In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better ...understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better ...understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Image-based plant phenotyping has become a powerful tool in unravelling genotype-environment interactions. The utilization of image analysis and machine learning have become paramount in extracting ...data stemming from phenotyping experiments. Yet we rely on observer (a human expert) input to perform the phenotyping process. We assume such input to be a 'gold-standard' and use it to evaluate software and algorithms and to train learning-based algorithms. However, we should consider whether any variability among experienced and non-experienced (including plain citizens) observers exists. Here we design a study that measures such variability in an annotation task of an integer-quantifiable phenotype: the leaf count.
We compare several experienced and non-experienced observers in annotating leaf counts in images of
to measure intra- and inter-observer variability in a controlled study using specially designed annotation tools but also citizens using a distributed citizen-powered web-based platform. In the controlled study observers counted leaves by looking at top-view images, which were taken with low and high resolution optics. We assessed whether the utilization of tools specifically designed for this task can help to reduce such variability. We found that the presence of tools helps to reduce intra-observer variability, and that although intra- and inter-observer variability is present it does not have any effect on longitudinal leaf count trend statistical assessments. We compared the variability of citizen provided annotations (from the web-based platform) and found that plain citizens can provide statistically accurate leaf counts. We also compared a recent machine-learning based leaf counting algorithm and found that while close in performance it is still not within inter-observer variability.
While expertise of the observer plays a role, if sufficient statistical power is present, a collection of non-experienced users and even citizens can be included in image-based phenotyping annotation tasks as long they are suitably designed. We hope with these findings that we can re-evaluate the expectations that we have from automated algorithms: as long as they perform within observer variability they can be considered a suitable alternative. In addition, we hope to invigorate an interest in introducing suitably designed tasks on citizen powered platforms not only to obtain useful information (for research) but to help engage the public in this societal important problem.
Root phenotyping is a challenging task, mainly because of the hidden nature of this organ. Only recently, imaging technologies have become available that allow us to elucidate the dynamic ...establishment of root structure and function in the soil. In root tips, optical analysis of the relative elemental growth rates in root expansion zones of hydroponically-grown plants revealed that it is the maximum intensity of cellular growth processes rather than the length of the root growth zone that control the acclimation to dynamic changes in temperature. Acclimation of entire root systems was studied at high throughput in agar-filled Petri dishes. In the present study, optical analysis of root system architecture showed that low temperature induced smaller branching angles between primary and lateral roots, which caused a reduction in the volume that roots access at lower temperature. Simulation of temperature gradients similar to natural soil conditions led to differential responses in basal and apical parts of the root system, and significantly affected the entire root system. These results were supported by first data on the response of root structure and carbon transport to different root zone temperatures. These data were acquired by combined magnetic resonance imaging (MRI) and positron emission tomography (PET). They indicate acclimation of root structure and geometry to temperature and preferential accumulation of carbon near the root tip at low root zone temperatures. Overall, this study demonstrated the value of combining different phenotyping technologies that analyse processes at different spatial and temporal scales. Only such an integrated approach allows us to connect differences between genotypes obtained in artificial high throughput conditions with specific characteristics relevant for field performance. Thus, novel routes may be opened up for improved plant breeding as well as for mechanistic understanding of root structure and function.
Volume carving is a well established method for visual hull reconstruction and has been successfully applied in plant phenotyping, especially for 3d reconstruction of small plants and seeds. When ...imaging larger plants at still relatively high spatial resolution (≤1 mm), well known implementations become slow or have prohibitively large memory needs. Here we present and evaluate a computationally efficient algorithm for volume carving, allowing e.g., 3D reconstruction of plant shoots. It combines a well-known multi-grid representation called "Octree" with an efficient image region integration scheme called "Integral image." Speedup with respect to less efficient octree implementations is about 2 orders of magnitude, due to the introduced refinement strategy "Mark and refine." Speedup is about a factor 1.6 compared to a highly optimized GPU implementation using equidistant voxel grids, even without using any parallelization. We demonstrate the application of this method for trait derivation of banana and maize plants.
Root system architecture and especially its plasticity in acclimation to variable environments play a crucial role in the ability of plants to explore and acquire efficiently soil resources and ...ensure plant productivity. Non-destructive measurement methods are indispensable to quantify dynamic growth traits. For closing the phenotyping gap, we have developed an automated phenotyping platform,
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, for non-destructive characterization of root and shoot traits of plants grown in transparent agar medium.
The phenotyping system is capable to phenotype root systems and correlate them to whole plant development of up to 280
plants within 15 min. The potential of the platform has been demonstrated by quantifying phenotypic differences within 78
accessions from the 1001 genomes project. The chosen concept 'plant-to-sensor' is based on transporting plants to the imaging position, which allows for flexible experimental size and design. As transporting causes mechanical vibrations of plants, we have validated that daily imaging, and consequently, moving plants has negligible influence on plant development. Plants are cultivated in square Petri dishes modified to allow the shoot to grow in the ambient air while the roots grow inside the Petri dish filled with agar. Because it is common practice in the scientific community to grow
plants completely enclosed in Petri dishes, we compared development of plants that had the shoot inside with that of plants that had the shoot outside the plate. Roots of plants grown completely inside the Petri dish grew 58% slower, produced a 1.8 times higher lateral root density and showed an etiolated shoot whereas plants whose shoot grew outside the plate formed a rosette. In addition, the setup with the shoot growing outside the plate offers the unique option to accurately measure both, leaf and root traits, non-destructively, and treat roots and shoots separately.
Because the
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system can be moved from one growth chamber to another, plants can be phenotyped under a wide range of environmental conditions including future climate scenarios. In combination with a measurement throughput enabling phenotyping a large set of mutants or accessions, the platform will contribute to the identification of key genes.
The efficient and biocompatible transfer of nucleic acids into mammalian cells for research applications or medical purposes is a long-standing, challenging task. Viral transduction is the most ...efficient transfer system, but often entails high safety levels for research and potential health impairments for patients in medical applications. Lipo- or polyplexes are commonly used transfer systems but result in comparably low transfer efficiencies. Moreover, inflammatory responses caused by cytotoxic side effects were reported for these transfer methods. Often accountable for these effects are various recognition mechanisms for transferred nucleic acids. Using commercially available fusogenic liposomes (Fuse-It-mRNA), we established highly efficient and fully biocompatible transfer of RNA molecules for in vitro as well as in vivo applications. We demonstrated bypassing of endosomal uptake routes and, therefore, of pattern recognition receptors that recognize nucleic acids with high efficiency. This may underlie the observed almost complete abolishment of inflammatory cytokine responses. RNA transfer experiments into zebrafish embryos and adult animals fully confirmed the functional mechanism and the wide range of applications from single cells to organisms.
The main objective of this study was to identify genomic regions involved in biomass heterosis using QTL, generation means, and mode-of-inheritance classification analyses. In a modified North ...Carolina Design III we backcrossed 429 recombinant inbred line and 140 introgression line populations to the two parental accessions, C24 and Col-0, whose F ₁ hybrid exhibited 44% heterosis for biomass. Mid-parent heterosis in the RILs ranged from −31 to 99% for dry weight and from −58 to 143% for leaf area. We detected ten genomic positions involved in biomass heterosis at an early developmental stage, individually explaining between 2.4 and 15.7% of the phenotypic variation. While overdominant gene action was prevalent in heterotic QTL, our results suggest that a combination of dominance, overdominance and epistasis is involved in biomass heterosis in this Arabidopsis cross.
In this contribution we introduce an almost lossless affine 2D image transformation method. To this end we extend the theory of the well-known Chirp-z transform to allow for fully affine ...transformation of general <inline-formula> <tex-math notation="LaTeX">n </tex-math></inline-formula>-dimensional images. In addition we give a practical spatial and spectral zero-padding approach dramatically reducing losses of our transform, where usual transforms introduce blurring artifacts due to sub-optimal interpolation. The proposed method improves the mean squared error by approx. a factor of 1800 compared to the commonly used linear interpolation, and by a factor of 250 to the best competitor. We derive the transform from basic principles with special attention to implementation details and supplement this paper with python code for 2D images. In demonstration experiments we show the superior image quality compared to usual approaches, when using our method. However runtimes are considerably larger than when using toolbox algorithms.
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