1,251 research outputs found
Left Ventricular Apical Aneurysm in a Cat With Primary Cardiomyopathy
Abstract A 13-year-old female Persian cat died suddenly after severe respiratory distress. At necropsy, the changes were compatible with congestive heart failure. The heart was enlarged with a flabby and puckered sac-like aneurysm at the apex of the left ventricle. The apical zone showed a thin muscular wall arising from the free wall of the left ventricle connected to a bulged saccular area through a wide communication. Microscopically, the wall of the aneurysm was composed of fibrous connective tissue with neovascularization and sparse atrophied myocardial cells at the margins. A few isolated cardiomyocytes in the lesion stained positively for desmin, and the inner lining of the aneurysm had immunoreactivity to von Willebrand factor and CD31.Mature fibrous connective tissue was interspersed with degenerated and disorganized cardiomyocytes elsewhere in the myocardium, and many small myocardial arteries were tortuous and thickened. In this case of sudden death, the diagnosis was primary cardiomyopathy, with formation of a left ventricular apical aneurysm within an area of marked myocardial fibrosis and cardiomyocyte atrophy.
Keywords left ventricle, apical aneurysm, cats, cardiomyopathy, heart failure, diverticulu
El conejo como modelo para el estudio del daño pulmonar generado por el lipopolisacarido de Pasteurella haemolytica mediante la reacción de Shwartzman y el fenómeno de Arthus
Tesis (Doctorado en Medicina Veterinaria) UANLUANLhttp://www.uanl.mx
Grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity
The process of extracting valuable and novel insights from raw data involves
a series of complex steps. In the realm of Automated Machine Learning (AutoML),
a significant research focus is on automating aspects of this process,
specifically tasks like selecting algorithms and optimising their
hyper-parameters. A particularly challenging task in AutoML is automatic
workflow composition (AWC). AWC aims to identify the most effective sequence of
data preprocessing and ML algorithms, coupled with their best hyper-parameters,
for a specific dataset. However, existing AWC methods are limited in how many
and in what ways they can combine algorithms within a workflow.
Addressing this gap, this paper introduces EvoFlow, a grammar-based
evolutionary approach for AWC. EvoFlow enhances the flexibility in designing
workflow structures, empowering practitioners to select algorithms that best
fit their specific requirements. EvoFlow stands out by integrating two
innovative features. First, it employs a suite of genetic operators, designed
specifically for AWC, to optimise both the structure of workflows and their
hyper-parameters. Second, it implements a novel updating mechanism that
enriches the variety of predictions made by different workflows. Promoting this
diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow
builds an ensemble whose workflows differ in their misclassified instances.
To evaluate EvoFlow's effectiveness, we carried out empirical validation
using a set of classification benchmarks. We begin with an ablation study to
demonstrate the enhanced performance attributable to EvoFlow's unique
components. Then, we compare EvoFlow with other AWC approaches, encompassing
both evolutionary and non-evolutionary techniques. Our findings show that
EvoFlow's specialised genetic operators and updating mechanism substantially
outperform current leading methods[..]Comment: 32 pages, 7 figures, 6 tables, journal pape
GEML: A Grammar-based Evolutionary Machine Learning Approach for Design-Pattern Detection
Design patterns (DPs) are recognised as a good practice in software
development. However, the lack of appropriate documentation often hampers
traceability, and their benefits are blurred among thousands of lines of code.
Automatic methods for DP detection have become relevant but are usually based
on the rigid analysis of either software metrics or specific properties of the
source code. We propose GEML, a novel detection approach based on evolutionary
machine learning using software properties of diverse nature. Firstly, GEML
makes use of an evolutionary algorithm to extract those characteristics that
better describe the DP, formulated in terms of human-readable rules, whose
syntax is conformant with a context-free grammar. Secondly, a rule-based
classifier is built to predict whether new code contains a hidden DP
implementation. GEML has been validated over five DPs taken from a public
repository recurrently adopted by machine learning studies. Then, we increase
this number up to 15 diverse DPs, showing its effectiveness and robustness in
terms of detection capability. An initial parameter study served to tune a
parameter setup whose performance guarantees the general applicability of this
approach without the need to adjust complex parameters to a specific pattern.
Finally, a demonstration tool is also provided.Comment: 27 pages, 18 tables, 10 figures, journal pape
Bovine diseases causing neurological signs and death in Mexican feedlots
Abstract The number of large feedlot operations, similar to
that of USA and Canada, has notably increased in Mexico in
the last three decades. Clinical and laboratory diagnoses of
neurological diseases in feedlot cattle are crucial in Mexico and Central America because of the high incidence of bovine paralytic rabies (BPR). Because of its zoonotic potential, BPR must be promptly diagnosed and differentiated from other bovine neurological diseases such as thrombotic meningoencephalitis (TME), polioencephalomalacia (PEM) and botulism. More recently, BPR and botulism have been diagnosed with increasing frequency in Mexican feedlots. Neither BPR nor botulism has relevant gross lesions, thus post-mortem diagnosis without laboratory support is impossible. Herein, we describe five outbreaks of neurological diseases in Mexican feedlots in which BPR, botulism and PEM were diagnosed either independently or in combination. A diagram illustrating the most conspicuous pathologic findings and ancillary laboratory test required to confirm the diagnoses of these neurological diseases in feedlot cattle is proposed
sSLAM: Speeded-Up Visual SLAM Mixing Artificial Markers and Temporary Keypoints
Environment landmarks are generally employed by visual SLAM (vSLAM) methods in the form of keypoints. However, these landmarks are unstable over time because they belong to areas that tend to change, e.g., shadows or moving objects. To solve this, some other authors have proposed the combination of keypoints and artificial markers distributed in the environment so as to facilitate the tracking process in the long run. Artificial markers are special elements (similar to beacons) that can be permanently placed in the environment to facilitate tracking. In any case, these systems keep a set of keypoints that is not likely to be reused, thus unnecessarily increasing the computing time required for tracking. This paper proposes a novel visual SLAM approach that efficiently combines keypoints and artificial markers, allowing for a substantial reduction in the computing time and memory required without noticeably degrading the tracking accuracy. In the first stage, our system creates a map of the environment using both keypoints and artificial markers, but once the map is created, the keypoints are removed and only the markers are kept. Thus, our map stores only long-lasting features of the environment (i.e., the markers). Then, for localization purposes, our algorithm uses the marker information along with temporary keypoints created just in the time of tracking, which are removed after a while. Since our algorithm keeps only a small subset of recent keypoints, it is faster than the state-of-the-art vSLAM approaches. The experimental results show that our proposed sSLAM compares favorably with ORB-SLAM2, ORB-SLAM3, OpenVSLAM and UcoSLAM in terms of speed, without statistically significant differences in accuracy
sSLAM: Speeded-Up Visual SLAM Mixing Artificial Markers and Temporary Keypoints
Environment landmarks are generally employed by visual SLAM (vSLAM) methods in the form of keypoints. However, these landmarks are unstable over time because they belong to areas that tend to change, e.g., shadows or moving objects. To solve this, some other authors have proposed the combination of keypoints and artificial markers distributed in the environment so as to facilitate the tracking process in the long run. Artificial markers are special elements (similar to beacons) that can be permanently placed in the environment to facilitate tracking. In any case, these systems keep a set of keypoints that is not likely to be reused, thus unnecessarily increasing the computing time required for tracking. This paper proposes a novel visual SLAM approach that efficiently combines keypoints and artificial markers, allowing for a substantial reduction in the computing time and memory required without noticeably degrading the tracking accuracy. In the first stage, our system creates a map of the environment using both keypoints and artificial markers, but once the map is created, the keypoints are removed and only the markers are kept. Thus, our map stores only long-lasting features of the environment (i.e., the markers). Then, for localization purposes, our algorithm uses the marker information along with temporary keypoints created just in the time of tracking, which are removed after a while. Since our algorithm keeps only a small subset of recent keypoints, it is faster than the state-of-the-art vSLAM approaches. The experimental results show that our proposed sSLAM compares favorably with ORB-SLAM2, ORB-SLAM3, OpenVSLAM and UcoSLAM in terms of speed, without statistically significant differences in accuracy.This research was funded by the project PID2019-103871GB-I00 of the Spanish Ministry of Economy, Industry and Competitiveness, FEDER, Project 1380047-F UCOFEDER-2021 of Andalusia and by the European Union–NextGeneration EU for requalification of Spanish University System 2021–2023
Protocolo de manejo de hernia inguinal en el servicio de cirugía general del hospital Dr. Teodoro Maldonado Carbo, Guayaquil
Se propone un protocolo de manejo para la patología herniaria en el Servicio de Cirugía General del Hospital Dr. Teodoro Maldonado Carbo de Guayaquil, cuyo propósito es unificar criterios para el manejo de esta patología tan frecuente en nuestro medio; siendo el objetivo principal disminuir las recurrencias, que fluctúan entre 4 y 6% según las estadísticas mundiales; independientemente de las variables atribuibles al equipo quirúrgico y evaluación exacta de cada caso.Como conclusiones podemos mencionar que la causa principal de recidiva es la tensión en la línea de sutura y que la técnica de Shouldice es la apropiada cuando existe defecto de la pared posterior. Este artículo resume una propuesta para el manejo de esta patología muy frecuente como resultado de la experiencia en este servicio
Non-Linear Multivariate Dependence between the Mexican Stock Market Index and the Exchange Rate: Efficiency Hypothesis and Political Cycle in Mexico (1994-2012)
Este trabajo utiliza una extensión multivariante de la prueba no paramétrica de no linealidad de Hinich (1991) con el objetivo de investigar si existe una relación no lineal entre el índice de la Bolsa Mexicana de Valores (IPC) y el tipo de cambio peso/dólar medida a través de la correlación cruzada y la bicorrelación cruzada en el periodo 1994-2012 durante tres subperíodos de administración presidencial. Este método divide la muestra en ventanas y proporciona información sobre la dependencia no lineal. El principal hallazgo es que no se detectan ventanas de correlación cruzada significativas. No obstante se observan ventanas de tiempo con una bicorrelación cruzada significativa, lo que sugiere una relación no lineal y bidireccional entre las series. Este trabajo concluye que para los tres subperíodos de administración presidencial ambas series mantienen la misma relación no lineal y bidireccional para cualquier cambio en el gobierno con ventanas significativas concentradas al principio del período presidencial sin importar el partido gobernante. Por último es importante destacar que los períodos no lineales bidireccionales se concentraron a mediados del último período presidencial mexicano, lo que indica que los factores financieros externos y económicos globales afectaron esta relación.This paper uses a multivariate extension of the non-parametric nonlinearity test from Hinich (1991) with the objective of investigating whether there is a nonlinear relation between the index of The Mexican Stock Exchange (IPC) and the peso/dollar exchange rate measured through the Cross-correlation and cross-correlation in the period 1994-2012 for three sub-periods of presidential administration. This method divides the sample into windows and provides information on nonlinear dependency. The main finding is that no significant cross-correlation windows are detected. However, time windows are observed with a significant cross bicorrelation, which suggests a non-linear and bidirectional relationship between the series. This paper concludes that for the three sub-periods of presidential administration both series maintain the same nonlinear and bidirectional relation for any change in the government with significant windows concentrated at the beginning of the presidential period regardless of the ruling party. Finally, It is important to note that the non-linear bidirectional periods were concentrated in the middle of the last Mexican presidential period, indicating that global external and economic financial factors affected this relationship.
The BRISA process as a path for efficient copper recovery from waste PCBs
In the present work, a two-stage biohydrometallurgical process for copper extraction from waste PCBs is developed. The main goal of this study is to check whether to separate the chemical leaching of copper with ferric iron from the regeneration of the leaching agent by bacterial oxidation of the ferrous iron is an efficient route for copper recovery from waste PCBs. To test this proposal, large waste PCBs pieces were retained in a stirred tank reactor (STR) in contact with a leaching liquor circulating at a high flow rate between this STR and a bioreactor.
The kinetics of leaching of large PCB pieces, when ferric iron is added in excess over the stoichiometric requirements, is limited by the rate of mass transfer of the leaching agent. A heterogeneous kinetic model was proposed to fit the experimental data. It was also found that by increasing the ferric iron concentration the leaching rate was increased.
Process separation has proven to be a promising configuration in which the productivity of the bioreactor has fulfilled the leaching agent demand and 90% of copper extraction was achieved in 48 h for large waste PCBs
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