Lactic Acid Bacteria in Durum Wheat Flour Are Endophytic Components of the Plant during Its Entire Life Cycle

This study aimed at assessing the dynamics of lactic acid bacteria and other Firmicutes associated with durum wheat organs and processed products. 16S rRNA gene-based high-throughput sequencing showed that Lactobacillus, Streptococcus, Enterococcus, and Lactococcus were the main epiphytic and endophytic genera among lactic acid bacteria. Bacillus, Exiguobacterium, Paenibacillus, and Staphylococcus completed the picture of the core genus microbiome. The relative abundance of each lactic acid bacterium genus was affected by cultivars, phenological stages, other Firmicutes genera, environmental temperature, and water activity (a_w) of plant organs. Lactobacilli, showing the highest sensitivity to a_w, markedly decreased during milk development (Odisseo) and physiological maturity (Saragolla). At these stages, Lactobacillus was mainly replaced by Streptococcus, Lactococcus, and Enterococcus. However, a key sourdough species, Lactobacillus plantarum, was associated with plant organs during the life cycle of Odisseo and Saragolla wheat. The composition of the sourdough microbiota and the overall quality of leavened baked goods are also determined throughout the phenological stages of wheat cultivation, with variations depending on environmental and agronomic factors.     

Long-Term Fungal Inhibition by Pisum sativum Flour Hydrolysate during Storage of Wheat Flour Bread

In order to identify antifungal compounds from natural sources to be used as ingredients in the bakery industry, water/salt-soluble extracts (WSE) from different legume flour hydrolysates obtained by the use of a fungal protease were assayed against Penicillium roqueforti DPPMAF1. The agar diffusion assays allowed the selection of the pea (Pisum sativum) hydrolysate as the most active. As shown by the hyphal radial growth rate, the WSE had inhibitory activity towards several fungi isolated from bakeries. The MIC of the WSE was 9.0 mg/ml. Fungal inhibition was slightly affected by heating and variations in pH. The antifungal activity was attributed to three native proteins (pea defensins 1 and 2 and a nonspecific lipid transfer protein [nsLTP]) and a mixture of peptides released during hydrolysis. The three proteins have been reported previously as components of the defense system of the plant. Five peptides were purified from WSE and were identified as sequences encrypted in leginsulin A, vicilin, provicilin, and the nsLTP. To confirm antifungal activity, the peptides were chemically synthesized and tested. Freeze-dried WSE were used as ingredients in leavened baked goods. In particular, breads made by the addition of 1.6% (wt/wt) of the extract and fermented by baker''s yeast or sourdough were characterized for their main chemical, structural, and sensory features, packed in polyethylene bags, stored at room temperature, and compared to controls prepared without pea hydrolysate. Artificially inoculated slices of a bread containing the WSE did not show contamination by fungi until at least 21 days of storage and behaved like the bread prepared with calcium propionate (0.3%, wt/wt).     

Extending networks of protected areas to optimize connectivity and population growth rate

Protected areas (PAs) are recognized as the flagship tool to offset biodiversity loss on Earth. Spatial conservation planning seeks optimal designs of PAs that meet multiple targets such as biodiversity representation and population persistence. Since connectivity between PAs is a fundamental requirement for population persistence, several methods have been developed to include connectivity into PA design algorithms. Among these, the eigenvalue decomposition of the connectivity matrix allows for identifying clusters of strongly connected sites and selecting the sites contributing the most to population persistence. So far, this method was only suited to optimize an entire network of PAs without considering existing PAs in the new design. However, a more cost‐effective and realistic approach is to optimize the design of an extended network to improve its connectivity and thus population persistence. Here, we develop a flexible algorithm based on eigenvalue decomposition of connectivity matrices to extend existing networks of PAs while optimizing connectivity and population growth rate. We also include a splitting algorithm to improve cluster identification. The new algorithm accounts for the change in connectivity due to the increased biological productivity often observed in existing PAs. We illustrate the potential of our algorithm by proposing an extension of the network of ～100 Mediterranean marine PAs to reach the targeted 10% surface area protection from the current 1.8%. We identify differences between the clean slate scenario, where all sites are available for protection, irrespective of their current protection status, and the scenario where existing PAs are forced to be included into the optimized solution. By integrating this algorithm to existing multi‐objective and multi‐specific algorithms of PA selection, the demographic effects of connectivity can be explicitly included into conservation planning.     

Odourant dominance in olfactory mixture processing: what makes a strong odourant?

The question of how animals process stimulus mixtures remains controversial as opposing views propose that mixtures are processed analytically, as the sum of their elements, or holistically, as unique entities different from their elements. Overshadowing is a widespread phenomenon that can help decide between these alternatives. In overshadowing, an individual trained with a binary mixture learns one element better at the expense of the other. Although element salience (learning success) has been suggested as a main explanation for overshadowing, the mechanisms underlying this phenomenon remain unclear. We studied olfactory overshadowing in honeybees to uncover the mechanisms underlying olfactory-mixture processing. We provide, to our knowledge, the most comprehensive dataset on overshadowing to date based on 90 experimental groups involving more than 2700 bees trained either with six odourants or with their resulting 15 binary mixtures. We found that bees process olfactory mixtures analytically and that salience alone cannot predict overshadowing. After normalizing learning success, we found that an unexpected feature, the generalization profile of an odourant, was determinant for overshadowing. Odourants that induced less generalization enhanced their distinctiveness and became dominant in the mixture. Our study thus uncovers features that determine odourant dominance within olfactory mixtures and allows the referring of this phenomenon to differences in neural activity both at the receptor and the central level in the insect nervous system.