<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>258</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>1996-12-02</completedDate>
    <publishedDate>1996-12-02</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Survivable Mobile Phone Architectures: Models and Solution Methods</title>
    <abstract language="eng">In the highly competitive area of telecommunications, cost, quality, and network management are among the most important aspects to be considered when designing a network. We study the problem of dimensioning a telecommunication network that is still operating in case of a failure of a network component. Given a demand between each pair of nodes of a telecommunication network and a finite set of possible capacities for each edge of the network, we consider the problem of deciding what capacity to install on each edge of the network in order to minimize the building cost of the network and to satisfy the demand between each pair of nodes, even if a network component fails. The routing of the demands must satisfy the following additional restrictions: (a) there is a maximum number of nodes allowed in each path between any pair of nodes (path length restriction), and (b) there is a maximum percentage of the demand between each pair of nodes that can be routed through any network component (diversification restriction). Moreover, the chosen capacities must be such that, for every single node or single edge failure, a certain percentage of the demand between any pair of nodes is reroutable (i.e. it ``survives'' the particular failure). We formulate the problem as a mixed integer linear programming problem and present a cutting plane algorithm as well as several heuristics for its solution. Furthermore, we discuss several ways to implement survivability into a telecommunication network.</abstract>
    <identifier type="serial">SC-96-48</identifier>
    <identifier type="opus3-id">259</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-2582</identifier>
    <enrichment key="SourceTitle">Appeared in: IEEE Communications Magazine 36 (1998) 88-93</enrichment>
    <author>Dimitris Alevras</author>
    <author>Martin Grötschel</author>
    <author>Peter Jonas</author>
    <author>Ulrich Paul</author>
    <author>Roland Wessäly</author>
    <series>
      <title>ZIB-Report</title>
      <number>SC-96-48</number>
    </series>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">ZIB Allgemein</collection>
    <collection role="persons" number="groetschel">Grötschel, Martin</collection>
    <file>https://opus4.kobv.de/opus4-zib/files/258/SC-96-48.ps</file>
    <file>https://opus4.kobv.de/opus4-zib/files/258/SC-96-48.pdf</file>
  </doc>
  <doc>
    <id>6418</id>
    <completedYear/>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>reportzib</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2017-05-29</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Prognostic value of MACC1 and proficient mismatch repair status for recurrence risk prediction in stage II colon cancer patients: the BIOGRID studies</title>
    <abstract language="eng">Background&#13;
We assessed the novel MACC1 gene to further stratify stage II colon cancer&#13;
patients with proficient mismatch repair (pMMR).&#13;
Patients and methods&#13;
Four cohorts with 596 patients were analyzed: Charité 1 discovery cohort&#13;
was assayed for MACC1 mRNA expression and MMR in cryo-preserved&#13;
tumors. Charité 2 comparison cohort was used to translate MACC1 qRT-&#13;
PCR analyses to FFPE samples. In the BIOGRID 1 training cohort MACC1&#13;
mRNA levels were related to MACC1 protein levels from&#13;
immunohistochemistry in FFPE sections; also analyzed for MMR.&#13;
Chemotherapy-naïve pMMR patients were stratified by MACC1 mRNA and&#13;
protein expression to establish risk groups based on recurrence-free&#13;
survival (RFS). Risk stratification from BIOGRID 1 was confirmed in the&#13;
BIOGRID 2 validation cohort. Pooled BIOGRID datasets produced a best&#13;
effect-size estimate.&#13;
Results&#13;
In BIOGRID 1, using qRT-PCR and immunohistochemistry for MACC1&#13;
detection, pMMR/MACC1-low patients had a lower recurrence probability&#13;
versus pMMR/MACC1-high patients (5-year RFS of 92% and 67% versus&#13;
100% and 68%, respectively). In BIOGRID 2, longer RFS was confirmed&#13;
for pMMR/MACC1-low versus pMMR/MACC1-high patients (5-year RFS of&#13;
100% versus 90%, respectively). In the pooled dataset, 6.5% of patients&#13;
were pMMR/MACC1-low with no disease recurrence, resulting in a 17%&#13;
higher 5-year RFS (95% CI (12.6-21.3%)) versus pMMR/MACC1-high&#13;
patients (P=0.037). Outcomes were similar for pMMR/MACC1-low and&#13;
deficient MMR (dMMR) patients (5-year RFS of 100% and 96%,&#13;
respectively).&#13;
Conclusions&#13;
MACC1 expression stratifies colon cancer patients with unfavorable pMMR&#13;
status. Stage II colon cancer patients with pMMR/MACC1-low tumors have&#13;
a similar favorable prognosis to those with dMMR with potential&#13;
implications for the role of adjuvant therapy.</abstract>
    <identifier type="issn">1438-0064</identifier>
    <identifier type="doi">10.1093/annonc/mdx207</identifier>
    <identifier type="urn">urn:nbn:de:0297-zib-64184</identifier>
    <enrichment key="SourceTitle">Appeared in: Annals of Oncology</enrichment>
    <author>Ulrich-Peter Rohr</author>
    <submitter>Marc Osterland</submitter>
    <author>Pia Herrmann</author>
    <author>Katharina Ilm</author>
    <author>Hai Zhang</author>
    <author>Sabine Lohmann</author>
    <author>Astrid Reiser</author>
    <author>Andrea Muranyi</author>
    <author>Janice Smith</author>
    <author>Susen Burock</author>
    <author>Marc Osterland</author>
    <author>Katherine Leith</author>
    <author>Shalini Singh</author>
    <author>Patrick Brunhoeber</author>
    <author>Rebecca Bowermaster</author>
    <author>Jeanne Tie</author>
    <author>Michael Christie</author>
    <author>Hui-Li Wong</author>
    <author>Paul Waring</author>
    <author>Kandavel Shanmugam</author>
    <author>Peter Gibbs</author>
    <author>Ulrike Stein</author>
    <series>
      <title>ZIB-Report</title>
      <number>17-28</number>
    </series>
    <collection role="msc" number="92-XX">BIOLOGY AND OTHER NATURAL SCIENCES</collection>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="projects" number="no-project">no-project</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
  <doc>
    <id>6389</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1869</pageFirst>
    <pageLast>1875</pageLast>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>28</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2017-04-28</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Prognostic value of MACC1 and proficient mismatch repair status for recurrence risk prediction in stage II colon cancer patients: the BIOGRID studies</title>
    <abstract language="eng">Background&#13;
We assessed the novel MACC1 gene to further stratify stage II colon cancer patients with proficient mismatch repair (pMMR).&#13;
Patients and methods&#13;
Four cohorts with 596 patients were analyzed: Charité 1 discovery cohort was assayed for MACC1 mRNA expression and MMR in cryo-preserved tumors. Charité 2 comparison cohort was used to translate MACC1 qRT-PCR analyses to FFPE samples. In the BIOGRID 1 training cohort MACC1 mRNA levels were related to MACC1 protein levels from immunohistochemistry in FFPE sections; also analyzed for MMR. Chemotherapy-naïve pMMR patients were stratified by MACC1 mRNA and protein expression to establish risk groups based on recurrence-free survival (RFS). Risk stratification from BIOGRID 1 was confirmed in the BIOGRID 2 validation cohort. Pooled BIOGRID datasets produced a best effect-size estimate.&#13;
Results&#13;
In BIOGRID 1, using qRT-PCR and immunohistochemistry for MACC1 detection, pMMR/MACC1-low patients had a lower recurrence probability versus pMMR/MACC1-high patients (5-year RFS of 92% and 67% versus 100% and 68%, respectively). In BIOGRID 2, longer RFS was confirmed for pMMR/MACC1-low versus pMMR/MACC1-high patients (5-year RFS of 100% versus 90%, respectively). In the pooled dataset, 6.5% of patients were pMMR/MACC1-low with no disease recurrence, resulting in a 17% higher 5-year RFS (95% CI (12.6-21.3%)) versus pMMR/MACC1-high patients (P=0.037). Outcomes were similar for pMMR/MACC1-low and deficient MMR (dMMR) patients (5-year RFS of 100% and 96%, respectively).&#13;
Conclusions&#13;
MACC1 expression stratifies colon cancer patients with unfavorable pMMR status. Stage II colon cancer patients with pMMR/MACC1-low tumors have a similar favorable prognosis to those with dMMR with potential implications for the role of adjuvant therapy.</abstract>
    <parentTitle language="eng">Annals of Oncology</parentTitle>
    <identifier type="doi">10.1093/annonc/mdx207</identifier>
    <enrichment key="PeerReviewed">yes</enrichment>
    <enrichment key="PreprintUrn">urn:nbn:de:0297-zib-64184</enrichment>
    <author>Ulrich-Peter Rohr</author>
    <submitter>Marc Osterland</submitter>
    <author>Pia Herrmann</author>
    <author>Katharina Ilm</author>
    <author>Hai Zhang</author>
    <author>Sabine Lohmann</author>
    <author>Astrid Reiser</author>
    <author>Andrea Muranyi</author>
    <author>Janice Smith</author>
    <author>Susen Burock</author>
    <author>Marc Osterland</author>
    <author>Katherine Leith</author>
    <author>Shalini Singh</author>
    <author>Patrick Brunhoeber</author>
    <author>Rebecca Bowermaster</author>
    <author>Jeanne Tie</author>
    <author>Michael Christie</author>
    <author>Hui-Li Wong</author>
    <author>Paul Waring</author>
    <author>Kandavel Shanmugam</author>
    <author>Peter Gibbs</author>
    <author>Ulrike Stein</author>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="projects" number="no-project">no-project</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
</export-example>
