Time-lapse Data Research Articles

Millions of seafloor pits, not pockmarks, induced by vertebrates in the North Sea

Seabed pockmarks are among the most prominent morphologic structures in the oceans. They are usually interpreted as surface manifestation of hydrocarbon fluids venting from sediments. Here we suggest an alternative hypothesis of pockmark formation based on latest multibeam echosounder data with a centimeter resolution. In the North Sea, >40,000 enigmatically shaped shallow depressions or ‘pits’ with a mean depth of 0.11 m were documented, that do not resemble known pockmark morphologies. Combining the new echosounder data with information from behavioral biology, physical oceanography, satellite remote sensing and habitat mapping, we conclude that harbor porpoises excavate sediments during benthic foraging. By grubbing the seabed, they cause sandeels to escape from the sediment and initiate the formation of seafloor pits. Time-lapse data reveals that the initially feeding pits serve as nuclei for scouring and eventually merge into larger scour-pits. With the immense number of vertebrates in the ocean, such megafauna-driven macro-bioturbation reshapes the seafloor, modulates sediment transport, and ultimately impacts associated ecosystems on a global scale.

An in silico micro-multiphysics agent-based approach for simulating bone regeneration in a mouse femur defect model.

Bone defects represent a challenging clinical problem as they can lead to non-union. In silico models are well suited to study bone regeneration under varying conditions by linking both cellular and systems scales. This paper presents an in silico micro-multiphysics agent-based (micro-MPA) model for bone regeneration following an osteotomy. The model includes vasculature, bone, and immune cells, as well as their interaction with the local environment. The model was calibrated by time-lapsed micro-computed tomography data of femoral osteotomies in C57Bl/6J mice, and the differences between predicted bone volume fractions and the longitudinal in vivo measurements were quantitatively evaluated using root mean square error (RMSE). The model performed well in simulating bone regeneration across the osteotomy gap, with no difference (5.5% RMSE, p = 0.68) between the in silico and in vivo groups for the 5-week healing period - from the inflammatory phase to the remodelling phase - in the volume spanning the osteotomy gap. Overall, the proposed micro-MPA model was able to simulate the influence of the local mechanical environment on bone regeneration, and both this environment and cytokine concentrations were found to be key factors in promoting bone regeneration. Further, the validated model matched clinical observations that larger gap sizes correlate with worse healing outcomes and ultimately simulated non-union. This model could help design and guide future experimental studies in bone repair, by identifying which are the most critical in vivo experiments to perform.

CO2 Injection Monitoring: Enhancing Time-Lapse Seismic Inversion for Injected Volume Estimation in the Utsira Formation, Sleipner Field, North Sea

This article presents an in-depth study of CO2 injection monitoring in the Sleipner Field, focusing on the Utsira Formation. The research leverages advanced time-lapse inversion techniques and 4D seismic data analysis to enhance the accuracy of volume estimations and provide a comprehensive understanding of the dynamic behavior of the injected CO2 plume. The analysis encompasses cross correlation, time shift, predictability, and other key elements to yield robust insights into the reservoir’s response to CO2 injection. Cross-correlation analysis results of 60% to 100% outside the injection zone and less than 50% within the injection zone reveal a distinct dissimilarity between the injection and non-injection zones, emphasizing phase, time, and frequency content changes due to CO2 injection. Time shifts are meticulously calibrated globally on a trace-by-trace basis, to account for shallow statics and velocity changes, improving the overall alignment of seismic data. Predictability analysis results of 0 to 0.34 within the injection zone and 0.45 to 0.96 at the background further reinforce the findings, highlighting high predictability values in areas untouched by production and markedly lower values within the injection zone. These results provide a measure of the reliability of the seismic data and its ability to reflect the subtle changes occurring in the reservoir. Crucially, the time-lapse inversion process excels in capturing the evolving state of the CO2 plume within the Utsira Formation. The seismic data reveals the migration and expansion of the plume over time and the dynamic nature of the reservoir’s response to CO2 injection. Integrating various data facets reduces non-uniqueness in inversion results, allowing for more precise volume estimations.

TreeJ: an ImageJ plugin for interactive cell lineage reconstruction from static images

BackgroundWith the emergence of deep-learning methods, tools are needed to capture and standardize image annotations made by experimentalists. In developmental biology, cell lineages are generally reconstructed from time-lapse data. However, some tissues need to be fixed to be accessible or to improve the staining. In this case, classical software do not offer the possibility of generating any lineage. Because of their rigid cell walls, plants present the advantage of keeping traces of the cell division history over successive generations in the cell patterns. To record this information despite having only a static image, dedicated tools are required.ResultsWe developed an interface to assist users in the building and editing of a lineage tree from a 3D labeled image. Each cell within the tree can be tagged. From the created tree, cells of a sub-tree or cells sharing the same tag can be extracted. The tree can be exported in a format compatible with dedicated software for advanced graph visualization and manipulation.ConclusionsThe TreeJ plugin for ImageJ/Fiji allows the user to generate and manipulate a lineage tree structure. The tree is compatible with other software to analyze the tree organization at the graphical level and at the cell pattern level. The code source is available at https://github.com/L-EL/TreeJ.

How to Use Time Lapse Data for Embryo Selection - A Different Perspective

How to Use Time Lapse Data for Embryo Selection - A Different Perspective

Solving multiphysics-based inverse problems with learned surrogates and constraints

Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO2 plume predictions near, and far away, from the monitoring wells.

Understanding the synergistic impact of stress release and cementation on sandstone using sound waves — Implications for exhumation estimation

Exhumation is the process that encompasses uplift and erosion, leading to the removal of overburden and the release of effective stress exerted on rocks. When estimating exhumation magnitude using the compaction trend method, it is commonly assumed that the physical properties of rocks are insensitive to stress reduction. However, recent laboratory evidence has indicated that porosity exhibits weaker sensitivity to stress release compared with velocity that can be significantly affected by stress release. This raises questions regarding the validity of irreversible compaction assumed by compaction trend method. It remains unclear whether the impact of stress release can be observed in real rocks in exhumed areas because there is a lack of methods to directly measure the impact of stress release on field data. In addition, studying real rocks is further complicated by the presence of rock diagenesis and its interaction with stress release. To address these knowledge gaps, this study uses stress-dependent burial and uplift modeling and interprets an extensive well-log data set using the modeling-derived evaluation metrics. Conceptual modeling suggests that methods that neglect the combined effect of cementation and stress release tend to underestimate the exhumation magnitude. Furthermore, we discover that the disparity between porosity sensitivity and velocity sensitivity to stress release can be leveraged to derive a metric we call “porosity inconsistency” that can serve as a qualitative and quantitative measure for identifying and evaluating stress release in sandstone using geophysical field measurements. We gather a significant amount of sonic velocity and porosity data from normally compacted and uplifted clean sandstones in the Norwegian Sea and the Barents Sea. Notably, we observe significant porosity inconsistency in the exhumed well 6510/2-1 in the Norwegian Sea. In the Barents Sea, which has experienced extensive Cenozoic exhumation, the well data reveal a varying pattern of porosity inconsistency increasing toward the north and decreasing toward the west. This distribution of porosity inconsistencies in the Barents Sea wells not only aligns with the spatial variation of exhumation reported in various studies but also exhibits a positive correlation with the magnitude of exhumation. Furthermore, the exhumation magnitude derived from velocity-depth trends is considerably lower than the magnitude obtained from porosity/density-depth trends for wells displaying significant porosity inconsistency. These observations provide support for the predictions made by the conceptual modeling. The results of this study enhance our understanding of the synergistic impact of stress release and cementation on sandstone. Moreover, these findings have implications for pore pressure prediction and core evaluation in exhumed areas. They also provide insights into the feasibility and interpretation of time-lapse data of reservoir injection, for which the effective stress is likely to decrease due to pore pressure buildup.

ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells

Detecting and tracking multiple moving objects in a video is a challenging task. For living cells, the task becomes even more arduous as cells change their morphology over time, can partially overlap, and mitosis leads to new cells. Differently from fluorescence microscopy, label-free techniques can be easily applied to almost all cell lines, reducing sample preparation complexity and phototoxicity. In this study, we present ALFI, a dataset of images and annotations for label-free microscopy, made publicly available to the scientific community, that notably extends the current panorama of expertly labeled data for detection and tracking of cultured living nontransformed and cancer human cells. It consists of 29 time-lapse image sequences from HeLa, U2OS, and hTERT RPE-1 cells under different experimental conditions, acquired by differential interference contrast microscopy, for a total of 237.9 hours. It contains various annotations (pixel-wise segmentation masks, object-wise bounding boxes, tracking information). The dataset is useful for testing and comparing methods for identifying interphase and mitotic events and reconstructing their lineage, and for discriminating different cellular phenotypes.

OpenFrame: A modular, sustainable, open microscopy platform with single-shot, dual-axis optical autofocus module providing high precision and long range of operation.

'openFrame' is a modular, low-cost, open-hardware microscopy platform that can be configured or adapted to most light microscopy techniques and is easily upgradeable or expandable to multiple modalities. The ability to freely mix and interchange both open-source and proprietary hardware components or software enables low-cost, yet research-grade instruments to be assembled and maintained. It also enables rapid prototyping of advanced or novel microscope systems. For long-term time-lapse image data acquisition, slide-scanning or high content analysis, we have developed a novel optical autofocus incorporating orthogonal cylindrical optics to provide robust single-shot closed-loop focus lock, which we have demonstrated to accommodate defocus up to ±37μm with <200nm accuracy, and a two-step autofocus mode which we have shown can operate with defocus up to ±68μm. We have used this to implement automated single molecule localisation microscopy (SMLM) in a relatively low-cost openFrame-based instrument using multimode diode lasers for excitation and cooled CMOS cameras.

3D DC inversion, visualization, and processing of dense time-lapse data in fine domains applied to remediation monitoring

Using electrical direct-current (DC) data to monitor the subsurface is an efficient solution for observing changes in the shallow subsurface. Recent advances in instrumentation enable the acquisition of large data sets over 3D domains in a reasonable time. We have developed an inversion approach capable of handling very large amounts of data on a finely discretized domain, which enables the delivery of time-lapse results of dense data acquisitions in a feasible time and with high resolution. To spatially represent 3D DC data, a visualization method is introduced that formally generalizes the focusing point of a common pseudosection in three dimensions. The method physically describes subsurface properties for simple geologic scenarios. In addition, the focusing points are used to design our 3D measuring protocol. We develop a DC data processing scheme using the full time-domain induced polarization (IP) waveform data by applying two novel physics-based criteria that enforce charge buildup and steady state. These criteria are tested on DCIP field data and find that 98% of the filtered DC data have less than 1% relative error. The 3D visualization method and inversion algorithm are evaluated on synthetic scenarios of 3D DC dense borehole data. Then, we indicate time-lapse results of DC field data acquired at an uncontaminated site where a remediation agent was injected at depth. The data are acquired with a new DCIP instrument capable of measuring dense data sets at fast acquisition times. Accounting for DCIP data acquisition, signal processing, and 3D inversion of 60,000 DC measurements, our results locate in 14 h the 3D spread of the remediation agent in a [Formula: see text] volume.

Learned multiphysics inversion with differentiable programming and machine learning

We present the Seismic Laboratory for Imaging and Modeling/Monitoring open-source software framework for computational geophysics and, more generally, inverse problems involving the wave equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, the software is designed to be both readable and scalable, allowing researchers to easily formulate problems in an abstract fashion while exploiting the latest developments in high-performance computing. The design principles and their benefits are illustrated and demonstrated by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which, aside from coupling of wave physics and multiphase flow, involves machine learning.

P-122 Evaluation of an updated artificial intelligence embryo viability model on implantation and miscarriage

Abstract Study question Is the implantation and miscarriage prediction performance of the iDAScore model v2.0 increased as compared to the original v1.0 model? Summary answer The predictive performance is better for iDAScore v2.0 than for v1.0 on both implantation and miscarriage, although the differences did not reach statistical significance. What is known already In recent years, several artificial intelligence (AI) models for the prediction of embryo implantation based on image or time-lapse data have been published and validated in various ways. Some of these models are commercial products while others are more of an academic nature. The long-term benefit of AI models in clinical practice relies on their use in clinical routine and that the models are continuously monitored and updated. The iDAScore time-lapse model has been available from Vitrolife since 2021 and several external validations of iDAScore v1.0 have been published. An updated version (iDAScore v2.0) has recently been released. Study design, size, duration This was an observational retrospective study with data collected during the years 2019 to 2022 from one Danish clinic from fresh single embryo transfers performed on day 5. Blastocysts were selected for transfer by experienced embryologists based on morphology (Gardner score). Within the study period, we included all 849 embryos for which we had information on hCG test results, implantation (fetal heartbeat (FH) detected by ultrasound) and live birth (LB). Participants/materials, setting, methods Embryos were cultured in an EmbryoScope+ incubator (Vitrolife A/S). Time-lapse images were automatically captured throughout the incubation period. We used the original time-lapse images to calculate scores for iDAScore v1.0 and v2.0 retrospectively. For each version, the predictive performance for miscarriage (early pregnancy loss and clinical miscarriage), FH and LB prediction was expressed in terms of area under the ROC curve (AUC). Differences in performance between versions were assessed in paired comparisons of the AUCs. Main results and the role of chance The median age of the transfer recipients was 34 years. One third of the blastocysts was derived from ICSI and the remaining from standard IVF cycles. Among all transferred blastocysts, the hCG rate was 58.0% (492/849), the FH rate was 46.5% (395/849), the LB rate was 42.4% (360/849), and the overall miscarriage rate from hCG to live birth was 26.8% (132/492).The overall AUC for FH prediction was 0.652 (0.616 to 0.689) for iDAScore v2.0 compared to 0.632 (0.595 to 0.669) for v1.0. Similarly, the AUC for LB prediction was 0.644 (95% CI: 0.607 to 0.681) for iDAScore v2.0 compared to 0.626 (95% CI: 0.588 to 0.663) for iDAScore v1.0. However, these differences were not statistically significant (P-values: 0.12 for FH and 0.17 for LB) in paired comparisons.For miscarriage, we found an increase in AUC from v1.0 (0.574; 95% CI: 0.514 to 0.633) to v2.0 (0.592; 95% CI: 0.534 to 0.649), P-value: 0.40. Limitations, reasons for caution This study only includes data from a single clinic. Patient characteristics and procedures followed might not be representative for other clinics. The data is limited to blastocysts that were chosen for transfer by morphology from trained embryologists. Furthermore, the limited sample size does not provide statistically significant differences in performance. Wider implications of the findings It is important that predictive models are constantly updated and improved. Updated versions should take into account a larger database as well as potential new methodologies that evolve in the rapidly developing AI field. This study shows that the iDAScore model update seems to represent an actual performance increase. Trial registration number 1-16-02-277-22

O-121 Temporal development of scores from a time-lapse based artificial intelligence provides no additional benefit compared to the latest score

Abstract Study question Does the temporal development of scores from an artificial intelligence (AI) based embryo selection model provide additional predictive power compared to the latest score? Summary answer Earlier predictions provide no additional predictive power given the latest prediction. Knowing the development of AI scores is thus not beneficial for embryo selection. What is known already Traditionally, embryo grading takes into consideration the final morphology score together with the history of embryo development events. Recent publications have shown that time-lapse based AI models trained on clinical outcomes can automatically rank embryos by the likelihood of implantation that equal or surpass traditional methods, without the necessity for manual evaluation. AI-based methods based on time-lapse data may account for the impact of the full temporal development on likelihood of implantation. In this study, we examine if the addition of previous scores adds to the performance of an embryo selection model. Study design, size, duration A retrospective multicenter study of transferred embryos (n = 2422) with known implantation data (KID) between 2012-2020 from 21 international clinics. Embryos were cultured in EmbryoScope time-lapse incubators for at least 5 days. Both single and multi-embryo transfers in fresh and warmed cycles were included. Embryos were classified as positive (KIDp) or negative (KIDn) by the presence of fetal heartbeat. Implantation likelihood was evaluated at cleavage- and blastocyst stages by a 3D CNN model; iDAScore v2.0. Participants/materials, setting, methods A kernel-based conditional independence test was used to evaluate if a score at x hours post insemination (hpi) provide extra information compared to a score at y hpi. The test estimates the likelihood that a prediction after x hpi does not provide any additional information with regards to predicting KIDp when a prediction after y hpi is available. These likelihoods (p-values) were Bonferroni corrected to adjust for multiple comparisons. Main results and the role of chance KIDp predictions were computed for all embryos at 40, 44, 64, 68, 112, and 116 hpi corresponding to early and median time of transfer for day 2, 3, and 5. For all time points, the conditional independence test showed that there was no added information from knowing predictions at an earlier time point (p &amp;gt; 0.21). Similarly, it was tested if a known later prediction will improve model performance for an earlier prediction. This was always the case (p &amp;lt; 0.01) except for predictions close in time (68 vs 64 hpi; p = 1.0) and (44 vs 40 hpi; p = 1.0). This may reflect that there was little additional embryo development information during a 4-hour period at this development stage. The above shows that knowledge of the temporal score development does not improve model performance compared to using latest available score. This means that an AI model based on time-lapse videos likely considers earlier embryo development events in the context of the entire development history. Limitations, reasons for caution This study is a retrospective study that only looks at AI based on time-lapse videos. There is a potential bias as the embryos were likely of higher quality having been chosen for transfer. It is possible that the results do not generalize to simpler non-3D CNNs. Wider implications of the findings Knowing the temporal score development can introduce subjectivity that might lower implantation rates as the earlier scores were shown to not provide any significant prediction improvement. Trial registration number not applicable

P-252 A roadmap to normal human embryo development – a timelapse study of embryos resulting in the live birth

Abstract Study question What are the morphokinetic parameters of preimplantation development in normal human embryos? Summary answer We provide a timeline of early developmental events and the incidence of embryo morphological features compatible with childbirth. What is known already Timelapse embryo monitoring provides unique insight into early human development. Implementation of this technique in clinical practice allows for assessing developmental dynamics for improved embryo selection. Several markers and ranking algorithms have been proposed to predict blastulation or implantation outcomes, but only a few studies assessed time-lapse data with live birth as the primary endpoint. Therefore, the comprehensive morphokinetic profile of embryos that gave rise to live birth has not been defined. Study design, size, duration Retrospective observational analysis of 300 time-lapse records of IVF embryos known to develop into healthy newborns. Out of total 73 quantitative/qualitative signs annotated, we focused on 9 developmental events and 14 morphological features and evaluated their timing, duration, and incidence. The influence of potential confounding factors (i.e., egg age, donor egg, donor sperm, sperm quality, and sex of the child) was evaluated by advanced statistical methods. Participants/materials, setting, methods Embryo monitoring was performed by a GERI-timelapse incubator. Image acquisition of individual embryo development lasted 5-6 days, starting 10 minutes after ICSI, which was used as reference time point. Transmitted light images were automatically captured in 5-minute intervals and 7-11 focal planes. The embryo selection for transfer was based solely on its morphology. The annotation data represent the consensus of minimum 3 experienced embryologists. Generalized linear models were used to explore relationships between dataset variables. Main results and the role of chance The average age of female participants was 27.5 years; 71.67% of embryos were derived from young donor eggs without fertility disorders. The severe andrological factor was present in 8% of male patients; donor sperm was used in 23% of cases; 16% of embryos were derived from dysmorphic oocytes. Both sexes were equally represented among analyzed embryos. Our analysis showed that early development was characterized by remarkably short first mitosis (2.68 ± 0.73h) and the interval between the second and third division (t4-t3 = 0.65 ± 0.66h), whereas the third cleavage was less synchronous (t8-t5 = 5.46 ± 4.9h) No association between quantitative signs and confounding factors was found. Interestingly, 2-cell stage embryos often contained multiple nuclei in at least one blastomere (51%), but multinucleation was reduced at the 4-cell stage (8%). Multipolar divisions were absent during zygotic division and rare (2.3%) during the second cleavage. Preterm and partial compaction was observed in 26.6% and 34% of embryos, respectively. Importantly, we noticed that 11.6% of embryos in our dataset deviated from conventional scenario of cleavage dynamics. These “atypical” embryos were characterized by a significantly higher incidence of multipolar divisions during the second cleavage compared to “typical,” regularly dividing embryos. Additionally, the odds of partial compaction increase 10 times in “atypical” embryos. Limitations, reasons for caution The presented analysis only involved embryos confirmed to result in a live birth after transfer. The comparison with data from embryos that did not produce pregnancy was not performed because it would be impossible to discriminate between the role of the embryo and endometrial factors in implantation failure. Wider implications of the findings Human embryos may develop into healthy children despite irregular cell division. A high incidence of partial compaction in “atypical” embryos suggests a self-correction mechanism rescuing their developmental potential. A larger-scale analysis of features compatible with live birth is needed to improve embryo selection criteria for evidence-based decision-making. Trial registration number not applicable

Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes

Predicting failure in solids has broad applications including earthquake prediction which remains an unattainable goal. However, recent machine learning work shows that laboratory earthquakes can be predicted using micro-failure events and temporal evolution of fault zone elastic properties. Remarkably, these results come from purely data-driven models trained with large datasets. Such data are equivalent to centuries of fault motion rendering application to tectonic faulting unclear. In addition, the underlying physics of such predictions is poorly understood. Here, we address scalability using a novel Physics-Informed Neural Network (PINN). Our model encodes fault physics in the deep learning loss function using time-lapse ultrasonic data. PINN models outperform data-driven models and significantly improve transfer learning for small training datasets and conditions outside those used in training. Our work suggests that PINN offers a promising path for machine learning-based failure prediction and, ultimately for improving our understanding of earthquake physics and prediction.

Evaluating the impact of 4D seismic data artefacts in data assimilation

Time-lapse or 4D seismic data are important constraints in reservoir studies because they enable monitoring of saturation and pressure changes that result from hydrocarbon production. 4D seismic data have been quantitatively added, along with production data, in history matching or data assimilation procedures to reduce uncertainty and improve production forecasts. Before performing quantitative studies, it is important to ensure that the 4D seismic data are reliable, with minimal artefacts such as side-lobe effects that can disturb the identification of anomalies. In this work, we propose different ways of treating 4D seismic data in data assimilation for a real reservoir. Explicitly, we evaluated the impact on data assimilation results when considering different amounts of 4D information and three treatments to the identified artefacts. The treatments were: ignoring them, excluding them from data assimilation or defining no seismic changes at their locations. The results show that well and seismic matches are improved when 4D seismic data are assimilated, also improving the production predictions. Despite being a thin reservoir, assimilating two single-layer maps allowed us to predict relevant observed dynamic behaviour, such as the evolved gas trapped in the lower interval. Furthermore, when a treatment was applied to the artefacts, they produced better models than using a single two-layer map (with lower production errors and visually closer impedances to the observed data). Our recommendation is the assimilation of well and 4D seismic data, with the exclusion of unreliable information, for better life-cycle decisions.

Automated time-lapse data segmentation reveals in vivo cell state dynamics.

Embryonic development proceeds as a series of orderly cell state transitions built upon noisy molecular processes. We defined gene expression and cell motion states using single-cell RNA sequencing data and in vivo time-lapse cell tracking data of the zebrafish tailbud. We performed a parallel identification of these states using dimensional reduction methods and a change point detection algorithm. Both types of cell states were quantitatively mapped onto embryos, and we used the cell motion states to study the dynamics of biological state transitions over time. The time average pattern of cell motion states is reproducible among embryos. However, individual embryos exhibit transient deviations from the time average forming left-right asymmetries in collective cell motion. Thus, the reproducible pattern of cell states and bilateral symmetry arise from temporal averaging. In addition, collective cell behavior can be a source of asymmetry rather than a buffer against noisy individual cell behavior.

Multidecadal changes in planform morphology of the confluence of River Niger and River Benue, West Africa

Analysis of 35 years (1987–2022) of time-lapse data derived from Landsat imagery and SRTM DEM has revealed significant changes in planform morphology at the confluence of the River Niger and River Benue in Lokoja, West Africa. In the study area, the Niger is flanked by high elevation plateaus of ca. 400 m altitude and low-lying floodplains on its western bank. The Benue is characterized by more abundant intra-channel and bank-attached bars relative to the Niger. Over the 35-year period, a net downstream migration of 880 m was observed at the junction, its angle decreased from ca. 175° in 1987 to 50° in 2006 and to ca. 16° in 2022 and showed varied segmentation patterns. We establish a linkage between this decrease in confluence angle and increase in annual runoff in the study area. Expansion along the banks of the Niger, Benue, and post-confluence channels was found to be low and non-uniform, suggesting varied resistance of the banks to erosion, which is linked to the abundance of vegetation. These decadal changes in confluence planform are important for understanding river confluence dynamics in major river systems around the world.

Comparing Performance of Spectral Image Analysis Approaches for Detection of Cellular Signals in Time-Lapse Hyperspectral Imaging Fluorescence Excitation-Scanning Microscopy.

Hyperspectral imaging (HSI) technology has been applied in a range of fields for target detection and mixture analysis. While HSI was originally developed for remote sensing applications, modern uses include agriculture, historical document authentication, and medicine. HSI has also shown great utility in fluorescence microscopy. However, traditional fluorescence microscopy HSI systems have suffered from limited signal strength due to the need to filter or disperse the emitted light across many spectral bands. We have previously demonstrated that sampling the fluorescence excitation spectrum may provide an alternative approach with improved signal strength. Here, we report on the use of excitation-scanning HSI for dynamic cell signaling studies-in this case, the study of the second messenger Ca2+. Time-lapse excitation-scanning HSI data of Ca2+ signals in human airway smooth muscle cells (HASMCs) were acquired and analyzed using four spectral analysis algorithms: linear unmixing (LU), spectral angle mapper (SAM), constrained energy minimization (CEM), and matched filter (MF), and the performances were compared. Results indicate that LU and MF provided similar linear responses to increasing Ca2+ and could both be effectively used for excitation-scanning HSI. A theoretical sensitivity framework was used to enable the filtering of analyzed images to reject pixels with signals below a minimum detectable limit. The results indicated that subtle kinetic features might be revealed through pixel filtering. Overall, the results suggest that excitation-scanning HSI can be employed for kinetic measurements of cell signals or other dynamic cellular events and that the selection of an appropriate analysis algorithm and pixel filtering may aid in the extraction of quantitative signal traces. These approaches may be especially helpful for cases where the signal of interest is masked by strong cellular autofluorescence or other competing signals.

4D interpretation of time-lapse electrical resistivity monitoring data to identify preferential flow path in a landfill, South Korea.

Monitoring the leakage of leachate from a landfill is critical in preventing possible contamination in the surrounding area. Time-lapse (TL) electrical resistivity tomography (ERT) has been performed along eleven survey lines at four different time points in a landfill in Korea. The TL data sets were interpreted using an in-house 4D inversion algorithm. Changes in 4D inversion results were analyzed in order to interpret a leachate-contaminant region. Since the rainy season started during obtaining TL ERT data sets, the effects of precipitation on TL ERT data are also analyzed. Changes in electrical resistivity (ER) showed that precipitation increases ER of contaminant zones. As hydrogeochemical data offer contamination information in some areas where boreholes are located, these are helpful to interpret and compare with ERT inversion results to evaluate the extent of the contaminated plume. We also classified soil textures from particle size analysis on soil samples and analyzed electrical conductivity (EC) and dissolved oxygen (DO) using groundwater samples obtained from observation wells in the survey site. The information on soil structure as well as the results of 4D inversion provided insight into the location of a preferential flow path.