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585 Research products

  • European Marine Science
  • 2018-2022
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  • UK Research and Innovation
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dixit, Tanmay; Choi, Gary P. T.; al-Mosleh, Salem; Lund, Jess; +4 Authors

    The persistence of imperfect mimicry in nature presents a challenge to mimicry theory. Some hypotheses for the existence of imperfect mimicry make differing predictions depending on how mimetic fidelity is measured. Here, we measure mimetic fidelity in a brood parasite–host system using both trait-based and response-based measures of mimetic fidelity. Cuckoo finches Anomalospiza imberbis lay imperfectly mimetic eggs that lack the fine scribbling characteristic of eggs of the tawny-flanked prinia Prinia subflava, a common host species. A trait-based discriminant analysis based on Minkowski functionals—that use geometric and topological morphometric methods related to egg pattern shape and coverage—reflects this consistent difference between host and parasite eggs. These methods could be applied to quantify other phenotypes including stripes and waved patterns. Furthermore, by painting scribbles onto cuckoo finch eggs and testing their rate of rejection compared to control eggs (i.e. a response-based approach to quantify mimetic fidelity), we show that prinias do not discriminate between eggs based on the absence of scribbles. Overall, our results support relaxed selection on cuckoo finches to mimic scribbles, since prinias do not respond differently to eggs with and without scribbles, despite the existence of this consistent trait difference. The dataset consists of pattern metrics extracted from egg images, and egg rejection data, from field experiments conducted from 2018-2020.

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    ZENODO
    Dataset . 2022
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    DRYAD
    Dataset . 2022
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    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Dataset . 2022
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      Data sources: ZENODO
      DRYAD
      Dataset . 2022
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      Data sources: Datacite
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    Authors: Dunkley, Katie; Whittey, Kathryn E.; Ellison, Amy; Perkins, Sarah E.; +2 Authors

    Mutualisms are driven by partners deciding to interact with one another to gain specific services or rewards. As predicted by biological market theory, partners should be selected based on the likelihood, quality, reward level, and or services each partner can offer. Third-party species that are not directly involved in the interaction, however, may indirectly affect the occurrence and or quality of the services provided, thereby affecting which partners are selected or avoided. We investigated how different clients of the sharknose goby (Elacatinus evelynae) cleaner fish were distributed across cleaning stations, and asked what characteristics, relating to biological market theory, affected this distribution. Through quantifying the visitation and cleaning patterns of client fish that can choose which cleaning station(s) to visit, we found that the relative species richness of visiting clients at stations was negatively associated with the presence of disruptive territorial damselfish at the station. Our study highlights, therefore, the need to consider the indirect effects of third-party species and their interactions (e.g. agonistic interactions) when attempting to understand mutualistic interactions between species. Moreover, we highlight how cooperative interactions may be indirectly governed by external partners. 

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    ZENODO
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    DRYAD
    Dataset . 2022
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    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
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      ZENODO
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      DRYAD
      Dataset . 2022
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Wass, Sam;

    Experimental participant details The project was approved by the Research Ethics Committee at the University of East London (Approval number: EXP 1617 04). Informed consent, and intent to publish, were obtained in the usual manner. Participants were recruited from the London, Essex, Hertfordshire and Cambridge regions of the UK. In total, 91 infant-caregiver dyads were recruited to participate in the study, of whom usable autonomic data were recorded from 82. Of these, usable paired autonomic data (from both caregiver and child) were obtained from 74 participants. Further details, including exclusion criteria, and detailed demographic details on the sample, are given in Appendix 1 section 1.1. The sample size was selected following power calculations presented in the original funding application ES/N017560/1. Of note, we excluded families in which the primary day-time care was performed by the male caregiver because the numbers were insufficient to provide an adequately gender-matched sample. All participating caregivers were, therefore, female. Participants received £30 in gift vouchers as a token of gratitude for participation, split over two visits. Experimental method details Participating caregivers were invited to select a day during which they would be spending the entire day with their child but which was otherwise, as far as possible, typical for them and their child. The researcher visited the participants’ homes in the morning (c. 7:30–10am) to fit the equipment, and returned later (c. 4–7pm) to pick it up. The mean (std) recording time per day was 7.3 (1.4) hours. The equipment consisted of two wearable layers, for both infant and caregiver. For the infant, a specially designed baby-grow was worn next to the skin, which contained a built-in Electrocardiogram (ECG) recording device (recording at 250Hz), accelerometer (30Hz), Global Positioning System (GPS) (1Hz), and microphone (11.6kHz). A T-shirt, worn on top of the device, contained a pocket to hold the microphone and a miniature video camera (a commercially available Narrative Clip 2 camera). For the caregiver, a specially designed chest strap was also worn next to the skin, containing the same equipment. A cardigan, worn as a top layer, contained the microphone and video camera. The clothes were comfortable when worn and, other than a request to keep the equipment dry, participants were encouraged to behave exactly as they would do on a normal day. At the start and end of each recording session, before the devices were inserted into the clothes worn by the participants, the researchers synchronised the two devices by holding them on top of one another and moving them sharply from side to side, once per second for 10 consecutive seconds. Post hoc trained coders identified the timings of these movements in the accelerometer data from each device independently. This information was used to synchronise the two recording devices. Quantification and statistical analysis Autonomic data parsing and calculation of the autonomic composite measure. Further details on the parsing of the heart rate, heart rate variability, and actigraphy are given here: https://tinyurl.com/yckzfxf8. Here we present our motivation for collapsing these three measures into a single composite measure of autonomic arousal. Home/Awake coding. Our preliminary analyses suggested that infants tended to be strapped-in to either a buggy or car seat for much of the time that they were outdoors, which strongly influenced their autonomic data. For this reason, all of the analyses presented in the paper only include data segments in which the dyad was at home and the infant was awake. A description of how these segments were identified is given in Appendix 1 (section 1.7). Following these exclusions, the mean (std) total amount of data available per dyad was 3.7 (1.7) hours, corresponding to 221.5 (102.4) 60-second epochs per dyad. Vocal coding. The microphone recorded a 5-second snapshot of the auditory environment every 60 seconds. Post hoc, trained coders identified samples in which the infant or caregiver was vocalising, and the following codings were applied. For each coding scheme, consistency of rating between coders was achieved through discussions and joint coding sessions based on an ersatz dataset, before the actual dataset was coded. All coders were blind to study design and hypothesised study outcome. Importantly, analyses conducted on a separate, continuous dataset (see Appendix 1, section S10) suggest that the temporal structure of our vocalisations was maintained despite this ‘sparse sampling’ approach. Furthermore, our analyses examine how arousal changes relative to observed vocalisations, and any arousal changes that we do observe time-locked to vocalisations would be weakened (not strengthened) by the fact that the vocalisation data were sparsely sampled (because power would have been reduced by missing vocalizations through the sparse sampling method, rather than increased). Infant data. i) vocalisation type. A morphological coding scheme was applied with the following categories: cry, laugh, squeal, growl, quasi-resonant vowel, fully-resonant vowel, marginal syllable, canonical syllable. Overall, 29% of vocalisations were cries; 1% laughs; 1% squeal; 3% growl; 18% quasi-resonant vowel; 18% fully-resonant vowel; 6% marginal syllable; 23% canonical syllable. For analyses presented in the main text these were collapsed into cries and speech-like vocalisations, which included the following non-cry categories: quasi-resonant vowel; fully-resonant vowel; marginal syllable; canonical syllable. Laughs, squeals and growls were excluded due to rarity. ii) vocal affect was coded on a three-point scale for vocal affect (negative (fussy and difficult), neutral or positive (happy and engaged). In order to assess inter-rater reliability, 11% of the sample was double coded; Cohen’s kappa was 0.70, which is considered substantial agreement. iii) vocal intensity was coded on a three-point scale from low emotional intensity, neutral, or high emotional intensity. Adult data. i) vocalisation type. A trained coder listened to vocalisations one by one and categorised them into the following categories: Imperative, Question, Praise, Singing, Imitation of Baby Vocalisation, Laughter, Reassurance, Sighing, Storytelling. These were then further collapsed into four supraordinate categories: Positive (Singing, Laughter); Stimulating (Question); Intrusive/negative affect (Imperative, Sighing); Sensitive (Praise, Imitation of Baby Vocalisation, Reassurance, Storytelling). Overall, 14% of vocalisations were Positive; 30% were Stimulating; 41% were Intrusive; 15% were Praise. In addition, ii) vocal affect and iii) vocal intensity were coded in the same way as for the infant data. In order to assess inter-rater reliability, 24% of the sample was double coded; Cohen’s kappa was 0.60, which is considered acceptable. Physical positions while vocalising. We also ascertained the physical position of our participants while vocalising (Appendix 1 section 1.8). Permutation-based temporal clustering analyses. To estimate the significance of time-series relationships, a permutation-based temporal clustering approach was used. This procedure, which is adapted from neuroimaging, allows us to estimate the probability of temporally contiguous relationships being observed in our results, a fact that standard approaches to correcting for multiple comparisons fail to account for. See further details in Appendix 1 section 1.9. ROC analyses. In order to assess the selection of visual features we employed a signal detection framework based on the Receiver Operator Characteristic (ROC) . This analyses the degree to which arousal levels predict the timings of vocalisations relative to the timings of randomly sampled comparison samples, epoch by epoch. See Results section and 67 for more details. Arousal stability. Arousal stability was measured by calculating the auto-correlation in infant and caregiver arousal, considered separately. The auto-correlation was calculated using the Matlab function nanautocorr.m, written by Fabio Oriani. Only the first lag term was reported as previous analyses have shown that autocorrelation data show a strong first order autoregressive tendency. Arousal coupling. Arousal coupling was measured by calculating the zero-lag cross-correlation between infant and caregiver arousal. The cross-correlation was calculated by first applying a linear detrend to each measure independently and then calculating the Spearman’s correlation between the infant and caregiver arousal data within that window. Moving window analyses. To estimate how stability and coupling changed relative to vocalisations, we used a moving window analysis (see Figure 8). Arousal data were downsampled to 1-minute epochs (0.016 Hz) (which was the sampling frequency of our microphone data). The size of the moving window was set arbitrarily at 10 epochs, with a shift of 5 epochs between windows. We excerpted the stability and coupling values around each individual vocalisation, and averaged these across all vocalisations. Control analysis. Participant by participant, for each vocalisation that was observed, a random ‘non-vocalisation’ moment was selected as a moment during the day when the dyad was at home and the infant was awake but no vocalisation occurred. The same moving window analysis described above was then repeated to examine change relative to this ‘non-vocalisation event’. The same procedure was repeated 1000 times and the results averaged. Real and observed data were compared using the permutation-based temporal clustering analyses described above. Appendices available here: https://doi.org/10.31234/osf.io/gmfk7 It has been argued that a necessary condition for the emergence of speech in humans is the ability to vocalize irrespectively of underlying affective states, but when and how this happens during development remains unclear. To examine this, we used wearable microphones and autonomic sensors to collect multimodal naturalistic datasets from 12-month-olds and their caregivers. We observed that, across the day, clusters of vocalisations occur during elevated infant and caregiver arousal. This relationship is stronger in infants than caregivers: caregivers' vocalizations show greater decoupling with their own states of arousal, and their vocal production is more influenced by the infant’s arousal than their own. Different types of vocalisation elicit different patterns of change across the dyad. Cries occur following reduced infant arousal stability and lead to increased child-caregiver arousal coupling, and decreased infant arousal. Speech-like vocalisations also occur at elevated arousal, but lead to longer-lasting increases in arousal, and elicit more parental verbal responses. Our results suggest that: 12-month-old infants’ vocalisations are strongly contingent on their arousal state (for both cries and speech-like vocalisations), whereas adults’ vocalisations are more flexibly tied to their own arousal; that cries and speech-like vocalisations alter the intra-dyadic dynamics of arousal in different ways, which may be an important factor driving speech development; and that this selection mechanism which drives vocal development is anchored in our stress physiology. These data files and associated processing scripts are designed to be run in Matlab R2022a. Only the Statistics and Machine Learning Toolbox is required. Details on open-source alternatives to Matlab are given here: https://opensource.com/alternatives/matlab.

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    ZENODO
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    DRYAD
    Dataset . 2022
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      ZENODO
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      Dataset . 2022
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    Authors: Smith, Thomas; Mombrikotb, Shorok; Ransome, Emma; Kontopoulos, Dimitrios - Georgios; +2 Authors

    How complex microbial communities respond to climatic fluctuations remains an open question. Due to their relatively short generation times and high functional diversity, microbial populations harbor great potential to respond as a community through a combination of strain-level phenotypic plasticity, adaptation, and species sorting. However, the relative importance of these mechanisms remains unclear. We conducted a laboratory experiment to investigate the degree to which bacterial communities can respond to changes in environmental temperature through a combination of phenotypic plasticity and species sorting alone. We grew replicate soil communities from a single location at six temperatures between 4°C and 50°C. We found that phylogenetically- and functionally-distinct communities emerge at each of these temperatures, with K-strategist taxa favoured under cooler conditions, and r-strategist taxa under warmer conditions. We show that this dynamic emergence of distinct communities across a wide range of temperatures (in essence, community-level adaptation), is driven by the resuscitation of latent functional diversity: the parent community harbors multiple strains pre-adapted to different temperatures that are able to "switch on" at their preferred temperature without immigration or adaptation. Our findings suggest that microbial community function in nature is likely to respond rapidly to climatic temperature fluctuations through shifts in species composition by resuscitation of latent functional diversity. Bacterial isolates were collected from a species sorting experiment. Isolates were grown at different temperatures, their growth, respiration and ATP content measured in order to produce thermal performance curves for these traits. Please see the accompanying published article: Smith, T.P., Mobrikotb, S., Ransome, E., Kontopoulos, D-G., Pawar, S., Bell, T. 2022. Latent functional diversity may accelerate microbial community responses to temperature fluctuations. eLife. Accepted. Data files provided in csv format. Please see the README document for full details of files and contents (README.md).

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    Dataset . 2022
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    Authors: Takigawa, Masahiro; Kukovska, Lilia; Tirole, Margot; Huelin, Marta; +1 Authors

    Animals Five male Lister-Hooded rats (350-450g) were implanted with a microdrive with 24 independently moveable tetrodes. Prior to surgery, rats were kept at 90% of their free-feeding weight and housed in pairs on a 12-hour light/dark cycle, with 1 hour of simulated dusk/dawn. All experimental procedures and postoperative care were approved and carried out in accordance with the UK Home Office, subject to the restrictions and provisions contained within the Animal (Scientific Procedures) Act of 1986 Surgery Animals were deeply anaesthetized under isoflurane anesthesia (1.5-3% at 2L/min) and implanted with a custom-made microdrive array carrying 24 independently moveable tetrodes (modified from microdrive first published by (Davidson et al., 2009)). Each tetrode consisted of a twisted bundle of four tungsten microwires (12µm diameter, Tungsten 99.95% CS, California Fine Wire), gold-plated to reduce impedance to < 200kΩ. Three rats were implanted with a dual-hippocampal microdrive targeting both dorsal hippocampal CA1 areas (AP: -3.48mm, ML: +/-2.4mm from Bregma), each output carrying 12 tetrodes. The two remaining rats were implanted with a microdrive targeting the right dorsal hippocampal CA1 area (AP: 3.72mm, ML: 2.5mm from Bregma) and the left primary visual cortex (AP: -5.76mm, ML: -3.8mm from Bregma), using 16 and 8 tetrodes respectively. After surgery, animals were housed individually and allowed to recover with food and water ad libitum for a week before returning to being kept at 90% of their free-feeding weight. Experimental design A given recording session started with a 1-hour rest period in which the rats were placed in a quiet, remote location (rest pot), to which they had been previously habituated. The rest pot consisted of a black circular enclosure of 20cm of diameter, surrounded by a 50cm tall black plastic sheet that isolated them from the surroundings. The animals went through one of the two following protocols: Following the rest period, the rats were exposed to two novel 2m linear tracks in which they were allowed to run back and forth for 15 min, except for one session in which the animal ran for 30 min in the second track. (Rat1 session1; Rat2 and Rat3 all sessions) Following the rest period, the rats were exposed to three novel 2m linear tracks in which they were allowed to run back and forth for 15 min. Data from the first track has been removed for this study to ensure consistency between protocols, such as temporal proximity to final rest session, and for all analyses. (Rat1 session2; Rat4 and Rat5 all sessions) Liquid reward was dispensed at each end of the track (0.1mL chocolate flavored soy milk) to encourage the animals to traverse the entirety of the track. In all except one session (Rat2 session2), the exposures to the two tracks were separated by a 10min rest period in the rest pot. The recording session finished with a final 2-hours rest period inside the rest pot. To simulate novel environments, the shape of the tracks was changed between recording sessions and their surfaces covered with different textured fabrics. In each session, the room was surrounded by black curtains with different high contrast visual cues. The tracks were separated using view-obstructing dividers. Spike detection and unit isolation Spiking data was sorted using the semi-automatic clustering software KlustaKwik 2.0 (K.Harris, http://klustakwik.sourceforge.net/) and then manually curated with either Phy-GUI (https://github.com/kwikteam/phy) or Klustaviewa (https://github.com/klusta-team/klustaviewa). Putative single units were discriminated based on the spike waveform, a clean inter-spike interval, and their stability across the recording session. The rest of the clustered activity was classified in either multi-unit activity or noise. local field potentials The power spectral density (PSD) of the LFP was calculated using Welch’s method (pwelch, MATLAB) to identify the channels with higher power for theta (4–12 Hz) and ripple (125–300 Hz) oscillations, as well as the channel with the largest difference in normalized theta to ripple power. The LFP of selected channels was next downsampled from 30 kHz to 1 kHz and band-passed filtered (MATLAB command filtfilt). The instantaneous phases were estimated using Hilbert transform. Replay, the sequential reactivation within a neuronal ensemble, is a central hippocampal mechanism postulated to drive memory processing. While both rate and place representations are used by hippocampal place cells to encode behavioral episodes, replay has been largely defined by only the latter – based on the fidelity of sequential activity across neighboring place fields. Here, we show that dorsal CA1 place cells in rats can modulate their firing rate between replay events of two different contexts. This experience-dependent phenomenon mirrors the same pattern of rate modulation observed during behavior and can be used independently from place information within replay sequences to discriminate between contexts. Our results reveal the existence of two complementary neural representations available for memory processes. Files can be viewed in Matlab. Details available in companion README.txt

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    ZENODO
    Dataset . 2023
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    DRYAD
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      ZENODO
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    Authors: Gallego-Carracedo, Cecilia; Perich, Matthew G.; Chowdhury, Raeed H.; Miller, Lee E.; +1 Authors

    This data set includes behavioral recordings and extracellular neural recordings from the motor cortex, premotor cortex, and area 2 of the primary somatosensory cortex of Rhesus macaques during an instructed-delayed reaching task. Matthew Perich and Raeed Chowdhury collected and processed the data in the laboratory of Lee Miller for use in Gallego-Carracedo et al. 2022, which characterised the relationship between neural population activity and local field potentials across these sensorimotor cortical regions. Results and methodology from these experiments are described in Gallego-Carracedo et al. 2022. In these experiments, monkeys controlled a cursor on a screen using a two-link, planar manipulandum. Monkeys performed a simple center-out task to one of the eight possible targets, after a variable delayed period. During this reaching task, we tracked the endpoint position of the hand using sensors on the manipulandum. In addition to the behavioral data, we collected neural data from one or two of these areas using Blackrock Utah multielectrode arrays, yielding ~100 to ~200 channels of extracellular recordings per monkey. Recordings from these channels were thresholded online to detect spikes, which were sorted offline into putative single units. Analysis code used to produce figures for Gallego-Carracedo et al. 2022 provides useful examples for how to work with this dataset. See https://github.com/BeNeuroLab/Relationship_between_latent_dynamics_and_LFP.git for code and README. If you publish any work using the data, please cite the publication above (Gallego-Carracedo et al. 2022) and also cite this data set. The spiking activity of populations of cortical neurons is well described by a small number of population-wide covariance patterns, the “latent dynamics”. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable across behaviour: in each of the primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recording.

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    ZENODO
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    DRYAD
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      ZENODO
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    Authors: Chudley, Thomas R.;

    This dataset is produced using the ArcticDEM v3 mosaic. If you would like to use the newer ArcticDEM v4.1 mosaic, please contact me at thomas.r.chudley@durham.ac.uk. Data Description Title: Greenland Ice Sheet crevasse map from ArcticDEMVersion: 1.00Format: GeoTiffProjection: WGS84 / NSIDC Sea Ice Polar Stereographic North (EPSG: 3413)Resolution: 2 m (binary) and 200 m (fraction)Size: 3.5 GB (total binary) and 22 MB (total fraction)Citation: Chudley et al. (2021)Contact: Tom ChudleyEmail: thomas.r.chudley@durham.ac.uk Products This dataset contains crevasse locations identified from the ArcticDEM v3 mosaic. There are two primary products: a 2 m binary crevasse map, and a 200 m crevasse fraction map. It is divided into the six IMBIE 'Rignot' drainage basins: central west (CW), southwest (SW), southeast (SE), northeast (NE), north (NO), northwest (NW). This dataset is for scientific purposes only. The method is not able to detect metre-scale and snow-covered crevasses, and should not be used for field safety purposes. 2 m crevasse binary Byte GeoTiff product indicating derived crevasses at 2 m resolution. File naming convention is crevasse_binary_XX_2m.tif, where XX is the IMBIE basin code. Values have the following meaning: 0: No data 1: No crevasses identified 2: Crevasses identified 200 m crevasse fraction Float32 GeoTiff product indicating fraction of 200 m grid cell identified as crevasses in the 200 m product. File naming convention is crevasse_fraction_XX_2m.tif, where XX is the IMBIE basin code. Values have the following meaning: 0 - 1: Fraction of grid cell identified as crevasse in 2 m dataset -9999: No data Method The full processing chain for data derivation is described in Chudley et al. (2021). A binary crevasse mask of the Greenland Ice Sheet is generated using ArcticDEM v3 mosaic data at 2 m resolution (Porter et al., 2018), with data processed in Google Earth Engine (Gorelick et al., 2017). The ArcticDEM is cropped to the GIMP ice mask (Howat et al., 2014), before a smoothed elevation model is generated by performing an image convolution with a circular kernel of 50 m radius. Residuals greater than 1 m between the smoothed and raw elevation values were identified as crevasses. To compare with public velocity datasets (and derived strain rates, stress, etc.), the 2 m dataset was aggregated (using GDAL) into grid cells to match the resolution (200 m) of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) ice sheet surface velocity grid (Joughin, 2010; 2021). Aggregated values represent the fraction of grid cell area classified as crevasses. Caveats The method, including kernel size was tuned manually based on the region of interest of the original Chudley et al. (2021) paper. As such, it may not be optimal for other regions of interest on the ice sheet, in particular in the east, where medial moraines and marginal valleys are more prevalent (see caveat #4). The ArcticDEM is derived from optical MAXAR imagery. As such, snow-filled crevasses will not be identified here, which will be problematic above the ablation zone. Following comparison with Uncrewed Aerial Vehicle (UAV) data in Chudley et al. (2021), the approximate lower bound of crevasse width identified is ~10 m. These are large crevasses, far greater than are commonly encountered in safe fieldwork environments. This method is relatively crude: at its core, it is effectively a high-pass filter applied to the ArcticDEM mosaic. As such, there are false positives that occur around other supraglacial features (rivers, moraines, etc.) as well as marginal features (proglacial geomorphology, fjord sikkusak, etc.) that are captured in regions where the GIMP ice mask does not accurately capture the terrestrial ice extent at the time of ArcticDEM data capture. Users are encouraged to critically evaluate data in their areas of interest using the 2 m binary map and external data, even when intending to use only the 200 m fraction dataset. Citation When using this data, please cite the method as being from: Chudley, T. R., Christoffersen, P., Doyle, S. H., Dowling, T. P. F., Law, R., Schoonman, C. M., Bougamont, M., & Hubbard, B. (2021). Controls on water storage and drainage in crevasses on the Greenland Ice Sheet. Journal of Geophysical Research: Earth Surface, 126, e2021JF006287. https://doi.org/10.1029/2021JF006287. Acknowledgements Initial processing chain created whilst Chudley was supported by a Natural Environment Research Council Doctoral Training Partnership Studentship (Grant No. NE/L002507/1). ArcticDEM v3 mosaic is provided by the Polar Geospatial Center under NSF-OPP awards 1043681, 1559691, and 1542736. This dataset is for scientific purposes only. The method is not able to detect metre-scale and snow-covered crevasses, and should not be used for field safety purposes.

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    ZENODO
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    ZENODO
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      ZENODO
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    Authors: Ivanov, Aleksandar; King, Andrew; Willmore, Ben; Walker, Kerry; +1 Authors

    In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation. Our brains usually cope well with reverberation, allowing us to recognize sound sources regardless of their environments. In contrast, reverberation can cause severe difficulties for speech recognition algorithms and hearing-impaired people. The present study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant counterparts. The model neurons achieved this by extending the inhibitory component of their receptive filters for more reverberant spaces, and did so in a frequency-dependent manner. These predicted effects were observed in the responses of auditory cortical neurons of ferrets in the same simulated reverberant environments. Together, these results suggest that auditory cortical neurons adapt to reverberation by adjusting their filtering properties in a manner consistent with dereverberation. We have provided our Matlab scripts for generating our figures on Github: https://github.com/PhantomSpike/DeReverb Spike data were recorded using Neuropixels electrodes in the auditory cortex of anaesthetised ferrets.

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    ZENODO
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      ZENODO
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    Authors: Vides, Edmundo; Pfeffer, Suzanne;

    Figure 1 zip file: -Figure1ABCD_ Prism files containing tabular data of mst experiments for recombinant Rab29 binding to LRRK2 Armadillo 1-552,1-159, 350-550, and Rab7 binding to LRRK2 Armadillo 1-552. Figure 2 zip file: -Figure2ABC_ Prism files containing tabular data of mst experiments for recombinant Rab8A binding to LRRK2 Armadillo 1-552,1-159, and 350-550. -Figure2DEF_ Prism files containing tabular data of mst experiments for recombinant Rab10 binding to LRRK2 Armadillo 1-552,1-159, and 350-550. Figure3 zip file: -Figure3B_ Prism file containing tabular data of GFP LRRK2 WT and HA Rab29 colocalization as measured by Mander's coefficient. -Figure3S1_ Images of GFP LRRK2 Nterminal truncations amd HA Rab29 co-expressing HeLa cells. -Figure3S3_ Images of GFP LRRK2 WT and GFP LRRK2 point mutants and HA Rab29 co-expressing HeLa cells used to generate figureB. -Figure3S4_ PDF of annotated 700 and 800 channel for immunoblots used to generate figure3C and figure3D. Figure4 zip file: -Figure4ABC_ Prism files containing tabular data of mst experiments for recombinant phosphoRab8A binding to LRRK2 Armadillo 1-552,1-159, and 350-550. -Figure4DEF_Prism files containing tabular data of mst experiments for recombinant phosphoRab10 binding to LRRK2 Armadillo 1-552,1-159, and 350-550. Figure5 zip file: -Figure5A_ Electrostatic mapping of LRRK2 Armadillo model. -Figure5CD_ Prism files containing tabular data of mst experiments for recombinant phosphoRab10 binding to LRRK2 Armadillo 1-552 K17A or K18A. Figure6 zip file: -Figure6AB_ Images of FLAG LRRK2 R1441G and FLAG LRRK2 R1441GK17AK18A expressing HeLa cells. Images from three different experiment per condition. -Figure6C_ Prism files containing tabular data of quantification by Mander’s Coefficient colocalization analysis. Figure7 zip file: -Figure7RAWdata_ 700 and 800 channels of blots for figure7ABCD. -Figure7EF_ Prism files containing tabular data of quantification of blots of MLi2 washout experiments. Figure8 zip file: -Figure8RAWdata_ TIRF movies of LRRK2 recruitment to Rab10-lipid bilayers. -Figure8A_ Prism files containing tabular data of quantification of R1441G, D2017A, or R1441GK17AK18A LRRK2 recruitment to Rab10-lipid bilayers fitted to a non-linear regression curve. -Figure8B_ Prism files containing tabular data of quantification of R1441G LRRK2 recruitment to noRab10 or Rab11-lipid bilayers fitted to a non-linear regression curve. -Figure8C_ Prism files containing tabular data of quantification of 0.18-2.5uM R1441G LRRK2 recruitment to Rab10-lipid bilayers fitted to a non-linear regression curve. Initial rates then plotted in separate prism file and fitted to a nonlinear regression curve to determine a Hill coefficient. Figure8S2_ 700 and 800 channels of blots for figure for figure 8S2. Figure9 zip file: -Figure9RAWdata_ 700 and 800 channels of blots for figure9AB. -Figure9CD_ Prism files containing tabular data of quantification of blots in figure9AB. Activating mutations in the Leucine Rich Repeat Kinase 2 (LRRK2) cause Parkinson’s disease and activated LRRK2 phosphorylates a subset of Rab GTPases. Moreover, Golgi-associated Rab29 can recruit LRRK2 to the surface of the Golgi and activate it there for both auto- and Rab substrate phosphorylation. Here we define the precise Rab29 binding region of the LRRK2 Armadillo domain between residues 360-450 and show that this site, termed “Site #1”, can also bind additional LRRK2 substrates, Rab8A and Rab10. Moreover, we identify a distinct, N-terminal, higher affinity interaction interface between LRRK2 phosphorylated Rab8 and Rab10 termed “Site #2”, that can retain LRRK2 on membranes in cells to catalyze multiple, subsequent phosphorylation events. Kinase inhibitor washout experiments and mutation analysis demonstrate that rapid recovery of kinase activity in cells depends on the ability of LRRK2 to associate with phosphorylated Rab reaction products. Reconstitution of purified LRRK2 recruitment onto planar lipid bilayers decorated with Rab10 protein demonstrates cooperative association of only active LRRK2 with phospho-Rab10-containing membrane surfaces. These experiments reveal a feed-forward pathway that provides spatial control and apparent membrane activation of LRRK2 kinase activity.

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    ZENODO
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    Authors: Christine McKenna;

    This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. Geophysical Research Letters, 45, 945-954. https://doi.org/10.1002/2017GL076433 Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'. All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are: ua: zonal winds zg: geopotential height ts: surface temperature hfls, hfss, rlds, rlus: surface heatfluxes Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February) The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files.

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    ZENODO
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    ZENODO
    Dataset . 2022
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      ZENODO
      Dataset . 2022
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      ZENODO
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585 Research products
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dixit, Tanmay; Choi, Gary P. T.; al-Mosleh, Salem; Lund, Jess; +4 Authors

    The persistence of imperfect mimicry in nature presents a challenge to mimicry theory. Some hypotheses for the existence of imperfect mimicry make differing predictions depending on how mimetic fidelity is measured. Here, we measure mimetic fidelity in a brood parasite–host system using both trait-based and response-based measures of mimetic fidelity. Cuckoo finches Anomalospiza imberbis lay imperfectly mimetic eggs that lack the fine scribbling characteristic of eggs of the tawny-flanked prinia Prinia subflava, a common host species. A trait-based discriminant analysis based on Minkowski functionals—that use geometric and topological morphometric methods related to egg pattern shape and coverage—reflects this consistent difference between host and parasite eggs. These methods could be applied to quantify other phenotypes including stripes and waved patterns. Furthermore, by painting scribbles onto cuckoo finch eggs and testing their rate of rejection compared to control eggs (i.e. a response-based approach to quantify mimetic fidelity), we show that prinias do not discriminate between eggs based on the absence of scribbles. Overall, our results support relaxed selection on cuckoo finches to mimic scribbles, since prinias do not respond differently to eggs with and without scribbles, despite the existence of this consistent trait difference. The dataset consists of pattern metrics extracted from egg images, and egg rejection data, from field experiments conducted from 2018-2020.

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    ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2022
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Dunkley, Katie; Whittey, Kathryn E.; Ellison, Amy; Perkins, Sarah E.; +2 Authors

    Mutualisms are driven by partners deciding to interact with one another to gain specific services or rewards. As predicted by biological market theory, partners should be selected based on the likelihood, quality, reward level, and or services each partner can offer. Third-party species that are not directly involved in the interaction, however, may indirectly affect the occurrence and or quality of the services provided, thereby affecting which partners are selected or avoided. We investigated how different clients of the sharknose goby (Elacatinus evelynae) cleaner fish were distributed across cleaning stations, and asked what characteristics, relating to biological market theory, affected this distribution. Through quantifying the visitation and cleaning patterns of client fish that can choose which cleaning station(s) to visit, we found that the relative species richness of visiting clients at stations was negatively associated with the presence of disruptive territorial damselfish at the station. Our study highlights, therefore, the need to consider the indirect effects of third-party species and their interactions (e.g. agonistic interactions) when attempting to understand mutualistic interactions between species. Moreover, we highlight how cooperative interactions may be indirectly governed by external partners. 

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    ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
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      ZENODO
      Dataset . 2022
      License: CC 0
      Data sources: ZENODO
      DRYAD
      Dataset . 2022
      License: CC 0
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Wass, Sam;

    Experimental participant details The project was approved by the Research Ethics Committee at the University of East London (Approval number: EXP 1617 04). Informed consent, and intent to publish, were obtained in the usual manner. Participants were recruited from the London, Essex, Hertfordshire and Cambridge regions of the UK. In total, 91 infant-caregiver dyads were recruited to participate in the study, of whom usable autonomic data were recorded from 82. Of these, usable paired autonomic data (from both caregiver and child) were obtained from 74 participants. Further details, including exclusion criteria, and detailed demographic details on the sample, are given in Appendix 1 section 1.1. The sample size was selected following power calculations presented in the original funding application ES/N017560/1. Of note, we excluded families in which the primary day-time care was performed by the male caregiver because the numbers were insufficient to provide an adequately gender-matched sample. All participating caregivers were, therefore, female. Participants received £30 in gift vouchers as a token of gratitude for participation, split over two visits. Experimental method details Participating caregivers were invited to select a day during which they would be spending the entire day with their child but which was otherwise, as far as possible, typical for them and their child. The researcher visited the participants’ homes in the morning (c. 7:30–10am) to fit the equipment, and returned later (c. 4–7pm) to pick it up. The mean (std) recording time per day was 7.3 (1.4) hours. The equipment consisted of two wearable layers, for both infant and caregiver. For the infant, a specially designed baby-grow was worn next to the skin, which contained a built-in Electrocardiogram (ECG) recording device (recording at 250Hz), accelerometer (30Hz), Global Positioning System (GPS) (1Hz), and microphone (11.6kHz). A T-shirt, worn on top of the device, contained a pocket to hold the microphone and a miniature video camera (a commercially available Narrative Clip 2 camera). For the caregiver, a specially designed chest strap was also worn next to the skin, containing the same equipment. A cardigan, worn as a top layer, contained the microphone and video camera. The clothes were comfortable when worn and, other than a request to keep the equipment dry, participants were encouraged to behave exactly as they would do on a normal day. At the start and end of each recording session, before the devices were inserted into the clothes worn by the participants, the researchers synchronised the two devices by holding them on top of one another and moving them sharply from side to side, once per second for 10 consecutive seconds. Post hoc trained coders identified the timings of these movements in the accelerometer data from each device independently. This information was used to synchronise the two recording devices. Quantification and statistical analysis Autonomic data parsing and calculation of the autonomic composite measure. Further details on the parsing of the heart rate, heart rate variability, and actigraphy are given here: https://tinyurl.com/yckzfxf8. Here we present our motivation for collapsing these three measures into a single composite measure of autonomic arousal. Home/Awake coding. Our preliminary analyses suggested that infants tended to be strapped-in to either a buggy or car seat for much of the time that they were outdoors, which strongly influenced their autonomic data. For this reason, all of the analyses presented in the paper only include data segments in which the dyad was at home and the infant was awake. A description of how these segments were identified is given in Appendix 1 (section 1.7). Following these exclusions, the mean (std) total amount of data available per dyad was 3.7 (1.7) hours, corresponding to 221.5 (102.4) 60-second epochs per dyad. Vocal coding. The microphone recorded a 5-second snapshot of the auditory environment every 60 seconds. Post hoc, trained coders identified samples in which the infant or caregiver was vocalising, and the following codings were applied. For each coding scheme, consistency of rating between coders was achieved through discussions and joint coding sessions based on an ersatz dataset, before the actual dataset was coded. All coders were blind to study design and hypothesised study outcome. Importantly, analyses conducted on a separate, continuous dataset (see Appendix 1, section S10) suggest that the temporal structure of our vocalisations was maintained despite this ‘sparse sampling’ approach. Furthermore, our analyses examine how arousal changes relative to observed vocalisations, and any arousal changes that we do observe time-locked to vocalisations would be weakened (not strengthened) by the fact that the vocalisation data were sparsely sampled (because power would have been reduced by missing vocalizations through the sparse sampling method, rather than increased). Infant data. i) vocalisation type. A morphological coding scheme was applied with the following categories: cry, laugh, squeal, growl, quasi-resonant vowel, fully-resonant vowel, marginal syllable, canonical syllable. Overall, 29% of vocalisations were cries; 1% laughs; 1% squeal; 3% growl; 18% quasi-resonant vowel; 18% fully-resonant vowel; 6% marginal syllable; 23% canonical syllable. For analyses presented in the main text these were collapsed into cries and speech-like vocalisations, which included the following non-cry categories: quasi-resonant vowel; fully-resonant vowel; marginal syllable; canonical syllable. Laughs, squeals and growls were excluded due to rarity. ii) vocal affect was coded on a three-point scale for vocal affect (negative (fussy and difficult), neutral or positive (happy and engaged). In order to assess inter-rater reliability, 11% of the sample was double coded; Cohen’s kappa was 0.70, which is considered substantial agreement. iii) vocal intensity was coded on a three-point scale from low emotional intensity, neutral, or high emotional intensity. Adult data. i) vocalisation type. A trained coder listened to vocalisations one by one and categorised them into the following categories: Imperative, Question, Praise, Singing, Imitation of Baby Vocalisation, Laughter, Reassurance, Sighing, Storytelling. These were then further collapsed into four supraordinate categories: Positive (Singing, Laughter); Stimulating (Question); Intrusive/negative affect (Imperative, Sighing); Sensitive (Praise, Imitation of Baby Vocalisation, Reassurance, Storytelling). Overall, 14% of vocalisations were Positive; 30% were Stimulating; 41% were Intrusive; 15% were Praise. In addition, ii) vocal affect and iii) vocal intensity were coded in the same way as for the infant data. In order to assess inter-rater reliability, 24% of the sample was double coded; Cohen’s kappa was 0.60, which is considered acceptable. Physical positions while vocalising. We also ascertained the physical position of our participants while vocalising (Appendix 1 section 1.8). Permutation-based temporal clustering analyses. To estimate the significance of time-series relationships, a permutation-based temporal clustering approach was used. This procedure, which is adapted from neuroimaging, allows us to estimate the probability of temporally contiguous relationships being observed in our results, a fact that standard approaches to correcting for multiple comparisons fail to account for. See further details in Appendix 1 section 1.9. ROC analyses. In order to assess the selection of visual features we employed a signal detection framework based on the Receiver Operator Characteristic (ROC) . This analyses the degree to which arousal levels predict the timings of vocalisations relative to the timings of randomly sampled comparison samples, epoch by epoch. See Results section and 67 for more details. Arousal stability. Arousal stability was measured by calculating the auto-correlation in infant and caregiver arousal, considered separately. The auto-correlation was calculated using the Matlab function nanautocorr.m, written by Fabio Oriani. Only the first lag term was reported as previous analyses have shown that autocorrelation data show a strong first order autoregressive tendency. Arousal coupling. Arousal coupling was measured by calculating the zero-lag cross-correlation between infant and caregiver arousal. The cross-correlation was calculated by first applying a linear detrend to each measure independently and then calculating the Spearman’s correlation between the infant and caregiver arousal data within that window. Moving window analyses. To estimate how stability and coupling changed relative to vocalisations, we used a moving window analysis (see Figure 8). Arousal data were downsampled to 1-minute epochs (0.016 Hz) (which was the sampling frequency of our microphone data). The size of the moving window was set arbitrarily at 10 epochs, with a shift of 5 epochs between windows. We excerpted the stability and coupling values around each individual vocalisation, and averaged these across all vocalisations. Control analysis. Participant by participant, for each vocalisation that was observed, a random ‘non-vocalisation’ moment was selected as a moment during the day when the dyad was at home and the infant was awake but no vocalisation occurred. The same moving window analysis described above was then repeated to examine change relative to this ‘non-vocalisation event’. The same procedure was repeated 1000 times and the results averaged. Real and observed data were compared using the permutation-based temporal clustering analyses described above. Appendices available here: https://doi.org/10.31234/osf.io/gmfk7 It has been argued that a necessary condition for the emergence of speech in humans is the ability to vocalize irrespectively of underlying affective states, but when and how this happens during development remains unclear. To examine this, we used wearable microphones and autonomic sensors to collect multimodal naturalistic datasets from 12-month-olds and their caregivers. We observed that, across the day, clusters of vocalisations occur during elevated infant and caregiver arousal. This relationship is stronger in infants than caregivers: caregivers' vocalizations show greater decoupling with their own states of arousal, and their vocal production is more influenced by the infant’s arousal than their own. Different types of vocalisation elicit different patterns of change across the dyad. Cries occur following reduced infant arousal stability and lead to increased child-caregiver arousal coupling, and decreased infant arousal. Speech-like vocalisations also occur at elevated arousal, but lead to longer-lasting increases in arousal, and elicit more parental verbal responses. Our results suggest that: 12-month-old infants’ vocalisations are strongly contingent on their arousal state (for both cries and speech-like vocalisations), whereas adults’ vocalisations are more flexibly tied to their own arousal; that cries and speech-like vocalisations alter the intra-dyadic dynamics of arousal in different ways, which may be an important factor driving speech development; and that this selection mechanism which drives vocal development is anchored in our stress physiology. These data files and associated processing scripts are designed to be run in Matlab R2022a. Only the Statistics and Machine Learning Toolbox is required. Details on open-source alternatives to Matlab are given here: https://opensource.com/alternatives/matlab.

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    ZENODO
    Dataset . 2022
    License: CC 0
    Data sources: ZENODO
    DRYAD
    Dataset . 2022
    License: CC 0
    Data sources: Datacite
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