Physiological and Behavioral Changes of Water Buffalo in Hot and Cold Systems: Review

This review's objective is to provide information on the mechanisms that buffaloes express during the thermoregulation process. Generally, the water buffalo is associated with warm and tropical climates. In these systems, the combination of high temperature, relative humidity, and radiation cause different physiological and behavioral changes, particularly during the summer months. Wallowing behavior in water or mud promotes heat dissipation through physical mechanisms, such as conduction, convection, and radiation. Furthermore, the provision of natural or artificial shades contributes to thermoregulation and maintains homeostasis. In production systems in cold climates, the wallowing behavior is inhibited by the water temperature, so it is important to keep the animals protected in stables to avoid the cold winds and rapid drops in temperature, causing increased illness pneumonia and sometimes death. Finally, in cold conditions, the animals require an appropriate diet since the use of energy is distributed mainly for the production of heat. Thus, heat stress and cold stress generates relevant problems in health, welfare, and productivity in water buffaloes. A comprehensive assessment of the severity of the resulting problems associated with thermal stress and specialty in cold stress in water buffaloes is necessary so far, and there's very little information about it in this species

Ciliates as Symbionts

Although many ciliates are free-living, more than 140 families of ciliates (Alveolata, Ciliophora) include symbiotic species of animals. Symbiosis, defined as an interaction between two species, is analyzed in this chapter to show a wide diversity of symbiotic systems in ciliates (epibiosis, commensalism, mutualism, and parasitism), providing some data about ciliate strategies showing their success as symbionts. Some species are free-living as well symbionts, facultative symbionts, and obligate symbionts. Analysis of reconstructions of ancestral state evidence that the parasitism arose numerous times and independently among the lineages of ciliates. At least three evolutionary routes can be traced: (1) transition from free-living to mutualism and parasitism, (2) transition from free-living to parasitism, and (3) regression from parasitism to free-living. The evolution of the symbiosis in ciliates demonstrates a higher diversification rate concerning free-living ciliates. The analysis of the evolution of the life cycles complexity, exploring molecular data of the phases of the ciliate cycle in their hosts is also essential. We propose new approaches for an integrative study of symbiotic ciliates

Mapping of social initiatives as a model of local development against depopulation in rural areas. The Valle del Genal case (Andalusia, Spain)

The cultural heritage of many rural areas, such as the villages of Valle del Genal in Andalusia, is endangered. Factors such as the depopulation suffered in the last 25 years have prevented the knowledge transfer from the elderly to the young. This paper focuses on mapping the social, economic and habitat resources as a preliminary step to the implementation of measures and policies against the abandonment of these areas. The aim is to create a map regarding the cultural identity and idiosyncrasy of each village in the valley. The mapping of these local entities is carried out through a combination of participatory work with the communities in the area and the data tracking from geo-positioning and social networks applications. During the identification and inventory process, the relationship between different citizen initiatives and social groups are analysed. This cartography pretends to offer a base of accessible knowledge for inhabitants and visitors.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

New prioritized value iteration for Markov decision processes

The problem of solving large Markov decision processes accurately and quickly is challenging. Since the computational effort incurred is considerable, current research focuses on finding superior acceleration techniques. For instance, the convergence properties of current solution methods depend, to a great extent, on the order of backup operations. On one hand, algorithms such as topological sorting are able to find good orderings but their overhead is usually high. On the other hand, shortest path methods, such as Dijkstra's algorithm which is based on priority queues, have been applied successfully to the solution of deterministic shortest-path Markov decision processes. Here, we propose an improved value iteration algorithm based on Dijkstra's algorithm for solving shortest path Markov decision processes. The experimental results on a stochastic shortest-path problem show the feasibility of our approach. © Springer Science+Business Media B.V. 2011.García Hernández, MDG.; Ruiz Pinales, J.; Onaindia De La Rivaherrera, E.; Aviña Cervantes, JG.; Ledesma Orozco, S.; Alvarado Mendez, E.; Reyes Ballesteros, A. (2012). New prioritized value iteration for Markov decision processes. Artificial Intelligence Review. 37(2):157-167. doi:10.1007/s10462-011-9224-zS157167372Agrawal S, Roth D (2002) Learning a sparse representation for object detection. In: Proceedings of the 7th European conference on computer vision. Copenhagen, Denmark, pp 1–15Bellman RE (1954) The theory of dynamic programming. Bull Amer Math Soc 60: 503–516Bellman RE (1957) Dynamic programming. Princeton University Press, New JerseyBertsekas DP (1995) Dynamic programming and optimal control. Athena Scientific, MassachusettsBhuma K, Goldsmith J (2003) Bidirectional LAO* algorithm. In: Proceedings of indian international conferences on artificial intelligence. p 980–992Blackwell D (1965) Discounted dynamic programming. Ann Math Stat 36: 226–235Bonet B, Geffner H (2003a) Faster heuristic search algorithms for planning with uncertainty and full feedback. In: Proceedings of the 18th international joint conference on artificial intelligence. Morgan Kaufmann, Acapulco, México, pp 1233–1238Bonet B, Geffner H (2003b) Labeled RTDP: improving the convergence of real-time dynamic programming. In: Proceedings of the international conference on automated planning and scheduling. Trento, Italy, pp 12–21Bonet B, Geffner H (2006) Learning depth-first search: a unified approach to heuristic search in deterministic and non-deterministic settings and its application to MDP. In: Proceedings of the 16th international conference on automated planning and scheduling. Cumbria, UKBoutilier C, Dean T, Hanks S (1999) Decision-theoretic planning: structural assumptions and computational leverage. J Artif Intell Res 11: 1–94Chang I, Soo H (2007) Simulation-based algorithms for Markov decision processes Communications and control engineering. Springer, LondonDai P, Goldsmith J (2007a) Faster dynamic programming for Markov decision processes. Technical report. Doctoral consortium, department of computer science and engineering. University of WashingtonDai P, Goldsmith J (2007b) Topological value iteration algorithm for Markov decision processes. In: Proceedings of the 20th international joint conference on artificial intelligence. Hyderabad, India, pp 1860–1865Dai P, Hansen EA (2007c) Prioritizing bellman backups without a priority queue. In: Proceedings of the 17th international conference on automated planning and scheduling, association for the advancement of artificial intelligence. Rhode Island, USA, pp 113–119Dibangoye JS, Chaib-draa B, Mouaddib A (2008) A Novel prioritization technique for solving Markov decision processes. In: Proceedings of the 21st international FLAIRS (The Florida Artificial Intelligence Research Society) conference, association for the advancement of artificial intelligence. Florida, USAFerguson D, Stentz A (2004) Focused propagation of MDPs for path planning. In: Proceedings of the 16th IEEE international conference on tools with artificial intelligence. pp 310–317Hansen EA, Zilberstein S (2001) LAO: a heuristic search algorithm that finds solutions with loops. Artif Intell 129: 35–62Hinderer K, Waldmann KH (2003) The critical discount factor for finite Markovian decision processes with an absorbing set. Math Methods Oper Res 57: 1–19Li L (2009) A unifying framework for computational reinforcement learning theory. PhD Thesis. The state university of New Jersey, New Brunswick. NJLittman ML, Dean TL, Kaelbling LP (1995) On the complexity of solving Markov decision problems.In: Proceedings of the 11th international conference on uncertainty in artificial intelligence. Montreal, Quebec pp 394–402McMahan HB, Gordon G (2005a) Fast exact planning in Markov decision processes. In: Proceedings of the 15th international conference on automated planning and scheduling. Monterey, CA, USAMcMahan HB, Gordon G (2005b) Generalizing Dijkstra’s algorithm and gaussian elimination for solving MDPs. Technical report, Carnegie Mellon University, PittsburghMeuleau N, Brafman R, Benazera E (2006) Stochastic over-subscription planning using hierarchies of MDPs. In: Proceedings of the 16th international conference on automated planning and scheduling. Cumbria, UK, pp 121–130Moore A, Atkeson C (1993) Prioritized sweeping: reinforcement learning with less data and less real time. Mach Learn 13: 103–130Puterman ML (1994) Markov decision processes. Wiley Editors, New YorkPuterman ML (2005) Markov decision processes. Wiley Inter Science Editors, New YorkRussell S (2005) Artificial intelligence: a modern approach. Making complex decisions (Ch-17), 2nd edn. Pearson Prentice Hill Ed., USAShani G, Brafman R, Shimony S (2008) Prioritizing point-based POMDP solvers. IEEE Trans Syst Man Cybern 38(6): 1592–1605Sniedovich M (2006) Dijkstra’s algorithm revisited: the dynamic programming connexion. Control Cybern 35: 599–620Sniedovich M (2010) Dynamic programming: foundations and principles, 2nd edn. Pure and Applied Mathematics Series, UKTijms HC (2003) A first course in stochastic models. Discrete-time Markov decision processes (Ch-6). Wiley Editors, UKVanderbei RJ (1996) Optimal sailing strategies. Statistics and operations research program, University of Princeton, USA ( http://www.orfe.princeton.edu/~rvdb/sail/sail.html )Vanderbei RJ (2008) Linear programming: foundations and extensions, 3rd edn. Springer, New YorkWingate D, Seppi KD (2005) Prioritization methods for accelerating MDP solvers. J Mach Learn Res 6: 851–88

Massive deworming, nutritional status and learning capacity in school children in a rural community

one hundred and ninety eight children of a rural village school were studied for parasitological variables, nutritional status (anthropometric variables and blood parameters) and assessment of their learning capacity. the study was designed as a baseline study and included both massive anthelminthic treatment and follow-up of the reinfection process. the initial prevalence of infection by soil helminths was: ascaris lumbricoides 36,4 %, trichuris trichiura 34.8%, hookworm 18,2 % and strongyloides stercoralis 4,5 %. 53,1 % of children were under risk of malnutrition and the values of hematocrit and hemoglobin concentration were below normal in 83 % and 55 % respectively. an important deficit in all the learning capacity tests was observed. some relationship was found between these results and infection by parasites.Se estudiaron 198 niños de 5 a 15 años de una comunidad escolar rural en variables parasitológicas, estado nutricional (antropometría y parámetros en sangre) y evaluación de la capacidad de aprendizaje. El estudio se diseño como línea de base e incluyó tratamiento masivo y seguimiento al proceso de reinfección. La prevalencia inicial de helmintos fue: Ascaris lumbricoides 36,4 %, Trichuris trichiura 34,8 %, Uncinaria 18,2 % y Strongyloides stercoralis 4,5 %. El 53,1 % de los niños estaban en riesgo de desnutrición y los valores del hematocrito y de la hemoglobina estaban por debajo de lo normal en el 83 y 55 % respectivamente. Así mismo, se detectó un importante déficit en todas las pruebas de capacidad de aprendizaje. Se encontraron algunas relaciones entre los resultados anteriores y la presencia de parásitos

Canagliflozin and renal outcomes in type 2 diabetes and nephropathy

BACKGROUND Type 2 diabetes mellitus is the leading cause of kidney failure worldwide, but few effective long-term treatments are available. In cardiovascular trials of inhibitors of sodium–glucose cotransporter 2 (SGLT2), exploratory results have suggested that such drugs may improve renal outcomes in patients with type 2 diabetes. METHODS In this double-blind, randomized trial, we assigned patients with type 2 diabetes and albuminuric chronic kidney disease to receive canagliflozin, an oral SGLT2 inhibitor, at a dose of 100 mg daily or placebo. All the patients had an estimated glomerular filtration rate (GFR) of 30 to <90 ml per minute per 1.73 m2 of body-surface area and albuminuria (ratio of albumin [mg] to creatinine [g], >300 to 5000) and were treated with renin–angiotensin system blockade. The primary outcome was a composite of end-stage kidney disease (dialysis, transplantation, or a sustained estimated GFR of <15 ml per minute per 1.73 m2), a doubling of the serum creatinine level, or death from renal or cardiovascular causes. Prespecified secondary outcomes were tested hierarchically. RESULTS The trial was stopped early after a planned interim analysis on the recommendation of the data and safety monitoring committee. At that time, 4401 patients had undergone randomization, with a median follow-up of 2.62 years. The relative risk of the primary outcome was 30% lower in the canagliflozin group than in the placebo group, with event rates of 43.2 and 61.2 per 1000 patient-years, respectively (hazard ratio, 0.70; 95% confidence interval [CI], 0.59 to 0.82; P=0.00001). The relative risk of the renal-specific composite of end-stage kidney disease, a doubling of the creatinine level, or death from renal causes was lower by 34% (hazard ratio, 0.66; 95% CI, 0.53 to 0.81; P<0.001), and the relative risk of end-stage kidney disease was lower by 32% (hazard ratio, 0.68; 95% CI, 0.54 to 0.86; P=0.002). The canagliflozin group also had a lower risk of cardiovascular death, myocardial infarction, or stroke (hazard ratio, 0.80; 95% CI, 0.67 to 0.95; P=0.01) and hospitalization for heart failure (hazard ratio, 0.61; 95% CI, 0.47 to 0.80; P<0.001). There were no significant differences in rates of amputation or fracture. CONCLUSIONS In patients with type 2 diabetes and kidney disease, the risk of kidney failure and cardiovascular events was lower in the canagliflozin group than in the placebo group at a median follow-up of 2.62 years

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Representación y aprendizaje de procesos de decisión de markov cualitativas

La planificación automática en problemas del mundo real se ha convertido en una disciplina de interás para la comunidad científica ya que permite establecer computacionalmente rumbos de acción en casos que, por la complejidad del problema, un humano no puede abordar adecuadamente. En particular, la planificación con incertidumbre permite generar estrategias de control en ambientes inciertos con metas multivaluadas, ponderando el costo de generación de un plan contra la ganancia de utilidad en el tiempo por ejecución del mismo (planes �utiles). Gracias a los recientes adelantos tácnológicos en procesamiento de información y al perfeccionamiento de algoritmos en materia de teoría de decisiones y razonamiento con incertidumbre, han resurgido las tácnicas basadas en los Procesos de Decisión de Markov (MDPs por sus siglas en inglás) como marco estándar para la planificación con incertidumbre. Una limitación de los MDPs es que ante problemas altamente dimensionales, con grandes espacios de acciones, y la existencia de variables continuas, se producen espacios de solución no manejables con algoritmos estándar. En este trabajo se propone una tácnica de representación de MDPs abstractos para simplificar espacios de estados muy grandes, que puede resolverse con mátodos estándar de programación dinámica. Dado que esta tácnica esta basada en restricciones cualitativas impuestas por características (ó factores) propias del mismo problema de decisión, la hemos llamado MDPs cualitativos. Aun cuando el mátodo de representación resulta novedoso y fácil de implementar, la especificación manual del modelo de decisión abstracto y sus parámetros puede volverse impráctica. En este trabajo, tal modelo se aproxima usando algoritmos de aprendizaje autómatico donde, a partir de un conjunto de datos de muestreo, se aprende una abstracción inicial del espacio de estados, y un modelo de transición sobre esta abstracción. La solución de este MDP abstracto inicial es una política de acción que en general es satisfactoria, sin embargo, para los casos donde ásta resulte insuficiente, se puede aplicar una segunda fase donde la solución es detallada o refinada. La calidad del mátodo se demuestra empíricamente usando problemas simples de planificación de movimiento en robótica, y un problema de control de procesos industriales con diferentes dimensiones y de los espacios de estados y de acciones. Los resultados muestran buenas soluciones con ahorros en el tama�no del espacio de estados, y reducciones en el tiempo de aprendizaje e inferencia al compararse con discretizaciones uniformes finas

Guía de conductas reactivas mediante planes abstractos en un problema de navegación robótica

Tesis de Maestría en Inteligencia Artificial presentada a la Faculta de Física e Inteligencia Artificial de la Universidad Veracruzana, Región Xalapa