TY  - JOUR
A1  - Karulin, Alexey Y.
A1  - Karacsony, Kinga
A1  - Zhang, Wenji
A1  - Targoni, Oleg S.
A1  - Moldova, Ioana
A1  - Dittrich, Marcus
A1  - Sundararaman, Srividya
A1  - Lehmann, Paul V.
T1  - ELISPOTs produced by CD8 and CD4 cells follow Log Normal size distribution permitting objective counting
JF  - Cells
N2  - Each positive well in ELISPOT assays contains spots of variable sizes that can range from tens of micrometers up to a millimeter in diameter. Therefore, when it comes to counting these spots the decision on setting the lower and the upper spot size thresholds to discriminate between non-specific background noise, spots produced by individual T cells, and spots formed by T cell clusters is critical. If the spot sizes follow a known statistical distribution, precise predictions on minimal and maximal spot sizes, belonging to a given T cell population, can be made. We studied the size distributional properties of IFN-γ, IL-2, IL-4, IL-5 and IL-17 spots elicited in ELISPOT assays with PBMC from 172 healthy donors, upon stimulation with 32 individual viral peptides representing defined HLA Class I-restricted epitopes for CD8 cells, and with protein antigens of CMV and EBV activating CD4 cells. A total of 334 CD8 and 80 CD4 positive T cell responses were analyzed. In 99.7% of the test cases, spot size distributions followed Log Normal function. These data formally demonstrate that it is possible to establish objective, statistically validated parameters for counting T cell ELISPOTs.
KW  - ELISPOT
KW  - software
KW  - IFN-γ
KW  - IL-17
KW  - T cells
KW  - Normal Distribution
KW  - spot size
KW  - gating
KW  - cytokines
KW  - IL-2
KW  - IL-4
KW  - IL-5
KW  - CD8
KW  - CD4
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-149648
VL  - 4
IS  - 1
ER  - 
TY  - JOUR
A1  - Ahmed, Zeeshan
A1  - Zeeshan, Saman
A1  - Dandekar, Thomas
T1  - Mining biomedical images towards valuable information retrieval in biomedical and life sciences
JF  - Database - The Journal of Biological Databases and Curation
N2  - Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.
KW  - humans
KW  - software
KW  - image processing
KW  - animals
KW  - computer-assisted
KW  - data mining/methods
KW  - natural language processing
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-162697
VL  - 2016
ER  - 
TY  - JOUR
A1  - Milanese, Alessio
A1  - Mende, Daniel R
A1  - Paoli, Lucas
A1  - Salazar, Guillem
A1  - Ruscheweyh, Hans-Joachim
A1  - Cuenca, Miguelangel
A1  - Hingamp, Pascal
A1  - Alves, Renato
A1  - Costea, Paul I
A1  - Coelho, Luis Pedro
A1  - Schmidt, Thomas S. B.
A1  - Almeida, Alexandre
A1  - Mitchell, Alex L
A1  - Finn, Robert D.
A1  - Huerta-Cepas, Jaime
A1  - Bork, Peer
A1  - Zeller, Georg
A1  - Sunagawa, Shinichi
T1  - Microbial abundance, activity and population genomic profiling with mOTUs2
JF  - Nature Communications
N2  - Metagenomic sequencing has greatly improved our ability to profile the composition of environmental and host-associated microbial communities. However, the dependency of most methods on reference genomes, which are currently unavailable for a substantial fraction of microbial species, introduces estimation biases. We present an updated and functionally extended tool based on universal (i.e., reference-independent), phylogenetic marker gene (MG)-based operational taxonomic units (mOTUs) enabling the profiling of >7700 microbial species. As more than 30% of them could not previously be quantified at this taxonomic resolution, relative abundance estimates based on mOTUs are more accurate compared to other methods. As a new feature, we show that mOTUs, which are based on essential housekeeping genes, are demonstrably well-suited for quantification of basal transcriptional activity of community members. Furthermore, single nucleotide variation profiles estimated using mOTUs reflect those from whole genomes, which allows for comparing microbial strain populations (e.g., across different human body sites).
KW  - microbiome
KW  - software
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-224089
VL  - 10
ER  -