Biology subjects

Werner, R.

Publications and source records attributed to Werner, R..

9 recordsLinked to original sources

bioRxiv2025

Dissociating mechanisms of heart-voice coupling

In a landmark paper in JASA, Orlikoff & Baken (1989a) demonstrated that the heartbeat carries through acoustic features of the human voice. Heart-voice coupling questions how vocal control is functionally regulated and has many potential engineering applications to extract heart rate from the human voice. However, laboratory phonology and bioacoustics seem to have forgotten about the phenomenon, such that its physiological underpinnings have been left unexplained. The current contribution showed that vocal acoustics contain signatures of the heartbeat as originally reported by Orlikoff & Baken (1989a). More specifically, the steady-state vowel vocalizations of 17 participants were analysed and a significant relationship between the vocal acoustic envelope and the cardiac activity (ECG) was found. Furthermore, two empirical tests were conducted to discriminate between vocal fold (vascularization) and pulmonary (heart-lung interaction) mechanisms of heart-voice coupling. In addition to a stable heart-voice coupling, a significant heart-expiration coupling during conditions of silent expiration was also found. Since the vocal folds are abducted during expiration, this result demonstrated that pulmonary mechanisms that modulate expiratory flow must be involved in heart-voice coupling. It is speculated that the hearts rhythm may be a weakly coupled pattern generator that possibly shaped the evolution of vocal and speech production.

neuroscience↗

bioRxiv2024

Multi-year drought strengthens positive and negative functional diversity effects on tree growth response

O_LIMixed-species forests are proposed as strategy to increase the resistance and resilience of forests to drought stress. However, evidence suggest that increasing tree species richness does not consistently enhance tree growth responses to drought. Moreover, tree diversity effects under unprecedented multiyear droughts remain uncertain, calling for a better understanding of the underlying processes. C_LIO_LIHere, we used a network of planted tree diversity experiments to investigate how drought-induced growth responses of individual trees are influenced by neighborhood tree diversity and the functional traits of the focal tree species. We analyzed tree cores (948 trees across 16 species) from nine experiments across Europe featuring gradients of tree species richness (1-6 species), which experienced severe droughts in recent years. Radial growth response to drought was quantified as tree-ring biomass increment using X-ray computed tomography. We applied hydraulic trait-based growth models to analyze single-year drought responses across all sites and site-specific responses during consecutive drought years for six sites as a function of neighborhood tree diversity. C_LIO_LIThe large variability in tree growth responses to a single-year drought was partially explained by the focal species hydraulic safety margin (representing species drought tolerance) and drought intensity, but independent of neighborhood species richness or functional trait diversity. However, tree diversity effects on growth responses strengthened during consecutive drought years and were site-specific, with contrasting direction (both positive and negative). This indicated opposing pathways of diversity effects under consecutive drought events, possibly resulting from competitive release or greater water consumption in diverse mixtures. C_LIO_LIWe conclude that tree diversity effects on growth responses to single-year droughts may differ considerably from responses to consecutive drought years. Our study highlights the need to consider trait-based approaches (specifically, hydraulic traits) and tree neighborhood scale processes to understand the multifaceted growth responses of tree mixtures under prolonged drought stress. C_LI

ecology↗

bioRxiv2024

Arm movements increase acoustic markers of expiratory flow

The gesture-speech physics theory suggests that there are biomechanical interactions of the voice with the whole body, driving speech to align fluctuations in loudness and F0 with upper-limb movement. This exploratory study offers a possible falsification of the gesture-speech physics theory, which would predict effects of upper-limb movement on voice as well as respiration. We therefore investigate co-movement expiration. Seventeen participants were asked to produce a continuous exhalation for several seconds. After 3s, they execute one of five within-subject movement conditions with their arm with and without a wrist weight (no movement, elbow flexion, elbow extension, internal arm rotation, external arm rotation). We analyzed the smoothed amplitude envelope of the acoustic signal in relation to arm movement. Compared to no movement, all four movements lead to higher positive peaks in the amplitude peaks, while weight did not influence the amplitude. We also found that across movement conditions, positive amplitude peaks are structurally timed relative to peaks in kine-matics (speed, acceleration). We conclude that the reason why upper-limb movements affect voice loudness is still best understood through gesture-speech physics theory, where upper-limb movements affect the voice directly by modulating sub-glottal pressures. Multimodal prosody is therefore partly literally embodied.

neuroscience↗

bioRxiv2023

The human voice aligns with whole-body kinetics

Humans often vocalize while concurrently gesturing with their hands. Fluctuations in the intensity and tone of the voice have been shown to synchronize with gestural upper limb movement. This research provides direct evidence that interactions between arm movements and postural muscle activity cause these voicing fluctuations. We show that specific muscles (e.g., pectoralis major, erector spinae), associated with upper limb movement and their postural anticipations, are especially likely to interact with the voice. Adding mass to the upper limb increased this interaction. Ground-reaction forces were also found to relate to postural muscles, and these measurements also directly covaried with fluctuations in the voice during some movement conditions. These results show that the voice co-patterns with whole-body kinetics, i.e. forces. We thereby go beyond kinematic analyses in studying interactions between gesturing and vocalization, invoking several implications for biomechanical modeling. We conclude that human voicing has evolved in a dynamical interaction with the whole-body motor system.

neuroscience↗

bioRxiv2022

Genetic variability, including gene duplication and deletion, in early sequences from the 2022 European monkeypox outbreak

Genome sequences from 47 monkeypox virus infections detected in a German university virology laboratory were analyzed in context of other sequences from the 2022 outbreak and earlier monkeypox genomes. Identical non-synonymous amino acid changes in six genes and the signature of APOBEC editing match other sequences from the European outbreak. Non-synonymous changes that were present in one to three sequences were found in 34 other genes. In sequences from two lesions of one patient, an 856 nucleotide translocation between genome termini resulted in the duplication of an initial 5 gene, and the disruption or complete deletion of four genes near the 3 genome end. Orthopoxvirus genome rearrangements of this nature are known to confer fitness advantages in the face of selection pressure. This change may therefore represent an early virus adaptation in the novel widespread and sustained human-to-human context of the current monkeypox outbreak.

bioinformatics↗

bioRxiv2021

Time-Dependent Image Restoration of Low-SNR Live Cell Ca2+ Fluorescence Microscopy Data

Live cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems, but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four data sets: synthetic data for in-depth evaluation of the algorithm behavior; two data sets acquired for analysis of initial Ca2+ microdomains in T cells; and, to illustrate transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.

bioinformatics↗

bioRxiv2021

Single Image-based Vignetting Correction for Improving the Consistency of Neural Activity Analysis in 2-Photon Functional Microscopy

High-resolution functional 2-photon microscopy of neural activity is a cornerstone technique in current neuroscience, enabling, for instance, the image-based analysis of relations of the organization of local neuron populations and their temporal neural activity patterns. Interpreting local image intensity as a direct quantitative measure of neural activity presumes, however, a consistent within- and across-image relationship between the image intensity and neural activity, which may be subject to interference by illumination artifacts. In particular, the so-called vignetting artifact - the decrease of image intensity towards the edges of an image - is, at the moment, widely neglected in the context of functional microscopy analyses of neural activity, but potentially introduces a substantial center-periphery bias of derived functional measures. In the present report, we propose a straightforward protocol for single image-based vignetting correction. Using immediate-early-gene-based 2-photon microscopic neural image data of the mouse brain, we show the necessity of correcting both image brightness and contrast to improve within- and across-image intensity consistency and demonstrate the plausibility of the resulting functional data.

neuroscience↗

bioRxiv2020

Single-cell analysis identifies TCF4 and ID3 as a molecular switch of mammary epithelial stem cell differentiation.

It is well known that the expansion of the mammary epithelium during the ovarian cycles in female mammals is supported by the transient increase in mammary epithelial stem cells (MaSCs). However, dissecting the molecular mechanisms that govern MaSC function and differentiation is poorly understood due to the lack of standardized methods for their identification and isolation. The development of robust single-cell mRNA sequencing () technologies and the computational methods to analyze them provides us with novel tools to approach the challenge of studying MaSCs in a completely unbiased way without. Here, we have performed the largest scRNA-seq analysis of individual mammary epithelial cells (~70,000 cells). Our study identified a distinct cell population presenting molecular features of MaSCs. Importantly, further purification and additional in-depth single-cell analysis of these cells revealed that they are not a fully homogenous entity. Instead, we identified three subpopulations representing early stages of lineage commitment. By tracking their molecular evolution through single-cell network analysis we found that one of these subpopulations represents bipotent MaSCs from which luminal and basal lineages diverge. Importantly, we also confirmed the presence of these cells in human mammary glands. Finally, through expression and network analysis studies, we have uncovered transcription factors that are activated early during lineage commitment. These data identified E2-2 (Tcf4) and ID3 as a potential molecular switch of mammary epithelial stem cell differentiation.

developmental biology↗

bioRxiv2018

Discrimination of the hierarchical structure of cortical layers in 2-photon microscopy data by combined unsupervised and supervised machine learning

The laminar organization of the cerebral cortex is a fundamental characteristic of the brain, with essential implications for cortical function. Due to the rapidly growing amount of high-resolution brain imaging data, a great demand arises for automated and flexible methods for discriminating the laminar texture of the cortex. Here, we propose a combined approach of unsupervised and supervised machine learning to discriminate the hierarchical cortical laminar organization in high-resolution 2-photon microscopic neural image data without observer bias, that is, without the prerequisite of manually labeled training data. For local cortical foci, we modify an unsupervised clustering approach to identify and represent the laminar cortical structure. Subsequently, supervised machine learning is applied to transfer the resulting layer labels across different locations and image data, to ensure the existence of a consistent layer label system. By using neurobiologically meaningful features, the discrimination results are shown to be consistent with the layer classification of the classical Brodmann scheme, and provide additional insight into the structure of the cerebral cortex and its hierarchical organization. Thus, our work paves a new way for studying the anatomical organization of the cerebral cortex, and potentially its functional organization.

neuroscience↗