Optimizing canopy‐forming algae conservation and restoration with a new herbivorous fish deterrent device

The role of herbivorous fish in threatening marine forests of temperate seas has been generally overlooked. Only recently, the scientific community has highlighted that high fish herbivory can lead to regime shifts from canopy‐forming algae to less complex turf communities. Here, we present an innovative herbivorous fish deterrent device (DeFish), which can be used for conservation and restoration of marine forests. Compared to most traditional fish exclusion systems, such as cages, the DeFish system does not need regular cleaning and maintenance, making it more cost‐efficient. Resistance of DeFish was tested by installing prototypes at different depths in the French Riviera and in Montenegro: more than 60% of the devices endured several years without maintenance, even if most of them were slightly damaged in the exposed site in Montenegro. The efficacy of DeFish in limiting fish herbivory was tested by an exclusion experiment on Cystoseira amentacea in the French Riviera. In a few months, the number of fish bite marks on the seaweed was decreased, causing a consequent increase in algal length. The device here presented has been conceived for Mediterranean canopy‐forming algae, but the same concept can be applied to other species vulnerable to fish herbivory, such as kelps or seagrasses. In particular, the DeFish design could be improved using more robust and biodegradable materials. Innovative engineering systems, such as DeFish, are expected to become useful tools in the conservation and restoration of marine forests, to complement other practices including active reforestation, herbivore regulation, and regular monitoring of their status.     

Response of transgenic tobacco overexpressing the CchGLP gene to cadmium and aluminium: phenotypic and microRNAs expression changes

Physiology and Molecular Biology of Plants - Transgenic tobacco (N. tabacum cv. Xanthi nc) expressing Capsicum chinense CchGLP gene that encodes an Mn-SOD, constitutively produces hydrogen...     

Contrasting the seasonal and elevational prevalence of generalist avian haemosporidia in co‐occurring host species

Understanding the ecology and evolution of parasites is contingent on identifying the selection pressures they face across their infection landscape. Such a task is made challenging by the fact that these pressures will likely vary across time and space, as a result of seasonal and geographical differences in host susceptibility or transmission opportunities. Avian haemosporidian blood parasites are capable of infecting multiple co‐occurring hosts within their ranges, yet whether their distribution across time and space varies similarly in their different host species remains unclear. Here, we applied a new PCR method to detect avian haemosporidia (genera Haemoproteus, Leucocytozoon, and Plasmodium) and to determine parasite prevalence in two closely related and co‐occurring host species, blue tits (Cyanistes caeruleus, N = 529) and great tits (Parus major, N = 443). Our samples were collected between autumn and spring, along an elevational gradient in the French Pyrenees and over a three‐year period. Most parasites were found to infect both host species, and while these generalist parasites displayed similar elevational patterns of prevalence in the two host species, this was not always the case for seasonal prevalence patterns. For example, Leucocytozoon group A parasites showed inverse seasonal prevalence when comparing between the two host species, being highest in winter and spring in blue tits but higher in autumn in great tits. While Plasmodium relictum prevalence was overall lower in spring relative to winter or autumn in both species, spring prevalence was also lower in blue tits than in great tits. Together, these results reveal how generalist parasites can exhibit host‐specific epidemiology, which is likely to complicate predictions of host–parasite co‐evolution.     

Teaching life cycle assessment in higher education

The International Journal of Life Cycle Assessment - Scientific Life Cycle Assessment (LCA) literature provides some examples of LCA teaching in higher education, but not a structured overview of...     

Differentiation of the binding of two ligands to a tetrameric protein with a single symmetric active site by 19F NMR

R67 dihydrofolate reductase (R67 DHFR) is a plasmid‐encoded enzyme that confers resistance to the antibacterial drug trimethoprim. R67 DHFR is a tetramer with a single active site that is unusual as both cofactor and substrate are recognized by symmetry‐related residues. Such promiscuity has limited our previous efforts to differentiate ligand binding by NMR. To address this problem, we incorporated fluorine at positions 4, 5, 6, or 7 of the indole rings of tryptophans 38 and 45 and characterized the spectra to determine which probe was optimal for studying ligand binding. Two resonances were observed for all apo proteins. Unexpectedly, the W45 resonance appeared broad, and truncation of the disordered N‐termini resulted in the appearance of one sharp W45 resonance. These results are consistent with interaction of the N‐terminus with W45. Binding of the cofactor broadened W38 for all fluorine probes, whereas substrate, dihydrofolate, binding resulted in the appearance of three new resonances for 4‐ and 5‐fluoroindole labeled protein and severe line broadening for 6‐ and 7‐fluoroindole R67 DHFR. W45 became slightly broader upon ligand binding. With only two peaks in the ¹⁹F NMR spectra, our data were able to differentiate cofactor and substrate binding to the single, symmetric active site of R67 DHFR and yield binding affinities.     

Correction to: Whole-Genome sequencing and comparative genomics of Mycobacterium spp. from farmed Atlantic and coho salmon in Chile

Antonie van Leeuwenhoek -     

Role of HRTPT in kidney proximal epithelial cell regeneration: Integrative differential expression and pathway analyses using microarray and scRNA-seq

Damage to proximal tubules due to exposure to toxicants can lead to conditions such as acute kidney injury (AKI), chronic kidney disease (CKD) and ultimately end-stage renal failure (ESRF). Studies have shown that kidney proximal epithelial cells can regenerate particularly after acute injury. In the previous study, we utilized an immortalized in vitro model of human renal proximal tubule epithelial cells, RPTEC/TERT1, to isolate HRTPT cell line that co-expresses stem cell markers CD133 and CD24, and HREC24T cell line that expresses only CD24. HRTPT cells showed most of the key characteristics of stem/progenitor cells; however, HREC24T cells did not show any of these characteristics. The goal of this study was to further characterize and understand the global gene expression differences, upregulated pathways and gene interaction using scRNA-seq in HRTPT cells. Affymetrix microarray analysis identified common gene sets and pathways specific to HRTPT and HREC24T cells analysed using DAVID, Reactome and Ingenuity software. Gene sets of HRTPT cells, in comparison with publicly available data set for CD133+ infant kidney, urine-derived renal progenitor cells and human kidney-derived epithelial proximal tubule cells showed substantial similarity in organization and interactions of the apical membrane. Single-cell analysis of HRTPT cells identified unique gene clusters associated with CD133 and the 92 common gene sets from three data sets. In conclusion, the gene expression analysis identified a unique gene set for HRTPT cells and narrowed the co-expressed gene set compared with other human renal–derived cell lines expressing CD133, which may provide deeper understanding in their role as progenitor/stem cells that participate in renal repair.     

Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.