<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>2072</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>26</pageLast>
    <pageNumber/>
    <edition/>
    <issue>12</issue>
    <volume>2022</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-12-16</completedDate>
    <publishedDate>2022-12-16</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments</title>
    <abstract language="eng">Ayahuasca is a blend of Amazonian plants that has been used for traditional medicine by&#13;
the inhabitants of this region for hundreds of years. Furthermore, this plant has been demon� strated to be a viable therapy for a variety of neurological and mental diseases. EEG experi� ments have found specific brain regions that changed significantly due to ayahuasca. Here,&#13;
we used an EEG dataset to investigate the ability to automatically detect changes in brain&#13;
activity using machine learning and complex networks. Machine learning was applied at&#13;
three different levels of data abstraction: (A) the raw EEG time series, (B) the correlation of&#13;
the EEG time series, and (C) the complex network measures calculated from (B). Further, at&#13;
the abstraction level of (C), we developed new measures of complex networks relating to&#13;
community detection. As a result, the machine learning method was able to automatically&#13;
detect changes in brain activity, with case (B) showing the highest accuracy (92%), followed&#13;
by (A) (88%) and (C) (83%), indicating that connectivity changes between brain regions are&#13;
more important for the detection of ayahuasca. The most activated areas were the frontal&#13;
and temporal lobe, which is consistent with the literature. F3 and PO4 were the most impor� tant brain connections, a significant new discovery for psychedelic literature. This connec� tion may point to a cognitive process akin to face recognition in individuals during&#13;
ayahuasca-mediated visual hallucinations. Furthermore, closeness centrality and assorta� tivity were the most important complex network measures. These two measures are also&#13;
associated with diseases such as Alzheimer’s disease, indicating a possible therapeutic&#13;
mechanism. Moreover, the new measures were crucial to the predictive model and sug� gested larger brain communities associated with the use of ayahuasca. This suggests that&#13;
the dissemination of information in functional brain networks is slower when this drug is&#13;
present. Overall, our methodology was able to automatically detect changes in brain activity&#13;
during ayahuasca consumption and interpret how these psychedelics alter brain networks,&#13;
as well as provide insights into their mechanisms of action</abstract>
    <parentTitle language="eng">PLOS ONE</parentTitle>
    <identifier type="doi">https://doi.org/10.1371/journal. pone.0277257</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Caroline L. Alves</author>
    <author>Rubens G. Cury</author>
    <author>Kirstin Roster</author>
    <author>Aruane M. Pineda</author>
    <author>Francisco A. Rodrigues</author>
    <author>Christiane Thielemann</author>
    <author>Manuel Ciba</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Ayahuasca</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektroencephalographie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Alzheimerkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <file>https://opus4.kobv.de/opus4-h-ab/files/2072/journal.pone.0277257.pdf</file>
  </doc>
  <doc>
    <id>2062</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>e37</pageFirst>
    <pageLast>e47</pageLast>
    <pageNumber/>
    <edition/>
    <issue>137</issue>
    <volume>2022</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-11-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Functional connectivity and convolutional neural networks for automatic classification of EEG data</title>
    <parentTitle language="eng">Clinical Neurophysiology</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/j.clinph.2022.01.086</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Caroline L. Alves</author>
    <author>Lennart Wissel</author>
    <author>Philipp Capetian</author>
    <author>Christiane Thielemann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektroencephalographie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neuronales Netz</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2063</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>1</pageFirst>
    <pageLast>2</pageLast>
    <pageNumber/>
    <edition/>
    <issue>November</issue>
    <volume>2022</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-11-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Revisiting the involvement of tau in complex neural network remodeling: analysis of the extracellular neuronal activity in organotypic brain slice co-cultures</title>
    <abstract language="deu">Objective: Tau ablation has a protective effect in epilepsy due to inhibition of the hyperexcitability/hypersynchrony. Protection may also occur in transgenic models of Alzheimer's disease by reducing the epileptic activity and normalizing the excitation/inhibition imbalance. However, it is difficult to determine the exact functions of tau, because tau knockout (tauKO) brain networks exhibit elusive phenotypes. In this study, we aimed to further explore the physiological role of tau using brain network remodeling. Approach: The effect of tau ablation was investigated in hippocampal-entorhinal slice co-cultures during network remodeling. We recorded the spontaneous extracellular neuronal activity over two weeks in single-slice cultures and co-cultures from control and tauKO mice. We compared the burst parameters and applied concepts and analytical tools intended for the analysis of the network synchrony and connectivity. Main results: Comparison of the control and tauKO co-cultures revealed that tau ablation had an anti-synchrony effect on the hippocampal-entorhinal two-slice networks at late stages of culture, in line with the literature. Differences were also found between the single-slice and co-culture conditions, which indicated that tau ablation had differential effects at the sub-network scale. For instance, tau ablation was found to have an anti-synchrony effect on the co-cultured hippocampal slices throughout the culture, possibly due to a reduction in the excitation/inhibition ratio. Conversely, tau ablation led to increased synchrony in the entorhinal slices at early stages of the co-culture, possibly due to homogenization of the connectivity distribution. Significance: The new methodology presented here proved useful for investigating the role of tau in the remodeling of complex brain-derived neural networks. The results confirm previous findings and hypotheses concerning the effects of tau ablation on neural networks. Moreover, the results suggest, for the first time, that tau has multifaceted roles that vary in different brain sub-networks.</abstract>
    <parentTitle language="deu">Journal of Neural Engineering</parentTitle>
    <identifier type="doi">DOI 10.1088/1741-2552/aca261</identifier>
    <enrichment key="copyright">0</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Thomas Bouillet</author>
    <author>Manuel Ciba</author>
    <author>Caroline L. Alves</author>
    <author>Francisco A. Rodrigues</author>
    <author>Christiane Thielemann</author>
    <author>Morvane Colin</author>
    <author>Luc Buée</author>
    <author>Sophie Halliez</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neuronales Netz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Alzheimerkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Gehirn</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schnittpräparat</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Material Testing &amp; Sensor Technology</collection>
  </doc>
  <doc>
    <id>2060</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>13</pageLast>
    <pageNumber/>
    <edition/>
    <issue>3</issue>
    <volume>2022</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-11-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer’s disease and schizophrenia</title>
    <abstract language="eng">Mental disorders are among the leading causes of disability worldwide. The first step in treating&#13;
these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a&#13;
possible solution to this problem, as we describe in this work. We present a method for the&#13;
automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG&#13;
time series and deep learning. We show that our approach can classify patients with Alzheimer’s&#13;
disease and schizophrenia with a high level of accuracy. The comparison with the traditional cases,&#13;
that use raw EEG time series, shows that our method provides the highest precision. Therefore, the&#13;
application of deep neural networks on data from brain connections is a very promising method&#13;
for the diagnosis of neurological disorders.</abstract>
    <parentTitle language="eng">Journal of Physics: complexity</parentTitle>
    <identifier type="doi">DOI 10.1088/2632-072X/ac5f8d</identifier>
    <enrichment key="copyright">0</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Caroline L. Alves</author>
    <author>Aruane M. Pineda</author>
    <author>Kirstin Roster</author>
    <author>Christiane Thielemann</author>
    <author>Francisco A. Rodrigues</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>complex networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Hirnfunktionsstörung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Alzheimerkrankheit</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Schizophrenie</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Elektroencephalographie</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
  </doc>
  <doc>
    <id>2068</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>0</pageFirst>
    <pageLast>0</pageLast>
    <pageNumber/>
    <edition/>
    <issue>November</issue>
    <volume>2022</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-12-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fabrication process for FEP piezoelectrets based on photolithographically structured thermoforming templates</title>
    <abstract language="eng">Piezoelectrets fabricated from fluoroethylenepropylene (FEP)-foils have shown drastic increase of their piezoelectric&#13;
properties during the last decade. This led to the development of FEP-based energy harvesters, which are about to evolve&#13;
into a technology with a power-generation-capacity of milliwatt per square-centimeter at their resonance frequency. Recent&#13;
studies focus on piezoelectrets with solely negative charges, as they have a better charge stability and a better suitability for&#13;
implementation in rising technologies, like the internet of things (IOT) or portable electronics. With these developments&#13;
heading towards applications of piezoelectrets in the near future, there is an urgent need to also address the fabrication&#13;
process in terms of scalability, reproducibility and miniaturization. In this study, we firstly present a comprehensive review&#13;
of the literature for a deep insight into the research that has been done in the field of FEP-based piezoelectrets. For the first&#13;
time, we propose the employment of microsystem-technology and present a process for the fabrication of thermoformed&#13;
FEP piezoelectrets based on thermoforming SU-8 templates. Following this process, unipolar piezoelectrets were fabri� cated with air void dimensions in the range of 300–1000 lm in width and approx. 90 lm in height. For samples with a void&#13;
size of 1000 lm, a d33-coefficient up to 26,508 pC/N has been achieved, depending on the applied seismic mass. Finally,&#13;
the properties as energy harvester were characterized. At the best, an electrical power output of 0.51 mW was achieved for&#13;
an acceleration of 1 �  g with a seismic mass of 101 g. Such piezoelectrets with highly defined dimensions show good&#13;
energy output in relation to volume, with high potential for widespread applications.</abstract>
    <parentTitle language="eng">Microsystem Technologies</parentTitle>
    <identifier type="url">Microsystem Technologies https://doi.org/10.1007/s00542-022-05405-6</identifier>
    <identifier type="doi">doi.org/10.1007/s00542-022-05405-6</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Dennis Flachs</author>
    <author>Florian Emmerich</author>
    <author>Christiane Thielemann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Mikrosystemtechnik</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Innovative Material Processing</collection>
    <collection role="forschungsschwerpunkte" number="">Material Testing &amp; Sensor Technology</collection>
    <file>https://opus4.kobv.de/opus4-h-ab/files/2068/f2675faa-60f0-4d35-8e9d-0d5db94997e6.pdf</file>
  </doc>
  <doc>
    <id>2061</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>2</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>2022</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-11-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Characterization of electrically conductive, printable ink based on alginate hydrogel and graphene nanoplatelets</title>
    <abstract language="eng">In recent years, there has been an increasing interest in electrically conductive hydrogels for a wide range of biomedical applications, like tissue engineering or biosensors. In this study, we present a cost-effective conductive hydrogel based on alginate and graphene nanoplatelets for extrusion-based bioprinters. The hydrogel is prepared under ambient conditions avoiding high temperatures detrimental for cell culture environments. Investigation of the hydrogel revealed a conductivity of up to 7.5 S/cm, depending on the ratio of platelets. Furthermore, in vitro tests with human embyronic kidney cells - as an example cell type - showed good adhesion of the cells to the surface of the conductive hydrogel. Electrochemical measurements revealed a low electrode impedance which is desirable for the extracellular recording, but also low electrode capacitance, which is unfavorable for electrical stimulation purposes. Therefore, future experiments with the graphene nanoplatelets-based hydrogels will focus on electrodes for biosensors and extracellular recordings of neurons or cardiac myocytes.</abstract>
    <parentTitle language="eng">Biomedical Engineering Advances</parentTitle>
    <identifier type="doi">https://doi.org/10.1016/j.bea.2022.100045</identifier>
    <enrichment key="copyright">0</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Dennis Flachs</author>
    <author>Johannes Etzel</author>
    <author>Margot Mayer</author>
    <author>Frederic Harbecke</author>
    <author>Stefan Belle</author>
    <author>Tim Rickmeyer</author>
    <author>Christiane Thielemann</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Hydrogel</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Biosensor</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Alginate</value>
    </subject>
    <collection role="institutes" number="">BIOMEMS Lab</collection>
    <collection role="forschungsschwerpunkte" number="">Innovative Material Processing</collection>
  </doc>
</export-example>
