Open-source Software Package Research Articles

Developing a nearly automated open-source pipeline for conducting computational fluid dynamics simulations in anterior brain vasculature: a feasibility study

Intracranial aneurysms (IA) pose significant health risks and are often challenging to manage. Computational fluid dynamics (CFD) simulation has emerged as a powerful tool for understanding lesion-specific hemodynamics in and around IAs, aiding in the clinical management of patients with an IA. However, the current workflow of CFD simulations is time-consuming, complex, and labor-intensive and, thus, does not fit the clinical environment. To address these challenges, we have developed a semi-automated pipeline integrating multiple open-source software packages to streamline the CFD simulation process. Specifically, the study utilized medical angiography data from 18 patients. An in-house open-source DL image segmentation model (ARU-Net) was employed to generate 3D computer models of the anterior circulation. The segmented intracranial vasculature models, including IAs, were further refined using the Vascular Modeling Toolkit (VMTK), an open-source Python package. This step involved smoothing the surface of the models and extending the inlet and outlet regions to ensure a realistic representation of the vascular geometry. The refined vascular models were then converted into computational meshes using an open-source mesh generator known as TetGen. This process was nearly automated and required minimal user interaction(s). Blood flow simulations of the cerebral vascular models were performed using established SimVascular solvers (an open-source finite element platform for vascular applications) through an application programming interface (API). The CFD simulation process was also conducted using the manual workflow for comparative purposes. The initial assessment compared the geometries derived from manual and DL-based segmentation. The DL-based segmentation demonstrated reliable performance, closely aligning with manually segmented results, evidenced by excellent Pearson correlation coefficient (PCC) values and low relative difference (RD) values ranging from 3% to 10% between the computed geometrical variables derived from both methods. The statistical analysis of the computed hemodynamic variables, including velocity informatics and WSS-related variables, indicated good to excellent reliability for most parameters (e.g., ICC of 0.85–0.95). Given the data investigated, the proposed automated workflow streamlines the process of conducting CFD simulations. It generates results consistent with the current standard manual CFD protocol while minimizing dependence on user input.

Killing Two Birds with One Stone: Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence

Open-source software (OSS) supply chain enlarges the attack surface of a software system, which makes package registries attractive targets for attacks. Recently, multiple package registries have received intensified attacks with malicious packages. Of those package registries, NPM and PyPI are two of the most severe victims. Existing malicious package detectors are developed with features from a list of packages of the same ecosystem and deployed within the same ecosystem exclusively, which is infeasible to utilize the knowledge of a new malicious NPM package detected recently to detect the new malicious package in PyPI. Moreover, existing detectors lack support to model malicious behavior of OSS packages in a sequential way To address the two limitations, we propose a single detection model using malicious behavior sequence, named Cerebro , to detect malicious packages in NPM and PyPI. We curate a feature set based on a high-level abstraction of malicious behavior to enable multi-lingual knowledge fusing. We organize extracted features into a behavior sequence to model sequential malicious behavior. We fine-tune the pre-trained language model to understand the semantics of malicious behavior. Extensive evaluation has demonstrated the effectiveness of Cerebro over the state-of-the-art as well as the practically acceptable efficiency. Cerebro has detected 683 and 799 new malicious packages in PyPI and NPM, and received 707 thank letters from the official PyPI and NPM teams.

Evaluating snow depth retrievals from Sentinel-1 volume scattering over NASA SnowEx sites

Abstract. Snow depth retrievals from spaceborne C-band synthetic aperture radar (SAR) backscatter have the potential to fill an important gap in the remote monitoring of seasonal snow. Sentinel-1 (S1) SAR data have been used previously in an empirical algorithm to generate snow depth products with near-global coverage, subweekly temporal resolution and spatial resolutions on the order of hundreds of meters to 1 km. However, there has been no published independent validation of this algorithm. In this work we develop the first open-source software package that implements this Sentinel-1 snow depth retrieval algorithm as described in the original papers and evaluate the snow depth retrievals against nine high-resolution lidar snow depth acquisitions collected during the winters of 2019–2020 and 2020–2021 at six study sites across the western United States as part of the NASA SnowEx mission. Across all sites, we find agreement between the Sentinel-1 snow depth retrievals and the lidar snow depth measurements to be considerably lower than requirements placed for remotely sensed observations of snow depth, with a mean root mean square error (RMSE) of 0.92 m and a mean Pearson correlation coefficient r of 0.46. Algorithm performance improves slightly in deeper snowpacks and at higher elevations. We further investigate the underlying Sentinel-1 data for a snow signal through an exploratory analysis of the cross- to co-backscatter ratio (σVH/σVV; i.e., cross ratio) relative to lidar snow depths. We find the cross ratio increases through the time series for snow depths over ∼ 1.5 m but that the cross ratio decreases for snow depths less than ∼ 1.5 m. We attribute poor algorithm performance to (a) the variable amount of apparent snow depth signal in the S1 cross ratio and (b) an algorithm structure that does not adequately convert S1 backscatter signal to snow depth. Our findings provide an open-source framework for future investigations, along with insight into the applicability of C-band SAR for snow depth retrievals and directions for future C-band snow depth retrieval algorithm development. C-band SAR has the potential to address gaps in radar monitoring of deep snowpacks; however, more research into retrieval algorithms is necessary to better understand the physical mechanisms and uncertainties of C-band volume-scattering-based retrievals.

Gene count estimation with pytximport enables reproducible analysis of bulk RNA sequencing data in Python.

Transcript quantification tools efficiently map bulk RNA sequencing reads to reference transcriptomes. However, their output consists of transcript count estimates that are subject to multiple biases and cannot be readily used with existing differential gene expression analysis tools in Python.Here we present pytximport, a Python implementation of the tximport R package that supports a variety of input formats, different modes of bias correction, inferential replicates, gene-level summarization of transcript counts, transcript-level exports, transcript-to-gene mapping generation and optional filtering of transcripts by biotype. pytximport is part of the scverse ecosystem of open-source Python software packages for omics analyses and includes both a Python as well as a command-line interface.With pytximport, we propose a bulk RNA sequencing analysis workflow based on Bioconda and scverse ecosystem packages, ensuring reproducible analyses through Snakemake rules. We apply this pipeline to a publicly available RNA-sequencing dataset, demonstrating how pytximport enables the creation of Python-centric workflows capable of providing insights into transcriptomic alterations. pytximport is licensed under the GNU General Public License version 3. The source code is available at https://github.com/complextissue/pytximport and via Zenodo with DOI: 10.5281/zenodo.13907917. A related Snakemake workflow is available through GitHub at https://github.com/complextissue/snakemake-bulk-rna-seq-workflow and Zenodo with DOI: 10.5281/zenodo.12713811. Documentation and a vignette for new users are available at: https://pytximport.readthedocs.io. Supplementary Material is available at Bioinformatics online.

Investigation on the wave focusing effects and energy capture of Oscillating Water Column array

The study of OWC array hydrodynamic performance has been a focus of attention. However, due to the complex flow and strong nonlinearity generated during its interaction with waves, the main challenge and key focus in current research is how to simulate this process both quickly and accurately. A fast-numerical model of the OWC is developed using the open-source software package OpenFOAM. The numerical model replaces the orifice plate with Forchheimer-flow and the vertical walls with an immersed boundary. Compared to the traditional numerical model, the fast-numerical model can speed up the simulation by at least 10 times. The impact of row spacing and column spacing on the array gain is systematically investigated through the simulations of two-OWCs array, four-OWCs lattice array, and five-OWCs interlaced array. The results show that a two-OWCs array can generate a wave focusing area at its rear, and the location of this area changes with row spacings. For the four-OWCs lattice array, the row spacing increasing causes the wave focusing area to move closer to the array. The proximity of the wave focusing area enhances the power output of the second row of OWCs. After increasing the row and column spacing, the reflection of waves from the second row of OWCs to the first row is decreased, resulting in a reduction in the power output of the first row of OWCs. In contrast to the four-OWC lattice array, the five-OWCs interlaced array demonstrates a lower array gain in the first row of OWCs, with the second row displaying a higher array gain.

Ssc-cdi: A Memory-Efficient, Multi-GPU Package for Ptychography with Extreme Data.

We introduce <tt>ssc-cdi</tt>, an open-source software package from the Sirius Scientific Computing family, designed for memory-efficient, single-node multi-GPU ptychography reconstruction. <tt>ssc-cdi</tt> offers a range of reconstruction engines in Python version 3.9.2 and C++/CUDA. It aims at developing local expertise and customized solutions to meet the specific needs of beamlines and user community of the Brazilian Synchrotron Light Laboratory (LNLS). We demonstrate ptychographic reconstruction of beamline data and present benchmarks for the package. Results show that <tt>ssc-cdi</tt> effectively handles extreme datasets typical of modern X-ray facilities without significantly compromising performance, offering a complementary approach to well-established packages of the community and serving as a robust tool for high-resolution imaging applications.

Modular and extendable 1D-simulation for microfluidic devices

Microfluidic devices have been the subject of considerable attention in recent years. The development of novel microfluidic devices, their evaluation, and their validation requires simulations. While common methods based on Computational Fluid Dynamics (CFD) can be time-consuming, 1D simulation provides an appealing alternative that leads to efficient results with reasonable quality. Current 1D simulation tools cover specific microfluidic applications; however, these tools are still rare and not widely adopted. There is a need for a more versatile and adaptable tool that covers novel applications, like mixing and the addition of membranes, and allows easy extension, resulting in one comprehensive 1D simulation tool for microfluidic devices. In this work, we present an open-source, modular, and extendable 1D simulation approach for microfluidic devices, which is available as an open-source software package at https://github.com/cda-tum/mmft-modular-1D-simulator. To this end, we propose an implementation that consists of a base module (providing the core functionality) that can be extended with dedicated application-specific modules (providing dedicated support for common microfluidic applications such as mixing, droplets, membranes, etc.). Case studies show that this indeed allows to efficiently simulate a broad spectrum of microfluidic applications in a quality that matches previous results or even fabricated devices.

An open-source framework for the generation of OpenSim models with personalised knee joint geometries for the estimation of articular contact mechanics

Musculoskeletal modelling pipelines typically use generic models scaled to individual’s anthropometry. The ability to represent variations in bone or joint geometry and alignment is highly limited. This may have a large effect, particularly when modelling contact between articular surfaces such as for the knee where articular contact mechanics are used to determine joint kinematics and the resulting cartilage contact pressures and locations. Here we describe a developed open-source framework for the personalisation of such models and compare dynamic simulation outputs. The framework involves three main steps: (1) positions personalised geometries from magnetic resonance imaging and replaces generic bone and contact geometries. (2) Repositions muscle and ligament attachments and via points and optimisation of wrapping surfaces to ensure physiological lengthening behaviour. Finally, (3) muscle and ligament properties are calibrated to ensure physiological behaviour. Following model creation, dynamic simulations from a single participant and gait trial were compared. Small changes in knee adduction/abduction and rotation angles were observed between models. Joint moment differences however were present in not only the knee but also hip and ankle joints. These differences resulted in changes in both the magnitude and location of knee joint contact pressure. The framework developed is automated and requires only minimal user interaction and is built using open-source software packages which can be freely downloaded and installed. The adoption of such personalised modelling approaches facilitates patient specific modelling and may provide more detailed information regarding disease progression, patient stratification and facilitate personalised rehabilitation and treatment planning.

A multi-objective approach for timber harvest scheduling to include management of at-risk species and spatial configuration objectives.

Sustainable forestry typically involves integration of several economic and ecological objectives which, at times, may not be compatible with one another. Multi-objective prioritization via harvest scheduling programs can be used to elucidate these relationships and explore solutions. One such program is a spatially explicit harvest scheduler that adopts the Metropolis-Hastings algorithm to iteratively find management solutions to achieve multiple objectives (Habplan). Although this program has been used to address forest management scheduling and simulation-based tasks, its utility is constrained by time-intensive data preparation and challenges with incorporating spatial configuration objectives. To address these shortcomings, we introduce an open-source software package, HabplanR, streamlines data preparation, sets parameters, visualizes results, and assesses spatial components of ecological objectives. We developed four example objectives to incorporate into a multi-objective management problem: habitat quality indices for three species "types" (open, closed, and intermediate-canopy-associated species), and harvested pine pulpwood (revenue). We demonstrate the utility of this package to find management schedules that can accommodate potentially conflicting habitat needs of species, while achieving economic targets. We produced 100 software runs and prioritized individual objectives to select four management schedules for further comparisons. We compared outcome differences of the four schedules, including a spatial comparison of two high performing schedules. The software package makes costs and benefits of different schedules explicit and allows for consideration of the spatial configuration of management outcomes in decision-making.

Clinical Phenotypes Associated With Cerebral Small Vessel Disease: A Study of 45,013 UK Biobank Participants.

Cerebral small vessel disease (cSVD) is the most common pathology underlying vascular cognitive impairment. Although other clinical features of cSVD are increasingly recognized, it is likely that certain symptoms are being overlooked. A comprehensive description of cSVD associations with clinical phenotypes at scale is lacking. The objective of this study was to conduct a large-scale, hypothesis-free study of associations between cSVD and clinical phenotypes in UK Biobank (UKB). We included participants from the UKB imaging study who had available information on total volume of white matter hyperintensities (WMHs), the most common cSVD neuroimaging feature. We included various UKB variables describing clinical phenotypes, defined as observable signs and symptoms of individuals with concurrent neuroimaging evidence of cSVD. We conducted a phenome scan using the open-source PHESANT software package. Total volume of WMHs was introduced as the independent variable and clinical phenotypes as the dependent variables in the regression model. The association of each phenotype with total volume of WMHs was tested using one of several regression analyses (all age at recruitment and sex-adjusted). All associations were corrected for multiple comparisons using the false discovery rate (FDR) correction method. We included 45,013 participants in the analysis (mean age = 54.97 years, SD = 7.55). We confirm previously reported associations with depression (odds ratio [OR] = 1.07 [95% CI 1.05-1.10]), apathy (OR = 1.11 [95% CI 1.08-1.14]), falls (OR = 1.11 [95% CI 1.09-1.13]), respiratory problems (OR = 1.14 [95% CI 1.04-1.25]), and sleep disturbance (OR = 1.07 [95% CI 1.04-1.09], all FDR-adjusted p < 0.001). We further identified associations with all-cause dental issues (OR = 0.94 [95% CI 0.96-0.92]), hearing problems (OR = 1.06 [95% CI 1.03-1.08]), and eye problems (OR = 0.93 [95% CI 0.91-0.95], all FDR-adjusted p < 0.001). Our findings suggest that presence of cSVD associates with concurrent clinical phenotypes across several body systems. We have corroborated established associations of cSVD and present novel ones. While our results do not provide causality or direction of association because of the cross-sectional nature of our study, they support the need for a more holistic view of cSVD in research, practice, and policy.

QArray: A GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Semiconductor quantum dot arrays are a leading architecture for the development of quantum technologies. Over the years, the constant capacitance model has served as a fundamental framework for simulating, understanding, and navigating the charge stability diagrams of small quantum dot arrays. However, while the size of the arrays keeps growing, solving the constant capacitance model becomes computationally prohibitive. This paper presents an open-source software package able to compute a 100 × 100100×100 pixels charge stability diagram of a 16-dot array in less than a second. Smaller arrays can be simulated in milliseconds - faster than they could be measured experimentally, enabling the creation of diverse datasets for training machine learning models and the creation of digital twins that can interface with quantum dot devices in real-time. Our software package implements its core functionalities in the systems programming language Rust and the high-performance numerical computing library JAX. The Rust implementation benefits from advanced optimisations and parallelisation, enabling the users to take full advantage of multi-core processors. The JAX implementation allows for GPU acceleration.

ScACT: Accurate Cross-modality Translation via Cycle-consistent Training from Unpaired Single-cell Data.

Single-cell sequencing technologies have revolutionized genomics by enabling the simultaneous profiling of various molecular modalities within individual cells. Their integration, especially cross-modality translation, offers deep insights into cellular regulatory mechanisms. Many methods have been developed for cross-modality translation, but their reliance on scarce high-quality co-assay data limits their applicability. Addressing this, we introduce scACT, a deep generative model designed to extract cross-modality biological insights from unpaired single-cell data. scACT tackles three major challenges: aligning unpaired multi-modal data via adversarial training, facilitating cross-modality translation without prior knowledge via cycle-consistent training, and enabling interpretable regulatory interconnections explorations via in-silico perturbations. To test its performance, we applied scACT on diverse single-cell datasets and found it outperformed existing methods in all three tasks. Finally, we have developed scACT as an individual open-source software package to advance single-cell omics data processing and analysis within the research community.

Codebase release 1.3 for QArray

Semiconductor quantum dot arrays are a leading architecture for the development of quantum technologies. Over the years, the constant capacitance model has served as a fundamental framework for simulating, understanding, and navigating the charge stability diagrams of small quantum dot arrays. However, while the size of the arrays keeps growing, solving the constant capacitance model becomes computationally prohibitive. This paper presents an open-source software package able to compute a 100 × 100100×100 pixels charge stability diagram of a 16-dot array in less than a second. Smaller arrays can be simulated in milliseconds - faster than they could be measured experimentally, enabling the creation of diverse datasets for training machine learning models and the creation of digital twins that can interface with quantum dot devices in real-time. Our software package implements its core functionalities in the systems programming language Rust and the high-performance numerical computing library JAX. The Rust implementation benefits from advanced optimisations and parallelisation, enabling the users to take full advantage of multi-core processors. The JAX implementation allows for GPU acceleration.

VAN-DAMME: GPU-accelerated and symmetry-assisted quantum optimal control of multi-qubit systems

We present an open-source software package, VAN-DAMME (Versatile Approaches to Numerically Design, Accelerate, and Manipulate Magnetic Excitations), for massively-parallelized quantum optimal control (QOC) calculations of multi-qubit systems. To enable large QOC calculations, the VAN-DAMME software package utilizes symmetry-based techniques with custom GPU-enhanced algorithms. This combined approach allows for the simultaneous computation of hundreds of matrix exponential propagators that efficiently leverage the intra-GPU parallelism found in high-performance GPUs. In addition, to maximize the computational efficiency of the VAN-DAMME code, we carried out several extensive tests on data layout, computational complexity, memory requirements, and performance. These extensive analyses allowed us to develop computationally efficient approaches for evaluating complex-valued matrix exponential propagators based on Padé approximants. To assess the computational performance of our GPU-accelerated VAN-DAMME code, we carried out QOC calculations of systems containing 10 - 15 qubits, which showed that our GPU implementation is 18.4× faster than the corresponding CPU implementation. Our GPU-accelerated enhancements allow efficient calculations of multi-qubit systems, which can be used for the efficient implementation of QOC applications across multiple domains. Program summaryProgram Title: VAN-DAMMECPC Library link to program files::https://doi.org/10.17632/zcgw2n5bjf.1Licensing provisions: GNU General Public License 3Programming language: C++ and CUDANature of problem: The VAN-DAMME software package utilizes GPU-accelerated routines and new algorithmic improvements to compute optimized time-dependent magnetic fields that can drive a system from a known initial qubit configuration to a specified target state with a large (≈1) transition probability.Solution method: Quantum control, GPU acceleration, analytic gradients, matrix exponential, and gradient ascent optimization.

Time-Dependent Failure Assessment of Ceramic Receivers

The outlet temperature targets for Gen 3 Concentrating Solar Power (CSP) systems pose a significant challenge to the structural reliability of high temperature metallic components, including those manufactured from nickel-based superalloys. Advanced ceramics present a potential solution due to their excellent high-temperature strength. However, accurate assessment of ceramic components requires an entirely different approach compared to metallic components. This paper describes the implementation of time-dependent reliability analysis of ceramic components in srlife – an open-source software package for estimating the life of high temperature CSP components. This new capability will allow high temperature CSP designers to make fair comparisons between competing metallic and ceramic designs and accurately assess the performance of different ceramic materials for CSP receivers and other components. The current version of the tool is available at https://github.com/Argonne-National-Laboratory/srlife.

NetSci: A Library for High Performance Biomolecular Simulation Network Analysis Computation.

We present the NetSci program-an open-source scientific software package designed for estimating mutual information (MI) between data sets using GPU acceleration and a k-nearest-neighbor algorithm. This approach significantly enhances calculation speed, achieving improvements of several orders of magnitude over traditional CPU-based methods, with data set size limits dictated only by available hardware. To validate NetSci, we accurately compute MI for an analytically verifiable two-dimensional Gaussian distribution and replicate the generalized correlation (GC) analysis previously conducted on the B1 domain of protein G. We also apply NetSci to molecular dynamics simulations of the Sarcoendoplasmic Reticulum Calcium-ATPase (SERCA) pump, exploring the allosteric mechanisms and pathways influenced by ATP and 2'-deoxy-ATP (dATP) binding. Our analysis reveals distinct allosteric effects induced by ATP compared to dATP, with predicted information pathways from the bound nucleotide to the calcium-binding domain differing based on the nucleotide involved. NetSci proves to be a valuable tool for estimating MI and GC in various data sets and is particularly effective for analyzing intraprotein communication and information transfer.

NCPAProp: an open-source, modular infrasound propagation modeling package

NCPAProp is a command-line-driven, fully open-source software package first released in 2016 and under continuous development since then. It aims to provide a comprehensive set of tested and validated full-wave numerical models to simulate long-range propagation of infrasound through realistic atmospheres, at frequencies nominally in the range of 0.1-10 Hz. The package currently includes two normal-mode models of varying complexity, and a parabolic equation model with the option to include a range-dependent atmosphere and orography. This presentation will focus on a description of the models, a discussion of their use, and a summary of the short-term and long-term ongoing development goals for the overall package.

OQuPy: A Python package to efficiently simulate non-Markovian open quantum systems with process tensors.

Non-Markovian dynamics arising from the strong coupling of a system to a structured environment is essential in many applications of quantum mechanics and emerging technologies. Deriving an accurate description of general quantum dynamics including memory effects is, however, a demanding task, prohibitive to standard analytical or direct numerical approaches. We present a major release of our open source software package, OQuPy (Open Quantum System in Python), which provides several recently developed numerical methods that address this challenging task. It utilizes the process tensor approach to open quantum systems (OQS) in which a single map, the process tensor, captures all possible effects of an environment on the system. The representation of the process tensor in a tensor network form allows for an exact yet highly efficient description of non-Markovian OQS (NM-OQS). The OQuPy package provides methods to (1) compute the dynamics and multi-time correlations of quantum systems coupled to single and multiple environments, (2) optimize control protocols for NM-OQS, (3) simulate interacting chains of NM-OQS, and (4) compute the mean-field dynamics of an ensemble of NM-OQS coupled to a common central system. Our aim is to provide an easily accessible and extensible tool for researchers of OQS in fields such as quantum chemistry, quantum sensing, and quantum information.

NPCoronaPredict: A Computational Pipeline for the Prediction of the Nanoparticle-Biomolecule Corona.

The corona of a nanoparticle immersed in a biological fluid is of key importance to its eventual fate and bioactivity in the environment or inside live tissues. It is critical to have insight into both the underlying bionano interactions and the corona composition to ensure biocompatibility of novel engineered nanomaterials. A prediction of these properties in silico requires the successful spanning of multiple orders of magnitude of both time and physical dimensions to produce results in a reasonable amount of time, necessitating the development of a multiscale modeling approach. Here, we present the NPCoronaPredict open-source software package: a suite of software tools to enable this prediction for complex multicomponent nanomaterials in essentially arbitrary biological fluids, or more generally any medium containing organic molecules. The package integrates several recent physics-based computational models and a library of both physics-based and data-driven parametrizations for nanomaterials and organic molecules. We describe the underlying theoretical background and the package functionality from the design of multicomponent NPs through to the evaluation of the corona.

Improved tracking of corneal immune cell dynamics using in vivo confocal microscopy.

In vivo confocal microscopy (IVCM) is a widely used technique for imaging the cornea of the eye with a confocal scanning light ophthalmoscope. Cellular resolution and high contrast are achieved without invasive procedures, suiting the study of living humans. However, acquiring useful image data can be challenging due to the incessant motion of the eye, such that images are typically limited by noise and a restricted field of view. These factors affect the degree to which the same cells can be identified and tracked over time. To redress these shortcomings, here we present a data acquisition protocol together with the details of a free, open-source software package written in Matlab. The software package automatically registers and processes IVCM videos to significantly improve contrast, resolution, and field of view. The software also registers scans acquired at progressive time intervals from the same tissue region, producing a time-lapsed video to facilitate visualization and quantification of individual cell dynamics (e.g., motility and dendrite probing). With minimal user intervention, to date, this protocol has been employed to both cross-sectionally and longitudinally assess the dynamics of immune cells in the human corneal epithelium and stroma, using a technique termed functional in vivo confocal microscopy (Fun-IVCM) in 68 eyes from 68 participants. Using the custom software, registration of 'sequence scan' data was successful in 97% of videos acquired from the corneal epithelium and 93% for the corneal stroma. Creation of time-lapsed videos, in which the averages from single videos were registered across time points, was successful in 93% of image series for the epithelium and 75% of image series for the stroma. The reduced success rate for the stroma occurred due to practical difficulties in finding the same tissue between time points, rather than due to errors in image registration. We also present preliminary results showing that the protocol is well suited to in vivo cellular imaging in the retina with adaptive optics scanning laser ophthalmoscopy (AOSLO). Overall, the approach described here substantially improves the efficiency and consistency of time-lapsed video creation to enable non-invasive study of cell dynamics across diverse tissues in the living eye.