arXiv subjects

Ricardo Henriques

Publications and source records attributed to Ricardo Henriques.

7 recordsLinked to original sources

arXiv2026

Packaging Jupyter notebooks as installable desktop apps using LabConstrictor

Life sciences research depends heavily on open-source academic software, yet many tools remain underused due to practical barriers. These include installation requirements that hinder adoption and limited developer resources for software distribution and long-term maintenance. Jupyter notebooks are popular because they combine code, documentation, and results into a single executable document, enabling quick method development. However, notebooks are often fragile due to reproducibility issues in coding environments, and sharing them, especially for local execution, does not ensure others can run them successfully. LabConstrictor closes this deployment gap by bringing CI/CD-style automation to academic developers without needing DevOps expertise. Its GitHub-based pipeline checks environments and packages notebooks into one-click installable desktop applications. After installation, users access a unified start page with documentation, links to the packaged notebooks, and version checks. Code cells can be hidden by default, and run-cell controls combined with widgets provide an app-like experience. By simplifying the distribution, installation, and sharing of open-source software, LabConstrictor allows faster access to new computational methods and promotes routine reuse across labs.

cs.SE↗

arXiv2026

EZInput: A Cross-Environment Python Library for Easy UI Generation in Scientific Computing

Researchers face a persistent barrier when applying computational algorithms with parameter configuration typically demanding programming skills, interfaces differing across environments, and settings rarely persisting between sessions. This fragmentation forces repetitive input, slows iterative exploration, and undermines reproducibility because parameter choices are difficult to record, share, and reuse. We present EZInput, a cross-runtime environment Python library enabling algorithm developers to automatically generate graphical user interfaces that make their computational tools accessible to end-users without programming expertise. EZInput employs a declarative specification system where developers define input requirements and validation constraints once; the library then handles environment detection, interface rendering, parameter validation, and session persistence across Jupyter notebooks, Google Colab, and terminal environments. This "write once, run anywhere" architecture enables researchers to prototype in notebooks and deploy identical parameter configurations for batch execution on remote systems without code changes or manual transcription. Parameter persistence, inspired by ImageJ/FIJI and adapted to Python workflows, saves and restores user configurations via lightweight YAML files, eliminating redundant input and producing shareable records that enhance reproducibility. EZInput supports diverse input types essential for scientific computing and it also includes built-in validation that ensures data integrity and clear feedback that reduces user friction.

cs.SE↗

arXiv2025

Rxiv-Maker: an automated template engine for streamlined scientific publications

The rapid growth of preprint servers has accelerated scientific dissemination but has also shifted the technical burden of manuscript preparation to authors. This challenge is particularly acute in computational research, where manuscripts must remain synchronised with evolving data and code. We present Rxiv-Maker, a framework that resolves this by converting simple Markdown files into professionally typeset, publication-ready PDFs. Its core feature is the ability to execute embedded code, creating a self-updating manuscript where figures and statistical values are generated directly from source data during compilation. This ensures that the final document is always current and fully reproducible. By building on standard tools like Git and Visual Studio (VS) Code, Rxiv-Maker offers a transparent, collaborative authoring workflow that applies software-engineering practices to academic writing.

cs.DL↗

arXiv2024

The Rise of Data-Driven Microscopy powered by Machine Learning

Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field-of-view, and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real-time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse, and adapt promise to transform optical imaging by opening new experimental possibilities.

q-bio.QM↗

arXiv2023

Harnessing Artificial Intelligence To Reduce Phototoxicity in Live Imaging

Fluorescence microscopy, widely used in the study of living cells, tissues, and organisms, often faces the challenge of photodamage. This is primarily caused by the interaction between light and biochemical components during the imaging process, leading to compromised accuracy and reliability of biological results. Methods necessitating extended high-intensity illumination, such as super-resolution microscopy or thick sample imaging, are particularly susceptible to this issue. As part of the solution to these problems, advanced imaging approaches involving artificial intelligence (AI) have been developed. Here we underscore the necessity of establishing constraints to maintain light-induced damage at levels that permit cells to sustain their live behaviour. From this perspective, data-driven live-cell imaging bears significant potential in aiding the development of AI-enhanced photodamage-aware microscopy. These technologies could streamline precise observations of natural biological dynamics while minimising phototoxicity risks.

q-bio.QM↗

arXiv2023

Roadmap on Deep Learning for Microscopy

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

physics.optics↗

arXiv2022

Mapping molecular complexes with Super-Resolution Microscopy and Single-Particle Analysis

Understanding the structure of supramolecular complexes provides insight into their functional capabilities and how they can be modulated in the context of disease. Super-resolution microscopy (SRM) excels in performing this task by resolving ultrastructural details at the nanoscale with molecular specificity. However, technical limitations, such as underlabelling, preclude its ability to provide complete structures. Single-particle analysis (SPA) overcomes this limitation by combining information from multiple images of identical structures and producing an averaged model, effectively enhancing the resolution and coverage of image reconstructions. This review highlights important studies using SRM-SPA, demonstrating how it broadens our knowledge by elucidating features of key biological structures with unprecedented detail.

q-bio.QM↗