Deep learning for automated river-level monitoring through river camera images: an approach based on water segmentation and transfer learning

River level estimation is a critical task required for the understanding of flood events, and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river camera images to estimate the river levels, but currently, the utility of this approach remains limited as it requires a large amount of manual intervention (ground topographic surveys and water image annotation). We develop an approach using an automated water semantic segmentation method to ease the process of river level estimation from river camera images. Our method is based on the application of a transfer learning methodology to deep semantic neural networks designed for water segmentation. Using datasets of image series extracted from four river cameras and manually annotated for the observation of a flood event on the Severn and Avon rivers, UK (21 November - 5 December 2012), we show that this algorithm is able to automate the annotation process with an accuracy greater than 91%. Then, we apply our approach to year-long image series from the same cameras observing the Severn and Avon (from 1 June 2019 to 31 May 2020) and compare the results with nearby river-gauge measurements. Given the high correlation (Pearson's Correlation Coefficient >0.94) between these results and the river-gauge measurements, it is clear that our approach to automation of the water segmentation on river camera images could allow for straightforward, inexpensive observation of flood events, especially at ungauged locations

Cow responses and evolution of the rumen bacterial and methanogen community following a complete rumen content transfer

Understanding the rumen microbial ecosystem requires the identification of factors that influence the community structure, such as nutrition, physiological condition of the host and host-microbiome interactions. The objective of the current study was to describe the rumen microbial communities before, during and after a complete rumen content transfer. The rumen contents of one donor cow were removed completely and used as inoculum for the emptied rumen of the donor itself and three acceptor cows under identical physiological and nutritional conditions. Temporal changes in microbiome composition and rumen function were analysed for each of four cows over a period of 6 weeks. Shortly after transfer, the cows showed different responses to perturbation of their rumen content. Feed intake depression in the first 2 weeks after transfer resulted in short-term changes in milk production, methane emission, fatty acid composition and rumen bacterial community composition. These effects were more pronounced in two cows, whose microbiome composition showed reduced diversity. The fermentation metrics and microbiome diversity of the other two cows were not affected. Their rumen bacterial community initially resembled the composition of the donor but evolved to a new community profile that resembled neither the donor nor their original composition. Descriptive data presented in the current paper show that the rumen bacterial community composition can quickly recover from a reduction in microbiome diversity after a severe perturbation. In contrast to the bacteria, methanogenic communities were more stable over time and unaffected by stress or host effects

Visceral artery aneurysm

Background: A 36-year-old female patient with no relevant clinical history was referred to the gastroenterology department for chronic epigastric pain. Blood analysis and gastroscopy were normal. Subsequently, abdominal ultrasonography and CT scan of the abdomen were made

Water vapour line assignments in the 9250-26 000 cm (-1) frequency range

Line parameters for water vapour in natural abundance have recently been determined for the 9250-13 000 cm(-1) region [M.-F. Wrienne, A. Jenouvrier, C. Hermans, A.C. Vandaele, M. Carleer, C. Clerbaux, P.-F. Coheur, R. Colin, S. Fally, M. Bach, J. Quant. Spectrosc. Radiat. Transfer 82 (2003) 99] and the 13 000-26 000 cm(-1) region [P.-F. Coheur, S. Fally, M. Carleer, C. Clerbaux, R. Colin, A. Jenouvrier, M.-F. Wrienne, C. Hermans, A.C. Vandaele, J. Quant. Spectrosc. Radial. Transfer 74 (2002) 493] using a high-resolution Fourier-transform spectrometer with a long-path absorption cell. These spectra are analysed using several techniques including variational line lists and assignments made. In total, over 15 000 lines were assigned to transitions involving more than 150 exited vibrational states of (H2O)-O-16. Twelve new vibrational band origins are determined and estimates for a further 16 are presented. (c) 2005 Elsevier Inc. All rights reserved

Calibration of NOMAD on ESA's ExoMars Trace Gas Orbiter: Part 2 – The Limb, Nadir and Occultation (LNO) channel

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).The Nadir and Occultation for MArs Discovery (NOMAD) instrument is a 3-channel spectrometer suite on the ESA ExoMars Trace Gas Orbiter. Since April 2018, when the nominal science mission began, it has been measuring the constituents of the Martian atmosphere. NOMAD contains three separate spectrometers, two of which operate in the infrared: the Solar Occultation (SO) channel makes only solar occultation observations, and therefore has the best resolving power (∼20,000) and a wider spectral region covering 2.2–4.3 ​μm. The Limb, Nadir and Occultation (LNO) channel covers the 2.2–3.8 ​μm spectral region and can operate in limb, nadir or solar occultation pointing modes. The Ultraviolet–VISible (UVIS) channel operates in the UV–visible region, from 200 to 650 ​nm, and can measure in limb, nadir or solar occultation modes like LNO. The LNO channel has a lower resolving power (∼10,000) than the SO channel, but is still typically an order of magnitude better than previous instruments orbiting Mars. The channel primarily operates in nadir-viewing mode, pointing directly down to the surface to measure the narrow atmospheric molecular absorption lines, clouds and surface features in the reflected sunlight. From the depth and position of the observed atmospheric absorption lines, the constituents of the Martian atmosphere and their column densities can be derived, leading to new insights into the processes that govern their distribution and transport. Surface properties can also be derived from nadir observations by observing the shape of the spectral continuum. Many calibration measurements were made prior to launch, on the voyage to Mars, and continue to be made in-flight during the science phase of the mission. This work, part 2, addresses the aspects of the LNO channel calibration that are not covered elsewhere, namely: the LNO ground calibration setup, the LNO occultation and nadir boresight pointing vectors, LNO detector characterisation and nadir/limb illumination pattern, instrument temperature effects, and finally the radiometric calibration of the LNO channel. An accompanying paper, part 1 (Thomas et al., 2021, this issue), addresses similar aspects for SO, the other infrared channel in NOMAD. A further accompanying paper (Cruz-Mermy et al., 2021, this issue) investigated the LNO radiometric calibration in more detail, approaching the work from a theoretical perspective. The two calibrations agree with each other to within 3%, validating each calibration method. © 2022 The Authors. Published by Elsevier Ltd.This project acknowledges funding by the Belgian Science Policy Office (BELSPO), with the financial and contractual coordination by the ESA Prodex Office (PEA 4000103401, 4000121493), by Spanish Ministry of Science and Innovation (MCIU) and by European funds under grants PGC2018-101836-B-I00 and ESP2017-87143-R (MINECO/FEDER), as well as by the UK Space Agency through grants ST/V002295/1, ST/V005332/1 and ST/S00145X/1 and ST/R001405/1 and Italian Space Agency through grant 2018-2-HH.0. This work was supported by the Belgian Fonds de la Recherche Scientifique – FNRS under grant number 30442502 (ET_HOME). The IAA/CSIC team acknowledges financial support from the State Agency for Research of the Spanish MCIU through the ‘Center of Excellence Severo Ochoa’ award for the Instituto de Astrofísica de Andalucía (SEV-2017-0709). SR thanks BELSPO for the FED-tWIN funding (Prf-2019-077 - RT-MOLEXO)