Scar/WAVE drives actin protrusions independently of its VCA domain using proline-rich domains

Cell migration requires the constant modification of cellular shape by reorganization of the actin cytoskeleton. Fine-tuning of this process is critical to ensure new actin filaments are formed only at specific times and in defined regions of the cell. The Scar/WAVE complex is the main catalyst of pseudopod and lamellipodium formation during cell migration. It is a pentameric complex highly conserved through eukaryotic evolution and composed of Scar/WAVE, Abi, Nap1/NCKAP1, Pir121/CYFIP, and HSPC300/Brk1. Its function is usually attributed to activation of the Arp2/3 complex through Scar/WAVE's VCA domain, while other parts of the complex are expected to mediate spatial-temporal regulation and have no direct role in actin polymerization. Here, we show in both B16-F1 mouse melanoma and Dictyostelium discoideum cells that Scar/WAVE without its VCA domain still induces the formation of morphologically normal, actin-rich protrusions, extending at comparable speeds despite a drastic reduction of Arp2/3 recruitment. However, the proline-rich regions in Scar/WAVE and Abi subunits are essential, though either is sufficient for the generation of actin protrusions in B16-F1 cells. We further demonstrate that N-WASP can compensate for the absence of Scar/WAVE's VCA domain and induce lamellipodia formation, but it still requires an intact WAVE complex, even if without its VCA domain. We conclude that the Scar/WAVE complex does more than directly activating Arp2/3, with proline-rich domains playing a central role in promoting actin protrusions. This implies a broader function for the Scar/WAVE complex, concentrating and simultaneously activating many actin-regulating proteins as a lamellipodium-producing core.

Development of Lung Cancer Risk Prediction Machine Learning Models for Equitable Learning Health System: Retrospective Study

BACKGROUND

A significant proportion of young at-risk patients and nonsmokers are excluded by the current guidelines for lung cancer (LC) screening, resulting in low-screening adoption. The vision of the US National Academy of Medicine to transform health systems into learning health systems (LHS) holds promise for bringing necessary structural changes to health care, thereby addressing the exclusivity and adoption issues of LC screening.

OBJECTIVE

This study aims to realize the LHS vision by designing an equitable, machine learning (ML)-enabled LHS unit for LC screening. It focuses on developing an inclusive and practical LC risk prediction model, suitable for initializing the ML-enabled LHS (ML-LHS) unit. This model aims to empower primary physicians in a clinical research network, linking central hospitals and rural clinics, to routinely deliver risk-based screening for enhancing LC early detection in broader populations.

METHODS

We created a standardized data set of health factors from 1397 patients with LC and 1448 control patients, all aged 30 years and older, including both smokers and nonsmokers, from a hospital's electronic medical record system. Initially, a data-centric ML approach was used to create inclusive ML models for risk prediction from all available health factors. Subsequently, a quantitative distribution of LC health factors was used in feature engineering to refine the models into a more practical model with fewer variables.

RESULTS

The initial inclusive 250-variable XGBoost model for LC risk prediction achieved performance metrics of 0.86 recall, 0.90 precision, and 0.89 accuracy. Post feature refinement, a practical 29-variable XGBoost model was developed, displaying performance metrics of 0.80 recall, 0.82 precision, and 0.82 accuracy. This model met the criteria for initializing the ML-LHS unit for risk-based, inclusive LC screening within clinical research networks.

CONCLUSIONS

This study designed an innovative ML-LHS unit for a clinical research network, aiming to sustainably provide inclusive LC screening to all at-risk populations. It developed an inclusive and practical XGBoost model from hospital electronic medical record data, capable of initializing such an ML-LHS unit for community and rural clinics. The anticipated deployment of this ML-LHS unit is expected to significantly improve LC-screening rates and early detection among broader populations, including those typically overlooked by existing screening guidelines.

BEATS: BEAmline for synchrotron X-ray microTomography at SESAME

The ID10 beamline of the SESAME (Synchrotron-light for Experimental Science and Applications in the Middle East) synchrotron light source in Jordan was inaugurated in June 2023 and is now open to scientific users. The beamline, which was designed and installed within the European Horizon 2020 project BEAmline for Tomography at SESAME (BEATS), provides full-field X-ray radiography and microtomography imaging with monochromatic or polychromatic X-rays up to photon energies of 100 keV. The photon source generated by a 2.9 T wavelength shifter with variable gap, and a double-multilayer monochromator system allow versatile application for experiments requiring either an X-ray beam with high intensity and flux, and/or a partially spatial coherent beam for phase-contrast applications. Sample manipulation and X-ray detection systems are designed to allow scanning samples with different size, weight and material, providing image voxel sizes from 13 µm down to 0.33 µm. A state-of-the-art computing infrastructure for data collection, three-dimensional (3D) image reconstruction and data analysis allows the visualization and exploration of results online within a few seconds from the completion of a scan. Insights from 3D X-ray imaging are key to the investigation of specimens from archaeology and cultural heritage, biology and health sciences, materials science and engineering, earth, environmental sciences and more. Microtomography scans and preliminary results obtained at the beamline demonstrate that the new beamline ID10-BEATS expands significantly the range of scientific applications that can be targeted at SESAME.

Illuminating the nanostructure of diffuse interfaces: Recent advances and future directions in reflectometry techniques

Diffuse soft matter interfaces take many forms, from end-tethered polymer brushes or adsorbed surfactants to self-assembled layers of lipids. These interfaces play crucial roles across a multitude of fields, including materials science, biophysics, and nanotechnology. Understanding the nanostructure and properties of these interfaces is fundamental for optimising their performance and designing novel functional materials. In recent years, reflectometry techniques, in particular neutron reflectometry, have emerged as powerful tools for elucidating the intricate nanostructure of soft matter interfaces with remarkable precision and depth. This review provides an overview of selected recent developments in reflectometry and their applications for illuminating the nanostructure of diffuse interfaces. We explore various principles and methods of neutron and X-ray reflectometry, as well as ellipsometry, and discuss advances in their experimental setups and data analysis approaches. Improvements to experimental neutron reflectometry methods have enabled greater time resolution in kinetic measurements and elucidation of diffuse structure under shear or confinement, while innovation in analysis protocols has significantly reduced data processing times, facilitated co-refinement of reflectometry data from multiple instruments and provided greater-than-ever confidence in proposed structural models. Furthermore, we highlight some significant research findings enabled by these techniques, revealing the organisation, dynamics, and interfacial phenomena at the nanoscale. We also discuss future directions and potential advancements in reflectometry techniques. By shedding light on the nanostructure of diffuse interfaces, reflectometry techniques enable the rational design and tailoring of interfaces with enhanced properties and functionalities.

Revisiting the model for coactivator recruitment: Med15 can select its target sites independent of promoter-bound transcription factors

Activation domains (ADs) within transcription factors (TFs) induce gene expression by recruiting coactivators such as the Mediator complex. Coactivators lack DNA binding domains (DBDs) and are assumed to passively follow their recruiting TFs. This is supported by direct AD-coactivator interactions seen in vitro but has not yet been tested in living cells. To examine that, we targeted two Med15-recruiting ADs to a range of budding yeast promoters through fusion with different DBDs. The DBD-AD fusions localized to hundreds of genomic sites but recruited Med15 and induced transcription in only a subset of bound promoters, characterized by a fuzzy-nucleosome architecture. Direct DBD-Med15 fusions shifted DBD localization towards fuzzy-nucleosome promoters, including promoters devoid of the endogenous Mediator. We propose that Med15, and perhaps other coactivators, possess inherent promoter preference and thus actively contribute to the selection of TF-induced genes.

Biogenesis of lysosome-related organelles complex-2 is an evolutionarily ancient proto-coatomer complex

Hermansky-Pudlak syndrome (HPS) is an inherited disorder of intracellular vesicle trafficking affecting the function of lysosome-related organelles (LROs). At least 11 genes underlie the disease, encoding four protein complexes, of which biogenesis of lysosome-related organelles complex-2 (BLOC-2) is the last whose molecular action is unknown. We find that the unicellular eukaryote Dictyostelium unexpectedly contains a complete BLOC-2, comprising orthologs of the mammalian subunits HPS3, -5, and -6, and a fourth subunit, an ortholog of the Drosophila LRO-biogenesis gene, Claret. Lysosomes from Dictyostelium BLOC-2 mutants fail to mature, similar to LROs from HPS patients, but for all endolysosomes rather than a specialized subset. They also strongly resemble lysosomes from WASH mutants. Dictyostelium BLOC-2 localizes to the same compartments as WASH, and in BLOC-2 mutants, WASH is inefficiently recruited, accounting for their impaired lysosomal maturation. BLOC-2 is recruited to endolysosomes via its HPS3 subunit. Structural modeling suggests that all four subunits are proto-coatomer proteins, with important implications for BLOC-2's molecular function. The discovery of Dictyostelium BLOC-2 permits identification of orthologs throughout eukaryotes. BLOC-2 and lysosome-related organelles, therefore, pre-date the evolution of Metazoa and have broader and more conserved functions than previously thought.

Scientific Data Spaces - Experiences from the EGI-ACE project

This paper presents the approach adopted by the EGI-ACE project for the setup and delivery of Data Spaces for various scientific domains. The work was implemented by members of the EGI e-infrastructure and of several European Research Infrastructures in the context of the European Open Science Cloud programme. Our results are several Data Space services that enable the reuse and exploitation of open, scientific big data for compute intensive use cases. The paper illustrates the EGI-ACE approach through two examples: (1) EMSO ERIC Data Portal for seafloor and water column research and (2) ENES Data Space for climate research.

Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories

To investigate the fundamental question of how cellular variations arise across spatiotemporal scales in a population of identical healthy cells, we focused on nuclear growth in hiPS cell colonies as a model system. We generated a 3D timelapse dataset of thousands of nuclei over multiple days, and developed open-source tools for image and data analysis and an interactive timelapse viewer for exploring quantitative features of nuclear size and shape. We performed a data-driven analysis of nuclear growth variations across timescales. We found that individual nuclear volume growth trajectories arise from short timescale variations attributable to their spatiotemporal context within the colony. We identified a strikingly time-invariant volume compensation relationship between nuclear growth duration and starting volume across the population. Notably, we discovered that inheritance plays a crucial role in determining these two key nuclear growth features while other growth features are determined by their spatiotemporal context and are not inherited.

Optical microscopic imaging, manipulation, and analysis methods for morphogenesis research

Morphogenesis is a developmental process of organisms being shaped through complex and cooperative cellular movements. To understand the interplay between genetic programs and the resulting multicellular morphogenesis, it is essential to characterize the morphologies and dynamics at the single-cell level and to understand how physical forces serve as both signaling components and driving forces of tissue deformations. In recent years, advances in microscopy techniques have led to improvements in imaging speed, resolution and depth. Concurrently, the development of various software packages has supported large-scale, analyses of challenging images at the single-cell resolution. While these tools have enhanced our ability to examine dynamics of cells and mechanical processes during morphogenesis, their effective integration requires specialized expertise. With this background, this review provides a practical overview of those techniques. First, we introduce microscopic techniques for multicellular imaging and image analysis software tools with a focus on cell segmentation and tracking. Second, we provide an overview of cutting-edge techniques for mechanical manipulation of cells and tissues. Finally, we introduce recent findings on morphogenetic mechanisms and mechanosensations that have been achieved by effectively combining microscopy, image analysis tools and mechanical manipulation techniques.

Microbial adaptation to spaceflight is correlated with bacteriophage-encoded functions

Evidence from the International Space Station suggests microbial populations are rapidly adapting to the spacecraft environment; however, the mechanism of this adaptation is not understood. Bacteriophages are prolific mediators of bacterial adaptation on Earth. Here we survey 245 genomes sequenced from bacterial strains isolated on the International Space Station for dormant (lysogenic) bacteriophages. Our analysis indicates phage-associated genes are significantly different between spaceflight strains and their terrestrial counterparts. In addition, we identify 283 complete prophages, those that could initiate bacterial lysis and infect additional hosts, of which 21% are novel. These prophage regions encode functions that correlate with increased persistence in extreme environments, such as spaceflight, to include antimicrobial resistance and virulence, DNA damage repair, and dormancy. Our results correlate microbial adaptation in spaceflight to bacteriophage-encoded functions that may impact human health in spaceflight.

Ultrafast Computational Screening of Molecules with Inverted Singlet-Triplet Energy Gaps Using the Pariser-Parr-Pople Semiempirical Quantum Chemistry Method

Molecules with an inverted energy gap between their first singlet and triplet excited states have promising applications in the next generation of organic light-emitting diode (OLED) materials. Unfortunately, such molecules are rare, and only a handful of examples are currently known. High-throughput virtual screening could assist in finding novel classes of these molecules, but current efforts are hampered by the high computational cost of the required quantum chemical methods. We present a method based on the semiempirical Pariser-Parr-Pople theory augmented by perturbation theory and show that it reproduces inverted gaps at a fraction of the cost of currently employed excited-state calculations. Our study paves the way for ultrahigh-throughput virtual screening and inverse design to accelerate the discovery and development of this new generation of OLED materials.

Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes

MOTIVATION

The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes.

RESULTS

We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments.

AVAILABILITY AND IMPLEMENTATION

Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.

Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark prototypes an implementation of a reproducible framework, where the provided Jupyter Book enables readers to reproduce or modify the figures on the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep. Most of the benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing was generally effective, but is incompatible with statistical analyses requiring the continuous sampling of brain signal, for which a simpler strategy, using motion parameters, average activity in select brain compartments, and global signal regression, is preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods.

Large language models improve annotation of prokaryotic viral proteins

Viral genomes are poorly annotated in metagenomic samples, representing an obstacle to understanding viral diversity and function. Current annotation approaches rely on alignment-based sequence homology methods, which are limited by the paucity of characterized viral proteins and divergence among viral sequences. Here we show that protein language models can capture prokaryotic viral protein function, enabling new portions of viral sequence space to be assigned biologically meaningful labels. When applied to global ocean virome data, our classifier expanded the annotated fraction of viral protein families by 29%. Among previously unannotated sequences, we highlight the identification of an integrase defining a mobile element in marine picocyanobacteria and a capsid protein that anchors globally widespread viral elements. Furthermore, improved high-level functional annotation provides a means to characterize similarities in genomic organization among diverse viral sequences. Protein language models thus enhance remote homology detection of viral proteins, serving as a useful complement to existing approaches.

From pixels to insights: Machine learning and deep learning for bioimage analysis

Bioimage analysis plays a critical role in extracting information from biological images, enabling deeper insights into cellular structures and processes. The integration of machine learning and deep learning techniques has revolutionized the field, enabling the automated, reproducible, and accurate analysis of biological images. Here, we provide an overview of the history and principles of machine learning and deep learning in the context of bioimage analysis. We discuss the essential steps of the bioimage analysis workflow, emphasizing how machine learning and deep learning have improved preprocessing, segmentation, feature extraction, object tracking, and classification. We provide examples that showcase the application of machine learning and deep learning in bioimage analysis. We examine user-friendly software and tools that enable biologists to leverage these techniques without extensive computational expertise. This review is a resource for researchers seeking to incorporate machine learning and deep learning in their bioimage analysis workflows and enhance their research in this rapidly evolving field.

Characterization and visualization of tandem repeats at genome scale

Tandem repeat (TR) variation is associated with gene expression changes and numerous rare monogenic diseases. Although long-read sequencing provides accurate full-length sequences and methylation of TRs, there is still a need for computational methods to profile TRs across the genome. Here we introduce the Tandem Repeat Genotyping Tool (TRGT) and an accompanying TR database. TRGT determines the consensus sequences and methylation levels of specified TRs from PacBio HiFi sequencing data. It also reports reads that support each repeat allele. These reads can be subsequently visualized with a companion TR visualization tool. Assessing 937,122 TRs, TRGT showed a Mendelian concordance of 98.38%, allowing a single repeat unit difference. In six samples with known repeat expansions, TRGT detected all expansions while also identifying methylation signals and mosaicism and providing finer repeat length resolution than existing methods. Additionally, we released a database with allele sequences and methylation levels for 937,122 TRs across 100 genomes.

Computational reproducibility of Jupyter notebooks from biomedical publications

BACKGROUND

Jupyter notebooks facilitate the bundling of executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share computational workflows, including for research publications. The reproducibility of computational aspects of research is a key component of scientific reproducibility but has not yet been assessed at scale for Jupyter notebooks associated with biomedical publications.

APPROACH

We address computational reproducibility at 2 levels: (i) using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks associated with publications indexed in the biomedical literature repository PubMed Central. We identified such notebooks by mining the article's full text, trying to locate them on GitHub, and attempting to rerun them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. (ii) This study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over the course of 2 years, during which the corpus of Jupyter notebooks from articles indexed in PubMed Central has grown in a highly dynamic fashion.

RESULTS

Out of 27,271 Jupyter notebooks from 2,660 GitHub repositories associated with 3,467 publications, 22,578 notebooks were written in Python, including 15,817 that had their dependencies declared in standard requirement files and that we attempted to rerun automatically. For 10,388 of these, all declared dependencies could be installed successfully, and we reran them to assess reproducibility. Of these, 1,203 notebooks ran through without any errors, including 879 that produced results identical to those reported in the original notebook and 324 for which our results differed from the originally reported ones. Running the other notebooks resulted in exceptions.

CONCLUSIONS

We zoom in on common problems and practices, highlight trends, and discuss potential improvements to Jupyter-related workflows associated with biomedical publications.

Controlling stable Bloch points with electric currents

The Bloch point is a point singularity in the magnetisation configuration, where the magnetisation vanishes. It can exist as an equilibrium configuration and plays an important role in many magnetisation reversal processes. In the present work, we focus on manipulating Bloch points in a system that can host stable Bloch points-a two-layer FeGe nanostrip with opposite chirality of the two layers. We drive Bloch points using spin-transfer torques and find that Bloch points can move collectively without any Hall effect and report that Bloch points are repelled from the sample boundaries and each other. We study pinning of Bloch points at wedge-shaped constrictions (notches) in the nanostrip and demonstrate that arrays of Bloch points can be moved past a series of notches in a controlled manner by applying consecutive current pulses of different strength. Finally, we simulate a T-shaped geometry and demonstrate that a Bloch point can be moved along different paths by applying current between suitable strip ends.

Subjective data models in bioinformatics and how wet lab and computational biologists conceptualise data

Biological science produces "big data" in varied formats, which necessitates using computational tools to process, integrate, and analyse data. Researchers using computational biology tools range from those using computers for communication, to those writing analysis code. We examine differences in how researchers conceptualise the same data, which we call "subjective data models". We interviewed 22 people with biological experience and varied levels of computational experience, and found that many had fluid subjective data models that changed depending on circumstance. Surprisingly, results did not cluster around participants' computational experience levels. People did not consistently map entities from abstract data models to the real-world entities in files, and certain data identifier formats were easier to infer meaning from than others. Real-world implications: 1) software engineers should design interfaces for task performance, emulating popular user interfaces, rather than targeting professional backgrounds; 2) when insufficient context is provided, people may guess what data means, whether or not they are correct, emphasising the importance of contextual metadata to remove the need for erroneous guesswork.

Unveiling the Anti-Cancer Potential of Onoceranoid Triterpenes from Lansium domesticum Corr. cv. kokosan: An In Silico Study against Estrogen Receptor Alpha

Breast cancer is a significant global concern, with tamoxifen, the standard treatment, raising long-term safety issues due to side effects. In this study, we evaluated the potential of five onoceranoid triterpenes from Lansium domesticum Corr. cv. kokosan against estrogen receptor alpha (ERα) using in silico techniques. Utilizing molecular docking, Lipinski's rule of five, in silico ADMET, and molecular dynamics simulations, we assessed the potency of five onoceranoid triterpenes against ERα. Molecular docking indicated competitive binding energies for these triterpenes relative to the active form of tamoxifen (4OHT) and estradiol, an ERα native ligand. Three triterpenes met drug-likeness criteria with favorable ADMET profiles. Notably, 2 demonstrated superior binding affinity in molecular dynamics simulations, outperforming estradiol, closely followed by 3 and 4. Hierarchical clustering on principal components (HCPC) and the spatial distribution of contact surface area (CSA) analyses suggest that these triterpenes, especially 2, may act as antagonist ligands akin to 4OHT. These findings highlight the potential of onoceranoid triterpenes in treating ERα-related breast cancer.

The five pillars of computational reproducibility: bioinformatics and beyond

Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.

OME-Zarr: a cloud-optimized bioimaging file format with international community support

A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself-OME-Zarr-along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain-the file format that underlies so many personal, institutional, and global data management and analysis tasks.

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the Hacking the Human Body machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We create a dataset containing 880 histology images with 12,901 segmented structures, engaging 1175 teams from 78 countries in community-driven, open-science development of machine learning models. Tissue variations in the dataset pose a major challenge to the teams which they overcome by using color normalization techniques and combining vision transformers with convolutional models. The best model will be productized in the HuBMAP portal to process tissue image datasets at scale in support of Human Reference Atlas construction.

Continuous Evaluation of Denoising Strategies in Resting-State fMRI Connectivity Using fMRIPrep and Nilearn

Reducing contributions from non-neuronal sources is a crucial step in functional magnetic resonance imaging (fMRI) connectivity analyses. Many viable strategies for denoising fMRI are used in the literature, and practitioners rely on denoising benchmarks for guidance in the selection of an appropriate choice for their study. However, fMRI denoising software is an ever-evolving field, and the benchmarks can quickly become obsolete as the techniques or implementations change. In this work, we present a denoising benchmark featuring a range of denoising strategies, datasets and evaluation metrics for connectivity analyses, based on the popular fMRIprep software. The benchmark is implemented in a fully reproducible framework, where the provided research objects enable readers to reproduce or modify core computations, as well as the figures of the article using the Jupyter Book project and the Neurolibre reproducible preprint server (https://neurolibre.org/). We demonstrate how such a reproducible benchmark can be used for continuous evaluation of research software, by comparing two versions of the fMRIprep software package. The majority of benchmark results were consistent with prior literature. Scrubbing, a technique which excludes time points with excessive motion, combined with global signal regression, is generally effective at noise removal. Scrubbing however disrupts the continuous sampling of brain images and is incompatible with some statistical analyses, e.g. auto-regressive modeling. In this case, a simple strategy using motion parameters, average activity in select brain compartments, and global signal regression should be preferred. Importantly, we found that certain denoising strategies behave inconsistently across datasets and/or versions of fMRIPrep, or had a different behavior than in previously published benchmarks. This work will hopefully provide useful guidelines for the fMRIprep users community, and highlight the importance of continuous evaluation of research methods. Our reproducible benchmark infrastructure will facilitate such continuous evaluation in the future, and may also be applied broadly to different tools or even research fields.

The Effect of Ethanol on Lipid Nanoparticle Stabilization from a Molecular Dynamics Simulation Perspective

Lipid nanoparticles (LNPs) have emerged as a promising delivery system, particularly for genetic therapies and vaccines. LNP formation requires a specific mixture of nucleic acid in a buffered solution and lipid components in ethanol. Ethanol acts as a lipid solvent, aiding the formation of the nanoparticle's core, but its presence can also affect LNP stability. In this study, we used molecular dynamics (MD) simulations to investigate the physicochemical effect of ethanol on LNPs and gain a dynamic understanding of its impact on the overall structure and stability of LNPs. Our results demonstrate that ethanol destabilizes LNP structure over time, indicated by increased root mean square deviation (RMSD) values. Changes in the solvent-accessible surface area (SASA), electron density, and radial distribution function (RDF) also suggest that ethanol affects LNP stability. Furthermore, our H-bond profile analysis shows that ethanol penetrates the LNP earlier than water. These findings emphasize the importance of immediate ethanol removal in lipid-based systems during LNP production to ensure stability.

A Caenorhabditis elegans Male Pheromone Feminizes Germline Gene Expression in Hermaphrodites and Imposes Life-History Costs

Sex pheromones not only improve the reproductive success of the recipients, but also impose costs, such as a reduced life span. The underlying mechanisms largely remain to be elucidated. Here, we show that even a brief exposure to physiological amounts of the dominant Caenorhabditis elegans male pheromone, ascr#10, alters the expression of thousands of genes in hermaphrodites. The most dramatic effect on the transcriptome is the upregulation of genes expressed during oogenesis and the downregulation of genes associated with male gametogenesis. This result reveals a way in which social signals help to resolve the inherent conflict between spermatogenesis and oogenesis in a simultaneous hermaphrodite, presumably to optimally align reproductive function with the presence of potential mating partners. We also found that exposure to ascr#10 increased the risk of persistent intestinal infections in hermaphrodites due to pathological pharyngeal hypertrophy. Thus, our study reveals ways in which the male pheromone can not only have beneficial effects on the recipients' reproduction, but also cause harmful consequences that reduce life span.

Bloch points in nanostrips

Complex magnetic materials hosting topologically non-trivial particle-like objects such as skyrmions are under intensive research and could fundamentally change the way we store and process data. One important class of materials are helimagnetic materials with Dzyaloshinskii-Moriya interaction. Recently, it was demonstrated that thin nanodisks consisting of two layers with opposite chirality can host a single stable Bloch point of two different types at the interface between the layers. Using micromagnetic simulations we show that FeGe nanostrips consisting of two layers with opposite chirality can host multiple coexisting Bloch points in an arbitrary combination of the two different types. We show that the number of Bloch points that can simultaneously coexist depends on the strip geometry and the type of the individual Bloch points. Our simulation results allow us to predict strip geometries suitable for an arbitrary number of Bloch points. We show an example of an 80-Bloch-point configuration verifying the prediction.

A C. elegans male pheromone feminizes germline gene expression in hermaphrodites and imposes life-history costs

Sex pheromones improve reproductive success, but also impose costs. Here we show that even brief exposure to physiological amounts of the dominant C. elegans male pheromone, ascr#10, alters the expression of thousands of genes in hermaphrodites. The most dramatic effect on the transcriptome was the upregulation of genes expressed during oogenesis and downregulation of genes associated with male gametogenesis. Among the detrimental effects of ascr#10 on hermaphrodites is the increased risk of persistent infections caused by pathological pharyngeal hypertrophy. Our results reveal a way in which social signals help to resolve the inherent conflict between spermatogenesis and oogenesis in a simultaneous hermaphrodite, presumably to optimally align reproductive function to the presence of potential mating partners. They also show that the beneficial effects of the pheromone are accompanied by harmful consequences that reduce lifespan.

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the "Hacking the Human Body" machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We showcase how 1,175 teams from 78 countries engaged in community- driven, open-science code development that resulted in machine learning models which successfully segment anatomical structures across five organs using histology images from two consortia and that will be productized in the HuBMAP data portal to process large datasets at scale in support of Human Reference Atlas construction. We discuss the benchmark data created for the competition, major challenges faced by the participants, and the winning models and strategies.

Five Guiding Principles to Make Jupyter Notebooks Fit for Earth Observation Data Education

There is a growing demand to train Earth Observation (EO) data users in how to access and use existing and upcoming data. A promising tool for data-related training is computational notebooks, which are interactive web applications that combine text, code and computational output. Here, we present the Learning Tool for Python (LTPy), which is a training course (based on Jupyter notebooks) on atmospheric composition data. LTPy consists of more than 70 notebooks and has taught over 1000 EO data users so far, whose feedback is overall positive. We adapted five guiding principles from different fields (mainly scientific computing and Jupyter notebook research) to make the Jupyter notebooks more educational and reusable. The Jupyter notebooks developed (i) follow the literate programming paradigm by a text/code ratio of 3, (ii) use instructional design elements to improve navigation and user experience, (iii) modularize functions to follow best practices for scientific computing, (iv) leverage the wider Jupyter ecosystem to make content accessible and (v) aim for being reproducible. We see two areas for future developments: first, to collect feedback and evaluate whether the instructional design elements proposed meet their objective; and second, to develop tools that automatize the implementation of best practices.

Guidelines for collaborative development of sustainable data treatment software

Software development for data reduction and analysis at large research facilities is increasingly professionalized, and internationally coordinated. To foster software quality and sustainability, and to facilitate collaboration, representatives from software groups of European neutron and muon facilities have agreed on a set of guidelines for development practices, infrastructure, and functional and non-functional product properties. These guidelines have been derived from actual practices in software projects from the EU funded consortium ‘Science and Innovation with Neutrons in Europe in 2020’ (SINE2020), and have been enriched through extensive literature review. Besides guiding the work of the professional software engineers in our computing groups, we hope to influence scientists who are willing to contribute their own data treatment software to our community. Moreover, this work may also provide inspiration to scientific software development beyond the neutron and muon field.

LOCAN: a python library for analyzing single-molecule localization microscopy data

SUMMARY

Single-molecule localization microscopy has become an important part of the super-resolution microscopy toolbox in biomedical research. Software platforms for applying analytical methods to the point-based data structures are needed that offer both routine application and flexible customization of analysis procedures. We present a python library called LOCAN that consists of well-defined data structures and analysis methods for analyzing localization data in a script or computable notebook.

AVAILABILITY AND IMPLEMENTATION

The package source code is released open-source under a BSD-3 license at https://github.com/super-resolution/Locan. It can be installed form the Python Package Index at https://pypi.org/project/locan. Documentation is available at https://locan.readthedocs.io.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.