Publications by authors named "Martin Strauch"

Background: Fine particulate matter (<2.5 μm, PM), and its subcomponent ultrafine carbonaceous particles (UFP), have been shown to cause adverse health effects, including respiratory and cardiovascular disease, and decline in kidney function. Previous research demonstrated the presence of these particles in the kidney, yet potential effects on kidney tissue remain elusive.

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Deep learning (DL) holds great promise to improve medical diagnostics, including pathology. Current DL research mainly focuses on performance. DL implementation potentially leads to environmental consequences but approaches for assessment of both performance and carbon footprint are missing.

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Modern microscopy techniques can be used to investigate soft nano-objects at the nanometer scale. However, time-consuming microscopy measurements combined with low numbers of observable polydisperse objects often limit the statistics. We propose a method for identifying the most representative objects from their respective point clouds.

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The physiological performance of any sensory organ is determined by its anatomy and physical properties. Consequently, complex sensory structures with elaborate features have evolved to optimize stimulus detection. Understanding these structures and their physical nature forms the basis for mechanistic insights into sensory function.

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Objectives: This experimental study focused on the intra- and inter-rater reproducibility of vertical bone level (VBL) measurements at strategic mini-implants (MI) using digital panoramic radiographs (PR).

Study Design: VBLs of 152 MIs for removable partial denture stabilization at 50 randomly chosen PRs from a clinical trial were digitally evaluated by three ratters. Rater deviations exceeding 0.

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Larvae of the fruit fly are a powerful study case for understanding the neural circuits underlying behavior. Indeed, the numerical simplicity of the larval brain has permitted the reconstruction of its synaptic connectome, and genetic tools for manipulating single, identified neurons allow neural circuit function to be investigated with relative ease and precision. We focus on one of the most complex neurons in the brain of the larva (of either sex), the GABAergic anterior paired lateral neuron (APL).

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The beet cyst nematode is a plant pest responsible for crop loss on a global scale. Here, we introduce a high-throughput system based on computer vision that allows quantifying beet cyst nematode infestation and measuring phenotypic traits of cysts. After recording microscopic images of soil sample extracts in a standardized setting, an instance segmentation algorithm serves to detect nematode cysts in these images.

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Article Synopsis
  • Image segmentation models using only image-level labels are gaining traction because they require less annotation effort compared to detailed methods like scribbles or bounding boxes.
  • The proposed deep network architecture incorporates Global Weighted Pooling and low-level image cues to enhance the segmentation of fine structures found in biological images.
  • Applied to datasets of nematodes and their cysts, this approach achieved Dice coefficients of 79.72% for nematodes and 58.51% for cysts, demonstrating effective segmentation.
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  • Aedes aegypti mosquitoes are highly specialized for biting humans, which makes them effective carriers of diseases.
  • Female mosquitoes prefer human odor over animal odors, but the mechanisms behind this preference are unclear.
  • The study identifies specific olfactory glomeruli in mosquitoes that respond differently to human and animal odors, highlighting a glomerulus that reacts strongly to components found in human skin, suggesting potential targets for new mosquito control methods.
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Imaging in three dimensions is necessary for thick tissues and small organisms. This is possible with tomographic optical microscopy techniques such as confocal, multiphoton and light sheet microscopy. All these techniques suffer from anisotropic resolution and limited penetration depth.

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Spermatogenesis, the complex process of male germ cell proliferation, differentiation, and maturation, is the basis of male fertility. In the seminiferous tubules of the testes, spermatozoa are constantly generated from spermatogonial stem cells through a stereotyped sequence of mitotic and meiotic divisions. The basic physiological principles, however, that control both maturation and luminal transport of the still immotile spermatozoa within the seminiferous tubules remain poorly, if at all, defined.

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An adaptive transition from exploring the environment in search of vital resources to exploiting these resources once the search was successful is important to all animals. Here we study the neuronal circuitry that allows larval of either sex to negotiate this exploration-exploitation transition. We do so by combining Pavlovian conditioning with high-resolution behavioral tracking, optogenetic manipulation of individually identified neurons, and EM data-based analyses of synaptic organization.

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Nematodes are plant parasites that cause damage to crops. In order to quantify nematode infestation based on soil samples, we propose an instance segmentation method that will serve as the basis of automatic quantitative analysis. We consider light microscopic images of cluttered object collections as they occur in realistic soil samples.

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Solid-liquid interfaces play an important role for functional devices. Hence, a detailed understanding of the interaction of soft matter objects with solid supports and of the often concomitant structural deformations is of great importance. We address this topic in a combined experimental and simulation approach.

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Article Synopsis
  • Neural mapping across individuals can be difficult when spatial relationships differ, leading researchers to use functional registration based on responses to specific reference stimuli.
  • The study focused on Drosophila flies, recording their receptor neuron responses to various volatile compounds.
  • The proposed method can advance the creation of reliable biological sensors, utilizing the natural sensitivity of olfactory receptors for better physiological measurements.
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The larval brain of the fruit fly Drosophila melanogaster is a small, tractable model system for neuroscience. Genes for fluorescent marker proteins can be expressed in defined, spatially restricted neuron populations. Here, we introduce the methods for 1) generating a standard template of the larval central nervous system (CNS), 2) spatial mapping of expression patterns from different larvae into a reference space defined by the standard template.

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Background: Plants under herbivore attack release volatiles that attract natural enemies, and herbivores in turn avoid such plants. Whilst herbivore-induced plant volatile blends appeared to reduce the attractiveness of host plants to herbivores, the volatiles that are key in this process and particularly the way in which deterrence is coded in the olfactory system are largely unknown. Here we demonstrate that herbivore-induced cotton volatiles suppress orientation of the moth Spodoptera littoralis to host plants and mates.

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Background: Calcium imaging in insects reveals the neural response to odours, both at the receptor level on the antenna and in the antennal lobe, the first stage of olfactory information processing in the brain. Changes of intracellular calcium concentration in response to odour presentations can be observed by employing calcium-sensitive, fluorescent dyes. The response pattern across all recorded units is characteristic for the odour.

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Article Synopsis
  • The study focuses on the honeybee's antennal lobe, where odours are processed through activity patterns in glomeruli, using optical imaging techniques.
  • A new tool called ImageBee has been developed for KNIME, which employs a matrix factorisation algorithm to isolate distinct glomerular signals from complex imaging data.
  • ImageBee proves effective in identifying biological signals in real and artificial datasets and offers flexible data processing options for further analysis projects within the KNIME platform.
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Cancer cells and non-cancer cells differ in their metabolism and they emit distinct volatile compound profiles, allowing to recognise cancer cells by their scent. Insect odorant receptors are excellent chemosensors with high sensitivity and a broad receptive range unmatched by current gas sensors. We thus investigated the potential of utilising the fruit fly's olfactory system to detect cancer cells.

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Neuronal plasticity allows an animal to respond to environmental changes by modulating its response to stimuli. In the honey bee (Apis mellifera), the biogenic amine octopamine plays a crucial role in appetitive odor learning, but little is known about how octopamine affects the brain. We investigated its effect in the antennal lobe, the first olfactory center in the brain, using calcium imaging to record background activity and odor responses before and after octopamine application.

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Article Synopsis
  • The study examines how honeybees encode different odors in their antennal lobe, focusing on the role of odor identity and concentration.
  • As the concentration of an odor increases, more olfactory glomeruli (the brain structures that process smells) are activated, leading to changes in the spatial patterns of responses.
  • The findings suggest that at higher concentrations, the glomerular response patterns become more similar to the actual chemical similarities of the odor molecules, enhancing the bees' ability to distinguish between different odors.
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Background: The calcium-imaging technique allows us to record movies of brain activity in the antennal lobe of the fruitfly Drosophila melanogaster, a brain compartment dedicated to information about odors. Signal processing, e.g.

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We have developed a new computational framework for merging odor response data sets from heterogeneous studies, creating a consensus metadatabase, the database of odor responses (DoOR). As a result, we obtained a functional atlas of all available odor responses in Drosophila melanogaster. Both the program and the data set are freely accessible and downloadable on the Internet (http://neuro.

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Article Synopsis
  • Cells adapt their gene expression in response to stimuli, forming modules of co-regulated genes that can be analyzed using time-series microarray datasets and clustering methods.
  • The EDISA algorithm introduces a new probabilistic clustering approach specifically designed for three-dimensional gene-condition-time datasets, improving the identification and refinement of gene expression modules.
  • The analysis identified three primary types of gene expression modules: independent response modules (most common), coherent modules with similar responses, and rare specific-response modules.
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