024 |
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Actualizado : |
23/02/2024 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
PARUELO, J.; TEXEIRA, M.; TOMASEL, F. |
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Afiliación : |
JOSÉ PARUELO, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; IFEVA, Universidad de Buenos Aires, CONICET, Facultad de Agronomía, Buenos Aires, Argentina; IECA, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay; MARCOS TEXEIRA, IFEVA, Universidad de Buenos Aires, CONICET, Facultad de Agronomía, Buenos Aires, Argentina; FERNANDO TOMASEL, Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, United States. |
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Título : |
Hybrid modeling for grassland productivity prediction: A parametric and machine learning technique for grazing management with applicability to digital twin decision systems. |
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Fecha de publicación : |
2024 |
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Fuente / Imprenta : |
Agricultural Systems. 2024. Volume 214, article 103847. https://doi.org/10.1016/j.agsy.2023.103847 |
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ISSN : |
0308-521X |
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DOI : |
10.1016/j.agsy.2023.103847 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received 1 August 2023; Received in revised form 5 December 2023; Accepted 18 December 2023; Available online 28 December 2023. -- Correspondence: Paruelo, J.M.; Instituto Nacional de Investigación Agropecuaria, INIA, La Estanzuela, Ruta 50 km 11, Colonia, Uruguay; email:jparuelo@inia.org.uy -- Funding: This work was supported by grants from ANII (Uruguay. FSDA_1_2018_1_154773 and IA_2021_1_04 and IA_2021_1_1010784), CSIC-Universidad de la República - Uruguay (Programa I + D Grupos 2018-433), Universidad de Buenos Aires (Argentina) and CONICET (2021-2024. PIP-2021. 11220200100956CO01). -- Supplementary data: https://doi.org/10.1016/j.agsy.2023.103847 -- |
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Contenido : |
ABSTRACT.- CONTEXT: Monitoring Aboveground Net Primary Production (ANPP) is critical to assess not only the current ecosystem status but also its long-term dynamics. In rangelands, the seasonal dynamics of ANPP determines forage availability, stock density, and livestock productivity. OBJECTIVE: To develop a hybrid model to be used as a prediction engine for ANPP in the native grasslands of Uruguay. The model combines a parametric component based on the seasonal dynamics of ANPP, and an artificial neural network (ANN) component used to model the remaining non-linearities, which are mainly related to precipitation and temperature variability. The output of hybrid model is proposed as the "virtual entity" of a digital twin support decision system where the "physical entity" is characterized by a collection of bi-weekly (fortnight) ANPP estimates. METHODS: Fortnight ANPP data were calculated from MODIS EVI for the 2001-2020 period. A sigmoidal functional response, having three parameters with an explicit biological interpretation, was fitted to the accumulated ANPP as a function of time. Forecasts were generated by extrapolating the sigmoidal functional response fit up to four fortnights ahead. From these fits, we obtained the fortnight ANPP values by differentiating the accumulated fortnight ANPP. Predictions (up to four fortnights) were generated for each fortnight and year. The residuals from these fits were modeled using a multilayer perceptron trained by backpropagation using climate variables as independent variables. RESULTS AND CONCLUSIONS: The sigmoidal functional response model fit was highly significant for the accumulated ANPP profile. This model also had a high explanatory power for the accumulated ANPP curve. The median of the percentage absolute residuals for forecasts made 1 to 4 fortnights ahead ranged from 17% to 18%. The ANN significantly reduced this unexplained variability in ANPP, showing a median reduction in residuals of 35%, 31%, 30%, and 30% for 1 to 4 fortnights ahead forecasts, respectively, when compared to predictions from the sigmoidal functional response fit. SIGNIFICANCE: By integrating both parametric and machine learning techniques, the hybrid model developed can make accurate predictions in a way that is both efficient and dependable. The hybrid model not only represents an advantage in terms of predictive power, but it also allows for a deeper understanding of the basic ecological processes involved in forage production. © 2023 MenosABSTRACT.- CONTEXT: Monitoring Aboveground Net Primary Production (ANPP) is critical to assess not only the current ecosystem status but also its long-term dynamics. In rangelands, the seasonal dynamics of ANPP determines forage availability, stock density, and livestock productivity. OBJECTIVE: To develop a hybrid model to be used as a prediction engine for ANPP in the native grasslands of Uruguay. The model combines a parametric component based on the seasonal dynamics of ANPP, and an artificial neural network (ANN) component used to model the remaining non-linearities, which are mainly related to precipitation and temperature variability. The output of hybrid model is proposed as the "virtual entity" of a digital twin support decision system where the "physical entity" is characterized by a collection of bi-weekly (fortnight) ANPP estimates. METHODS: Fortnight ANPP data were calculated from MODIS EVI for the 2001-2020 period. A sigmoidal functional response, having three parameters with an explicit biological interpretation, was fitted to the accumulated ANPP as a function of time. Forecasts were generated by extrapolating the sigmoidal functional response fit up to four fortnights ahead. From these fits, we obtained the fortnight ANPP values by differentiating the accumulated fortnight ANPP. Predictions (up to four fortnights) were generated for each fortnight and year. The residuals from these fits were modeled using a multilayer perceptron trained by backpropagation us... Presentar Todo |
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Palabras claves : |
Agroecological transitions; ANPP; Artificial neural networks; Grasslands; Remote sensing; Uruguay. |
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Asunto categoría : |
-- |
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Marc : |
LEADER 04040naa a2200253 a 4500
001 1064472
005 2024-02-23
008 2024 bl uuuu u00u1 u #d
022 $a0308-521X
024 7 $a10.1016/j.agsy.2023.103847$2DOI
100 1 $aPARUELO, J.
245 $aHybrid modeling for grassland productivity prediction$bA parametric and machine learning technique for grazing management with applicability to digital twin decision systems.$h[electronic resource]
260 $c2024
500 $aArticle history: Received 1 August 2023; Received in revised form 5 December 2023; Accepted 18 December 2023; Available online 28 December 2023. -- Correspondence: Paruelo, J.M.; Instituto Nacional de Investigación Agropecuaria, INIA, La Estanzuela, Ruta 50 km 11, Colonia, Uruguay; email:jparuelo@inia.org.uy -- Funding: This work was supported by grants from ANII (Uruguay. FSDA_1_2018_1_154773 and IA_2021_1_04 and IA_2021_1_1010784), CSIC-Universidad de la República - Uruguay (Programa I + D Grupos 2018-433), Universidad de Buenos Aires (Argentina) and CONICET (2021-2024. PIP-2021. 11220200100956CO01). -- Supplementary data: https://doi.org/10.1016/j.agsy.2023.103847 --
520 $aABSTRACT.- CONTEXT: Monitoring Aboveground Net Primary Production (ANPP) is critical to assess not only the current ecosystem status but also its long-term dynamics. In rangelands, the seasonal dynamics of ANPP determines forage availability, stock density, and livestock productivity. OBJECTIVE: To develop a hybrid model to be used as a prediction engine for ANPP in the native grasslands of Uruguay. The model combines a parametric component based on the seasonal dynamics of ANPP, and an artificial neural network (ANN) component used to model the remaining non-linearities, which are mainly related to precipitation and temperature variability. The output of hybrid model is proposed as the "virtual entity" of a digital twin support decision system where the "physical entity" is characterized by a collection of bi-weekly (fortnight) ANPP estimates. METHODS: Fortnight ANPP data were calculated from MODIS EVI for the 2001-2020 period. A sigmoidal functional response, having three parameters with an explicit biological interpretation, was fitted to the accumulated ANPP as a function of time. Forecasts were generated by extrapolating the sigmoidal functional response fit up to four fortnights ahead. From these fits, we obtained the fortnight ANPP values by differentiating the accumulated fortnight ANPP. Predictions (up to four fortnights) were generated for each fortnight and year. The residuals from these fits were modeled using a multilayer perceptron trained by backpropagation using climate variables as independent variables. RESULTS AND CONCLUSIONS: The sigmoidal functional response model fit was highly significant for the accumulated ANPP profile. This model also had a high explanatory power for the accumulated ANPP curve. The median of the percentage absolute residuals for forecasts made 1 to 4 fortnights ahead ranged from 17% to 18%. The ANN significantly reduced this unexplained variability in ANPP, showing a median reduction in residuals of 35%, 31%, 30%, and 30% for 1 to 4 fortnights ahead forecasts, respectively, when compared to predictions from the sigmoidal functional response fit. SIGNIFICANCE: By integrating both parametric and machine learning techniques, the hybrid model developed can make accurate predictions in a way that is both efficient and dependable. The hybrid model not only represents an advantage in terms of predictive power, but it also allows for a deeper understanding of the basic ecological processes involved in forage production. © 2023
653 $aAgroecological transitions
653 $aANPP
653 $aArtificial neural networks
653 $aGrasslands
653 $aRemote sensing
653 $aUruguay
700 1 $aTEXEIRA, M.
700 1 $aTOMASEL, F.
773 $tAgricultural Systems. 2024. Volume 214, article 103847. https://doi.org/10.1016/j.agsy.2023.103847
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