SoftwareX

1. Video-tracking programs have now become an essential tool for researchers measuring animal behavior across biological fields. The panel of available programs is growing rapidly, providing researchers with numerous specific tools that will match their precise needs. However, their proliferation may complicate post-tracking data processing, and some programs do not even provide tools for correcting tracking errors or analysing tracking data. In the case of commercial software, the loss of access to a program due to budget limitations or researchers' mobility from one institution to another could prevent them from accessing and visualizing their tracking data. 2. There is therefore a growing need for an accessible and flexible tool to handle post-tracking processes such as the correction and analysis of tracking data obtained across different video-tracking programs. 3. We present here the latest update of the video tracking and analysis program AnimalTA. With this new release, we propose to solve the above-mentioned problems by providing the scientific community with a program that will allow for data importation from other video-tracking programs. Like in its previous versions, AnimalTA remains a free, open-source, and highly user-friendly program, ensuring that it will always be accessible without restriction. Now, with this new importation option, users who performed their tracking with other programs can benefit from AnimalTA's complete toolset of data visualization, correction, and analysis. 4. Finally, this article gives an overview of the other main improvements associated with this new release. The program is now faster in both video importation and tracking, proposes an amplified toolset for data visualisation and correction, and features new options for data analysis.

A major challenge in behavioral neuroscience is the lack of a unified software framework capable of implementing diverse paradigms across species and experimental setups. Researchers currently face a trade-off: they must either spend significant time developing custom, siloed solutions that hinder reproducibility, or incur substantial costs purchasing inflexible, closed systems. Here, we present Neurokraken, an open-source, Python-native platform designed to overcome these limitations. Neurokraken allows writing experiment progression entirely in standard python, while its core architecture automatically sets up a microcontroller for the connected hardware components and enables python side access with millisecond-precision timing and automatic logging. The system prioritizes ease of use and flexibility, enabling advanced series of events and conditions, the usage of python ecosystem code and packages within experiments, and the addition of any arduino-compatible electronic devices for custom experiments. As a result, users can easily create interactive virtual and real environments to engage, monitor, and record subjects. We present Neurokraken's versatility across a wide range of paradigms, for human and non-human primate psychophysics, and complex rodent behavior in both head-fixed and freely moving paradigms. Its modular design allows for rapid hardware reconfiguration, while a fully customizable user interface enables real-time monitoring and interactive experimental control without compromising timing precision. By uniting laboratory-grade precision with an accessible and flexible open-source philosophy, Neurokraken provides a single, powerful solution to design and execute next-generation behavioral experiments. We hope Neurokraken helps accelerate research, improve reproducibility throughout the neuroscience community, and make advanced behavioral experimentation more accessible through its substantial cost-efficiency.

Neurophysiology datasets are becoming increasingly complex, combining behavioral measurements with high-dimensional neuronal activity recordings coming from optical and/or electrophysiological measurements. The Neurodata Without Borders (NWB) standard has emerged in the community as the format of record. While standardized and widely used preprocessing tools generating NWB files have been developed, extensible frameworks for scientific analysis downstream of the NWB ecosystem are still under-represented. We present CICADA, a Python framework dedicated to analysis of neurophysiological data in the standardized NWB format. The toolbox is built as three hierarchically-organized packages: cicada-nwb (NWB access layer), cicada-analysis (plugin-based analysis engine and tool library), and cicada-gui (PyQt5 desktop application at the head of the pipeline). Beyond this architectural separation, CICADA is built around a central design principle: supporting a continuum from turnkey use to full modularity. Researchers can use the complete GUI-driven cicada-gui workflow without writing code, programmatically use existing analysis plugins from cicada-analysis, contribute to new analysis plugins, reuse utilities from cicada-tools, or build entirely custom pipelines on top of the cicada-nwb access layer alone. The same analysis plugin runs identically in interactive GUI and parameter-configured headless modes, enabling reproducible multi-session, multi-animal group analyses. We illustrate the versatility of CICADA with example analyses of behavioral, calcium imaging (two-photon and widefield) and extracellular electrophysiology datasets from rodent laboratories. CICADA is open source, actively maintained, and designed so that any laboratory can contribute at any level of the stack without modifying the core framework.

Chemical imaging is a powerful branch of modern microscopy encumbered by a lack of flexible, high-throughput analysis tools. Bespoke analytical pipelines typically perform ratiometric analysis on two layers in a multispectral image to describe the relative composition of molecules in a sample. This strategy has been implemented across fields, spanning histopathology, cell biology, environmental science, and materials science. The commercialization of chemical imaging microscopes has facilitated the collection of large multispectral datasets, necessitating accessible ways to process them. This paper describes Multispectral Analysis Graphical User Interface (msaGUI), a desktop graphical user interface to analyze individual and batch datasets of multispectral images. Data is loaded as CSV, TSV, or TIFFs and processed through a user-defined sequence of modular image operations that can be flexibly combined, e.g. to reduce spectral crosstalk or background noise. After analysis, data is visualized as exportable images, histograms, and statistics. To yield publication-quality figures, outputted images are fully customizable. Written in Python with open-source libraries, the msaGUI program is packaged into an executable for Windows and Mac for a fully no-code application. Other operating systems are supported via the Python source code. In summary, msaGUI provides a rapid and user-friendly solution for analyzing and visualizing multispectral data.

pylimma is a faithful Python port of limma, intended to bring one of the most widely used tools for differential expression analysis to the developing Python ecosystem for transcriptomics and proteomics. We validated pylimma against the existing R implementation through 227 function-level comparisons and across six real world datasets spanning microarray, RNAseq, proteomics and single-cell transcriptomics. pylimma reproduces limmas numerical output to a median agreement of 13 significant figures and calls identical sets of differentially expressed features and gene sets. This supports its use as a drop-in replacement for the R implementation.

Imaging Mass Cytometry (IMC) combines metal-tagged antibody labelling with laser ablation mass spectrometry to generate highly multiplexed spatial images of tissue sections. However, the area that can be acquired within a single region of interest (ROI) is limited by hardware and software constraints, requiring large tissues to be imaged as multiple tiled ROIs. Reconstructing these ROIs into whole-slide images requires additional processing, while the proprietary .mcd file format can hinder integration with standard bioimage analysis workflows. Here, we present MCD Stitcher, an open-source Python package for converting .mcd files into OME-TIFF images with automated whole-slide stitching. The tool supports rectangular and polygonal ROIs, accommodates variable pixel sizes between ROIs, and uses memory-aware chunked reading during data ingestion to process large datasets on standard workstations. The generated OME-TIFF outputs preserve spatial, channel, and acquisition metadata for downstream analysis in tools such as QuPath, napari, and ImageJ/Fiji. MCD Stitcher provides a reproducible workflow for converting raw IMC data into interoperable image formats, enabling whole-slide spatial analysis without reliance on vendor-specific software.

golgi is an open-source platform that takes a peripheral nerve from image to stimulated fiber population through a single graphical interface, with an equivalent scriptable Python API and command-line interface for batch and high-performance use. It integrates promptable image segmentation, automated multi-region tetrahedral meshing, anisotropic finite-element solution of the extracellular field with an explicit perineurium contact impedance, generation of realistic fiber populations and their three-dimensional trajectories, and biophysical activation thresholds through interchangeable backends-- NEURON (via PyFibers) and a GPU-accelerated surrogate (AxonML). Every study exports as an integrity-hashed bundle whose image-to-recruitment provenance is verifiable byte-for-byte. golgi lowers the barrier to in-silico peripheral nerve stimulation modeling for experimentalists and clinicians, using a fully open finite-element stack with no commercial dependencies.

Pathway and functional enrichment analysis is a cornerstone of omics data interpretation, enabling researchers to map differentially expressed proteins or genes onto curated biological processes, signaling cascades, and molecular functions. While tools such as Ingenuity Pathway Analysis (IPA), g:Profiler, and Enrichr are widely used to generate ranked enrichment results, translating these tabular outputs into clear, publication-ready figures remains a time-consuming step that typically requires custom scripting and familiarity with visualization libraries -- a significant barrier for researchers without a computational background. Here we present EnrichViz, a self-contained, browser-based R Shiny application that enables interactive, code-free visualization of pathway and functional enrichment results from quantitative proteomics, transcriptomics, and metabolomics experiments. EnrichViz accepts three standard CSV files as input -- a normalized abundance matrix, a sample annotation or metadata file, and enrichment results from any platform that exports tabular output -- and produces six complementary, publication-ready visualizations: bar and bubble plots for ranking enriched terms by significance, chord diagrams for exploring pathway-molecule connectivity, clustered heatmaps for displaying Z-score normalized expression patterns across experimental groups, and boxplots or violin plots for examining the abundance distribution of individual proteins, genes, or metabolites. The application supports both raw p-values and pre-transformed -log10(p) values through automatic detection, and all plot parameters are adjustable in real time through a graphical sidebar. Every figure can be exported as a high-resolution PNG file at 300 dpi. EnrichViz is implemented in R using the Shiny, ggplot2, pheatmap, and circlize packages, and is freely available at https://rgmilian.shinyapps.io/EnrichViz/.

O_LIStudying animal movement at the population scale requires a stable, modern software substrate. Within R, the legacy move package supplied that substrate for over a decade, but its sp/rgeos backbone has been retired. The successor package move2 deliberately confined its scope to the data class and core movebank API functions. C_LIO_LIThe analytical machinery of move, namely dynamic Brownian-bridge utilisation distributions, the directional bivariate-Gaussian variant, corridor segmentation, and along-track thinning, was left to port to the modern sf/terra stack. C_LIO_LIWe present move2utils, an R package that completes and complements that transition. move2utils provides move2-native ports of the move analytical functions, preserves the original C kernels where they exist, and replaces the deprecated spatial scaffolding around them. It additionally ports some of the legacy R-based code to faster C kernels to improve computational speed. move2utils also exposes novel outlier-detection methodology described in detail in a companion paper. C_LIO_LIThe package is open-source (GPL [≤] 3), is developed on the MPCDF GitLab and mirrored on GitHub for public installation, and ships with vignettes and a CI-tested check suite. We illustrate it with a worked example on real tracking data and synthetic datasets. C_LI

Images in the electron microscope are formed by electron scattering and focusing. The spherical geometry of these processes gives rise to two coherent, conjugate spherical wave fronts, known as Ewald spheres. These spheres are associated with the two halves of the contrast transfer function (CTF), and their widths are determined by the focal gradient through the specimen. To properly correct for the CTF, each half of the CTF must be applied to an image individually and integrated into the reconstruction into the corresponding Ewald sphere. Theory indicates that this dual Ewald sphere reconstruction method should recover the maximal amount of information possible. This method was compared to the other reconstruction methods commonly used: the projection approximation (ignoring the Ewald sphere), the simple insertion and the single sideband methods. In simulated reconstructions the dual Ewald sphere method recovered the most information when the correct half of the CTF is matched to the corresponding Ewald sphere. If the wrong half is matched, the result worse than the projection approximation method. Examining reconstructions from real data indicated that the dual Ewald sphere method performs at least as well as the simple insertion method, but not as good as in simulations. The likely reason is the two-fold ambiguity in the assigned orientations of the particle images, which remains an issue to pursue in further studies. In conclusion, the dual Ewald sphere reconstruction method may offer the best way to calculate very high resolution reconstructions when the micrograph quality warrants it. HighlightsO_LIThe dual Ewald sphere reconstruction corrects for the two halves of the CTF. C_LIO_LIThe signs of the two halves of the CTF must correspond to the focal gradient. C_LIO_LIDetermining the focal gradient for individual particle images remains unresolved. C_LIO_LIComplex reconstructions indicate any real space phases are artifacts. C_LI

Spectral bleed-through remains a persistent practical problem in multichannel fluorescence microscopy. Signal from one fluorophore can be recorded in the detection channel of another, thereby biasing intensity measurements, inflating apparent colocalization, and complicating the interpretation of dynamic microscopy data. Although many correction strategies exist, routine workflows often remain fragmented across ad hoc scripts, manually tuned graphical procedures, or method-specific blind-unmixing implementations with limited provenance. Here we present spectral-unmixing, an open-source Python package for reproducible linear spectral unmixing in multidimensional microscopy stacks. The package unifies directed two-channel correction with multiple alpha-estimation strategies, optional bidirectional two-channel correction through explicit inversion of a 2 x 2 mixing model, and PICASSO-family blind unmixing for multichannel data. Microscopy inputs are normalized at the API boundary to canonical TZCY X stacks, allowing the same unmixing code to be applied across file formats without manual axis handling. Machine-readable sidecar reports preserve the effective processing configuration and estimated coefficients for every output, so that workflows can be audited and reproduced. Synthetic and real-data-derived benchmarks show that the implemented workflows accurately estimate and correct bleed-through when their model assumptions are satisfied. In fixed-alpha two-channel simulations, the mean-ratio and linear-fit estimators recovered {approx} 0.283 for a ground-truth value of 0.28 and reduced target-channel normalized root mean squared error from approximately 0.029 to 0.003. In time-varying simulations, per-time-point estimation tracked coefficient drift substantially better than reference-time-point estimation. Bidirectional inversion recovered reciprocally mixed channels accurately when coefficients were known or well estimated. PICASSO-family benchmarks further showed a practical trade-off between reducing residual inter-channel dependence and preserving fluorophore identity, with MATLAB-style workflows behaving more conservatively and source-sink formulations providing stronger dependence suppression when meaningful directional priors are available. Together, these elements make spectral-unmixing a practical, transparent, and extensible platform for reproducible spectral unmixing of fluorescence microscopy data in neuroscience and other quantitative bioimage-analysis settings.

Microscopes are essential tools for discoveries on a scale invisible to the unaided human eye. The development of immuno-fluorescence followed by molecular biology techniques and fluorescent fusion proteins have revolutionised the use of optical microscopy in bioscience. The quality of the data produced is dependent upon the sample, its preparation and the instrument used. However, instruments can degrade over time without easily visible changes to the produced images and, in turn, negatively impacts results. By testing instruments and doing comparisons between results over time and between different instruments, problems can be highlighted and corrective action can be taken. Using small fluorescent beads the point spread function (PSF) of the microscope can be recorded and the image resolution measured. Beads were prepared in a concentration matched to the field of view size and dried onto coverslips and mounted on slides. The beads were then imaged as 3D Z-stacks of sufficient size to fully enclose the PSF of the system. This data was uploaded to OMERO and processed using OMERO-metrics, an OMERO plugin developed for this purpose. This paper summarizes the development of workflows and protocols to enable this process, presents the results obtained and demonstrates the detection of significant instrument issues.

Flower-visiting insect populations are declining since the 1990s, especially because of the decrease of floral resources in agricultural settings. Mass flowering crops can help increase resource availability, and plant breeding can be directed towards selecting varieties attracting more flower-visiting insects. This requires the implementation of an automated high-throughput phenotyping tool for assessing the attractiveness of plant genotypes to flower-visiting insects. In this study, (i) we present a procedure to take standardized images of sunflower heads with camera traps continuously at day and night in the field; (ii) we trained two versions of a deep learning model, named PolliCrop, to automatically detect and identify three classes of the main insects visiting sunflower on these images (non-Bombus bees, bumble bees, lepidopterans); (iii) we assessed and validated the ability of PolliCrop to correctly predict the true visitation frequencies of the insect classes on three sunflower genotypes; (iv) we presented two statistical approaches to compare the insect visitation frequencies between plant genotypes, one including weather variables, and the other one without. One PolliCrop version yielded satisfying performance to correctly detect the three insect classes. In particular, it correctly predicted the insect visitation frequencies on two sunflower genotypes in a range of {+/-}10%. The other PolliCrop version can be useful in certain contexts of images and objectives. PolliCrop can be extended in the future to other crop species by training PolliCrop on new images captured in these crops. The field experimental design to set up for comparing the attractiveness between genotypes is also discussed.

Multi-omics and spatial-omics technologies are exploding in use, producing increasingly complex datasets. Existing bioinformatics tools are developing rapidly but fail to fully enforce the FAIR principles, leaving the field vulnerable to escalating issues in computational reproducibility. Here, we introduce a reproducible-by-design paradigm represented in an omics data processing package, RomicsProcessor. At its core, the "Romics_object", which is a self-contained digital artifact that encapsulates the full history of the data from the original data to the fully processed state, capturing the details of the transformative steps and the required dependencies. This architecture ensures that computational workflows are fully portable and reproducible. In this manuscript, we demonstrate RomicProcessors computational capabilities and scalability on diverse datasets, including bulk proteomics, large-scale multiplexed immunofluorescence, and multi-batch mass spectrometry imaging. Providing a robust framework for truly FAIR Data Principles-based analysis, RomicsProcessor is a blueprint for the next generation of reproducible bioinformatics tools that can dramatically accelerate discovery in multi-omics biology in the era of artificial intelligence.

The preparation of multi panel figures remains a labor intensive step in scientific publication. Albeit there are specific tools available to solve this problem, they are often highly specialized, difficult to install, or time consuming to learn. Griphus is a standalone graphical application designed for rapid composition and experimentation with multi panel figures, developed by and for zoological taxonomists. Functions specifically designed for multi panel composition include automatic figure numbering and placement, aspect ratio operations, spacers, layout rotation, layout suggestions, and automatic generation of figure legends, including scale bar descriptions. The software can perform both spatial interpretation of images on the canvas and work with a simple, editable layout formula. It also enables instant multi panel composition, with numbered images and automatic contrast selection for the numbers, obtained simply by loading images. User defined parameters such as target printable dimensions, resolution, spacing, and color mode are preserved throughout the work. The program produces coordinated outputs consisting of the final composite figure, a readable file describing the layout structure, and a .gri file storing images, transformations, and parameters for exact regeneration. Griphus is intended as a complementary tool to professional image software, providing a simple and efficient environment for constructing high quality multi panel figures.

Apolipoprotein E (ApoE) is the strongest genetic predictor of Alzheimers disease (AD) risk, with ApoE4 increasing and ApoE2 decreasing risk relative to ApoE3. Using a global LC-MS proteomic approach, we integrated protein abundance and kinetics in Human-APOE knock-in mice for young (3-month) and aged (18-month) cohorts to quantify the changes in steady-state proteostasis. By mapping 6,052 identified proteins and 3,986 associated turnover rates into ontological groups, we observed that vesicle trafficking and mitochondrial dysregulation occur as early as 3 months in ApoE4 mice accompanied by hyperactive metabolism that eventually reduces with age. In contrast, young and old ApoE2 mice retain similar signatures to ApoE3 mice in metabolic, mitochondrial, cellular regulation, and membrane trafficking ontologies. We found that females had more isoform-induced ontological changes relative to ApoE3, providing insight into sex-dependent vulnerabilities. Our global proteomic approach for ApoE proteostasis crucially unifies independent literature observations while providing turnover kinetics to uncover the underlying mechanism behind abundance changes. Data are available via ProteomeXchange with identifier PXD079261. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=189 SRC="FIGDIR/small/735293v1_ufig2.gif" ALT="Figure 1000"> View larger version (45K): org.highwire.dtl.DTLVardef@54c14borg.highwire.dtl.DTLVardef@5e490dorg.highwire.dtl.DTLVardef@df5b1org.highwire.dtl.DTLVardef@7d7717_HPS_FORMAT_FIGEXP M_FIG C_FIG O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/735293v1_ufig1.gif" ALT="Figure 1001"> View larger version (48K): org.highwire.dtl.DTLVardef@115c4cdorg.highwire.dtl.DTLVardef@2baac8org.highwire.dtl.DTLVardef@d965e6org.highwire.dtl.DTLVardef@b0e49a_HPS_FORMAT_FIGEXP M_FIG C_FIG

Ribonucleic acid (RNA) modifications, with over 170 identified types, play diverse roles in cellular processes. The past decade has witnessed surging demand for accurate identification and localization of RNA modifications in both endogenous and synthetic therapeutic RNAs. With accurate spectral annotation for RNA, tandem mass spectrometry (MS/MS) can meet this demand. Here we present RNabel, a user-friendly software tool for in-depth annotation of MS/MS spectra of RNA oligonucleotides. RNabel considers a full set of backbone-cleavage ions (a, b, c, d, a-B, w, x, y, z) in which the ribonucleotide unit could be A, U, C, G, Y (pseudouridine), or I (Inosine). Additionally, RNabel considers 196 modifications on the base, the phosphoribose linkage, the 5' or the 3' terminus, or detachment of a sub-nucleotide fragment as a neutral or charged group. Users can create new components if needed, including ribonucleotides, modifications, neutral or charged groups that could detach from a ribonucleotide. RNabel efficiently processes large datasets in four acceptable formats including .mgf, .raw, .txt from msConvert, and RNabel batch files. Multiple statistical metrics are provided for quality assessment of spectral annotation. To accelerate RNA modification analysis, RNabel is made freely available for Mac and Windows users at https://github.com/songge1111/RNabel/releases. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/733900v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@8ccae5org.highwire.dtl.DTLVardef@15c8cfaorg.highwire.dtl.DTLVardef@12b93a2org.highwire.dtl.DTLVardef@1e9aab9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Mass spectrometry laboratories must turn lists of submitted samples into acquisition queues. The run order and the placement of quality-control (QC) injections determine whether a design controls batch effects and signal drift, and whether those effects stay correctable afterward. Yet operators usually set them by hand in vendor worklist editors that neither randomize run order nor offer configurable, pattern-driven QC. We present *qg*, an open-source tool that builds acquisition queues with systematic run-order handling: four run-order modes (none, simple, blocked/randomized-complete-block, and group-uniform blocked), pattern-driven QC and standard injections, and sampler- and plate-aware positioning. Unlike plate-design tools that stop at a generic sample sheet, *qg* writes the native vendor acquisition file directly, for three instrument ecosystems (Thermo Fisher XCalibur, Axel Semrau Chronos, Bruker HyStar) across proteomics, metabolomics, and lipidomics. It separates a small, stateless generation pipeline from a declarative configuration layer, so a laboratory adapts instruments, QC patterns, layouts, and naming by editing version-controlled configuration through a validating editor rather than changing code. *qg* runs from a reactive web interface or a scripted command-line interface, integrated with a LIMS (B-Fabric) or standalone from uploaded tables; randomized runs record their seed and reproduce from exported parameters. On an unbalanced design, group-uniform blocked randomization spreads biological groups evenly across acquisition time, whereas textbook block randomization leaves a tail of the largest group and can track acquisition time worse than a plain shuffle. *qg* is released under the Apache-2.0 license.

Sorted lists of elements are particularly good for computing set operations. A single scan of the two lists is sufficient to materialize or count the results of the union, intersection, difference, and xor operators. In bioinformatics, only a few tools are designed to perform these operations on k-mers. A fast tool like KMC allows set operations at the cost of storing individual k-mers. In this paper, we introduce a novel way to represent sorted k-mers as a collection of recomposed super-k-mer sorted lists. We introduce the concept of virtual super-k-mer and show how to construct, query and perform set operations on sorted lists of virtual super-k-mers. In the implementation sklib, we demonstrate high throughput of the data structure for construction and set operations, while remaining competitive in query capabilities, within a controlled memory footprint (2-5x decrease in bits/element compared to KMC).

Quantifying blood-brain barrier (BBB) integrity from fluorescence microscopy remains limited by subjective scoring and categorical classification methods that lack reproducibility. For objective and consistent BBB phenotyping, we present two semi-automated image-analysis pipelines that replace manual scoring with quantitative, continuous-variable measurements. Our in vitro pipeline, implemented in Python, quantifies the connectivity of tight junction structures by measuring discrete ZO-1 fragment objects within manually traced junction regions. It outputs continuous metrics including average fragment area, total junctional area, and a junctional fragmentation ratio that captures degree of ZO-1 continuity versus discontinuity. In human brain microvascular endothelial cells subjected to glycocalyx component knockdown, the pipeline detected significantly reduced fragment area (37% decrease for both CD44 and syndecan-1 (SDC1) knockdown, p = 0.0148 and 0.0084) and junctional fragmentation ratio (p = 0.0061 and 0.0137). Our in vivo pipeline integrates ilastik-based pixel classification with FIJI macro automation to quantify vascular marker colocalization and to separate vessel signal from microglial contamination within a single fluorescence channel, eliminating the need for dedicated counterstains. Applied across four mouse cohorts [young, aged, Alzheimer's, traumatic brain injury (TBI)] and three brain regions [prefrontal cortex (PFC), hippocampus, midbrain], the pipeline revealed concurrent ZO-1 loss and ICAM-1 elevation in the PFC and hippocampus of aged and Alzheimer's mice, with Alzheimer's-specific doubling of eNOS occurring in the PFC (p = 0.0013). TBI mice showed persistent ZO-1 loss with transient ICAM-1 and eNOS changes. Both deterministic pipelines are available on GitHub and designed for adoption beyond the specific markers and systems analyzed here.