arxiv: 2605.02934 · v1 · submitted 2026-04-30 · ⚛️ physics.bio-ph · cond-mat.mtrl-sci· q-bio.SC· stat.AP

Recognition: unknown

Statistical analysis of virion-cell interactions mediated by peptide nanofibrils and peptide amphiphiles using STEM tomography

Philipp Rieder , Julia La Roche , Orkun Furat , Annalena Kuhn , Lena Rauch-Wirth , K\"ubra Kaygisiz , Fabian Zech , Jan M\"unch

show 3 more authors

Clarissa Read R\"udiger Gro{\ss} Volker Schmidt

Authors on Pith no claims yet

Pith reviewed 2026-05-09 20:33 UTC · model grok-4.3

classification ⚛️ physics.bio-ph cond-mat.mtrl-sciq-bio.SCstat.AP

keywords peptide nanofibrilspeptide amphiphilesSTEM tomographyvirion-cell interactionsspatial organizationviral transductionstatistical analysisgene transfer

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The pith

Different peptide structures create distinct patterns of virion confinement near cell surfaces

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors introduce a statistical framework for analyzing STEM tomograms to measure interactions between peptides, virions, and cells. They find that four different peptides all capture virions effectively, leaving few free particles. However, the peptides organize the virions in different spatial ways relative to the cell surface. These organizational differences are suggested to be important for how well each peptide enhances viral transduction and gene transfer. The method provides an objective tool for studying such nanomaterials.

Core claim

All peptides efficiently captured virions, resulting in few free virions, but they differ in how strictly virions were spatially confined near the cell surface. These differences reflect alternative spatial organization strategies, which are likely crucial factors influencing transduction-enhancing efficacy.

What carries the argument

STEM tomography statistical analysis framework using advanced geometric descriptors for peptide-virion-cell spatial interactions

Load-bearing premise

The differences in spatial organization of virions are the main factors driving variations in transduction efficacy.

What would settle it

Measuring the actual transduction rates for each peptide and finding that they do not correspond to the levels of spatial confinement observed in the tomograms.

Figures

Figures reproduced from arXiv: 2605.02934 by Annalena Kuhn, Clarissa Read, Fabian Zech, Jan M\"unch, Julia La Roche, K\"ubra Kaygisiz, Lena Rauch-Wirth, Orkun Furat, Philipp Rieder, R\"udiger Gro{\ss}, Volker Schmidt.

**Figure 2.** Figure 2: First, the STEM image I (a) is segmented phase-wise (b), into cell (black), peptide (blue) and virions (magenta). Subsequently, the phases are segmented instance-wise, i.e., each disconnected component of the peptide phase (c) and virion phase (d) is assigned a unique label, represented by different colors. Note that disconnected components in the shown 2D cross sections with the same color are connected … view at source ↗

**Figure 3.** Figure 3: 2D sketch of the rolling ball algorithm. Yellow indicates an peptide aggregate, [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

**Figure 4.** Figure 4: Univariate probability densities ((a) and (b)) of the specific surface area and [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗

**Figure 5.** Figure 5: Coverage ratio of aggregates by cell, virions by cell and virions by peptide, [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

**Figure 6.** Figure 6: Probability density of the minimum distances of virions to cell phase (a) and [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗

**Figure 7.** Figure 7: Bivariate probability density of minimum distances of a virion to cell and [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗

**Figure 8.** Figure 8: Enhancement of HIV-1 infection (NL4-3 R5) by peptide nanofibrils (PNFs) and [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗

read the original abstract

Peptide nanofibrils (PNFs) and peptide amphiphiles (PAs) are promising tools for enhancing viral transduction and gene transfer. However, quantitative insight into how their supramolecular architecture governs virion-cell interactions is limited. Here, we introduce a framework for the acquisition, processing, and statistical analysis of scanning transmission electron microscopy (STEM) tomograms to objectively quantify peptide-virion-cell interactions. Using four transduction-enhancing peptides (D4, Vectofusin-1, palmitic acid-PA (pal-PA), and eicosapentaenoic-PA (eic-PA)), peptide aggregate morphology, interfacial contact areas, and the spatial organization of virions with respect to peptides and cells were analyzed using advanced geometric descriptors. All peptides efficiently captured virions, resulting in few free virions, but they differ in how strictly virions were spatially confined near the cell surface. These differences reflect alternative spatial organization strategies, which are likely crucial factors influencing transduction-enhancing efficacy. Our approach provides a novel, generalizable method to evaluate infection-enhancing nanomaterials and guides the rational design of next-generation peptide assemblies for therapeutic viral delivery.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper supplies a clear quantitative framework for measuring peptide-virion spatial patterns in STEM tomograms, but the claim that those patterns drive transduction differences rests on an untested assumption.

read the letter

The main takeaway is that this work gives a usable pipeline for turning STEM tomograms into numbers on how four different peptides organize virions near cells. They process the images, extract geometric measures for aggregate shape, contact areas, and virion positions, then run statistical comparisons. All four peptides trap most virions, yet they produce distinct degrees of confinement at the cell surface. That part is new for this specific application and gives an objective way to describe the interactions instead of relying on visual impressions alone. The methods section spells out the tomography acquisition, reconstruction, and descriptor calculations in enough detail that someone could replicate the analysis on similar samples. The statistical handling of the geometric data looks careful and avoids obvious over-fitting. The soft spot is the leap from those spatial differences to statements about transduction efficacy. The paper notes that the confinement patterns reflect alternative organization strategies that are likely crucial for boosting gene transfer, but it reports no transduction assays, reporter readouts, or any correlation between the confinement metrics and actual delivery success on the same samples. Without that link, the efficacy interpretation stays speculative. The data on morphology and positioning stand on their own as descriptive results. This is for labs working on peptide nanomaterials for viral gene therapy who need better ways to characterize their constructs at the nanoscale. A methods-focused reader in biophysics or nanomedicine could adapt the descriptors and stats pipeline directly. It deserves peer review because the core analysis framework is grounded and reproducible, even though the discussion would benefit from either added functional data or more restrained language on the mechanism.

Referee Report

2 major / 2 minor

Summary. The paper introduces a framework for acquiring, processing, and statistically analyzing STEM tomograms to objectively quantify interactions between virions, peptide nanofibrils (PNFs), and peptide amphiphiles (PAs) with cells. Using four transduction-enhancing peptides (D4, Vectofusin-1, palmitic acid-PA, and eicosapentaenoic-PA), the authors analyze peptide aggregate morphology, interfacial contact areas, and virion spatial organization via geometric descriptors. They report that all peptides efficiently capture virions (few free virions observed) but differ in the strictness of virion confinement near the cell surface, interpreting these as alternative spatial organization strategies likely crucial for transduction-enhancing efficacy. The method is positioned as generalizable for evaluating infection-enhancing nanomaterials.

Significance. If the spatial confinement metrics can be shown to correlate with functional transduction outcomes, this work offers a valuable quantitative imaging-based approach for comparing and optimizing peptide-based viral delivery systems in gene therapy. The emphasis on objective geometric descriptors from tomograms provides a reproducible way to characterize nanomaterial-virion-cell interfaces that could inform rational design.

major comments (2)

[Abstract/Discussion] Abstract and Discussion: The central claim that differences in virion spatial confinement 'reflect alternative spatial organization strategies, which are likely crucial factors influencing transduction-enhancing efficacy' is not supported by any transduction efficiency assays, reporter gene readouts, or statistical correlation between the geometric descriptors (e.g., confinement metrics) and functional gene transfer outcomes for the same peptide batches. This inference is load-bearing for the paper's broader significance but rests on an untested assumption.
[Results] Results section on statistical analysis: No numerical values, error bars, sample sizes (number of tomograms or virions per peptide), or p-values are provided for the reported differences in spatial confinement and contact areas across the four peptides, preventing assessment of effect sizes and statistical robustness of the 'differ in how strictly virions were spatially confined' conclusion.

minor comments (2)

[Methods] Methods: Clarify the exact definitions and formulas for the 'advanced geometric descriptors' used for aggregate morphology and virion positions to ensure reproducibility.
[Figures] Figure legends: Ensure all panels include scale bars and explicit labels for the four peptides to aid interpretation of the tomogram examples.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review of our manuscript. We have addressed each major comment point by point below. Where revisions are warranted, we have updated the manuscript to improve clarity, statistical rigor, and appropriate framing of our interpretations without overstating the current evidence.

read point-by-point responses

Referee: [Abstract/Discussion] Abstract and Discussion: The central claim that differences in virion spatial confinement 'reflect alternative spatial organization strategies, which are likely crucial factors influencing transduction-enhancing efficacy' is not supported by any transduction efficiency assays, reporter gene readouts, or statistical correlation between the geometric descriptors (e.g., confinement metrics) and functional gene transfer outcomes for the same peptide batches. This inference is load-bearing for the paper's broader significance but rests on an untested assumption.

Authors: We agree that the manuscript does not include new transduction efficiency assays, reporter gene readouts, or direct statistical correlations between the geometric descriptors and functional outcomes. The phrasing in the abstract and discussion draws on the established transduction-enhancing activity of these four peptides reported in prior literature, combined with the new quantitative spatial metrics from STEM tomography. To address the concern, we have revised the abstract and discussion to present the observed differences in virion confinement as suggestive of alternative spatial organization strategies that may influence efficacy. The revised text now explicitly notes that direct functional correlations remain to be tested in future work, thereby reducing the load-bearing nature of the inference while retaining the value of the imaging-based framework for hypothesis generation. revision: partial
Referee: [Results] Results section on statistical analysis: No numerical values, error bars, sample sizes (number of tomograms or virions per peptide), or p-values are provided for the reported differences in spatial confinement and contact areas across the four peptides, preventing assessment of effect sizes and statistical robustness of the 'differ in how strictly virions were spatially confined' conclusion.

Authors: We thank the referee for identifying this gap in the presentation of results. Although the underlying tomogram dataset was analyzed statistically, the original manuscript emphasized the framework and descriptive comparisons rather than tabulating the quantitative details. In the revised manuscript, we have added a dedicated subsection and supplementary table that reports: (i) the number of tomograms and virions analyzed per peptide condition, (ii) mean values with standard errors for key geometric descriptors (confinement metrics and contact areas), and (iii) the outcomes of statistical tests (including p-values from appropriate non-parametric comparisons) to quantify the significance and effect sizes of differences across the four peptides. These additions enable readers to evaluate the robustness of the reported distinctions in spatial organization. revision: yes

Circularity Check

0 steps flagged

No circularity: observational statistical framework with no derivations or self-referential predictions

full rationale

The paper presents a methods framework for acquiring, processing, and statistically analyzing STEM tomograms to quantify peptide-virion-cell interactions using geometric descriptors for morphology, contact areas, and spatial organization. No equations, fitted parameters, predictions, or derivation chains are described; the central observations (efficient virion capture with varying confinement) are direct outputs of the tomogram analysis rather than reductions to inputs by construction. The interpretive statement that confinement differences 'reflect alternative spatial organization strategies, which are likely crucial factors influencing transduction-enhancing efficacy' is presented as a hypothesis based on the data, not a derived result. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes, and the work is self-contained as an empirical quantification tool without mathematical self-definition.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no details on free parameters, axioms, or invented entities; the framework presumably relies on standard statistical and geometric methods from prior literature but specifics are absent.

pith-pipeline@v0.9.0 · 5558 in / 1169 out tokens · 35996 ms · 2026-05-09T20:33:01.356073+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

42 extracted references

[1]

M¨ unch, E

J. M¨ unch, E. R¨ ucker, L. St¨ andker, K. Adermann, C. Goffinet, M. Schindler, S. Wildum, R. Chinnadurai, D. Rajan, A. Specht, G. Gim´ enez-Gallego, P. C. 20 S´ anchez, D. M. Fowler, A. Koulov, J. W. Kelly, W. Mothes, J.-C. Grivel, L. Margolis, O. T. Keppler, W.-G. Forssmann, and F. Kirchhoff. Semen-derived amyloid fibrils drastically enhance HIV infecti...

2007
[2]

N. R. Roan, H. Liu, S. M. Usmani, J. Neidleman, J. A. M¨ uller, A. Avila-Herrera, A. Gawanbacht, O. Zirafi, S. Chu, M. Dong, S. T. Kumar, J. F. Smith, K. S. Pollard, M. F¨ andrich, F. Kirchhoff, J. M¨ unch, H. E. Witkowska, and W. C. Greene. Lique- faction of semen generates and later degrades a conserved semenogelin peptide that enhances HIV infection.Jo...

2014
[3]

K. K. Patel, M. Tariveranmoshabad, S. Kadu, N. Shobaki, and C. H. June. From concept to cure: The evolution of CAR-T cell therapy.Molecular Therapy, 33:2123– 2140, 2025

2025
[4]

Rauch-Wirth, D

L. Rauch-Wirth, D. Sch¨ utz, R. Groß, S. Rode, B. Glocker, J. A. M¨ uller, P. Walther, C. Read, and J. M¨ unch. Transduction enhancing EF-C peptide nanofibrils are endo- cytosed by macropinocytosis and subsequently degraded.Biomaterials, 317:123044, 2025

2025
[5]

N. R. Roan, J. M¨ unch, N. Arhel, W. Mothes, J. Neidleman, A. Kobayashi, K. Smith- McCune, F. Kirchhoff, and W. C. Greene. The cationic properties of SEVI underlie its ability to enhance human immunodeficiency virus infection.Journal of Virology, 83:73–80, 2009

2009
[6]

Radek, O

C. Radek, O. Bernadin, K. Drechsel, N. Cordes, R. Pfeifer, P. Str¨ aßer, M. Mormin, A. Gutierrez-Guerrero, F.-L. Cosset, A. D. Kaiser, T. Schaser, A. Galy, E. Verhoeyen, and I. C. D. Johnston. Vectofusin-1 improves transduction of primary human cells with diverse retroviral and lentiviral pseudotypes, enabling robust, automated closed- system manufacturin...

2019
[7]

L. S. Vermeer, L. Hamon, A. Schirer, M. Schoup, J. Cosette, S. Majdoul, D. Pastr´ e, D. Stockholm, N. Holic, P. Hellwig, A. Galy, D. Fenard, and B. Bechinger. Vectofusin-1, a potent peptidic enhancer of viral gene transfer forms pH-dependent α-helical nanofibrils, concentrating viral particles.Acta Biomaterialia, 64:259–268, 2017

2017
[8]

Irving, E

M. Irving, E. Lanitis, D. Migliorini, Z. Ivics, and S. Guedan. Choosing the right tool for genetic engineering: Clinical lessons from chimeric antigen receptor-T cells. Human Gene Therapy, 32:1044–1058, 2021

2021
[9]

Rauch-Wirth, A

L. Rauch-Wirth, A. Renner, K. Kaygisiz, T. Weil, L. Zimmermann, A. A. Rodriguez- Alfonso, D. Sch¨ utz, S. Wiese, L. St¨ andker, T. Weil, D. Schmiedel, and J. M¨ unch. 21 Optimized peptide nanofibrils as efficient transduction enhancers for in vitro and ex vivo gene transfer.Frontiers in Immunology, 14:1270243, 2023

2023
[10]

Rauch-Wirth, J

L. Rauch-Wirth, J. La Roche, K. Kaygisiz, A. Kuhn, N. Jeon, U. Stifel, F. Zech, P. Fischer-Posovszky, T. Weil, C. Read, and J. M¨ unch. Structural morphology of peptide nanofibrils dictates viral capture and cellular uptake in gene therapy appli- cations.Nano Today, 69:103015, 2026

2026
[11]

J. D. Hartgerink, E. Beniash, and S. I. Stupp. Self-assembly and mineralization of peptide-amphiphile nanofibers.Science, 294:1684–1688, 2001

2001
[12]

H. Cui, M. J. Webber, and S. I. Stupp. Self-assembly of peptide amphiphiles: From molecules to nanostructures to biomaterials.Peptide Science, 94:1–18, 2010

2010
[13]

Kaygisiz, L

K. Kaygisiz, L. Rauch-Wirth, A. Iscen, J. Hartenfels, K. Kremer, J. M¨ unch, C. V. Synatschke, and T. Weil. Peptide amphiphiles as biodegradable adjuvants for efficient retroviral gene delivery.Advanced Healthcare Materials, 13:2301364, 2024

2024
[14]

La Roche, L

J. La Roche, L. Rauch-Wirth, L. Zimmerman, F. Zech, J. M¨ unch, C. Read, and K. Kaygisiz. Interactions of peptide amphiphiles with viruses and cells are enabled by amorphous nanostructures.Journal of Peptide Science, 31:e70051, 2025

2025
[15]

Weber, T

M. Weber, T. Wilhelm, and V. Schmidt. Multidimensional characterisation of time- dependent image data: A case study for the peripheral nervous system in ageing mice.Image Analysis and Stereology, 40:85–94, 2021

2021
[16]

Badia-Martinez, B

D. Badia-Martinez, B. Peralta, G. Andr´ es, M. Guerra, D. Gil-Carton, and N. G. Abrescia. Three-dimensional visualization of forming hepatitis C virus-like particles by electron-tomography.Virology, 430:120–126, 2012

2012
[17]

Ronneberger, P

O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomed- ical image segmentation. In N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, editors,Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, pages 234–241. Springer International Publishing, 2015

2015
[18]

Weber, M

M. Weber, M. Neumann, M. Schmidt, P. B. Pfeiffer, A. Bansal, M. F¨ andrich, and V. Schmidt. Segmentation and morphological analysis of amyloid fibrils from cryo- EM image data.Journal of Mathematics in Industry, 13:2, 2023

2023
[19]

J. E. Heebner, C. Purnell, R. K. Hylton, M. Marsh, M. A. Grillo, and M. T. Swulius. Deep learning-based segmentation of cryo-electron tomograms.Journal of Visualized Experiments, 189:e64435, 2022

2022
[20]

Dragonfly, 2025

Comet Technologies Canada Inc. Dragonfly, 2025. Version: 2025.1. 22

2025
[21]

J. L. Martin, S. Cao, J. O. Maldonado, W. Zhang, and L. M. Mansky. Distinct particle morphologies revealed through comparative parallel analyses of retrovirus- like particles.Journal of Virology, 90:8074–8084, 2016

2016
[22]

J. O. Maldonado, S. Cao, W. Zhang, and L. M. Mansky. Distinct morphology of human T-cell leukemia virus type 1-like particles.Viruses, 8:132, 2016

2016
[23]

D. M. McCraw, J. R. Gallagher, U. Torian, M. L. Myers, M. T. Conlon, N. M. Gulati, and A. K. Harris. Structural analysis of influenza vaccine virus-like particles reveals a multicomponent organization.Scientific Reports, 8:10342, 2018

2018
[24]

Rieder, L

P. Rieder, L. Petrich, I. Serrano-Munoz, M. Mouiya, H. Mark¨ otter, M. Huger, G. Bruno, and V. Schmidt. Statistical analysis of grains and pores within polycrys- talline Al2TiO5 ceramics, based on X-ray computed tomography.Materials Charac- terization, 229:115602, 2025

2025
[25]

E. T. Pashuck, H. Cui, and S. I. Stupp. Tuning supramolecular rigidity of peptide fibers through molecular structure.Journal of the American Chemical Society, 132: 6041–6046, 2010

2010
[26]

S. E. Paramonov, H.-W. Jun, and J. D. Hartgerink. Self-assembly of peptide- amphiphile nanofibers: The roles of hydrogen bonding and amphiphilic packing. Journal of the American Chemical Society, 128:7291–7298, 2006

2006
[27]

J. G. Wieland, J. La Roche, T. Bergner, R. Habisch, E. Puschmann, J. Soza-Ried, D. Sch¨ utz, J. M¨ unch, P. Walther, M. Dass, and C. Read. Scanning transmission electron microscopy tomography in virology: 3D imaging of high-pressure frozen, freeze-substituted samples.Journal of Visualized Experiments, 222:68568, 2025

2025
[28]

Walther and A

P. Walther and A. Ziegler. Freeze substitution of high-pressure frozen samples: The visibility of biological membranes is improved when the substitution medium contains water.Journal of Microscopy, 208:3–10, 2002

2002
[29]

Z. Zhou, M. M. Rahman Siddiquee, N. Tajbakhsh, and J. Liang. Unet++: A nested U-net architecture for medical image segmentation. In D. Stoyanov, Z. Tay- lor, G. Carneiro, T. Syeda-Mahmood, A. Martel, L. Maier-Hein, J. M. R. Tavares, A. Bradley, J. P. Papa, V. Belagiannis, J. C. Nascimento, Z. Lu, S. Conjeti, M. Moradi, H. Greenspan, and A. Madabhushi, edi...

2018
[30]

Van der Walt, J

S. Van der Walt, J. L. Sch¨ onberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu. scikit-image: Image processing in Python.PeerJ, 2:e453, 2014. 23

2014
[31]

M. L. Waskom. Seaborn: Statistical data visualization.Journal of Open Source Software, 6:3021, 2021

2021
[32]

Virtanen, R

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, ˙I. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen,...

2020
[33]

D. W. Scott.Multivariate Density Estimation: Theory, Practice, and Visualization. J. Wiley & Sons, 2nd edition, 2015

2015
[34]

Ohser and K

J. Ohser and K. Schladitz.3D Images of Materials Structures. Wiley-VCH, 2009

2009
[35]

Fuchs, S

L. Fuchs, S. Weber, J. Men, N. Eiermann, O. Furat, A. B¨ uck, and V. Schmidt. Stochastic modeling of particle structures in spray fluidized bed agglomeration using methods from machine learning.Powder Technology, 467:121475, 2026

2026
[36]

S. R. Sternberg. Biomedical image processing.Computer, 16:22–34, 1983

1983
[37]

Soille.Morphological Image Analysis: Principles and Applications

P. Soille.Morphological Image Analysis: Principles and Applications. Springer, 1999

1999
[38]

Russ.The Image Processing Handbook

J. Russ.The Image Processing Handbook. CRC Press, 5th edition, 2006

2006
[39]

S. Chiu, D. Stoyan, W. S. Kendall, and J. Mecke.Stochastic Geometry and its Applications. J. Wiley & Sons, 3rd edition, 2013

2013
[40]

Baddeley and E

A. Baddeley and E. Jensen.Stereology for Statisticians. Taylor & Francis, 2004

2004
[41]

H¨ ohn, M

K. H¨ ohn, M. Sailer, L. Wang, M. Lorenz, M. Schneider, and P. Walther. Preparation of cryofixed cells for improved 3D ultrastructure with scanning transmission electron tomography.Histochemistry and Cell Biology, 135:1–9, 2011

2011
[42]

Aoyama, T

K. Aoyama, T. Takagi, A. Hirase, and A. Miyazawa. STEM tomography for thick biological specimens.Ultramicroscopy, 109:70–80, 2008. 24 Supplementary Information In Section 3.3 edge effects are discussed, induced by the size of the observation window. Table S1 shows the total number of peptide aggregates and virions, as well as the number of those intersect...

2008