Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea.

Although the Mediterranean Sea is a crucial hotspot in marine biodiversity, it has been threatened by numerous anthropogenic pressures. As flagship species, Cetaceans are exposed to those anthropogenic impacts and global changes. Assessing their conservation status becomes strategic to set effective management plans. The aim of this paper is to understand the habitat requirements of cetaceans, exploiting the advantages of a machine-learning framework. To this end, 28 physical and biogeochemical variables were identified as environmental predictors related to the abundance of three odontocete species in the Northern Ionian Sea (Central-eastern Mediterranean Sea). In fact, habitat models were built using sighting data collected for striped dolphins Stenella coeruleoalba, common bottlenose dolphins Tursiops truncatus, and Risso's dolphins Grampus griseus between July 2009 and October 2021. Random Forest was a suitable machine learning algorithm for the cetacean abundance estimation. Nitrate, phytoplankton carbon biomass, temperature, and salinity were the most common influential predictors, followed by latitude, 3D-chlorophyll and density. The habitat models proposed here were validated using sighting data acquired during 2022 in the study area, confirming the good performance of the strategy. This study provides valuable information to support management decisions and conservation measures in the EU marine spatial planning context.

A Digital Twin modelling framework for the assessment of seagrass Nature Based Solutions against storm surges.

In this paper we demonstrate a novel framework for assessing nature-based solutions (NBSs) in coastal zones using a new suite of numerical models that provide a virtual "replica" of the natural environment. We design experiments that use a Digital Twin strategy to establish the wave, sea level and current attenuation due to seagrass NBSs. This Digital Twin modelling framework allows us to answer "what if" scenario questions such as: (i) are indigenous seagrass meadows able to reduce the energy of storm surges, and if so how? (ii) what are the best seagrass types and their landscaping for optimal wave and current attenuation? An important result of the study is to show that the landscaping of seagrasses is an important design choice and that seagrass does not directly attenuate the sea level but the current amplitudes. This framework reveals the link between seagrass NBS and the components of the disruptive potential of storm surges (waves and sea level) and opens up new avenues for future studies.

Defatted chia flour as functional ingredient in sweet cookies. How do Processing, simulated gastrointestinal digestion and colonic fermentation affect its antioxidant properties?

The aim of this work was to improve the antioxidant quality of cookies using defatted chia flour (DCF), which is a by-|product of the food industry. We prepared cookies containing DFC (5, 10 and 20%), and evaluated the technological and sensory qualities of cookies. Additionally, we verified the effects of processing and simulated gastrointestinal digestion on polyphenols content. The addition of DFC did not affect the technological quality of cookies, with the exception of color. Furthermore, cookies supplemented with 10% DFC were sensorial preferred over the others. The addition of DFC increased the polyphenol content and the in vitro antioxidant capacity of cookies. Besides, the simulated gastrointestinal digestion suggested that 73% of total polyphenols could be absorbed in the intestine, showing an antioxidant effect greater than expected, also showing prebiotic effects. Supplementation of cookies with 10% DFC could be recommended to improve antioxidant quality without reducing the technological or sensorial properties.

Towards a deeper understanding of structural biomass recalcitrance using phase-contrast tomography.

BACKGROUND: The development of technological routes to convert lignocellulosic biomass to liquid fuels requires an in-depth understanding of the cell wall architecture of substrates. Essential pretreatment processes are conducted to reduce biomass recalcitrance and usually increase the reactive surface area. Quantitative three-dimensional information about both bulk and surface structural features of substrates needs to be obtained to expand our knowledge of substrates. In this work, phase-contrast tomography (PCT) was used to gather information about the structure of a model lignocellulosic biomass (piassava fibers). RESULTS: The three-dimensional cellular organization of piassava fibers was characterized by PCT using synchrotron radiation. This technique enabled important physical features that describe the substrate piassava fibers to be visualized and quantified. The external surface area of a fiber and internal surface area of the pores in a fiber could be determined separately. More than 96% of the overall surface area available to enzymes was in the bulk substrate. The pore surface area and length exhibited a positive linear relationship, where the slope of this relationship depended on the plant tissue. CONCLUSIONS: We demonstrated that PCT is a powerful tool for the three-dimensional characterization of the cell wall features related to biomass recalcitrance. Original and relevant quantitative information about the structural features of the analyzed material were obtained. The data obtained by PCT can be used to improve processing routes to efficiently convert biomass feedstock into sugars.