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Exploring the Relationship Between Astringency and Lingual Tactile Sensitivity
No full textInternational audienceWhen lingual tactile sensitivity has received less attention than taste, it plays a critical role in food textural preferences and eating behavior. The anterior tongue, rich in specialized mechanoreceptors, is particularly sensitive to tactile stimuli. This study investigated inter‐individual variability in lingual tactile sensitivity and its potential relationship with astringency sensitivity. To this end, three distinct aspects of lingual tactile sensitivity as well as tongue strength were assessed in 39 subjects (26 F, mean age = 35 ± 5 years). The three tactile dimensions included: (i) light touch (assessed with Von Frey monofilaments in a one‐point pressure test), (ii) spatial perception (two‐point discrimination test), and (iii) roughness sensitivity (using paper coupons with varying grit sizes). Astringency detection thresholds, previously determined using tannic acid solutions, were available for all participants. Results revealed significant inter‐individual variability across all tactile measures. Notably, no significant correlations were found between the three different tactile sensitivity tests (light touch, spatial perception, roughness), suggesting that each one captures a distinct aspect of lingual tactile functions. Tongue strength was not associated with either tactile or astringency sensitivity. A key finding was a significant positive correlation between the tannic acid astringency detection threshold and the pressure discrimination threshold, suggesting that astringency sensitivity may involve a tactile component possibly mediated by the slowly adapting mechanoreceptors (SAI and SAII types). However, no relationship was found between astringency sensitivity and roughness sensitivity. The study emphasizes the need for standardized methods to better understand distinct dimensions of lingual tactile sensitivity and their influence on food texture perception
Pollinators in blackcurrant orchards: Spatio-temporal environmental effects and no evidence of competition with co-flowering resources
No full textAgricultural intensification is threatening pollinators and associated ecosystem services. Flower strips and spontaneous floral cover are commonly recommended to support pollinators, but farmers often hesitate to adopt them, fearing competition with their crops. Here, we studied nine blackcurrant orchards in Burgundy, France, differing in local floral resources and surrounding landscape composition. We thus investigated (i) the potential competition for pollinators between blackcurrant flowers and flowers in adjacent inter-rows, and (ii) the effects of local and landscape factors through time on the abundance of five pollinator morpho-groups: Bombus spp., Apis mellifera, large bees, small bees, and hoverflies. We conducted transects in orchards at three different periods in spring to quantify the abundance of local flowers and pollinators. We characterised the landscape composition around sites within a 100 m and 500 m radii. We showed that in each morpho-group, pollinator abundance in blackcurrant bushes increased with pollinators in adjacent inter-rows and was unaffected by flower abundance there, suggesting an absence of competition. Pollinator morpho-groups responded differently to environmental factors, with variations throughout spring. Small bees were the most negatively affected by the distance from orchard edge, while hoverfly abundance highly increased with the number of flowers at the end of spring. Floral resources in the inter-row could support pollinators in spring, particularly hoverflies and poor dispersers like small bees. Given the variability in pollinator traits and rapid environmental changes, future research should focus on functional groups and fine-scale spatio-temporal analysis to better understand environmental impacts on pollinators and effectively guide conservation efforts
Pourquoi les fonds verts peinent à convaincre malgré une épargne record
No full textInternational audienceUne étude cherche à comprendre les freins à l’investissement vert chez les investisseurs individuels. Et si la ruse marketing de quelques acteurs nuisait à l’ensemble de la filière
Tarsal lipids regulate xenobiotic penetration in Drosophila melanogaster
No full textInternational audienceInsects touch their proximal environment with their tarsi. The immediate contact with xenobiotics occurs with the tarsal cuticle surface that is covered with cuticular hydrocarbons (CHCs). In this work, we tested the hypothesis that xenobiotics entry through the tarsi depended on CHC amounts and composition. Applying RNA interference, we suppressed the expression of genes coding for the key enzymes of CHC production Cyp4G1 (total CHC), desat1 (unsaturated CHCs) and FASN2 (branched CHCs) in lipid producing oenocytes and analyzed the penetration efficiency of the insecticides DDT and chlorantraniliprole and of the inert dye Eosin Y in the respective flies. As expected, in walking experiments, reduction of CHC amounts (cyp4G1RNAi) enhanced insecticide and dye penetration into the tarsi. In the same assay, we identified unsaturated CHCs as the main CHC component attenuating DDT and chlorantraniliprole at low concentrations. Likewise, tarsal adhesion and uptake of Eosin Y depended rather on unsaturated than on branched CHCs. Extrapolating from our data, we propose a two‐step model of xenobiotics penetration through the tarsal cuticle: first, modulated by unsaturated CHCs, the molecule is repelled or adheres to the cuticle surface; upon adhesion, the molecule penetrates the cuticle and accumulates in the tarsal lumen in a second step. Whether these mechanisms apply to molecules other than Eosin Y remains to be investigated. Taken together, the tarsal cuticle constitutes a selective bipartite barrier against uncontrolled uptake of contact xenobiotics
A correspondence between quantum error correcting codes and quantum reference frames
No full textIn a gauge theory, a collection of kinematical degrees of freedom is used to redundantly describe a smaller amount of gauge-invariant information. In a quantum error correcting code (QECC), a collection of computational degrees of freedom that make up a device's physical layer is used to redundantly encode a smaller amount of logical information. We elaborate this parallel in terms of quantum reference frames (QRFs), which are a universal toolkit for dealing with symmetries in quantum systems and which define the gauge theory analog of encodings. The result is a precise dictionary between QECCs and QRF setups within the perspective-neutral framework for gauge systems. Concepts from QECCs like error sets and correctability translate to novel insights into the informational architecture of gauge theories. Conversely, the dictionary provides a systematic procedure for constructing symmetry-based QECCs and characterizing their error correcting properties. In this initial work, we scrutinize the dictionary between Pauli stabilizer codes and their corresponding QRF setups. We show that there is a one-to-one correspondence between maximal correctable error sets and tensor factorizations splitting system from error-generated QRF degrees of freedom. Relative to this split, errors corrupt only redundant frame data, leading to a novel characterization of correctability. When passed through the dictionary, standard Pauli errors behave as electric excitations that are dual, via Pontryagin duality, to magnetic excitations related to gauge-fixing. This gives rise to a new class of correctable errors and a systematic error duality. We illustrate our findings in surface codes, which themselves connect quantum error correction with gauge systems. Our exploratory investigations pave the way for foundational applications to gauge theories and for eventual practical applications to quantum simulation
Human emotional odours influence horses’ behaviour and physiology
No full textInternational audienceOlfaction is the most widespread sensory modality animals use to communicate, yet much remains to be discovered about its role. While most studies focused on intraspecific interactions and reproduction, new evidence suggests chemosignals may influence interspecific interactions and emotional communication. This study explores this possibility, investigating olfactory signals in emotional contagion from the example of human-horse interactions. Cotton pads carrying human odours from fear and joy contexts, or unused pads (control odour) were applied to 43 horses’ nostrils during fear tests (suddenness and novelty tests) and human interaction tests (grooming and approach tests). Principal component analysis showed that overall, when exposed to fear-related human odours, horses exhibited significantly heightened fear responses and reduced interaction with humans compared to joy-related and control odours. More precisely, when exposed to fear-related odours, horses touched the human less in the human approach test, gazed more at the novel object, and were more startled (startle intensity and maximum heart rate) by a sudden event. These results highlight the significance of chemosignals in interspecific interactions and provides insights into questions about the impact of domestication on emotional communication
Improving on early exaggeration in t -SNE: Early hierarchization better preserves global structure
No full textInternational audienceIn dimensionality reduction, t-SNE is a local method of neighbor embedding that requires to be carefully initialized in order to preserve the global structure of data to a good extent. In standard t-SNE, the low-dimensional embedding is initialized either randomly or with PCA. Next, gradient descent runs for two successive phases to refine the embedding coordinates iteratively. In the first phase, named early exaggeration, the attractive forces between points are artificially strengthened to prevent the repulsive forces to scatter fragments of the still poorly organized embedding, before the second phase takes over with the genuine gradient, until final convergence. A novel initialization of t-SNE is proposed in this extended work. It proceeds by hierarchizing the data points into a space-partitioning binary tree that yields faithful subsamples of data with 4,8, 16,...2[log2 N], N points; t-SNE runs on these growing subsamples, each obtained embedding initializing the next run. Between two runs, the prototypical point in each tree branch is split into its two children and the embedding is rescaled to account for the increased population. Extended experimental results with 5 repetitions show quantitatively the effectiveness of the method on a variety of artificial and real data sets, while running times get only multiplied by a small constant factor, leaving the computational complexity unchanged. This confirms that early hierarchization can advantageously replace initialization and early exaggeration, making t-SNE a more homogeneous method with fewer meta-parameters. The proposed method is compatible with any method of neighbor embedding (t-SNE, UMAP, etc.) with quadratic, log-linear, or even linear iterations, provided early exaggeration can be disabled and initial coordinates of the embedded data points can be specified
Optical photothermal infrared (OPTIR) spectroscopy assisted by machine learning for lactic acid bacteria identification at strain level
No full textInternational audienceLactic acid bacteria (LAB) are widely used in food, health, and biotechnology sectors, where accurate strain level identification is critical. Conventional methods, such as 16S rRNA sequencing, PCR-based fingerprinting (RAPD, AFLP), and MALDI-TOF mass spectrometry are powerful tools to identify bacteria at species level but often fail to resolve closely related strains due to limited taxonomic resolution, protocol sensitivity, or database dependence. In this study, we evaluated the capacity of Optical photothermal infrared (OPTIR) spectroscopy, a single-cell vibrational imaging technique, combined with supervised neural networks, to classify LAB at both species and strain levels. A total of 13 strains were analysed, including five Lactiplantibacillus plantarum, one Lactiplantibacillus pentosus, one Limosilactobacillus fermentum, three Lacticaseibacillus casei/paracasei, and three Streptococcus thermophilus, covering both intra- and inter-species diversity. Spectral data from LAB were acquired using a mIRage LS OPTIR system, preprocessed, and used to train a fully connected neural network for each level. The models achieved macro F1-scores of 97% for species level and 91% for strain level classification. These results demonstrate the potential of OPTIR, when integrated with machine learning, as a robust tool for high-resolution bacterial classification, with promising applications in microbiological quality control, probiotic selection, and microbial ecology
The negative impact of urban sprawl on biodiversity: A simulation approach to genetic diversity in European cities
No full textInternational audienceCurrent urbanization trends pose multiple challenges to biodiversity conservation and the provision of ecosystem services to city dwellers. Beside attempts to halt urbanization, urban planners can control urban forms, i.e., the spatial configuration of artificial areas within and around cities. Yet, a lack of consensus on their influence on biodiversity hinders appropriate decision-making. It is commonly predicted that compact cities should exhibit low biodiversity levels at their center while preserving the biodiversity of peri-urban areas, whereas the opposite pattern should be observed in sprawled cities. To test whether these trade-offs can actually emerge from existing urban forms, we simulated the genetic diversity of urban and peri-urban animal populations in 325 European cities. We delineated them by standardizing the proportion of artificial areas to 20 %, and distinguishing urban green spaces from forest habitats. We then modeled the indirect interplay of urban forms (irrespective of the degree of urbanization), the connectivity of these two habitats, and their respective genetic diversity. Our statistical path modeling results revealed an overall negative effect of urban sprawl on habitat connectivity, and consequently on genetic diversity in both habitat types. Interestingly, forest habitat connectivity was a better predictor of genetic diversity in the urban populations of the simulated species than was urban green space connectivity. This reflected the importance of preserving peri-urban habitats from urban sprawl, as they may act as biodiversity sources for city centers. Accordingly, efforts to foster biodiversity within cities should not overshadow the large-scale impacts of urban sprawl on peri-urban biodiversity