Summary1. The disease costs of sociality have largely been understood through the link between group size and transmission. However, infectious disease spread is driven primarily by the social organization of interactions in a group and not its size.2. We used statistical models to review the social network organization of 47 species, including mammals, birds, reptiles, fish and insects by categorizing each species into one of three social systems, relatively solitary, gregarious *
Social network analysis is an invaluable tool to understand the patterns, evolution, and consequences of sociality. Comparative studies over a range of social systems across multiple taxonomic groups are particularly valuable. Such studies however require quantitative social association or interaction data across multiple species which is not easily available. We introduce the Animal Social Network Repository (ASNR) as the first multi-taxonomic repository that collates 790 social networks from more than 45 species, including those of mammals, reptiles, fish, birds, and insects. The repository was created by consolidating social network datasets from the literature on wild and captive animals into a consistent and easy-to-use network data format. The repository is archived at https://bansallab.github.io/asnr/ . ASNR has tremendous research potential, including testing hypotheses in the fields of animal ecology, social behavior, epidemiology and evolutionary biology.
Social network analysis is an invaluable tool to understand the patterns, evolution, and consequences of sociality. Comparative studies over the spectrum of sociality across taxonomic groups are particularly valuable. Such studies however require quantitative information on social interactions across multiple species which is not easily available. We introduce the Animal Social Network Repository (ASNR) as the first multitaxonomic repository that collates more than 650 social networks from 47 species, including those of mammals, reptiles, fish, birds, and insects. The repository was created by consolidating social network datasets from the literature on wild and captive animals into a consistent and easy-to-use network data format. The repository is archived at https://bansallab.github.io/asnr/. ASNR has tremendous research potential, including testing hypotheses in the fields of animal ecology, social behavior, epidemiology and evolutionary biology.
Purpose: We conducted our study to develop a tool capable of automatically detecting dental artifacts in a CT scan on a slice-by-slice basis and to assess the dosimetric impact of implementing the tool into the Radiation Planning Assistant (RPA), a web-based platform designed to fully automate the radiation therapy treatment planning process. Methods: We developed an automatic dental artifact identification tool and assessed the dosimetric impact of its use in the RPA. Three users manually annotated 83,676 head-and-neck (HN) CT slices (549 patients). Majority-voting was applied to the individual annotations to determine the presence or absence of dental artifacts. The patients were divided into train, cross-validation, and test data sets (ratio: 3:1:1, respectively). A random subset of images without dental artifacts was used to balance classes (1:1) in the training data set. The Inception-V3 deep learning model was trained with the binary cross-entropy loss function. With use of this model, we automatically identified artifacts on 15 RPA HN plans on a slice-by-slice basis and investigated three dental artifact management methods applied before and after volumetric modulated arc therapy (VMAT) plan optimization. The resulting dose distributions and target coverage were quantified. Results: Per-slice accuracy, sensitivity, and specificity were 99 %, 91 %, and 99 %, respectively. The model identified all patients with artifacts. Small dosimetric differences in total plan dose were observed between the various density-override methods (±1 Gy). For the pre- and post-optimized plans, 90 % and 99 %, respectively, of dose comparisons resulted in normal structure dose differences of ±1 Gy. Differences in the volume of structures receiving 95 % of the prescribed dose (V95[%]) were ≤0.25 % for 100 % of plans. Conclusion: The dosimetric impact of applying dental artifact management before and after artifact plan optimization was minor. Our results suggest that not accounting for dental artifacts in the current RPA workflow (where only post-optimization dental artifact management is possible) may result in minor dosimetric differences. If RPA users choose to override CT densities as a solution to managing dental artifacts, our results suggest segmenting the volume of the artifact and overriding its density to water is a safe option.
La introducción del concepto de Cosecha Asistida en la recolección de café en Colombia implica la adopción de tres prácticas que pueden usarse de manera aditiva y escalarse independientemente del tamaño de la finca: retención de pases, recolección con lonas y uso de la Derribadora DSC18. Las pruebas de campo indican que el aumento en la masa de café cosechable producto de la retención, más la reducción de movimientos para desprender los frutos del árbol al usar las lonas, pueden incrementar la eficiencia de la recolección en un 40%, y que el uso de la máquina DSC-18 puede aumentar esa eficiencia en 180% en promedio. La reducción del costo unitario (COP/kg), que puede ser de un 50% o más, permite un margen de maniobra donde el recolector y el productor de café puedan acordar precios de cosecha que generen ganancias para ambas partes. De esta manera, la Cosecha Asistida contribuye a mejorar la rentabilidad en la finca a través de beneficios económicos por una mayor productividad de la mano de obra en la labor de recolección, y favorece a los recolectores haciendo más cómoda y eficiente su labor, con una mejor remuneración.
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