Die chronologische Liste zeigt aktuelle Veröffentlichungen aus dem Forschungsbetrieb der Hochschule Weihenstephan-Triesdorf. Zuständig ist das Zentrum für Forschung und Wissenstransfer (ZFW).
8 Ergebnisse
Fedir PERTSEVYI,
Volodymyr Ladyka,
Prof. Dr. Iryna Smetanska
Technology of thermostable and frozen fillings from dairy raw materials and sesame seeds concentrate. (2022) Dissa+ 2022 , S. 1-192.
Hepatic stellate cells (HSCs) are also known as lipocytes, fat-storing cells, perisinusoidal cells, or Ito cells. These liver-specific mesenchymal cells represent about 5% to 8% of all liver cells, playing a key role in maintaining the microenvironment of the hepatic sinusoid. Upon chronic liver injury or in primary culture, these cells become activated and transdifferentiate into a contractile phenotype, i.e., the myofibroblast, capable of producing and secreting large quantities of extracellular matrix compounds. Based on their central role in the initiation and progression of chronic liver diseases, cultured HSCs are valuable in vitro tools to study molecular and cellular aspects of liver diseases. However, the isolation of these cells requires special equipment, trained personnel, and in some cases needs approval from respective authorities. To overcome these limitations, several immortalized HSC lines were established. One of these cell lines is CFSC, which was originally established from cirrhotic rat livers induced by carbon tetrachloride. First introduced in 1991, this cell line and derivatives thereof (i.e., CFSC-2G, CFSC-3H, CFSC-5H, and CFSC-8B) are now used in many laboratories as an established in vitro HSC model. We here describe molecular features that are suitable for cell authentication. Importantly, chromosome banding and multicolor spectral karyotyping (SKY) analysis demonstrate that the CFSC-2G genome has accumulated extensive chromosome rearrangements and most chromosomes exist in multiple copies producing a pseudo-triploid karyotype. Furthermore, our study documents a defined short tandem repeat (STR) profile including 31 species-specific markers, and a list of genes expressed in CFSC-2G established by bulk mRNA next-generation sequencing (NGS).
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M.Sc. Isabel Möhrle,
Prof. Dr. Peter Breunig,
Prof. Dr. Martin Döring,
Prof. Dr. Manfred Geißendörfer,
Eberhard Groß,
Prof. Dr. Andreas Hoffmann,
Friedrich Gronauer-Weddige,
Prof. Dr. Michael Rudner
Weeds are undesired plants in agricultural fields that affect crop yield and quality by competing for nutrients, water, sunlight and space. For centuries, farmers have used several strategies and resources to remove weeds. The use of herbicide is still the most common control strategy. To reduce the amount of herbicide and impact caused by uniform spraying, site-specific weed management (SSWM) through variable rate herbicide application and mechanical weed control have long been recommended. To implement such precise strategies, accurate detection and classification of weeds in crop fields is a crucial first step. Due to the phenotypic similarity between some weeds and crops as well as changing weather conditions, it is challenging to design an automated system for general weed detection. For efficiency, unmanned aerial vehicles (UAV) are commonly used for image capturing. However, high wind pressure and different drone settings have a severe effect on the capturing quality, what potentially results in degraded images, e.g., due to motion blur. In this paper, we investigate the generalization capabilities of Deep Learning methods for early weed detection in sorghum fields under such challenging capturing conditions. For this purpose, we developed weed segmentation models using three different state-of-the-art Deep Learning architectures in combination with residual neural networks as feature extractors.We further publish a manually annotated and expert-curated UAV imagery dataset for weed detection in sorghum fields under challenging conditions. Our results show that our trained models generalize well regarding the detection of weeds, even for degraded captures due to motion blur. An UNet-like architecture with a ResNet-34 feature extractor achieved an F1-score of over 89 % on a hold-out test-set. Further analysis indicate that the trained model performed well in predicting the general plant shape, while most misclassifications appeared at borders of the plants. Beyond that, our approach can detect intra-row weeds without additional information as well as partly occluded plants in contrast to existing research.All data, including the newly generated and annotated UAV imagery dataset, and code is publicly available on GitHub: https://github.com/grimmlab/UAVWeedSegmentation and Mendeley Data: https://doi.org/10.17632/4hh45vkp38.3
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Maura John,
Prof. Dr. Florian Haselbeck,
Rupashree Dass,
Christoph Malisi,
Christian Dreischer,
Sebastian J Schultheiss,
Prof. Dr. Dominik Grimm
Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare twelve different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allows us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.
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Betreuung der Publikationsseiten
Gerhard Radlmayr
Referent für Wissenstransfer und Forschungskommunikation
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