@@ -1,10 +1,25 @@
-@inproceedings{moraza2026geometry,
-  title={Geometry-Based Differentiable Camera Placement for Optimal 3D Coverage.},
-  author={Ane Moraza and Xiao Lin and Daniel Mejia-Parra and Nagore Barrena},
-  booktitle={VISAPP (3)},
-  pages={473--483},
+@article{lekunberri2026deep,
+  title={Deep learning for accurate vision-based catch composition in tropical tuna purse seiners.},
+  author={Xabier Lekunberri and Ahmad Kamal and Izaro Goienetxea and Jon Ruiz and Iñaki Quincoces and Jaime Valls Miro and Ignacio Arganda-Carreras and Jose A Fernandes-Salvador},
+  journal={ICES Journal of Marine Science},
+  volume={83},
+  number={8},
+  pages={fsag151},
   year={2026},
-  doi={10.5220/0014335800004084},
-  url={https://www.scitepress.org/Papers/2026/143358/143358.pdf},
-  abstract={Optimal camera placement is fundamental for inspection and 3D reconstruction tasks. We address this traditionally discrete problem by reformulating it into a continuous optimization task using Differentiable Rendering (DR), a technique that enables gradient computation from 3D camera poses to a final loss value. Assuming a known scene, our pipeline treats camera poses as learnable parameters. We implement a novel differentiable visibility formulation that compares observed depths from the rendered scene, with the expected depth of the mesh vertices. Based on this, we define a geometry-based loss function using two specific coverage criteria: ”At-Least-Once” (ALO) and ”Exactly-Once” (EO), which find optimal placement configurations. Experiments conducted on diverse 3D objects and camera configurations demonstrate that our approach outperforms the SOTA baseline Neural Observation Field Guided Hybrid Optimization of Camera Placement (NeOF). Our methods yield superior quality, as demonstrated by our quantitative evaluation, and achieve lower computation times. Furthermore, the quality and efficiency of the generated poses show strong potential for future integration into trajectory planning systems.}
+  doi={10.1093/icesjms/fsag151},
+  url={https://doi.org/10.1093/icesjms/fsag151},
+  abstract={Purse seiners play a crucial role in tuna fishing, as approximately 69\% of the world's tropical tuna is caught using this gear. All tuna Regional Fisheries Management Organizations have established minimum standards for the use of electronic monitoring (EM) in fisheries in addition to traditional observers. These EM systems produce a massive amount of video data that human analysts must process. Integrating artificial intelligence (AI) into their workflow can decrease that workload and improve the accuracy of the reports. However, species identification still poses significant challenges for AI, as achieving balanced performance across all species requires appropriate training data. Here, we quantify the difficulty experts face to distinguish bigeye tuna (BET, Thunnus obesus) from yellowfin tuna (YFT, Thunnus albacares) using images captured by EM systems. We found inter-expert agreements of 42.9\% ± 35.6\% for BET and 57.1\% ± 35.6\% for YFT. We then present a multi-stage pipeline to estimate the species composition of the catches using a reliable ground-truth dataset based on identifications made by observers on board. Three segmentation approaches are compared: Mask R-CNN, a combination of DINOv2 with SAM2, and an integration of YOLOv9 with SAM2. We found that the latter performs the best, with a validation mean average precision of 0.66 ± 0.03 and a recall of 0.85 ± 0.03. Segmented individuals are tracked using ByteTrack. For classification, we evaluated a standard multiclass classification model and a hierarchical approach. Their performance depended on the fishing trip considered, suggesting that both approaches are promising, but additional testing is needed to determine which strategy generalizes better overall. All our models were cross-validated during training and tested on fishing operations with fully known catch composition. Combining YOLOv9-SAM2 with the hierarchical classification produced the best estimations, with 84.8\% of the individuals being segmented and classified with a mean absolute error of 4.5\%.}
+}
+
+@article{dangelo2026three,
+  title={Three-dimensional mapping of intact ovaries reveals the aging dynamics of the ovarian reserve.},
+  author={Arturo D'Angelo and Daniel Franco-Barranco and Marco Musy and Juan Manuel Duran and James Sharpe and Nicholas Stroustrup and Ignacio Arganda-Carreras and Elvan Böke},
+  journal={Nature Aging},
+  volume={6},
+  number={8},
+  pages={1580--1591},
+  year={2026},
+  doi={10.1038/s43587-026-01178-z},
+  url={https://doi.org/10.1038/s43587-026-01178-z},
+  abstract={Female fertility depends on a finite pool of oocytes that depletes during aging1,2, yet the spatiotemporal dynamics of this depletion remain poorly understood. Traditional methods obscure the three-dimensional architecture of the ovary, limiting quantitative insights. Here we combine light-sheet microscopy, artificial intelligence-driven segmentation and mathematical modeling to map over 85,000 oocytes in whole ovaries across the reproductive lifespan in mouse. We find that newly activated oocytes represent a fixed fraction of the total oocyte pool despite an age-related decline in oocyte numbers. Spatial analysis revealed that oocytes are enriched along the lateral ovarian axis, and local oocyte density positively correlates with activation. We also uncover a bimodal distribution of oocyte sizes, suggesting a bottleneck during oogenesis. Finally, a differential equation-based model captures the kinetics of oocyte activation and loss. Our findings establish a quantitative framework for understanding ovarian aging and suggest that an organ-scale regulatory mechanism coordinates the age-related decline in oocyte numbers.}
 }