Lack of Assortative Mating for Tail, Body Size, or Condition in the Elaborate Monomorphic Turquoise-Browed Motmot (\u3cem\u3eEumomota superciliosa\u3c/em\u3e)

Elaborate male and female plumage can be maintained by mutual sexual selection and function as a mate-choice or status signal in both sexes. Both male and female Turquoise-browed Motmot (Eumomota superciliosa) have long tails that terminate in widened blue-and-black rackets that appear to hang, unattached, below the body of the bird. I tested whether mutual sexual selection maintains the Turquoise-browed Motmot’s elaborate tail plumage by testing the prediction that mating occurs in an assortative manner for tail plumage. I also tested whether assortative mating occurs for body size, a potential measure of dominance, and for phenotypic condition, a measure of individual quality. Assortative mating was measured (1) within all pairs in the study population, (2) within newly formed pairs, and (3) within experimentally induced pairs that formed after removal of females from stable pairs. Assortative mating was not found for tail plumage, body size, or phenotypic condition in any of these samples. Therefore, there was no support for the “mutual sexual selection” hypothesis. I discuss the hypothesis that the tail is sexually selected in males only, and that natural selection accounts for the evolutionary maintenance of the elaborate female tail. La existencia de plumaje elaborado en los machos y las hembras puede ser mantenida por selecci´on sexual mutua, y funcionar como una se˜nal para la selecci´on de parejas o del estatus de los individuos en ambos sexos. Tanto los machos como las hembras de la especie Eumomota superciliosa tienen colas largas que terminan en unas raquetas ensanchadas de color azul y negro, que parecen colgar debajo del cuerpo de las aves. En este estudio prob´e si el plumaje elaborado de la cola de esta especie es mantenido mediante selecci´on sexual mutua, evaluando la predicci´on de que el apareamiento es asociativo con respecto al plumaje de la cola. Tambi´en prob´e si existe apareamiento asociativo con respecto al tama˜no (una medida potencial de la dominancia) y con respecto a la condici´on fenot´ıpica (una medida de la calidad de los individuos). El apareamiento asociativo fue medido para todas las parejas de la poblaci´on de estudio, para parejas formadas recientemente y para parejas cuya formaci´on fue inducida experimentalmente mediante la remoci´on de las hembras de parejas estables. No se encontr´o apareamiento asociativo con respecto al plumaje de la cola, al tama˜no corporal, ni a la condici´on fenot´ıpica en ninguna de estas muestras. Por lo tanto, no existi´o respaldo para la hip´otesis de selecci´on sexual mutua. Discuto la hip´otesis que plantea que la cola es objeto de selecci´on sexual s´olo en los machos, y que la selecci´on natural permite explicar el mantenimiento evolutivo de la cola elaborada en las hembras

Small primary adenocarcinoma in adenomyosis with nodal metastasis: a case report

<p>Abstract</p> <p>Background</p> <p>Malignant transformation of adenomyosis is a very rare event. Only about 30 cases of this occurrence have been documented till now.</p> <p>Case presentation</p> <p>The patient was a 57-year-old woman with a slightly enlarged uterus, who underwent total hysterectomy and unilateral adnexectomy. On gross inspection, the uterine wall displayed a single nodule measuring 5 cm and several small gelatinous lesions. Microscopic examination revealed a common leiomyoma and multiple adenomyotic foci. A few of these glands were transformed into a moderately differentiated adenocarcinoma. The endometrium was completely examined and tumor free. The carcinoma was, therefore, considered to be an endometrioid adenocarcinoma arising from adenomyosis. Four months later, an ultrasound scan revealed enlarged pelvic lymph nodes: a cytological diagnosis of metastatic adenocarcinoma was made.</p> <p>Immunohistochemical studies showed an enhanced positivity of the tumor site together with the neighbouring adenomyotic foci for estrogen receptors, aromatase, p53 and COX-2 expression when compared to the distant adenomyotic glands and the endometrium. We therefore postulate that the neoplastic transformation of adenomyosis implies an early carcinogenic event involving p53 and COX-2; further tumor growth is sustained by an autocrine-paracrine loop, based on a modulation of hormone receptors as well as aromatase and COX-2 local expression.</p> <p>Conclusion</p> <p>Adenocarcinoma in adenomyosis may be affected by local hormonal influence and, despite its small size, may metastasize.</p

Ruteo de buses escolares con consideraciones ambientales mediante Búsqueda Tabú Granular1

in the current context it is important that the School Bus Routing Problem (SBRP) in addition to efficiency also addresses the social and environmental dimensions, to ensure sustainable solutions. The environmental dimension has been widely addressed in the VRP, however, the SBRP has not had the same development, although there are studies on the relationship between environmental problems in school transport and children's health, however, there are no jobs that address the environmental dimension in the SBRP, in this sense this article addresses the SBRP with environmental considerations. A mathematical model is formulated to minimize fuel consumption, which is calculated based on the distance traveled, the weight of the vehicles and the students. The model is optimally solved for small instances, and for larger instances an algorithm based on Granular Tabu Search is used. The initial solution is generated by the savings algorithm. The performance of the algorithm is evaluated by comparing the times and values of objective function with respect to the exact method, the times of solution with the metaheuristic were 99.96% on average faster than the exact method, however the objective values was far on average 13.98% of the exact method. In this work an extension to the SBRP is made, the environmental dimension is added, approximate the fuel consumption according to the distance and the weight.En el contexto actual es importante que el ruteo de buses escolares (SBRP) además de la eficiencia aborde también las dimensiones social y ambiental, para garantizar soluciones sostenibles. La dimensión ambiental ha sido abordada ampliamente en el VRP, sin embargo, el SBRP no ha contado con la misma suerte, a pesar de que existen estudios sobre la relación entre los problemas ambientales en el trasporte escolar y salud de los niños, no obstante, no se encuentran trabajos que aborden la dimensión ambiental en el ruteo de buses escolares, en tal sentido este artículo aborda el SBRP con consideraciones ambientales. Se formula un modelo matemático que minimiza el consumo de combustible, que se calcula en función de la distancia recorrida, el peso de los vehículos y el de los estudiantes. El modelo es resuelto de manera óptima para instancias pequeñas, y para instancias de mayor tamaño se emplea un algoritmo basado en Búsqueda Tabú Granular. La solución inicial es generada por el algoritmo de ahorros. Se evalúa el rendimiento del algoritmo comparando los tiempos y valores de función objetivo con respecto al método exacto, la meta heurística generó soluciones 99,96% en promedio más rápido que el método exacto y generó soluciones con valor de función objetivo alejado en promedio 13,98% de las del método exacto. En este trabajo se hace una extensión al SBRP, adicionando la dimensión ambiental, aproximado el consumo de combustible en función de la distancia y el peso

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance