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4,059 results for “mammal”
Data from: Consequences of repeated sarcoptic mange outbreaks in an endangered mammal population
<p>Diseases and parasites are important drivers of population dynamics in wild mammal populations. Small and endangered populations that overlap with larger, reservoir populations are particularly vulnerable to diseases and parasites, especially in ecosystems highly influenced by climate change. Sarcoptic mange, caused by a parasitic mite (Sarcoptes scabiei), constitutes a severe threat to many wildlife populations and is today considered a panzootic. The Scandinavian arctic fox (Vulpes lagopus) is endangered with a fragmented distribution and is threatened by e.g., red fox (Vulpes vulpes) expansion, prey scarcity and inbreeding depression. Moreover, one of the subpopulations in Scandinavia has suffered from repeated outbreaks of sarcoptic mange during the past decade, most likely spread by red foxes. This was first documented in 2013 and then again 2014, 2017, 2019, 2020 and 2021. We used field inventories and wildlife cameras to follow the development of sarcoptic mange outbreaks in this arctic fox subpopulation with specific focus on disease transmission and consequences for reproductive output. In 2013-14, we documented visual symptoms of sarcoptic mange in about 30% of the total population. Despite medical treatment, we demonstrate demographic consequences where the number of arctic fox litters plateaued and litter size was reduced after the introduction of S. scaibei. Furthermore, we found indications that mange likely was transmitted by a few arctic foxes travelling between several dens, i.e., "super-spreaders". This study highlights sarcoptic mange as a severe threat to small populations and can put the persistence of the entire Scandinavian arctic fox population at risk.</p>
Data from: The role of conflict in the formation and maintenance of variant sex chromosome systems in mammals
<p>The XX/XY sex chromosome system is deeply conserved in therian mammals, as is the role of <em>Sry</em> in testis determination, giving the impression of stasis relative to other taxa. However, the long tradition of cytogenetic studies in mammals documents sex chromosome karyotypes that break this norm in myriad ways, ranging from fusions between sex chromosomes and autosomes to Y chromosome loss. Evolutionary conflict, in the form of sexual antagonism or meiotic drive, is the primary predicted driver of sex chromosome transformation and turnover. Yet conflict-based hypotheses are less considered in mammals, perhaps because of the perceived stability of the sex chromosome system. To address this gap, we catalogue and characterize all described sex chromosome variants in mammals, test for family-specific rates of accumulation, and consider the role of conflict between the sexes or within the genome in the evolution of these systems. We identify 152 species with sex chromosomes that differ from the ancestral state and find evidence for different rates of ancestral to derived transitions among families. Sex chromosome-autosome fusions account for 80% of all variants whereas documented sex chromosome fissions are limited to three species. We propose that meiotic drive and drive suppression provide viable explanations for the evolution of many of these variant systems, particularly those involving autosomal fusions. We highlight taxa particularly worthy of further study and provide experimental predictions for testing the role of conflict and its alternatives in generating observed sex chromosome diversity.</p>
Variance in offspring sex ratio and maternal allocation in a highly invasive mammal
<p>Skewed sex ratios at birth are widely reported in wild populations, however the extent to which parents are able to modulate the sex ratio of offspring to maximize their own fitness remains unclear. This is particularly true for highly polytocous species as maximizing fitness may include trade-offs between sex ratio and the size and number of offspring in litters. In such cases, it may be adaptive for mothers to adjust both the number of offspring per litter and offspring sex to maximize individual fitness. Investigating maternal sex allocation in wild pigs (<em>Sus scrofa</em>) under stochastic environmental conditions, we predicted that, under favorable conditions, high quality mothers (larger, older) would produce male-biased litters and invest more in producing larger litters with more males. We also predicted sex ratio would vary relative to litter size, with a male-bias among smaller litters. We found evidence that increasing wild boar ancestry, maternal age and condition, and resource availability may weakly contribute to male-biased sex ratio, however, unknown factors not measured in this study are assumed to be more influential. High quality mothers allocated more resources in litter production, but this relationship was driven by adjustment of litter size, not sex ratio. There was no relationship between sex ratio and litter size. Collectively, our results emphasized that adjustment of litter size appeared to be the primary reproductive characteristic manipulated in wild pigs to increase fitness rather than adjustment of offspring sex ratio.</p>
Fig. 14 in On The Study Of Fauna (Macroinvertebrates, Fish, Amphibians, Reptiles, Birds And Mammals) Of The Lower Course Of Shokhdara River Valley In Pamir, Mountain Bodakhshan, Tajikistan
Fig. 14. Grey draft hamster.
Fig. 9 in On The Study Of Fauna (Macroinvertebrates, Fish, Amphibians, Reptiles, Birds And Mammals) Of The Lower Course Of Shokhdara River Valley In Pamir, Mountain Bodakhshan, Tajikistan
Fig. 9. Young individual of spotted whip snake, road kill, Sebzor village.
Fig. 8. N in On The Study Of Fauna (Macroinvertebrates, Fish, Amphibians, Reptiles, Birds And Mammals) Of The Lower Course Of Shokhdara River Valley In Pamir, Mountain Bodakhshan, Tajikistan
Fig. 8. N. tessellata specimen found near Shokhdara River.
Fig. 5. Caught Tibetian stone loach N in On The Study Of Fauna (Macroinvertebrates, Fish, Amphibians, Reptiles, Birds And Mammals) Of The Lower Course Of Shokhdara River Valley In Pamir, Mountain Bodakhshan, Tajikistan
Fig. 5. Caught Tibetian stone loach N. stoliczkai.
Fig. 11 in On The Study Of Fauna (Macroinvertebrates, Fish, Amphibians, Reptiles, Birds And Mammals) Of The Lower Course Of Shokhdara River Valley In Pamir, Mountain Bodakhshan, Tajikistan
Fig. 11. Grey thin-toed geckos registered within the study area.
Fig. 15 in On The Study Of Fauna (Macroinvertebrates, Fish, Amphibians, Reptiles, Birds And Mammals) Of The Lower Course Of Shokhdara River Valley In Pamir, Mountain Bodakhshan, Tajikistan
Fig. 15. Dr. Abdulnazarov with female of snow leopard in Pamir biological institute.
Fig. 12 in On The Study Of Fauna (Macroinvertebrates, Fish, Amphibians, Reptiles, Birds And Mammals) Of The Lower Course Of Shokhdara River Valley In Pamir, Mountain Bodakhshan, Tajikistan
Fig. 12. Scheme of the findings of amphibians and reptiles at the study area.
The effect of competitor presence on the foraging decisions of small mammals
<p>Competitive interactions between species can have marked effects on the diets and foraging behaviours of the interactants. Dominant competitors may constrain the foraging decisions of subordinate competitors, reducing the individual fitness of subordinates, and potentially driving their populations to low levels. Following a sustained population decline of the bush rat (<em>Rattus fuscipes</em>) in the presence of the competitively dominant common brushtail possum (<em>Trichosurus vulpecula</em>) at Booderee National Park in south-eastern Australia, we investigated whether possums affected the foraging decisions of bush rats. Using a modified giving-up density experiment, we predicted that bush rats would: (a) increase visits to baited sites where possums had restricted access, and (b) restrict visits to baited sites where possums had free access. We used camera traps to investigate visitation patterns and foraging bout lengths at 40 baited sites with two treatments, one that allowed full access by both species (full access), and the other that attempted to prevent possum access (restricted access). We also measured additional covariate factors that may influence visitation. Bush rats visited both treatments less when there were more possum visits. We also found that bush rats spent less time eating bait at regularly visited sites, regardless of possums' access level. Our results suggest a negative, potentially competitive interaction between the two species that is detrimental to bush rat foraging and is a potential factor contributing to bush rat's decline at Booderee National Park.</p>
The origins of mammal growth patterns during the Jurassic mammalian radiation
<p>We use synchrotron X-ray tomography of annual growth increments in the dental cementum of mammaliaforms (stem and crown fossil mammals) from three faunas across the Jurassic to map the origin of patterns of determinate growth, which is intrinsically related to mammalian endothermy. Although all fossils studied exhibited slower growth rates, longer lifespans, and delayed sexual maturity relative to comparably sized extant mammals, the earliest crown mammals developed significantly faster growth rates in early life that reduced at sexual maturity (determinate growth), compared to stem mammaliaforms. Estimation of basal metabolic rates (BMRs) suggests some fossil crown mammals had BMRs approaching the lowest rates of extant mammals. We suggest mammalian determinate growth first evolved during their mid-Jurassic adaptive radiation, although growth remained slower than in extant mammals.</p>
Codes and data for the article: Biodiversity on the Line: Life Cycle Impact Assessment of Power Lines on Birds and Mammals in Norway
<p>This repository contains all input data required to run the habitat conversion, collision, and electrocution LCIA models and reproduce the results, as well as all output data generated in various formats. The models are described in the paper "Biodiversity on the Line: Life Cycle Impact Assessment of Power Lines on Birds and Mammals in Norway" (https://doi.org/10.1088/2634-4505/ad5bfd).</p> <p>"The files "01_Get_GBIF_points.R, "02_SDMs_maxent.R" describe how to create the species distribution maps.</p> <p>"03_Data_preparation.R", "04_Pylon_cleaning.py" are to prepare and modify the raw data for the analysis. The raw data are not provided, yet links to the sources are provided either in the R codes or the paper.</p> <p>To run the models, run the "05_SHR_modelling.R" and "06_Collision_electrocution_models.R" files.</p> <p>To calculate characterization factors, run the "07_Characterisation_factors.R" file.</p> <p>To export the tables in the Supporting Information 1, run the file "08_Supporting_Information.R".</p> <p>Finally, the file "09_Sensitivity_analysis.R" performs the sensitivity analyses.</p>
Fig.1. Vitamin A in Vitamins A And E In Physiological Adaptation Of Mammals With Different Ecogenesis
Fig.1. Vitamin A content in the organs of animals.
Combining local ecological knowledge with camera traps to assess the link between African mammal life history traits and their occurrence in anthropogenic landscapes
<p>Understanding what influences species and trait composition is critical for predicting changes in communities driven by landscape transformation. </p> <p>We explored how life history traits are associated with the persistence of mammal species in human-dominated habitats within the Garden Route Biosphere Reserve, South Africa. We combined data from a camera trap and a local ecological knowledge-based survey in an integrated occupancy model to analyze species occurrence along a gradient of anthropogenic landscape transformation. </p> <p>Results confirmed that mammal occurrence in human-modified habitats was related to specific life history traits. Species with more specialist diets, as well as larger body mass species were more likely to stay in protected areas. Species with slow reproductive strategies occupied more natural areas. </p> <p>Our study also showed that combining different monitoring methods enabled us to increase spatial coverage and mammal sighting numbers. This approach fostered research participation by various stakeholders, an important step for co-designing wildlife-friendly anthropogenic spaces. </p> <p><strong>Synthesis and applications: </strong>Integrating data from a standard ecological protocol and structured participatory citizen knowledge allowed us to identify the species functional traits associated with mammal species occurrence in anthropogenic landscapes at a local scale. These results advocate for wisely combining methods, and will guide conservation orientated land-use planning towards the protection of natural habitats in the Garden Route Biosphere Reserve. This methodological approach will enable managers and conservationists to use data obtain from diverse protocols. This should catalyze the involvement of citizens in biodiversity monitoring and conservation.</p>
Figure S4 in Small mammals and microhabitat selection in forest fragments in the transition zone between Atlantic Forest and Pampa biome
Figure S4. Rarefaction curve for both studied fragments in the Atlantic Forest biome, Brazil. Sample coverage is the proportion of the total number of individuals that belong to the species detected in the sample. F1 = Fragment 1 (28°08′38″S, 54°45′36″W); F2 = Fragment 2 (28°07′33″S, 54°44′57″W).
Figure 3 in Small mammals and microhabitat selection in forest fragments in the transition zone between Atlantic Forest and Pampa biome
Figure 3. Variables coefficients and their confidence intervals in the models selected (with ΔAIC ≤ 2) for each small mammal species. (A) Akodon montensis; (B) Oligoryzomys nigripes; (C) Sooretamys angouya; (D) Didelphis albiventris. PC1GC = first axis of the PCA for soil variables; PC2GC = second axis of the PCA for soil variables; PC1VS = first axis of the PCA for vegetation structure; PC2VS = second axis of the PCA for vegetation structure.
Investigating small mammal microhabitat selection in logged and unlogged tropical forests
<b>Description: </b><p>Spool and line tracking data on small mammals</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/87"><b>Investigating small mammal microhabitat selection in logged and unlogged tropical forests</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=60">here</a></p><p><b>Data worksheets: </b>There are 3 data worksheets in this dataset:</p><ol><li><p><b>Tracking data</b> (Worksheet Tracking)</p><p>Dimensions: 1396 rows by 15 columns</p><p>Description: Individual trap segment details for all spool-and-line tracked individuals</p><p>Fields: </p><ul><li><b>block</b>: SAFE Project Block in which trapping grid was located (Field type: Location)</li><li><b>Date</b>: Date individual was spooled (Field type: Date)</li><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>Trap</b>: Trap number on the grid where individual was captured (Field type: ID)</li><li><b>Respool</b>: Has this individual been tracked previously? (Field type: Categorical)</li><li><b>track</b>: Track ID for that day (Field type: Replicate)</li><li><b>Pit.tag.no.</b>: PIT tag number of the individual being tracked (Field type: ID)</li><li><b>Sex</b>: Sex oFxthe individual (Field type: Categorical)</li><li><b>Weight</b>: Body mass of the individual (Field type: Numeric)</li><li><b>Distance</b>: Length of straight line portion of track (Field type: Numeric)</li><li><b>Bearing</b>: Compass bearing of track portion (Field type: Numeric)</li><li><b>Dominant.habitat.feature</b>: Habitat taype on the observed route (Field type: Categorical)</li><li><b>feature.control</b>: Habitat type on the control route (Field type: Categorical)</li><li><b>height</b>: Height above ground (Field type: Ordered Categorical)</li></ul><br></li><li><p><b>microhabitat data</b> (Worksheet microhabitat)</p><p>Dimensions: 597 rows by 15 columns</p><p>Description: Microhabitat details recorded along spool tracks</p><p>Fields: </p><ul><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>individual</b>: PIT tag number of the individual being tracked (Field type: ID)</li><li><b>Control</b>: Is the record for the observed track of the control track? (Field type: Categorical)</li><li><b>ground</b>: Leaf density <0.5m above ground (Field type: Ordered Categorical)</li><li><b>understorey</b>: Leaf density 0.5m to 3m above ground (Field type: Ordered Categorical)</li><li><b>midstorey</b>: Leaf density 3m to 20m above ground (Field type: Ordered Categorical)</li><li><b>canopy</b>: Leaf density >20m above ground (Field type: Ordered Categorical)</li><li><b>forest.quality</b>: Forest quality (Field type: Ordered Categorical)</li><li><b>Predator</b>: Predation risk (Field type: Ordered Categorical)</li><li><b>Densiometer</b>: Canopy cover (Field type: Numeric)</li><li><b>Relascope</b>: Tree volume (Field type: Numeric)</li><li><b>height</b>: Height above ground (Field type: Ordered Categorical)</li><li><b>rain</b>: Rainfall (Field type: Ordered Categorical)</li><li><b>moon.phase</b>: Lunar phase (Field type: Ordered Categorical)</li></ul><br></li><li><p><b>environmental data</b> (Worksheet trap_covariates)</p><p>Dimensions: 295 rows by 14 columns</p><p>Description: Environmental quality metrics at trap locations</p><p>Fields: </p><ul><li><b>Site</b>: Site locality in camera trap grid (Field type: Location)</li><li><b>trap</b>: Trap identity within block (Field type: ID)</li><li><b>ground</b>: Leaf density <0.5m above ground (Field type: Ordered Categorical)</li><li><b>understorey</b>: Leaf density 0.5m to 3m above ground (Field type: Ordered Categorical)</li><li><b>midstorey</b>: Leaf density 3m to 20m above ground (Field type: Ordered Categorical)</li><li><b>canopy</b>: Leaf density >20m above ground (Field type: Ordered Categorical)</li><li><b>forest quality (O)</b>: Forest quality (Field type: Ordered Categorical)</li><li><b>Predator</b>: Predation risk (Field type: Ordered Categorical)</li><li><b>Densiometer 1</b>: Canopy cover - 1 of 4 measurements (Field type: Numeric)</li><li><b>Densiometer 2</b>: Canopy cover - 1 of 4 measurements (Field type: Numeric)</li><li><b>Densiometer 3</b>: Canopy cover - 1 of 4 measurements (Field type: Numeric)</li><li><b>Densiometer 4</b>: Canopy cover - 1 of 4 measurements (Field type: Numeric)</li><li><b>Relascope</b>: Tree volume (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2012-06-01 to 2012-07-21</p><p><b>Latitudinal extent: </b>4.6931 to 4.7166</p><p><b>Longitudinal extent: </b>117.5304 to 117.5975</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Mammalia<br> -  -  - Rodentia<br> -  -  -  - Muridae<br> -  -  -  -  - <i>Chrotomys</i><br> -  -  -  -  -  - <i>Chrotomys whiteheadi</i> (as <i>Maxomys whiteheadi</i>)<br> -  -  -  -  - <i>Leopoldamys</i><br> -  -  -  -  -  - <i>Leopoldamys sabanus</i><br> -  -  -  -  - <i>Maxomys</i><br> -  -  -  -  -  - <i>Maxomys surifer</i><br> -  -  -  -  - <i>Rattus</i><br> -  -  -  -  -  - <i>Rattus rattus</i><br></div><p></p>
Do logging roads impede small mammal movement in Borneo's tropical rainforests?
<b>Description: </b><p>Data from small mammal trap grid adjacent to roads and an associated movement/translocation experiment</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/37"><b>Do logging roads impede small mammal movement in Borneo's tropical rainforests?</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=113">here</a></p><p><b>Files: </b>This consists of 1 file: template_Heon.xlsx</p><p><b>template_Heon.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Road crossing field data</b> (described in worksheet RoadCrossing)</p><p>Description: Data from trap grid plus translocation experiment; NB: Paper accompanying this dataset used data from Site1-7 only; no translocations were conducted at Site8 so this was not used in the paper.</p><p>Number of fields: 23</p><p>Number of data rows: 3456</p><p>Fields: </p><ul><li><b>Site</b>: Site code (sites not georeferenced) (Field type: Location)</li><li><b>Treatment</b>: Experimental treatment trap was assigned to (Field type: Categorical)</li><li><b>Road.width</b>: Width of the road adjacent to trap grid (Field type: Numeric)</li><li><b>Date</b>: Date trap was set (Field type: Date)</li><li><b>Trap.Night</b>: Number of nights trap had been set at this site for (Field type: Numeric)</li><li><b>Trap.Number</b>: For field reference on distance and location of each trap. (Field type: ID)</li><li><b>Set.distance</b>: Distance from the road's edge or the start point in control sites. (Field type: Numeric)</li><li><b>Rat.Present</b>: Trap status (Field type: Categorical)</li><li><b>Distance.fromroad</b>: distance of the trap site from a road (Field type: Numeric)</li><li><b>Trap.Status</b>: Basic information about captured individuals (Field type: Categorical)</li><li><b>Species</b>: Species identity (Field type: Taxa)</li><li><b>Weight</b>: Body mass (Field type: Numeric)</li><li><b>Hind.foot</b>: Length of hind foot (Field type: Numeric)</li><li><b>Ear</b>: Length of ear (Field type: Numeric)</li><li><b>AGD</b>: Anal-genital distance (Field type: Numeric)</li><li><b>Sex</b>: Sex of the individual (Field type: Categorical)</li><li><b>Age</b>: Age of the individual (Field type: Categorical)</li><li><b>Release.distance</b>: Distance on the trap grid that the rat was released from (Field type: Numeric)</li><li><b>Returned.translocation</b>: Was the rat recaptured after translocation? (Field type: Categorical)</li><li><b>Recaptured.trap</b>: Trap ID code (Field type: ID)</li><li><b>Recaptured.Date</b>: Date rat was recaptured on trap grid after translocation (Field type: Date)</li><li><b>Recaptured.Distance</b>: Distance on the trap grid where the rat was re-captured (returned) from translocation (Field type: Numeric)</li><li><b>Recaptured.Day</b>: Day the rat is re-captured /return from translocation to trap grid (Field type: Numeric)</li></ul></li></ol><p><b>Date range: </b>2016-04-19 to 2016-07-20</p><p><b>Latitudinal extent: </b>4.6893 to 4.7472</p><p><b>Longitudinal extent: </b>117.5817 to 117.6158</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div>Animalia<br> - Chordata<br> -  - Mammalia<br> -  -  - Rodentia<br> -  -  -  - Muridae<br> -  -  -  -  - <i>Chrotomys</i><br> -  -  -  -  -  - <i>Chrotomys whiteheadi</i><br> -  -  -  -  - <i>Leopoldamys</i><br> -  -  -  -  -  - <i>Leopoldamys sabanus</i><br> -  -  -  -  - <i>Maxomys</i><br> -  -  -  -  -  - <i>Maxomys baeodon</i><br> -  -  -  -  -  - <i>Maxomys rajah</i><br> -  -  -  -  -  - <i>Maxomys surifer</i><br> -  -  -  -  - <i>Niviventer</i><br> -  -  -  -  -  - <i>Niviventer cremoriventer</i><br> -  -  -  -  - <i>Rattus</i><br> -  -  -  -  -  - <i>Rattus exulans</i><br> -  -  -  -  - <i>Sundamys</i><br> -  -  -  -  -  - <i>Sundamys muelleri</i><br></div><p></p>
Envixlab/OpenMICE: OpenMICE: an open spatial and temporal data set of small mammals in south-central Italy based on owl pellet data
<p>Provided in support of the Data-paper: OpenMICE: an open spatial and temporal data set of small mammals in south-central Italy based on owl pellet data by Paniccia, C., M. Di Febbraro, L. Delucchi, R. Oliveto, M. Marchetti, and A. Loy. 2018. Ecology. <a href="https://github.com/Envixlab/OpenMICE/files/2273658/OpenMICE.sqlite.zip">OpenMICE.sqlite.zip</a></p>
ScienceDex guides
Understand access before you commit
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.