ORCID Profile
0000-0002-9709-073X
Current Organisations
Universidade de São Paulo
,
Hellenic Agricultural Organisation DIMITRA
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Publisher: Copernicus GmbH
Date: 22-09-2022
DOI: 10.5194/ESSD-14-4315-2022
Abstract: Abstract. Marine particles of different nature are found throughout the global ocean. The term “marine particles” describes detritus aggregates and fecal pellets as well as bacterioplankton, phytoplankton, zooplankton and nekton. Here, we present a global particle size distribution dataset obtained with several Underwater Vision Profiler 5 (UVP5) camera systems. Overall, within the 64 µm to about 50 mm size range covered by the UVP5, detrital particles are the most abundant component of all marine particles thus, measurements of the particle size distribution with the UVP5 can yield important information on detrital particle dynamics. During deployment, which is possible down to 6000 m depth, the UVP5 images a volume of about 1 L at a frequency of 6 to 20 Hz. Each image is segmented in real time, and size measurements of particles are automatically stored. All UVP5 units used to generate the dataset presented here were inter-calibrated using a UVP5 high-definition unit as reference. Our consistent particle size distribution dataset contains 8805 vertical profiles collected between 19 June 2008 and 23 November 2020. All major ocean basins, as well as the Mediterranean Sea and the Baltic Sea, were s led. A total of 19 % of all profiles had a maximum s ling depth shallower than 200 dbar, 38 % s led at least the upper 1000 dbar depth range and 11 % went down to at least 3000 dbar depth. First analysis of the particle size distribution dataset shows that particle abundance is found to be high at high latitudes and in coastal areas where surface productivity or continental inputs are elevated. The lowest values are found in the deep ocean and in the oceanic gyres. Our dataset should be valuable for more in-depth studies that focus on the analysis of regional, temporal and global patterns of particle size distribution and flux as well as for the development and adjustment of regional and global biogeochemical models. The marine particle size distribution dataset (Kiko et al., 2021) is available at 0.1594/PANGAEA.924375.
Publisher: Elsevier BV
Date: 12-2022
DOI: 10.1016/J.PLAPHY.2022.10.023
Abstract: L-Ascorbic acid (AsA), a strong antioxidant, serves as an enzyme cofactor and redox status marker, modulating a plethora of biological processes. As tomato commercial varieties and hybrids possess relatively low amounts of AsA, the improvement of fruit AsA represents a strategic goal for enhanced human health. Previously, we have suggested that GDP-L-Galactose phosphorylase (GGP) and L-galactose-1-phosphate phosphatase (GPP) can serve as possible targets for AsA manipulation in tomato (Solanum lycopersicon L.) fruit. To this end, we produced and evaluated T3 transgenic tomato plants carrying these two genes under the control of CaMV-35S and two fruit specific promoters, PPC2 and PG-GGPI. The transgenic lines had elevated levels of AsA, with the PG-GGP1 line containing 3-fold more AsA than WT, without affecting fruit characteristics. Following RNA-Seq analysis, 164 and 13 DEGs were up- or down-regulated, respectively, between PG-GGP1 and WT pink fruits. PG-GGP1 fruit had a distinct number of up-regulated transcripts associated with cell wall modification, ethylene biosynthesis and signaling, pollen fertility and carotenoid metabolism. The elevated AsA accumulation resulted in the up regulation of AsA associated transcripts and alternative biosynthetic pathways suggesting that the entire metabolic pathway was influenced, probably via master regulation. We show here that AsA-fortification of tomato ripe fruit via GGP1 overexpression under the action of a fruit specific promoter PG affects fruit development and ripening, reduces ethylene production, and increased the levels of sugars, and carotenoids, supporting a robust database to further explore the role of AsA induced genes for agronomically important traits, breeding programs and precision gene editing approaches.
Publisher: American Society for Microbiology
Date: 24-04-2012
DOI: 10.1128/JB.00377-12
Publisher: Research Square Platform LLC
Date: 25-04-2022
DOI: 10.21203/RS.3.RS-1186379/V1
Abstract: For over 50 years, the conceptualisation of low-nutrient oligotrophic systems having longer food chains and thus lower energy transfer to fish than their high-nutrient eutrophic counterparts 1 has achieved the status of an ecological paradigm. However, recent global assessments indicate global fish biomass could be much higher than previously thought 2–4 , suggesting that our traditional understanding of food webs may need to be revisited. Here, we challenge the classical paradigm by exploring the role of zooplankton in food webs across the world’s oceans. Using observed zooplankton size spectra, and output from a size-spectrum model that resolves nine zooplankton groups, we conclude that food chains in oligotrophic (low-nutrient) and eutrophic (high-nutrient) systems have similar lengths. We offer a compelling hypothesis to explain this emergent pattern: self-organisation of zooplankton groups across the global productivity gradient regulates food chain length. We find that in oligotrophic systems the increased carnivory and longer food chains are offset by relatively large gelatinous filter feeders eating the dominant small phytoplankton, resulting in shorter-than-expected food chains, but decreasing food quality for fish. Our findings highlight the pivotal role zooplankton play in regulating energy transfer. Better resolution of zooplankton groups, their feeding relationships and carbon content in models will increase our ability to estimate current global fish biomass 5 , project future fish biomass under climate change 6–8 , and provide more-robust forecasts of nutrient 9 and carbon cycling 10 .
Publisher: Oxford University Press (OUP)
Date: 03-08-2023
Abstract: 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 ersity 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.
Publisher: Frontiers Media SA
Date: 25-04-2019
No related grants have been discovered for Rubens Lopes.