Bayesian Multi-Targets Strategy to Track Apis mellifera Movements at Colony Level.

Interactive movements of bees facilitate the division and organization of collective tasks, notably when they need to face internal or external environmental challenges. Here, we present a Bayesian and computational approach to track the movement of several honey bee, Apis mellifera, workers at colony level. We applied algorithms that combined tracking and Kernel Density Estimation (KDE), allowing measurements of entropy and Probability Distribution Function (PDF) of the motion of tracked organisms. We placed approximately 200 recently emerged and labeled bees inside an experimental colony, which consists of a mated queen, approximately 1000 bees, and a naturally occurring beehive background. Before release, labeled bees were fed for one hour with uncontaminated diets or diets containing a commercial mixture of synthetic fungicides (thiophanate-methyl and chlorothalonil). The colonies were filmed (12 min) at the 1st hour, 5th and 10th days after the bees' release. Our results revealed that the algorithm tracked the labeled bees with great accuracy. Pesticide-contaminated colonies showed anticipated collective activities in peripheral hive areas, far from the brood area, and exhibited reduced swarm entropy and energy values when compared to uncontaminated colonies. Collectively, our approach opens novel possibilities to quantify and predict potential alterations mediated by pollutants (e.g., pesticides) at the bee colony-level.

Chronic Undernutrition Differentially Changes Muscle Fiber Types Organization and Distribution in the EDL Muscle Fascicles.

Fiber type composition, organization, and distribution are key elements in muscle functioning. These properties can be modified by intrinsic and/or extrinsic factors, such as undernutrition and injuries. Currently, there is no methodology to quantitatively analyze such modifications. On one hand, we propose a fractal approach to determine fiber type organization, using the fractal correlation method in software Fractalyse. On the other hand, we applied the kernel methodology from machine learning to build radial-basis functions for the spatial distribution of fibers (distribution functions), by dividing into square cells a two-dimensional binary image for the spatial distribution of fibers from a muscle fascicle and mounting on each cell a radial-basis function in such a way that the sum of all cell functions creates a smooth version of the fiber histogram on the cell grid. The distribution functions thus created belong in a reproducing kernel Hilbert space which permits us to regard them as vectors and measure distances and angles between them. In the present study, we analyze fiber type organization and distribution in fascicles (F2, F3, F4, and F5) of the extensor digitorum longus muscle (EDLm) from control and undernourished male rats. Fibers were classified according to the ATPase activity in slow, intermediate, and fast. Then, (x, y) coordinates of fibers were used to build binary images and distribution functions for each fiber type and both conditions. The fractal organization analysis showed that fast and intermediate fibers, from both groups, had a fractal organization within the four fascicles, i.e., the fiber assembly is distributed in clusters. We also show that chronic undernutrition altered the organization of fast fibers in the F3, although it still is considered a fractal organization. Distribution function analysis showed that each fiber type (slow, intermediate, and fast) has a unique distribution within the fascicles, in both conditions. However, chronic undernutrition modified the intra-fascicular fiber type distributions, except in the F2. Altogether, these results showed that the methodology herein proposed allows for analyzing fiber type organization and distribution modifications. On the other side, we show that chronic undernutrition alters not only the fiber type composition but also the organization and distribution, which could affect the muscle functioning, and ultimately, its behavior (e.g., locomotion).

Bioglass 45S5: Structural characterization of short range order and analysis of biocompatibility with adipose-derived mesenchymal stromal cells in vitro and in vivo.

Bioactive glasses have potential applications in the field of regenerative medicine due to their bioactivity that permits interaction with both hard and soft tissues. In the same way, mesenchymal stromal cells (MSCs) have been experimentally tested as part of engineered constructs considering their self-renewal and multipotent capacities. However, to design an association, it is crucial to investigate the physical properties of bioglass 45S5, as well as its biocompatibility. Therefore, we investigated the structural short range order of the stoichiometric 45S5, by obtaining its total structure factors (S(K)) and total pair distribution function G(r). The in vitro compatibility of human MSCs with 45S5 was verified by viability, morphometry and osteoinduction assays, F-actin staining and scanning electron (SEM) analysis. The compatibility outcome was verified through a subcutaneous implantation in a murine model by grafting the 45S5 as a scaffold for allogeneic MSCs. The cell-substrate modulation includes the maintenance of the MSC viability and osteoinduction potential after being exposed to the 45S5 extract. A low spreading during cell adhesion was detected. Both normal actin cytoskeleton organization and nuclei irregularities were observed, besides an increase of hydroxyapatite (HA) depositions around cells. Cells showed satisfactory compatibility patterns when growing over 45S5 for 7, 30 and 90 days. The implant did not show any apparent toxicity for organs, or strong immunogenic reactions, and it was accompanied by a dense capsule formation around the graft. Our results indicate that MSCs can grow in the long term on the 45S5 while maintaining their characteristics. This fact, together with a non-toxicity to animals means that the 45S5 can be implemented in pre-clinical trials aiming MSC's transplantation leading to further bone and tissue repair.

A Trivariate Extreme Value Distribution Applied to Flood Frequency Analysis.

A trivariate extreme value distribution has been derived from the logistic model for the multivariate extreme value distribution. The construction of its corresponding probability distribution and density function is described. In order to obtain the parameters of such a trivariate distribution, a generalized maximum likelihood estimation procedure is described to allow for the cases of samples with different record lengths. Furthermore the reliability of the estimated parameters of the irivariate extreme value distribution is measured through the use of relative information ratios. A region in Northern Mexico with six gauging stations has been selected to apply the trivariate model. Results produced by the proposed model have been compared with those obtained by general extreme value (GEV) distribution functions.