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Figures 2–4. Orsilochides scurrilis. 2 in First record of Orsilochides scurrilis (Stål) (Hemiptera: Heteroptera: Scutelleridae: Pachycorinae) in the United States, with notes on the biology and distribution of U.S. species of Orsilochides Kirkaldy
Figures 2–4. Orsilochides scurrilis. 2) Lateral view. 3) Ventral view. 4) Dorsal view. Dimensional lines = 1.0 mm.
Porous single crystal unit-cell simulation database for ductile fracture by void growth and coalescence
<p>Ductile fracture through void growth to coalescence occurs at the grain scale in numerous metallic alloys encountered in engineering applications. In order to perform mechanical homogenization of porous single crystals, a database of porous single crystal unit-cell simulation results has been gathered through Finite Element Modeling and Fast-Fourrier Transform simulations, respectively performed on Z-set and Amitex_FFTP. In these simulations, a cubic unit-cell with a unique central spherical void undergo axisymmetric mechanical loading. Mechanical simulations are performed within finite strain theory. Input parameters of interest are stress triaxiality, crystallographic orientation, initial porosity and strain hardening law type; results include macroscopic stress, macroscopic deformation gradient, porosity, void aspect ratio, ligament size and cell aspect ratio.</p>
The Unit Re-Balancing Problem
<p>The unit re-balancing problem is about a number of defensive military units distributed over a geographic area. Each unit consists of a number of components (e.g., people, armor, or equipment). A value between 0 and 1 describes the current rating of each component. By a nonlinear function this value is converted into a nominal status assessment. This allows a comparison of different components of all units. The lowest of the statuses determines the efficiency of a unit, and the highest status its cost. An unbalanced unit has a gap between these two. When too many units are unbalanced, the entire system is costly and inefficient. To re-balance the units, people and material can be transferred. The goal is to have all units equally well equipped at the lowest possible cost. On a secondary level, the cost for the re-balancing should also be minimal. We present a mixed-integer nonlinear programming formulation for this problem, which describes the potential movement of components as a multi-commodity flow. Nonlinear constraints are needed to obtain the lowest and the highest status. Since we assume that these functions are piecewise linear, we reformulate them using inequalities and binary variables. This results in a mixed-integer linear program, and numerical standard solvers are able to compute proven optimal solutions for instances with up to 100 units. The dataset consists of the models and test instances that were presented at the (virtual) 6th IMA Conference on Mathematics in Defence and Security, March 30-31, 2021. </p>
Optimal sequence similarity thresholds for clustering of molecular operational taxonomic units in DNA metabarcoding studies
<p><span>Clustering approaches are pivotal to handle the many sequence variants obtained in DNA metabarcoding datasets, therefore they have become a key step of metabarcoding analysis pipelines. Clustering often relies on a sequence similarity threshold to gather sequences in Molecular Operational Taxonomic Units (MOTUs), each of which ideally representing a homogeneous taxonomic entity, e.g. a species or a genus. However, the choice of the clustering threshold is rarely justified, and its impact on MOTU over-splitting or over-merging even less tested. Here, we evaluated clustering threshold values for several metabarcoding markers under different criteria: limitation of MOTU over-merging, limitation of MOTU over-splitting, and trade-off between over-merging and over-splitting. We extracted sequences from a public database for nine markers, ranging from generalist markers targeting Bacteria or Eukaryota, to more specific markers targeting a class or a subclass (e.g. Insecta, Oligochaeta). Based on the distributions of pairwise sequence similarities within species and within genera, and on the rates of over-splitting and over-merging across different clustering thresholds, we were able to propose threshold values minimizing the risk of over-splitting, that of over-merging, or offering a trade-off between the two risks. For generalist markers, high similarity thresholds (0.96-0.99) are generally appropriate, while more specific markers require lower values (0.85-0.96). These results do not support the use of a fixed clustering threshold. Instead, we advocate a careful examination of the most appropriate threshold based on the research objectives, the potential costs of over-splitting and over-merging, and the features of the studied markers.</span></p>
Wastewater Treatment Unit Processes Datasets: Pollutant removal efficiencies, evaluation criteria and cost estimations
<p>This dataset provides data on typical pollutant removal efficiencies, evaluation critera and cost estimation for 37 common wastewater treatment unit processes. The data is based on literature research, expert workshops and estimations.</p> <p>The following files are available:</p> <ul> <li>Pollutant removal efficiencies, evaluation criteria and cost estimations for wastewater treatment unit processes (dataset) - Microsoft Excel:<br> Database of pollutant removal efficiencies for 11 paramameters, cost estimation and evaluation criteria.</li> <li>Pollutant Removal Efficiencies for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Evaluation Criteria for Wastewater Treatment Unit Processes (Dataset) - pdf</li> <li>Cost Estimation for Wastewater Treatment Unit Processes (Dataset) - pdf</li> </ul> <p> </p> <p> </p>
Adeno-associated virus type 2 in children from the United States with acute severe hepatitis
<p>This repository contains the metadata and code used in the analysis of the manuscript, <em>Adeno-associated virus type 2 in children from the United States with acute severe hepatitis.</em></p>
Figure 30 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 30. Elytral apex and basal antennomeres in Aneflomorpha. a-b) A. fisheri. c-d) A. unispinosa.
Figure 21 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 21. Morphological features of Aneflomorpha. a) A. texana, elytral setae. b) A. texana, antennomere three spine. c) A. linsleyae, flattened protibia, lateral and dorsal views. d) A. linsleyae, elytral setae. e) A. linsleyae, antennomere three spine.
Figure 16 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 16. Abdomen and metasternum of Aneflomorpha. a) A. aculeata. b) A. citrana, allotype. c) A. parowana, lectotype. d) A. rectilinea, Davis Mountains, Texas. e) A. rectilinea, Huachuca Mountains, Arizona. f) A. seminuda, holotype. g) A. subpubescens. h) A. yumae, paratype.
Figure 18 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 18. Neaneflus fuchsii (Wickham) variation. a) male, Texas (Big Bend), dorsal. b) male, Texas (Big Bend), ventral. c) female, Texas (Big Bend), dorsal. d) male holotype, California (Independence), dorsal. e) male holotype, California (Independence), ventral. f) antennomeres 6–9 of male (left) and female (right) of Texas (Big Bend) specimens. g) prosternal process of male specimen from Big Bend, Texas. h) prosternal process of holotype male from Independence, California.
Figure 15 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 15. Comparison of Aneflomorpha linearis and A. paralinearis, n. sp. a) A. linearis, holotype, head. b) A. parowana, lectotype, head. c) A. paralinearis n. sp., holotype, head. d) A. linearis, holotype, abdomen and metathorax. e) A. linearis, Nevada specimen, mesothorax. f) A. paralinearis, n. sp., paratype, Cochise County, Arizona, mesothorax. f) A. linearis, Nevada specimens, mesothorax. g) A. paralinearis, n. sp., holotype, abdomen. h) A. linearis, Calaveras County, California, aedeagus. i) A. paralinearis, n. sp., paratype, Cochise County, Arizona, aedeagus.
Figure 26 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 26. Protibia of Aneflomorpha. a) A. paralinearis, lateral. b) A. paralinearis, dorsal. c) A. rectilinea, lateral. d) A. rectilinea, dorsal.
Figure 14 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 14. Aneflomorpha arizonica Linsley, holotype compared to A. unispinosa Casey, holotype. a) A. arizonica, dorsal, lateral habitus. b) A. unispinosa, dorsal, lateral habitus. c) A. arizonica, detail of pronotum and basal antennomeres. d) A. unispinosa, detail of pronotum and basal antennomeres.
Figure 12 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 12. Ventral structures of Aneflomorpha crypta Lingafelter, n. sp., A. minuta Chemsak, and A. rectilinea Casey. a) A. crypta, head and prothorax. b) A. crypta, partially cleared mesosternum. c) A. minuta, head and prothorax. d) A. minuta, partially cleared mesosternum. e) A. rectilinea, partially cleared mesosternum.
Figure 11 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 11. Protibiae of Aneflomorpha, lateral views. a) A. citrana, holotype. b) A. gilana. c) A. linsleyae. d) A. parkeri, holotype. e) A. linearis, holotype. f) A. rectilinea, lectotype. g) A. paralinearis Lingafelter, n. sp., paratype (also showing dorsal view and basal carina). h) A. rectilinea, Hereford, Arizona (also showing dorsal view and absence of basal carina). i) A. seminuda, holotype. j) A. yumae, paratype.
Figure 19 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 19. Elytral pubescence of Aneflomorpha, lateral view. a) A. delongi, mostly erect. b) A. rectilinea, mostly recurved, recumbent.
Figure 9 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 9. Basal antennomeres of Aneflomorpha Casey. a) A. aculeata (LeConte), holotype. b) A. cazieri Chemsak. c) A. citrana Chemsak, holotype. d) A. crypta Lingafelter, n. sp., holotype. e) A. delongi (Champlain and Knull). f) A. fisheri Linsley. g) A. gilana Casey. h) A. linearis (LeConte), holotype. i) A. linsleyae Chemsak. j) A. luteicornis Linsley. k) A. minuta Chemsak, holotype. l) A. opacicornis Linsley (Neaneflus, herein). m) A. paralinearis Lingafelter, n. sp., holotype. n) A. parowana Knull, lectotype. o) A. rectilinea Casey. p) A. seminuda Casey, holotype. q) A. subpubescens (LeConte). r) A. tenuis (LeConte). s) A. unispinosa Casey. t) A. texana Linsley, holotype. u) A. yumae Giesbert and Hovore, paratype.
Figure 36 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 36. Morphological structures in Aneflomorpha. a) A. linearis, prosternal process. b) A. linearis, mesosternum. c) A. paralinearis, mesosternum. d) A. paralinearis, prosternal process. e) A. linearis, aedeagus, top arrow showing paramere shape, bottom arrow showing median lobe apex shape. f) A. paralinearis, aedeagus.
Figure 23. Elytral pubescence and basal antennomere 3–4 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 23. Elytral pubescence and basal antennomere 3–4 spination of Aneflomorpha. a, b) A. delongi. c, d) A. tenuis.
Figure 7 in Revision of Aneflomorpha Casey and Neaneflus Linsley (Coleoptera: Cerambycidae) of the United States with an illustrated key to species
Figure 7. Elytral pubescence of Aneflomorpha Casey. a) A. aculeata (LeConte), holotype. b) A. arizonica Linsley. c) A. cazieri Chemsak. d) A. citrana Chemsak, holotype. e) A. crypta Lingafelter, n. sp. f) A. delongi (Champlain and Knull). g) A. fisheri Linsley. h) A. gilana Casey. i) A. linearis (LeConte), holotype. j) A. linsleyae Chemsak. k) A. luteicornis Linsley. l) A. minuta Chemsak. m) A. opacicornis Linsley (Neaneflus, herein). n) A. parkeri Knull, holotype. o) A. parowana Casey, lectotype. p) A. paralinearis Lingafelter, n. sp., holotype. q) A. rectilinea Casey, Arizona. r) A. rectilinea Casey, Texas. s) A. seminuda Casey, holotype. t) A. subpubescens (LeConte). u) A. tenuis (LeConte). v) A. unispinosa Casey, holotype. w) A. texana Linsley. x) A. yumae Giesbert and Hovore, paratype.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.