Agresividad en inculpados por casos de feminicidio y tentativa de feminicidio en Piura, 2018

Esta investigación tuvo como objetivo principal, analizar si existen o no diferencias a nivel de agresividad en inculpados por casos de feminicidio y tentativa de feminicidio en Piura, 2018. La metodología fue de tipo descriptivo comparativa, además se trabajó con una muestra de 80 inculpados, en donde 32 inculpados eran del delito de feminicidio y 48 inculpados eran del delito de tentativa de feminicidio. El instrumento utilizado para la investigación fue el Cuestionario de Agresión de Buss y Perry, adaptado por Matalinares, Reyes y Yarigaño (2012), para su evaluación se divide en cuatro subescalas, entre ellas encontramos: agresividad física, agresividad verbal, hostilidad e ira. Concluyendo que ambas unidades muestrales difieren en porcentajes a sus medianas, demostrando la significancia estadística, siendo de igual manera las dimensiones, excepto la ira al ser una dimensión de tipo emocional, por ende, en ambas muestras existen diferencias a nivel de agresividad.This research had as main objective, to analyze whether or not there are differences in the level of aggressiveness in those accused of cases of feminicide and attempt of feminicide in Piura, 2018. The methodology was of a comparative descriptive type, in addition, we worked with a sample of 80 defendants, where 32 defendants were from the crime of femicide and 48 defendants were from the crime of attempted femicide. The instrument used for the investigation was the Buss and Perry Aggression Questionnaire, adapted by Matalinares, Reyes and Yarigaño (2012), for its evaluation it is divided into four subscales, among them we find: physical aggressiveness, verbal aggressiveness, hostility and anger. Concluding that both sample units differ in percentages from their medians, demonstrating statistical significance, the dimensions being the same, except anger as being an emotional type dimension, therefore, in both samples there are differences at the level of aggressiveness.Tesi

DIVERSIMAR Project: marine citizen science in the North and Northwest Iberian coast

Marine citizen science can play an important role in understanding the ocean responses to global change and other pressures to marine systems. Citizen science projects guide public participation combining research with environmental education and science divulgation [1, 4]. The DIVERSIMAR project (https://diversimar.cesga.es/) aims to register biodiversity data of the North and Northwest Iberian coast and is a way for science and society to interact and collaborate [3]. A system to integrate both the available scientific information (on distribution, biology and ecology of marine species) and the new information provided by volunteers has been designed. In a first step, volunteers contact directly the scientists providing photos, videos and any other information about their findings. Technological innovations such as smartphone devices equipped with cameras become a powerful tool for data collection because the images have associated metadata such as date and position [2]. In a second step, these records are verified, validated and stored in the project GIS database that can be consulted in the DIVERSIMAR Map Viewer (https://diversimar.cesga.es/visor/index.php). Different stakeholders, from scientists to citizens, and from fishermen to marine environmental organisations, can get involved in this citizen project. The wide-ranging observations on coastal flora and fauna (such as the occurrence and regularity of jellyfish blooms, the sporadic report of species that have never been observed in a region before, the apparition of invasive species, the presence of kelp forests or the sighting of protected species) allow to increase the temporal and spatial data acquisition and play an important role in monitoring the coastline and the intertidal zones. The information gathered by mapping habitats and by determination of abundance and distribution of native and invasive species demonstrate the scientific value of citizen monitoring to help managers to develop management plans and conservation strategies such as EU Marine Strategy framework Directive.Fundación Biodiversida

Mathematical modelling of Echinococcus multilocularis abundance in foxes in Zurich, Switzerland

Background In Europe, the red fox (Vulpes vulpes) is the main definitive host of Echinococcus multilocularis, the aetiological agent of a severe disease in humans called alveolar echinococcosis. The distribution of this zoonotic parasite among the fox population is remarkably aggregated with few heavily infected animals harbouring much of the parasite burdens and being responsible for most of the environmental parasitic egg contamination. Important research questions explored were: (i) spatial differences in parasite infection pressure related to the level of urbanization; (ii) temporal differences in parasite infection pressure in relation to time of the year; (iii) is herd immunity or an age-dependent infection pressure responsible for the observed parasite abundance; (iv) assuming E. multilocularis infection is a clumped process, how many parasites results from a regular infection insult. Methods By developing and comparing different transmission models we characterised the spatio-temporal variation of the infection pressure, in terms of numbers of parasites that foxes acquired after exposure per unit time, in foxes in Zurich (Switzerland). These included the variations in infection pressure with age of fox and season and the possible regulating effect of herd immunity on parasite abundance. Results The model fitting best to the observed data supported the existence of spatial and seasonal differences in infection pressure and the absence of parasite-induced host immunity. The periodic infection pressure had different amplitudes across urbanization zones with higher peaks during autumn and winter. In addition, the model indicated the existence of variations in infection pressure among age groups in foxes from the periurban zone. Conclusions These heterogeneities in infection exposure have strong implications for the implementation of targeted control interventions to lower the intensity of environmental contamination with parasite eggs and, ultimately, the infection risk to humans

ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation

[EN] Query-by-example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given an acoustic (spoken) query containing the term of interest as the input. This paper presents the systems submitted to the ALBAYZIN QbE STD 2016 Evaluation held as a part of the ALBAYZIN 2016 Evaluation Campaign at the IberSPEECH 2016 conference. Special attention was given to the evaluation design so that a thorough post-analysis of the main results could be carried out. Two different Spanish speech databases, which cover different acoustic and language domains, were used in the evaluation: the MAVIR database, which consists of a set of talks from workshops, and the EPIC database, which consists of a set of European Parliament sessions in Spanish. We present the evaluation design, both databases, the evaluation metric, the systems submitted to the evaluation, the results, and a thorough analysis and discussion. Four different research groups participated in the evaluation, and a total of eight template matching-based systems were submitted. We compare the systems submitted to the evaluation and make an in-depth analysis based on some properties of the spoken queries, such as query length, single-word/multi-word queries, and in-language/out-of-language queries.This work was partially supported by Fundacao para a Ciencia e Tecnologia (FCT) under the projects UID/EEA/50008/2013 (pluriannual funding in the scope of the LETSREAD project) and UID/CEC/50021/2013, and Grant SFRH/BD/97187/2013. Jorge Proenca is supported by the SFRH/BD/97204/2013 FCT Grant. This work was also supported by the Galician Government ('Centro singular de investigacion de Galicia' accreditation 2016-2019 ED431G/01 and the research contract GRC2014/024 (Modalidade: Grupos de Referencia Competitiva 2014)), the European Regional Development Fund (ERDF), the projects "DSSL: Redes Profundas y Modelos de Subespacios para Deteccion y Seguimiento de Locutor, Idioma y Enfermedades Degenerativas a partir de la Voz" (TEC2015-68172-C2-1-P) and the TIN2015-64282-R funded by Ministerio de Economia y Competitividad in Spain, the Spanish Government through the project "TraceThem" (TEC2015-65345-P), and AtlantTIC ED431G/04.Tejedor, J.; Toledano, DT.; Lopez-Otero, P.; Docio-Fernandez, L.; Proença, J.; Perdigão, F.; García-Granada, F.... (2018). ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation. EURASIP Journal on Audio, Speech and Music Processing. 1-25. https://doi.org/10.1186/s13636-018-0125-9S125Jarina, R, Kuba, M, Gubka, R, Chmulik, M, Paralic, M (2013). UNIZA system for the spoken web search task at MediaEval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 791–792).Ali, A, & Clements, MA (2013). Spoken web search using and ergodic hidden Markov model of speech. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 861–862).Buzo, A, Cucu, H, Burileanu, C (2014). SpeeD@MediaEval 2014: Spoken term detection with robust multilingual phone recognition. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 721–722).Caranica, A, Buzo, A, Cucu, H, Burileanu, C (2015). SpeeD@MediaEval 2015: Multilingual phone recognition approach to Query By Example STD. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 781–783).Kesiraju, S, Mantena, G, Prahallad, K (2014). IIIT-H system for MediaEval 2014 QUESST. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 761–762).Ma, M, & Rosenberg, A (2015). CUNY systems for the Query-by-Example search on speech task at MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 831–833).Takahashi, J, Hashimoto, T, Konno, R, Sugawara, S, Ouchi, K, Oshima, S, Akyu, T, Itoh, Y (2014). An IWAPU STD system for OOV query terms and spoken queries. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 384–389).Makino, M, & Kai, A (2014). Combining subword and state-level dissimilarity measures for improved spoken term detection in NTCIR-11 SpokenQuery & Doc task. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 413–418).Konno, R, Ouchi, K, Obara, M, Shimizu, Y, Chiba, T, Hirota, T, Itoh, Y (2016). An STD system using multiple STD results and multiple rescoring method for NTCIR-12 SpokenQuery & Doc task. In Proc. of NTCIR-12. National Institute of Informatics, Tokyo, (pp. 200–204).Sakamoto, N, Yamamoto, K, Nakagawa, S (2015). Combination of syllable based N-gram search and word search for spoken term detection through spoken queries and IV/OOV classification. In Proc. of ASRU. IEEE, New York, (pp. 200–206).Hou, J, Pham, VT, Leung, C-C, Wang, L, 2, HX, Lv, H, Xie, L, Fu, Z, Ni, C, Xiao, X, Chen, H, Zhang, S, Sun, S, Yuan, Y, Li, P, Nwe, TL, Sivadas, S, Ma, B, Chng, ES, Li, H (2015). The NNI Query-by-Example system for MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 141–143).Vavrek, J, Viszlay, P, Lojka, M, Pleva, M, Juhar, J, Rusko, M (2015). TUKE at MediaEval 2015 QUESST. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 451–453).Mantena, G, Achanta, S, Prahallad, K (2014). Query-by-example spoken term detection using frequency domain linear prediction and non-segmental dynamic time warping. IEEE/ACM Transactions on Audio, Speech and Language Processing, 22(5), 946–955.Anguera, X, & Ferrarons, M (2013). Memory efficient subsequence DTW for query-by-example spoken term detection. In Proc. of ICME. IEEE, New York, (pp. 1–6).Tulsiani, H, & Rao, P (2015). The IIT-B Query-by-Example system for MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 341–343).Bouallegue, M, Senay, G, Morchid, M, Matrouf, D, Linares, G, Dufour, R (2013). LIA@MediaEval 2013 spoken web search task: An I-Vector based approach. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 771–772).Rodriguez-Fuentes, LJ, Varona, A, Penagarikano, M, Bordel, G, Diez, M (2013). GTTS systems for the SWS task at MediaEval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 831–832).Wang, H, Lee, T, Leung, C-C, Ma, B, Li, H (2013). Using parallel tokenizers with DTW matrix combination for low-resource spoken term detection. In Proc. of ICASSP. IEEE, New York, (pp. 8545–8549).Wang, H, & Lee, T (2013). The CUHK spoken web search system for MediaEval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 681–682).Proenca, J, Veiga, A, Perdigão, F (2014). The SPL-IT query by example search on speech system for MediaEval 2014. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 741–742).Proenca, J, Veiga, A, Perdigao, F (2015). Query by example search with segmented dynamic time warping for non-exact spoken queries. In Proc. of EUSIPCO. Springer, Berlin, (pp. 1691–1695).Proenca, J, Castela, L, Perdigao, F (2015). The SPL-IT-UC Query by Example search on speech system for MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 471–473).Proenca, J, & Perdigao, F (2016). Segmented dynamic time warping for spoken Query-by-Example search. In Proc. of Interspeech. ISCA, Baixas, (pp. 750–754).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2015). GTM-UVigo systems for the Query-by-Example search on speech task at MediaEval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 521–523).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2015). Phonetic unit selection for cross-lingual Query-by-Example spoken term detection. In Proc. of ASRU. IEEE, New York, (pp. 223–229).Saxena, A, & Yegnanarayana, B (2015). Distinctive feature based representation of speech for Query-by-Example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 3680–3684).Skacel, M, & Szöke, I (2015). BUT QUESST 2015 system description. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 721–723).Chen, H, Leung, C-C, Xie, L, Ma, B, Li, H (2016). Unsupervised bottleneck features for low-resource Query-by-Example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 923–927).Yuan, Y, Leung, C-C, Xie, L, Chen, H, Ma, B, Li, H (2017). Pairwise learning using multi-lingual bottleneck features for low-resource Query-by-Example spoken term detection. In Proc. of ICASSP. IEEE, New York, (pp. 5645–5649).Torbati, AHHN, & Picone, J (2016). A nonparametric Bayesian approach for spoken term detection by example query. In Proc. of Interspeech. ISCA, Baixas, (pp. 928–932).Popli, A, & Kumar, A (2015). Query-by-example spoken term detection using low dimensional posteriorgrams motivated by articulatory classes. In Proc. of MMSP. IEEE, New York, (pp. 1–6).Yang, P, Leung, C-C, Xie, L, Ma, B, Li, H (2014). Intrinsic spectral analysis based on temporal context features for query-by-example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 1722–1726).George, B, Saxena, A, Mantena, G, Prahallad, K, Yegnanarayana, B (2014). Unsupervised query-by-example spoken term detection using bag of acoustic words and non-segmental dynamic time warping. In Proc. of Interspeech. ISCA, Baixas, (pp. 1742–1746).Hazen, TJ, Shen, W, White, CM (2009). Query-by-example spoken term detection using phonetic posteriorgram templates. In Proc. of ASRU. IEEE, New York, (pp. 421–426).Abad, A, Astudillo, RF, Trancoso, I (2013). The L2F spoken web search system for mediaeval 2013. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 851–852).Szöke, I, Skácel, M, Burget, L (2014). BUT QUESST 2014 system description. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 621–622).Szöke, I, Burget, L, Grézl, F, Černocký, JH, Ondel, L (2014). Calibration and fusion of query-by-example systems - BUT SWS 2013. In Proc. of ICASSP. IEEE, New York, (pp. 621–622).Abad, A, Rodríguez-Fuentes, LJ, Penagarikano, M, Varona, A, Bordel, G (2013). On the calibration and fusion of heterogeneous spoken term detection systems. In Proc. of Interspeech. ISCA, Baixas, (pp. 20–24).Yang, P, Xu, H, Xiao, X, Xie, L, Leung, C-C, Chen, H, Yu, J, Lv, H, Wang, L, Leow, SJ, Ma, B, Chng, ES, Li, H (2014). The NNI query-by-example system for MediaEval 2014. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 691–692).Leung, C-C, Wang, L, Xu, H, Hou, J, Pham, VT, Lv, H, Xie, L, Xiao, X, Ni, C, Ma, B, Chng, ES, Li, H (2016). Toward high-performance language-independent Query-by-Example spoken term detection for MediaEval 2015: Post-evaluation analysis. In Proc. of Interspeech. ISCA, Baixas, (pp. 3703–3707).Xu, H, Hou, J, Xiao, X, Pham, VT, Leung, C-C, Wang, L, Do, VH, Lv, H, Xie, L, Ma, B, Chng, ES, Li, H (2016). Approximate search of audio queries by using DTW with phone time boundary and data augmentation. In Proc. of ICASSP. IEEE, New York, (pp. 6030–6034).Oishi, S, Matsuba, T, Makino, M, Kai, A (2016). Combining state-level and DNN-based acoustic matches for efficient spoken term detection in NTCIR-12 SpokenQuery &Doc-2 task. In Proc. of NTCIR-12. National Institute of Informatics, Tokyo, (pp. 205–210).Oishi, S, Matsuba, T, Makino, M, Kai, A (2016). Combining state-level spotting and posterior-based acoustic match for improved query-by-example spoken term detection. In Proc. of Interspeech. ISCA, Baixas, (pp. 740–744).Obara, M, Kojima, K, Tanaka, K, Lee, S-w, Itoh, Y (2016). Rescoring by combination of posteriorgram score and subword-matching score for use in Query-by-Example. In Proc. of Interspeech. ISCA, Baixas, (pp. 1918–1922).NIST. The Ninth Text REtrieval Conference (TREC 9). http://trec.nist.gov . Accessed Feb 2018.Anguera, X, Rodriguez-Fuentes, LJ, Szöke, I, Buzo, A, Metze, F (2014). Query by Example Search on Speech at Mediaeval 2014. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 351–352).Joho, H, & Kishida, K (2014). Overview of the NTCIR-11 SpokenQuery&Doc Task. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 1–7).NIST. Draft KWS16 Keyword Search Evaluation Plan. https://www.nist.gov/sites/default/files/documents/itl/iad/mig/KWS16-evalplan-v04.pdf . Accessed Feb 2018.Anguera, X, Metze, F, Buzo, A, Szöke, I, Rodriguez-Fuentes, LJ (2013). The spoken web search task. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 921–922).Taras, B, & Nadeu, C (2011). Audio segmentation of broadcast news in the Albayzin-2010 evaluation: overview, results, and discussion. EURASIP Journal on Audio, Speech, and Music Processing, 2011(1), 1–10.Zelenák, M, Schulz, H, Hernando, J (2012). Speaker diarization of broadcast news in Albayzin 2010 evaluation campaign. EURASIP Journal on Audio, Speech, and Music Processing, 2012(19), 1–9.Rodríguez-Fuentes, LJ, Penagarikano, M, Varona, A, Díez, M, Bordel, G (2011). The Albayzin 2010 Language Recognition Evaluation. In Proc. of Interspeech. ISCA, Baixas, (pp. 1529–1532).Tejedor, J, Toledano, DT, Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C, Cardenal, A, Echeverry-Correa, JD, Coucheiro-Limeres, A, Olcoz, J, Miguel, A (2015). Spoken term detection ALBAYZIN 2014 evaluation: overview, systems, results, and discussion. EURASIP, Journal on Audio, Speech and Music Processing, 2015(21), 1–27.Tejedor, J, Toledano, DT, Anguera, X, Varona, A, Hurtado, LF, Miguel, A, Colás, J (2013). Query-by-example spoken term detection ALBAYZIN 2012 evaluation: overview, systems, results, and discussion. EURASIP, Journal on Audio, Speech, and Music Processing, 2013(23), 1–17.Tejedor, J, Toledano, DT, Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2016). Comparison of ALBAYZIN query-by-example spoken term detection 2012 and 2014 evaluations. EURASIP, Journal on Audio, Speech and Music Processing, 2016(1), 1–19.Méndez, F, Docío, L, Arza, M, Campillo, F (2010). The Albayzin 2010 text-to-speech evaluation. In Proc. of FALA. UniversidadeVigo, Vigo, (pp. 317–340).Billa, J, Ma, KW, McDonough, JW, Zavaliagkos, G, Miller, DR, Ross, KN, El-Jaroudi, A (1997). Multilingual speech recognition: the 1996 Byblos Callhome system. In Proc. of Eurospeech. ISCA, Baixas, (pp. 363–366).Killer, M, Stuker, S, Schultz, T (2003). Grapheme based speech recognition. In Proc. of Eurospeech. ISCA, Baixas, (pp. 3141–3144).Burget, L, Schwarz, P, Agarwal, M, Akyazi, P, Feng, K, Ghoshal, A, Glembek, O, Goel, N, Karafiat, M, Povey, D, Rastrow, A, Rose, RC, Thomas, S (2010). Multilingual acoustic modeling for speech recognition based on subspace gaussian mixture models. In Proc. of ICASSP. IEEE, New York, (pp. 4334–4337).Cuayahuitl, H, & Serridge, B (2002). Out-of-vocabulary word modeling and rejection for Spanish keyword spotting systems. In Proc. of MICAI. Springer, Berlin, (pp. 156–165).Tejedor, J (2009). Contributions to keyword spotting and spoken term detection for information retrieval in audio mining. PhD thesis, Universidad Autónoma de Madrid, Madrid, Spain.Tejedor, J, Toledano, DT, Wang, D, King, S, Colás, J (2014). Feature analysis for discriminative confidence estimation in spoken term detection. Computer Speech and Language, 28(5), 1083–1114.Li, J, Wang, X, Xu, B (2014). An empirical study of multilingual and low-resource spoken term detection using deep neural networks. In Proc. of Interspeech. ISCA, Baixas, (pp. 1747–1751).NIST. The Spoken Term Detection (STD) 2006 evaluation plan. http://berlin.csie.ntnu.edu.tw/Courses/Special%20Topics%20in%20Spoken%20Language%20Processing/Lectures2008/SLP2008S-Lecture12-Spoken%20Term%20Detection.pdf . Accessed Feb 2018.Fiscus, JG, Ajot, J, Garofolo, JS, Doddingtion, G (2007). Results of the 2006 spoken term detection evaluation. In Proc. of SSCS. ACM, New York, (pp. 45–50).Martin, A, Doddington, G, Kamm, T, Ordowski, M, Przybocki, M (1997). The DET curve in assessment of detection task performance. In Proc. of Eurospeech. ISCA, Baixas, (pp. 1895–1898).NIST. Evaluation Toolkit (STDEval) software. https://www.nist.gov/itl/iad/mig/tools . Accessed Feb 2018.Union, IT. ITU-T Recommendation P.563: Single-ended method for objective speech quality assessment in narrow-band telephony applications. http://www.itu.int/rec/T-REC-P.563/en . Accessed Feb 2018.Rajput, N, & Metze, F (2011). Spoken web search. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 1–2).Metze, F, Barnard, E, Davel, M, van Heerden, C, Anguera, X, Gravier, G, Rajput, N (2012). The spoken web search task. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 41–42).Szöke, I, Rodriguez-Fuentes, LJ, Buzo, A, Anguera, X, Metze, F, Proenca, J, Lojka, M, Xiong, X (2015). Query by Example Search on Speech at Mediaeval 2015. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 81–82).Szöke, I, & Anguera, X (2016). Zero-cost speech recognition task at Mediaeval 2016. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 81–82).Akiba, T, Nishizaki, H, Nanjo, H, Jones, GJF (2014). Overview of the NTCIR-11 spokenquery &doc task. In Proc. of NTCIR-11. National Institute of Informatics, Tokyo, (pp. 1–15).Akiba, T, Nishizaki, H, Nanjo, H, Jones, GJF (2016). Overview of the NTCIR-12 spokenquery &doc-2. In Proc. of NTCIR-12. National Institute of Informatics, Tokyo, (pp. 1–13).Schwarz, P (2008). Phoneme recognition based on long temporal context. PhD thesis, FIT, BUT, Brno, Czech Republic.Varona, A, Penagarikano, M, Rodríguez-Fuentes, LJ, Bordel, G (2011). On the use of lattices of time-synchronous cross-decoder phone co-occurrences in a SVM-phonotactic language recognition system. In Proc. of Interspeech. ISCA, Baixas, (pp. 2901–2904).Eyben, F, Wollmer, M, Schuller, B (2010). OpenSMILE—the munich versatile and fast open-source audio feature extractor. In Proc. of ACM Multimedia (MM). ACM, New York, (pp. 1459–1462).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2016). Finding relevant features for zero-resource query-by-example search on speech. Speech Communication, 84(1), 24–35.Zhang, Y, & Glass, JR (2009). Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams. In Proc. of ASRU. IEEE, New York, (pp. 398–403).Povey, D, Ghoshal, A, Boulianne, G, Burget, L, Glembek, O, Goel, N, Hannemann, M, Motlicek, P, Qian, Y, Schwarz, P, Silovsky, J, Stemmer, G, Vesely, K (2011). The KALDI speech recognition toolkit. In Proc. of ASRU. IEEE, New York, (pp. 1–4).Muller, M. (2007). Information retrieval for music and motion. New York: Springer.Szöke, I, Skacel, M, Burget, L (2014). BUT QUESST 2014 system description. In Proc. of MediaEval. Ruzica Piskac, New Haven, (pp. 621–622).Brümmer, N, & van Leeuwen, D (2006). On calibration of language recognition scores. In Proc of the IEEE Odyssey: The speaker and language recognition workshop. IEEE, New York, (pp. 1–8).Brümmer, N, & de Villiers, E. The BOSARIS toolkit user guide: Theory, algorithms and code for binary classifier score processing. Technical report. https://sites.google.com/site/nikobrummer . Accessed Feb 2018.Meinedo, H, & Neto, J (2005). A stream-based audio segmentation, classification and clustering pre-processing system for broadcast news using ANN models. In Proc. of Interspeech. ISCA, Baixas, (pp. 237–240).Morgan, N, & Bourlard, H (1995). An introduction to hybrid HMM/connectionist continuous speech recognition. IEEE Signal Processing Magazine, 12(3), 25–42.Meinedo, H, Abad, A, Pellegrini, T, Trancoso, I, Neto, J (2010). The L2F broadcast news speech recognition system. In Proc. of FALA. UniversidadeVigo, Vigo, (pp. 93–96).Abad, A, Luque, J, Trancoso, I (2011). Parallel transformation network features for speaker recognition. In Proc. of ICASSP. IEEE, New York, (pp. 5300–5303).Diez, M, Varona, A, Penagarikano, M, Rodriguez-Fuentes, LJ, Bordel, G (2012). On the use of phone log-likelihood ratios as features in spoken language recognition. In Proc. of SLT. IEEE, New York, (pp. 274–279).Diez, M, Varona, A, Penagarikano, M, Rodriguez-Fuentes, LJ, Bordel, G (2014). New insight into the use of phone log-likelihood ratios as features for language recognition. In Proc. of Interspeech. ISCA, Baixas, (pp. 1841–1845).Abad, A, Ribeiro, E, Kepler, F, Astudillo, R, Trancoso, I (2016). Exploiting phone log-likelihood ratio features for the detection of the native language of non-native English speakers. In Proc. of Interspeech. ISCA, Baixas, (pp. 2413–2417).Rodríguez-Fuentes, LJ, Varona, A, Peñagarikano, M, Bordel, G, Díez, M (2014). High-performance query-by-example spoken term detection on the SWS 2013 evaluation. In Proc. of ICASSP. IEEE, New York, (pp. 7819–7823).Vesely, K, Ghoshal, A, Burget, L, Povey, D (2013). Sequence-discriminative training of deep neural networks. In Proc. of Interspeech. ISCA, Baixas, (pp. 2345–2349).Ghahremani, P, BabaAli, B, Povey, D, Riedhammer, K, Trmal, J, Khudanpur, S (2014). A pitch extraction algorithm tuned for automatic speech recognition. In Proc. of ICASSP. IEEE, New York, (pp. 2494–2498).Povey, D, Hannemann, M, Boulianne, G, Burget, L, Ghoshal, A, Janda, M, Karafiat, M, Kombrink, S, Motlicek, P, Qian, Y, Riedhammer, K, Vesely, K, Vu, NT (2012). Generating exact lattices in the WFST framework. In Proc. of ICASSP. IEEE, New York, (pp. 4213–4216).Garcia-Mateo, C, Dieguez-Tirado, J, Docio-Fernandez, L, Cardenal-Lopez, A (2004). Transcrigal: A bilingual system for automatic indexing of broadcast news. In Proc. of LREC. ELRA, Paris, (pp. 2061–2064).Stolcke, A (2002). SRILM—an extensible language modeling toolkit. In Proc. of Interspeech. ISCA, Baixas, (pp. 901–904).Lopez-Otero, P, Docio-Fernandez, L, Garcia-Mateo, C (2016). GTM-UVigo systems for Albayzin 2016 search on speech evaluation. In Proc. of Iberspeech. Springer, Berlin, (pp. 65–74).Chen, G, Khudanpur, S, Povey, D, Trmal, J, Yarowsky, D, Yilmaz, O (2013). Quantifying the value of pronunciation lexicons for keyword search in low resource languages. In Proc. of ICASSP. IEEE, New York, (pp. 8560–8564).Pham, VT, Chen, NF, Sivadas, S, Xu, H, Chen, I-F, Ni, C, Chng, ES, Li, H (2014). System and keyword dependent fusion for spoken term detection. In Proc. of SLT. IEEE, New York, (pp. 430–435).Can, D, & Saraclar, M (2011). Lattice indexing for spoken term detection. IEEE Transactions on Audio, Speech and Language Processing, 19(8), 2338–2347.Miller, DRH, K

América Latina 2019: vuelta a la inestabilidad

Tres décadas después de la tercera ola democrática, la cual puso fin a las dictaduras en América Latina, la democracia parece haber entrado en crisis. En los últimos meses, diferentes estallidos sociales han puesto en evidencia la fragilidad institucional de algunos de los países de la región y han desatado numerosas olas de violencia que han puesto en discusión la estabilidad democrática del continente. Durante los últimos meses, Perú, Ecuador, Chile, Bolivia y Colombia han experimentado movilizaciones que han puesto de relieve un evidente descontento social. Antes lo habían hecho, entre otros, venezolanos y portorriqueños. Como contraste, otros países como Argentina fueron capaces de gestionar sin violencia el fin de mandato de un gobierno desgastado como el de Macri y celebrar unas nuevas elecciones que, aun polarizadas, antepusieron el respeto a la institucionalidad a la movilización social y la protesta

New heterobimetallic ferrocenyl derivatives are promising antitrypanosomal agents

In the search for a more effective chemotherapy for the treatment of Chagas´ disease and human African trypanosomiasis, caused by Trypanosoma cruzi and Trypanosoma brucei parasites, respectively, the use of organometallic compounds may be a promising strategy. In this work, eight new heterobimetallic compounds are described including four 5-nitrofuryl containing thiosemicarbazones as bioactive ligands (HL1-HL4) and dppf = 1,1′-bis(diphenylphosphino) ferrocene as an organometallic co-ligand. Complexes of the formula [MII(L)(dppf)](PF6) with M = Pd or Pt were synthesized and fully characterized in the solid state and in solution, including the determination of the molecular structure of four of them by single crystal X-ray diffraction methods. Most compounds showed activity in the low micromolar or submicromolar range against both parasites, with the platinum compounds being more active than the palladium analogues. Activity was significantly increased by generation of the M-dppf compounds (3-24 fold increase with respect to free ligands HL for T. cruzi and up to 99 fold increase with respect to HL for T. brucei). The inclusion of the organometallic co-ligand also led to lower toxicity in mammalian cells and higher selectivity towards both parasites when compared to the free HL compounds. The complexes interact with DNA and affect the redox metabolism of the parasites. Furthermore, the most active and selective compound of the new series showed no in vivo toxicity in zebrafish embryos.Fil: Rodríguez Arce, Esteban. Universidad de la República; UruguayFil: Putzu, Eugenia. Universidad de la República; UruguayFil: Lapier, Michel. Universidad de Chile; ChileFil: Maya, Juan Diego. Universidad de Chile; ChileFil: Olea Azar, Claudio. Universidad de Chile; ChileFil: Echeverría, Gustavo Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Piro, Oscar Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Física La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Física La Plata; ArgentinaFil: Medeiros, Andrea. Instituto Pasteur de Montevideo; Uruguay. Universidad de la República; UruguayFil: Sardi, Florencia. Instituto Pasteur de Montevideo; UruguayFil: Comini, Marcelo. Instituto Pasteur de Montevideo; UruguayFil: Risi, Gastón. Instituto Pasteur de Montevideo; UruguayFil: Salinas, Gustavo. Instituto Pasteur de Montevideo; UruguayFil: Abad Villamor, Ana Isabel. Instituto Superior Técnico; PortugalFil: Pessoa, João Costa. Instituto Superior Técnico; PortugalFil: Otero, Lucía. Universidad de la República; UruguayFil: Gambino, Dinorah. Universidad de la República; Urugua

Randomised multicentre clinical trial to evaluate voriconazole pre-emptive genotyping strategy in patients with risk of aspergillosis: vorigenipharm study protocol.

Introduction Invasive aspergillosis is the most important cause of morbidity and mortality in patients with haematological diseases. At present, voriconazole is the first-line treatment for invasive fungal disease. The pharmacokinetic interindividual variability of voriconazole depends on genetic factors. CYP450 is involved in 70%–75% of total metabolism of voriconazole, mainly CYP3A4 and CYP2C19, with the remaining 25%–30% of metabolism conducted by monooxygenase flavins. CYP2C19 single nucleotide polymorphisms could explain 50%–55% of variability in voriconazole metabolism. Materials and methods The main objective is to compare efficiency of pre-emptive voriconazole genotyping with routine practice. The primary outcome is serum voriconazole on the fifth day within the therapeutic range. The secondary outcome is the combined variables of therapeutic failure and adverse events within 90 days of first administration, associated with voriconazole. A total of 146 patients at risk of invasive aspergillosis who will potentially receive voriconazole will be recruited, and CYP2C19 will be genotyped. If the patient ultimately receives voriconazole, they will be randomised (1:1 experimental/control). In the experimental arm, patients will receive a dose according to a pharmacogenetic algorithm, including CYP2C19 genotype and clinical and demographic information. In the control arm, patients will receive a dose according to clinical practice guidelines. In addition, a Spanish National Healthcare System (NHS) point-of-view cost-effectiveness evaluation will be performed. Direct cost calculations for each arm will be performed. Conclusion This trial will provide information about the viability and cost-effectiveness of the mplementation of a pre-emptive voriconazole genotyping strategy in the Spanish NHS. Ethics and dissemination A Spanish version of this protocol has been evaluated and approved by the La Paz University Hospital Ethics Committee and the Spanish Agency of Medicines and Medical Devices. Trial results will be submitted for publication in an open peer-reviewed medical speciality-specific publication. Trial registration number Eudra-CT: 2019-000376-41 and NCT04238884; Pre-results.post-print441 K

Involvement of Mechanical Cues in the Migration of Cajal-Retzius Cells in the Marginal Zone During Neocortical Development

Emerging evidence points to coordinated action of chemical and mechanical cues during brain development. At early stages of neocortical development, angiogenic factors and chemokines such as CXCL12, ephrins, and semaphorins assume crucial roles in orchestrating neuronal migration and axon elongation of postmitotic neurons. Here we explore the intrinsic mechanical properties of the developing marginal zone of the pallium in the migratory pathways and brain distribution of the pioneer Cajal-Retzius cells. These neurons are generated in several proliferative regions in the developing brain (e.g., the cortical hem and the pallial subpallial boundary) and migrate tangentially in the preplate/marginal zone covering the upper portion of the developing cortex. These cells play crucial roles in correct neocortical layer formation by secreting several molecules such as Reelin. Our results indicate that the motogenic properties of Cajal-Retzius cells and their perinatal distribution in the marginal zone are modulated by both chemical and mechanical factors, by the specific mechanical properties of Cajal-Retzius cells, and by the differential stiffness of the migratory routes. Indeed, cells originating in the cortical hem display higher migratory capacities than those generated in the pallial subpallial boundary which may be involved in the differential distribution of these cells in the dorsal-lateral axis in the developing marginal zone