Climate change may have minor impact on zooplankton functional diversity in the Mediterranean Sea

Aim To assess the impact of climate change on the functional diversity of marine zooplankton communities. Location The Mediterranean Sea. Methods We used the functional traits and geographic distributions of 106 copepod species to estimate the zooplankton functional diversity of Mediterranean surface assemblages for the 1965–1994 and 2069–2098 periods. Multiple environmental niche models were trained at the global scale to project the species habitat suitability in the Mediterranean Sea and assess their sensitivity to climate change predicted by several scenarios. Simultaneously, the species traits were used to compute a functional dendrogram from which we identified seven functional groups and estimated functional diversity through Faith's index. We compared the measured functional diversity to the one originated from null models to test if changes in functional diversity were solely driven by changes in species richness. Results All but three of the 106 species presented range contractions of varying intensity. A relatively low decrease of species richness (−7.42 on average) is predicted for 97% of the basin, with higher losses in the eastern regions. Relative sensitivity to climate change is not clustered in functional space and does not significantly vary across the seven copepod functional groups defined. Changes in functional diversity follow the same pattern and are not different from those that can be expected from changes in richness alone. Main conclusions Climate change is not expected to alter copepod functional traits distribution in the Mediterranean Sea, as the most and the least sensitive species are functionally redundant. Such redundancy should buffer the loss of ecosystem functions in Mediterranean zooplankton assemblages induced by climate change. Because the most negatively impacted species are affiliated to temperate regimes and share Atlantic biogeographic origins, our results are in line with the hypothesis of increasingly more tropical Mediterranean communities

Precise Method to Identify Kinase Drug Targets in Complex Diseases: The First Step Towards Sustainable and Effective Treatment

Background: Kinases are enzymes that have proven to be important drug targets due to their role in critical biological mechanisms such as phosphorylation. Phosphorylation happens when a kinase catalyzes the transfer of a phosphate group to a protein in a phosphorylated site, which then becomes known as the substrate of the kinase. Any dysregulation of protein phosphorylation causes a wide range of complex diseases including cancer. Thus, discovering the links between kinases and their substrates (i.e. predicting kinase-substrate associations (KSAs)) is crucial in developing effective and sustainable treatments. Presently, less than 5% of phosphorylated sites have an associated kinase, and existing prediction methods tend to favor well-known kinases. In this project, we use algorithms (NetREX) that were developed in the context of gene regulatory networks to create a network of kinases and substrates to improve prediction precision by uncovering hidden KSAs, while also comprehensively analyzing existing computational methods. Methods: We modify NetRex, a method developed to find the links between transcription factors and genes, to create a network, where kinases and their phosphorylated sites are nodes and the known associations between them (KSAs) are represented by edges. We use the network component analysis model (NCA), which assumes that each kinase is characterized by its activity. NCA explains the phosphorylation of each phosphosite as a linear combination of the activities of its regulating kinases. We iteratively add and remove edges in the network, where co-phosphorylated sites are co-regulated, and kinases with correlated activities co-regulate the same site. Lastly, our edges are ranked by their confidence score, which is their impact on the overall performance of the linear model. Results: Once we ran the algorithms on our data, we were able to obtain a network of kinases and substrates. We are currently in the process of performing cross validation to assess sensitivity and specificity of our network using methods such as 5-fold and leave-one-out. In addition, we will comprehensively analyze and compare our results with other computational methods to see if an improvement was achieved. Conclusions: Identifying kinases causing abnormal phosphorylation is essential for drug discovery and effective treatment. Current computational prediction methods tend to find KSAs for kinases that already have several phosphorylated sites associated with them. Based on our results, we anticipate to uncover KSAs for kinases that do not have many phosphorylated sites associated with them priorly

Electronic Properties and Magnetic Moment Distribution on Perovskite Type Slabs: Sr2FeMoO6, SrFeO3 and SrMoO3

AbstractPerovskite type slabs were excised from the Sr2FeMoO6, SrFeO3 and SrMoO3 bulk double perovskites, respectively, leaving (001) free surfaces. Supercells were built up for each slab, keeping a 10Å initial free space, to optimize the geometry. Once the minimum energy state was identified, the electronic and magnetic properties of the [001] oriented slabs have been calculated within the Density Functional Theory (DFT) scheme, with the Hubbard-corrected Local Density Approximation (LDA+U) and the CA−PZ functional. Magnetic moment for each atom in the systems was calculated; spin values for the Mo atoms are –0.02ħ, − 0.13ħ and 0.56ħ for the SrMoO3 slab system case and they are aligned antiferromagnetically. Contrarily, Mo magnetic moments in the Sr2FeMoO3 slab system align antiferromagnetically to the corresponding Fe atoms, being around 10% in magnitude; meanwhile, Fe moments increase and align ferromagnetically in SrFeO3. The Densities of States (DOS) and band structures were calculated also to study the electronic behaviors. The vacuum region changes from the initial 10Å, as geometry stabilizes for all the slab cases; however, slab images separation evolves notoriously different for each model

Effect of gaseous cement industry effluents on four species of microalgae

International audienceExperiments were performed at lab scale in order to test the possibility to grow microalgae with CO2 from gaseous effluent of cement industry. Four microalgal species (Dunaliella tertiolecta, Chlorella vulgaris, Thalassiosira weissflogii, and Isochrysis galbana), representing four different phyla were grown with CO2 enriched air or with a mixture of gasses mimicking the composition of a typical cement flue gas (CFG). In a second stage, the culture submitted to the CFG received an increasing concentration of dust characteristic of cement industry. Results show that growth for the four species is not affected by the CFG. Dust added at realistic concentrations do not have any impact on growth. For dust concentrations in two ranges of magnitude higher, microalgae growth was inhibited

Theory of Raman Scattering by Phonons in Germanium Nanostructures

Within the linear response theory, a local bond-polarization model based on the displacement–displacement Green’s function and the Born potential including central and non-central interatomic forces is used to investigate the Raman response and the phonon band structure of Ge nanostructures. In particular, a supercell model is employed, in which along the [001] direction empty-column pores and nanowires are constructed preserving the crystalline Ge atomic structure. An advantage of this model is the interconnection between Ge nanocrystals in porous Ge and then, all the phonon states are delocalized. The results of both porous Ge and nanowires show a shift of the highest-energy Raman peak toward lower frequencies with respect to the Raman response of bulk crystalline Ge. This fact could be related to the confinement of phonons and is in good agreement with the experimental data. Finally, a detailed discussion of the dynamical matrix is given in the appendix section

Temporal evolution of plankton and particles distribution across a mesoscale front during the spring bloom

The effect of mesoscale features on the distribution of planktonic organisms are well documented. Yet, the interaction between these spatial features and the temporal scale, which can result in sudden increases of the planktonic biomass, is less known and not described at high resolution. A permanent mesoscale front in the Ligurian Sea (north-western Mediterranean) was repeatedly sampled between January and June 2021 using a SeaExplorer glider equipped with an Underwater Vision Profiler 6 (UVP6), a versatile in situ imager. Both plankton and particle distributions were resolved throughout the spring bloom to assess whether the front was a location of increased zooplankton concentration and whether it constrained particle distribution. Over the 5 months, the glider performed more than 5000 dives and the UVP6 collected 1.1 million images. We focused our analysis on shallow (300 m) transects, which gave a horizontal resolution of 900 m. About 13,000 images of planktonic organisms were retained. Ordination methods applied to particles and plankton concentrations revealed strong temporal variations during the bloom, with a succession of various zooplankton communities. Changes in particle abundance and size could be explained by changes in the plankton community. The front had a strong influence on particle distribution, while the signal was not as clear for plankton, probably because of the relatively small number of imaged organisms. This work confirms the need to sample both plankton and particles at fine scale to understand their interactions, a task for which automated in situ imaging is particularly adapted

Sinking Organic Particles in the Ocean—Flux Estimates From in situ Optical Devices

Optical particle measurements are emerging as an important technique for understanding the ocean carbon cycle, including contributions to estimates of their downward flux, which sequesters carbon dioxide (CO2) in the deep sea. Optical instruments can be used from ships or installed on autonomous platforms, delivering much greater spatial and temporal coverage of particles in the mesopelagic zone of the ocean than traditional techniques, such as sediment traps. Technologies to image particles have advanced greatly over the last two decades, but the quantitative translation of these immense datasets into biogeochemical properties remains a challenge. In particular, advances are needed to enable the optimal translation of imaged objects into carbon content and sinking velocities. In addition, different devices often measure different optical properties, leading to difficulties in comparing results. Here we provide a practical overview of the challenges and potential of using these instruments, as a step toward improvement and expansion of their applications

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio