99 research outputs found
Evaluación de fuentes convencionales y no convencionales de energÃa y proteina en la alimentación de cerdos en el Piedemonte Llanero: informe técnico.
Informe técnico del proyecto de investigación aplicada, ejecutado en fincas de pequeños y medianos productores porcÃcolas, con el objeto de incrementar la rentabilidad de dichas explotaciones mediante el empleo de alimentos no convencionales. A partir de un diagnóstico de identificación de los principales sistemas de explotación porcina existentes en los municipios donde se desarrolló el estudio, se realizaron 10 experimentos para igual número de dietas donde se compararon el efecto de la dieta sobre el incremento de peso y los costos asociados. Los resultados obtenidos muestran que la región piedemonte llanero tiene un elevado potencial para la explotación porcicola que con tecnologÃas apropiadas permite reducir costos de producción, mejorar rendimientos de ganancias de peso de los animales y aumentar el rendimiento económico del productor. Se lograron pesos para mercadeo entre 160 y 175 dÃas, con rentabilidades totales del orden del 67 por cient
Electrophysiological characterization of texture information slip-resistance dependent in the rat vibrissal nerve
<p>Abstract</p> <p>Background</p> <p>Studies in tactile discrimination agree that rats are able to learn a rough-smooth discrimination task by actively touching (whisking) objects with their vibrissae. In particular, we focus on recent evidence of how neurons at different levels of the sensory pathway carry information about tactile stimuli. Here, we analyzed the multifiber afferent discharge of one vibrissal nerve during active whisking. Vibrissae movements were induced by electrical stimulation of motor branches of the facial nerve. We used sandpapers of different grain size as roughness discrimination surfaces and we also consider the change of vibrissal slip-resistance as a way to improve tactile information acquisition. The amplitude of afferent activity was analyzed according to its Root Mean Square value (RMS). The comparisons among experimental situation were quantified by using the information theory.</p> <p>Results</p> <p>We found that the change of the vibrissal slip-resistance is a way to improve the roughness discrimination of surfaces. As roughness increased, the RMS values also increased in almost all cases. In addition, we observed a better discrimination performance in the retraction phase (maximum amount of information).</p> <p>Conclusions</p> <p>The evidence of amplitude changes due to roughness surfaces and slip-resistance levels allows to speculate that texture information is slip-resistance dependent at peripheral level.</p
Inhibición de butirilcolinesterasa en dos perros intoxicados y confirmación analÃtica de carbofuran como agente causal
Ferré, D.M.; Saldeña, E.L.; AlbarracÃn, L.; Neuilly, V.; Gorla, N.B.: Inhibición de butirilcolinesterasa en dos perros intoxicados y confirmación analÃtica de carbofuran como agente causal. Rev. vet. 26: 1, 43-48, 2015
Short-Term Low Temperature Induces Nitro-Oxidative Stress that Deregulates the NADP-Malic Enzyme Function by Tyrosine Nitration in Arabidopsis thaliana
Low temperature (LT) negatively affects plant growth and development via the alteration of the metabolism of reactive oxygen and nitrogen species (ROS and RNS). Among RNS, tyrosine nitration, the addition of an NO2 group to a tyrosine residue, can modulate reduced nicotinamide-dinucleotide phosphate (NADPH)-generating systems and, therefore, can alter the levels of NADPH, a key cofactor in cellular redox homeostasis. NADPH also acts as an indispensable electron donor within a wide range of enzymatic reactions, biosynthetic pathways, and detoxification processes, which could affect plant viability. To extend our knowledge about the regulation of this key cofactor by this nitric oxide (NO)-related post-translational modification, we analyzed the effect of tyrosine nitration on another NADPH-generating enzyme, the NADP-malic enzyme (NADP-ME), under LT stress. In Arabidopsis thaliana seedlings exposed to short-term LT (4 °C for 48 h), a 50% growth reduction accompanied by an increase in the content of superoxide, nitric oxide, and peroxynitrite, in addition to diminished cytosolic NADP-ME activity, were found. In vitro assays confirmed that peroxynitrite inhibits cytosolic NADP-ME2 activity due to tyrosine nitration. The mass spectrometric analysis of nitrated NADP-ME2 enabled us to determine that Tyr-73 was exclusively nitrated to 3-nitrotyrosine by peroxynitrite. The in silico analysis of the Arabidopsis NADP-ME2 protein sequence suggests that Tyr73 nitration could disrupt the interactions between the specific amino acids responsible for protein structure stability. In conclusion, the present data show that short-term LT stress affects the metabolism of ROS and RNS, which appears to negatively modulate the activity of cytosolic NADP-ME through the tyrosine nitration processThis research was funded by ERDF grants co-financed by the Ministry of Economy and Competitiveness
(project PGC2018-096405-B-I00) and the Junta de AndalucÃa (group BIO286) in Spain. Research in FJ-C lab is
supported by an ERDF-co-financed grant from the Ministry of Economy and Competitiveness (AGL2015-65104-P)
and Junta de AndalucÃa (group BIO-192), Spain. Postdoctoral researcher J.B.-M. was funded by the Ministry of
Economy and Competitiveness (Spain) within Juan de la Cierva-Incorporación program (IJCI-2015-23438)
Aplicaciones prácticas de la citogenética animal
El Grupo de Genética Veterinaria dela FCVA UMazadesde el año 2005 realiza investigación cientÃfica en Citogenética, rama dela Genéticaque estudia la estructura y herencia de los cromosomas, aplicada a la práctica de la biologÃa de los organismos y de la genética clÃnica. El estudio del cariotipo aporta al conocimiento integral de los animales ya sea en su taxonomÃa y manejo eficiente en cautiverio, determinación sexual principalmente de aves sin dimorfismo sexual, y establecimiento de etiologÃas cromosómicas de determinadas patologÃas reproductivas u oncológicas
Compensatory Evolution of pbp Mutations Restores the Fitness Cost Imposed by β-Lactam Resistance in Streptococcus pneumoniae
The prevalence of antibiotic resistance genes in pathogenic bacteria is a major challenge to treating many infectious diseases. The spread of these genes is driven by the strong selection imposed by the use of antibacterial drugs. However, in the absence of drug selection, antibiotic resistance genes impose a fitness cost, which can be ameliorated by compensatory mutations. In Streptococcus pneumoniae, β-lactam resistance is caused by mutations in three penicillin-binding proteins, PBP1a, PBP2x, and PBP2b, all of which are implicated in cell wall synthesis and the cell division cycle. We found that the fitness cost and cell division defects conferred by pbp2b mutations (as determined by fitness competitive assays in vitro and in vivo and fluorescence microscopy) were fully compensated by the acquisition of pbp2x and pbp1a mutations, apparently by means of an increased stability and a consequent mislocalization of these protein mutants. Thus, these compensatory combinations of pbp mutant alleles resulted in an increase in the level and spectrum of β-lactam resistance. This report describes a direct correlation between antibiotic resistance increase and fitness cost compensation, both caused by the same gene mutations acquired by horizontal transfer. The clinical origin of the pbp mutations suggests that this intergenic compensatory process is involved in the persistence of β-lactam resistance among circulating strains. We propose that this compensatory mechanism is relevant for β-lactam resistance evolution in Streptococcus pneumoniae
SARS-CoV-2 Catalonia contact tracing program : evaluation of key performance indicators
Background: Guidance on SARS-CoV-2 contact tracing indicators have been recently revised by international public health agencies. The aim of the study is to describe and analyse contact tracing indicators based on Catalonia's (Spain) real data and proposing to update them according to recommendations. Methods: Retrospective cohort analysis including Catalonia's contact tracing dataset from 20 May until 31 December 2020. Descriptive statistics are performed including sociodemographic stratification by age, and differences are assessed over the study period. Results: We analysed 923,072 contacts from 301,522 SARS-CoV-2 cases with identified contacts (67.1% contact tracing coverage). The average number of contacts per case was 4.6 (median 3, range 1-243). A total of 403,377 contacts accepted follow-up through three phone calls over a 14-day quarantine period (84.5% of contacts requiring follow-up). The percentage of new cases declared as contacts 14 days prior to diagnosis evolved from 33.9% in May to 57.9% in November. All indicators significantly improved towards the target over time (p < 0.05 for all four indicators). Conclusions: Catalonia's SARS-CoV-2 contact tracing indicators improved over time despite challenging context. The critical revision of the indicator's framework aims to provide essential information in control policies, new indicators proposed will improve system delay's follow-up. The study provides information on COVID-19 indicators framework experience from country's real data, allowing to improve monitoring tools in 2021-2022. With the SARS-CoV-2 pandemic being so harmful to health systems and globally, is important to analyse and share contact tracing data with the scientific community
Desafios en la diversidad 2. Desplazamiento lingüÃstico y revitalización: Reflexiones y metodologÃas emergentes
Desde distintas miradas, este libro escudriña las muchas facetas que tiene la diversidad y los retos que enfrentamos al tratar de entenderlas y aceptarlas. Incentivados por la lingüÃstica, los procesos educativos, el contacto lingüÃstico, el translingüismo, y la creciente vulnerabilidad lingüÃstica, analizamos múltiples situaciones de lenguas en desplazamiento, reflexionamos sobre algunas de las respuestas que se han ido generando y que apuntan a la revitalización lingüistico-cultural a largo plazo, y ponemos en la mesa de discusión teorÃas, conceptos y metodologÃas que consideramos aportarán a la investigación y a la acción, desde nuestras propias realidades. Esperamos que este libro nos lleve a reflexionar y a posicionarnos frente a las innumerables inequidades sociales, lingüÃsticas y culturales que nos rodean y, sobre todo, que nos incentive a vivir en la diversidad con todos y cada uno de nuestros sentidos
Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification
Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.EFG was supported by Programa Torres Quevedo, Ministerio de Educacion y Ciencia, co-funded by the European Social Fund (PTQ-1205693). EFG, JMGG, and JVM were supported by Red Tematica de Investigacion Cooperativa en Cancer, (RTICC) 2013-2016 (RD12/0036/0020). JMGG was supported by Project TIN2013-43457-R: Caracterizacion de firmas biologicas de glioblastomas mediante modelos no-supervisados de prediccion estructurada basados en biomarcadores de imagen, co-funded by the Ministerio de Economia y Competitividad of Spain; CON2014001 UPV-IISLaFe: Unsupervised glioblastoma tumor components segmentation based on perfusion multiparametric MRI and spatio/temporal constraints; and CON2014002 UPV-IISLaFe: Empleo de segmentacion no supervisada multiparametrica basada en perfusion RM para la caracterizacion del edema peritumoral de gliomas y metastasis cerebrales unicas, funded by Instituto de Investigacion Sanitaria H. Universitario y Politecnico La Fe. This work was partially supported by the Instituto de Aplicaciones de las Tecnologias de la Informacion y las Comunicaciones Avanzadas (ITACA). Veratech for Health S.L. provided support in the form of salaries for author EF-G, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author is articulated in the "author contributions" section. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.Juan AlbarracÃn, J.; Fuster GarcÃa, E.; Manjón Herrera, JV.; Robles Viejo, M.; Aparici, F.; Marti-Bonmati, L.; GarcÃa Gómez, JM. (2015). Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification. PLoS ONE. 10(5):1-20. https://doi.org/10.1371/journal.pone.0125143S120105Wen, P. Y., Macdonald, D. R., Reardon, D. A., Cloughesy, T. F., Sorensen, A. G., Galanis, E., … Chang, S. M. (2010). Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group. Journal of Clinical Oncology, 28(11), 1963-1972. doi:10.1200/jco.2009.26.3541Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor studies. Physics in Medicine and Biology, 58(13), R97-R129. doi:10.1088/0031-9155/58/13/r97Dolecek, T. A., Propp, J. M., Stroup, N. E., & Kruchko, C. (2012). CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2005-2009. Neuro-Oncology, 14(suppl 5), v1-v49. doi:10.1093/neuonc/nos218Gordillo, N., Montseny, E., & Sobrevilla, P. (2013). State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426-1438. doi:10.1016/j.mri.2013.05.002Verma, R., Zacharaki, E. I., Ou, Y., Cai, H., Chawla, S., Lee, S.-K., … Davatzikos, C. (2008). Multiparametric Tissue Characterization of Brain Neoplasms and Their Recurrence Using Pattern Classification of MR Images. Academic Radiology, 15(8), 966-977. doi:10.1016/j.acra.2008.01.029Jensen, T. R., & Schmainda, K. M. (2009). Computer-aided detection of brain tumor invasion using multiparametric MRI. Journal of Magnetic Resonance Imaging, 30(3), 481-489. doi:10.1002/jmri.21878Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Wagstaff KL. Intelligent Clustering with instance-level constraints. PhD Thesis, Cornell University. 2002.Fletcher-Heath, L. M., Hall, L. O., Goldgof, D. B., & Murtagh, F. R. (2001). Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artificial Intelligence in Medicine, 21(1-3), 43-63. doi:10.1016/s0933-3657(00)00073-7Nie, J., Xue, Z., Liu, T., Young, G. S., Setayesh, K., Guo, L., & Wong, S. T. C. (2009). Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field. Computerized Medical Imaging and Graphics, 33(6), 431-441. doi:10.1016/j.compmedimag.2009.04.006Zhu, Y., Young, G. S., Xue, Z., Huang, R. Y., You, H., Setayesh, K., … Wong, S. T. (2012). Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation. Academic Radiology, 19(8), 977-985. doi:10.1016/j.acra.2012.03.026Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45-57. doi:10.1109/42.906424Vijayakumar, C., Damayanti, G., Pant, R., & Sreedhar, C. M. (2007). Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps. Computerized Medical Imaging and Graphics, 31(7), 473-484. doi:10.1016/j.compmedimag.2007.04.004Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., & Gerig, G. (2003). Automatic brain tumor segmentation by subject specific modification of atlas priors1. Academic Radiology, 10(12), 1341-1348. doi:10.1016/s1076-6332(03)00506-3Gudbjartsson, H., & Patz, S. (1995). The rician distribution of noisy mri data. Magnetic Resonance in Medicine, 34(6), 910-914. doi:10.1002/mrm.1910340618Buades, A., Coll, B., & Morel, J. M. (2005). A Review of Image Denoising Algorithms, with a New One. Multiscale Modeling & Simulation, 4(2), 490-530. doi:10.1137/040616024Manjón, J. V., Coupé, P., MartÃ-BonmatÃ, L., Collins, D. L., & Robles, M. (2009). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203. doi:10.1002/jmri.22003Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1), 87-97. doi:10.1109/42.668698Tustison, N. J., Avants, B. B., Cook, P. A., Yuanjie Zheng, Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310-1320. doi:10.1109/tmi.2010.2046908Manjón, J. V., Coupé, P., Buades, A., Collins, D. L., & Robles, M. (2010). MRI Superresolution Using Self-Similarity and Image Priors. International Journal of Biomedical Imaging, 2010, 1-11. doi:10.1155/2010/425891Rousseau, F. (2010). A non-local approach for image super-resolution using intermodality priors☆. Medical Image Analysis, 14(4), 594-605. doi:10.1016/j.media.2010.04.005Protter, M., Elad, M., Takeda, H., & Milanfar, P. (2009). Generalizing the Nonlocal-Means to Super-Resolution Reconstruction. IEEE Transactions on Image Processing, 18(1), 36-51. doi:10.1109/tip.2008.2008067Manjón, J. V., Coupé, P., Buades, A., Fonov, V., Louis Collins, D., & Robles, M. (2010). Non-local MRI upsampling. Medical Image Analysis, 14(6), 784-792. doi:10.1016/j.media.2010.05.010Kassner, A., & Thornhill, R. E. (2010). Texture Analysis: A Review of Neurologic MR Imaging Applications. American Journal of Neuroradiology, 31(5), 809-816. doi:10.3174/ajnr.a2061Ahmed, S., Iftekharuddin, K. M., & Vossough, A. (2011). Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI. IEEE Transactions on Information Technology in Biomedicine, 15(2), 206-213. doi:10.1109/titb.2011.2104376Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. doi:10.1109/tit.1982.1056489Dunn, J. C. (1973). A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3(3), 32-57. doi:10.1080/01969727308546046Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. doi:10.1007/978-1-4757-0450-1Hammersley, JM, Clifford, P. Markov fields on finite graphs and lattices. 1971.Komodakis, N., & Tziritas, G. (2007). Approximate Labeling via Graph Cuts Based on Linear Programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(8), 1436-1453. doi:10.1109/tpami.2007.1061Komodakis, N., Tziritas, G., & Paragios, N. (2008). Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies. Computer Vision and Image Understanding, 112(1), 14-29. doi:10.1016/j.cviu.2008.06.007Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011). Unbiased average age-appropriate atlases for pediatric studies. NeuroImage, 54(1), 313-327. doi:10.1016/j.neuroimage.2010.07.033Fonov, V., Evans, A., McKinstry, R., Almli, C., & Collins, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47, S102. doi:10.1016/s1053-8119(09)70884-5Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., … Parsey, R. V. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3), 786-802. doi:10.1016/j.neuroimage.2008.12.037AVANTS, B., EPSTEIN, C., GROSSMAN, M., & GEE, J. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26-41. doi:10.1016/j.media.2007.06.004Saez C, Robles M, Garcia-Gomez JM. Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research 2014; In press
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