Environment-sensitive probes are frequently used in spectral/multi-channel microscopy to study alterations in cell homeostasis. However, the few open-source packages available for processing of spectral images are limited in scope. Here, we present VISION, a stand-alone software based on Python for spectral analysis with improved applicability. In addition to classical intensity-based analysis, our software can batch-process multidimensional images with an advanced single-cell segmentation capability and apply user-defined mathematical operations on spectra to calculate biophysical and metabolic parameters of single cells. VISION allows for 3D and temporal mapping of properties such as membrane fluidity and mitochondrial potential. We demonstrate the broad applicability of VISION by applying it to study the effect of various drugs on cellular biophysical properties; the correlation between membrane fluidity and mitochondrial potential; protein distribution in cell-cell contacts; and properties of nanodomains in cell-derived vesicles. Together with the code, we provide a graphical user interface for facile adoption.

The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows, including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.

The rat sciatic nerve model is commonly used to test novel therapies for nerve injury repair. The static sciatic index (SSI) is a useful metric for quantifying functional recovery, and involves comparing an operated paw versus a control paw using a weighted ratio between the toe spread and the internal toe spread. To calculate it, rats are placed in a transparent box, photos are taken from underneath and the toe distances measured manually. This is labour intensive and subject to human error due to the challenge of consistently taking photos, identifying digits and making manual measurements. Although several commercial kits have been developed to address this challenge, they have seen little dissemination due to cost. Here we develop a novel algorithm for automatic measurement of SSI metrics based on video data using casted U-Nets. The algorithm consists of three U-Nets, one to segment the hind paws and two for the two pairs of digits which input into the SSI calculation. A training intersection over union error of 60 % and 80 % was achieved for the back paws and for both digit segmentation U-Nets, respectfully. The algorithm was tested against video data from three separate experiments. Compared to manual measurements, the algorithm provides the same profile of recovery for every experiment but with a tighter standard deviation in the SSI measure. Through the open-source release of this algorithm, we aim to provide an inexpensive tool to more reliably quantify functional recovery metrics to the nerve repair research community.

Understanding the mechanical behavior of heterogeneous materials is becoming increasingly crucial across various fields, including aerospace engineering, composite materials development, geology, and biomechanics. While substantial literature exists on this topic, conventional methods often rely on commercial software packages. This study presents a framework for computed tomography (CT) scan-based finite element (FE) analysis of such materials using open-source software in most of the workflow. Our work focuses on three key aspects:1.Mesh generation that incorporates spatially varying mechanical properties and well-defined boundary conditions.2.Validation of the FE results through comparison with digital image correlation (DIC) system measurements.3.Open-source software utilization throughout the entire process, making it more accessible and cost-effective.This work aims to demonstrate the effectiveness of this framework for analyzing heterogeneous materials in various fields, offering a more accessible and affordable approach.

The COVID-19 Research Network Lower Saxony (COFONI) is a German state network of experts in Coronavirus research and development of strategies for future pandemics. One of the pillars of the COFONI technology platform is its established research data repository (Available at https://forschungsdb.cofoni.de/), which enables provision of pseudonymised data and cross-location data retrieval for heterogeneous datasets. The platform consistently uses open standards (openEHR) and open source components (EHRbase) for its data repository, taking into account the FAIR criteria. Available data include both clinical and socio-demographic patient information. A comprehensive AQL query builder interface and an integrated research request process enable new research approaches, rapid cohort assembly and customized data export for researchers from participating institutions. Our flexible and scalable platform approach can be regarded as a blueprint. It contributes, to pandemic preparedness by providing easily accessible cross-location research data in a fully standardised and open representation.

The screening and diagnosis of breast cancer is a major public health issue. Although deep learning models are proving highly effective in breast imaging, these models are not yet readily accessible to a wide audience. In order to promote the widespread dissemination of such models, this article introduces a free and open-source, integrated platform for the automated detection of masses on mammograms. A state-of-the-art RetinaNet model is trained on this task and the results of the inference are encoded using the DICOM-SR interoperable format. These contributions present a significant step towards overcoming the accessibility gap in deep learning for breast imaging.

Formal infectious disease surveillance in Ukraine has been disrupted by Russia\'s 2022 invasion, leading to challenges with tracking and containing epidemics. To analyze the effects of the war on infectious disease epidemiology, we used open-source data from EPIWATCH, an artificial intelligence early-warning system. We analyzed patterns of infectious diseases and syndromes before (November 1, 2021-February 23, 2022) and during (February 24-July 31, 2022) the conflict. We compared case numbers for the most frequently reported diseases with numbers from formal sources and found increases in overall infectious disease reports and in case numbers of cholera, botulism, tuberculosis, HIV/AIDS, rabies, and salmonellosis during compared with before the invasion. During the conflict, although open-source intelligence captured case numbers for epidemics, such data (except for diphtheria) were unavailable/underestimated by formal surveillance. In the absence of formal surveillance during military conflicts, open-source data provide epidemic intelligence useful for infectious disease control.

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In an era marked by scientific stagnation, Decentralized Science (DeSci) challenges the inefficiencies of traditional funding and publishing systems. DeSci employs blockchain technology to address the misalignment of incentives in academic research, emphasizing transparency, rapid funding, and open-source principles. Centralized institutions have been linked to a deceleration of progress, which is acutely felt in the field of longevity science-a critical discipline as aging is the #1 risk factor for most diseases. DeSci proposes a transformative model where decentralized autonomous organizations (DAOs) facilitate community-driven funding, promoting high-risk, high-reward research. DeSci, particularly within longevity research, could catalyze a paradigm shift towards an equitable, efficient, and progressive scientific future.

Cryo-transfer stations are essential tools in the field of cryo-electron microscopy, enabling the safe transfer of frozen vitreous samples between different stages of the workflow. However, existing cryo-transfer stations are typically configured for only the two most popular sample holder geometries and are not commercially available for all electron microscopes. Additionally, they are expensive and difficult to customize, which limits their accessibility and adaptability for research laboratories. Here, we present a new modular cryo-transfer station that addresses these limitations. The station is composed entirely of 3D-printed and off the shelf parts, allowing it to be reconfigured to a fit variety of microscopes and experimental protocols. We describe the design and construction of the station and report on the results of testing the cryo-transfer station, including its ability to maintain cryogenic temperatures and transfer frozen vitreous samples as demonstrated by vibrational spectroscopy. Our findings demonstrate that the cryo-transfer station performs comparably to existing commercial models, while offering greater accessibility and customizability. The design for the station is open source to encourage other groups to replicate and build on this development. We hope that this project will increase access to cryo-transfer stations for researchers in a variety of disciplines with nonstandard equipment.