LIVIVO - Search results -

Search results

Result 1 - 10 of total 63

Search options

Article ; Online: Ten simple rules for providing a scientific Web resource.

Schultheiss, Sebastian J

2011 Volume 7, Issue 5, Page(s) e1001126

MeSH term(s)	Computational Biology ; Information Dissemination ; Internet ; Reproducibility of Results ; Software
Language	English
Publishing date	2011-05-26
Publishing country	United States
Document type	Editorial
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1001126
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Article: A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species.

John, Maura / Haselbeck, Florian / Dass, Rupashree / Malisi, Christoph / Ricca, Patrizia / Dreischer, Christian / Schultheiss, Sebastian J / Grimm, Dominik G

Frontiers in plant science

2022 Volume 13, Page(s) 932512

Abstract: 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 ... ...

Abstract	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 12 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
Language	English
Publishing date	2022-11-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2613694-6
ISSN	1664-462X
ISSN	1664-462X
DOI	10.3389/fpls.2022.932512
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Disclosure of salicylic acid and jasmonic acid-responsive genes provides a molecular tool for deciphering stress responses in soybean.

Beyer, Sebastian F / Bel, Paloma Sánchez / Flors, Victor / Schultheiss, Holger / Conrath, Uwe / Langenbach, Caspar J G

Scientific reports

2021 Volume 11, Issue 1, Page(s) 20600

Abstract: Hormones orchestrate the physiology of organisms. Measuring the activity of defense hormone-responsive genes can help understanding immune signaling and facilitate breeding for plant health. However, different from model species like Arabidopsis, genes ... ...

Abstract	Hormones orchestrate the physiology of organisms. Measuring the activity of defense hormone-responsive genes can help understanding immune signaling and facilitate breeding for plant health. However, different from model species like Arabidopsis, genes that respond to defense hormones salicylic acid (SA) and jasmonic acid (JA) have not been disclosed in the soybean crop. We performed global transcriptome analyses to fill this knowledge gap. Upon exogenous application, endogenous levels of SA and JA increased in leaves. SA predominantly activated genes linked to systemic acquired resistance and defense signaling whereas JA mainly activated wound response-associated genes. In general, SA-responsive genes were activated earlier than those responding to JA. Consistent with the paradigm of biotrophic pathogens predominantly activating SA responses, free SA and here identified most robust SA marker genes GmNIMIN1, GmNIMIN1.2 and GmWRK40 were induced upon inoculation with Phakopsora pachyrhizi, whereas JA marker genes did not respond to infection with the biotrophic fungus. Spodoptera exigua larvae caused a strong accumulation of JA-Ile and JA-specific mRNA transcripts of GmBPI1, GmKTI1 and GmAAT whereas neither free SA nor SA-marker gene transcripts accumulated upon insect feeding. Our study provides molecular tools for monitoring the dynamic accumulation of SA and JA, e.g. in a given stress condition.
MeSH term(s)	Cyclopentanes/metabolism ; Cyclopentanes/pharmacology ; Gene Expression/genetics ; Gene Expression Profiling/methods ; Gene Expression Regulation, Plant/genetics ; Oxylipins/metabolism ; Oxylipins/pharmacology ; Plant Growth Regulators/metabolism ; Plant Growth Regulators/pharmacology ; Salicylic Acid/metabolism ; Salicylic Acid/pharmacology ; Signal Transduction/drug effects ; Glycine max/genetics ; Glycine max/metabolism ; Stress, Physiological/genetics ; Stress, Physiological/physiology ; Transcriptome/genetics
Chemical Substances	Cyclopentanes ; Oxylipins ; Plant Growth Regulators ; jasmonic acid (6RI5N05OWW) ; Salicylic Acid (O414PZ4LPZ)
Language	English
Publishing date	2021-10-18
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-021-00209-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Coherent Magnons with Giant Nonreciprocity at Nanoscale Wavelengths.

Gallardo, Rodolfo / Weigand, Markus / Schultheiss, Katrin / Kakay, Attila / Mattheis, Roland / Raabe, Jörg / Schütz, Gisela / Deac, Alina / Lindner, Jürgen / Wintz, Sebastian

ACS nano

2024

Abstract: Nonreciprocal wave propagation arises in systems with broken time-reversal symmetry and is key to the functionality of devices, such as isolators or circulators, in microwave, photonic, and acoustic applications. In magnetic systems, collective wave ... ...

Abstract	Nonreciprocal wave propagation arises in systems with broken time-reversal symmetry and is key to the functionality of devices, such as isolators or circulators, in microwave, photonic, and acoustic applications. In magnetic systems, collective wave excitations known as magnon quasiparticles have so far yielded moderate nonreciprocities, mainly observed by means of incoherent thermal magnon spectra, while their occurrence as coherent spin waves (magnon ensembles with identical phase) is yet to be demonstrated. Here, we report the direct observation of strongly nonreciprocal propagating coherent spin waves in a patterned element of a ferromagnetic bilayer stack with antiparallel magnetic orientations. We use time-resolved scanning transmission X-ray microscopy (TR-STXM) to directly image the layer-collective dynamics of spin waves with wavelengths ranging from 5 μm down to 100 nm emergent at frequencies between 500 MHz and 5 GHz. The experimentally observed nonreciprocity factor of these counter-propagating waves is greater than 10 with respect to both group velocities and specific wavelengths. Our experimental findings are supported by the results from an analytic theory, and their peculiarities are further discussed in terms of caustic spin-wave focusing.
Language	English
Publishing date	2024-02-05
Publishing country	United States
Document type	Journal Article
ISSN	1936-086X
ISSN (online)	1936-086X
DOI	10.1021/acsnano.3c08390
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Engineered coumarin accumulation reduces mycotoxin-induced oxidative stress and disease susceptibility.

Beesley, Alexander / Beyer, Sebastian F / Wanders, Verena / Levecque, Sophie / Bredenbruch, Sandra / Habash, Samer S / Schleker, A Sylvia S / Gätgens, Jochem / Oldiges, Marco / Schultheiss, Holger / Conrath, Uwe / Langenbach, Caspar J G

Plant biotechnology journal

2023 Volume 21, Issue 12, Page(s) 2490–2506

Abstract: Coumarins can fight pathogens and are thus promising for crop protection. Their biosynthesis, however, has not yet been engineered in crops. We tailored the constitutive accumulation of coumarins in transgenic Nicotiana benthamiana, Glycine max and ... ...

Abstract	Coumarins can fight pathogens and are thus promising for crop protection. Their biosynthesis, however, has not yet been engineered in crops. We tailored the constitutive accumulation of coumarins in transgenic Nicotiana benthamiana, Glycine max and Arabidopsis thaliana plants, as well as in Nicotiana tabacum BY-2 suspension cells. We did so by overexpressing A. thaliana feruloyl-CoA 6-hydroxylase 1 (AtF6'H1), encoding the key enzyme of scopoletin biosynthesis. Besides scopoletin and its glucoside scopolin, esculin at low level was the only other coumarin detected in transgenic cells. Mechanical damage of scopolin-accumulating tissue led to a swift release of scopoletin, presumably from the scopolin pool. High scopolin levels in A. thaliana roots coincided with reduced susceptibility to the root-parasitic nematode Heterodera schachtii. In addition, transgenic soybean plants were more tolerant to the soil-borne pathogenic fungus Fusarium virguliforme. Because mycotoxin-induced accumulation of reactive oxygen species and cell death were reduced in the AtF6'H1-overexpressors, the weaker sensitivity to F. virguliforme may be caused by attenuated oxidative damage of coumarin-hyperaccumulating cells. Together, engineered coumarin accumulation is promising for enhanced disease resilience of crops.
MeSH term(s)	Arabidopsis/metabolism ; Scopoletin/metabolism ; Mycotoxins/metabolism ; Disease Susceptibility/metabolism ; Coumarins/metabolism ; Oxidative Stress ; Plant Roots/genetics ; Plant Roots/metabolism
Chemical Substances	coumarin (A4VZ22K1WT) ; Scopoletin (KLF1HS0SXJ) ; Mycotoxins ; Coumarins
Language	English
Publishing date	2023-08-14
Publishing country	England
Document type	Journal Article
ZDB-ID	2136367-5
ISSN	1467-7652 ; 1467-7652
ISSN (online)	1467-7652
ISSN	1467-7652
DOI	10.1111/pbi.14144
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Kernel-based identification of regulatory modules.

Schultheiss, Sebastian J

Methods in molecular biology (Clifton, N.J.)

2010 Volume 674, Page(s) 213–223

Abstract: ... the regulation of genes (Schultheiss et al. (2009) Bioinformatics 25, 2126-2133). Our software is called KIRMES ...

Abstract	The challenge of identifying cis-regulatory modules (CRMs) is an important milestone for the ultimate goal of understanding transcriptional regulation in eukaryotic cells. It has been approached, among others, by motif-finding algorithms that identify overrepresented motifs in regulatory sequences. These methods succeed in finding single, well-conserved motifs, but fail to identify combinations of degenerate binding sites, like the ones often found in CRMs. We have developed a method that combines the abilities of existing motif finding with the discriminative power of a machine learning technique to model the regulation of genes (Schultheiss et al. (2009) Bioinformatics 25, 2126-2133). Our software is called KIRMES: , which stands for kernel-based identification of regulatory modules in eukaryotic sequences. Starting from a set of genes thought to be co-regulated, KIRMES: can identify the key CRMs responsible for this behavior and can be used to determine for any other gene not included on that list if it is also regulated by the same mechanism. Such gene sets can be derived from microarrays, chromatin immunoprecipitation experiments combined with next-generation sequencing or promoter/whole genome microarrays. The use of an established machine learning method makes the approach fast to use and robust with respect to noise. By providing easily understood visualizations for the results returned, they become interpretable and serve as a starting point for further analysis. Even for complex regulatory relationships, KIRMES: can be a helpful tool in directing the design of biological experiments.
MeSH term(s)	Arabidopsis/genetics ; Arabidopsis/metabolism ; Arabidopsis Proteins/metabolism ; Artificial Intelligence ; Binding Sites ; Computational Biology/methods ; Genome, Plant/genetics ; Homeodomain Proteins/metabolism ; Regulatory Sequences, Nucleic Acid/genetics ; Software
Chemical Substances	Arabidopsis Proteins ; Homeodomain Proteins ; WUSCHEL protein, Arabidopsis
Language	English
Publishing date	2010
Publishing country	United States
Document type	Journal Article
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-60761-854-6_13
Database	MEDical Literature Analysis and Retrieval System OnLINE

Full text online

Accessible to users with ZB MED library card

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Details ▾
- Full text online
- Order with fees

Article: Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops.

Genze, Nikita / Bharti, Richa / Grieb, Michael / Schultheiss, Sebastian J / Grimm, Dominik G

Plant methods

2020 Volume 16, Issue 1, Page(s) 157

Abstract: Background: Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical ... ...

Abstract	Background: Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments. Results: We generated labeled imaging data of the germination process of more than 2400 seeds for three different crops, Zea mays (maize), Secale cereale (rye) and Pennisetum glaucum (pearl millet), with a total of more than 23,000 images. Different state-of-the-art convolutional neural network (CNN) architectures with region proposals have been trained using transfer learning to automatically identify seeds within petri dishes and to predict whether the seeds germinated or not. Our proposed models achieved a high mean average precision (mAP) on a hold-out test data set of approximately 97.9%, 94.2% and 94.3% for Zea mays, Secale cereale and Pennisetum glaucum respectively. Further, various single-value germination indices, such as Mean Germination Time and Germination Uncertainty, can be computed more accurately with the predictions of our proposed model compared to manual countings. Conclusion: Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.
Language	English
Publishing date	2020-12-22
Publishing country	England
Document type	Journal Article
ZDB-ID	2203723-8
ISSN	1746-4811
ISSN	1746-4811
DOI	10.1186/s13007-020-00699-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Ten simple rules for providing a scientific Web resource.

Sebastian J Schultheiss

PLoS Computational Biology, Vol 7, Iss 5, p e

2011 Volume 1001126

Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2011-05-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: MethylScore, a pipeline for accurate and context-aware identification of differentially methylated regions from population-scale plant whole-genome bisulfite sequencing data.

Hüther, Patrick / Hagmann, Jörg / Nunn, Adam / Kakoulidou, Ioanna / Pisupati, Rahul / Langenberger, David / Weigel, Detlef / Johannes, Frank / Schultheiss, Sebastian J / Becker, Claude

Quantitative plant biology

2022 Volume 3, Page(s) e19

Abstract: Whole-genome bisulfite sequencing (WGBS) is the standard method for profiling DNA methylation at single-nucleotide resolution. Different tools have been developed to extract differentially methylated regions (DMRs), often built upon assumptions from ... ...

Abstract	Whole-genome bisulfite sequencing (WGBS) is the standard method for profiling DNA methylation at single-nucleotide resolution. Different tools have been developed to extract differentially methylated regions (DMRs), often built upon assumptions from mammalian data. Here, we present MethylScore, a pipeline to analyse WGBS data and to account for the substantially more complex and variable nature of plant DNA methylation. MethylScore uses an unsupervised machine learning approach to segment the genome by classification into states of high and low methylation. It processes data from genomic alignments to DMR output and is designed to be usable by novice and expert users alike. We show how MethylScore can identify DMRs from hundreds of samples and how its data-driven approach can stratify associated samples without prior information. We identify DMRs in the
Language	English
Publishing date	2022-09-26
Publishing country	England
Document type	Journal Article
ISSN	2632-8828
ISSN (online)	2632-8828
DOI	10.1017/qpb.2022.14
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops

Nikita Genze / Richa Bharti / Michael Grieb / Sebastian J. Schultheiss / Dominik G. Grimm

Plant Methods, Vol 16, Iss 1, Pp 1-

2020 Volume 11

Abstract: Abstract Background Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. ... ...

Abstract	Abstract Background Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done manually, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks with region proposals for accurate seed germination detection and high-throughput seed germination experiments. Results We generated labeled imaging data of the germination process of more than 2400 seeds for three different crops, Zea mays (maize), Secale cereale (rye) and Pennisetum glaucum (pearl millet), with a total of more than 23,000 images. Different state-of-the-art convolutional neural network (CNN) architectures with region proposals have been trained using transfer learning to automatically identify seeds within petri dishes and to predict whether the seeds germinated or not. Our proposed models achieved a high mean average precision (mAP) on a hold-out test data set of approximately 97.9%, 94.2% and 94.3% for Zea mays, Secale cereale and Pennisetum glaucum respectively. Further, various single-value germination indices, such as Mean Germination Time and Germination Uncertainty, can be computed more accurately with the predictions of our proposed model compared to manual countings. Conclusion Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.
Keywords	Seed germination ; Germination prediction ; Germination indices ; Machine learning ; Faster R-CNN ; Plant culture ; SB1-1110 ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2020-12-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED