Publications

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We present a method for the layout of anatomical structures and blood vessels based on information from the Foundational Model of Anatomy (FMA). Our approach integrates a novel vascular layout into the hierarchical treemap representation of anatomy as used in ApiNATOMY. Our method aims to improve the comprehension of complex anatomical and vascular data by providing readable visual representations. The effectiveness of our method is demonstrated through a prototype developed in VANTED, showing potential for application in research, education, and clinical settings.

Animal vocal communication research traditionally focuses on acoustic and contextual features of calls, yet substantial information is also contained in response selectivity and timing during vocalization events. By examining the spatiotemporal structure of vocal interactions, we can distinguish between ‘broadcast’ and ‘exchange’ signalling modes, with the former potentially serving to transmit signallers’ general state and the latter reflecting more interactive signalling behaviour. Here, we tracked the movements and vocalizations of wild meerkat (Suricata suricatta) groups simultaneously using collars to explore this distinction. We found evidence that close calls (used for maintaining group cohesion) are given as signal exchanges. They are typically given in temporally structured call–response sequences and are also strongly affected by the social environment, with individuals calling more when they have more neighbours and juveniles responding more to adults than the reverse. In contrast, short note calls appear mainly in sequences produced by single individuals and show little dependence on social surroundings, suggesting a broadcast signalling mode. Despite these differences, both call categories show similar clustering in space and time at a group level. Our results highlight how the fine-scale structure of vocal interactions can give important insights into the usage and function of signals in social groups. This article is part of the theme issue ‘The power of sound: unravelling how acoustic communication shapes group dynamics.'

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dc.contributor.author: Lehmann, Joris; Khosravian, Maryam; Cassau, Sina; Libnow, Johanna S.; Chang, Hetan; Fleischer, Joerg; Krieger, Jürgen

The olfactory system is an ideal and tractable system for exploring how the brain transforms sensory inputs into behaviour. The basic tasks of any olfactory system include

odour detection, discrimination and categorization. The challenge for the olfactory system is to transform the high-dimensional space of olfactory stimuli into the much smaller space of perceived objects and valence that endows odours with meaning. Our current understanding of how neural circuits address this challenge has come primarily from observations of the mechanisms of the brain for processing other sensory modalities, such as vision and hearing, in which optimized deep hierarchical circuits are used

to extract sensory features that vary along continuous physical dimensions. The olfactory system, by contrast, contends with an ill-defined, high-dimensional stimulus space and discrete stimuli using a circuit architecture that is shallow and parallelized. Here, we present recent observations in vertebrate and invertebrate systems that relate the statistical structure and state-dependent modulation of olfactory codes to mechanisms of perception and odour-guided behaviour.

One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned task by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.

Understanding how animals meet their daily energy requirements is critical in our rapidly changing world. Small organisms with high metabolic rates can conserve stored energy when food availability is low or increase energy intake when energetic requirements are high, but how they balance this in the wild remains largely unknown. Using miniaturized heart rate transmitters, we continuously quantified energy expenditure, torpor use and foraging behaviour of free-ranging male bats (Nyctalus noctula) in spring and summer. In spring, bats used torpor extensively, characterized by lowered heart rates and consequently low energy expenditures. In contrast, in summer, bats consistently avoided torpor, even though they could have used this low-energy mode. As a consequence, daytime heart rates in summer were three times as high compared with the heart rates in spring. Daily energy use increased by 42% during summer, despite lower thermogenesis costs at higher ambient temperatures. Likely, as a consequence, bats nearly doubled their foraging duration. Overall, our results indicate that summer torpor avoidance, beneficial for sperm production and self-maintenance, comes with a high energetic cost. The ability to identify and monitor such vulnerable energetic life-history stages is particularly important to predict how species will deal with increasing temperatures and changes in their resource landscapes.

Desert locust plagues threaten the food security of millions. Central to their formation is crowding-induced plasticity, with social phenotypes changing from cryptic (solitarious) to swarming (gregarious). Here, we elucidate the implications of this transition on foraging decisions and corresponding neural circuits. We use behavioral experiments and Bayesian modeling to decompose the multi-modal facets of foraging, revealing olfactory social cues as critical. To this end, we investigate how corresponding odors are encoded in the locust olfactory system using in-vivo calcium imaging. We discover crowding-dependent synergistic interactions between food-related and social odors distributed across stable combinatorial response maps. The observed synergy was specific to the gregarious phase and manifested in distinct odor response motifs. Our results suggest a crowding-induced modulation of the locust olfactory system that enhances food detection in swarms. Overall, we demonstrate how linking sensory adaptations to behaviorally relevant tasks can improve our understanding of social modulation in non-model organisms.

Background: While aldosterone plays an important role in blood pressure regulation, its role in essential hypertension (EHT) remains unclear. Here, we systematically investigated the secretion of biologically-active free aldosterone in saliva in response to awakening (AldAR) and during the day (AldDay) in EHT compared to normotensive controls (NT).In 30 men with EHT and 30 age-matched NT, AldAR saliva samples were collected immediately after awakening and 15, 30, 45, and 60 min thereafter and AldDay samples were collected from 08:30-22:00 h on two consecutive days.Results: Over the course of the day, men with EHT had higher repeated AldDay levels compared to NT (p = .002) with higher concentrations in the morning hours (p's ≤ .047), a steeper decline over the course of the day (p's ≤ .018), and similar concentrations in the evening (p's ≥ .21). Regarding AldAR, we observed higher concentrations in EHT at awakening (p = .017) and borderline higher concentrations at 15 min (p = .086). No differences were found 30-60 min after awakening (p's ≥ .34). Analyses with repeated and aggregated AldAR levels resulted in borderline significantly higher free aldosterone in EHT (p's ≤ .077). Complementary analyses confirmed linear associations between higher blood pressure and higher AldAR and AldDay levels.Conclusions: Our data point to elevated salivary free aldosterone secretion in EHT over the course of the day, particularly in the morning hours. As the free aldosterone fraction is considered biologically active, our data may point to a biological mechanism underlying EHT.

Thermal soaring, a technique used by birds and gliders to utilize updrafts of hot air, is an appealing model-problem for studying motion control and how it is learned by animals and engineered autonomous systems. Thermal soaring has rich dynamics and nontrivial constraints, yet it uses few control parameters and is becoming experimentally accessible. Following recent developments in applying reinforcement learning methods for training deep neural-network (deep-RL) models to soar autonomously both in simulation and real gliders, here we develop a simulation-based deep-RL system to study the learning process of thermal soaring. We find that this process has learning bottlenecks, we define a new efficiency metric and use it to characterize learning robustness, we compare the learned policy to data from soaring vultures, and find that the neurons of the trained network divide into function clusters that evolve during learning. These results pose thermal soaring as a rich yet tractable model-problem for the learning of motion control.

The application of supervised machine learning methods to identify behavioural modes from inertial measurements of bio-loggers has become a standard tool in behavioural ecology. Several design choices can affect the accuracy of identifying the behavioural modes. One such choice is the inclusion or exclusion of segments consisting of more than a single behaviour (mixed segments) in the machine learning model training data. Currently, the common practice is to ignore such segments during model training. In this paper we tested the hypothesis that including mixed segments in model training will improve accuracy, as the model would perform better in identifying them in the test data. We test this hypothesis using a series of data simulations on four datasets of accelerometer data coupled with behaviour observations, obtained from four study species (Damaraland mole-rats, meerkats, olive baboons, polar bears). Results show that when a substantial proportion of the test data are mixed behaviour segments (above ~ 10%), including mixed segments in machine learning model training improves the accuracy of classification. These results were consistent across the four study species, and robust to changes in segment length, sample size, and degree of mixture within the mixed segments. However, we also find that in some cases (particularly in baboons) models trained with mixed segments show reduced accuracy in classifying test data containing only single behaviour (pure) segments, compared to models trained without mixed segments. Based on these results, we recommend that when the classification model is expected to deal with a substantial proportion of mixed behaviour segments (>10%), it is beneficial to include them in model training, otherwise, it is unnecessary but also not harmful. The exception is when there is a basis to assume that the training data contains a higher rate of mixed segments than the actual (unobserved) data to be classified—such a situation may occur particularly when training data are collected in captivity and used to classify data from the wild. In this case, excess inclusion of mixed segments in training data should probably be avoided.

Motivation: High-throughput omics methods increasingly result in large datasets including metabolomics data, which are often difficult to analyse.

Result: To help researchers to handle and analyse those datasets by mapping and investigating metabolomics data of multiple sampling conditions (e. g., different time points or treatments) in the context of pathways, PathwayNexus has been developed, which presents the mapping results in a matrix format, allowing users to easily observe the relations between the compounds and the pathways. It also offers functionalities like ranking, sorting, clustering, pathway views and further analytical tools. Its primary objective is to condense large sets of pathways into smaller, more relevant subsets that align with the specific interests of the user.

Availability and Implementation: The methodology presented here is implemented in PathwayNexus, an open-source add-on for Vanted available at www.cls.uni-konstanz.de/software/pathway-nexus.

Supplementary Information: Website: www.cls.uni-konstanz.de/software/pathway-nexus

A change to a population’s social network is a change to the substrate of cultural transmission, affecting behavioral diversity and adaptive cultural evolution. While features of network structure such as population size and density have been well studied, less is understood about the influence of social processes such as population turnover—or the repeated replacement of individuals by naive individuals. Experimental data have led to the hypothesis that naive

learners can drive cultural evolution by better assessing the relative value of behaviors, although this hypothesis has been expressed only verbally.We conducted a formal exploration of this hypothesis using a generative model that concurrently simulated its two key ingredients: social transmission and reinforcement learning. We simulated competition between high- and low-reward behaviors while varying turnover magnitude and tempo. Variation in turnover influenced changes in the distributions of cultural behaviors, irrespective of initial knowledgestate conditions. We found optimal turnover regimes that amplified the production of higher reward behaviors through two key mechanisms:

repertoire composition and enhanced valuation by agents that knew both behaviors. These effects depended on network and learning parameters. Our model provides formal theoretical support for, and predictions about, the hypothesis that naive learners can shape cultural change through their enhanced sampling ability. By moving from experimental data to theory, we illuminate an underdiscussed generative process that can lead to changes in cultural behavior, arising

from an interaction between social dynamics and learning.

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dc.contributor.author: Page, Rachel A.