Arctic sea ice dynamics forecasting through interpretable machine learning

Machine Learning (ML) has become an increasingly popular tool to model the evolution of sea ice in the Arctic region. ML tools produce highly accurate and computationally efficient forecasts on specific tasks. Yet, they generally lack physical interpretability and do not support the understanding of system dynamics and interdependencies among target variables and driving factors. Here, we present a 2-step framework to model Arctic sea ice dynamics with the aim of balancing high performance and accuracy typical of ML and result interpretability. We first use time series clustering to obtain homogeneous subregions of sea ice spatiotemporal variability. Then, we run an advanced feature selection algorithm, called Wrapper for Quasi Equally Informative Subset Selection (W-QEISS), to process the sea ice time series barycentric of each cluster. W-QEISS identifies neural predictors (i.e., extreme learning machines) of the future evolution of the sea ice based on past values and returns the most relevant set of input variables to describe such evolution. Monthly output from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) from 1978 to 2020 is used for the entire Arctic region. Sea ice thickness represents the target of our analysis, while sea ice concentration, snow depth, sea surface temperature and salinity are considered as candidate drivers. Results show that autoregressive terms have a key role in the short term (with lag time 1 and 2 months) as well as the long term (i.e., in the previous year); salinity along the Siberian coast is frequently selected as a key driver, especially with a one-year lag; the effect of sea surface temperature is stronger in the clusters with thinner ice; snow depth is relevant only in the short term. The proposed framework is an efficient support tool to better understand the physical process driving the evolution of sea ice in the Arctic region

Esterase-Cleavable 2D Assemblies of Magnetic Iron Oxide Nanocubes: Exploiting Enzymatic Polymer Disassembling to Improve Magnetic Hyperthermia Heat Losses

Here, we report a nanoplatform based on iron oxide nanocubes (IONCs) coated with a bioresorbable polymer that, upon exposure to lytic enzymes, can be disassembled increasing the heat performances in comparison with the initial clusters. We have developed two-dimensional (2D) clusters by exploiting benchmark IONCs as heat mediators for magnetic hyperthermia and a polyhydroxyalkanoate (PHA) copolymer, a biodegradable polymer produced by bacteria that can be digested by intracellular esterase enzymes. The comparison of magnetic heat performance of the 2D assemblies with 3D centrosymmetrical assemblies or single IONCs emphasizes the benefit of the 2D assembly. Moreover, the heat losses of 2D assemblies dispersed in water are better than the 3D assemblies but worse than for single nanocubes. On the other hand, when the 2D magnetic beads (2D-MNBs) are incubated with the esterase enzyme at a physiological temperature, their magnetic heat performances began to progressively increase. After 2 h of incubation, specific absorption rate values of the 2D assembly double the ones of individually coated nanocubes. Such an increase can be mainly correlated to the splitting of the 2D-MNBs into smaller size clusters with a chain-like configuration containing few nanocubes. Moreover, 2D-MNBs exhibited nonvariable heat performances even after intentionally inducing their aggregation. Magnetophoresis measurements indicate a comparable response of 3D and 2D clusters to external magnets (0.3 T) that is by far faster than that of single nanocubes. This feature is crucial for a physical accumulation of magnetic materials in the presence of magnetic field gradients. This system is the first example of a nanoplatform that, upon exposure to lytic enzymes, such as those present in a tumor environment, can be disassembled from the initial 2D-MNB organization to chain-like assemblies with clear improvement of the heat magnetic losses resulting in better heat dissipation performances. The potential application of 2D nanoassemblies based on the cleavable PHAs for preserving their magnetic losses inside cells will benefit hyperthermia therapies mediated by magnetic nanoparticles under alternating magnetic fields

Comparative analysis of different spatial clustering techniques to model Arctic sea ice dynamics

The dynamics of sea ice in Polar regions has attracted increasing scientific interest over the last few decades, for its role in the global climate processes, as well as economic interest for the potential effect of new arctic routes on international trades, and natural gas and oil exploitation. Sea ice evolution is usually represented by adopting multi-layer thermodynamic-dynamic models coupled with atmosphere-ocean general circulation models. First-order physical representations of sea ice are currently included in large-scale models, but whether they are sufficient or not depends on the application. For instance, their spatial and temporal scales may not be sufficiently resolved for operational forecasting. The most relevant model upgrades for improving sea-ice predictions might be made in the atmosphere-ocean interplay mechanisms, more than details of the ice physics. The combination of unprecedented satellite datasets, increased computational power, and the advances in machine learning offer exciting opportunities for expanding our knowledge of the sea-ice trends and their main drivers. Arctic ice dynamic is usually interpreted as the combination of a long-term trend, most of the times considered as linear, and the deviation from this trend, i.e., the interannual variability. Yet, this approach is highly dependent on the linearity assumption, that appears simplistic and could affect the following analyses. We thus focus on the whole time series of ice data and explore its spatiotemporal evolution via time series clustering. We comparatively analyze the ability of three clustering algorithms to detect patterns in the PIOMAS ice thickness dataset, that reports monthly reanalysis data from 1978 to 2020. K-means, mean-shift, and hierarchical algorithms are adopted to represent centroid-, density- and connectivity-based clustering. Our results show that unsupervised machine learning can advance the interpretability of the complex phenomena occurring in the Arctic region. In addition, the proposed clustering analysis is a promising preprocessing tool for supervised tasks, such as forecasting and input selection. The methodology developed can be applied to other variables and spatial domains, and can also be easily extended to the multivariate case to consider the cross correlations

Management and long-term results in patients with two-thirds gastrectomy and stomal ulcer

In 67 patients with two-thirds gastrectomy and endoscopically proven stomal ulcer, serum gastrin levels were measured under basal conditions and after intravenous infusion of bombesin (15 ng/kg/ min), calcium (4 mg/kg/hour) and secretin (2 units/ kg). All patients underwent medical or surgical therapy. The long-term results were evaluated according to the Visick grading system (average follow-up, 3.1 years). © 1981