@inproceedings{18117, abstract = {We investigate parameter-efficient fine-tuning (PEFT) methods that can provide good accuracy under limited computational and memory budgets in the context of large language models (LLMs). We present a new PEFT method called Robust Adaptation (RoSA) inspired by robust principal component analysis that jointly trains low-rank and highly-sparse components on top of a set of fixed pretrained weights to efficiently approximate the performance of a full-fine-tuning (FFT) solution. Across a series of challenging generative tasks such as grade-school math and SQL query generation, which require fine-tuning for good performance, we show that RoSA outperforms LoRA, pure sparse fine-tuning, and alternative hybrid methods at the same parameter budget, and can even recover the performance of FFT on some tasks. We provide system support for RoSA to complement the training algorithm, specifically in the form of sparse GPU kernels which enable memory- and computationally-efficient training, and show that it is also compatible with low-precision base weights, resulting in the first joint representation combining quantization, low-rank and sparse approximations. Our code is available at https://github.com/IST-DASLab/RoSA.}, author = {Nikdan, Mahdi and Tabesh, Soroush and Crncevic, Elvir and Alistarh, Dan-Adrian}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, issn = {2640-3498}, location = {Vienna, Austria}, pages = {38187--38206}, publisher = {ML Research Press}, title = {{RoSA: Accurate parameter-efficient fine-tuning via robust adaptation}}, volume = {235}, year = {2024}, } @article{15315, abstract = {Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description.}, author = {Brückner, David and Broedersz, Chase P.}, issn = {1361-6633}, journal = {Reports on Progress in Physics}, number = {5}, publisher = {IOP Publishing}, title = {{Learning dynamical models of single and collective cell migration: a review}}, doi = {10.1088/1361-6633/ad36d2}, volume = {87}, year = {2024}, } @article{15250, abstract = {Orphan solute carrier (SLC) represents a group of membrane transporters whose exact functions and substrate specificities are not known. Elucidating the function and regulation of orphan SLC transporters is not only crucial for advancing our knowledge of cellular and molecular biology but can potentially lead to the development of new therapeutic strategies. Here, we provide evidence for the biological function of a ubiquitous orphan lysosomal SLC, the Major Facilitator Superfamily Domain-containing Protein 1 (MFSD1), which has remained phylogenetically unassigned. Targeted metabolomics revealed that dipeptides containing either lysine or arginine residues accumulate in lysosomes of cells lacking MFSD1. Whole-cell patch-clamp electrophysiological recordings of HEK293-cells expressing MFSD1 on the cell surface displayed transport affinities for positively charged dipeptides in the lower mM range, while dipeptides that carry a negative net charge were not transported. This was also true for single amino acids and tripeptides, which MFSD1 failed to transport. Our results identify MFSD1 as a highly selective lysosomal lysine/arginine/histidine-containing dipeptide exporter, which functions as a uniporter.}, author = {Boytsov, Danila and Madej, Gregor M. and Horn, Georg and Blaha, Nadine and Köcher, Thomas and Sitte, Harald H. and Siekhaus, Daria E and Ziegler, Christine and Sandtner, Walter and Roblek, Marko}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {13}, publisher = {National Academy of Sciences}, title = {{Orphan lysosomal solute carrier MFSD1 facilitates highly selective dipeptide transport}}, doi = {10.1073/pnas.2319686121}, volume = {121}, year = {2024}, } @article{15024, abstract = {Electrostatic correlations between ions dissolved in water are known to impact their transport properties in numerous ways, from conductivity to ion selectivity. The effects of these correlations on the solvent itself remain, however, much less clear. In particular, the addition of salt has been consistently reported to affect the solution’s viscosity, but most modeling attempts fail to reproduce experimental data even at moderate salt concentrations. Here, we use an approach based on stochastic density functional theory, which accurately captures charge fluctuations and correlations. We derive a simple analytical expression for the viscosity correction in concentrated electrolytes, by directly linking it to the liquid’s structure factor. Our prediction compares quantitatively to experimental data at all temperatures and all salt concentrations up to the saturation limit. This universal link between the microscopic structure and viscosity allows us to shed light on the nanoscale dynamics of water and ions under highly concentrated and correlated conditions.}, author = {Robin, Paul}, issn = {1089-7690}, journal = {Journal of Chemical Physics}, number = {6}, publisher = {AIP Publishing}, title = {{Correlation-induced viscous dissipation in concentrated electrolytes}}, doi = {10.1063/5.0188215}, volume = {160}, year = {2024}, } @article{17892, abstract = {Enzyme-substrate kinetics form the basis of many biomolecular processes. The interplay between substrate binding and substrate geometry can give rise to long-range interactions between enzyme binding events. Here we study a general model of enzyme-substrate kinetics with restricted long-range interactions described by an exponent −𝛾. We employ a coherent-state path integral and renormalization group approach to calculate the first moment and two-point correlation function of the enzyme-binding profile. We show that starting from an empty substrate the average occupancy follows a power law with an exponent 1/(1−𝛾) over time. The correlation function decays algebraically with two distinct spatial regimes characterized by exponents −𝛾 on short distances and −(2/3)⁢(2−𝛾) on long distances. The crossover between both regimes scales inversely with the average substrate occupancy. Our work allows associating experimental measurements of bound enzyme locations with their binding kinetics and the spatial conformation of the substrate.}, author = {Olmeda, Fabrizio and Rulands, Steffen}, issn = {2470-0053}, journal = {Physical Review E}, number = {2}, publisher = {American Physical Society}, title = {{Field theory of enzyme-substrate systems with restricted long-range interactions}}, doi = {10.1103/PhysRevE.110.024404}, volume = {110}, year = {2024}, } @article{17104, abstract = {The homeostasis of epithelial tissue relies on a balance between the self-renewal of stem cell populations, cellular differentiation, and loss. Although this balance needs to be tightly regulated to avoid pathologies, such as tumor growth, the regulatory mechanisms, both cell-intrinsic and collective, which ensure tissue steady-state are still poorly understood. Here, we develop a computational model that incorporates basic assumptions of stem cell renewal into distinct populations and mechanical interactions between cells. We find that the model generates unexpected dynamic features: stem cells repel each other in the bulk tissue and are thus found rather isolated, as in a number of in vivo contexts. By mapping the system onto a gas of passive Brownian particles with effective repulsive interactions, that arise from the generated flows of differentiated cells, we show that we can quantitatively describe such stem cell distribution in tissues. The interaction potential between a pair of stem cells decays exponentially with a characteristic length that spans several cell sizes, corresponding to the volume of cells generated per stem cell division. Our findings may help understanding the dynamics of normal and cancerous epithelial tissues.}, author = {Krämer, Johannes C. and Hannezo, Edouard B and Gompper, Gerhard and Elgeti, Jens}, issn = {2542-4653}, journal = {SciPost Physics}, number = {4}, publisher = {SciPost Foundation}, title = {{Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells}}, doi = {10.21468/scipostphys.16.4.097}, volume = {16}, year = {2024}, } @article{18919, abstract = {The integration of theory and experiment makes possible tracking the slow evolution of a photodoped Mott insulator to a distinct non-equilibrium metallic phase under the influence of electron-lattice coupling.}, author = {Baykusheva, Denitsa Rangelova}, issn = {1745-2481}, journal = {Nature Physics}, number = {5}, pages = {684--685}, publisher = {Springer Nature}, title = {{Through the slopes of a light-induced phase transition}}, doi = {10.1038/s41567-024-02401-7}, volume = {20}, year = {2024}, } @misc{23022, abstract = {Contains animations, data, and source code used for the paper "Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells" by the same authors. This repository contains all the data and analysis scripts to support the manuscript The file structure is as follows: - "analysis" contains all the analysis scripts - "gfx" and "animations" the figures and supporting animations - "1-SCpair", "2-SCtissue", "3-BDsimulation", "4-tissue-stochdiv", "6-inertia-effects" all the source code and data for the respective models.}, author = {Elgeti, Jens and Krämer, Johannes}, publisher = {Zenodo}, title = {{Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells}}, doi = {10.5281/zenodo.8410956}, year = {2024}, } @article{17123, abstract = {A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack a principled framework to quantify the performance of such stochastic self-organizing systems. To that end, we introduce an information-theoretic measure for self-organized fate specification during embryonic development. We show that the proposed measure assesses the total information content of fate patterns and decomposes it into interpretable contributions corresponding to the positional and correlational information. By optimizing the proposed measure, our framework provides a normative theory for developmental circuits, which we demonstrate on lateral inhibition, cell type proportioning, and reaction–diffusion models of self-organization. This paves a way toward a classification of developmental systems based on a common information-theoretic language, thereby organizing the zoo of implicated chemical and mechanical signaling processes.}, author = {Brückner, David and Tkačik, Gašper}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {23}, publisher = {National Academy of Sciences}, title = {{Information content and optimization of self-organized developmental systems}}, doi = {10.1073/pnas.2322326121}, volume = {121}, year = {2024}, } @misc{23023, abstract = {In this repository, you will find the R-scripts used to generate the figures, along with the data necessary to make the plots. You will also find the Python-scripts used to compute the pair-wise distance between embryos, to estimate the surface, the volume, the α-parameter, to identify the closest rigid packing and to generate the morphomap. Additionally, you have access to the base model used with Plantseg to predict and label the cells at the 8-cell stage. Note that a more accurate and more specific model was generated for each embryo based on the curation of at least 5 timepoints. Figures The plots in Figure 1-7 and Figure S1-S7 can be generated with the respective files. The code is written in R and requires RStudio to work out-of-the-box. The R scripts are located in figures/. Figures and supplemental figures are generated with the same file (e.g., Figure 1.R generates all the computed panels of Figure 1 and Figure S1). Data required to generate the plots are located in figures/data/. Note that the code to work with the morphomap and the tracking data is located in local packages figures/fabreges.morphomap and figures/fabreges.tracking. An additional package figures/fabreges.generic contains utility functions. Python packages For the needs of this study, we developed a series of package for Python located in python-packages/. The packages can be directly imported within your scripts. Along with the packages, you will find 6 scripts used to compute, extract or estimate the data from the source. The folder is organised as follow: package interfaces is located in python-packages/interfaces/ and can be tested by running the scripts to extract the angles an estimate the surface of contact (compute-angles.py and estimate-contacts.py respectively) in folder python-packages, package morphomap is located in python-packages/morphomap/ and can be tested by running the script to compute the geometrical distances between embryos, project the data in 2D and compute the volume of the cells (compute-distances.py, process-distances.py and compute-volumes.py respectively) in folder python-packages, package splinefit is included in morphomap in python-packages/morphomap/splinefit/, but can function independently, package rbb is located in python-packages/rbb/ and can be tested by running the script to identify the closest rigid packing of an embryo at the 8-cell stage (identify-packing.py) in folder python-packages. We provide example of segmented data in python-packages/samples/. CNN We provide a convolutional neural network (CNN) used in our study, located in cnn. Please note that we optimised the CNN further for each dataset by doing manual curation of at least 5 time points and by retraining the network with those ground truth. The CNN were trained using pytorch-3dunet. The CNN can be used with Plantseg. Currently (plantseg v.1.6.0 and pytorch-3dunet v.1.6.0), the output model from pytorch-3dunet must currently be modified to work with plantseg: # python import torch for model in ('best_checkpoint.pytorch', 'last_checkpoint.pytorch'): state = torch.load(model, map_location='cpu') state = state['model_state_dict'] torch.save(state, model) Requierements Figures The figures were generated with R (v. 4.4.0) and RStudio (v. 2024.04.1+748). The following packages must be installed prior to execution: alphashape3d (v. 1.3.2) devtools (v. 2.4.5) ggplot2 (v. 3.5.1) ggrepel (v. 0.9.5) rstudioapi (v. 0.16.0) To install all these packages, run the following command in the RStudio console: # R install.packages(c("alphashape3d@1.3.2", "devtools@2.4.5", "ggplot2@3.5.1", "ggrepel@0.9.5", "rstudioapi@0.16")) More recent version of R, RStudio and the packages may also work (not tested). Python packages Python 3.12.3 was used to analyse the data. The scripts may work with more recent version of Python and with Python 3.8 or earlier (not tested). To run the code, you must first install the following packages: numpy (v. 1.26.4) imageio (v. 2.34.1) scipy (v. 1.13.1) scikit-image (v. 0.23.2) scikit-learn (v. 1.5.0) Alternatively, you can create a conda environment: # Shell script conda create -y --name fabreges2024 python=3.12.3 conda activate fabreges2024 python -m pip install numpy==1.26.4 imageio==2.34.1 scipy==1.13.1 scikit-image==0.23.2 scikit-learn==1.5.0 More recent version of the dependencies may work (not tested). Usage Figures Open one of the figure script in RStudio and run the entire code. You may have to create a folder output in figures/ if it does not exist. The code has local dependencies that will be installed on your computer when encountering them for the first time. They start with fabreges.* and are necessary to generate the figures. Python packages (example) Compute angles # Shell script # Compute a summary (mean, median, s.d.) of the angles in embryo C1 at time 40 python compute-angles.py -s -r 15 -R 20 samples/C1_t040.tif.gz output/C1_t040_angles.csv # Compute a detailed list of angle values python compute-angles.py -r 15 -R 20 samples/C1_t041.tif.gz output/C1_t041_angles.csv Compute distances # Shell script # Compute the distance between two embryos python compute-distances.py -c 5 -s 5 samples/C1_t040.tif.gz samples/C1_t041.tif.gz output/distances.csv # Append another distance measurement python compute-distances.py -a -c 5 -s 5 samples/C1_t040.tif.gz samples/C1_t042.tif.gz output/distances.csv Compute volumes # Shell script # Compute the volume in isotropic data python compute-volumes.py -r 0.416 0.416 0.416 samples/C1_t040.tif.gz output/volumes.csv Estimate contacts # Shell script # Estimate the contacts and the free surface area python estimate-contacts.py -r 0.416 0.416 0.416 samples/C1_t040.tif.gz output/contacts.csv Identify the closest rigid packing # Shell script # Assuming the file `output/contacts.csv` exists and contains the output # from command `estimate-contacts.py`. # Identify the closest rigid packings python identify-packing.py output/contacts.csv output/backbones.csv Generate the seeds for Surface Evolver # Shell script # Assuming the file `output/contacts.csv` exists and contains the output # from command `estimate-contacts.py`, and the file `output/volumes.csv` # exists and contains the output from command `compute-volumes.py` # Generate the seeds for Surface Evolver using automatic thresholding # Automatic threshold will determine the highest cell-cell contact threshold # that preserves rigidity of the embryo (or 0 if not rigid) python generate-seed.py output/contacts.csv output/volumes.csv output/seed.fe # Generate the seeds for Surface Evolver with custom threshold python generate-seed.py --threshold 0.1 output/contacts.csv output/volumes.csv output/seed.fe Project in 2D and generate a distance matrix # Shell script # Assuming the folder `foo` only contains one or more CSV # from command `compute-distances.py`. # Compute the tSNE 2D projection python process-distances.py -D2 -t output/tsne_2d.csv foo/ # Compute the tSNE 3D projection python process-distances.py -D3 -t output/tsne_2d.csv foo/ # Generate the distance matrix python process-distances.py -m output/dismat.csv foo/}, author = {Corominas-Murtra, Bernat and Moghe, Prachiti and Kickuth, Alison and Ichikawa, Takafumi and Iwatani, Chizuru and Tsukiyama, Tomoyuki and Daniel, Nathalie and Gering, Julie and Stokkermans, Anniek and Wolny, Adrian and Kreshuk, Anna and Duranthon, Véronique and Uhlmann, Virginie and Hannezo, Edouard and Hiiragi, Takashi}, publisher = {Institute of Science and Technology Austria}, title = {{Data and software from: Temporal variability and cell mechanics control robustness in mammalian embryogenesis}}, doi = {10.5281/zenodo.12819780}, year = {2024}, } @inproceedings{18913, abstract = {With the proliferation of blockchain technology in high-value sectors, consensus protocols are becoming critical infrastructures. The rapid innovation cycle in Byzantine fault tolerant (BFT) consensus protocols has culminated in HotStuff, which provides linear message complexity in the partially synchronous setting. To achieve this, HotStuff leverages a leader that collects, aggregates, and broadcasts the messages of other validators. This paper analyzes the security implications of such approaches in practice, from the perspective of liveness and availability. By implementing attacks in a globally-distributed testbed, we show that state-of-the-art leader-based protocols are vulnerable to denial-of-service (DoS) attacks on the leader. Our attacks, demonstrated on committees of up to 64 validators, manage to disrupt liveness within seconds, using only a few tens of Mbps of attack bandwidth per validator. Crucially, the cost and effectiveness of the attacks are independent of the committee size. Based on the outcome of these experiments, we then propose and test effective mitigations. Our findings show that advancements in both protocol design and network-layer defenses can greatly improve the practical resilience of BFT consensus protocols.}, author = {Giuliari, Giacomo and Sonnino, Alberto and Frei, Marc and Streun, Fabio and Kokoris Kogias, Eleftherios and Perrig, Adrian}, booktitle = {Proceedings of the 19th ACM Asia Conference on Computer and Communications Security}, isbn = {9798400704826}, location = {Singapore, Singapore}, pages = {1345--1360}, publisher = {ACM}, title = {{An empirical study of consensus protocols’ DoS resilience}}, doi = {10.1145/3634737.3656997}, year = {2024}, } @inproceedings{18957, abstract = {Sui Lutris is the first smart-contract platform to sustainably achieve sub-second finality. It achieves this significant decrease by employing consensusless agreement not only for simple payments but for a large variety of transactions. Unlike prior work, Sui Lutris neither compromises expressiveness nor throughput and can run perpetually without restarts. Sui Lutris achieves this by safely integrating consensuless agreement with a high-throughput consensus protocol that is invoked out of the critical finality path but ensures that when a transaction is at risk of inconsistent concurrent accesses, its settlement is delayed until the total ordering is resolved. Building such a hybrid architecture is especially delicate during reconfiguration events, where the system needs to preserve the safety of the consensusless path without compromising the long-term liveness of potentially misconfigured clients. We thus develop a novel reconfiguration protocol, the first to provably show the safe and efficient reconfiguration of a consensusless blockchain. Sui Lutris is currently running in production and underpins the Sui smart-contract platform. Combined with the use of Objects instead of accounts it enables the safe execution of smart contracts that expose objects as a first-class resource. In our experiments Sui Lutris achieves latency lower than 0.5 seconds for throughput up to 5,000 certificates per second (150k ops/s with transaction blocks), compared to the state-of-the-art real-world consensus latencies of 3 seconds. Furthermore, it gracefully handles validators crash-recovery and does not suffer visible performance degradation during reconfiguration.}, author = {Blackshear, Sam and Chursin, Andrey and Danezis, George and Kichidis, Anastasios and Kokoris Kogias, Eleftherios and Li, Xun and Logan, Mark and Menon, Ashok and Nowacki, Todd and Sonnino, Alberto and Williams, Brandon and Zhang, Lu}, booktitle = {Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security}, isbn = {9798400706363}, location = {Salt Lake City, UT, United States}, pages = {2606--2620}, publisher = {ACM}, title = {{Sui Lutris: A blockchain combining broadcast and consensus}}, doi = {10.1145/3658644.3670286}, year = {2024}, } @article{17269, abstract = {The directed migration of epithelial cell collectives through coordinated movements plays a crucial role in various physiological processes and is increasingly understood at the level of large confluent monolayers. However, numerous processes rely on the migration of small groups of polarized epithelial clusters in complex environments, and their responses to external geometries remain poorly understood. To address this, we cultivate primary epithelial keratocyte tissues on adhesive microstripes to create autonomous epithelial clusters with well-defined geometries. We show that their migration efficiency is strongly influenced by the contact geometry and the orientation of cell–cell contacts with respect to the direction of migration. A combination of velocity and polarity alignment with contact regulation of locomotion in an active matter model captures quantitatively the experimental data. Furthermore, we predict that this combination of rules enables efficient navigation in complex geometries, which we confirm experimentally. Altogether, our findings provide a conceptual framework for extracting the interaction rules of active systems from their interaction with physical boundaries, as well as design principles for collective navigation in complex microenvironments.}, author = {Vercruysse, Eléonore and Brückner, David and Gómez-González, Manuel and Remson, Alexandre and Luciano, Marine and Kalukula, Yohalie and Rossetti, Leone and Trepat, Xavier and Hannezo, Edouard B and Gabriele, Sylvain}, issn = {1745-2481}, journal = {Nature Physics}, pages = {1492--1500}, publisher = {Springer Nature}, title = {{Geometry-driven migration efficiency of autonomous epithelial cell clusters}}, doi = {10.1038/s41567-024-02532-x}, volume = {20}, year = {2024}, } @article{18446, abstract = {How living systems achieve precision in form and function despite their intrinsic stochasticity is a fundamental yet ongoing question in biology. We generated morphomaps of preimplantation embryogenesis in mouse, rabbit, and monkey embryos, and these morphomaps revealed that although blastomere divisions desynchronized passively, 8-cell embryos converged toward robust three-dimensional shapes. Using topological analysis and genetic perturbations, we found that embryos progressively changed their cellular connectivity to a preferred topology, which could be predicted by a physical model in which actomyosin contractility and noise facilitate topological transitions, lowering surface energy. This mechanism favored regular embryo packing and promoted a higher number of inner cells in the 16-cell embryo. Synchronized division reduced embryo packing and generated substantially more misallocated cells and fewer inner-cell–mass cells. These findings suggest that stochasticity in division timing contributes to robust patterning.}, author = {Fabrèges, Dimitri and Corominas-Murtra, Bernat and Moghe, Prachiti and Kickuth, Alison and Ichikawa, Takafumi and Iwatani, Chizuru and Tsukiyama, Tomoyuki and Daniel, Nathalie and Gering, Julie and Stokkermans, Anniek and Wolny, Adrian and Kreshuk, Anna and Duranthon, Véronique and Uhlman, Virginie and Hannezo, Edouard B and Hiiragi, Takashi}, issn = {1095-9203}, journal = {Science}, number = {6718}, publisher = {AAAS}, title = {{Temporal variability and cell mechanics control robustness in mammalian embryogenesis}}, doi = {10.1126/science.adh1145}, volume = {386}, year = {2024}, } @inproceedings{18071, abstract = {Recent advancements on DAG-based consensus protocols allow for blockchains with improved metrics and properties, such as throughput and censorship-resistance. Variants of the Bullshark [18] consensus protocol are adopted for practical use by the Sui blockchain, for improved latency. However, the protocol is leader-based, and is strongly affected by crashed leaders that can lead to various performance issues, for example, decreased transaction throughput. In this paper, we propose HammerHead, a DAG-based consensus protocol, that is inspired by Carousel [8] and provides Leader-Utilization. Our proposal differs from Carousel, which is built for a chained consensus protocol; in HammerHead chain quality is inherited by the DAG. HammerHead needs to preserve safety and liveness, despite validators committing leader vertices asynchronously. The key idea is to update leader schedules dynamically, based on the validators' scores during the previous schedule. We implement HammerHead and show a minor improvement in performance for cases without faults. The major improvements in comparison to Bullshark appear in faulty settings. Specifically, we show a drastic, 2x-latency improvement and up to 40% increased throughput when crash faults occur (100 validators, 33 faults).}, author = {Tsimos, Giorgos and Kichidis, Anastasios and Sonnino, Alberto and Kokoris Kogias, Eleftherios}, booktitle = {Proceedings - International Conference on Distributed Computing Systems}, isbn = {9798350386059}, issn = {2575-8411}, location = {Jersey City, NJ, United States}, pages = {1377--1387}, publisher = {IEEE}, title = {{HammerHead: Leader reputation for dynamic scheduling}}, doi = {10.1109/ICDCS60910.2024.00129}, year = {2024}, } @article{17284, abstract = {Platelet homeostasis is essential for vascular integrity and immune defence1,2. Although the process of platelet formation by fragmenting megakaryocytes (MKs; thrombopoiesis) has been extensively studied, the cellular and molecular mechanisms required to constantly replenish the pool of MKs by their progenitor cells (megakaryopoiesis) remains unclear3,4. Here we use intravital imaging to track the cellular dynamics of megakaryopoiesis over days. We identify plasmacytoid dendritic cells (pDCs) as homeostatic sensors that monitor the bone marrow for apoptotic MKs and deliver IFNα to the MK niche triggering local on-demand proliferation and maturation of MK progenitors. This pDC-dependent feedback loop is crucial for MK and platelet homeostasis at steady state and under stress. pDCs are best known for their ability to function as vigilant detectors of viral infection5. We show that virus-induced activation of pDCs interferes with their function as homeostatic sensors of megakaryopoiesis. Consequently, activation of pDCs by SARS-CoV-2 leads to excessive megakaryopoiesis. Together, we identify a pDC-dependent homeostatic circuit that involves innate immune sensing and demand-adapted release of inflammatory mediators to maintain homeostasis of the megakaryocytic lineage.}, author = {Gärtner, Florian R and Ishikawa-Ankerhold, Hellen and Stutte, Susanne and Fu, Wenwen and Weitz, Jutta and Dueck, Anne and Nelakuditi, Bhavishya and Fumagalli, Valeria and Van Den Heuvel, Dominic and Belz, Larissa and Sobirova, Gulnoza and Zhang, Zhe and Titova, Anna and Navarro, Alejandro Martinez and Pekayvaz, Kami and Lorenz, Michael and Von Baumgarten, Louisa and Kranich, Jan and Straub, Tobias and Popper, Bastian and Zheden, Vanessa and Kaufmann, Walter and Guo, Chenglong and Piontek, Guido and Von Stillfried, Saskia and Boor, Peter and Colonna, Marco and Clauß, Sebastian and Schulz, Christian and Brocker, Thomas and Walzog, Barbara and Scheiermann, Christoph and Aird, William C. and Nerlov, Claus and Stark, Konstantin and Petzold, Tobias and Engelhardt, Stefan and Sixt, Michael K and Hauschild, Robert and Rudelius, Martina and Oostendorp, Robert A.J. and Iannacone, Matteo and Heinig, Matthias and Massberg, Steffen}, issn = {1476-4687}, journal = {Nature}, pages = {645--653}, publisher = {Springer Nature}, title = {{Plasmacytoid dendritic cells control homeostasis of megakaryopoiesis}}, doi = {10.1038/s41586-024-07671-y}, volume = {631}, year = {2024}, } @inbook{18052, abstract = {Sodium dodecyl sulfate-digested freeze-fracture replica labeling (SDS-FRL) is an electron microscope (EM) sample preparation technique which allows for high-resolution visualization of membrane proteins with high sensitivity. However, image acquisition of specific replica profiles such as synapses in a large field of EM view needs a valid experience and a long time for manual searching. Here, we describe how to utilize deep learning for automatizing image acquisition of specific profiles of interest in replica samples. This protocol facilitates the labor-intensive collection of EM images, in particular for rare profiles. We provide instructions for using SerialEM image acquisition software in conjunction with object detection by our newly developed deep learning software DarEM, to automatically acquire tilt series of all synapses in a selected region. We then show how to perform a mostly automated analysis of gold particle labeling in the acquired images by utilizing Darea software.}, author = {Kleindienst, David and Costanzo, Tommaso and Shigemoto, Ryuichi}, booktitle = {New Aspects in Analyzing the Synaptic Organization of the Brain}, editor = {Lübke, Joachim H.R. and Rollenhagen, Astrid}, isbn = {9781071640180}, issn = {1940-6045}, pages = {123--137}, publisher = {Springer Nature}, title = {{Automated Imaging and Analysis of Synapses in Freeze-Fracture Replica Samples with Deep Learning}}, doi = {10.1007/978-1-0716-4019-7_8}, year = {2024}, } @misc{14842, abstract = {Eva Benkova received a PhD in Biophysics at the Institute of Biophysics of the Czech Academy of Sciences in 1998. After working as a postdoc at the Max Planck Institute in Cologne and the Center for Plant Molecular Biology (ZMBP) in Tübingen, she became a group leader at the Plant Systems Biology Department of the Vlaams Instituut voor Biotechnologie (VIB) in Gent. In 2012, she transitioned to an Assistant Professor position at the Institute of Science and Technology Austria (ISTA) where she was later promoted to Professor. Since 2021, she has served as the Dean of the ISTA Graduate School. As a plant developmental biologist, she focuses on unraveling the molecular mechanisms and principles that underlie hormonal interactions in plants. In her current work, she explores the intricate connections between hormones and regulatory pathways that mediate the perception of environmental stimuli, including abiotic stress and nitrate availability.}, author = {Benková, Eva}, booktitle = {Current Biology}, issn = {1879-0445}, number = {1}, pages = {R3--R5}, publisher = {Elsevier}, title = {{Eva Benkova}}, doi = {10.1016/j.cub.2023.11.039}, volume = {34}, year = {2024}, } @article{15146, abstract = {The extracellular matrix (ECM) serves as a scaffold for cells and plays an essential role in regulating numerous cellular processes, including cell migration and proliferation. Due to limitations in specimen preparation for conventional room-temperature electron microscopy, we lack structural knowledge on how ECM components are secreted, remodeled, and interact with surrounding cells. We have developed a 3D-ECM platform compatible with sample thinning by cryo-focused ion beam milling, the lift-out extraction procedure, and cryo-electron tomography. Our workflow implements cell-derived matrices (CDMs) grown on EM grids, resulting in a versatile tool closely mimicking ECM environments. This allows us to visualize ECM for the first time in its hydrated, native context. Our data reveal an intricate network of extracellular fibers, their positioning relative to matrix-secreting cells, and previously unresolved structural entities. Our workflow and results add to the structural atlas of the ECM, providing novel insights into its secretion and assembly.}, author = {Zens, Bettina and Fäßler, Florian and Hansen, Jesse and Hauschild, Robert and Datler, Julia and Hodirnau, Victor-Valentin and Zheden, Vanessa and Alanko, Jonna H and Sixt, Michael K and Schur, Florian KM}, issn = {1540-8140}, journal = {Journal of Cell Biology}, number = {6}, publisher = {Rockefeller University Press}, title = {{Lift-out cryo-FIBSEM and cryo-ET reveal the ultrastructural landscape of extracellular matrix}}, doi = {10.1083/jcb.202309125}, volume = {223}, year = {2024}, } @article{17463, abstract = {Allosteric modulation of protein function, wherein the binding of an effector to a protein triggers conformational changes at distant functional sites, plays a central part in the control of metabolism and cell signalling1,2,3. There has been considerable interest in designing allosteric systems, both to gain insight into the mechanisms underlying such ‘action at a distance’ modulation and to create synthetic proteins whose functions can be regulated by effectors4,5,6,7. However, emulating the subtle conformational changes distributed across many residues, characteristic of natural allosteric proteins, is a significant challenge8,9. Here, inspired by the classic Monod–Wyman–Changeux model of cooperativity10, we investigate the de novo design of allostery through rigid-body coupling of peptide-switchable hinge modules11 to protein interfaces12 that direct the formation of alternative oligomeric states. We find that this approach can be used to generate a wide variety of allosterically switchable systems, including cyclic rings that incorporate or eject subunits in response to peptide binding and dihedral cages that undergo effector-induced disassembly. Size-exclusion chromatography, mass photometry13 and electron microscopy reveal that these designed allosteric protein assemblies closely resemble the design models in both the presence and absence of peptide effectors and can have ligand-binding cooperativity comparable to classic natural systems such as haemoglobin14. Our results indicate that allostery can arise from global coupling of the energetics of protein substructures without optimized side-chain–side-chain allosteric communication pathways and provide a roadmap for generating allosterically triggerable delivery systems, protein nanomachines and cellular feedback control circuitry.}, author = {Pillai, Arvind and Idris, Abbas and Philomin, Annika and Weidle, Connor and Skotheim, Rebecca and Leung, Philip J.Y. and Broerman, Adam and Demakis, Cullen and Borst, Andrew J. and Praetorius, Florian M and Baker, David}, issn = {1476-4687}, journal = {Nature}, pages = {911–920 }, publisher = {Springer Nature}, title = {{De novo design of allosterically switchable protein assemblies}}, doi = {10.1038/s41586-024-07813-2}, volume = {632}, year = {2024}, }