Alejandra Jayme

Overview
Related items

About Alejandra Jayme:

No description specified

SEEK ID: https://publications.h-its.org/people/70

Location: Germany

ORCID: Not specified

Expertise: Not specified

Tools: Not specified

Their tags

This person has not yet tagged anything.

Related items

Advanced Research Groups list for this Person with search and filtering

Data Mining and Uncertainty Quantification

No description specified

Public web page: Not specified

Organisms: Not specified

Advanced Institutions list for this Person with search and filtering

Heidelberg Institute for Theoretical Studies

Country: Germany

City: Heidelberg

Web page: https://www.h-its.org

Advanced Publications list for this Person with search and filtering

Multi-view-AE: A Python package for multi-view autoencoder models

Data Mining and Uncertainty Quantification

Abstract

Not specified

Authors: Ana Lawry Aguila, Alejandra Jayme, Nina Montaña-Brown, Vincent Heuveline, Andre Altmann

Date Published: 1st May 2023

Publication Type: Journal

DOI: 10.21105/joss.05093

Citation: JOSS 8(85):5093

Created: 16th Feb 2024 at 13:22, Last updated: 5th Mar 2024 at 21:25

aRgus: Multilevel visualization of non-synonymous single nucleotide variants & advanced pathogenicity score modeling for genetic vulnerability assessment

Data Mining and Uncertainty Quantification

Abstract

Not specified

Authors: Julian Schröter, Tal Dattner, Jennifer Hüllein, Alejandra Jayme, Vincent Heuveline, Georg F. Hoffmann, Stefan Kölker, Dominic Lenz, Thomas Opladen, Bernt Popp, Christian P. Schaaf, Christian Staufner, Steffen Syrbe, Sebastian Uhrig, Daniel Hübschmann, Heiko Brennenstuhl

Date Published: 2023

Publication Type: Journal

DOI: 10.1016/j.csbj.2023.01.027

Citation: Computational and Structural Biotechnology Journal 21:1077-1083

Created: 15th Feb 2023 at 14:09, Last updated: 5th Mar 2024 at 21:25

Natural variability in bee brain size and symmetry revealed by micro-CT imaging and deep learning

Data Mining and Uncertainty Quantification

(Show All)

Abstract (Expand)

Abstract Analysing large numbers of brain samples can reveal minor, but statistically and biologically relevant variations in brain morphology that provide critical insights into animal behaviour, …into animal behaviour, ecology and evolution. So far, however, such analyses have required extensive manual effort, which considerably limits the scope for comparative research. Here we used micro-CT imaging and deep learning to perform automated analyses of 3D image data from 187 honey bee and bumblebee brains. We revealed strong inter-individual variations in total brain size that are consistent across colonies and species, and may underpin behavioural variability central to complex social organisations. In addition, the bumblebee dataset showed a significant level of lateralization in optic and antennal lobes, providing a potential explanation for reported variations in visual and olfactory learning. Our fast, robust and user-friendly approach holds considerable promises for carrying out large-scale quantitative neuroanatomical comparisons across a wider range of animals. Ultimately, this will help address fundamental unresolved questions related to the evolution of animal brains and cognition. Author Summary Bees, despite their small brains, possess a rich behavioural repertoire and show significant variations among individuals. In social bees this variability is key to the division of labour that maintains their complex social organizations, and has been linked to the maturation of specific brain areas as a result of development and foraging experience. This makes bees an ideal model for understanding insect cognitive functions and the neural mechanisms that underlie them. However, due to the scarcity of comparative data, the relationship between brain neuro-architecture and behavioural variance remains unclear. To address this problem, we developed an AI-based approach for automated analysis of brain images and analysed an unprecedentedly large dataset of honey bee and bumblebee brains. Through this process, we were able to identify previously undescribed anatomical features that correlate with known behaviours, supporting recent evidence of lateralized behaviour in foraging and pollination. Our method is open-source, easily accessible online, user-friendly, fast, accurate, and robust to different species, enabling large-scale comparative analyses across the animal kingdom. This includes investigating the impact of external stressors such as environmental pollution and climate change on cognitive development, helping us understand the mechanisms underlying the cognitive abilities of animals and the implications for their survival and adaptation.

Authors: Philipp D. Lösel, Coline Monchanin, Renaud Lebrun, Alejandra Jayme, Jacob Relle, Jean-Marc Devaud, Vincent Heuveline, Mathieu Lihoreau

Date Published: 17th Oct 2022

Publication Type: Journal

DOI: 10.1101/2022.10.12.511944

Citation: biorxiv;2022.10.12.511944v3,[Preprint]

Created: 15th Feb 2023 at 14:15, Last updated: 5th Mar 2024 at 21:25

Introducing Biomedisa as an open-source online platform for biomedical image segmentation

Data Mining and Uncertainty Quantification

(Show All)

Abstract

Not specified

Authors: Philipp D. Lösel, Thomas van de Kamp, Alejandra Jayme, Alexey Ershov, Tomáš Faragó, Olaf Pichler, Nicholas Tan Jerome, Narendar Aadepu, Sabine Bremer, Suren A. Chilingaryan, Michael Heethoff, Andreas Kopmann, Janes Odar, Sebastian Schmelzle, Marcus Zuber, Joachim Wittbrodt, Tilo Baumbach, Vincent Heuveline

Date Published: 1st Dec 2020

Publication Type: Journal

DOI: 10.1038/s41467-020-19303-w

Citation: Nat Commun 11(1),5577

Created: 11th Feb 2021 at 10:53, Last updated: 5th Mar 2024 at 21:24

(v.1.14.2)

About HITS Publication SEEK | About HITS-Publication-SEEK | Funding and Programmes | Credits