Enhancing the usability of Satellite Earth Observations through Data Driven Models. An application to Sea Water Quality

Earth Observation from satellites has the potential to provide comprehensive, rapid and inexpensive information about land and water bodies. Marine monitoring could gain in effectiveness if integrated with approaches that are able to collect data from wide geographic areas, such as satellite observation. Integrated with in situ measurements, satellite observations enable to extend the punctual information of sampling campaigns to a synoptic view, increase the spatial and temporal coverage, and thus increase the representativeness of the natural diversity of the monitored water bodies, their inter-annual variability and water quality trends, providing information to support EU Member States’ action plans. Turbidity is one of the optically active water quality parameters that can be derived from satellite data, and is one of the environmental indicator considered by EU directives monitoring programmes. Turbidity is a visual property of water, related to the amount of light scattered by particles in water, and it can act as simple and convenient indirect measure of the concentration of suspended solids and other particulate material. A review of the state-of-the-art shows that most traditional methods to estimate turbidity from optical satellite images are based on semi-empirical models relying on few spectral bands. The choice of the most suitable bands to be used is often site and season specific, as it is related to the type and concentration of suspended particles. When investigating wide areas or long time series that include different optical water types, the application of machine learning algorithms seems to be promising due to their flexibility, responding to the need of a model that can adapt to varying water conditions with smooth transition, and their ability to exploit the wealth of spectral information. Moreover, machine learning models have shown to be less affected by atmospheric and other background factors. Atmospheric correction for water leaving reflectance, in fact, still remains one of the major challenges in aquatic remote sensing. The use of machine learning for remotely sensed water quality estimation has spread in recent years thanks to the advances in algorithm development, computing power, and availability of higher spatial resolution data. Among all existing algorithms, the choice of the complexity of the model derives from the nature and number of available data. The present study explores the use of Sentinel-2 MultiSpectral Instrument (MSI) Level-1C Top of Atmosphere spectral radiance to derive water turbidity, through application of a Polynomial Kernel Regularized Least Squares regression. This algorithms is characterized by a simple model structure, good generalization, global optimal solution, especially suitable for non-linear and high dimension problems. The study area is located in the North Tyrrhenian Sea (Italy), covering a coastline of about 100 km, characterized by a varied shoreline, embracing environments worthy of protection and valuable biodiversity, but also relevant ports, and three main river flow and sediment discharge. The coastal environment in this area has been monitored since 2001, according to the 2000/60/EC Water Framework Directive, and in 2008 EU Marine Strategy Framework Directive 2008/56/EC further strengthened the investigation in the area. A dataset of combination of turbidity measurements, expressed in nephelometric turbidity units (NTU), and values of the 13 spectral bands in the pixel corresponding to the sample location was used to calibrate and validate the model. The developed turbidity model shows good agreement of the estimated satellite-derived surface turbidity with the measured one, confirming that the use of ML techniques allows to reach a good accuracy in turbidity estimation from satellite Top of Atmosphere reflectance. Comparison between turbidity estimates obtained from the model with turbidity data from Copernicus CMEMS dataset named ’Mediterranean Sea, Bio-Geo-Chemical, L3, daily observation’, which was used as benchmark, produced consistent results. A band importance analysis revealed the contribution of the different spectral bands and the main role of the red-edge range. Finally, turbidity maps from satellite imagery were produced for the study area, showing the ability of the model to catch extreme events and, overall, how it represents an important tool to improve our understanding of the complex factors that influence water quality in our oceans

Tyr78Phe Transthyretin Mutation with Predominant Motor Neuropathy as the Initial Presentation

Transthyretin (TTR) amyloidosis, the most frequent form of hereditary amyloidosis, is caused by dominant mutations in the TTR gene. More than 100 mutations have been identified. Clinical manifestations of TTR amyloidosis are usually induced by extracellular amyloid deposition in several organs. The major neurological manifestation is motor-sensory neuropathy associated with dysautonomic impairment. Here, we describe a 63-year-old man who came to our institution due to a suspected motor neuron disease. During a 4-year follow-up period, he underwent extensive clinical examination, electromyographic studies, sural nerve biopsy and TTR gene analysis by direct sequencing. Despite the predominant motor involvement, the detailed clinical examination also showed some mild sensory and dysautonomic signs. In addition, his clinical and family history included multiorgan disorders, such as carpal tunnel syndrome, as well as conditions with cardiac, renal, eye, and hepatic involvement. The sural nerve biopsy disclosed amyloid deposition, and the sequence analysis of the TTR gene detected a heterozygous Tyr78Phe substitution. The TTR gene variant found in our patient had only been described once so far, in a French man of Italian origin presenting with late-onset peripheral neuropathy and bilateral carpal tunnel syndrome. The predominant motor involvement presented by our patient is an uncommon occurrence and demonstrates the clinical heterogeneity of TTR amyloidosis

A Dynamic Approach to Natech Risk Assessment Applied to an LPG Storage Facility in a Landslides Sensitive Italian Area

Due to the climate change, extreme weather phenomena are becoming increasingly intense and occur with higher frequencies, even in unusual areas. Nevertheless, historical data showed as Natech accidents can be triggered not only by natural disasters, like earthquakes or tornadoes, but even by natural phenomena that are considered of minor importance, such as rain and lightning. Only recently, the Natech issue has gained a great deal of attention, but there is still a lack of consolidated Natech risk-analysis methodologies and tools. The focus of this work is to include natural hazards into a dynamic risk assessment system beside the typical parameters of process risks. In Italy, rainfall represents the most common triggering factor for landslides. Generally, the determination of trigger and propagation can rely on physically-based approaches, which require the calibration of many parameters and are often difficult to apply, or on empirical correlations between rainfall and landslide built from historical data. On the other hand, by using a data driven approach, available data can be exploited to define the system state over time, anticipate the systems outcome, support decision-making, and adopt the most appropriate adjustments, allowing to enhance system resilience and knowledge. The actual capability of the proposed approach was evaluated on a simple case-study represented by an LPG storage facility located in landslides sensitive zone of Liguria Region

Transcriptional Profiling of Rat Prefrontal Cortex after Acute Inescapable Footshock Stress

Stress is a primary risk factor for psychiatric disorders such as Major Depressive Disorder (MDD) and Post Traumatic Stress Disorder (PTSD). The response to stress involves the regulation of transcriptional programs, which is supposed to play a role in coping with stress. To evaluate transcriptional processes implemented after exposure to unavoidable traumatic stress, we applied microarray expression analysis to the PFC of rats exposed to acute footshock (FS) stress that were sacrificed immediately after the 40 min session or 2 h or 24 h after. While no substantial changes were observed at the single gene level immediately after the stress session, gene set enrichment analysis showed alterations in neuronal pathways associated with glia development, glia-neuron networking, and synaptic function. Furthermore, we found alterations in the expression of gene sets regulated by specific transcription factors that could represent master regulators of the acute stress response. Of note, these pathways and transcriptional programs are activated during the early stress response (immediately after FS) and are already turned off after 2 h-while at 24 h, the transcriptional profile is largely unaffected. Overall, our analysis provided a transcriptional landscape of the early changes triggered by acute unavoidable FS stress in the PFC of rats, suggesting that the transcriptional wave is fast and mild, but probably enough to activate a cellular response to acute stress

Progressive myoclonus epilepsies due to SEMA6B mutations. New variants and appraisal of published phenotypes

Variants of SEMA6B have been identified in an increasing number of patients, often presenting with progressive myoclonus epilepsy (PME), and to lesser extent developmental encephalopathy, with or without epilepsy. The exon 17 is mainly involved, with truncating mutations causing the production of aberrant proteins with toxic gain of function. Herein, we describe three adjunctive patients carrying de novo truncating SEMA6B variants in this exon (c.1976delC and c.2086C > T novel; c.1978delC previously reported). These subjects presented with PME preceded by developmental delay, motor and cognitive impairment, worsening myoclonus, and epilepsy with polymorphic features, including focal to bilateral seizures in two, and non-convulsive status epilepticus in one. The evidence of developmental delay in these cases suggests their inclusion in the “PME plus developmental delay” nosological group. This work further expands our knowledge of SEMA6B variants causing PMEs. However, the data to date available confirms that phenotypic features do not correlate with the type or location of variants, aspects that need to be further clarified by future studie

Stormorken syndrome caused by a p.R304W STIM1 mutation: The first Italian patient and a review of the literature

Stormorken syndrome is a rare autosomal dominant disease that is characterized by a complex phenotype that includes tubular aggregate myopathy (TAM), bleeding diathesis, hyposplenism, mild hypocalcemia and additional features, such as miosis and a mild intellectual disability (dyslexia). Stormorken syndrome is caused by autosomal dominant mutations in the STIM1 gene, which encodes an endoplasmic reticulum Ca2+ sensor. Here, we describe the clinical and molecular aspects of a 21-year-old Italian female with Stormorken syndrome. The STIM1 gene sequence identified a c.910C T transition in a STIM1 allele (p.R304W). The p.R304W mutation is a common mutation that is responsible for Stormorken syndrome and is hypothesized to cause a gain of function action associated with a rise in Ca2+ levels. A review of published STIM1 mutations (n = 50) and reported Stormorken patients (n = 11) indicated a genotype-phenotype correlation with mutations in a coiled coil cytoplasmic domain associated with complete Stormorken syndrome, and other pathological variants outside this region were more often linked to an incomplete phenotype. Our study describes the first Italian patient with Stormorken syndrome, contributes to the genotype/phenotype correlation and highlights the possibility of directly investigating the p.R304W mutation in the presence of a typical phenotype