<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1050</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>e904</pageFirst>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>23</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mobile microscopy for the examination of blood samples</title>
    <abstract language="eng">The analysis of blood is one of the best possibilities to diagnose and control diseases and deficiency symptoms. Common blood tests that are performed in medical laboratories are time-consuming and work-intensive. In under-developed areas, there is often also a lack of specialised staff and facilities. The development of a mobile microscopic system that contains an automated image analysis and that can be used via a smartphone, could represent a valuable help to improve the diagnostic care, especially in those areas. it aims to enable a very fast, cheap, location- and knowledge-independent application for many use cases.</abstract>
    <parentTitle language="eng">EMBnet.journal</parentTitle>
    <identifier type="issn">2226-6089</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-10509</identifier>
    <enrichment key="SourceTitle">Pfeil, J., Frohme, M., &amp; Schulze, K. (2018). Mobile microscopy for the examination of blood samples. EMBnet.journal, 23, e904. doi:https://doi.org/10.14806/ej.23.0.904</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.14806/ej.23.0.904</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Juliane Pfeil</author>
    <author>Marcus Frohme</author>
    <author>Katja Schulze</author>
    <collection role="ddc" number="570">Biowissenschaften; Biologie</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="green_open_access" number="3">Diamond Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1050/904-6278-5-PB.pdf</file>
  </doc>
  <doc>
    <id>1580</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>23</volume>
    <type>article</type>
    <publisherName>BioMed Central</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Examination of blood samples using deep learning and mobile microscopy</title>
    <abstract language="eng">Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards.</abstract>
    <parentTitle language="eng">BMC Bioinformatics</parentTitle>
    <identifier type="issn">1471-2105</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-15802</identifier>
    <enrichment key="opus.import.date">2022-02-14T09:00:12+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="opus.import.file">filename=phpFXy8uO</enrichment>
    <enrichment key="opus.import.checksum">011f6eec170f6aa4094c35fc8e058106</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.1186/s12859-022-04602-4</enrichment>
    <enrichment key="SourceTitle">Pfeil, J., Nechyporenko, A., Frohme, M. et al. Examination of blood samples using deep learning and mobile microscopy. BMC Bioinformatics 23, 65 (2022). https://doi.org/10.1186/s12859-022-04602-4</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Juliane Pfeil</author>
    <author>Alina Nechyporenko</author>
    <author>Marcus Frohme</author>
    <author>Frank T. Hufert</author>
    <author>Katja Schulze</author>
    <collection role="ddc" number="570">Biowissenschaften; Biologie</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="Funding" number="">Projekt DEAL</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1580/s12859-022-04602-4.pdf</file>
  </doc>
  <doc>
    <id>2011</id>
    <completedYear>2018</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Standardisation in life-science research - Making the case for harmonization to improve communication and sharing of data amongst researchers</title>
    <abstract language="eng">Modern, high-throughput methods for the analysis of genetic information, gene and metabolic products and their interactions offer new opportunities to gain comprehensive information on life processes. The data and knowledge generated open diverse application possibilities with enormous innovation potential. To unlock that potential skills in generating but also properly annotating the data for further data integration and analysis are needed. The data need to be made computer readable and interoperable to allow integration with existing knowledge leading to actionable biological insights. To achieve this, we need common standards and standard operating procedures as well as workflows that allow the combination of data across standards. Currently, there is a lack of experts who understand the principles and possess knowledge of the principles  and  relevant  tools.  This  is  a  major barrier hindering the implementation of FAIR (findable, accessible, interoperable and reusable) data principles and the actual reusability of data. This is mainly due to insufficient and unequal education of the scientists and other stakeholders involved in producing and handling big data in  life  science  that  is inherently varied and complex  in nature,  and  large  in  volume. Due  to  the  interdisciplinary  nature  of  life  science research,  education within  this  field faces numerous hurdles including institutional barriers, lack of local availability of all required expertise, as well as lack of appropriate teaching material and appropriate adaptation of curricula.</abstract>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-20118</identifier>
    <enrichment key="opus.import.date">2025-02-27T11:43:55+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">sword</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.29007/4xkd</enrichment>
    <enrichment key="SourceTitle">Hollmann, S., Regierer, B., D’Elia, D., Gruden, K., Baebler, Š., Frohme, M., Pfeil, J., Sezerman, U.O., Evelo, C.T., Ehrhart, F., Huppertz, B., Bongcam-Rudloff, E., Trefois, C., Gruca, A., Duca, D., Colotti, G., Merino-Martinez, R., Ouzounis, C.A., Hunewald, O., He, F., &amp; Kremer, A. (2018). Standardisation in life-science research - Making the case for harmonization to improve communication and sharing of data amongst researchers. EasyChair Preprints.</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Susanne Hollmann</author>
    <author>Babette Regierer</author>
    <author>Domenica D'Elia</author>
    <author>Marcus Frohme</author>
    <author>Kristina Gruden</author>
    <author>Juliane Pfeil</author>
    <author>Spela Baebler</author>
    <author>Ugur Sezerman</author>
    <author>Chris T. Evelo</author>
    <author>Friederike Erhart</author>
    <author>Berthold Huppertz</author>
    <author>Erik Bongcam-Rudloff</author>
    <author>Christophe Trefois</author>
    <author>Aleksandra Gruca</author>
    <author>Deborah Duca</author>
    <author>Gianni Colotti</author>
    <author>Roxana Merino-Martinez</author>
    <author>Christos Ouzounis</author>
    <author>Oliver Hunewald</author>
    <author>Feng He</author>
    <author>Andreas Kremer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>FAIR data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>standardization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>interoperability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>standard operating procedures (SOPs)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>quality management (QM)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>quality control (QC)</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>education</value>
    </subject>
    <collection role="ddc" number="005">Computerprogrammierung, Programme, Daten</collection>
    <collection role="ddc" number="570">Biowissenschaften; Biologie</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="Import" number="import">Import</collection>
    <collection role="green_open_access" number="2">Green Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/2011/EasyChair-Preprint-580.pdf</file>
  </doc>
  <doc>
    <id>1718</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>7</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition</title>
    <abstract language="eng">Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96% accuracy. Samples from healthy persons were detected with a specificity of 97% and those from T2D individuals with a sensitivity of 92%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera.</abstract>
    <parentTitle language="eng">Big Data and Cognitive Computing</parentTitle>
    <identifier type="issn">2504-2289</identifier>
    <identifier type="urn">urn:nbn:de:kobv:526-opus4-17184</identifier>
    <enrichment key="opus.import.data">@Articlebdcc7010051, AUTHOR = "Pfeil, Juliane and Siptroth, Julienne and Pospisil, Heike and Frohme, Marcus and Hufert, Frank T. and Moskalenko, Olga and Yateem, Murad and Nechyporenko, Alina", TITLE = "Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition", JOURNAL = "Big Data and Cognitive Computing", VOLUME = "7", YEAR = "2023", NUMBER = "1", ARTICLE-NUMBER = "51", ISSN = "2504-2289", ABSTRACT = "Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96 DOI = "10.3390/bdcc7010051"</enrichment>
    <enrichment key="opus.import.dataHash">md5:cb887d16bf2ce2c31059d7996ce52f4c</enrichment>
    <enrichment key="opus.import.date">2023-03-21T11:03:55+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/phpfvw7fa</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">64198f1b699ce8.26273883</enrichment>
    <enrichment key="DOI_VoR">https://doi.org/10.3390/bdcc7010051</enrichment>
    <enrichment key="SourceTitle">Pfeil, J.; Siptroth, J.; Pospisil, H.; Frohme, M.; Hufert, F.T.; Moskalenko, O.; Yateem, M.; Nechyporenko, A. Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition. Big Data Cogn. Comput. 2023, 7, 51. https://doi.org/10.3390/bdcc7010051</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Juliane Pfeil</author>
    <author>Julienne Siptroth</author>
    <author>Heike Pospisil</author>
    <author>Marcus Frohme</author>
    <author>Frank T. Hufert</author>
    <author>Olga Moskalenko</author>
    <author>Murad Yateem</author>
    <author>Alina Nechyporenko</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>human intestinal microbiome</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>next-generation sequencing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>type 2 diabetes</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>image classification</value>
    </subject>
    <collection role="ddc" number="006">Spezielle Computerverfahren</collection>
    <collection role="ddc" number="570">Biowissenschaften; Biologie</collection>
    <collection role="institutes" number="">Fachbereich Ingenieur- und Naturwissenschaften</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="green_open_access" number="1">Gold Open Access</collection>
    <thesisPublisher>Technische Hochschule Wildau</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-th-wildau/files/1718/BDCC-07-00051.pdf</file>
  </doc>
</export-example>
