Open-source Algorithm Research Articles

3D Amplified Single-Cell RNA and Protein Imaging Identifies Oncogenic Transcript Subtypes in B-Cell Acute Lymphoblastic Leukemia.

Simultaneous in situ detection of transcript and protein markers at the single-cell level is essential for gaining a better understanding of tumor heterogeneity and for predicting and monitoring treatment responses. However, the limited accessibility to advanced 3D imaging techniques has hindered their rapid implementation. Here, we present a 3D single-cell imaging technique, termed 3D digital rolling circle amplification (4DRCA), capable of the multiplexed and amplified simultaneous digital quantification of single-cell RNAs and proteins using standard fluorescence microscopy and off-the-shelf reagents. We generated spectrally distinguishable DNA amplicons from molecular markers through an integrative protocol combining single-cell RNA and protein assays and directly enumerated the amplicons by leveraging an open-source algorithm for 3D deconvolution with a custom-built automatic gating algorithm. With 4DRCA, we were able to simultaneously quantify surface protein markers and cytokine transcripts in T-lymphocytes. We also show that 4DRCA can distinguish BCR-ABL1 fusion transcript positive B-cell acute lymphoblastic leukemia cells with or without CD19 protein expression. The accessibility and extensibility of 4DRCA render it broadly applicable to other cell-based diagnostic workflows, enabling sensitive and accurate single-cell RNA and protein profiling.

Machine learning classification of autism spectrum disorder based on reciprocity in naturalistic social interactions

Autism spectrum disorder is characterized by impaired social communication and interaction. As a neurodevelopmental disorder typically diagnosed during childhood, diagnosis in adulthood is preceded by a resource-heavy clinical assessment period. The ongoing developments in digital phenotyping give rise to novel opportunities within the screening and diagnostic process. Our aim was to quantify multiple non-verbal social interaction characteristics in autism and build diagnostic classification models independent of clinical ratings. We analyzed videos of naturalistic social interactions in a sample including 28 autistic and 60 non-autistic adults paired in dyads and engaging in two conversational tasks. We used existing open-source computer vision algorithms for objective annotation to extract information based on the synchrony of movement and facial expression. These were subsequently used as features in a support vector machine learning model to predict whether an individual was part of an autistic or non-autistic interaction dyad. The two prediction models based on reciprocal adaptation in facial movements, as well as individual amounts of head and body motion and facial expressiveness showed the highest precision (balanced accuracies: 79.5% and 68.8%, respectively), followed by models based on reciprocal coordination of head (balanced accuracy: 62.1%) and body (balanced accuracy: 56.7%) motion, as well as intrapersonal coordination processes (balanced accuracy: 44.2%). Combinations of these models did not increase overall predictive performance. Our work highlights the distinctive nature of non-verbal behavior in autism and its utility for digital phenotyping-based classification. Future research needs to both explore the performance of different prediction algorithms to reveal underlying mechanisms and interactions, as well as investigate the prospective generalizability and robustness of these algorithms in routine clinical care.

Maximizing the Volume of Collocated Data from Two Coordinated Suborbital Platforms

Abstract Suborbital (e.g., airborne) campaigns that carry advanced remote sensing and in situ payloads provide detailed observations of atmospheric processes, but can be challenging to use when it is necessary to geographically collocate data from multiple platforms that make repeated observations of a given geographic location at different altitudes. This study reports on a data collocation algorithm that maximizes the volume of collocated data from two coordinated suborbital platforms and demonstrates its value using data from the NASA Aerosol Cloud Meteorology Interactions Over the western Atlantic Experiment (ACTIVATE) suborbital mission. A robust data collocation algorithm is critical for the success of the ACTIVATE mission goal to develop new and improved remote sensing algorithms, and quantify their performance. We demonstrate the value of these collocated data to quantify the performance of a recently developed vertically resolved lidar + polarimeter–derived aerosol particle number concentration (Na) product, resulting in a range-normalized mean absolute deviation (NMAD) of 9% compared to in situ measurements. We also show that this collocation algorithm increases the volume of collocated ACTIVATE data by 21% compared to using only nearest-neighbor finding algorithms alone. Additional to the benefits demonstrated within this study, the data files and routines produced by this algorithm have solved both the critical collocation and the collocation application steps for researchers who require collocated data for their own studies. This freely available and open-source collocation algorithm can be applied to future suborbital campaigns that, like ACTIVATE, use multiple platforms to conduct coordinated observations, e.g., a remote sensing aircraft together with in situ data collected from suborbital platforms. Significance Statement This study describes a data collocation (i.e., selection) process that aims to maximize the volume of data identified to be simultaneously collected in time and space from two coordinated measurement platforms. The functional utility of the resultant dataset is also demonstrated by extending the validation of aerosol particle number concentration derived from standard lidar and polarimeter data products from a suborbital mission that used two aircraft platforms.

Open-source algorithm for high-throughput, non-contact elasticity measurements of IPSC-derived cardiomyocytes

Open-source algorithm for high-throughput, non-contact elasticity measurements of IPSC-derived cardiomyocytes

The influence of cardiac arrhythmias on the detection of heartbeats in the photoplethysmogram: benchmarking open-source algorithms.

Objective.Cardiac arrhythmias are a leading cause of mortality worldwide. Wearable devices based on photoplethysmography give the opportunity to screen large populations, hence allowing for an earlier detection of pathological rhythms that might reduce the risks of complications and medical costs. While most of beat detection algorithms have been evaluated on normal sinus rhythm or atrial fibrillation recordings, the performance of these algorithms in patients with other cardiac arrhythmias, such as ventricular tachycardia or bigeminy, remain unknown to date.Approach.ThePPG-beatsopen-source framework, developed by Charlton and colleagues, evaluates the performance of the beat detectors namedQPPG,MSPTDandABDamong others. We applied thePPG-beatsframework on two newly acquired datasets, one containing seven different types of cardiac arrhythmia in hospital settings, and another dataset including two cardiac arrhythmias in ambulatory settings.Main Results.In a clinical setting, theQPPGbeat detector performed best on atrial fibrillation (with a medianF1score of 94.4%), atrial flutter (95.2%), atrial tachycardia (87.0%), sinus rhythm (97.7%), ventricular tachycardia (83.9%) and was ranked 2nd for bigeminy (75.7%) behindABDdetector (76.1%). In an ambulatory setting, theMSPTDbeat detector performed best on normal sinus rhythm (94.6%), and theQPPGdetector on atrial fibrillation (91.6%) and bigeminy (80.0%).Significance.Overall, the PPG beat detectorsQPPG,MSPTDandABDconsistently achieved higher performances than other detectors. However, the detection of beats from wrist-PPG signals is compromised in presence of bigeminy or ventricular tachycardia.

Corn, soybeans and winter wheat water requirements over the contiguous United States between 2013 and 2021: The application of the SEBALIGEE v2 global model

The open-source fully-automated Surface Energy Balance Algorithm for Land-Improved (SEBALI) Google Earth Engine (SEBALIGEE) estimates 30-m actual evapotranspiration (ET) at a monthly rate, a much needed parameter in many hydrological and agricultural applications. An improved version of the basin-based SEBALIGEE v1 is proposed in this paper. The improvement of SEBALIGEE v1, designated as v2, focuses primarily on incorporating advanced machine learning approaches which have enabled for the first time to implement SEBALIGEE over any scale application and enhance its overall performance. More particularly, an evaluation of the monthly ET estimated from the new algorithm across several fluxnet sites in the US, China, Italy, Belgium, Germany, and France, yielded a Mean Absolute Error (MAE) of 12.22 mm/month versus 14.54 mm/month in the original SEBALIGEE v1. Furthermore, we applied the new any-scale capability of SEBALIGEE v2 over the contiguous United States (CONUS) while emphasizing on the three main crops, including corn, soybeans and winter wheat. Our analysis indicated that all three crops presented two similar ET seasonal cycles with peaks occurring in late spring to the summer (May-Aug) and between October and January, corresponding well to the key stages of crop life cycle. Moreover, corn and soybeans exhibited similar magnitudes of ET (36 ∼ 168 mm/month) and higher than winter wheat (33 ∼ 122 mm/month), with large standard deviations observed in the ET estimates amogst all the crops. On interannual comparisons, the corn and soybeans ET and aKc showed higher values than winter wheat, with the highest and lowest years identified and discussed. Following an exploratory analysis against some of the most common interfering variables such as air temperature, dewpoint temperature, surface net solar radiation, wind speed, SPEI drought index calculated on 14 days and 30-days, it was noted that the surface net solar radiation had the most influencing factor on ET in corn and soybeans plantations with R2 values of ∼0.72. The SPEI-30 stands out for winter wheat, showing a water scarcity tolerance up to a month in most of its developing stages. Different management practices are then recommended in each of these two crops’ categories (corn and soybeans vs. winter wheat).

Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights

The heart coordinates its functional parameters for optimal beat-to-beat mechanical activity. Reliable detection and quantification of these parameters still represent a hot topic in cardiovascular research. Nowadays, computer vision allows the development of open-source algorithms to measure cellular kinematics. However, the analysis software can vary based on analyzed specimens. In this study, we compared different software performances in in-silico model, in-vitro mouse adult ventricular cardiomyocytes and cardioids. We acquired in-vitro high-resolution videos during suprathreshold stimulation at 0.5-1-2 Hz, adapting the protocol for the cardioids. Moreover, we exposed the samples to inotropic and depolarizing substances. We analyzed in-silico and in-vitro videos by (i) MUSCLEMOTION, the gold standard among open-source software; (ii) CONTRACTIONWAVE, a recently developed tracking software; and (iii) ViKiE, an in-house customized video kinematic evaluation software. We enriched the study with three machine-learning algorithms to test the robustness of the motion-tracking approaches. Our results revealed that all software produced comparable estimations of cardiac mechanical parameters. For instance, in cardioids, beat duration measurements at 0.5 Hz were 1053.58 ms (MUSCLEMOTION), 1043.59 ms (CONTRACTIONWAVE), and 937.11 ms (ViKiE). ViKiE exhibited higher sensitivity in exposed samples due to its localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE offered temporal correlation, combining global assessment with time-efficient analysis. Finally, machine learning reveals greater accuracy when trained with MUSCLEMOTION dataset in comparison with the other software (accuracy > 83%). In conclusion, our findings provide valuable insights for the accurate selection and integration of software tools into the kinematic analysis pipeline, tailored to the experimental protocol.

Open-source software for respiratory rate estimation using single-lead electrocardiograms

Respiratory rate (RR) is a critical vital sign used to assess pulmonary function. Currently, RR estimating instrumentation is specialized and bulky, therefore unsuitable for remote health monitoring. Previously, RR was estimated using proprietary software that extract surface electrocardiogram (ECG) waveform features obtained at several thoracic locations. However, developing a non-proprietary method that uses minimal ECG leads, generally available from mobile cardiac monitors is highly desirable. Here, we introduce an open-source and well-documented Python-based algorithm that estimates RR requiring only single-stream ECG signals. The algorithm was first developed using ECGs from awake, spontaneously breathing adult human subjects. The algorithm-estimated RRs exhibited close linear correlation to the subjects’ true RR values demonstrating an R2 of 0.9092 and root mean square error of 2.2 bpm. The algorithm robustness was then tested using ECGs generated by the ischemic hearts of anesthetized, mechanically ventilated sheep. Although the ECG waveforms during ischemia exhibited severe morphologic changes, the algorithm-determined RRs exhibited high fidelity with a resolution of 1 bpm, an absolute error of 0.07 ± 0.07 bpm, and a relative error of 0.67 ± 0.64%. This optimized Python-based RR estimation technique will likely be widely adapted for remote lung function assessment in patients with cardiopulmonary disease.

Evaluating Step Counting Algorithms on Subsecond Wrist-Worn Accelerometry: A Comparison Using Publicly Available Data Sets

Background: Walking-based metrics, including step count and total time walking, are easily interpretable measures of physical activity. Algorithms can estimate steps from accelerometry, which increasingly is measured with accelerometers located on the wrist. However, many existing step counting algorithms have not been validated in free-living settings, exhibit high error rates, or cannot be used without proprietary software. We compare the performance of several existing open-source step counting algorithms on three publicly available data sets, including one with free-living data. Methods: We applied five open-source algorithms: Adaptive Empirical Pattern Transformation, Oak, Step Detection Threshold, Verisense, and stepcount, and one proprietary algorithm (ActiLife) to three publicly available data sets with ground truth step counts: Clemson Ped-Eval, Movement Analysis in Real-World Environments Using Accelerometers, and OxWalk. We evaluate F1 score, precision, recall, mean absolute percent error (MAPE), and mean bias for each algorithm and setting. Results: The machine learning-based stepcount algorithm exhibited the highest F1 score (0.89 ± 0.11) and lowest MAPE (8.6 ± 9%) across all data sets and had the best, or comparable, F1 scores and MAPE in each individual data set. All algorithms performed worse with respect to both F1 score and MAPE in free-living compared with regular walking scenarios, and stepcount and Verisense were most sensitive to sampling frequency of input data. Conclusion: Machine learning-based algorithms, including stepcount, are a promising avenue for step counting. More free-living accelerometry data sets with ground truth step counts are needed for testing, validation, and continued refinement of algorithms.

Comparing open-source optimisation algorithms for functionally graded material design: a thermoelastic case study

Comparing open-source optimisation algorithms for functionally graded material design: a thermoelastic case study

A live cell imaging-based assay for tracking particle uptake by clathrin-mediated endocytosis.

A popular strategy for therapeutic delivery to cells and tissues is to encapsulate therapeutics inside particles that cells internalize via endocytosis. The efficacy of particle uptake by endocytosis is often studied in bulk using flow cytometry and Western blot analysis and confirmed using confocal microscopy. However, these techniques do not reveal the detailed dynamics of particle internalization and how the inherent heterogeneity of many types of particles may impact their endocytic uptake. Toward addressing these gaps, here we present a live-cell imaging-based method that utilizes total internal reflection fluorescence microscopy to track the uptake of a large ensemble of individual particles in parallel, as they interact with the cellular endocytic machinery. To analyze the resulting data, we employ an open-source tracking algorithm in combination with custom data filters. This analysis reveals the dynamic interactions between particles and endocytic structures, which determine the probability of particle uptake. In particular, our approach can be used to examine how variations in the physical properties of particles (size, targeting, rigidity), as well as heterogeneity within the particle population, impact endocytic uptake. These data impact the design of particles toward more selective and efficient delivery of therapeutics to cells.

Comparing open-source optimisation algorithms for functionally graded material design: a thermoelastic case study

Comparing open-source optimisation algorithms for functionally graded material design: a thermoelastic case study

Comparative Analysis and Conversion Between Actiwatch and ActiGraph Open-Source Counts

Body-worn sensors have contributed to a rich and growing body of literature in public health and clinical research in the last decades. A major challenge in sensor research is the lack of consistency and standardization of the collection and reporting of the sensor data. The algorithms used to derive these activity counts can be vastly different between manufactures and not always transparent to the researchers. With Philips, one of the major research-grade wearable device manufacturers, discontinuing this product line, many researchers are left in need of alternative solutions and at the risk of not being able to relate their historical data using the Philips Actiwatch 2 devices to future findings with other devices. We herein provide a comparison analysis and conversion method that can be used to convert activity counts from Philips to those from ActiGraph, another major manufacturer who provide both raw acceleration data and count data based on their open-source algorithm to the research community. This work provides an approach to maximize the scientific value of historical actigraphy data collected by the Actiwatch devices to support research continuity in this community. The conversion, however, is not perfect and only offers an approximation, due to the intrinsic difference in the count algorithms between the two accelerometers, and the permanent information loss during data reduction. We encourage future research using body-worn sensors to retain the raw sensor data to ensure data consistency, comparability, and the ability to leverage future algorithm improvement.

Gaitmap-An Open Ecosystem for IMU-Based Human Gait Analysis and Algorithm Benchmarking.

Goal: Gait analysis using inertial measurement units (IMUs) has emerged as a promising method for monitoring movement disorders. However, the lack of public data and easy-to-use open-source algorithms hinders method comparison and clinical application development. To address these challenges, this publication introduces the gaitmap ecosystem, a comprehensive set of open source Python packages for gait analysis using foot-worn IMUs. Methods: This initial release includes over 20 state-of-the-art algorithms, enables easy access to seven datasets, and provides eight benchmark challenges with reference implementations. Together with its extensive documentation and tooling, it enables rapid development and validation of new algorithm and provides a foundation for novel clinical applications. Conclusion: The published software projects represent a pioneering effort to establish an open-source ecosystem for IMU-based gait analysis. We believe that this work can democratize the access to high-quality algorithm and serve as a driver for open and reproducible research in the field of human gait analysis and beyond.

Feasibility of an open-source algorithm for predicting sea surface temperature based on three multi-resolution data sources

Feasibility of an open-source algorithm for predicting sea surface temperature based on three multi-resolution data sources

Realizing in-house algorithm-driven free fibula flap set up within 24 hours: a pilot study evaluating accuracy with open-source tools.

This study aims to critically evaluate the effectiveness and accuracy of a time safing and cost-efficient open-source algorithm for in-house planning of mandibular reconstructions using the free osteocutaneous fibula graft. The evaluation focuses on quantifying anatomical accuracy and assessing the impact on ischemia time. A pilot study was conducted, including patients who underwent in-house planned computer-aided design and manufacturing (CAD/CAM) of free fibula flaps between 2021 and 2023. Out of all patient cases, we included all with postoperative 3D imaging in the study. The study utilized open-source software tools for the planning step, and three-dimensional (3D) printing techniques. The Hausdorff distance and Dice coefficient metrics were used to evaluate the accuracy of the planning procedure. The study assessed eight patients (five males and three females, mean age 61.75 ± 3.69 years) with different diagnoses such as osteoradionecrosis and oral squamous cell carcinoma. The average ischemia time was 68.38 ± 27.95 min. For the evaluation of preoperative planning vs. the postoperative outcome, the mean Hausdorff Distance was 1.22 ± 0.40. The Dice Coefficients yielded a mean of 0.77 ± 0.07, suggesting a satisfactory concordance between the planned and postoperative states. Dice Coefficient and Hausdorff Distance revealed significant correlations with ischemia time (Spearman's rho = -0.810, p = 0.015 and Spearman's rho = 0.762, p = 0.028, respectively). Linear regression models adjusting for disease type further substantiated these findings. The in-house planning algorithm not only achieved high anatomical accuracy, as reflected by the Dice Coefficients and Hausdorff Distance metrics, but this accuracy also exhibited a significant correlation with reduced ischemia time. This underlines the critical role of meticulous planning in surgical outcomes. Additionally, the algorithm's open-source nature renders it cost-efficient, easy to learn, and broadly applicable, offering promising avenues for enhancing both healthcare affordability and accessibility.

High-pressure structural behavior and elastic properties of Ce2In and Ce3In: A combined in situ synchrotron X-ray diffraction and density functional theory study

Across the cerium (Ce)-indium (In) binary phase diagram, there are five line compounds including Ce3In, Ce2In, Ce3In5, CeIn2 and CeIn3. So far, CeIn3 is the only compound whose high-pressure structural behavior and elastic properties have been studied in detail. In this work, we investigated the compression behavior of Ce3In and Ce2In at pressures up to 26.2 GPa using in situ high-pressure angle dispersive synchrotron powder X-ray diffraction (XRD). Our results indicate that Ce3In retains its Cu3Au-type structure in the pressure range investigated, whereas Ce2In undergoes a phase transformation from the Ni2In-type structure to an unidentified phase at ∼4.6 GPa. Unit-cell parameters as a function of pressure were obtained by Rietveld analysis of XRD data. Unit cell volumes were fitted to a second order Birch-Murnaghan equation of state (EOS). The experimentally determined bulk moduli (K0) of the two cerium indides are 50 ± 3 GPa for Ce3In and 39.2 ± 1.6 GPa for Ce2In. The zero-pressure compressibilites of the a-axis (βa,0) for Ce3In is 6.67 (±0.36) × 10–3 GPa–1 and of the a- and c-axis for Ce2In are βa,0 = 8.85 (±0.39) × 10−3 GPa−1 and βc,0 = 9.26 (±0.51) × 10−3 GPa−1, respectively. These results are in general agreement with those calculated by density functional theory (DFT + U) and open-source machine learning algorithms.

A DRONE SYSTEM FOR AUTONOMOUS MAPPING FLIGHTS INSIDE A FOREST – A FEASIBILITY STUDY AND FIRST RESULTS

Abstract. In recent years, drones have gained wide popularity in forest research and operational applications. Over the forest canopy, where Global Navigation Satellite Systems (GNSS) are available, the flights are already highly automated. However, under the canopy of dense forests, the flights still need active manual control by a human pilot due to missing GNSS signal and obstacles. The objective of this study was to design and implement a prototype of a drone autonomously flying inside a forest for future boreal forest research purposes by utilizing open-source algorithms. Based on a literature survey, EGO-Planner-v2 with VINS-Fusion localization and stereo-depth camera-based mapping was chosen as the base of the implemented prototype. The algorithms were first tested in a simulator and later a custom drone hardware was built to evaluate the performance and suitability in real boreal forest environments. The evaluation criteria for the performance were the success of the mission, the reliability of the obstacle avoidance, and the accuracy of the localization. Based on the results, the performance of the prototype was promising, but in dense forests, the sensing of small needleless branches and leafless understory vegetation needs to be improved to increase reliability. In a dense spruce forest, nine of 19 test flights were successful, when approximate flight distances varied between 35 m and 80 m. In the longest of those test flights, the error of the VINS-Fusion estimate of the trajectory length was approximately 1 m.

Application of an explainable glass-box machine learning approach for prognostic analysis of a biogas-powered small agriculture engine

Biogas has developed as a potential substitute fuel source due to its renewable and sustainable nature, which can help reduce greenhouse gas emissions. In this paper, along with the experimental investigation, a novel interpretable expert system was constructed to address these issues and provide meaningful explanations for model predictions using extreme gradient boosting (XGB). For this purpose, the SHapley Additive exPlanations (SHAP) tool was coupled with XGB open-source algorithm to simulate five efficiency parameters of the biogas-powered engines, including brake thermal efficiency (BTE), peak pressure (PP), hydrocarbon (HC), oxides of nitrogen (NOx), and carbon monoxide (CO). Here, four experimental-based variables comprised of fuel injection timing, fuel injection pressure, compression ratio, and engine load were employed as predictors. Apart from the main framework, two advanced ensemble machine learning (ML), namely the light gradient-boosting machine (LightGBM) and Extra Tree algorithms, were adopted to validate the primary model. In addition, we use the SHAP framework to understand the impact of input features on engine performance and emission outputs. XGB-SHAP owing to its best predictive performance (BTE|R = 0.994 and RMSE = 0.567, PP|R = 0.984 and RMSE = 0.846, HC|R = 0.994 and RMSE = 6.215, NOx|R = 0.998 and RMSE = 1.407, and CO|R = 0.985 and RMSE = 3.464) outperformed the Extra Tree and LightGBM, respectively.

A LiDAR-IMU-GNSS fused mapping method for large-scale and high-speed scenarios

In the field of autonomous navigation and surveying, constructing large-scale and globally consistent environmental maps in real-time is a critical problem. In this case, the algorithm faces many challenges, such as high-speed motion, GNSS-denied tunnels and open fields with few features. To solve the aforementioned problems, this paper proposes a mapping method that fuses LiDAR, IMU and GNSS information simultaneously. The proposed method mainly includes two parts: LiDAR-IMU joint optimization and LIO-GNSS joint optimization. In the LiDAR-IMU joint optimization, we realize a tightly-coupled LiDAR inertial odometry (LIO) and propose a new degradation processing mechanism that enables more robust mapping in degraded environments, such as tunnels and open fields. Furthermore, we propose a new constraint method based on ground points to solve the drift problem in the vertical direction. This method maintains a local map based on ground grids, providing more effective constraints for the vertical direction. In the LIO-GNSS joint optimization, we use the factor graph optimization(FGO) algorithm based on sliding window to realize the fusion of LIO and GNSS data, so as to achieve real-time, no drift and global consistency mapping. We conducted experiments in many complex environments with challenges such as high-speed motion, LiDAR degradation, GNSS-denied scenarios and so on. While most open-source algorithms struggled to produce satisfactory mapping results or even failed, our algorithm proved to be robust and achieved the best mapping results.