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@@ -299,49 +299,52 @@ Marie-Lannelongue</a><sup>2</sup>
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+<div class="content">
+  <h2 class="title is-3">An organization structured into 8 work packages</h2>
 
-    <!-- Concurrent Work. -->
-    <div class="columns is-centered">
-      <div class="column is-full-width">
-        <h2 class="title is-3">Related Links</h2>
+  <ul>
+    <li>
+      <i class="fas fa-database" style="margin-right: 8px;"></i>
+      <strong>WP1 – Data collection and governance:</strong> Structuring, accessing and securing health data.
+    </li>
 
-        <div class="content has-text-justified">
-          <p>
-            There's a lot of excellent work that was introduced around the same time as ours.
-          </p>
-          <p>
-            <a href="https://arxiv.org/abs/2104.09125" target="_blank">Progressive Encoding for Neural Optimization</a> introduces an idea similar to our windowed position encoding for coarse-to-fine optimization.
-          </p>
-          <p>
-            <a href="https://www.albertpumarola.com/research/D-NeRF/index.html" target="_blank">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/" target="_blank">NR-NeRF</a>
-            both use deformation fields to model non-rigid scenes.
-          </p>
-          <p>
-            Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/" target="_blank">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/" target="_blank">NSFF</a>, and <a href="https://neural-3d-video.github.io/" target="_blank">DyNeRF</a>
-          </p>
-          <p>
-            There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/" target="_blank">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF" target="_blank">Yen-Chen Lin's curated list of NeRF papers</a>.
-          </p>
-        </div>
-      </div>
-    </div>
-    <!--/ Concurrent Work. -->
+    <li>
+      <i class="fas fa-cogs" style="margin-right: 8px;"></i>
+      <strong>WP2 – Data preprocessing and harmonization:</strong> Standardizing and preparing datasets for model training.
+    </li>
 
-  </div>
-</section>
+    <li>
+      <i class="fas fa-brain" style="margin-right: 8px;"></i>
+      <strong>WP3 – Foundation model development:</strong> Designing and training large-scale models for health data.
+    </li>
 
+    <li>
+      <i class="fas fa-vial" style="margin-right: 8px;"></i>
+      <strong>WP4 – Use cases in oncology:</strong> Identification of tumor biomarkers and clinical applications.
+    </li>
+
+    <li>
+      <i class="fas fa-bacteria" style="margin-right: 8px;"></i>
+      <strong>WP5 – Use cases in infectious diseases:</strong> Automatic detection and support for combating antibiotic resistance.
+    </li>
+
+    <li>
+      <i class="fas fa-chart-line" style="margin-right: 8px;"></i>
+      <strong>WP6 – Evaluation and benchmarking:</strong> Assessing model performance, robustness and reproducibility.
+    </li>
+
+    <li>
+      <i class="fas fa-network-wired" style="margin-right: 8px;"></i>
+      <strong>WP7 – Deployment and infrastructure:</strong> Integration into secure environments and operationalization.
+    </li>
+
+    <li>
+      <i class="fas fa-users" style="margin-right: 8px;"></i>
+      <strong>WP8 – Dissemination and collaboration:</strong> Promoting open science, sharing resources and engaging stakeholders.
+    </li>
+  </ul>
+</div>
 
-<section class="section" id="BibTeX">
-  <div class="container is-max-desktop content">
-    <h2 class="title">BibTeX</h2>
-    <pre><code>@article{park2021nerfies,
-  author    = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
-  title     = {Nerfies: Deformable Neural Radiance Fields},
-  journal   = {ICCV},
-  year      = {2021},
-}</code></pre>
-  </div>
-</section>
 
 
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