30 research outputs found

    Development of a dynamic energy-partitioning model for enteric methane emissions and milk production in goats using energy balance data from indirect calorimetry studies

    Full text link
    [EN] The main objective of this study was to develop a dynamic energy balance model for dairy goats to describe and quantify energy partitioning between energy used for work (milk) and that lost to the environment. Increasing worldwide concerns regarding livestock contribution to global warming underscore the importance of improving energy efficiency utilization in dairy goats by reducing energy losses in feces, urine and methane (CH4). A dynamic model of CH(4)emissions from experimental energy balance data in goats is proposed and parameterized (n= 48 individual animal observations). The model includes DM intake, NDF and lipid content of the diet as explanatory variables for CH(4)emissions. An additional data set (n= 122 individual animals) from eight energy balance experiments was used to evaluate the model. The model adequately (root MS prediction error,RMSPE) represented energy in milk (E-milk;RMSPE = 5.6%), heat production (HP;RMSPE = 4.3%) and CH(4)emissions (E-CH4; RMSPE = 11.9%). Residual analysis indicated that most of the prediction errors were due to unexplained variations with small mean and slope bias. Some mean bias was detected for HP (1.12%) and E-CH4(1.27%) but was around zero for E-milk (0.14%). The slope bias was zero for HP (0.01%) and close to zero for E-milk (0.10%) and E-CH4(0.22%). Random bias was >98% for E-CH4, HP and E-milk, indicating non-systematic errors and that mechanisms in the model are properly represented. As predicted energy increased, the model tended to underpredict E-CH(4)and E-milk. The model is a first step toward a mechanistic description of nutrient use by goats and is useful as a research tool for investigating energy partitioning during lactation. The model described in this study could be used as a tool for making enteric CH(4)emission inventories for goats.This study was supported by LOW CARBON FEED Project reference LIFE2016/CCM/ES/000088.Fernández Martínez, CJ.; Hernando, I.; Moreno-Latorre, E.; Loor, J. (2020). Development of a dynamic energy-partitioning model for enteric methane emissions and milk production in goats using energy balance data from indirect calorimetry studies. Animal. 14:s382-s395. https://doi.org/10.1017/S1751731120001470Ss382s39514Agricultural and Food Research Council (AFRC) 1997. The nutrition of goats. Nutrition Abstract and Reviews (Series B) 67, 776–861.Aguilera, J. F., Prieto, C., & FonollÁ, J. (1990). Protein and energy metabolism of lactating Granadina goats. British Journal of Nutrition, 63(2), 165-175. doi:10.1079/bjn19900104Bannink, A., France, J., Lopez, S., Gerrits, W. J. J., Kebreab, E., Tamminga, S., & Dijkstra, J. (2008). Modelling the implications of feeding strategy on rumen fermentation and functioning of the rumen wall. Animal Feed Science and Technology, 143(1-4), 3-26. doi:10.1016/j.anifeedsci.2007.05.002Bava, L., Rapetti, L., Crovetto, G. M., Tamburini, A., Sandrucci, A., Galassi, G., & Succi, G. (2001). Effects of a Nonforage Diet on Milk Production, Energy, and Nitrogen Metabolism in Dairy Goats throughout Lactation. Journal of Dairy Science, 84(11), 2450-2459. doi:10.3168/jds.s0022-0302(01)74695-4Beauchemin K, McAllister T and McGinn S 2009. Dietary mitigation of enteric CH4 from cattle. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 4, 035.Blaxter, K. L., & Clapperton, J. L. (1965). Prediction of the amount of methane produced by ruminants. British Journal of Nutrition, 19(1), 511-522. doi:10.1079/bjn19650046Brouwer E 1965. Report of sub-committee on constants and factors. In Proceeding of the 3th EAAP Symposium on Energy Metabolism (ed. KL Blaxter ), pp. 441–443. Academic Press, London, UK.Criscioni, P., Marti, J. V., Pérez-Baena, I., Palomares, J. L., Larsen, T., & Fernández, C. (2016). Replacement of alfalfa hay ( Medicago sativa ) with maralfalfa hay ( Pennisetum sp.) in diets of lactating dairy goats. Animal Feed Science and Technology, 219, 1-12. doi:10.1016/j.anifeedsci.2016.05.020Ellis, J. L., Kebreab, E., Odongo, N. E., McBride, B. W., Okine, E. K., & France, J. (2007). Prediction of Methane Production from Dairy and Beef Cattle. Journal of Dairy Science, 90(7), 3456-3466. doi:10.3168/jds.2006-675Statistical data base Food and Agriculture Organization (FAOSTAT) 2018. FAO Statistical data base Food and Agriculture Organization of the United Nations, Rome, Italy. Retrieved on 25 June 2018 from http://faostat.fao.org/FERNÁNDEZ, C., LÓPEZ, M. C., & LACHICA, M. (2015). Low-cost mobile open-circuit hood system for measuring gas exchange in small ruminants: from manual to automatic recording. The Journal of Agricultural Science, 153(7), 1302-1309. doi:10.1017/s0021859615000416Fernández, C., Martí, J. V., Pérez-Baena, I., Palomares, J. L., Ibáñez, C., & Segarra, J. V. (2018). Effect of lemon leaves on energy and C–N balances, methane emission, and milk performance in Murciano-Granadina dairy goats. Journal of Animal Science, 96(4), 1508-1518. doi:10.1093/jas/sky028Fernández, C. (2018). Dynamic model development of enteric methane emission from goats based on energy balance measured in indirect open circuit respiration calorimeter. Global Ecology and Conservation, 15, e00439. doi:10.1016/j.gecco.2018.e00439Fernández, C., Pérez-Baena, I., Marti, J. V., Palomares, J. L., Jorro-Ripoll, J., & Segarra, J. V. (2019). Use of orange leaves as a replacement for alfalfa in energy and nitrogen partitioning, methane emissions and milk performance of murciano-granadina goats. Animal Feed Science and Technology, 247, 103-111. doi:10.1016/j.anifeedsci.2018.11.008Fernández, C., Gomis-Tena, J., Hernández, A., & Saiz, J. (2019). An Open-Circuit Indirect Calorimetry Head Hood System for Measuring Methane Emission and Energy Metabolism in Small Ruminants. Animals, 9(6), 380. doi:10.3390/ani9060380Grainger, C., & Beauchemin, K. A. (2011). Can enteric methane emissions from ruminants be lowered without lowering their production? Animal Feed Science and Technology, 166-167, 308-320. doi:10.1016/j.anifeedsci.2011.04.021Howarth, R. (2015). Methane emissions and climatic warming risk from hydraulic fracturing and shale gas development: implications for policy. Energy and Emission Control Technologies, 45. doi:10.2147/eect.s61539Hristov, A. N., Kebreab, E., Niu, M., Oh, J., Bannink, A., Bayat, A. R., … Yu, Z. (2018). Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models. Journal of Dairy Science, 101(7), 6655-6674. doi:10.3168/jds.2017-13536Ibáñez, C., López, M. C., Criscioni, P., & Fernández, C. (2015). Effect of replacing dietary corn with beet pulp on energy partitioning, substrate oxidation and methane production in lactating dairy goats. Animal Production Science, 55(1), 56. doi:10.1071/an13119Institute Nationale Recherche Agronomique (INRA) 2017. Feeding system for ruminants. Wageningen Academic Publishers, Wageningen, the Netherlands.Jørgensen, S. E. (2015). New method to calculate the work energy of information and organisms. Ecological Modelling, 295, 18-20. doi:10.1016/j.ecolmodel.2014.09.001Kebreab, E., Johnson, K. A., Archibeque, S. L., Pape, D., & Wirth, T. (2008). Model for estimating enteric methane emissions from United States dairy and feedlot cattle1. Journal of Animal Science, 86(10), 2738-2748. doi:10.2527/jas.2008-0960Knapp, J. R., Laur, G. L., Vadas, P. A., Weiss, W. P., & Tricarico, J. M. (2014). Invited review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. Journal of Dairy Science, 97(6), 3231-3261. doi:10.3168/jds.2013-7234Lin, L. I.-K. (1989). A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics, 45(1), 255. doi:10.2307/2532051López, M. C., Estellés, F., Moya, V. J., & Fernández, C. (2014). Use of dry citrus pulp or soybean hulls as a replacement for corn grain in energy and nitrogen partitioning, methane emissions, and milk performance in lactating Murciano-Granadina goats. Journal of Dairy Science, 97(12), 7821-7832. doi:10.3168/jds.2014-8424López, M. C., & Fernández, C. (2013). Energy partitioning and substrate oxidation by Murciano-Granadina goats during mid lactation fed soy hulls and corn gluten feed blend as a replacement for corn grain. Journal of Dairy Science, 96(7), 4542-4552. doi:10.3168/jds.2012-6473Martí JV, Pérez-Baena I and Fernández C 2012. Replacement of barley grain with lemon pulp on energy partitioning in lactating goats. Unpublished.Merino, P., Ramirez-Fanlo, E., Arriaga, H., del Hierro, O., Artetxe, A., & Viguria, M. (2011). Regional inventory of methane and nitrous oxide emission from ruminant livestock in the Basque Country. Animal Feed Science and Technology, 166-167, 628-640. doi:10.1016/j.anifeedsci.2011.04.081Mills, J. A. N., Kebreab, E., Yates, C. M., Crompton, L. A., Cammell, S. B., Dhanoa, M. S., … France, J. (2003). Alternative approaches to predicting methane emissions from dairy cows1. Journal of Animal Science, 81(12), 3141-3150. doi:10.2527/2003.81123141xMoorby, J. M., Fleming, H. R., Theobald, V. J., & Fraser, M. D. (2015). Can live weight be used as a proxy for enteric methane emissions from pasture-fed sheep? Scientific Reports, 5(1). doi:10.1038/srep17915Niu, M., Kebreab, E., Hristov, A. N., Oh, J., Arndt, C., Bannink, A., … Yu, Z. (2018). Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database. Global Change Biology, 24(8), 3368-3389. doi:10.1111/gcb.14094Patra, A. K., & Lalhriatpuii, M. (2016). Development of statistical models for prediction of enteric methane emission from goats using nutrient composition and intake variables. Agriculture, Ecosystems & Environment, 215, 89-99. doi:10.1016/j.agee.2015.09.018Pérez-Baena I, Martí JV and Fernández C 2012. Effect of replace barley grain with beet pulp in lactating goats diet; energy balance and milk performance. Unpublished.R Core Team 2016. R: A language and environment for statistical computing. Version 1.1.447. R Foundation for Statistical Computing, Vienna, Austria. Retrieved from https://www.R-project.org/Ramin, M., & Huhtanen, P. (2013). Development of equations for predicting methane emissions from ruminants. Journal of Dairy Science, 96(4), 2476-2493. doi:10.3168/jds.2012-6095Tovar-Luna, I., Puchala, R., Sahlu, T., Freetly, H. C., & Goetsch, A. L. (2010). Effects of stage of lactation and dietary concentrate level on energy utilization by Alpine dairy goats. Journal of Dairy Science, 93(10), 4818-4828. doi:10.3168/jds.2010-3315United Nations Framework Convention on Climate Change 2015. UN Climate Change Newsroom. Historic Paris agreement on climate change. 195 nations set path to keep temperature rise well below 2 degrees Celsius. Retrieved on 1 July 2018 from http://newsroom.unfccc.int/unfccc-newsroom/finale-cop21/Yan, T., Porter, M. G., & Mayne, C. S. (2009). Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters. Animal, 3(10), 1455-1462. doi:10.1017/s175173110900473

    Team dynamics in emergency surgery teams: results from a first international survey

    Get PDF
    Background: Emergency surgery represents a unique context. Trauma teams are often multidisciplinary and need to operate under extreme stress and time constraints, sometimes with no awareness of the trauma\u2019s causes or the patient\u2019s personal and clinical information. In this perspective, the dynamics of how trauma teams function is fundamental to ensuring the best performance and outcomes. Methods: An online survey was conducted among the World Society of Emergency Surgery members in early 2021. 402 fully filled questionnaires on the topics of knowledge translation dynamics and tools, non-technical skills, and difficulties in teamwork were collected. Data were analyzed using the software R, and reported following the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). Results: Findings highlight how several surgeons are still unsure about the meaning and potential of knowledge translation and its mechanisms. Tools like training, clinical guidelines, and non-technical skills are recognized and used in clinical practice. Others, like patients\u2019 and stakeholders\u2019 engagement, are hardly implemented, despite their increasing importance in the modern healthcare scenario. Several difficulties in working as a team are described, including the lack of time, communication, training, trust, and ego. Discussion: Scientific societies should take the lead in offering training and support about the abovementioned topics. Dedicated educational initiatives, practical cases and experiences, workshops and symposia may allow mitigating the difficulties highlighted by the survey\u2019s participants, boosting the performance of emergency teams. Additional investigation of the survey results and its characteristics may lead to more further specific suggestions and potential solutions

    Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

    Get PDF
    BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

    Subcortical brain volume, regional cortical thickness, and cortical surface area across disorders: findings from the ENIGMA ADHD, ASD, and OCD Working Groups

    Get PDF
    Objective Attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) are common neurodevelopmental disorders that frequently co-occur. We aimed to directly compare all three disorders. The ENIGMA consortium is ideally positioned to investigate structural brain alterations across these disorders. Methods Structural T1-weighted whole-brain MRI of controls (n=5,827) and patients with ADHD (n=2,271), ASD (n=1,777), and OCD (n=2,323) from 151 cohorts worldwide were analyzed using standardized processing protocols. We examined subcortical volume, cortical thickness and surface area differences within a mega-analytical framework, pooling measures extracted from each cohort. Analyses were performed separately for children, adolescents, and adults using linear mixed-effects models adjusting for age, sex and site (and ICV for subcortical and surface area measures). Results We found no shared alterations among all three disorders, while shared alterations between any two disorders did not survive multiple comparisons correction. Children with ADHD compared to those with OCD had smaller hippocampal volumes, possibly influenced by IQ. Children and adolescents with ADHD also had smaller ICV than controls and those with OCD or ASD. Adults with ASD showed thicker frontal cortices compared to adult controls and other clinical groups. No OCD-specific alterations across different age-groups and surface area alterations among all disorders in childhood and adulthood were observed. Conclusion Our findings suggest robust but subtle alterations across different age-groups among ADHD, ASD, and OCD. ADHD-specific ICV and hippocampal alterations in children and adolescents, and ASD-specific cortical thickness alterations in the frontal cortex in adults support previous work emphasizing neurodevelopmental alterations in these disorders

    Artículo: Aislamiento e identificación de bacterias tolerantes a Petróleo

    No full text
    El petróleo es una importante fuente de contaminación de suelo y agua, lo que origina que se desarrolle tolerancia a la presencia de este compuesto, induciendo la selectividad y la disminución de la diversidad microbiana en los diferentes nichos ecológicos contaminados. Se ha encontrado que el 96% de bacterias aisladas de medios líquidos (lagos, ríos y lagunas) presentan capacidad para emulsionar hidrocarburos derivados del petróleo
    corecore