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Biology subjects

Bacci, M.

Publications and source records attributed to Bacci, M..

3 recordsLinked to original sources

Atta Leafcutter Ants are Fine-Scale Bioindicators of Geographic and Seasonal Climate Changes Across the Americas

AimWe develop Atta leafcutter-ants as bioindicators that respond at fine scales to geographic and seasonal climate changes in the Americas, thereby addressing the paucity of versatile insect bioindicator systems capable of monitoring climate in both Southern and Northern Hemispheres. LocationAmerican tropics and sub-tropics from latitudes S33.6{degrees} to N33.2{degrees}, with case studies from Colombia, Mexico, and southern USA. Time Period2012-2024. Taxon StudiedAtta leafcutter-ants. MethodsWe elucidate biogeographic patterns of mating-flight phenology of Atta leafcutter-ants across the entire Atta range from Uruguay/Argentina to the USA, using 2335 records of Atta reproductives from the community database iNaturalist, then ground-truth these patterns by comparison with (i) mating-flight records (n=806) from the Atta literature; and (ii) mating-flight observations (n=836) accumulated by a consortium of experts who have researched Atta for a combined 1000+ work-years. Onset of mating flights can be timed with great precision in Atta populations because mass-mating flights are synchronized and triggered by the first major rainfall of a rainy season. ResultsBiogeographic patterns in climate-dependent mating-flight phenologies recorded at iNaturalist are corroborated by observations accumulated in the literature and by Atta experts. Analyses reveal so-far unknown gradients in mating-flight phenology (e.g., Colombia to USA) that are correlated to geographic climate gradients, and season switches of mating flights from early to late in the year between proximate Atta populations (e.g., 200 kilometers apart), for example in the climatically complex Andean regions of Colombia. Regional differences in Atta mating-flight phenology correspond to temporal differences in rainfall between ecoregions of Colombia. Main ConclusionsAtta ants are tractable insect bioindicators to monitor climate impacts with detailed spatial and temporal resolution across a 9200-kilometer trans-equatorial transect in the Americas. We outline future research directions to explore climate-dependent biology using the continuously growing, and now ground-truthed, information at iNaturalist on Atta mating behavior.

ecology↗

FADS1/2-mediated lipid metabolic reprogramming drives ferroptosis sensitivity in triple-negative breast cancer

Triple-negative breast cancer (TNBC) has limited therapeutic options, is highly metastatic and characterized by early recurrence. Lipid metabolism is generally deregulated in TNBC and might reveal vulnerabilities to be targeted or used as biomarkers with clinical value. Ferroptosis is a type of cell death caused by iron-dependent lipid peroxidation which is facilitated by the presence of polyunsaturated fatty acids (PUFA). Here we identify fatty acid desaturases 1 and 2 (FADS1/2), which are responsible for PUFA biosynthesis, lipid susceptible to peroxidation, to be highly expressed in a subset of TNBC with a poorer prognosis. Lipidomic analysis, coupled with functional metabolic assays, showed that FADS1/2 high-expressing TNBC are susceptible to ferroptosis-inducing agents and that targeting FADS1/2 renders those tumors ferroptosis-resistant. These findings were validated in vitro and in vivo in mouse and human-derived clinically relevant models and in a retrospective cohort of TNBC patients. One sentence summaryThe availability of intracellular PUFA depends on FADS1/2 desaturases, expressed at higher levels in aggressive triple-negative breast cancers highly susceptible to ferroptosis.

cancer biology↗

Reproducible evaluation of classification methods in Alzheimer’s disease: framework and application to MRI and PET data

A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimers disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS, performing better than the classifiers trained and tested on each of these datasets independently. All the code of the framework and the experiments is publicly available.

neuroscience↗