Overexpression of SIRT1 in Mouse Forebrain Impairs Lipid/Glucose Metabolism and Motor Function

SIRT1 plays crucial roles in glucose and lipid metabolism, and has various functions in different tissues including brain. The brain-specific SIRT1 knockout mice display defects in somatotropic signaling, memory and synaptic plasticity. And the female mice without SIRT1 in POMC neuron are more sensitive to diet-induced obesity. Here we created transgenic mice overexpressing SIRT1 in striatum and hippocampus under the control of CaMKIIα promoter. These mice, especially females, exhibited increased fat accumulation accompanied by significant upregulation of adipogenic genes in white adipose tissue. Glucose tolerance of the mice was also impaired with decreased Glut4 mRNA levels in muscle. Moreover, the SIRT1 overexpressing mice showed decreased energy expenditure, and concomitantly mitochondria-related genes were decreased in muscle. In addition, these mice showed unusual spontaneous physical activity pattern, decreased activity in open field and rotarod performance. Further studies demonstrated that SIRT1 deacetylated IRS-2, and upregulated phosphorylation level of IRS-2 and ERK1/2 in striatum. Meanwhile, the neurotransmitter signaling in striatum and the expression of endocrine hormones in hypothalamus and serum T3, T4 levels were altered. Taken together, our findings demonstrate that SIRT1 in forebrain regulates lipid/glucose metabolism and motor function

Detecting and Mitigating Hallucinations in Multilingual Summarisation

Hallucinations pose a significant challenge to the reliability of neural models for abstractive summarisation. While automatically generated summaries may be fluent, they often lack faithfulness to the original document. This issue becomes even more pronounced in low-resource settings, such as cross-lingual transfer. With the existing faithful metrics focusing on English, even measuring the extent of this phenomenon in cross-lingual settings is hard. To address this, we first develop a novel metric, mFACT, evaluating the faithfulness of non-English summaries, leveraging translation-based transfer from multiple English faithfulness metrics. We then propose a simple but effective method to reduce hallucinations with a cross-lingual transfer, which weighs the loss of each training example by its faithfulness score. Through extensive experiments in multiple languages, we demonstrate that mFACT is the metric that is most suited to detect hallucinations. Moreover, we find that our proposed loss weighting method drastically increases both performance and faithfulness according to both automatic and human evaluation when compared to strong baselines for cross-lingual transfer such as MAD-X. Our code and dataset are available at https://github.com/yfqiu-nlp/mfact-summ

Aplicación de algoritmos de búsqueda en la optimización de caminos de coste mínimo en grafos de decisión

Debido a la gran cantidad de estaciones de metro que hay en Comunidad de Madrid, para las personas que no utilizan coches y que tienen que ir a trabajar lejos, es muy importante encontrar una ruta óptima entre su estación de partida y su estación de meta. El presente proyecto consiste en desarrollar una aplicación de toma de decisión en un mapa para desplazarse entre estaciones de ese mapa. Se ha elegido el mapa de metro de la Comunidad de Madrid como el mapa por defecto y, el algoritmo A* para determinar la ruta óptima entre estación de partida y estación de meta. El mapa de metro de Madrid contiene 241 estaciones (no están incluidas las estaciones de metro ligero), el usuario de la aplicación elige una estación de partida y una de meta, como resultado la aplicación muestra una ruta óptima al usuario. Una vez implementado la función de cálculo de la ruta óptima entre las estaciones de metro de Madrid, se ha hecho que aparte del mapa de metro de la Comunidad de Madrid, la aplicación permite al usuario introducir cualquier otro mapa al sistema, siempre y cuando introduzca también las informaciones de dicho mapa (nombre, coordenadas, líneas…), y que deja al usuario calcular la ruta óptima entre las estaciones de dicho mapa.---ABSTRACT---Due to the large number of metro stations that are in Community of Madrid, for people who do not use cars and they must go to work far, it is very important to find an optimal path between his origin station and his destiny station. The present project consists of developing an application of pathfinding on a map to move between stations of that map. The metro map of the Community of Madrid has been chosen as the default map and the algorithm A * to determine the optimal path between the origin station and the destiny station. The chosen map contains 241 stations (light rail stations are not included), the user of the application chooses an origin station and a destiny station, as a result the application shows an optimal path to the user. Once the function of calculating the optimal path between Madrid's metro stations has been successful implemented, apart from the metro map of the Community of Madrid, the application allows the user to introduce any other map to the system, as long as it also introduces the information of the map (name, coordinates, lines ...), and that allows the user to calculate the optimal path between the stations of the map

Aplicación de algoritmos de búsqueda en la optimización de caminos de coste mínimo en grafos de decisión

Debido a la gran cantidad de estaciones de metro que hay en Comunidad de Madrid, para las personas que no utilizan coches y que tienen que ir a trabajar lejos, es muy importante encontrar una ruta óptima entre su estación de partida y su estación de meta. El presente proyecto consiste en desarrollar una aplicación de toma de decisión en un mapa para desplazarse entre estaciones de ese mapa. Se ha elegido el mapa de metro de la Comunidad de Madrid como el mapa por defecto y, el algoritmo A* para determinar la ruta óptima entre estación de partida y estación de meta. El mapa de metro de Madrid contiene 241 estaciones (no están incluidas las estaciones de metro ligero), el usuario de la aplicación elige una estación de partida y una de meta, como resultado la aplicación muestra una ruta óptima al usuario. Una vez implementado la función de cálculo de la ruta óptima entre las estaciones de metro de Madrid, se ha hecho que aparte del mapa de metro de la Comunidad de Madrid, la aplicación permite al usuario introducir cualquier otro mapa al sistema, siempre y cuando introduzca también las informaciones de dicho mapa (nombre, coordenadas, líneas…), y que deja al usuario calcular la ruta óptima entre las estaciones de dicho mapa.---ABSTRACT---Due to the large number of metro stations that are in Community of Madrid, for people who do not use cars and they must go to work far, it is very important to find an optimal path between his origin station and his destiny station. The present project consists of developing an application of pathfinding on a map to move between stations of that map. The metro map of the Community of Madrid has been chosen as the default map and the algorithm A * to determine the optimal path between the origin station and the destiny station. The chosen map contains 241 stations (light rail stations are not included), the user of the application chooses an origin station and a destiny station, as a result the application shows an optimal path to the user. Once the function of calculating the optimal path between Madrid's metro stations has been successful implemented, apart from the metro map of the Community of Madrid, the application allows the user to introduce any other map to the system, as long as it also introduces the information of the map (name, coordinates, lines ...), and that allows the user to calculate the optimal path between the stations of the map

The model of direct relative orientation with seven constraints for geological landslides measurement and 3D reconstruction

With the development of computer vision and high-precision 3D model reconstruction, used for the measurement and 3D reconstruction of the geological landslides, acquiring a high-precision relative orientation basing multiple images is crucial and the key point to ensuring and improving the accuracy of 3D model and space position. Currently, the conventional relative orientation model includes five independent parameters. For the linear relative orientation model, there are nine parameters to construct the linear space geometric relationship between the imaging and space point. To eliminate the impact of more parameterization and improve the accuracy and stability of solved parameters for the conventional direct relative orientation model, a new relative orientation model with seven constraints is proposed and validated in this paper. The additional constraints are derived from the orthogonal property of the rotation matrix of a stereo imaging pair and associated with the least squares adjustment to obtain a high-precision result of the relative orientation. Through the accuracy assessment using space position, it is revealed that the new proposed model is more advantage for the conventional type of direct relative orientation, especially at 3D model reconstruction and close range photogrammetric and applications for the geological landslides measurement. El modelo de orientación relativa directa con siete restricciones para la medida de deslizamientos de tierra y reconstrucción tridimensional ResumenCon el desarrollo del entorno computacional y la alta precisión del modelo de reconstrucción tridimensional, utilizados para la medida y reconstrucción de desprendimientos geológicos, es crucial la obtención de la orientación relativa de alta precisión basada en imágenes múltiples y es el punto clave para asegurar y mejorar la exactitud del modelo 3D y la posición espacial. Actualmente el modelo de orientación relativa incluye cinco parámetros independientes. En el modelo linear de orientación relativa hay nueve parámetros para construir la relación geométrica espacial linear entre el sondeo y la posición espacial. Para eliminar el impacto de más parametrización y mejorar la exactitud y la estabilidad de los parámetros resueltos el modelo de orientación relativa convencional, este artículo propone y valida un nuevo modelo de orientación relativa con siete restricciones. Las restricciones adicionales se derivan de la propiedad ortogonal de la matriz de rotación de la imagen estéreo y se asocian con el ajuste de los cuadrados mínimos para obtener un resultado de alta precisión de la orientación relativa. Al medir la exactitud con la posición espacial se revela que el nuevo modelo propuesto tiene más ventajas que aquel de orientación relativa directa, especialmente en el modelo de reconstrucción 3D y en las aplicaciones fotográmetricas de rango cercano para la evaluación de desprendimientos geológicos

The model of direct relative orientation with seven constraints for geological landslides measurement and 3D reconstruction

With the development of computer vision and high-precision 3D model reconstruction, used for the measurement and 3D reconstruction of the geological landslides, acquiring a high-precision relative orientation basing multiple images is crucial and the key point to ensuring and improving the accuracy of 3D model and space position. Currently, the conventional relative orientation model includes five independent parameters. For the linear relative orientation model, there are nine parameters to construct the linear space geometric relationship between the imaging and space point. To eliminate the impact of more parameterization and improve the accuracy and stability of solved parameters for the conventional direct relative orientation model, a new relative orientation model with seven constraints is proposed and validated in this paper. The additional constraints are derived from the orthogonal property of the rotation matrix of a stereo imaging pair and associated with the least squares adjustment to obtain a high-precision result of the relative orientation. Through the accuracy assessment using space position, it is revealed that the new proposed model is more advantage for the conventional type of direct relative orientation, especially at 3D model reconstruction and close range photogrammetric and applications for the geological landslides measurement.   El modelo de orientación relativa directa con siete restricciones para la medida de deslizamientos de tierra y reconstrucción tridimensional   Resumen Con el desarrollo del entorno computacional y la alta precisión del modelo de reconstrucción tridimensional, utilizados para la medida y reconstrucción de desprendimientos geológicos, es crucial la obtención de la orientación relativa de alta precisión basada en imágenes múltiples y es el punto clave para asegurar y mejorar la exactitud del modelo 3D y la posición espacial. Actualmente el modelo de orientación relativa incluye cinco parámetros independientes. En el modelo linear de orientación relativa hay nueve parámetros para construir la relación geométrica espacial linear entre el sondeo y la posición espacial. Para eliminar el impacto de más parametrización y mejorar la exactitud y la estabilidad de los parámetros resueltos el modelo de orientación relativa convencional, este artículo propone y valida un nuevo modelo de orientación relativa con siete restricciones. Las restricciones adicionales se derivan de la propiedad ortogonal de la matriz de rotación de la imagen estéreo y se asocian con el ajuste de los cuadrados mínimos para obtener un resultado de alta precisión de la orientación relativa. Al medir la exactitud con la posición espacial se revela que el nuevo modelo propuesto tiene más ventajas que aquel de orientación relativa directa, especialmente en el modelo de reconstrucción 3D y en las aplicaciones fotográmetricas de rango cercano para la evaluación de desprendimientos geológicos

Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass

Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates