First survey of Wolf-Rayet star populations over the full extension of nearby galaxies observed with CALIFA

The search of extragalactic regions with conspicuous presence of Wolf-Rayet (WR) stars outside the Local Group is challenging task due to the difficulties in detecting their faint spectral features. In this exploratory work, we develop a methodology to perform an automated search of WR signatures through a pixel-by-pixel analysis of integral field spectroscopy (IFS) data belonging to the Calar Alto Legacy Integral Field Area survey, CALIFA. This technique allowed us to build the first catalogue of Wolf-Rayet rich regions with spatially-resolved information, allowing to study the properties of these complexes in a 2D context. The detection technique is based on the identification of the blue WR bump (around He II 4686 {\AA}, mainly associated to nitrogen-rich WR stars, WN) and the red WR bump (around C IV 5808 {\AA} and associated to carbon-rich WR stars, WC) using a pixel-by-pixel analysis. We identified 44 WR-rich regions with blue bumps distributed in 25 galaxies of a total of 558. The red WR bump was identified only in 5 of those regions. We found that the majority of the galaxies hosting WR populations in our sample are involved in some kind of interaction process. Half of the host galaxies share some properties with gamma-ray burst (GRB) hosts where WR stars, as potential candidates to being the progenitors of GRBs, are found. We also compared the WR properties derived from the CALIFA data with stellar population synthesis models, and confirm that simple star models are generally not able to reproduce the observations. We conclude that other effects, such as the binary star channel (which could extend the WR phase up to 10 Myr), fast rotation or other physical processes that causes the loss of observed Lyman continuum photons, are very likely affecting the derived WR properties, and hence should be considered when modelling the evolution of massive stars.Comment: 33 pages, accepted for publication in A&

Aperture effects on the oxygen abundance determinations from CALIFA data

This paper aims at providing aperture corrections for emission lines in a sample of spiral galaxies from the Calar Alto Legacy Integral Field Area Survey (CALIFA) database. In particular, we explore the behavior of the log([OIII]5007/Hbeta)/([NII]6583/Halpha) (O3N2) and log[NII]6583/Halpha (N2) flux ratios since they are closely connected to different empirical calibrations of the oxygen abundances in star forming galaxies. We compute median growth curves of Halpha, Halpha/Hbeta, O3N2 and N2 up to 2.5R_50 and 1.5 disk R_eff. The growth curves simulate the effect of observing galaxies through apertures of varying radii. The median growth curve of the Halpha/Hbeta ratio monotonically decreases from the center towards larger radii, showing for small apertures a maximum value of ~10% larger than the integrated one. The median growth curve of N2 shows a similar behavior, decreasing from the center towards larger radii. No strong dependence is seen with the inclination, morphological type and stellar mass for these growth curves. Finally, the median growth curve of O3N2 increases monotonically with radius. However, at small radii it shows systematically higher values for galaxies of earlier morphological types and for high stellar mass galaxies. Applying our aperture corrections to a sample of galaxies from the SDSS survey at 0.02<=z<=0.3 shows that the average difference between fiber-based and aperture corrected oxygen abundances, for different galaxy stellar mass and redshift ranges, reaches typically to ~11%, depending on the abundance calibration used. This average difference is found to be systematically biased, though still within the typical uncertainties of oxygen abundances derived from empirical calibrations. Caution must be exercised when using observations of galaxies for small radii (e.g. below 0.5R_eff) given the high dispersion shown around the median growth curves.Comment: Accepted for publication in Ap

Fault diagnosis in industrial process by using LSTM and an elastic net

[EN] Fault diagnosis is important for industrial processes because it permits to determine the necessity of emergency stops in a process and/or to propose a maintenance plan. Two strategies for fault diagnosis are compared in this work. On the one hand, the data are preprocessed using the independent components analysis for dimension reduction, then the wavelet transform is used in order to highlight the faulty signals, with this information an artificial neural network was fed. On the other hand, the second strategy, the main contribution of this work, is the implementation of a long short term memory. This memory is fed with the most representative variables selected by an elastic net to use both, the L1 and L2 norms. These strategies are applied in the Tennessee Eastman process, a benchmark widely used for fault diagnosis. The fault isolation had better results than those reported in the literature.[ES] El diagnóstico de fallas es importante en los procesos industriales, ya que permite determinar si es necesario detener el proceso en operación y/o proponer un plan de mantenimiento. En el presente trabajo se comparan dos estrategias para diagnosticar fallas. La primera realiza un preprocesamiento de datos usando el análisis de componentes independientes para reducir la dimensión de los datos, posteriormente, se emplea la transformada wavelet para resaltar las señales de falla, con esta información se alimenta una red neuronal artificial. Por su parte, la segunda estrategia, principal contribución de este trabajo, usa una memoria de corto y largo plazo. Esta memoria es alimentada por las variables más significativas seleccionadas mediante una red elástica para usar tanto la norma $L_1$ como la $L_2$. Como ejemplo de aplicación se utilizó el proceso químico Tennessee Eastman, un proceso ampliamente usado en el diagnóstico de fallas. El aislamiento de fallas mostró mejores resultados con respecto a los reportados en la literatura.Márquez-Vera, MA.; López-Ortega, O.; Ramos-Velasco, LE.; Ortega-Mendoza, RM.; Fernández-Neri, BJ.; Zúñiga-Peña, NS. (2021). Diagnóstico de fallas mediante una LSTM y una red elástica. Revista Iberoamericana de Automática e Informática industrial. 18(2):164-175. https://doi.org/10.4995/riai.2020.13611OJS164175182Adewole, A., Tzoneva, R., Behardien, S., 2016. Distribution network fault section identification and fault location using wavelet entropy and neural networks. Applied Soft Computing 46, 296-306. https://doi.org/10.1016/j.asoc.2016.05.013Alkaya, A., Eker, I., 2011. Variance sensitive adaptive threshold-based PCA method for fault detection with experimental application. ISA Transactions 50, 287-302. https://doi.org/10.1016/j.isatra.2010.12.004Barakat, S., Eteiba, M., Wahba, W., 2014. Fault location in underground cables using anfis nets and discrete wavelet transform. Journal of Electrical Systems and Information Technology 1, 198-211. https://doi.org/10.1016/j.jesit.2014.12.003Bathelt, A., Ricker, N., Jelali, M., 2015. Revision of the Tennessee Eastman process model. IFAC Papers-Online 48 (8), 309-314. https://doi.org/10.1016/j.ifacol.2015.08.199Boldt, F., Rauber, T., Varejao, F., October 2014. Evaluation of the extreme learning machine for automatic fault diagnosis of the Tennessee Eastman chemical process. In: IEEE (Ed.), Annual Conference of the IEEE Industrial Electronics Society. Vol. 40. Dallas, Texas, pp. 2551-2557. https://doi.org/10.1109/IECON.2014.7048865Chen, H., Tino, P., Yao, X., 2014. Cognitive fault diagnosis in Tennessee Eastman process using learning in the model space. Computers and Chemical Engineering 67, 33-42. https://doi.org/10.1016/j.compchemeng.2014.03.015Rodrigues, J., Filho, P., PeixotoJr., E., Kumar, A., deAlbuquerque, V., 2019. Classification of EEG signals to detect alcoholism using machine learning techniques. Pattern Recognition Letters 125, 140-149. https://doi.org/10.1016/j.patrec.2019.04.019Dixit, A., Majumdar, S., 2013. Comparative analysis of coiflet and daubechies wavelets using global threshold for image denoising. Intenational Journal of Advances in Engineering & Technology 6 (5), 2247-2252.Downs, J., Vogel, E., 1993. A plant-wide industrial process control problem. Computers and Chemical Engineering 17 (3), 245-255. https://doi.org/10.1016/0098-1354(93)80018-IFischer, T., Krauss, C., 2018. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research 270, 654-669. https://doi.org/10.1016/j.ejor.2017.11.054Gao, X., Hou, J., 2016. An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process. Neurocomputing 174, 906-911. https://doi.org/10.1016/j.neucom.2015.10.018Geng, Z., Li, Z., Han, Y., 2018. A new deep belief network based on RBM with glial chains. Information Sciences 463, 294-306. https://doi.org/10.1016/j.ins.2018.06.043Goodfellow, I., Bengio, Y., Courville, A., 2016. Deep Learning. MIT Press, United States of America, http://www.deeplearningbook.ogr.Han, L., Li, C., Guo, S., Su, X., 2015. Feature extraction method of bearing AE signal based on improved Fast-ICA and wavelet packet energy. Mechanical Systems and Signal Processing 62-63, 91-99. https://doi.org/10.1016/j.ymssp.2015.03.009Hastie, T., Tibshirani, R., Friedman, J., 2009. The elements of statistical learning: data mining, inference and prediction. Springer, New York. https://doi.org/10.1007/978-0-387-84858-7Hoang, D., Kang, H., 2019. A survey on deep learning based bearing fault diagnosis. Neurocomputing 335, 327-335. ttps://doi.org/10.1016/j.neucom.2018.06.078Hochreiter, S., Schmidhuber, J., 1997. Long short term memory. Neural Computation 9 (8), 1735-1780. ttps://doi.org/10.1162/neco.1997.9.8.1735Hyvärinen, A., Oja, E., 2000. Independent component analysis: Algorithms and applications. Neural Networks 13, 411-430. ttps://doi.org/10.1016/S0893-6080(00)00026-5Jing, C., Gao, X., Zhu, X., Lang, S., July 2014. Fault classificaction on Tennessee Eastman process: PCA and SVM. In: IEEE (Ed.), Intenational Conference on Mecatronics and Control. Jinzhou, China, pp. 2194-2197. https://doi.org/10.1109/ICMC.2014.7231958Jung, C., Kim, K., Lee, J., Klockl, B., 2007. Wavelet and neuro-fuzzy based fault location for combined transmission systems. Energy Systems 29, 445-454. https://doi.org/10.1016/j.ijepes.2006.11.003Kandula, V. K., 2011. Fault detection in process control plants using principal component analysis. Master's thesis, Louisiana State University, Department of Electrical Engineering.Karpenko, M., Sepehri, N., Octubre 2001. A neural network based fault detection and identification scheme for pneumatic process control valves. In: IEEE (Ed.), International Conference on Systems, Man and Cybernetics. Tucson, USA, pp. 93-98. https://doi.org/10.1109/ICSMC.2001.969794Khakipour, M., Safavi, A., Setoodeh, P., 2017. Bearing fault diagnosis with morphological gradient wavelet. Journal of the Franklin Institute 354, 2465-2476. https://doi.org/10.1016/j.jfranklin.2016.11.013Kuang, T., Yang, Z., Yao, Y., 2015. Multivariate fault isolation via variable selection in discriminant analysis. Journal of Process Control 35, 30-40. https://doi.org/10.1016/j.isatra.2017.06.014Kumar, R., Bansal, H., 2019. Hardware in the loop implementation of wavelet based strategy in shuntactive powerfilter to mitigate power quality issues. Electric Power Systems Research 169, 92-104. https://doi.org/10.1016/j.epsr.2019.01.001Lau, C., Ghosh, K., Hussain, M., Hassan, C. C., 2013. Fault disgnosis of Tennessee Eastman process with multi-scale PCA and ANFIS. Chemom. Intell. Lab. Syst. 120, 1-14. https://doi.org/10.1016/j.chemolab.2012.10.005Lee, J., Yoo, C., Lee, I., 2004. Statistical process monitoring with independent component analysis. Journal of Process Control 14 (5), 467-485. https://doi.org/10.1016/j.jprocont.2003.09.004Lei, J., Liu, C., Jiang, D., 2019. Fault diagnosis of wind turbine based on long short-term memory networks. Renewable Energy 133, 422-432. https://doi.org/10.1016/j.renene.2018.10.031Li, W., Monti, A., Ponci, F., 2014. Fault detection and classification in medium voltage DC shipboard power systems with wavelets and artificial neural networks. IEEE Transactions on Instrumentation and Measurement 63 (11), 2651-2665. https://doi.org/10.1109/TIM.2014.2313035Liang, P., Deng, C.,Wu, J., Yang, Z., Zhu, J., Zhang, Z., 2019. Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform. Computers in Industry 113, 103132. https://doi.org/10.1016/j.compind.2019.103132Lin, J., Zhang, A., 2005. Fault feature separation using wavelet-ICA filter. NDT&E International 38, 421-427. https://doi.org/10.1016/j.ndteint.2004.11.005Linker, R., Gutman, P., Seginer, I., 2002. Observer-based robust failure detection and isolation in greenhouses. Control Engineering Practice 10 (5), 519- 531. https://doi.org/10.1016/S0967-0661(02)00002-3Lou, W., Loparo, K., 2004. Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mechanical Systems and Signal Processing 18, 1077-1095. https://doi.org/10.1016/S0888-3270(03)00077-3Lv, F.,Wen, C., Bao, Z., Liu, M., 2016. Fault diagnosis based on deep learning. In: AACC (Ed.), American Control Conference. Boston, USA, pp. 6851-6856. https://doi.org/10.1109/ACC.2016.7526751Lv, F., Wen, C., Liu, M., Bao, Z., 2017. Weighted time series fault diagnosis based on a staked sparce autoencoder. Journal of Chemometrics 31, 16 pages. https://doi.org/10.1002/cem.2912Lv, F., Fan, X., Wen, C., Bao, Z., 2018. Stacked sparse auto encoder network based multimode process monitoring. In: IEEE (Ed.), International Conference on Control Automation & Information Science. Hangzhou, China, pp. 227-232. https://doi.org/10.1109/ICCAIS.2018.8570618Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., Strintzis, M., 1998. ECG pattern recognition and classification using non-linear transfor mations and neural networks: A review. International Journal of Medical Informatics 52, 191-208. https://doi.org/10.1016/S1386-5056(98)00138-5Methnani, S., Lafont, F., Gautier, J., Damak, T., Toumi, A., 2013. Actuator and sensor fault detection, isolation and identification in nonlinear dynamical systems, with applications to a waste water treatment plant. Journal of Computer Engineering and Informatics 1 (4), 112-125. https://doi.org/10.1080/21642583.2014.888525Muñoz-Cobo, J., Mendizábal, R., Miquel, A., Berna, C., Escrivá, A., 2017. Use of the principles of maximum entropy and maximum relative entropy for the determination of uncertain parameter distributions in engineering applications. Entropy 19, 486, 37 pages. https://doi.org/10.3390/e19090486Nguyen, B., Quyen, A., Nguyen, P., Ton, T., July 2017. Wavelet-based neural network for recognition of faults at nhabe power substation of the vietnam power system. In: IEEE (Ed.), International Conference on System Science and Engineering. Ho Chi Minh City, Vietnam, pp. 165-168. https://doi.org/10.1109/ICSSE.2017.8030858Ojeda-González, A., Mendes-Jr., O., Oliveira-Domingues, M., Menconi, V., 2014. Daubechies wavelet coeffcients: a tool to study interplanetary magnetic field fluctuations. Geof'ısica Internacional 53 (2), 101-115. https://doi.org/10.1016/S0016-7169(14)71494-1Oliveira, J., Pontes, K., Santori, I., Embirucu, M., 2017. Fault detection and diagnosis in dynamic systems using weightless neural networks. Expert Systems With Applications 84, 200-219. https://doi.org/10.1016/j.eswa.2017.05.020Patan, K., 2008. Artificial neural networks for the modelling and fault diagnosis of technical process. Lecture Notes in Control and Information Sciences. Springer, India.Rafiee, J., Rafiee, M., Tse, P., 2010. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications 37, 4568-4579. https://doi.org/10.1016/j.eswa.2009.12.051Ramos-Velasco, L., Ramos-Fernández, J., Islar-Gómez, O., Espejel-Rivera, M., García-Lamont, J., Márquez-Vera, M., 2013. Identificación y control wavenet de un motor de ca. Revista Iberoamericana de Automática e Informática Industrial 10, 269-278. https://doi.org/10.1016/j.riai.2013.05.002Rato, T., Reis, M., 2013. Defining the structure of DPCA models and its impact on process monitoring and prediction ctivities. Chemometrics and Intelligent Laboratory Systems 125, 74-86. https://doi.org/10.1016/j.chemolab.2013.03.009Rockinger, M., Jondeau, E., 2002. Entropy densities with an application to autoregressive conditional skewness and kurtosis. Journal of Econometrics 106, 119-142. https://doi.org/10.1016/S0304-4076(01)00092-6Salahschoor, K., Kiasi, F., July 2008. On-line process monitoring based on wavelet-ICA methodology. In: IFAC (Ed.), Proceedings of the 17th World Congress. Seul- Korea, pp. 6-11. https://doi.org/10.3182/20080706-5-KR-1001.01253Salahshoor, K., Khoshro, M., Kordestani, M., 2011. Fault detection and diagnosis of an industrial steam turbine using a distributed configuration of adaptive neuro-fuzzy inference systems. Simulation Modelling Practice and Theory 19, 1280-1293. https://doi.org/10.1016/j.simpat.2011.01.005Sharif, I., Khare, S., 2014. Comparative analysis of Haar and Daubechies wavelet for hyper spectral image classification. In: Commission, I. T. (Ed.), VIII Symposium of The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science. Hyderabad-India, pp. 937-941. https://doi.org/10.5194/isprsarchives-XL-8-937-2014Smirnov, E., Timoshenko, D., Adrianov, S., 2014. Comparison of regularization methods for imagenet classification with deep convolutional neural networks. AASRI Procedia 6, 89-94. https://doi.org/10.1016/j.aasri.2014.05.013Sobhani-Tehrani, E., Khorasani, K., 2009. Fault diagnosis of nonlinear systems using a hybrid approach. Fault detetion and diagnosis. Springer, Berlin, Ch. 2, pp. 22-49. https://doi.org/10.1007/978-0-387-92907-1_2Tayarani-Bathaie, S., Vanini, Z., Khorasani, K., 2014. Dynamic neural networkbased fault diagnosis of gas turbine engines. Neurocomputing 125, 153-165. https://doi.org/10.1016/j.neucom.2012.06.050Zvokelj, M., Zupan, S., Prebil, I., 2016. EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. Journal of Sound and Vibration 26, 394-423. https://doi.org/10.1016/j.jsv.2016.01.046Wang, X., Qin, Y., Wang, Y., Xiang, S., Chen, H., 2019. ReLTanh: An activation function with vanishing gradient resistance for SAE-based DNNs and its application to rotating machinery fault diagnosis. Neurocomputing 363, 88-98. https://doi.org/10.1016/j.neucom.2019.07.017Wu, F., Tong, F., Yang, Z., 2016. EMGdi signal enhancement based on ICA decomposition and wavelet transform. Applied Soft Computing 43, 561-571. https://doi.org/10.1016/j.asoc.2016.03.002Wu, J., Hsu, C., Wu, G., 2009. Fault gear identification and classification using discrete wavelet transform and adaptive neuro-fuzzy inference. Expert Systems with Applications 36, 6244-6255. https://doi.org/10.1016/j.eswa.2008.07.023Wu, Q., Law, R., Wu, S., 2011. Fault diagnosis of car assembly line based on fuzzy wavelet kernel support vector classifier machine and modified genetic algorithm. Expert Systems with Applications 38, 9096-9104. https://doi.org/10.1016/j.eswa.2010.12.109Wu, H., Zhao., Jinsong, 2018. Deep convolutional neural network model based chemical process fault diagnosis. Computers and Chemical Engineering 115, 185-197. https://doi.org/10.1016/j.compchemeng.2018.04.009Xiao, C., Chen, N., Hu, C., Wang, K., Gong, J., Chen, Z., 2019. Short and midterm sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sensing of Environment 233, 111358. https://doi.org/10.1016/j.rse.2019.111358Xie, D., Bai, L., December 2015. A hierarchical deep neural network for fault diagnosis on Tennessee-Eastman process. In: IEEE (Ed.), International Conference on Machine Learning and Applications. Vol. 14. Miami, USA, pp. 745-748. https://doi.org/10.1109/ICMLA.2015.208Yan, R., Gao, R., Chen, X., 2014. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing 351, 4555-4569. https://doi.org/10.1016/j.sigpro.2013.04.015Yan, Z., Yao, Y., 2015. Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO). Chemometrics and Intelligent Laboratory Systems 146, 136-146. https://doi.org/10.1016/j.chemolab.2015.05.019Yao, G., Lei, T., Zhong, J., 2019. A review of convolutional-neural-networkbased action recognition. Pattern Recognition Letters 118, 14-22. https://doi.org/10.1016/j.patrec.2018.05.018Yin, S., Ding, S., Haghani, A., Hao, H., Zhang, P., 2012. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process. Journal of Process Control 22, 1567-1581. https://doi.org/10.1016/j.jprocont.2012.06.009Zhang, Q., Yang, L., Chen, Z., Li, P., 2018. A survey on deep learning for big data. Information Fusion 42, 146-157. https://doi.org/10.1016/j.inffus.2017.10.006Zhang, X., Polycarpou, M., Parisini, T., 2002. A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems. IEEE Transactions on Automatic Control 47 (4), 576-593. https://doi.org/10.1109/9.995036Zhang, Y., Zhang, L., Zhang, H., 2012. Fault detection for industrial processes. Mathematical Problems in Engineering 2012, 18 pages. https://doi.org/10.1155/2012/757828Zhang, Z., Zhao, J., 2017. A deep belief network based fault diagnosis model for complex chemical process. Computers and Chemical Engineering 107, 395-407. https://doi.org/10.1016/j.compchemeng.2017.02.041Zhao, H., 2018. Neural component analysis for fault detection. Chemometrics and Intelligent Laboratory Systems 176, 11-21. https://doi.org/10.1016/j.chemolab.2018.02.001Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., Gao, R., 2019. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing 115, 213-237. https://doi.org/10.1016/j.ymssp.2018.05.050Zheng, J., Huang, W., Wang, Z., Liang, J., 2019. Mutual information-based sparse multiblock dissimilarity method for incipient fault detection and diagnosis in plant-wide process. Journal of Process Control 83, 63-76. https://doi.org/10.1016/j.jprocont.2019.09.00

Imprints of galaxy evolution on H ii regions Memory of the past uncovered by the CALIFA survey

H ii regions in galaxies are the sites of star formation and thus particular places to understand the build-up of stellar mass in the universe. The line ratios of this ionized gas are frequently used to characterize the ionization conditions. We use the Hii regions catalogue from the CALIFA survey (~5000 H ii regions), to explore their distribution across the classical [OIII]/Hbeta vs. [NII]/Halpha diagnostic diagram, and how it depends on the oxygen abundance, ionization parameter, electron density, and dust attenuation. We compared the line ratios with predictions from photoionization models. Finally, we explore the dependences on the properties of the host galaxies, the location within those galaxies and the properties of the underlying stellar population. We found that the location within the BPT diagrams is not totally predicted by photoionization models. Indeed, it depends on the properties of the host galaxies, their galactocentric distances and the properties of the underlying stellar population. These results indicate that although H ii regions are short lived events, they are affected by the total underlying stellar population. One may say that H ii regions keep a memory of the stellar evolution and chemical enrichment that have left an imprint on the both the ionizing stellar population and the ionized gasComment: 18 pages, 8 figures, accepted for publishing in A&

Stellar Population gradients in galaxy discs from the CALIFA survey

While studies of gas-phase metallicity gradients in disc galaxies are common, very little has been done in the acquisition of stellar abundance gradients in the same regions. We present here a comparative study of the stellar metallicity and age distributions in a sample of 62 nearly face-on, spiral galaxies with and without bars, using data from the CALIFA survey. We measure the slopes of the gradients and study their relation with other properties of the galaxies. We find that the mean stellar age and metallicity gradients in the disc are shallow and negative. Furthermore, when normalized to the effective radius of the disc, the slope of the stellar population gradients does not correlate with the mass or with the morphological type of the galaxies. Contrary to this, the values of both age and metallicity at $\sim$2.5 scale-lengths correlate with the central velocity dispersion in a similar manner to the central values of the bulges, although bulges show, on average, older ages and higher metallicities than the discs. One of the goals of the present paper is to test the theoretical prediction that non-linear coupling between the bar and the spiral arms is an efficient mechanism for producing radial migrations across significant distances within discs. The process of radial migration should flatten the stellar metallicity gradient with time and, therefore, we would expect flatter stellar metallicity gradients in barred galaxies. However, we do not find any difference in the metallicity or age gradients in galaxies with without bars. We discuss possible scenarios that can lead to this absence of difference.Comment: 24 pages, 17 figures, accepted for publication in A&

Tetrahydropyrazolo[1,5-a]Pyrimidine-3-Carboxamide and N-Benzyl-6′,7′-Dihydrospiro[Piperidine-4,4′-Thieno[3,2-c]Pyran] analogues with bactericidal efficacy against Mycobacterium tuberculosis targeting MmpL3

Mycobacterium tuberculosis is a major human pathogen and the causative agent for the pulmonary disease, tuberculosis (TB). Current treatment programs to combat TB are under threat due to the emergence of multi-drug and extensively-drug resistant TB. As part of our efforts towards the discovery of new anti-tubercular leads, a number of potent tetrahydropyrazolo[1,5-a]pyrimidine-3-ca​rboxamide(THPP) and N-benzyl-6′,7′-dihydrospiro[piperidine-4,​4′-thieno[3,2-c]pyran](Spiro) analogues were recently identified against Mycobacterium tuberculosis and Mycobacterium bovis BCG through a high-throughput whole-cell screening campaign. Herein, we describe the attractive in vitro and in vivo anti-tubercular profiles of both lead series. The generation of M. tuberculosis spontaneous mutants and subsequent whole genome sequencing of several resistant mutants identified single mutations in the essential mmpL3 gene. This ‘genetic phenotype’ was further confirmed by a ‘chemical phenotype’, whereby M. bovis BCG treated with both the THPP and Spiro series resulted in the accumulation of trehalose monomycolate. In vivo efficacy evaluation of two optimized THPP and Spiro leads showed how the compounds were able to reduce >2 logs bacterial cfu counts in the lungs of infected mice

Campaña de sensibilización para el proyecto Ciudad Amigable con los mayores en Guadalajara, Jalisco: Equipo de producción

El presente reporte se enfoca en la creación de materiales audiovisuales para transformar la percepción actual de las y los adultos mayores, esto a partir de un eje rector que fue la diversidad en las formas de envejecer. El objetivo fue crear una campaña de sensibilización a partir de contenidos que se habían hecho en periodos anteriores del PAP, y con estos, mostrar una imagen digna de las y los adultos mayores para generar una transformación de los estereotipos que giran en torno a ellos. El trabajo se realizó a partir de un diagnóstico para conocer las formas en que se estaban siendo representados los adultos mayores en distintos medios de comunicación; y a partir de ahí se identificaron los recursos y elementos que ayudaban a generar una imagen digna e íntegra de ellos y los que no para usar como referencia de lo que se debía hacer y lo que no. Dentro de los resultados, encontramos distintos productos audiovisuales y gráficos que forman parte de una campaña de sensibilización y en los que se construye una imagen digna de los adultos mayores teniendo en cuenta las distintas formas de envejecer.ITESO, A.C

Dependence of the ttˉt\bar{t} production cross section on the transverse momentum of the top quark

We present a measurement of the differential cross section for $t\bar{t}$ events produced in $p\bar{p}$ collisions at $\sqrt{s}=1.96$ TeV as a function of the transverse momentum ($p_T$) of the top quark. The selected events contain a high-$p_T$ lepton ($\ell$), four or more jets, and a large imbalance in $p_T$, and correspond to 1 fb${}^{-1}$ of integrated luminosity recorded with the D0 detector. Each event must have at least one candidate for a $b$ jet. Objects in the event are associated through a constrained kinematic fit to the $t\bar{t}\to WbW\bar{b} \to \ell\nu b q\bar{q}'\bar{b}$ process. Results from next-to-leading-order perturbative QCD calculations agree with the measured differential cross section. Comparisons are also provided to predictions from Monte Carlo event generators using QCD calculations at different levels of precision.Comment: 8 pages, 6 figures, 4 tables, updated to reflect the published versio

Double parton interactions in photon+3 jet events in ppbar collisions sqrt{s}=1.96 TeV

We have used a sample of photon+3 jets events collected by the D0 experiment with an integrated luminosity of about 1 fb^-1 to determine the fraction of events with double parton scattering (f_DP) in a single ppbar collision at sqrt{s}=1.96 TeV. The DP fraction and effective cross section (sigma_eff), a process-independent scale parameter related to the parton density inside the nucleon, are measured in three intervals of the second (ordered in pT) jet transverse momentum pT_jet2 within the range 15 < pT_jet2 < 30 GeV. In this range, f_DP varies between 0.23 < f_DP < 0.47, while sigma_eff has the average value sigma_eff_ave = 16.4 +- 0.3(stat) +- 2.3(syst) mb.Comment: 15 pages, 13 figure