Recent behavioral evidence suggests that dogs, like humans and monkeys, are capable of visual face recognition. But do dogs also exhibit specialized cortical face regions similar to humans and monkeys? Using functional magnetic resonance imaging (fMRI) in six dogs trained to remain motionless during scanning without restraint or sedation, we found a region in the canine temporal lobe that responded significantly more to movies of human faces than to movies of everyday objects. Next, using a new stimulus set to investigate face selectivity in this predefined candidate dog face area, we found that this region responded similarly to images of human faces and dog faces, yet significantly more to both human and dog faces than to images of objects. Such face selectivity was not found in dog primary visual cortex. Taken together, these findings: (1) provide the first evidence for a face-selective region in the temporal cortex of dogs, which cannot be explained by simple low-level visual feature extraction; (2) reveal that neural machinery dedicated to face processing is not unique to primates; and (3) may help explain dogs’ exquisite sensitivity to human social cues.

Keystone individuals are expected to disproportionately contribute to group stability. For instance, rhesus macaques (Macaca mulatta) who police conflict contribute towards stability. Not all individuals’ motivations align with mechanisms of group stability. In wild systems, males typically disperse at maturity and attempt to ascend via contest competition. In a captive system, dispersal is not naturally enabled – individuals attempt to ascend in their natal groups, which can be enabled by matrilineal kin potentially destabilizing group dynamics. We relocated select high-ranking natal males from five groups and assessed group stability before and after. We quantified hierarchical metrics at the individual and group level. After removal, we found significantly higher aggression against the established hierarchy (reversals), indicative of opportunistic attempts to change the hierarchy. Mixed-sex social signaling became more hierarchical, but the strength of this effect varied. Stable stru..., These data are primarily social networks of five groups, observed for 12 weeks. Observation periods were divided into: 6-week baseline and 6-week post-removal, with the natal male removal event occurring early in week 7. Agonistic data were collected using event sampling; affiliative data were collected using scan sampling., , # Social stability via management of natal males in captive rhesus macaques Macaca mulatta

Alexander J. Pritchard a, c

Brianne A. Beisner b

Amy Nathman a

Brenda McCowan a, c

a: California National Primate Research Center, University of California Davis, Davis, CA, USA;

b: Emory National Primate Research Center Field Station, Division of Animal Resources, Emory University, Lawrenceville, GA, USA;

c: Department of Population Health & Reproduction, School of Veterinary Medicine, University of California Davis, Davis, CA, USA

Article

Data Dryad repository

Description of the data and file structure

Data are contained in the NMKO_Data_List.Rdata file. This can be read into R and preprocessed using Run-This-First_NMKO_Extract-Dataframes.R and will assemble 26 R objects. These objects are as follows:

Alf_NM_Codes = a dataframe with columns, in order, identifying the groups with a code, the alpha male ID (alfID) and the removed natal males' ID...

This repository contains the datasets for the findings reported in the manuscript titled "Upper-limit agricultural dietary exposure to streptomycin in the lab reduces learning and foraging in bumble bees".The data was collected from experiments with Bombus impatiens during 2019 and 2020 at Emory University. There are files for three separate experiments:a) Free Movement Proboscis Extension Reflex (FMPER) b) Foraging in automated foraging arenasc) Nectar consumption (three to four days) & survival data (first three days)Details of each experiment are presented in the manuscript and in three R Markdown (.rmd) data reports (included in this repository). All statistical analysis were carried out in R Studio.

This dataset accompanies the eLife publication "Automated annotation of birdsong with a neural network that segments spectrograms". In the article, we describe and benchmark a neural network architecture, TweetyNet, that automates the annotation of birdsong as we describe in the text. Here we provide checkpoint files that contain the weights of trained TweetyNet models. The checkpoints we provide correspond to the models that obtained the lowest error rates on the benchmark datasets used (as reported in the Results section titled "TweetyNet annotates with low error rates across individuals and species"). We share these checkpoints to enable other researchers to replicate our key result, and to allow users of our software to leverage them, for example to improve performance on their data by adapting pre-trained models with transfer learning methods.

Methods Checkpoint files were generated using the vak library (https://vak.readthedocs.io/en/latest/), running it with configuration files that are part of the code repository associated with the TweetyNet manuscript (https://github.com/yardencsGitHub/tweetynet). Those "config files" are in the directory "article/data/configs" and can be run on the appropriate datasets (as described in the paper). The "source data" files used to generate the figures were created by running scripts on the final results of running the vak library. Those source data files and scripts are in the code repository as well. For further detail, please see the methods section in https://elifesciences.org/articles/63853

The AneuX morphology database is an open-access, multi-centric database containing 3D geometries of 750 intracranial aneurysms curated in the context of the AneuX project (2015-2020). The database combines data from three different projects (AneuX, @neurIST and Aneurisk) standardized using a single processing pipeline. The code to process and view the 3D geometries is provided under this public repository: https://github.com/hirsch-lab/aneuxdb

The database at a glance:

750 aneurysm domes (surface meshes)

668 vessel trees (surface meshes)

3 different data sources (AneuX, @neurIST, Aneurisk)

3 different mesh resolutions (original resolution, 0.01mm² and 0.05mm² target cell area)

4 different cut configurations (including planar and free-form cuts)

5 clinical parameters (aneurysm rupture status, location and side; patient age and sex)

170 pre-computed morphometric indices for each of the aneurysm domes

Terms of use / License:

The data is provided "as is", without any warranties of any kind. It is provided under the CC BY-NC 4.0 license, with the additional requirements (A) that the use of the database is declared using the sentence below, and (B) to cite our peer reviewed journal article below.

[This project] uses data from the AneuX morphology database, an open-access, multi-centric database combining data from three European projects: AneuX project (www.aneux.ch; @neurIST protocol v5; ethics autorisations Geneva BASEC PB_2018‐00073; supported by the grant from the Swiss SystemsX.ch initiative, evaluated by the Swiss National Science Foundation), @neurIST project (www.aneurist.org; @neurIST protocol v1; ethics autorisations AmsterdamMEC 07-159, Barcelona2007-3507, Geneva CER 07-056, Oxfordshire REC AQ05/Q1604/162, Pècs RREC MC P 06 Jul 2007; supported by the 6th framework program of the European Commission FP6-IST-2004–027703) and Aneurisk (http://ecm2.mathcs.emory.edu/aneuriskweb/index).

Please cite the following journal article when referring to our dataset.

Juchler, Schilling, Bijlenga, Kurtcuoglu, Hirsch. Shape trumps size: Image-based morphological analysis reveals that the 3D shape discriminates intracranial aneurysm disease status better than aneurysm size. Frontiers in Neurology (2022), DOI: 10.3389/fneur.2022.809391

The AneuX morphology database contains parts (geometric models, clinical data) of the publicly available Aneurisk dataset released under the CC BY-NC 3.0 license (which is compatible with the license used here). Like all geometric models in this database, the Aneurisk models were preprocessed using the same procedure. See here for a description.

Acknowledgments:

The AneuX project was supported by SystemsX.ch, and evaluated by the Swiss National Science Foundation (SNSF). This database would not be possible without the support of the Zurich University of Applied Sciences (ZHAW) and University Hospitals Geneva (HUG).

We thank the following people for their support and contributions to the AneuX morphology database.

From the AneuX project (in alphabetical order):

Daniel Rüfenacht

Diana Sapina

Isabel Wanke

Karl Lovblad

Karl Schaller

Olivier Brina

Paolo Machi

Rafik Ouared

Sabine Schilling

Sandrine Morel

Ueli Ebnöther

Vartan Kurtucuoglu

Vitor Mendes Pereira

Zolt Kuscàr

From the @neurIST project (in alphabetical order)

Alan Waterworth

Alberto Marzo

Alejandro Frangi

Alison Clarke

Ana Marcos Gonzalez

Ana Paula Narata

Antonio Arbona

Bawarjan Schatlo

Daniel Rüfenacht

Elio Vivas

Ferenc Kover

Gulam Zilani

Guntram Berti

Guy Lonsdale

Istvan Hudak

James Byrne

Jimison Iavindrasana

Jordi Blasco

Juan Macho

Julia Yarnold

Mari Cruz Villa Uriol

Martin Hofmann-Apitius

Max Jägersberg

Miriam CJM Sturkenboom

Nicolas Roduit

Pankaj Singh

Patricia Lawford

Paul Summers

Peer Hasselmeyer

Peter Bukovics

Rod Hose

Roelof Risselada

Stuart Coley

Tamas Doczi

Teresa Sola

Umang Patel

From the Aneurisk project (list from AneuriskWeb, in alphabetical order):

Alessandro Veneziani

Andrea Remuzzi

Edoardo Boccardi

Francesco. Migliavacca

Gabriele Dubini

Laura Sangalli

Luca Antiga

Maria Piccinelli

Piercesare Secchi

Simone Vantini

Susanna Bacigaluppi

Tiziano Passerini