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5,805 results for “Data model”
Data from: Spatially explicit models of dynamic histories: examination of the genetic consequences of Pleistocene glaciation and recent climate change on the American Pika.
A central goal of phylogeography is to identify and characterize the processes underlying divergence. One of the biggest impediments currently faced is how to capture the spatiotemporal dynamic under which a species evolved. Here we described an approach that couples species distribution models (SDMs), demographic and genetic models in a spatiotemporally explicit manner. Analyses of American Pika (Ochotona priniceps) from the sky islands of the central Rocky Mountains of North America are used to provide insights into key questions about integrative approaches in landscape genetics, population genetics and phylogeography. This includes (i) general issues surrounding the conversion of time-specific SDMs into simple continuous, dynamic landscapes from past to current, and (ii) the utility of SDMs to inform demographic models with deme-specific carrying capacities and migration potentials, as well as (iii) the contribution of the temporal dynamic of colonization history in shaping genetic patterns of contemporary populations. Our results support that the inclusion of a spatiotemporal dynamic is an important factor when studying the impact of distributional shifts on patterns of genetic data. Our results also demonstrate the utility of SDMs to generate species-specific predictions about patterns of genetic variation that account for varying degrees of habitat specialization and life-history characteristics of taxa. Nevertheless, the results highlight some key issues when converting SDMs for use in demographic models. Because the transformations have direct affects on the genetic consequence of population expansion by prescribing how habitat heterogeneity and spatiotemporal variation is related to the species-specific demographic model, it is important to consider alternative transformations when studying the genetic consequences of distributional shifts.
Data from: A field test of a model for the stability of androdioecy in the freshwater shrimp, Eulimnadia texana
The evolution of hermaphroditism from dioecy is a poorly studied transition. Androdioecy (the coexistence of males and hermaphrodites) has been suggested as an intermediate step in this evolutionary transition or could be a stable reproductive mode. Freshwater crustaceans in the genus Eulimnadia have reproduced via androdioecy for 24+ million years and thus are excellent organisms to test models of the stability of androdioecy. Two related models that allow for the stable maintenance of males and hermaphrodites rely on the counterbalancing of three life history parameters. We tested these models in the field over three field seasons and compared the results to previous laboratory estimates of these three parameters. Male and hermaphroditic ratios within years were not well predicted using either the simpler original model or a version of this model updated to account for differences between hermaphroditic types ('monogenic' and 'amphigenic' hermaphrodites). Using parameter estimates of the previous year to predict the next year's sex ratios revealed a much better fit to the original relative to the updated version of the model. Therefore, counter to expectations, accounting for differences between the two hermaphroditic types did not improve the fit of these models. At the moment, we lack strong evidence that the long-term maintenance of androdioecy in these crustaceans is the result of a balancing of life history parameters; other factors, such as metapopulation dynamics or evolutionary constraints, may better explain the 24+ million year maintenance of androdioecy in clam shrimp.
Data from: Identifying fishing grounds from vessel tracks: model-based inference for small scale fisheries
Recent technological developments facilitate the collection of location data from fishing vessels at an increasing rate. The development of low-cost electronic systems allows tracking of small-scale fishing vessels, a sector of fishing fleets typically characterised by many, relatively small vessels. The imminent production of large spatial datasets for this previously data-poor sector, creates a challenge in terms of data analysis. Several methods have been used to infer the spatial distribution of fishing activities from positional data. Here, we compare five approaches using either vessel speed, or speed and turning angle, to infer fishing activity in the Scottish inshore fleet. We assess the performance of each approach using observational records of true vessel activity. Although results are similar across methods, a trip-based Gaussian mixture model provides the best overall performance and highest computational efficiency for our use-case, allowing accurate estimation of the spatial distribution of active fishing (97% of true area captured). When vessel movement data can be validated, we recommend assessing the performance of different methods. These results illustrate the feasibility of designing a monitoring system to efficiently generate information on fishing grounds, fishing intensity, or monitoring of compliance to regulations at a nationwide scale in near-real time.
Data from: A unified model explains commonness and rarity on coral reefs
Abundance patterns in ecological communities have important implications for biodiversity maintenance and ecosystem functioning. However, ecological theory has been largely unsuccessful at capturing multiple macroecological abundance patterns simultaneously. Here, we propose a parsimonious model that unifies widespread ecological relationships involving local aggregation, species-abundance distributions, and species associations, and we test this model against the metacommunity structure of reef-building corals and coral reef fishes across the western and central Pacific. For both corals and fishes, the unified model simultaneously captures extremely well local species-abundance distributions, interspecific variation in the strength of spatial aggregation, patterns of community similarity, species accumulation, and regional species richness, performing far better than alternative models also examined here and in previous work on coral reefs. Our approach contributes to the development of synthetic theory for large-scale patterns of community structure in nature, and to addressing ongoing challenges in biodiversity conservation at macroecological scales.
Data from: A dynamical model for invasive round goby populations reveals efficient and effective management options
1. When prevention of invasive species' introductions fails, society faces the challenge to manage these invasive species in an effective and efficient way. The success of this depends on biological aspects and on cooperation between decision makers and scientists. Using the case of the round goby Neogobius melanostomus, one of Europe's worst invasive species, we propose an approach guiding scientists to co-produce effective and efficient population control measures in collaboration with decision makers. 2. We surveyed the effectiveness, urgency and simplicity perceived by decision makers as well as the support of two population control options: removal of eggs and/or adults. Using a field study and a dynamical population model, we investigated the effectiveness and efficiency for both options in different population contexts. 3. Decision makers initially seemed to lack a clear preference for either control option. After being presented with preliminary field and modelling results, decision makers mostly approved measures being developed to implement the two control options. 4. Starting population control early after detecting the species requires in total fewer years for eradication than controlling an established population: to reach an eradication success rate of 95%, 13 years for early start vs. 18 years for late start are needed when removing eggs and adults; when removing adults only, 20 vs. 29 years are needed. Removing eggs and adults combined results in a yearly effort of 5.01 h m-2, while removing adults only results in a yearly effort of 1.76 h m-2. Thus, removing adults only proved to be the most efficient option to eradicate the population. Nonetheless, considerable effort is needed: when removing less than 57% of the adult population, eradication is not feasible, even assuming low survival and fecundity rates for the population. Further, inflow of new propagules renders eradication efforts ineffective. 5. Synthesis and applications. Scientists who aim to support decision makers in finding an optimal control strategy for invasive species need to be able to provide scientific knowledge on effectiveness and efficiency of different options. For round goby and most non-native species, eradication is only feasible if started early in recently arrived populations and if inflow of new propagules can be prevented.
Data from: Modelling the consequence of glacier retreat on mixotrophic nanoflagellate bacterivory: a Bayesian approach
<p>This repository comprises data and statisticial analysis from the paper Schenone et al. 2020 entitled "Modelling the Consequence of Glacier Retreat on Mixotrophic Nanoflagellate Bacterivory: A Bayesian Approach" which was accepted for publication in Oikos. The main contribution of this paper is a model for mixotrophic nanoflagellate bacterivory as a function of light availability and the presence of non edible particles such as glacial clay. The dataset consists in results from eight bacterivory experiments carried out during November 2018 and January-February 2019 using natural mixotrophic nanoflagellate community of six moutain lakes from north Patagonia, Argentina. A Bayesian approach was adopted to estimate relevant parameters from the model using our experimental data. The analysis was performed using JAGS interfaced through R Studio, the script is available in this repository.</p>
Data from: Modelling of dysregulated glucagon secretion in type 2 diabetes by considering mitochondrial alterations in pancreatic alpha cells
Type 2 diabetes mellitus (T2DM) has been associated with insulin resistance and the failure of β-cells to produce and secrete enough insulin as the disease progresses. However, clinical treatments based solely on insulin secretion and action have had limited success. The focus is therefore shifting towards α-cells, in particular to the dysregulated secretion of glucagon. Our qualitative electron-microscopy-based observations gave an indication that mitochondria in α-cells are altered in Western-diet-induced T2DM. In particular, α-cells extracted from mouse pancreatic tissue showed a lower density of mitochondria, a less expressed matrix, and a lower number of cristae. These deformities in mitochondrial ultrastructure imply a decreased efficiency in mitochondrial ATP production, which prompted us to theoretically explore and clarify one of the most challenging problems associated with T2DM, namely the lack of glucagon secretion in hypoglycemia and its oversecretion at high blood glucose concentrations. To this purpose, we constructed a novel computational model that links α-cell metabolism with their electrical activity and glucagon secretion. Our results show that defective mitochondrial metabolism in α-cells can account for dysregulated glucagon secretion in T2DM, thus improving our understanding of T2DM pathophysiology and indicating possibilities for new clinical treatments.
Data from: Modelling habitat distributions for multiple species using phylogenetics
In this paper, we describe an empirical approach to model community structure using phylogenetic signals. That approach combines information about the species (i.e. traits and phylogeny) with information about the habitat (i.e. environmental conditions and spatial distribution of sampling sites) and their interactions to predict the species responses (e.g. the local densities). As an application, we use the approach to model fish densities in rivers. In the model, the different species and size classes were described using a functional trait, body length, and phylogenetic eigenvectors maps whereas the sites were described using water velocity, depth, substrate composition, macrophyte cover, degree-days, total phosphorus, and spatial eigenvector maps. The model (estimated using a regularised Poisson-family Generalised Linear Modelling approach) fitted the data well (likelihood-based R2adj=0.512) and showed fair predictive power (likelihood-based cross-validation R2=0.283) to predict the density of fish pertaining to 48 species totalling 143 combinations of species and size classes in 15 unregulated Canadian rivers. Using the model as a baseline to estimate the effect of flow regulation on community composition, we found that, with few exceptions, the densities of most fish species were lower in regulated than in unregulated rivers. Phylogenetics have been proposed to study community structure, but this is, to our knowledge, the first time phylogenetic information is used explicitly for numerical habitat modelling. We expect that models of that type will be in increasing demand now that development projects are routinely assessed through impact studies.
Data from: Dissimilarity of individual microsatellite profiles under different mutation models: empirical approach
Microsatellites (simple sequence repeats, SSRs) still remain popular molecular markers for studying neutral genetic variation. Two alternative models outline how new microsatellite alleles evolve. Infinite alleles model (IAM) assumes that all possible alleles are equally likely to result from a mutation, while stepwise mutation model (SMM) describes microsatellite evolution as stepwise adding or subtracting single repeat units. Genetic relationships between individuals can be analyzed in higher precision when assuming the SMM scenario with allele size differences as a proxy of genetic distance. If population structure is not predetermined in advance, an empirical data analysis usually includes (a) estimating proximity between individual SSR profiles with a selected dissimilarity measure and (b) determining putative genetic structure of a given set of individuals using methods of clustering and/or ordination for the obtained dissimilarity matrix. We developed new dissimilarity indices between SSR profiles of haploid, diploid, or polyploid organisms assuming different mutation models and compared the performance of these indices for determining genetic structure with population data and with simulations. More specifically, we compared SMM with a constant or variable mutation rate at different SSR loci to IAM using data from natural populations of a freshwater bryozoan Cristatella mucedo (diploid), wheat leaf rust Puccinia triticina (dikaryon), and wheat powdery mildew Blumeria graminis (monokaryon). We show that inferences about population genetic structure are sensitive to the assumed mutation model. With simulations, we found that Bruvo's distance performs generally poorly, while the new metrics are capturing the differences in the genetic structure of the populations.
Data from: Model-based inference for estimating shifts in species distribution, area occupied and centre of gravity
Changing climate is already impacting the spatial distribution of many taxa, including bees, plants, birds, butterflies and fishes. A common goal is to detect range shifts in response to climate change, including changes in the centre of the population's distribution (the centre of gravity, COG), population boundaries and area occupied. Conventional estimators, such as the abundance-weighted average (AWA) estimator for COG, confound range shifts with changes in the spatial distribution of available survey data and may be biased when the distribution of survey data shifts over time. AWA also does not estimate the standard error of COG in individual years and cannot incorporate data from multiple survey designs. To explicitly account for changes in the spatial distribution of survey effort, we propose an alternative species distribution function (SDF) estimator. The SDF approach involves calculating distribution metrics, including COG, population boundary and area occupied, directly from the predicted species distribution or density function. We illustrate the SDF approach using a spatiotemporal model that is available as an r package. Using simulated data, we confirm that the SDF substantially decreases bias in COG estimates relative to the AWA estimator. We then illustrate the method by analysing data from two data sets spanning 1977–2013 for 18 marine fishes along the U.S. West Coast. In our case study, the SDF estimator shows significant northward shifts for six of 18 species (with southward shifts for only 2), where two species (darkblotched and greenstriped rockfishes) have both a northward shift and a decreased area occupied. Pelagic species (e.g. Pacific hake and spiny dogfish) have more variable distribution than bottom-associated species. We also find substantial differences between AWA and SDF estimates of COG that are likely caused by shifts in sampling distribution (which affect the AWA but not the SDF estimator). We caution that common estimators for range shift can yield inappropriate inference whenever sampling designs have shifted over time. We conclude by suggesting further improvements in model-based approaches to analysing climate impacts, including methods addressing the impact of local and regional temperature changes on species distribution.
Data from: Using a full annual cycle model to evaluate long-term population viability of the conservation-reliant Kirtland's warbler after successful recovery
Long-term management planning for conservation-reliant migratory songbirds is particularly challenging because habitat quality in different stages and geographic locations of the annual cycle can have direct and carry-over effects that influence the population dynamics. The Neotropical migratory songbird Kirtland's warbler Setophaga kirtlandii (Baird 1852) is listed as endangered under the U.S. Endangered Species Act and Near Threatened under the IUCN Red List. This conservation-reliant species is being considered for U.S. federal delisting because the species has surpassed the designated 1000 breeding pairs recovery threshold since 2001. To help inform the delisting decision and long-term management efforts, we developed a population simulation model for the Kirtland's warbler that incorporated both breeding and wintering grounds habitat dynamics, and projected population viability based on current environmental conditions and potential future management scenarios. Future management scenarios included the continuation of current management conditions, reduced productivity and carrying capacity due to the changes in habitat suitability from the creation of experimental jack pine Pinus banksiana (Lamb.) plantations, and reduced productivity from alteration of the brown-headed cowbird Molothrus ater (Boddaert 1783) removal programme. Linking wintering grounds precipitation to productivity improved the accuracy of the model for replicating past observed population dynamics. Our future simulations indicate that the Kirtland's warbler population is stable under two potential future management scenarios: (i) continuation of current management practices and (ii) spatially restricting cowbird removal to the core breeding area, assuming that cowbirds reduce productivity in the remaining patches by ≤41%. The additional future management scenarios we assessed resulted in population declines. Synthesis and applications. Our study indicates that the Kirtland's warbler population is stable under current management conditions and that the jack pine plantation and cowbird removal programmes continue to be necessary for the long-term persistence of the species. This study represents one of the first attempts to incorporate full annual cycle dynamics into a population viability analysis for a migratory bird, and our results indicate that incorporating wintering grounds dynamics improved the model performance.
Data from: Developing state and transition models of floodplain vegetation dynamics as a tool for conservation decision-making: a case study of the Macquarie Marshes Ramsar wetland
1. Floodplain vegetation states (communities) exhibit spatiotemporal dynamics in vegetation structure and composition, which reflect unique hydrological and connectivity patterns. Shifts in inundation regimes can drive succession and establish new stable states, determined by the magnitude and duration of the hydrological perturbation. 2. We aimed to develop a modelling approach that is able to capture ecosystem dynamics, identify and quantify the main drivers of change, and provide a tool for conservation decision-making. We developed state and transition models for floodplain vegetation states based on surveys in 1991 and 2008 in the Macquarie Marshes (Australia), a Ramsar wetland of international importance. We used a Bayesian logistic regression approach to model state and transitions between vegetation states and investigated how flood frequency, distance to stream and fire frequency were associated with vegetation dynamics during this period. 3. During 1991–2008, significant transitions have occurred towards drier states. Semi-permanent wetland vegetation had the lowest persistence probability (ppsis = 0·456) and a significant threshold response of transitioning to terrestrial vegetation (ptran = 0·505). Transition to drier states was driven by lower inundation probabilities followed by increased fire probability, and distance to nearest stream. 4. Using developed models, we predicted persistence probabilities of vegetation states under an unregulated (i.e. no dams or diversions) and regulated water availability system. Under a regulated system, semi-permanent wetland vegetation had an average persistence of ppsis = 0. 67 and 0·08 in the northern and southern sections of the nature reserve, respectively. Under an unregulated system, the predicted persistence of semi-permanent wetland vegetation was considerably higher: ppsis = 0·87 and 0·38, respectively. 5. Synthesis and applications. Developing quantitative models of state transitions significantly improved our understanding of ecosystem dynamics, identifying sensitive indicators for monitoring and thus supporting conservation decision-making. This helps managers understand potential trajectories of change in ecosystems in response to management options. For example, increasing environmental flows in the Macquarie Marshes is predicted to shift the community towards more of a wetland than the terrestrial state, resulting from river regulation. State and transition models identified how key ecological assets respond to drivers of change, particularly where these can be managed. This is critical for ensuring that all ecosystem components are managed and that these do not shift into undesirable states.
Data from: Computational research on mobile pastoralism using agent-based modeling and satellite imagery
Dryland pastoralism has long attracted considerable attention from researchers in diverse fields. However, rigorous formal study is made difficult by the high level of mobility of pastoralists as well as by the sizable spatio-temporal variability of their environment. This article presents a new computational approach for studying mobile pastoralism that overcomes these issues. Combining multi-temporal satellite images and agent-based modeling allows a comprehensive examination of pastoral resource access over a realistic dryland landscape with unpredictable ecological dynamics. The article demonstrates the analytical potential of this approach through its application to mobile pastoralism in northeast Nigeria. Employing more than 100 satellite images of the area, extensive simulations are conducted under a wide array of circumstances, including different land-use constraints. The simulation results reveal complex dependencies of pastoral resource access on these circumstances along with persistent patterns of seasonal land use observed at the macro level.
Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"
<p>Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for U.S. Southern High Plains (SHP) un-irrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005-2019 commodity prices and fixed production costs. Both crop's profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.</p>
Data from: Enamel hypoplasia and dental wear of North American late Pleistocene horses and bison: an assessment of nutritionally-based extinction models
Approximately 50,000 – 11,000 years ago many species around the world became extinct or were extirpated at a continental scale. The causes of the late Pleistocene extinctions have been extensively debated and continue to be poorly understood. Several extinction models have been proposed, including two nutritionally-based extinction models: coevolutionary disequilibrium and mosaic-nutrient models. These models draw upon the individualistic response of plant species to climate change to present a plausible scenario in which nutritional stress is considered one of the primary causes for the late Pleistocene extinctions. In this study, we tested predictions of the coevolutionary disequilibrium and mosaic-nutrient extinction models through the study of dental wear and enamel hypoplasia of Equus and Bison from various North American localities. The analysis of the dental wear (microwear and mesowear) of the samples yielded results which are consistent with predictions established for the coevolutionary disequilibrium model, but not for the mosaic-nutrient model. These ungulate species show statistically different dental wear patterns (suggesting dietary resource partitioning) during preglacial and full-glacial time intervals, but not during the postglacial in accordance with predictions of the coevolutionary disequilibrium model. In addition to changes in diet, these ungulates, specifically the equid species, show increased levels of enamel hypoplasia during the postglacial indicating higher levels of systemic stress, a result which is consistent with the models tested and with other climate-based extinction models. The extent to which the increase in systemic stress was detrimental to equid populations remains to be further investigated, but suggests that environmental changes during the late Pleistocene significantly impacted North American equids.
Data from: Wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types
<p>Concerns about gender bias in word embedding models have captured substantial attention in the algorithmic bias research literature. Other bias types however have received lesser amounts of scrutiny. This work describes a large-scale analysis of sentiment associations in popular word embedding models along the lines of gender and ethnicity but also along the less frequently studied dimensions of socioeconomic status, age, physical appearance, sexual orientation, religious sentiment and political leanings. Consistent with previous scholarly literature, this work has found systemic bias against given names popular among African-Americans in most embedding models examined. Gender bias in embedding models however appears to be multifaceted and often reversed in polarity to what has been regularly reported. Interestingly, using the common operationalization of the term bias in the fairness literature, novel types of so far unreported bias types in word embedding models have also been identified. Specifically, the popular embedding models analyzed here display negative biases against middle and working-class socioeconomic status, male children, senior citizens, plain physical appearance and intellectual phenomena such as Islamic religious faith, non-religiosity and conservative political orientation. Reasons for the paradoxical underreporting of these bias types in the relevant literature are probably manifold but widely held blind spots when searching for algorithmic bias and a lack of widespread technical jargon to unambiguously describe a variety of algorithmic associations could conceivably be playing a role. The causal origins for the multiplicity of loaded associations attached to distinct demographic groups within embedding models are often unclear but the heterogeneity of said associations and their potential multifactorial roots raises doubts about the validity of grouping them all under the umbrella term bias. Richer and more fine-grained terminology as well as a more comprehensive exploration of the bias landscape could help the fairness epistemic community to characterize and neutralize algorithmic discrimination more efficiently.</p>
Model data for: Drawdown of atmospheric pCO2 via dynamic particle export stoichiometry in the ocean twilight zone
<p>Understanding the global carbon cycle is key to understanding the climate system. One of the large unknowns is the processes happening in the twilight zone of the ocean. Here, we focus on how elemental stoichiometry of particulate organic matter in the twilight zone affects the strength of the biological pump and atmospheric CO2. We show through modeling that atmospheric CO2 is very sensitive to the change in C:P ratio in the twilight zone. Numerous model studies study the link between the carbon cycle and flexible elemental stoichiometry of organic matter in the surface ocean. However, our model study is unique. It investigates the effects of stoichiometric changes both at the surface and in the subsurface ocean that also involve stoichiometric interaction between phytoplankton and zooplankton.<br> <br> We use a 3D numerical model to illustrate how C:P variability in the twilight zone can significantly modulate the strength of carbon sequestration and atmospheric CO2. We used the biogeochemical model MOPS (Kriest and Oschlies, Geosci. Model Dev., 8, 2929–2957, 2015) coupled to ECCO Transport Matrices.</p> <p>This repository contains model input and output files for each sensitivity run outlined in the paper. The run ID corresponds to different sensitivity run. See README for more details. </p>
Open Datasets - available file for Design of Experimental (RSM) model, ANOVA table, SEM, VSM and Zeta potecial data for the nanocrystal clusters based on iron oxide.
<p>The open-access data from the statistical model for the design of experiment optimization and as well as the experimental data are available for the community. The data are based on an obtained results published in the article: https://www.mdpi.com/2079-4991/11/2/360</p>
Data from: Reducing complexity and unidentifiability when modelling human atrial cells
<p>Mathematical models of a cellular action potential in cardiac modelling have become increasingly complex, particularly in gating kinetics which control the opening and closing of individual ion channel currents. As cardiac models advance towards use in personalised medicine to inform clinical decision- making, it is critical to understand the uncertainty hidden in parameter estimates from their calibration to experimental data. This study applies approximate Bayesian computation to re-calibrate the gating kinetics of four ion channels in two existing human atrial cell models to their original datasets, providing a measure of uncertainty from the parameter posterior distributions. Two approaches are investigated to reduce the uncertainty present: firstly to re-calibrate the models to a more complete 'unified' dataset and, secondly, the use of a standardised formulation with fewer parameters to constrain. The study shows that the use of more complete datasets does not eliminate uncertainty present in parameter estimates. The standardised model, particularly for the fast sodium current, shows reduced residuals from experimental data alongside lower parameter uncertainty and improved performance.</p>
Data from: A mouse model of cochlear implantation with chronic electric stimulation
Objectives: Cochlear implants provide an effective treatment option for those with severe hearing loss, including those with preserved low frequency hearing. However, certain issues can reduce implant efficacy including intracochlear tissue response and delayed loss of residual acoustic hearing. We describe a mouse model of cochlear implantation with chronic electric stimulation that can be used to study cochlear implant biology and related pathologies. Methods: Twelve normal hearing adult CBA/J mice underwent unilateral cochlear implantation and were evenly divided into one group receiving electric stimulation and one not. Serial impedance and neural response telemetry (NRT) measurements were made to assess implant functionality. Functionality was defined as having at least one electrode with an impedance 35 kOhms. Mouse cochleae were harvested for histology and 3D X-ray microscopy 21 days post-operatively, or, in case the implant was still functional, at a later time point when the implant failed. A separate experiment measured the hearing preservation rate in 7 adult CBA/J mice undergoing unilateral cochlear implantation with serial auditory brainstem response (ABR) and distortion product otoacoustic emissions (DPOAE). Results: Implants maintained functionality for a mean of 35 days in the non-stimulated group and 19.8 days in the stimulated group. Reliable NRT and behavioral responses to electric stimulation were recorded. A robust intracochlear peri-implant tissue response with neo-ossification was seen in all cochleae. Six of seven mice maintained intact low frequency hearing up to 6 weeks following cochlear implantation. Conclusions: We demonstrate the feasibility of cochlear implantation and behaviorally significant electric stimulation in the mouse, with the potential for hearing preservation. This model may be combined with established mouse models of hearing loss and the large genetic and molecular research toolkit unique to the mouse for mechanistic and therapeutic investigations of cochlear implant biology.
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.