What can we learn about changes in coastal food web structure after the round goby invasion?

The round goby (Neogobius melanostomus) is extremely invasive fish species, capable to reach high abundance and make significant impact on invaded ecosystem. First round gobies occured in the Lithuanian part of the Baltic Sea in 2002. The population passed establishment (2002-2010), expansion (2011-2012) and adjustment (2013-2015) phases. Rapid round goby abundance increment induced dramatic decline of its major prey - the blue mussel (Mytilus edulis), which in turn negatively affected population of wintering long-tailed duck (Clangula hyemalis). Food competition between round goby and long-tailed duck was evaluated analysing changes in their diet composition, feeding efficiency and feeding niche overlap during different round goby invasion phases. During establishment both species preyed mainly on M. edulis; during expansion, the diet of round goby was dominated by M. edulis, Crangon crangon and Macoma balthica, while in the last invasion period it shifted to polychaetes. Long-tailed duck shifted its diet from epibenthic blue mussel to fish prey during round goby expansion and adjustment phases. Feeding efficiency of the round goby decreased from 100% (percentage of full guts) determined during establishment to 80% and 68% during expansion and adjustment phases, respectively. The highest percentage of feeding long-tailed ducks was observed in the beginning of round goby invasion (74%), it dramatically declined during 2011-2012 (26%) and recovered (53%) during 2015-2016. Feeding niche overlap between round goby and long-tailed duck was biologically significant during round goby establishment, but it lost significance after drastic decline of the blue mussel in natural environment

Direct holographic imaging of ultrafast laser damage process in thin films

International audienc

Exploring the necessity of mosaicking for underwater imagery semantic segmentation using deep learning

Deep learning applications are attracting considerable interest nowadays and image analysis pipelines are no exception. Benthic studies often rely on the subjective evaluation of video material recorded using underwater drones. The demand for automatic image segmentation and quantitative evaluation arises due to the large volume of video data collected. This study performed a semantic segmentation task by training the PSPNet architecture with ResNet-34 backbone for 50 epochs using imagery prepared by simply extracting a few video frames or stitch- ing a multitude of frames into a large 2D mosaic. Mosaicking is a particularly resource-intensive step, therefore, the possibility to skip such preprocessing would result in a more rapid analysis. The effect on the resulting seg- mentation quality was investigated by estimating the seabed coverage of three classes (Furcellaria lumbricalis, Mytilus edulis trossulus, and boulders) in a video material obtained from the Baltic Sea. Segmentation success, measured by intersection over union, varied between 0.56 and 0.84, usually slightly better for frames than for the mosaic overall. Absolute differences in estimated coverage were negligible (mosaic vs. frames): 0.24% vs. 1.26% for furcellaria, 0.44% vs. 2.46% for mytilus, and 4.02% vs. 2.06% for boulders. Due to the differences between predicted coverage and the mosaic-based ground truth being in an acceptable range, the findings suggest that the mosaicking step could be safely skipped in favor of a few equally spaced sample frames

Time Resolved Damage Effects

International audienc

Geriatric care management system powered by the IoT and computer vision techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients’ data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient’s position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff