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Fig. 4 in Synonymy of the Scale Worm Hesperonoe urechis with Arctonoella sinagawaensis (Annelida: Polynoidae), Newly Recorded from the Seto Inland Sea, Western Japan, with Remarks on Symbiosis with the Spoon Worm Urechis unicinctus (Annelida: Thalassematidae)

Fig. 4. Arctonoella sinagawaensis. Frontal margin of prostomium, preserved specimens (A: NSMT-Pol 113484; B: NSMT-Pol 113479; C: NSMT-Pol 113483; D: NSMT-Pol 113482; E: NSMT-Pol 113481; F: NSMT-Pol 113480). A, B, Lacking cephalic peaks; C–E, intermediate forms having round or slightly tapering protrusions; F, presence of typical tapering peaks. Scale bars: 0.1 mm.

Fig. 3 in Synonymy of the Scale Worm Hesperonoe urechis with Arctonoella sinagawaensis (Annelida: Polynoidae), Newly Recorded from the Seto Inland Sea, Western Japan, with Remarks on Symbiosis with the Spoon Worm Urechis unicinctus (Annelida: Thalassematidae)

Fig. 3. Arctonoella sinagawaensis, preserved specimens (A, C: NSMT-Pol 113479; B: NSMT-Pol 113481; D: NSMT-Pol 113480). A, Anterior end, dorsal view; B, anterior end, ventral view. C, posterior end, with last smallest elytron on segment 33 (white arrow), dorsal view; D, midbody with largest elytra, dorsal view. Abbreviations: ac, anal cirrus; dc, dorsal cirrus; la, lateral antenna; ma, median antenna; pa, palp; pr, proboscis; tc, tentacular cirrus; vbc, ventral buccal cirrus; 2–4, segments 2–4. Scale bars: 1 mm.

Fig. 2. Arctonoella sinagawaensis. A–D in Synonymy of the Scale Worm Hesperonoe urechis with Arctonoella sinagawaensis (Annelida: Polynoidae), Newly Recorded from the Seto Inland Sea, Western Japan, with Remarks on Symbiosis with the Spoon Worm Urechis unicinctus (Annelida: Thalassematidae)

Fig. 2. Arctonoella sinagawaensis. A–D, Living specimens (A, B: NSMT-Pol 113479; C, D: NSMT-Pol 113482) associated with Urechis unicinctus; E, preserved specimen (NSMT-Pol 113482). A, Ventral view; B, lateral view, tips of dorsal cirri in contact with host body (black arrows); C, dorsal view of a symbiont on its host (white arrow), just after being collected; D, dorsal view of symbiont in C, detached from its host; E, dorsal view. Scale bars: A, B, 10 mm; D, E, 5 mm.

Fig. 7 in Synonymy of the Scale Worm Hesperonoe urechis with Arctonoella sinagawaensis (Annelida: Polynoidae), Newly Recorded from the Seto Inland Sea, Western Japan, with Remarks on Symbiosis with the Spoon Worm Urechis unicinctus (Annelida: Thalassematidae)

Fig. 7. Reevaluation of numbers of pairs of elytra in photographs of living specimens (dorsal view) shown in previous studies. A, Holotype of Hesperonoe urechis (71 mm BL), modified from Marin and Antokhina (2020: fig. 8c); B, non-type specimen of H. urechis, modified from Marin and Antokhina (2020: figs 1e, 8a) and Buzhinskaja (2013: fig. E; shown as Hesperonoe sp.). 1–16: number of left elytra from anterior to posterior ends; brackets indicate elytron absent. Scale bar: 10 mm.

Fig. 1 in Synonymy of the Scale Worm Hesperonoe urechis with Arctonoella sinagawaensis (Annelida: Polynoidae), Newly Recorded from the Seto Inland Sea, Western Japan, with Remarks on Symbiosis with the Spoon Worm Urechis unicinctus (Annelida: Thalassematidae)

Fig. 1. Distribution of Arctonoella sinagawaensis. A, Japan and neighboring countries; B, Seto Inland Sea (western Japan). Closed circles, present study; open circles, previous studies. 1, mouth of Saba-gawa River, Yamaguchi Prefecture; 2, Mukaishima Island, Onomichi-shi, Hiroshima Prefecture; 3, Takasu-higata, Kurashikishi, Okayama Prefecture; a, Shinagawa, Tokyo Bay (type locality; Izuka 1912); b, Peter the Great Bay (Marin and Antokhina 2020); c, Possjet Bay (Buzhinskaja 1967; Marin and Antokhina 2020); d, Chefoo Bay, Yellow Sea (Fauvel 1933; Uschakov 1982).

Fig. 6 in Synonymy of the Scale Worm Hesperonoe urechis with Arctonoella sinagawaensis (Annelida: Polynoidae), Newly Recorded from the Seto Inland Sea, Western Japan, with Remarks on Symbiosis with the Spoon Worm Urechis unicinctus (Annelida: Thalassematidae)

Fig. 6. Arctonoella sinagawaensis, distal part of chaetae in chaetiger 14, preserved specimen (NSMT-Pol 113479). A, Superior notochaeta; B, inferior notochaeta; C, D, supra-acicular neurochaeta; E, infra-acicular neurochaeta. Scale bars: 0.1 mm.

YJMob100K: City-Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories

The YJMob100K human mobility datasets (YJMob100K_dataset1.csv.gz and YJMob100K_dataset1.csv.gz) contain the movement of a total of 100,000 individuals across a 75 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of 80,000 individuals across a 75-day business-as-usual period, while the second dataset contains the movement of 20,000 individuals across a 75-day period (including the last 15 days during an emergency) with unusual behavior.  While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (cell_POIcat.csv.gz). The list of 85 POI categories can be found in POI_datacategories.csv.  For details of the dataset, see Data Descriptor:  <ul> <li>Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. Scientific Data, 11(1), 397. <a href="https://www.nature.com/articles/s41597-024-03237-9" target="_blank" rel="noopener">https://www.nature.com/articles/s41597-024-03237-9</a>  </li> </ul>     --- Details about the Human Mobility Prediction Challenge 2023 (ended November 13, 2023) ---  The challenge takes place in a mid-sized and highly populated metropolitan area, somewhere in Japan. The area is divided into 500 meters x 500 meters grid cells, resulting in a 200 x 200 grid cell space. The human mobility datasets (task1_dataset.csv.gz and task2_dataset.csv.gz) contain the movement of a total of 100,000 individuals across a 90 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of a 75 day business-as-usual period, while the second dataset contains the movement of a 75 day period during an emergency with unusual behavior. There are 2 tasks in the Human Mobility Prediction Challenge. In task 1, participants are provided with the full time series data (75 days) for 80,000 individuals, and partial (only 60 days) time series movement data for the remaining 20,000 individuals (task1_dataset.csv.gz). Given the provided data, Task 1 of the challenge is to predict the movement patterns of the individuals in the 20,000 individuals during days 60-74. Task 2 is similar task but uses a smaller dataset of 25,000 individuals in total, 2,500 of which have the locations during days 60-74 masked and need to be predicted (task2_dataset.csv.gz). While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (which is optional for use in the challenge) (cell_POIcat.csv.gz). For more details, see https://connection.mit.edu/humob-challenge-2023

opencc-by-4.0Mar 2024View details →

Maintaining habitat diversity at small scales benefits wild bees and pollination services in mountain apple orchards

In 2021, we conducted our study in apple orchards in South Tyrol, an Alpine region in Italy, using pan-traps, direct observations of visitation frequency, and a pollinator exclusion experiment. We investigated the scale-dependent effects of landscape heterogeneity and other parameters on wild bee assemblages and the related pollination service they provide at five spatial scales (radius 100 – 2,000 m).

opencc-by-4.0Mar 2024View details →

Scripts, Data, and Figures for "MesoHOPS: Size-invariant scaling calculations of multi-excitation open quantum systems"

<div> This archive contains the scripts required to run all calculations presented in "MesoHOPS: Size-invariant scaling calculations of multi-excitation open quantum systems," the figure generation scripts and attendant processed data, and a copy of MesoHOPS version 1.4.0, as used in the paper. </div>

dryad40/100

Hierarchically embedded scales of movement shape the social networks of vampire bats

Social structure can emerge from hierarchically embedded scales of movement, where movement at one scale is constrained within a larger scale (e.g., among branches, trees, forests). In most studies of animal social networks, some scales of movement are unobserved, and the relative importance of the observed scales of movement is unclear. Here, we asked: how does individual variation in movement, at multiple nested spatial scales, influence each individual's social connectedness? Using existing data from common vampire bats (Desmodus rotundus), we created an agent-based model of how three nested scales of movement—among roosts, clusters, and grooming partners—each influence a bat's grooming network centrality. In each of 10 simulations, virtual bats lacking social and spatial preferences moved at each scale at empirically-derived rates that were either fixed or individually variable and either independent or correlated across scales. We found the number of partners groomed per bat was driven more by within-roost movements than by roost switching, highlighting that co-roosting networks do not fully capture bat social structure. Simulations revealed how individual variation in movement at nested spatial scales can cause false discovery and misidentification of preferred social relationships. Our model provides several insights into how nonsocial factors shape social networks.

opencc-zeroMar 2024View details →

F I G U R E 3 A in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 3 A priori discriminant analysis of principal components (DAPC) plot of Camel trout. Each point represents the genotype of an individual fish, with centroids for each site labelled. Discriminant function 1 (DF1) is represented by the x axis, and discriminant function 2 (DF2) by the y-axis

F I G U R E 1 in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 1 Map showing the location of rivers sampled for brown trout within the UK, France and Ireland. The left panel shows the rivers used to assess the performance of the single nucleotide polymorphisms (SNP) panel at characterising genetic parameters within and outside the target region. The top right (blue) panel shows the locations of the four sampled rivers in Mount's Bay, Cornwall (Case Study 1). The bottom right (red) panel shows the location of the sample locations in the Camel catchment (Case Study 2). The red box within the bottom right panel gives the position of the impassable De Lank quarry site

F I G U R E 2 A in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 2 A priori discriminant analysis of principal components (DAPC) of trout genotypes from rivers flowing into Mount's Bay, Cornwall. Individuals are represented by individual points, with centroids for each river labelled. Discriminant function 1 (DF1) is represented by the x axis, and discriminant function 2 (DF2) by the y-axis

opencc-by-4.0Apr 2024View details →

Direct Measurement of Learning Outcomes in Engineering Programs: A Proposal for Nine Standardized Scales

This database presents the results of nine different scales aimed at directly evaluating learning outcomes as generic attributes in engineering programs, as defined by the Washington Accord and the International Alliance of Engineering. Data was collected at a higher education institution focused on engineering and technology as part of quality assurance processes. Each scale features a distinct number of indicators. The data correspond to the following scales: AC (Lifelong Learning), AF (Project Management and Finance), AP (Problem Analysis), DI (Design/Development of Solutions), EE (Ethics), HC (Communication), HI (Tool Usage), IN (Investigation), and TE (Individual and Collaborative Teamwork).

F I G U R E 4 in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 4 Correlation between geographic distance (km) against genetic distance (linear FST) for the trout samples from the River Camel. The red points represent those between the De Lank and all other sites, the black points for all pair-wise comparisons excluding the De Lank. Linear regression for all sites including the De Lank is given by the red line (r2 = 0.321, P = 0.231), and linear regression for all pair-wise sites excluding the De Lank is given by the black line (r2 = 0.658, P = 0.0671)

opencc-by-4.0Apr 2024View details →

Data for "Quantum Monte Carlo studies of a trimer scaling function with microscopic two- and three-body interactions"

Data for "Quantum Monte Carlo studies of a trimer scaling function with microscopic two- and three-body interactions", Phys. Rev. A 104, 033301 (2021).

F I G U R E 3 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus

F I G U R E 3 Relationships between fast-start performance, body size (standard length), acclimation temperature, and test temperature in adult mummichogs. Graphs are arranged by performance variable in rows and by test temperature (24, 30, and 36 C) in columns. Colors indicate acclimation temperatures (blue is 24 C, purple is 30 C, and red is 36 C). Lines are the fitted performance–size relationships based on parameter estimates from best-fit mixed-effects models or model-averaged parameter estimates from supported models (see Table 1 and Table S3.). Effects of size on performance are apparent as nonzero slopes within individual graphs; effects of test temperature are evident as variation in average intercepts across test temperature graphs (i.e., comparing average intercepts for acclimation temperature–specific lines across columns within each row of graphs); effects of acclimation temperature are seen as differences in intercepts between acclimation temperature–specific lines within graphs; and interactions between size and temperature are apparent as variations in average slopes (i.e., averaged over acclimation-specific slopes) across test temperature graphs and as differences in acclimation temperature–specific slopes within graphs.

F I G U R E 1 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus

F I G U R E 1 Morphological scaling in mummichog ranging from YOY to larger adults. Morphological variables are body dimensions (top row) and median fin areas (bottom row), and standard length is a measure of the overall body size. All data were log transformed. Sample sizes for fin areas were reduced because individuals with damaged fins were not measured. Estimates for scaling coefficients (i.e., the slope of the scaling relationship, b1) ± 1 S.E. are given for each morphological variable. Isometric expectations for scaling coefficients are 1.0 for body dimensions and 2.0 for fin areas; only the caudal fin area deviates significantly from isometry.

F I G U R E 2 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus

F I G U R E 2 Representative kinematics for fast-starts in small (41 mm SL) and large (66 mm SL) adult mummichogs controlling for temperature. The top panel shows frames from high-speed videos at 10 ms intervals. To facilitate interpretation, original videos have been cropped to the same spatial dimensions and rotated to standardize starting orientation and escape direction (but note that digitization used unmodified videos). The graph in the bottom panel shows the angular displacement of the head (triangles) and the linear displacement of the fish center of mass (circles) for the trials depicted in the video frames, which were maximum performance trials for the small (filled shapes) and large (open shapes) fish tested at their acclimation temperature (24 C). The curve for head rotation was truncated at its maximum value to highlight the difference in time to form the initial C-bend. The curve for linear displacement continued until the end of stage 2 of the fast-start. Whereas small fish had higher rotation rates and executed the fast-start in less time, small and large fish moved their bodies over similar distances given equal amounts of time.