No abstract

This paper introduces an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size and imperfect labels. The data consists of audio recordings from captive marmoset monkeys housed in pairs, with several other cages nearby. The network in this paper can classify both the call type and which animal made it with a single pass through a single network using raw spectrogram images as input. The network vastly increases data analysis capacity for researchers interested in studying marmoset vocalizations, and allows data collection in the home cage, in group housed animals. V

Vocal communication in animals often involves taking turns vocalizing. In humans, turn-taking is a fundamental rule in conversation. Among non-human primates, the common marmoset is known to engage in antiphonal calling using phee calls and trill calls. Calls of the trill type are the most common, yet difficult to study, because they are not very loud and uttered in conditions when animals are in close proximity to one another. Here we recorded trill calls in captive pair-housed marmosets using wearable microphones, while the animals were together with their partner or separated, but within trill call range. Trills were exchanged mainly with the partner and not with other animals in the room. Animals placed outside the home cage increased their trill call rate and uttered more trills in response to their partner compared to strangers. The fundamental frequency, F0, of trills increased when animals were placed outside the cage. Our results indicate that trill calls can be monitored using wearable audio equipment and that minor changes in social context affect trill call interactions and spectral properties of trill calls.

BackgroundLyme neuroborreliosis (LNB), caused by the spirochete Borrelia burgdorferi (Bb), could result in cognitive impairment, motor dysfunction, and radiculoneuritis. We hypothesized that inflammation is a key factor in LNB pathogenesis and recently evaluated the effects of dexamethasone, a steroidal anti-inflammatory drug, and meloxicam a non-steroidal anti-inflammatory drug (NSAID), in a rhesus monkey model of acute LNB. Dexamethasone treatment significantly reduced the levels of immune mediators, and prevented inflammatory and/or neurodegenerative lesions in the central and peripheral nervous systems, and apoptosis in the dorsal root ganglia (DRG). However, infected animals treated with meloxicam showed levels of inflammatory mediators, inflammatory lesions, and DRG cell apoptosis that were similar to that of the infected animals that were left untreated.MethodsTo address the differential anti-inflammatory effects of dexamethasone and meloxicam on neuronal and myelinating cells of the peripheral nervous system (PNS), we evaluated the potential of these drugs to alter the levels of Bb-induced inflammatory mediators in rhesus DRG cell cultures and primary human Schwann cells (HSC), using multiplex enzyme-linked immunosorbent assays (ELISA). We also ascertained the ability of these drugs to modulate cell death as induced by live Bb in HSC using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) viability assay and the potential of dexamethasone to modulate Bb-induced apoptosis in HSC by the TUNEL assay.ResultsEarlier, we reported that dexamethasone significantly reduced Bb-induced immune mediators and apoptosis in rhesus DRG cell cultures. Here, we report that dexamethasone but not meloxicam significantly reduces the levels of several cytokines and chemokines as induced by live Bb, in HSC and DRG cell cultures. Further, meloxicam does not significantly alter Bb-induced cell death in HSC, while dexamethasone protects HSC against Bb-induced cell death.ConclusionsThese data help further explain our in vivo findings of significantly reduced levels of inflammatory mediators, DRG-apoptosis, and lack of inflammatory neurodegenerative lesions in the nerve roots and DRG of Bb-infected animals that were treated with dexamethasone, but not meloxicam. Evaluating the role of the signaling mechanisms that contribute to the anti-inflammatory potential of dexamethasone in the context of LNB could serve to identify therapeutic targets for limiting radiculitis and axonal degeneration in peripheral LNB.

We introduce an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size and imperfect labels. The data consists of audio recordings from captive marmoset monkeys housed in pairs, with several other cages nearby. Our network can classify both the call type and which animal made it with a single pass through a single network using raw spectrogram images as input. The network vastly increases data analysis capacity for researchers interested in studying marmoset vocalizations, and allows data collection in the home cage, in group housed animals.

Cross-species evidence suggests that the ability to exert control over a stressor is a key dimension of stress exposure that may sensitize frontostriatal-amygdala circuitry to promote more adaptive responses to subsequent stressors. The present study examined neural correlates of stressor controllability in young adults. Participants (N = 56; Mage = 23.74, range = 18–30 years) completed either the controllable or uncontrollable stress condition of the first of two novel stressor controllability tasks during functional magnetic resonance imaging (fMRI) acquisition. Participants in the uncontrollable stress condition were yoked to age- and sex-matched participants in the controllable stress condition. All participants were subsequently exposed to uncontrollable stress in the second task, which is the focus of fMRI analyses reported here. A whole-brain searchlight classification analysis revealed that patterns of activity in the right dorsal anterior insula (dAI) during subsequent exposure to uncontrollable stress could be used to classify participants' initial exposure to either controllable or uncontrollable stress with a peak of 73% accuracy. Previous experience of exerting control over a stressor may change the computations performed within the right dAI during subsequent stress exposure, shedding further light on the neural underpinnings of stressor controllability.

No abstract

AbstractVocal communication in animals often involves taking turns vocalizing. In humans, turn taking is a fundamental rule in conversation. Among non-human primates, the common marmoset is known to engage in antiphonal calling using phee calls and trill calls. Calls of the trill type are the most common, yet difficult to study, because they are not very loud and uttered in conditions when animals are in close proximity to one another. Here we recorded trill calls in captive pair-housed marmosets using wearable microphones, while the animals were together with their partner or separated, but within trill call range. Trills were exchanged mainly with the partner and not with other animals in the room. Animals placed outside the home cage increased their trill call rate and uttered more trills in response more to their partner. The fundamental frequency, F0, of trills increased when animals were placed outside the cage. Our results indicate that trill calls can be monitored using wearable audio equipment. Relatively minor changes in social context affect trill call interactions and spectral properties of trill calls, indicating that marmosets can communicate subtle information to their partner vocally.