Association of paratuberculosis sero-status with milk production and somatic cell counts across 5 lactations, using multilevel mixed models, in dairy cows

The aim of this work was to investigate associations between individual cow Mycobacterium avium ssp. paratuberculosis (MAP) seropositivity, 305- d corrected milk production, and somatic cell count during 5 lactations lifespan in Portuguese dairy herds using multilevel mixed models. We used MAP serum ELISA (Idexx MAP Ac, Idexx Laboratories Inc., Westbrook, ME) results (n = 23,960) from all the 20,221 adult cows present in 329 farms and corresponding 47,586 lactation records from the National Dairy Improvement Association. Cows and farms were classified as positive or negative. Multilevel mixed models were used to investigate the association of cow MAP status with variation in milk production and somatic cell count. Cow MAP status, farm status, and lactation number were considered as independent variables. A quadratic function of lactation number was used to mimic the effect of lactation order on milk production. The models considered 3 levels: measurement occasion (level 1) within cow (level 2) and cow within farm (level 3). Four final models were produced, including all herds and cows, to address the effect of farm status (models 1 and 2) or the effect of cow status (models 3 and 4) on the outcome variables. Our results show that MAP status affects milk production. Losses are detectable from third lactation onward. During the first 5 lactations, positive cows accumulated an average loss of 1,284.8 kg of milk when compared with the negative cows. We also observed that somatic cell counts were higher in positive cows and a positive interaction occurs between cow status and lactation number, suggesting a positive association between MAP infection and increased so- matic cell counts. Our results are in line with previous studies, suggesting a possible positive relation between cow milk production and susceptibility to MAP infection

Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems

[EN] Prediction models have become essential for the improvement of decision-making processes in public management and, particularly, for water supply utilities. Accurate estimation often needs to solve multimeasurement, mixed-mode, and space-time problems, typical of many engineering applications. As a result, accurate estimation of real world variables is still one of the major problems in mathematical approximation. Several individual techniques have shown very good estimation abilities. However, none of them are free from drawbacks. This paper faces the challenge of creating accurate water demand predictive models at urban scale by using so-called committee machines, which are ensemble frameworks of single machine learning models. The proposal is able to combine models of varied nature. Specifically, this paper analyzes combinations of such techniques as multilayer perceptrons, support vector machines, extreme learning machines, random forests, adaptive neural fuzzy inference systems, and the group method for data handling. Analyses are checked on two water demand datasets from Franca (Brazil). As an ensemble tool, the combined response of a committee machine outperforms any single constituent model.Ambrosio, JK.; Brentan, BM.; Herrera Fernández, AM.; Luvizotto, E.; Ribeiro, L.; Izquierdo Sebastián, J. (2019). Committee Machines for Hourly Water Demand Forecasting in Water Supply Systems. Mathematical Problems in Engineering. 2019:1-11. https://doi.org/10.1155/2019/97654681112019Montalvo, I., Izquierdo, J., Pérez-García, R., & Herrera, M. (2010). Improved performance of PSO with self-adaptive parameters for computing the optimal design of Water Supply Systems. Engineering Applications of Artificial Intelligence, 23(5), 727-735. doi:10.1016/j.engappai.2010.01.015Donkor, E. A., Mazzuchi, T. A., Soyer, R., & Alan Roberson, J. (2014). Urban Water Demand Forecasting: Review of Methods and Models. Journal of Water Resources Planning and Management, 140(2), 146-159. doi:10.1061/(asce)wr.1943-5452.0000314Adamowski, J. F. (2008). Peak Daily Water Demand Forecast Modeling Using Artificial Neural Networks. Journal of Water Resources Planning and Management, 134(2), 119-128. doi:10.1061/(asce)0733-9496(2008)134:2(119)Ghiassi, M., Zimbra, D. K., & Saidane, H. (2008). Urban Water Demand Forecasting with a Dynamic Artificial Neural Network Model. Journal of Water Resources Planning and Management, 134(2), 138-146. doi:10.1061/(asce)0733-9496(2008)134:2(138)Clemen, R. T. (1989). Combining forecasts: A review and annotated bibliography. International Journal of Forecasting, 5(4), 559-583. doi:10.1016/0169-2070(89)90012-5Herrera, M., García-Díaz, J. C., Izquierdo, J., & Pérez-García, R. (2011). Municipal Water Demand Forecasting: Tools for Intervention Time Series. Stochastic Analysis and Applications, 29(6), 998-1007. doi:10.1080/07362994.2011.610161Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Barzegar, R., & Asghari Moghaddam, A. (2016). Combining the advantages of neural networks using the concept of committee machine in the groundwater salinity prediction. Modeling Earth Systems and Environment, 2(1). doi:10.1007/s40808-015-0072-8Nadiri, A. A., Gharekhani, M., Khatibi, R., Sadeghfam, S., & Moghaddam, A. A. (2017). Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Science of The Total Environment, 574, 691-706. doi:10.1016/j.scitotenv.2016.09.093Brentan, B. M., Meirelles, G., Herrera, M., Luvizotto, E., & Izquierdo, J. (2017). Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models. Mathematical Problems in Engineering, 2017, 1-10. doi:10.1155/2017/6343625Brentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., & Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532-541. doi:10.1016/j.cam.2016.02.009Johansson, C., Bergkvist, M., Geysen, D., Somer, O. D., Lavesson, N., & Vanhoudt, D. (2017). Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms. Energy Procedia, 116, 208-216. doi:10.1016/j.egypro.2017.05.068Polikar, R. (2006). Ensemble based systems in decision making. IEEE Circuits and Systems Magazine, 6(3), 21-45. doi:10.1109/mcas.2006.1688199Ferreira, R. P., Martiniano, A., Ferreira, A., Ferreira, A., & Sassi, R. J. (2016). Study on Daily Demand Forecasting Orders using Artificial Neural Network. IEEE Latin America Transactions, 14(3), 1519-1525. doi:10.1109/tla.2016.7459644Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/bf00994018Schölkop, B. (2003). An Introduction to Support Vector Machines. Recent Advances and Trends in Nonparametric Statistics, 3-17. doi:10.1016/b978-044451378-6/50001-6Huang, G.-B., Wang, D. H., & Lan, Y. (2011). Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics, 2(2), 107-122. doi:10.1007/s13042-011-0019-yIvakhnenko, A. G. (1970). Heuristic self-organization in problems of engineering cybernetics. Automatica, 6(2), 207-219. doi:10.1016/0005-1098(70)90092-

Adenosine A2A receptor modulation of hippocampal CA3-CA1 synapse plasticity during associative learning in behaving mice

© 2009 Nature Publishing Group All rights reservedPrevious in vitro studies have characterized the electrophysiological and molecular signaling pathways of adenosine tonic modulation on long-lasting synaptic plasticity events, particularly for hippocampal long-term potentiation(LTP). However, it remains to be elucidated whether the long-term changes produced by endogenous adenosine in the efficiency of synapses are related to those required for learning and memory formation. Our goal was to understand how endogenous activation of adenosine excitatory A2A receptors modulates the associative learning evolution in conscious behaving mice. We have studied here the effects of the application of a highly selective A2A receptor antagonist, SCH58261, upon a well-known associative learning paradigm - classical eyeblink conditioning. We used a trace paradigm, with a tone as the conditioned stimulus (CS) and an electric shock presented to the supraorbital nerve as the unconditioned stimulus(US). A single electrical pulse was presented to the Schaffer collateral–commissural pathway to evoke field EPSPs (fEPSPs) in the pyramidal CA1 area during the CS–US interval. In vehicle-injected animals, there was a progressive increase in the percentage of conditioning responses (CRs) and in the slope of fEPSPs through conditioning sessions, an effect that was completely prevented (and lost) in SCH58261 (0.5 mg/kg, i.p.)-injected animals. Moreover, experimentally evoked LTP was impaired in SCH58261- injected mice. In conclusion, the endogenous activation of adenosine A2A receptors plays a pivotal effect on the associative learning process and its relevant hippocampal circuits, including activity-dependent changes at the CA3-CA1 synapse.This study was supported by grants from the Spanish Ministry of Education and Research (BFU2005-01024 and BFU2005-02512), Spanish Junta de Andalucía (BIO-122 and CVI-02487), and the Fundación Conocimiento y Cultura of the Pablo de Olavide University (Seville, Spain).B. Fontinha was in receipt of a studentship from a project grant (POCI/SAU-NEU/56332/2004) supported by Fundação para a Ciência e Tecnologia (FCT, Portugal), and of an STSM from Cost B30 concerted action of the EU

The luminosity function of field galaxies

Schmidt's method for construction of luminosity function of galaxies is generalized by taking into account the dependence of density of galaxies from the distance in the near Universe. The logarithmical luminosity function (LLF) of field galaxies depending on morphological type is constructed. We show that the LLF for all galaxies, and also separately for elliptical and lenticular galaxies can be presented by Schechter function in narrow area of absolute magnitudes. The LLF of spiral galaxies was presented by Schechter function for enough wide area of absolute magnitudes: . Spiral galaxies differ slightly by parameter . At transition from early spirals to the late spirals parameter in Schechter function is reduced. The reduction of mean luminosity of galaxies is observed at transition from elliptical galaxies to lenticular galaxies, to early spiral galaxies, and further, to late spiral galaxies, in a bright end, . The completeness and the average density of samples of galaxies of different morphological types are estimated. In the range the mean number density of all galaxies is equal 0.127 Mpc-3.Comment: 14 page, 8 figures, to appear in Astrophysic

Hyaluronidase of Bloodsucking Insects and Its Enhancing Effect on Leishmania Infection in Mice

Hyaluronidases are enzymes degrading the extracellular matrix of vertebrates. Bloodsucking insects use them to cleave the skin of the host, enlarge the feeding lesion and acquire the blood meal. In addition, resulting fragments of extracellular matrix modulate local immune response of the host, which may positively affect transmission of vector-borne diseases, including leishmaniasis. Leishmaniases are diseases with a wide spectrum of clinical forms, from a relatively mild cutaneous affection to life-threatening visceral disease. Their causative agents, protozoans of the genus Leishmania, are transmitted by phlebotomine sand flies. Sand fly saliva was described to enhance Leishmania infection, but the information about molecules responsible for this exacerbating effect is still very limited. In the present work we demonstrated hyaluronidase activity in salivary glands of various Diptera and in fleas. In addition, we showed that hyaluronidase exacerbates Leishmania lesions in mice and propose that salivary hyaluronidase may facilitate the spread of other vector-borne microorganisms

The Effect of Anandamide on Uterine Nitric Oxide Synthase Activity Depends on the Presence of the Blastocyst

Nitric oxide production, catalyzed by nitric oxide synthase (NOS), should be strictly regulated to allow embryo implantation. Thus, our first aim was to study NOS activity during peri-implantation in the rat uterus. Day 6 inter-implantation sites showed lower NOS activity (0.19±0.01 pmoles L-citrulline mg prot−1 h−1) compared to days 4 (0.34±0.03) and 5 (0.35±0.02) of pregnancy and to day 6 implantation sites (0.33±0.01). This regulation was not observed in pseudopregnancy. Both dormant and active blastocysts maintained NOS activity at similar levels. Anandamide (AEA), an endocannabinoid, binds to cannabinoid receptors type 1 (CB1) and type 2 (CB2), and high concentrations are toxic for implantation and embryo development. Previously, we observed that AEA synthesis presents an inverted pattern compared to NOS activity described here. We adopted a pharmacological approach using AEA, URB-597 (a selective inhibitor of fatty acid amide hydrolase, the enzyme that degrades AEA) and receptor selective antagonists to investigate the effect of AEA on uterine NOS activity in vitro in rat models of implantation. While AEA (0.70±0.02 vs 0.40±0.04) and URB-597 (1.08±0.09 vs 0.83±0.06) inhibited NOS activity in the absence of a blastocyst (pseudopregnancy) through CB2 receptors, AEA did not modulate NOS on day 5 pregnant uterus. Once implantation begins, URB-597 decreased NOS activity on day 6 implantation sites via CB1 receptors (0.25±0.04 vs 0.40±0.05). While a CB1 antagonist augmented NOS activity on day 6 inter-implantation sites (0.17±0.02 vs 0.27±0.02), a CB2 antagonist decreased it (0.17±0.02 vs 0.12±0.01). Finally, we described the expression and localization of cannabinoid receptors during implantation. In conclusion, AEA levels close to and at implantation sites seems to modulate NOS activity and thus nitric oxide production, fundamental for implantation, via cannabinoid receptors. This modulation depends on the presence of the blastocyst. These data establish cannabinoid receptors as an interesting target for the treatment of implantation deficiencies

An application of the Rasch model to reading comprehension measurement

An effective reading comprehension measurement demands robust psychometric tools that allow teachers and researchers to evaluate the educational practices and track changes in students’ performance. In this study, we illustrate how Rasch model can be used to attend such demands and improve reading comprehension measurement. We discuss the construction of two reading comprehension tests: TRC-n, with narrative texts, and TRC-e, with expository texts. Three vertically scaled forms were generated for each test (TRC-n-2, TRC-n-3, TRC-n-4; TRC-e-2, TRC-e-3 and TRC-e-4), each meant to assess Portuguese students in second, third and fourth grade of elementary school. The tests were constructed according to a nonequivalent groups with anchor test design and data were analyzed using the Rasch model. The results provided evidence for good psychometric qualities for each test form, including unidimensionality and local independence and adequate reliability. A critical view of this study and future researches are discussed.CIEC – Research Centre on Child Studies, IE, UMinho (FCT R&D unit 317), PortugalThis research was supported by Grant FCOMP-01-0124-FEDER-010733 from Fundação para a Ciência e Tecnologia (FCT) and the European Regional Development Fund (FEDER) through the European program COMPETE (Operational Program for Competitiveness Factors) under the National Strategic Reference Framework (QREN).info:eu-repo/semantics/publishedVersio

Stress-Induced Reinstatement of Drug Seeking: 20 Years of Progress

In human addicts, drug relapse and craving are often provoked by stress. Since 1995, this clinical scenario has been studied using a rat model of stress-induced reinstatement of drug seeking. Here, we first discuss the generality of stress-induced reinstatement to different drugs of abuse, different stressors, and different behavioral procedures. We also discuss neuropharmacological mechanisms, and brain areas and circuits controlling stress-induced reinstatement of drug seeking. We conclude by discussing results from translational human laboratory studies and clinical trials that were inspired by results from rat studies on stress-induced reinstatement. Our main conclusions are (1) The phenomenon of stress-induced reinstatement, first shown with an intermittent footshock stressor in rats trained to self-administer heroin, generalizes to other abused drugs, including cocaine, methamphetamine, nicotine, and alcohol, and is also observed in the conditioned place preference model in rats and mice. This phenomenon, however, is stressor specific and not all stressors induce reinstatement of drug seeking. (2) Neuropharmacological studies indicate the involvement of corticotropin-releasing factor (CRF), noradrenaline, dopamine, glutamate, kappa/dynorphin, and several other peptide and neurotransmitter systems in stress-induced reinstatement. Neuropharmacology and circuitry studies indicate the involvement of CRF and noradrenaline transmission in bed nucleus of stria terminalis and central amygdala, and dopamine, CRF, kappa/dynorphin, and glutamate transmission in other components of the mesocorticolimbic dopamine system (ventral tegmental area, medial prefrontal cortex, orbitofrontal cortex, and nucleus accumbens). (3) Translational human laboratory studies and a recent clinical trial study show the efficacy of alpha-2 adrenoceptor agonists in decreasing stress-induced drug craving and stress-induced initial heroin lapse

Chagas Cardiomiopathy: The Potential of Diastolic Dysfunction and Brain Natriuretic Peptide in the Early Identification of Cardiac Damage

Chagas disease remains a major cause of morbidity and mortality in several countries of Latin America and has become a potential public health problem in countries where the disease is not endemic as a result of migration flows. Cardiac involvement represents the main cause of mortality, but its diagnosis is still based on nonspecific criteria with poor sensitivity. Early identification of patients with cardiac damage is desirable, since early treatment may improve prognosis. Diastolic dysfunction and elevated brain natriuretic peptide levels are present in different cardiomyopathies and in advanced phases of Chagas disease. However, there are scarce data about the role of these parameters in earlier forms of the disease. We conducted a study to assess the diastolic function, regional systolic abnormalities and brain natriuretic peptide levels in the different forms of Chagas disease. The main finding of our investigation is that diastolic dysfunction occurs before any cardiac dilatation or motion abnormality. In addition, BNP levels identify patients with diastolic dysfunction and Chagas disease with high specificity. The results reported in this study could help to early diagnose myocardial involvement and better stratify patients with Chagas disease