Thirty-seven essential oil samples have been isolated from leaves of individual plants of Juniperus oxycedrus (subspecies oxycedrus and macrocarpa) growing wild in Tlemcen and Aïn Témouchent provinces (Northwestern Algeria). Analysis of eight selected oil samples by GC(RI), GC/MS and C NMR, allowed the identification of 88 components that accounted for 84.4-99.
View Article and Find Full Text PDFNeonicotinoids represent over a quarter of the global pesticide market. Research on their environmental impact has revealed their adverse effect on the cognitive functions of pollinators, in particular of bees. Cognitive impairments, mostly revealed by behavioural studies, are the phenotypic expression of an alteration in the underlying neural circuits, a matter deserving greater attention.
View Article and Find Full Text PDFOdour processing exhibits multiple parallels between vertebrate and invertebrate olfactory systems. Insects, in particular, have emerged as relevant models for olfactory studies because of the tractability of their olfactory circuits. Here, we used fast calcium imaging to track the activity of projection neurons in the honey bee antennal lobe (AL) during olfactory stimulation at high temporal resolution.
View Article and Find Full Text PDFThe motion, settling, and dispersion of microplastics in the ocean are determined by their rotational dynamics. We present experiments on elongated, large aspect ratio, and mildly curved plastic fibers slightly longer than the Kolmogorov length scale. Exploiting their uniquely identifiable three-dimensional orientation, we perform original optical Lagrangian investigations and provide a set of homogeneous data on their rotation rates around their longitudinal axis: spinning rate, and transversal axes: tumbling rates, which we explain in the context of the general features of turbulence.
View Article and Find Full Text PDFRecent advances in calcium imaging, including the development of fast and sensitive genetically encoded indicators, high-resolution camera chips for wide-field imaging, and resonant scanning mirrors in laser scanning microscopy, have notably improved the temporal and spatial resolution of functional imaging analysis. Nonetheless, the variability of imaging approaches and brain structures challenges the development of versatile and reliable segmentation methods. Standard techniques, such as manual selection of regions of interest or machine learning solutions, often fall short due to either user bias, non-transferability among systems, or computational demand.
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