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8,595 results on '"Jungbluth, A. A."'

1. M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Data

Author

Allen, Matthew J, Dorr, Francisco, Mejia, Joseph Alejandro Gallego, Martínez-Ferrer, Laura, Jungbluth, Anna, Kalaitzis, Freddie, and Ramos-Pollán, Raúl

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, I.4, I.4.6, I.4.8, I.4.9, I.5, I.5.4
Abstract

Satellite-based remote sensing has revolutionised the way we address global challenges. Huge quantities of Earth Observation (EO) data are generated by satellite sensors daily, but processing these large datasets for use in ML pipelines is technically and computationally challenging. While some preprocessed Earth observation datasets exist, their content is often limited to optical or near-optical wavelength data, which is ineffective at night or in adverse weather conditions. Synthetic Aperture Radar (SAR), an active sensing technique based on microwave length radiation, offers a viable alternative. However, the application of machine learning to SAR has been limited due to a lack of ML-ready data and pipelines, particularly for the full diversity of SAR data, including polarimetry, coherence and interferometry. In this work, we introduce M3LEO, a multi-modal, multi-label Earth observation dataset that includes polarimetric, interferometric, and coherence SAR data derived from Sentinel-1, alongside multispectral Sentinel-2 imagery and auxiliary data describing terrain properties such as land use. M3LEO spans approximately 17M 4x4 km data chips from six diverse geographic regions. The dataset is complemented by a flexible PyTorch Lightning framework configured using Hydra to accommodate its use across diverse ML applications in Earth observation. We provide tools to process any dataset available on popular platforms such as Google Earth Engine for seamless integration with our framework. We show that the distribution shift in self-supervised embeddings is substantial across geographic regions, even when controlling for terrain properties. Data: huggingface.co/M3LEO, Code: github.com/spaceml-org/M3LEO., Comment: 10 pages, 5 figures

Published
2024

2. Metropolitan-scale heralded entanglement of solid-state qubits

Author

Stolk, Arian J., van der Enden, Kian L., Slater, Marie-Christine, Raa-Derckx, Ingmar te, Botma, Pieter, van Rantwijk, Joris, Biemond, Benjamin, Hagen, Ronald A. J., Herfst, Rodolf W., Koek, Wouter D., Meskers, Arjan J. H., Vollmer, René, van Zwet, Erwin J., Markham, Matthew, Edmonds, Andrew M., Geus, Jan Fabian, Elsen, Florian, Jungbluth, Bernd, Haefner, Constantin, Tresp, Christoph, Stuhler, Jürgen, Ritter, Stephan, and Hanson, Ronald

Subjects
Quantum Physics
Abstract

A key challenge towards future quantum internet technology is connecting quantum processors at metropolitan scale. Here, we report on heralded entanglement between two independently operated quantum network nodes separated by 10km. The two nodes hosting diamond spin qubits are linked with a midpoint station via 25km of deployed optical fiber. We minimize the effects of fiber photon loss by quantum frequency conversion of the qubit-native photons to the telecom L-band and by embedding the link in an extensible phase-stabilized architecture enabling the use of the loss-resilient single-photon entangling protocol. By capitalizing on the full heralding capabilities of the network link in combination with real-time feedback logic on the long-lived qubits, we demonstrate the delivery of a predefined entangled state on the nodes irrespective of the heralding detection pattern. Addressing key scaling challenges and being compatible with different qubit systems, our architecture establishes a generic platform for exploring metropolitan-scale quantum networks., Comment: 10 pages, 4 figures, supplementary materials

Published
2024
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5. SuNeRF: 3D reconstruction of the solar EUV corona using Neural Radiance Fields

Author

Jarolim, Robert, Tremblay, Benoit, Muñoz-Jaramillo, Andrés, Bintsi, Kyriaki-Margarita, Jungbluth, Anna, Santos, Miraflor, Vourlidas, Angelos, Mason, James P., Sundaresan, Sairam, Downs, Cooper, and Caplan, Ronald M.

Subjects
Astrophysics - Solar and Stellar Astrophysics
Abstract

To understand its evolution and the effects of its eruptive events, the Sun is permanently monitored by multiple satellite missions. The optically-thin emission of the solar plasma and the limited number of viewpoints make it challenging to reconstruct the geometry and structure of the solar atmosphere; however, this information is the missing link to understand the Sun as it is: a three-dimensional evolving star. We present a method that enables a complete 3D representation of the uppermost solar layer (corona) observed in extreme ultraviolet (EUV) light. We use a deep learning approach for 3D scene representation that accounts for radiative transfer, to map the entire solar atmosphere from three simultaneous observations. We demonstrate that our approach provides unprecedented reconstructions of the solar poles, and directly enables height estimates of coronal structures, solar filaments, coronal hole profiles, and coronal mass ejections. We validate the approach using model-generated synthetic EUV images, finding that our method accurately captures the 3D geometry of the Sun even from a limited number of 32 ecliptic viewpoints ($|\text{latitude}| \leq 7^\circ$). We quantify uncertainties of our model using an ensemble approach that allows us to estimate the model performance in absence of a ground-truth. Our method enables a novel view of our closest star, and is a breakthrough technology for the efficient use of multi-instrument datasets, which paves the way for future cluster missions.

Published
2024
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6. Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery

Author

Gallego-Mejia, Joseph A., Jungbluth, Anna, Martínez-Ferrer, Laura, Allen, Matt, Dorr, Francisco, Kalaitzis, Freddie, and Ramos-Pollán, Raúl

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.4.8, I.5
Abstract

Self-supervised learning (SSL) models have recently demonstrated remarkable performance across various tasks, including image segmentation. This study delves into the emergent characteristics of the Self-Distillation with No Labels (DINO) algorithm and its application to Synthetic Aperture Radar (SAR) imagery. We pre-train a vision transformer (ViT)-based DINO model using unlabeled SAR data, and later fine-tune the model to predict high-resolution land cover maps. We rigorously evaluate the utility of attention maps generated by the ViT backbone and compare them with the model's token embedding space. We observe a small improvement in model performance with pre-training compared to training from scratch and discuss the limitations and opportunities of SSL for remote sensing and land cover segmentation. Beyond small performance increases, we show that ViT attention maps hold great intrinsic value for remote sensing, and could provide useful inputs to other algorithms. With this, our work lays the groundwork for bigger and better SSL models for Earth Observation., Comment: 9 pages, 5 figures

Published
2023

7. Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction

Author

Martínez-Ferrer, Laura, Jungbluth, Anna, Gallego-Mejia, Joseph A., Allen, Matt, Dorr, Francisco, Kalaitzis, Freddie, and Ramos-Pollán, Raúl

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.4.8, I.5
Abstract

In this work we pre-train a DINO-ViT based model using two Synthetic Aperture Radar datasets (S1GRD or GSSIC) across three regions (China, Conus, Europe). We fine-tune the models on smaller labeled datasets to predict vegetation percentage, and empirically study the connection between the embedding space of the models and their ability to generalize across diverse geographic regions and to unseen data. For S1GRD, embedding spaces of different regions are clearly separated, while GSSIC's overlaps. Positional patterns remain during fine-tuning, and greater distances in embeddings often result in higher errors for unfamiliar regions. With this, our work increases our understanding of generalizability for self-supervised models applied to remote sensing., Comment: 10 pages, 9 figures

Published
2023

8. Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data

Author

Allen, Matt, Dorr, Francisco, Gallego-Mejia, Joseph A., Martínez-Ferrer, Laura, Jungbluth, Anna, Kalaitzis, Freddie, and Ramos-Pollán, Raúl

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, I.4.8, I.5
Abstract

Satellite-based remote sensing is instrumental in the monitoring and mitigation of the effects of anthropogenic climate change. Large scale, high resolution data derived from these sensors can be used to inform intervention and policy decision making, but the timeliness and accuracy of these interventions is limited by use of optical data, which cannot operate at night and is affected by adverse weather conditions. Synthetic Aperture Radar (SAR) offers a robust alternative to optical data, but its associated complexities limit the scope of labelled data generation for traditional deep learning. In this work, we apply a self-supervised pretraining scheme, masked autoencoding, to SAR amplitude data covering 8.7\% of the Earth's land surface area, and tune the pretrained weights on two downstream tasks crucial to monitoring climate change - vegetation cover prediction and land cover classification. We show that the use of this pretraining scheme reduces labelling requirements for the downstream tasks by more than an order of magnitude, and that this pretraining generalises geographically, with the performance gain increasing when tuned downstream on regions outside the pretraining set. Our findings significantly advance climate change mitigation by facilitating the development of task and region-specific SAR models, allowing local communities and organizations to deploy tailored solutions for rapid, accurate monitoring of climate change effects., Comment: 12 pages, 6 figures. Tackling Climate Change with Machine Learning: Workshop at NeurIPS 2023

Published
2023

9. A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Author

Park, Helen, Joachimiak, Marcin P, Jungbluth, Sean P, Yang, Ziming, Riehl, William J, Canon, R Shane, Arkin, Adam P, and Dehal, Paramvir S

Subjects
Data Management and Data Science, Information and Computing Sciences, Biological Sciences, Machine Learning and Artificial Intelligence, Humans, Metagenomics, Microbiota, Metagenome, Machine Learning, Earth, Planet, metagenomics, machine learning, histidine kinase, sensory transduction processes, human microbiome, feature importance
Abstract

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and "transduce" signals to adjust internal processes. We hypothesized that an ecosystem's unique stimuli leave a sensor "fingerprint," able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection's nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient's disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.IMPORTANCEMicrobes infect, colonize, and proliferate due to their ability to sense and respond quickly to their surroundings. In this research, we extract the sensory proteins from a diverse range of environmental, engineered, and host-associated metagenomes. We trained machine learning classifiers using sensors as features such that it is possible to predict the ecosystem for a metagenome from its sensor profile. We use the optimized model's feature importance to identify the most impactful and predictive sensors in different environments. We next use the sensor profile from human gut metagenomes to classify their disease states and explore which sensors can explain differences between diseases. The sensors most predictive of environmental labels here, most of which correspond to uncharacterized proteins, are a useful starting point for the discovery of important environment signals and the development of possible diagnostic interventions.

Published
2024

15. Converging and evolving immuno-genomic routes toward immune escape in breast cancer

Author

Blanco-Heredia, Juan, Souza, Carla Anjos, Trincado, Juan L., Gonzalez-Cao, Maria, Gonçalves-Ribeiro, Samuel, Gil, Sara Ruiz, Pravdyvets, Dmytro, Cedeño, Samandhy, Callari, Maurizio, Marra, Antonio, Gazzo, Andrea M., Weigelt, Britta, Pareja, Fresia, Vougiouklakis, Theodore, Jungbluth, Achim A., Rosell, Rafael, Brander, Christian, Tresserra, Francesc, Reis-Filho, Jorge S., Tiezzi, Daniel Guimarães, de la Iglesia, Nuria, Heyn, Holger, and De Mattos-Arruda, Leticia

Published
2024
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17. Direct visualization of the charge transfer state dynamics in dilute-donor organic photovoltaic blends

Author

Gareth John Moore, Florian Günther, Kaila M. Yallum, Martina Causa’, Anna Jungbluth, Julien Réhault, Moritz Riede, Frank Ortmann, and Natalie Banerji

Subjects
Science
Abstract

Abstract The interconversion dynamics between charge transfer state charges (CTCs) and separated charges (SCs) is still an unresolved issue in the field of organic photovoltaics. Here, a transient absorption spectroscopy (TAS) study of a thermally evaporated small-molecule:fullerene system (α6T:C60) in different morphologies (dilute intermixed and phase separated) is presented. Spectral decomposition reveals two charge species with distinct absorption characteristics and different dynamics. Using time-dependent density functional theory, these species are identified as CTCs and SCs, where the spectral differences arise from broken symmetry in the charge transfer state that turns forbidden transitions into allowed ones. Based on this assignment, a kinetic model is formulated allowing the characterization of the charge generation, separation, and recombination mechanisms. We find that SCs are either formed directly from excitons within a few picoseconds or more slowly (~30–80 ps) from reversible splitting of CTCs. These findings constitute the first unambiguous observation of spectrally resolved CTCs and SCs.

Published
2024
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18. Inferring disease course from differential exon usage in the wide titinopathy spectrum

Author

Maria Francesca Di Feo, Ali Oghabian, Ella Nippala, Mathias Gautel, Heinz Jungbluth, Francesca Forzano, Edoardo Malfatti, Claudia Castiglioni, Ilona Krey, David Gomez Andres, Angela F. Brady, Maria Iascone, Anna Cereda, Lidia Pezzani, Daniel Natera De Benito, Andres Nascimiento Osorio, Berta Estévez Arias, Sergei A. Kurbatov, Tania Attie‐Bitach, Sheela Nampoothiri, Erin Ryan, Michelle Morrow, Svetlana Gorokhova, Brigitte Chabrol, Juha Sinisalo, Heli Tolppanen, Johanna Tolva, Francina Munell, Jessica Camacho Soriano, Maria Angeles Sanchez Duran, Mridul Johari, Homa Tajsharghi, Peter Hackman, Bjarne Udd, and Marco Savarese

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Objective Biallelic titin truncating variants (TTNtv) have been associated with a wide phenotypic spectrum, ranging from complex prenatal muscle diseases with dysmorphic features to adult‐onset limb‐girdle muscular dystrophy, with or without cardiac involvement. Given the size and complexity of TTN, reaching an unequivocal molecular diagnosis and precise disease prognosis remains challenging. Methods In this case series, 12 unpublished cases and one already published case with biallelic TTNtv were collected from multiple international medical centers between November 2022 and September 2023. TTN mutations were detected through exome or genome sequencing. Information about familial and personal clinical history was collected in a standardized form. RNA‐sequencing and analysis of TTN exon usage were performed on an internal sample cohort including postnatal skeletal muscles, fetal skeletal muscles, postnatal heart muscles, and fetal heart muscles. In addition, publicly available RNA‐sequencing data was retrieved from ENCODE. Results We generated new RNA‐seq data on TTN exons and identified genotype–phenotype correlations with prognostic implications for each titinopathy patient (whether worsening or improving in prenatal and postnatal life) using percentage spliced in (PSI) data for the involved exons. Interestingly, thanks to exon usage, we were also able to rule out a titinopathy diagnosis in one prenatal case. Interpretation This study demonstrates that exon usage provides valuable insights for a more exhaustive clinical interpretation of TTNtv; additionally, it may serve as a model for implementing personalized medicine in many other genetic diseases, since most genes undergo alternative splicing.

Published
2024
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19. 2024 ESA-ECMWF workshop report: current status, progress and opportunities in machine learning for Earth system observation and prediction

Author

Patrick Ebel, Rochelle Schneider, Massimo Bonavita, Mariana Clare, Anna Jungbluth, Maryam Pourshamsi, Matthew Chantry, Mihai Alexe, Alessandro Sebastianelli, and Marcin Chrust

Subjects
Environmental sciences, GE1-350, Meteorology. Climatology, QC851-999
Abstract

This report summarises the main outcomes of the 4th edition of the workshop on Machine Learning (ML) for Earth System Observation and Prediction (ESOP / ML4ESOP) co-organised by the European Space Agency (ESA) and the European Centre for Medium-Range Weather Forecasts (ECMWF). The 4-day workshop was held on 7-10 May 2024 in a hybrid format at the ESA Frascati site with an interactive online component, featuring over 46 expert talks with a record number of submissions and about 800 registrations. The workshop offered leading experts a platform to exchange on the current opportunities, challenges and future directions for applying ML methodology to ESOP. To structure the presentations and discussions, the workshop featured five main thematic areas covering key topics and emerging trends. The most promising research directions and significant outcomes were identified by each thematic area’s Working Group and are the focus of this document.

Published
2024
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20. Fewshot learning on global multimodal embeddings for earth observation tasks

Author

Allen, Matt, Dorr, Francisco, Gallego-Mejia, Joseph A., Martínez-Ferrer, Laura, Jungbluth, Anna, Kalaitzis, Freddie, and Ramos-Pollán, Raúl

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.4.8, I.5
Abstract

In this work we pretrain a CLIP/ViT based model using three different modalities of satellite imagery across five AOIs covering over ~10\% of Earth's total landmass, namely Sentinel 2 RGB optical imagery, Sentinel 1 SAR radar amplitude and interferometric coherence. This model uses $\sim 250$ M parameters. Then, we use the embeddings produced for each modality with a classical machine learning method to attempt different downstream tasks for earth observation related to vegetation, built up surface, croplands and permanent water. We consistently show how we reduce the need for labeled data by 99\%, so that with ~200-500 randomly selected labeled examples (around 4K-10K km$^2$) we reach performance levels analogous to those achieved with the full labeled datasets (about 150K image chips or 3M km$^2$ in each area of interest - AOI) on all modalities, AOIs and downstream tasks. This leads us to think that the model has captured significant earth features useful in a wide variety of scenarios. To enhance our model's usability in practice, its architecture allows inference in contexts with missing modalities and even missing channels within each modality. Additionally, we visually show that this embedding space, obtained with no labels, is sensible to the different earth features represented by the labelled datasets we selected., Comment: 9 pages, 6 figures, presented on NeurIPS workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models

Published
2023

21. NEMO reshapes the α-Synuclein aggregate interface and acts as an autophagy adapter by co-condensation with p62.

Author

Furthmann, Nikolas, Bader, Verian, Angersbach, Lena, Blusch, Alina, Goel, Simran, Sánchez-Vicente, Ana, Krause, Laura, Chaban, Sarah, Grover, Prerna, Trinkaus, Victoria, van Well, Eva, Jaugstetter, Maximilian, Tschulik, Kristina, Damgaard, Rune, Saft, Carsten, Ellrichmann, Gisa, Gold, Ralf, Koch, Arend, Englert, Benjamin, Westenberger, Ana, Klein, Christine, Jungbluth, Lisa, Sachse, Carsten, Behrends, Christian, Glatzel, Markus, Hartl, F, Christine, Chadwick, Huang, Eric, Tatzelt, Jörg, Winklhofer, Konstanze, and Nakamura, Ken

Subjects
Humans, NF-kappa B, I-kappa B Kinase, alpha-Synuclein, Ubiquitin, Autophagy
Abstract

NEMO is a ubiquitin-binding protein which regulates canonical NF-κB pathway activation in innate immune signaling, cell death regulation and host-pathogen interactions. Here we identify an NF-κB-independent function of NEMO in proteostasis regulation by promoting autophagosomal clearance of protein aggregates. NEMO-deficient cells accumulate misfolded proteins upon proteotoxic stress and are vulnerable to proteostasis challenges. Moreover, a patient with a mutation in the NEMO-encoding IKBKG gene resulting in defective binding of NEMO to linear ubiquitin chains, developed a widespread mixed brain proteinopathy, including α-synuclein, tau and TDP-43 pathology. NEMO amplifies linear ubiquitylation at α-synuclein aggregates and promotes the local concentration of p62 into foci. In vitro, NEMO lowers the threshold concentrations required for ubiquitin-dependent phase transition of p62. In summary, NEMO reshapes the aggregate surface for efficient autophagosomal clearance by providing a mobile phase at the aggregate interphase favoring co-condensation with p62.

Published
2023

23. The DeepCAR Method: Forecasting Time-Series Data That Have Change Points

Author

Jungbluth, Ayla and Lederer, Johannes

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Methodology
Abstract

Many methods for time-series forecasting are known in classical statistics, such as autoregression, moving averages, and exponential smoothing. The DeepAR framework is a novel, recent approach for time-series forecasting based on deep learning. DeepAR has shown very promising results already. However, time series often have change points, which can degrade the DeepAR's prediction performance substantially. This paper extends the DeepAR framework by detecting and including those change points. We show that our method performs as well as standard DeepAR when there are no change points and considerably better when there are change points. More generally, we show that the batch size provides an effective and surprisingly simple way to deal with change points in DeepAR, Transformers, and other modern forecasting models.

Published
2023

26. Automated Communication for Fault Diagnosis in Flexible Production Environments

Author

Rübel, Pascal, Jungbluth, Simon, Motsch, William, Ruskowski, Martin, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Tolio, Tullio A. M., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Schmitt, Robert, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Wang, Yi-Chi, editor, Chan, Siu Hang, editor, and Wang, Zih-Huei, editor

Published
2024
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28. SuNeRF: Validation of a 3D Global Reconstruction of the Solar Corona Using Simulated EUV Images

Author

Bintsi, Kyriaki-Margarita, Jarolim, Robert, Tremblay, Benoit, Santos, Miraflor, Jungbluth, Anna, Mason, James Paul, Sundaresan, Sairam, Vourlidas, Angelos, Downs, Cooper, Caplan, Ronald M., and Jaramillo, Andrés Muñoz

Subjects
Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Extreme Ultraviolet (EUV) light emitted by the Sun impacts satellite operations and communications and affects the habitability of planets. Currently, EUV-observing instruments are constrained to viewing the Sun from its equator (i.e., ecliptic), limiting our ability to forecast EUV emission for other viewpoints (e.g. solar poles), and to generalize our knowledge of the Sun-Earth system to other host stars. In this work, we adapt Neural Radiance Fields (NeRFs) to the physical properties of the Sun and demonstrate that non-ecliptic viewpoints could be reconstructed from observations limited to the solar ecliptic. To validate our approach, we train on simulations of solar EUV emission that provide a ground truth for all viewpoints. Our model accurately reconstructs the simulated 3D structure of the Sun, achieving a peak signal-to-noise ratio of 43.3 dB and a mean absolute relative error of 0.3\% for non-ecliptic viewpoints. Our method provides a consistent 3D reconstruction of the Sun from a limited number of viewpoints, thus highlighting the potential to create a virtual instrument for satellite observations of the Sun. Its extension to real observations will provide the missing link to compare the Sun to other stars and to improve space-weather forecasting., Comment: Accepted at Machine Learning and the Physical Sciences workshop, NeurIPS 2022

Published
2022

29. Digenic inheritance involving a muscle-specific protein kinase and the giant titin protein causes a skeletal muscle myopathy

Author

Töpf, Ana, Cox, Dan, Zaharieva, Irina T., Di Leo, Valeria, Sarparanta, Jaakko, Jonson, Per Harald, Sealy, Ian M., Smolnikov, Andrei, White, Richard J., Vihola, Anna, Savarese, Marco, Merteroglu, Munise, Wali, Neha, Laricchia, Kristen M., Venturini, Cristina, Vroling, Bas, Stenton, Sarah L., Cummings, Beryl B., Harris, Elizabeth, Marini-Bettolo, Chiara, Diaz-Manera, Jordi, Henderson, Matt, Barresi, Rita, Duff, Jennifer, England, Eleina M., Patrick, Jane, Al-Husayni, Sundos, Biancalana, Valerie, Beggs, Alan H., Bodi, Istvan, Bommireddipalli, Shobhana, Bönnemann, Carsten G., Cairns, Anita, Chiew, Mei-Ting, Claeys, Kristl G., Cooper, Sandra T., Davis, Mark R., Donkervoort, Sandra, Erasmus, Corrie E., Fassad, Mahmoud R., Genetti, Casie A., Grosmann, Carla, Jungbluth, Heinz, Kamsteeg, Erik-Jan, Lornage, Xavière, Löscher, Wolfgang N., Malfatti, Edoardo, Manzur, Adnan, Martí, Pilar, Mongini, Tiziana E., Muelas, Nuria, Nishikawa, Atsuko, O’Donnell-Luria, Anne, Ogonuki, Narumi, O’Grady, Gina L., O’Heir, Emily, Paquay, Stéphanie, Phadke, Rahul, Pletcher, Beth A., Romero, Norma B., Schouten, Meyke, Shah, Snehal, Smuts, Izelle, Sznajer, Yves, Tasca, Giorgio, Taylor, Robert W., Tuite, Allysa, Van den Bergh, Peter, VanNoy, Grace, Voermans, Nicol C., Wanschitz, Julia V., Wraige, Elizabeth, Yoshimura, Kimihiko, Oates, Emily C., Nakagawa, Osamu, Nishino, Ichizo, Laporte, Jocelyn, Vilchez, Juan J., MacArthur, Daniel G., Sarkozy, Anna, Cordell, Heather J., Udd, Bjarne, Busch-Nentwich, Elisabeth M., Muntoni, Francesco, and Straub, Volker

Published
2024
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30. Limiting factors for charge generation in low-offset fullerene-based organic solar cells

Author

Anna Jungbluth, Eunkyung Cho, Alberto Privitera, Kaila M. Yallum, Pascal Kaienburg, Andreas E. Lauritzen, Thomas Derrien, Sameer V. Kesava, Irfan Habib, Saied Md Pratik, Natalie Banerji, Jean-Luc Brédas, Veaceslav Coropceanu, and Moritz Riede

Subjects
Science
Abstract

Abstract Free charge generation after photoexcitation of donor or acceptor molecules in organic solar cells generally proceeds via (1) formation of charge transfer states and (2) their dissociation into charge separated states. Research often either focuses on the first component or the combined effect of both processes. Here, we provide evidence that charge transfer state dissociation rather than formation presents a major bottleneck for free charge generation in fullerene-based blends with low energetic offsets between singlet and charge transfer states. We investigate devices based on dilute donor content blends of (fluorinated) ZnPc:C60 and perform density functional theory calculations, device characterization, transient absorption spectroscopy and time-resolved electron paramagnetic resonance measurements. We draw a comprehensive picture of how energies and transitions between singlet, charge transfer, and charge separated states change upon ZnPc fluorination. We find that a significant reduction in photocurrent can be attributed to increasingly inefficient charge transfer state dissociation. With this, our work highlights potential reasons why low offset fullerene systems do not show the high performance of non-fullerene acceptors.

Published
2024
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31. Aberrant myonuclear domains and impaired myofiber contractility despite marked hypertrophy in MYMK-related, Carey-Fineman-Ziter Syndrome

Author

Hannah F. Dugdale, Yotam Levy, Heinz Jungbluth, Anders Oldfors, and Julien Ochala

Subjects
MYMK gene, Myomaker, Skeletal muscle, Myonuclear domain, Single myofiber, Carey-Fineman-Ziter Syndrome, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Carey Fineman Ziter Syndrome (CFZS) is a rare autosomal recessive disease caused by mutations in the MYMK locus which encodes the protein, myomaker. Myomaker is essential for fusion and concurrent myonuclei donation of muscle progenitors during growth and development. Strikingly, in humans, MYMK mutations appear to prompt myofiber hypertrophy but paradoxically, induce generalised muscle weakness. As the underlying cellular mechanisms remain unexplored, the present study aimed to gain insights by combining myofiber deep-phenotyping and proteomic profiling. Hence, we isolated individual muscle fibers from CFZS patients and performed mechanical, 3D morphological and proteomic analyses. Myofibers from CFZS patients were ~ 4x larger than controls and possessed ~ 2x more myonuclei than those from healthy subjects, leading to disproportionally larger myonuclear domain volumes. These greater myonuclear domain sizes were accompanied by smaller intrinsic cellular force generating-capacities in myofibers from CFZS patients than in control muscle cells. Our complementary proteomic analyses indicated remodelling in 233 proteins particularly those associated with cellular respiration. Overall, our findings suggest that myomaker is somewhat functional in CFZS patients, but the associated nuclear accretion may ultimately lead to non-functional hypertrophy and altered energy-related mechanisms in CFZS patients. All of these are likely contributors of the muscle weakness experienced by CFZS patients.

Published
2024
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32. Publisher Correction: Metagenome-assembled genome extraction and analysis from microbiomes using KBase

Author

Chivian, Dylan, Jungbluth, Sean P, Dehal, Paramvir S, Wood-Charlson, Elisha M, Canon, Richard S, Allen, Benjamin H, Clark, Mikayla M, Gu, Tianhao, Land, Miriam L, Price, Gavin A, Riehl, William J, Sneddon, Michael W, Sutormin, Roman, Zhang, Qizhi, Cottingham, Robert W, Henry, Chris S, and Arkin, Adam P

Subjects
Biological Sciences, Genetics, Human Genome, Chemical Sciences, Medical and Health Sciences, Bioinformatics
Abstract

In the version of this article initially published, the affiliation for Qizhi Zhang and Chris S. Henry was incorrectly shown as the University of Tennessee instead of Argonne National Laboratory. The affiliations have been corrected in the HTML and PDF versions of the article.

Published
2023

33. 12,500+ and counting: biodiversity of the Brazilian Pampa

Author

Andrade, Bianca O., Dröse, William, Aguiar, Cassiana Alves de, Aires, Elisa Teixeira, Alvares, Diego Janisch, Barbieri, Rosa Lia, Carvalho, Claudio José Barros de, Bartz, Marie, Becker, Fernando Gertum, Bencke, Glayson Ariel, Beneduzi, Anelise, Silva, Jorge Bernardo, Blochtein, Betina, Boldrini, Ilsi Iob, Boll, Piter Kehoma, Bordin, Juçara, Silveira, Rosa Mara Borges da, Martins, Márcio Borges, Bosenbecker, Camila, Braccini, João, Braun, Bruna, Brito, Rosângela, Brown, George G., Büneker, Henrique Mallmann, Buzatto, Cristiano Roberto, Cavalleri, Adriano, Cechin, Sonia Zanini, Colombo, Patrick, Constantino, Reginaldo, Costa, Cíntia Fernanda da, Dalzochio, Marina S., Oliveira, Marcelo Gehlen de, Dias, Rafael Antunes, Santos, Luana Amaral dos, Duarte, Adriane da Fonseca, Duarte, Juliano Lessa Pinto, Durigon, Jaqueline, da Silva, Mayara Escobar, Ferreira, Priscila Porto Alegre, Ferreira, Talita, Ferrer, Juliano, Ferro, Viviane G., Fontana, Carla Suertegaray, Freire, Marcelo Duarte, Freitas, Thales Renato Ochotorena, Galiano, Daniel, Garcia, Marinês, dos Santos, Tiago Gomes, Gomes, Lucas Roberto Pereira, Gonzatti, Felipe, Gottschalk, Marco Silva, Graciolli, Gustavo, Granada, Camille E., Grings, Martin, Guimarães, Pablo Santos, Heydrich, Ingrid, Iop, Samanta, Jarenkow, João André, Jungbluth, Patrícia, Käffer, Márcia Isabel, Kaminski, Lucas Augusto, Kenne, Diego Costa, Kirst, Frederico Dutra, Krolow, Tiago Kütter, Krüger, Rodrigo Ferreira, Kubiak, Bruno Busnello, Leal-Zanchet, Ana Maria, Loebmann, Daniel, Lucas, Dióber Borges, Lucas, Elaine Maria, Luza, André Luís, Machado, Ibere Farina, Madalozzo, Bruno, Maestri, Renan, Malabarba, Luiz R., Maneyro, Raúl, Marinho, Marco Antonio Tonus, Marques, Roberta, Marta, Kimberly da Silva, Martins, Diego da Silveira, Martins, Giovana da Silva, Martins, Thiago Rambo, Mello, Anderson Santos de, Mello, Ramon Luciano, Mendonça Junior, Milton de Souza, Morais, Ana Beatriz Barros de, Moreira, Felipe F. F., Moreira, Leonardo Felipe Bairos, Moura, Luciano de A., Nervo, Michelle Helena, Ott, Ricardo, Paludo, Patrícia, Passaglia, Luciane M. P., Périco, Eduardo, Petzhold, Erika Sant'Anna, Pires, Mateus M., Poppe, Jean Lucas, Quintela, Fernando Marques, Raguse-Quadros, Mateus, and Pereira, Maria João Ramos

Subjects
animal diversity, bacteria, biodiversity assessment, biogeographic survey, Brazilian Pampa, Campos, conservation, fungi, grassland, open ecosystem, plant diversity
Abstract

Knowledge on biodiversity is fundamental for conservation strategies. The Brazilian Pampa region, located in subtropical southern Brazil, is neglected in terms of conservation, and knowledge of its biodiversity is fragmented. We aim to answer the question: how many, and which, species occur in the Brazilian Pampa? In a collaborative effort, we built species lists for plants, animals, bacteria, and fungi that occur in the Brazilian Pampa. We included information on distribution patterns, main habitat types, and conservation status. Our study resulted in referenced lists totaling 12,503 species (12,854 taxa, when considering infraspecific taxonomic categories [or units]). Vascular plants amount to 3,642 species (including 165 Pteridophytes), while algae have 2,046 species (2,378 taxa) and bryophytes 316 species (318 taxa). Fungi (incl. lichenized fungi) contains 1,141 species (1,144 taxa). Animals total 5,358 species (5,372 taxa). Among the latter, vertebrates comprise 1,136 species, while invertebrates are represented by 4,222 species. Our data indicate that, according to current knowledge, the Pampa holds approximately 9% of the Brazilian biodiversity in an area of little more than 2% of Brazil’s total land. The proportion of species restricted to the Brazilian Pampa is low (with few groups as exceptions), as it is part of a larger grassland ecoregion and in a transitional climatic setting. Our study yielded considerably higher species numbers than previously known for many species groups; for some, it provides the first published compilation. Further efforts are needed to increase knowledge in the Pampa and other regions of Brazil. Considering the strategic importance of biodiversity and its conservation, appropriate government policies are needed to fund studies on biodiversity, create accessible and constantly updated biodiversity databases, and consider biodiversity in school curricula and other outreach activities.

Published
2023

43. Guidance on mucositis assessment from the MASCC Mucositis Study Group and ISOO: an international Delphi studyResearch in context

Author

Ragda Abdalla-Aslan, Pierluigi Bonomo, Dorothy Keefe, Nicole Blijlevens, Katrina Cao, Yin Ting Cheung, Eduardo Rodrigues Fregnani, Robert Miller, Judith Raber-Durlacher, Joel Epstein, Ysabella Van Sebille, Elisa Kauark-Fontes, Abhishek Kandwal, Emma McCurdy-Franks, Joel Finkelstein, Victoria McCarvell, Yehuda Zadik, Giulia Ottaviani, Rui Amaral Mendes, Caroline Margina Speksnijder, Hannah Rose Wardill, Paolo Bossi, Alexa Laheij, Arghavan Tonkaboni, Jacqui Scott, Rania Abasaeed, Adel Kauzman, Adriana Do Socorro Lima Flato, Adwaita Gore, Anne-Marie Hardman, Agnes Horvath, Allan Hovan, Aisha Al-Jamaei, Aya koizumi, Alan Santos-Silva, Alessandra Majorana, Alexandre Giannini, Aléxia Teixeira, Muhammad Ali Shazib, Alison Melvin, Aluísio Miranda Filho, Amanda Gruza, Amber Brown-Dahl, Amit Harilall, Amr El Maghrabi, Ana Andabak Rogulj, Ana Raphaela Curvo, Ana Laura Soares, Andrea Stringer, Andréa Moreira, Andy Kurtzweil, Angelyn Salaberry, Anne Blazy, Anne Margrete Gussgard, Anne Marie Lynge Pedersen, Annette Freidank, Anura Ariyawardana, Adrian Ramseier, Jann Arends, Ariel Blanchard, Adriana Sesti Paz, Angela Thermann, Augusto Poropat, Azael Freites-Martinez, Abdul Rahman Al-Azri, Bente Brokstad Herlofson, Sitaraman BalajiSubramanian, Barbara Ballantyne, Kivanc Bektas-Kayhan, Bengt Hasséus, Benjamin Kaffenberger, Bernar Benites, Bernice Kwong, Beth Test, Fernando Chiantia, Bo Pettersson, Bomi Framroze, Božana Lončar Brzak, Brittany Dulmage, Sorin Buga, Caroline Spekssnijder, Carlton Brown, Antonio Carlos Moura de Melo, Ana Carolina Ribeiro, Caroline Silva, Caroline Fulop, Carryn Anderson, Catherine Flaitz, Cathy Massoud, Cesar Migliorati, Callie Gross, Chiara Gandini, Charles Loprinzi, Charlotte de Mooij, Catherine Hong, Ying Chu Choi, Maria Choy, Christine Boers-Doets, Leonard Schmeel, Cibele Nagano, Maria Coeli Franco, Courtney Subramaniam, Carolyn Patrick, Catherine Poh, Cristina Neuenschwander, Cesar Virgen, Dorothea Riesenbeck, Dale Weaver, Daniel Cohen Goldemberg, Daniel Sundaresan, Daniela Nunes, Danyel Perez, Daphine Travassos, David Yang, Daniela Ribeiro, Dean Kolbinson, Deborah Buick, Deborah Saunders, Juliane De Bortolli, Deepika Chugh, Denise Markstrom, Denise Travassos, Dianna Weikel, Dimitra Galiti, Dinusha Peiris, Fedja Djordjevic, Pankaj Singhai, Douglas Peterson, Douglas Fonseca, Doreen Pon, Iuliia Kovalenko, Aleksandra Polonskaia, Rogério Caldas, Kevin Saganski, Julia Néri, Dennis Abbott, Abhijna Vithal Yergolkar, Cristina Del Conte, Januaria Passos, Katia Uezu, Paula Silva, Steven Gilbert, Keng Yeoh, Kunal Jain, Madhup Rastogi, Satheeshkumar Poolakkad Sankaran, Deborah Manne, Evgeniya Shatokhina, Esther Adebayo-Olojo, Eszter Somogyi-Ganss, Eli Ehrenpreis, Wilber Bernaola-Paredes, Eduardo Fregnani, Elaci Cardoso, Elena Bardellini, Eleni Arvanitou, Elisa Kauark Fontes, Elise Bruning, Eloise Neumann, Elsa Madureira, Marcia Ramires, Erofili Papadopoulou, Etiene Munhoz, Fred Spijkervet, Fabiana Granzotto, Fabiana Martins, Fabio Alves, Farah Mougeot, Federica Aielli, Fernanda Pigatti, Fernanda Fonseca, Firoozeh Samim, Flavia Carvalho, Florence Cuadra Zelaya, Cesar Freytes, Gabriela da Silveira, Gabriela Torino, Gabriela Martins, Geisa Silva, Gemma Caro, Gemma Bryan, Georgette Radford, Ghanyah Al-Qadami, Giorgia Albini, Gisele Mainville, Georgios Gkardiakos, Gleidston Potter, Gulbin Hoeberechts, Gordon Howarth, Grace Bradley, Gunjan Verma, Gustavo dos Santos, Margaret Randles-Guzzardi, Hanlie Engelbrecht, Hannah Wardill, Heidi Hansen, Iquebal Hasan, Hironobu Hata, Helena Ullgren, Heliton Spindola Antunes, Heloísa Laís dos Santos, Howard Weld, Helen McInnes, Hans Peter Jungbluth, Hsiaofen Weng, Ian Hewson, Ingrid Santos, Jorge Illarramendi, Ines Semendric, Rol Menge, Inger Von Bultzingslowen, Maria da Gloria de Melo, Iona Leong, Isabella Fonseca, Isadora Kalif, James Carroll, Janet Coller, Johann Beck-Mannagetta, Joanne Bowen, Jose Meurer, Ricky McCullough, Jennifer Powers, Jesus Gomez, Jimma Lenjisa, jaya Vangara, Jasna Leko, Jane Fall-Dickson, Jean-Luc Mougeot, Joan Fox, Jolien Robijns, Jonn Wu, Patricio Palma, Jaya Amaram-Davila, Jim Siderov, Juliana Dantas, Juliana Jasper, Juliana Monteiro, Julia Bruno, julie pfeffer, Julija Jovanovic Ristivojevic, Juliana Brito, Jyothsna Kuriakose, Yuji Kabasawa, Kanan Dave, Karin Barczyszyn, Karol Sartori Lima, Kate Secombe, Kate White, Kate Cooper, Kouji Katsura, Karen Biggs, Katharine Ciarrocca, Kristopher Dennis, Ken Tomizuka, Kevin Hendler, Ikuko Komo, Kristina Skallsjö, Kristy Hodgins, Katia Rupel, Keiko Tanaka, Seema Kurup, Luiz Gueiros, Larissa Agatti, Laura Garzona-Navas, Letícia Guerra, Leila Portela, Leilani Iossi, Linda Elting, Lene Baad-Hansen, Leslie Reeder, Leticia Lang, Liciane Menezes, Liliana Braun, Liliane Grando, Mathew Lim, Lina Fernandez, Lucy McKeage, Luana Campos, Luciana Simonato, Luciana Muniz, Leah van Draanen, Mieko Mizutani, Tsai-Wei Huang, Mahfujul Riad, Mahnoor Nazar, Maíra Souza, Mariana Minamisako, Manoela Pereira, Carlos Mantelato, Márcio Diniz-Freitas, Marco Montezuma, Marco Andrade, Marcos Santos, Margherita Gobbo, Maria Caterina Fortuna, Mariana Vitor, Joana Marinho, Alina Markova, Marlyse Knuchel, Marta Carlesimo, Marta Neves, Andrew Mazar, Maria Cristina Gomez Amarilla, Mark Chambers, Melissa de Araujo, Alexandre Melo, Melody Cole, Mohamed Elsayed, Monica Fliedner, Martin Hauer-Jensen, Micaela Bouchacourt, Michael Brennan, Michael Thirlwell, Michio Nakamura, Midori Nakagaki, Camila Rossi, Mireille Kaprilian, Michael Kase, Michael Dougan, Monique Stokman, Ragnhild Monsen, Alisha Morgan, Jocelyn Harding, Maryam Taleghani, Marie-Therese Genot, Mukund Seshadri, Brian Muzyka, Nancy Batista, Nancy Gadd, Naoko Tanda, Narmin Nasr, Natália Garcia, Nathan Lee, Natalia Palmier, Norman Brito-Dellan, Nancy Corbitt, Neli Pieralisi, Verônica Serrano, Nicola Alessandro Iacovelli, Norma Lúcia Sampaio, Nour Karra, Niveditha Venkatesh, Noam Yarom, Renata Cristina Borin, Olivia Lemenchick, Ondina Mendes, Ourania Nicolatou-Galitis, Vasiliy Shchitka, Paula Reis, Paulo Sérgio Santos, Paz Fernandez-Ortega, Ira Parker, Raquel García, Peter Fritz, Edmund Peters, Pamela Gardner, Pierre Saint Girons, Priya Tiwari, Pravin Chaturvedi, Tais de Moraes, Priscila Andrade, Raj Nair, Rachel Gibson, Rachita Gururaj, Raghu Thota, Rajesh Lalla, Raquel Almeida Prado, Ravikiran Ongole, George Taybos, Regina Mackey, Renata Rego, Renata Camilotti, Renata Ferrari, Renato Junior, Rene-Jean Bensadoun, Richard Logan, Roberta Sales, Roberta Zanicotti, Roberta Tunes, Rodolfo Mauceri, Rosiene Feitoza, Kathryn Ruddy, Cynthia Rybczyk, Stephanie Trager, Sachiyo Mitsunaga, Sahani Gunathilake, Rajan Saini, Viola Salvestrini, Sandip Mukhopadhyay, Sandrina Angeloz, Pramod Sankar S, Luciana S Barbosa Barbosa, Elena Volkova, Sharon Elad, Sergio Cantoreggi, Sharon Gordon, Shelly Brown, Shu Yie Janine Tam, Sibelle Faleiro, Silmara da Silva, Silvia de Oliveira, Siri Beier Jensen, Ivana Skrinjar, Sophie Beaumont, Felipe Sperandio, Sandra Reese, Steven Roser, Sachiko Seo, Stephanie van Leeuwen, Stephen Sonis, Stephen Bernard, Stephen Rajan Samuel, Stuart Taylor, Suranjan Maitra, Susanne Skulski, Suzanne Carlisle, Sylvie Louise Avon, Tomoya Yokota, Takashi Yurikusa, Tabata Santos Polvora, Tabitha Kelock, Tauana Fernandes, Taylor Wain, Timothy Brown, Tetsuhito konishi, Thalyta Amanda Ferreira, Tomoko Kataoka, Thomas Kelly, Takehiko Mori, Tomoko Higuchi, Toshiaki Saeki, Nikolaos Tsoukalas, Typhaine Maupoint De Vandeul, Masatoshi Usubuchi, Vanessa Lacerda, Vanessa Tilly, Emmanuelle Vigarios, Alessandro Villa, Vinicius Torregrossa, Vinodh Kumar Selvaraj, Viviane Sarmento, Vivien Heng, Wagner Gomes-Silva, Petter Wilberg, Wanessa Miranda e Silva, Wan Nor I'zzah Wan Mohamad Zain, Wonse Park, Wim Tissing, Yoshihiko Soga, Bella Van Sebille, and Yuhei Matsuda

Subjects
Oral mucositis, Gastrointestinal mucositis, Mucositis assessment tools, Patient-reported outcome measures, Medicine (General), R5-920
Abstract

Summary: Background: Mucositis is a common and highly impactful side effect of conventional and emerging cancer therapy and thus the subject of intense investigation. Although common practice, mucositis assessment is heterogeneously adopted and poorly guided, impacting evidence synthesis and translation. The Multinational Association of Supportive Care in Cancer (MASCC) Mucositis Study Group (MSG) therefore aimed to establish expert recommendations for how existing mucositis assessment tools should be used, in clinical care and trials contexts, to improve the consistency of mucositis assessment. Methods: This study was conducted over two stages (January 2022–July 2023). The first phase involved a survey to MASCC-MSG members (January 2022–May 2022), capturing current practices, challenges and preferences. These then informed the second phase, in which a set of initial recommendations were prepared and refined using the Delphi method (February 2023–May 2023). Consensus was defined as agreement on a parameter by >80% of respondents. Findings: Seventy-two MASCC-MSG members completed the first phase of the study (37 females, 34 males, mainly oral care specialists). High variability was noted in the use of mucositis assessment tools, with a high reliance on clinician assessment compared to patient reported outcome measures (PROMs, 47% vs 3%, 37% used a combination). The World Health Organization (WHO) and Common Terminology Criteria for Adverse Events (CTCAE) scales were most commonly used to assess mucositis across multiple settings. Initial recommendations were reviewed by experienced MSG members and following two rounds of Delphi survey consensus was achieved in 91 of 100 recommendations. For example, in patients receiving chemotherapy, the recommended tool for clinician assessment in clinical practice is WHO for oral mucositis (89.5% consensus), and WHO or CTCAE for gastrointestinal mucositis (85.7% consensus). The recommended PROM in clinical trials is OMD/WQ for oral mucositis (93.3% consensus), and PRO-CTCAE for gastrointestinal mucositis (83.3% consensus). Interpretation: These new recommendations provide much needed guidance on mucositis assessment and may be applied in both clinical practice and research to streamline comparison and synthesis of global data sets, thus accelerating translation of new knowledge into clinical practice. Funding: No funding was received.

Published
2024
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50. Converging and evolving immuno-genomic routes toward immune escape in breast cancer

Author

Juan Blanco-Heredia, Carla Anjos Souza, Juan L. Trincado, Maria Gonzalez-Cao, Samuel Gonçalves-Ribeiro, Sara Ruiz Gil, Dmytro Pravdyvets, Samandhy Cedeño, Maurizio Callari, Antonio Marra, Andrea M. Gazzo, Britta Weigelt, Fresia Pareja, Theodore Vougiouklakis, Achim A. Jungbluth, Rafael Rosell, Christian Brander, Francesc Tresserra, Jorge S. Reis-Filho, Daniel Guimarães Tiezzi, Nuria de la Iglesia, Holger Heyn, and Leticia De Mattos-Arruda

Subjects
Science
Abstract

Abstract The interactions between tumor and immune cells along the course of breast cancer progression remain largely unknown. Here, we extensively characterize multiple sequential and parallel multiregion tumor and blood specimens of an index patient and a cohort of metastatic triple-negative breast cancers. We demonstrate that a continuous increase in tumor genomic heterogeneity and distinct molecular clocks correlated with resistance to treatment, eventually allowing tumors to escape from immune control. TCR repertoire loses diversity over time, leading to convergent evolution as breast cancer progresses. Although mixed populations of effector memory and cytotoxic single T cells coexist in the peripheral blood, defects in the antigen presentation machinery coupled with subdued T cell recruitment into metastases are observed, indicating a potent immune avoidance microenvironment not compatible with an effective antitumor response in lethal metastatic disease. Our results demonstrate that the immune responses against cancer are not static, but rather follow dynamic processes that match cancer genomic progression, illustrating the complex nature of tumor and immune cell interactions.

Published
2024
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