ACME: Automatic feature extraction for cell migration examination through intravital microscopy imaging.

Cell detection and tracking applied to in vivo fluorescence microscopy has become an essential tool in biomedicine to characterize 4D (3D space plus time) biological processes at the cellular level. Traditional approaches to cell motion analysis by microscopy imaging, although based on automatic frameworks, still require manual supervision at some points of the system. Hence, when dealing with a large amount of data, the analysis becomes incredibly time-consuming and typically yields poor biological information. In this paper, we propose a fully-automated system for segmentation, tracking and feature extraction of migrating cells within blood vessels in 4D microscopy imaging. Our system consists of a robust 3D convolutional neural network (CNN) for joint blood vessel and cell segmentation, a 3D tracking module with collision handling, and a novel method for feature extraction, which takes into account the particular geometry in the cell-vessel arrangement. Experiments on a large 4D intravital microscopy dataset show that the proposed system achieves a significantly better performance than the state-of-the-art tools for cell segmentation and tracking. Furthermore, we have designed an analytical method of cell behaviors based on the automatically extracted features, which supports the hypotheses related to leukocyte migration posed by expert biologists. This is the first time that such a comprehensive automatic analysis of immune cell migration has been performed, where the total population under study reaches hundreds of neutrophils and thousands of time instances.This work has been partially supported by the National Grant TEC2017-84395-P of the Spanish Ministry of Economy and Competitiveness, Madrid Regional Government and Universidad Carlos III de Madrid through the project SHARON-CM-UC3M, RTI2018- 095497-B-I00 from Ministerio de Ciencia e Innovación (MICINN) and HR17_00527 from Fundación La Caixa to A.H. M.M-M. is supported by the Spanish Ministry of Education, Culture and Sports FPU Grant FPU18/02825. M.P-S. is supported by a Federation of European Biochemical Societies long-term fellowship. J.S. is supported by a fellowship (PRE2019-089130) from MICINN.S

Robust sound event detection in bioacoustic sensor networks

Bioacoustic sensors, sometimes known as autonomous recording units (ARUs), can record sounds of wildlife over long periods of time in scalable and minimally invasive ways. Deriving per-species abundance estimates from these sensors requires detection, classification, and quantification of animal vocalizations as individual acoustic events. Yet, variability in ambient noise, both over time and across sensors, hinders the reliability of current automated systems for sound event detection (SED), such as convolutional neural networks (CNN) in the time-frequency domain. In this article, we develop, benchmark, and combine several machine listening techniques to improve the generalizability of SED models across heterogeneous acoustic environments. As a case study, we consider the problem of detecting avian flight calls from a ten-hour recording of nocturnal bird migration, recorded by a network of six ARUs in the presence of heterogeneous background noise. Starting from a CNN yielding state-of-the-art accuracy on this task, we introduce two noise adaptation techniques, respectively integrating short-term (60 milliseconds) and long-term (30 minutes) context. First, we apply per-channel energy normalization (PCEN) in the time-frequency domain, which applies short-term automatic gain control to every subband in the mel-frequency spectrogram. Secondly, we replace the last dense layer in the network by a context-adaptive neural network (CA-NN) layer. Combining them yields state-of-the-art results that are unmatched by artificial data augmentation alone. We release a pre-trained version of our best performing system under the name of BirdVoxDetect, a ready-to-use detector of avian flight calls in field recordings.Comment: 32 pages, in English. Submitted to PLOS ONE journal in February 2019; revised August 2019; published October 201

Radio-telemetry observations of the first 650 km of the migration of Bar-tailed Godwits Limosa lapponica from the Wadden Sea to the Russian Arctic

In 1999 and 2000, 45 Bar-tailed Godwits Limosa lapponica were supplied with radio-transmitters during spring staging on the island Texel in the western Wadden Sea. With the use of Automatic Radio Tracking Stations (ARTS) on Texel and in south Sweden, and hand-held receivers on Texel, it was possible to follow the later part of the stopover period on Texel for 34 birds (76%) and the passage over south Sweden for 26 birds (58%). Thus, the method of automatic tracking of overflying migrating shorebirds works successfully where the migration corridor is narrow and predictable, as in the case with late spring shorebird migration from the Wadden Sea towards arctic Russia. The timing of departure from Texel and passage over south Sweden of radio-marked birds, with median dates of 30 May and 2 June respectively, were in agreement with published data on the spring migration of Siberian-breeding Bar-tailed Godwits L. l. taymyrensis. The individual variation in migration dates was larger than expected, with birds passing south Sweden between 25 May and 10 June, indicating that the time-window for departure might be broader than previously thought. There was no clear difference between males and females in timing of migration. The time difference between departure from Texel and passage over south Sweden (average 3.3 days) indicates that most Bar-tailed Godwits do not embark on the long flight towards Siberia directly from the western Wadden Sea, but are more likely to stop in the more easterly portion of the Wadden Sea before the final take-off. This pattern is similar to what has been found in other shorebirds and geese (e.g. Red Knots Calidris canutus and Dark-bellied Brent Geese Branta bernicla) migrating along the same route.

Digital Preservation Services : State of the Art Analysis

Research report funded by the DC-NET project.An overview of the state of the art in service provision for digital preservation and curation. Its focus is on the areas where bridging the gaps is needed between e-Infrastructures and efficient and forward-looking digital preservation services. Based on a desktop study and a rapid analysis of some 190 currently available tools and services for digital preservation, the deliverable provides a high-level view on the range of instruments currently on offer to support various functions within a preservation system.European Commission, FP7peer-reviewe