Publications

El tratamiento quirúrgico del cáncer de mama ha evolucionado, desde cirugías radicales que incluían la pared torácica hasta cirugías conservadoras de resección tumoral con margen oncológico seguro complementadas con radioterapia; estas se consideran alternativa a la cirugía radical. La supervivencia es similar en ambos procedimientos. Metodología: Estudio de cohorte retrospectivo que evaluó las características demográficas, patológicas y desenlaces clínicos, como recaída y mortalidad, en mujeres con cáncer invasivo, sometidas a cirugía conservadora entre 1998 y 2007 en el INC. Resultados: Se incluyeron 358 pacientes con edad promedio de 53 anos y estados tumora- ˜ les tempranos en su mayoría. Con mediana de seguimiento de cuatro anos se presentaron 40 ˜ recaídas entre locales, regionales y sistémicas con una tasa de recaída de 2,6 recaídas por 100 pacientes/ano. Se presentó un mayor porcentaje de recaídas en estado clínico avanzado ˜ (p=0,022), tamano tumoral mayor de 2 centímetros (p=0,02 ˜ ) y a mayor número de ganglios comprometidos en el vaciamiento axilar (p=0,004). La tasa de mortalidad fue 1,2 muertes por 100 pacientes/ano. Los márgenes positivos se relacionaron con estado clínico avanzado (p=0,010) ˜ y las pacientes con márgenes positivos que recibieron manejo no quirúrgico presentaron un porcentaje mayor de recaída, comparado con las llevadas a cirugía (p=0,023). Esta diferencia se conservó al comparar manejo quirúrgico con no quirúrgico en márgenes positivos invasivos (p=0,037). Conclusiones: El estado clínico avanzado, se relacionó con márgenes positivos y recaída tumoral. El compromiso ganglionar axilar y el manejo no quirúrgico de los márgenes positivos determinaron un mayor porcentaje de recaída.

Abstract Animal behavior is elicited, in part, in response to external conditions, but understanding how animals perceive the environment and make the decisions that bring about these behavioral responses is challenging. Animal heads often move during specific behaviors and, additionally, typically have sensory systems (notably vision, smell, and hearing) sampling in defined arcs (normally to the front of their heads). As such, head‐mounted electronic sensors consisting of accelerometers and magnetometers, which can be used to determine the movement and directionality of animal heads (where head “movement” is defined here as changes in heading [azimuth] and/or pitch [elevation angle]), can potentially provide information both on behaviors in general and also clarify which parts of the environment the animals might be prioritizing (“environmental framing”). We propose a new approach to visualize the data of such head‐mounted tags that combines the instantaneous outputs of head heading and pitch in a single intuitive spherical plot. This sphere has magnetic heading denoted by “longitude” position and head pitch by “latitude” on this “orientation sphere” (O‐sphere). We construct the O‐sphere for the head rotations of a number of vertebrates with contrasting body shape and ecology (oryx, sheep, tortoises, and turtles), illustrating various behaviors, including foraging, walking, and environmental scanning. We also propose correcting head orientations for body orientations to highlight specific heading‐independent head rotation, and propose the derivation of O‐sphere‐metrics, such as angular speed across the sphere. This should help identify the functions of various head behaviors. Visualizations of the O‐sphere provide an intuitive representation of animal behavior manifest via head orientation and rotation. This has ramifications for quantifying and understanding behaviors ranging from navigation through vigilance to feeding and, when used in tandem with body movement, should provide an important link between perception of the environment and response to it in free‐ranging animals.

User-generated content plays a key role in social networking, allowing a more active participation, socialisation, and collaboration among users. In particular, media content has been gaining a lot of ground, allowing users to express themselves through different types of formats such as images, GIFs and videos. The majority of this growing type of online content remains inaccessible to a part of the population, despite available tools to mitigate this source of exclusion. We sought to understand how people are perceiving these online contents in their networks and how to support tools are being used. To do so, we performed an online survey of 258 social network users and a follow-up interview conducted with 20 of them - 7 of them self-reporting blind and 13 sighted users without a disability. Results show how the different approaches being employed by major platforms are still not sufficient to properly address this issue. Our findings reveal that mainstream users are not aware of the possibility and the benefits of adopting accessible practices. From the general perspectives of end-users experiencing accessible practices, concerning barriers encountered, and motivational factors, we also discuss further approaches to create more user engagement and awareness.

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The database compiles published and unpublished data on carbonate (CaCO3) and/or particulate inorganic carbon (PIC) along with particulate organic carbon (POC) stocks if available, in sediments of seagrass meadows and adjacent un-vegetated patches. We considered the total pool of CaCO3 reported without distinguishing between the different biogenic carbonate mineral forms (calcite, Mg-calcite and aragonite). Fourqurean et al. (2012) provided data for 201 sites, and a literature search using both the Web of Knowledge (using the search terms “seagrass*” AND “inorganic carbon*” AND [“calcific* OR sediment* OR CaCO3 OR dissolut* OR diagenesis”]) and Google Scholar (using the search terms “seagrass carbonate”) yielded data for an additional 82 sites. The database was amended with unpublished values for 154 additional sites sampled by the authors. The database compiles data on sediment carbonate concentration for a total of 437 sites, of which 34 corresponded to sand patches adjacent to seagrass meadows. The final database comprises estimates for 403 seagrass vegetated sites, including 219 estimates for sediment surface samples (ca. 1-30 cm depth) and 184 estimates for sediment cores of variable length (149 cores < 100 cm-long, and 35 cores ≥ 100 cm-long). When only one of the variables, CaCO3 or PIC was reported, the other was estimated assuming that PIC is 12% of the total molar mass of the CaCO3. In most cases, particulate inorganic and organic carbon (PIC and POC) were reported as a percentage of dry weight (%DW), where PIC and POC, in mg cm-3, was calculated as the product of the fraction of sediment dry weight composed by PIC or POC and the dry bulk density (DBD) of a given sediment section (n = 340 sites). When DBD was not reported (n = 113 sites), we used the average DBD (1.03 g cm-3) reported by Fourqurean et al. (2012) for seagrass sediments in the calculations. Enquiries about the dabaset may be sent to Ines Mazarrasa .

A Marine Climate Impacts Workshop was held from 29 April to 3 May 2012 at the US National Center of Ecological Analysis and Synthesis in Santa Barbara. This workshop was the culmination of a series of six meetings over the past three years, which had brought together 25 experts in climate change ecology, analysis of large datasets, palaeontology, marine ecology and physical oceanography. Aims of these workshops were to produce a global synthesis of climate impacts on marine biota, to identify sensitive habitats and taxa, to inform the current Intergovernmental Panel on Climate Change (IPCC) process, and to strengthen research into ecological impacts of climate change.

The automatic classification of animal sounds presents an enduring challenge in bioacoustics, owing to the diverse statistical properties of sound signals, variations in recording equipment, and prevalent low Signal-to-Noise Ratio (SNR) conditions. Deep learning models like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) have excelled in human speech recognition but have not been effectively tailored to the intricate nature of animal sounds, which exhibit substantial diversity even within the same domain. We propose an automated classification framework applicable to general animal sound classification. Our approach first optimizes audio features from Mel-frequency cepstral coefficients (MFCC) including feature rearrangement and feature reduction. It then uses the optimized features for the deep learning model, i.e., an attention-based Bidirectional LSTM (Bi-LSTM), to extract deep semantic features for sound classification. We also contribute an animal sound benchmark dataset encompassing oceanic animals and birds1. Extensive experimentation with real-world datasets demonstrates that our approach consistently outperforms baseline methods by over 25% in precision, recall, and accuracy, promising advancements in animal sound classification.