Biology subjects

Borras, A.

Publications and source records attributed to Borras, A..

3 recordsLinked to original sources

bioRxiv2023

Host space, not energy or symbiont size, constrains feather mite abundance across passerine bird species

Comprehending symbiont abundance among host species is a major ecological endeavour, and the metabolic theory of ecology has been proposed to understand what constraints symbiont populations. We parameterized metabolic theory equations to predict how bird species body size and the body size of their feather mites relate to mite abundance according to four potential energy (microbial abundance, uropygial gland size) and space constraints (wing area, number of feather barbs). Predictions were compared with the empirical scaling of feather mite abundance from 26,604 birds of 106 passerine species, using phylogenetic modelling and quantile regression. Feather mite populations were strongly constrained by host space (number of feather barbs) and not energy. Moreover, feather mite species body size was unrelated to their abundance or to the body size of their host species. We discuss the implications of our results for our understanding of the bird-feather mite system and for symbiont abundance in general.

ecology↗

bioRxiv2019

Augmentation of Virtual Endoscopic Images with Intra-operative Data using Content-Nets

Medical imaging applications are challenging for machine learning and computer vision methods, in general, for two main reasons: it is difficult to generate reliable ground truth and databases are usually too small in size for training state of the art methods. Virtual images obtained from computer simulations could be used to train classifiers and validate image processing methods if their appearances were comparable (in texture and color) to the actual appearance of intra-operative medical images. Recent works focus on style transfer to generate artistic images by combining the content of an image and the style of another one. A main challenge is the generation of pairs with similar content ensuring preservation of anatomical features, especially across multi-modal data. This paper presents a deep-learning approach to content-preserving style transfer of intra-operative medical data for realistic virtual endoscopy. We propose a multi-objective optimization strategy for Generative Adversarial Networks (GANs) to obtain content-matching pairs that are blended using a siamese u-net architecture (called Content-net) that uses a measure of the content of activations to modulate skip connections. Our approach has been applied to transfer the appearance of bronchoscopic intra-operative videos to virtual bronchoscopies. Experiments assess images in terms of, both, content and appearance and show that our simulated data can substitute intra-operative videos for the design and training of image processing methods.

bioengineering↗

bioRxiv2019

Augmentation of Virtual Endoscopic Images with Intra-operative Data using Content-Nets

State of the art methods in computer vision need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. The particular field of intervention guiding has the extra difficulty of intra-operative recordings probably requiring the alteration of standard protocols.

bioengineering↗