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Coastal morphology is key to modern understanding of threats and nature risks to coastal communities. A quarter of the world’s beaches are eroding due to natural or man-made activities. Dynamics of coastal morphology occurs in space and time (4D), making it difficult to measure using standard survey approaches. We introduce a technique that uses multispectral satellite data and satellite lidar to provide frequent spatial observations of coastal zones. Thanks to international cooperation through the Horizon 2020 program, the analytical processes are based on a highly automatic inversion of the radiative transfer equation (RTE), which enables to model shallow water depth without the access to local survey data. The workflow is installed on AWS cloud environment with directly linked to the satellite data archive. Leveraging these Copernicus satellite data, the solution can generate high frequent and high-resolution shallow water bathymetric data continuously and for the past decade. In combination with land topographic models derived from stereo satellite imagery and verification with bathy-topo data from a space-born green lidar system (IceSat-2 Atlas), this allows for verification and control of vertical accuracies.This solution allows for a rapid and scalable approach to generate shallow water bathymetric and topographic surfaces from small sites to a national level. It serves as a crucial dataset to manage coastal resilience strategies and reduce disaster risk. This will be demonstrated in pilot sites from the southern Baltic to the Caribbean where we can quantify and visualise the changes of the coastal morphology in 4D., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Background Neonatal units’ volume of activity, and other quantitative and qualitative variables, such as staffing, workload, work environment, care organization and geographical location, may influence the outcome of high risk newborns. Data about the distribution of these variables and their relationships among Italian neonatal units are lacking. Methods Between March 2010-April 2011, 63 neonatal intensive care units adhering to the Italian Neonatal Network participated in the SONAR Nurse study. Their main features and work environment were investigated by questionnaires compiled by the chief and by physicians and nurses of each unit. Twelve cross-sectional monthly-repeated surveys on different shifts were performed, collecting data on number of nurses on duty and number and acuity of hospitalized infants. Results Six hundred forty five physicians and 1601 nurses compiled the questionnaires. In the cross-sectional surveys 702 reports were collected, with 11082 infant and 3226 nurse data points. A high variability was found for units’ size (4–50 total beds), daily number of patients (median 14.5, range 3.4-48.7), number of nurses per shift (median 4.2, range 0.7-10.8) and number of team meetings per month. Northern regions performed better than Central and Southern regions for frequency of training meetings, qualitative assessment of performance, motivation within the unit and nursing work environment; mean physicians’ and nurses’ age increased moving from North to South. After stratification by terciles of the mean daily number of patients, the median number of nurses per shift increased at increasing volume of activity, while the opposite was found for the nurse-to-patient ratio adjusted by patients’ acuity. On average, in units belonging to the lower tercile there was 1 nurse every 2.5 patients, while in those belonging to the higher tercile the ratio was 1 nurse every 5 patients. Conclusions In Italy, there is a high variability in organizational characteristics and work environment among neonatal units and an uneven distribution of human resources in relation to volume of activity, suggesting that the larger the unit the greater the workload for each nurse. Urgent modifications in planning and organization of services are needed in order to pursue more efficient, homogeneous and integrated regionalized neonatal care systems.

A challenging problem in systems biology is parameter inference in mechanistic models of signalling pathways. In the present article, we investigate an approach based on gradient matching and nonparametric Bayesian modelling with Gaussian processes. We evaluate the method on two biological systems, related to the regulation of PIF4/5 in Arabidopsis thaliana, and the JAK/STAT signal transduction pathway.

The important research objective of identifying genes with similar behavior with respect to different conditions has recently been tackled with biclustering techniques. In this paper we introduce a new approach to the biclustering problem using the Possibilistic Clustering paradigm. The proposed Possibilistic Biclustering algorithm finds one bicluster at a time, assigning a membership to the bicluster for each gene and for each condition. The biclustering problem, in which one would maximize the size of the bicluster and minimizing the residual, is faced as the optimization of a proper functional. We applied the algorithm to the Yeast database, obtaining fast convergence and good quality solutions. We discuss the effects of parameter tuning and the sensitivity of the method to parameter values. Comparisons with other methods from the literature are also presented.