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A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: an Application to Xco2 to Estimate Benchmarks and Assess Model Bias
There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.
A Segmentation Algorithm for Characterizing Rise and Fall Segments in Seasonal Cycles: An Application to XCO2 to Estimate Benchmarks and Assess Model Bias
There is more useful information in the time series of satellite-derived column-averaged carbon dioxide (XCO2) than is typically characterized. Often, the entire time series is treated at once without considering detailed features at shorter timescales, such as nonstationary changes in signal characteristics – amplitude, period and phase. In many instances, signals are visually and analytically differentiable from other portions in a time series. Each rise (increasing) and fall (decreasing) segment in the seasonal cycle is visually discernable in a graph of the time series. The rise and fall segments largely result from seasonal differences in terrestrial ecosystem production, which means that the segment's signal characteristics can be used to establish observational benchmarks because the signal characteristics are driven by similar underlying processes. We developed an analytical segmentation algorithm to characterize the rise and fall segments in XCO2 seasonal cycles. We present the algorithm for general application of the segmentation analysis and emphasize here that the segmentation analysis is more generally applicable to cyclic time series. We demonstrate the utility of the algorithm with specific results related to the comparison between satellite- and model-derived XCO2 seasonal cycles (2009–2012) for large bioregions across the globe. We found a seasonal amplitude gradient of 0.74–0.77 ppm for every 10∘ of latitude in the satellite data, with similar gradients for rise and fall segments. This translates to a south–north seasonal amplitude gradient of 8 ppm for XCO2, about half the gradient in seasonal amplitude based on surface site in situ CO2 data (∼19 ppm). The latitudinal gradients in the period of the satellite-derived seasonal cycles were of opposing sign and magnitude (−9 d per 10∘ latitude for fall segments and 10 d per 10∘ latitude for rise segments) and suggest that a specific latitude (∼2∘ N) exists that defines an inversion point for the period asymmetry. Before (after) the point of asymmetry inversion, the periods of rise segments are lesser (greater) than the periods of fall segments; only a single model could reproduce this emergent pattern. The asymmetry in amplitude and the period between rise and fall segments introduces a novel pattern in seasonal cycle analyses, but, while we show these emergent patterns exist in the data, we are still breaking ground in applying the information for science applications. Maybe the most useful application is that the segmentation analysis allowed us to decompose the model biases into their correlated parts of biases in amplitude, period and phase independently for rise and fall segments. We offer an extended discussion on how such information about model biases and the emergent patterns in satellite-derived seasonal cycles can be used to guide future inquiry and model development.
Automated correlative segmentation of large Transmission X-ray Microscopy (TXM) tomograms using deep learning
Highlights: • A unique automated segmentation approach for large 3D nanotomography datasets obtained by Transmission X-ray Microscopy (TXM) in an Al-Cu alloy. • Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. • This extremely versatile technique is capable of emulating the manual segmentation process effectively. • Quantitative comparison between manual segmentation and CNN architecture established the accuracy of this automated technique. • Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis. - Abstract: A unique correlative approach for automated segmentation of large 3D nanotomography datasets obtained using Transmission X-ray Microscopy (TXM) in an Al-Cu alloy has been introduced. Automated segmentation using a Convolutional Neural Network (CNN) architecture based on a deep learning approach was employed. This extremely versatile technique is capable of emulating the manual segmentation process effectively. Coupling this technique with post-scanning SEM imaging ensured precise estimation of 3D morphological parameters from nanotomography. The segmentation process as well as subsequent analysis was expedited by several orders of magnitude. Quantitative comparison between segmentation performed manually and using the CNN architecture established the accuracy of this automated technique. Its ability to robustly process ultra-large volumes of data in relatively small time frames can exponentially accelerate tomographic data analysis, possibly opening up novel avenues for performing 4D characterization experiments with finer time steps.
Data for Clumping Index Estimation With 30°-tilted Cameras in Row Crops: Evaluation of Methods and Segment Size Effects
The clumping index (CI) quantifies the spatial distribution of foliage elements and is essential for accurately estimating the plant area index (PAI), canopy radiative transfer, and photosynthesis. Traditionally, the finite-length averaging method (LX), the gap size distribution method (CC), and a combined approach of CC and LX (CLX) have been applied to instruments like TRAC and digital hemispherical photography to estimate CI. However, a comprehensive evaluation of these methods in row crops remains limited, especially regarding the influence of segment size on CI. Meanwhile, digital cameras offer a cost-effective and user-friendly solution for canopy measurements in row crops, yet their application in this context remains underexplored. In this study, we employed a new approach using a 30°-tilted digital camera to estimate CI in corn and soybean fields, applying the LX, CC, and CLX methods. We systematically assessed the performance of these three methods by combining field measurements in real-world fields with simulations using the LESS 3D radiative transfer model. Our results showed that CLX applied to the whole image and 45° segment offered accurate estimation of CI (bias within ±0.1, RMSE < 0.2) and PAI (bias within ±0.4, RMSE < 1) in real-world fields and LESS simulations. The accuracy of the LX method was highly sensitive to segment size, with the best performance observed at the 15° segment (PAI bias within ±0.4). In contrast, the CC method remained stable across different segment sizes, and its performance was generally comparable to that of LX, except at the 15° segment. Across view zenith angles, CI derived from CC generally showed a continuous increase, while those from LX and CLX followed a rising trend at small zenith angles but began to decline at 68°, likely due to an increasing proportion of no-gap segments. Seasonally, LX tended to show decreasing CI during early growth stages but increased as the canopy matured, whereas CC and CLX showed gradually increasing CI before plateauing at peak PAI. The 30°-tilted camera effectively captured CI variations across different angles and growth stages, making it a practical and robust instrument for row crop canopy structure analysis. Applying these CI methods to digital cameras offers a low-cost and accessible CI estimation alternative, improving canopy structure monitoring accuracy in row crops.
Keck Segment Surface Artifacts: Impacts on Segment Phasing and Image Quality
We show that the segments of the Keck telescopes su er from small but signi cant surface artifacts that limit segment phasing accuracy. Both the segment surface artifacts themselves and the limits they place on segment phasing have important implications for adaptive optics, high contrast imaging, and seeing limited observations. These surface artifacts are likely by-products of the segment polishing, most likely the Ion Beam Figuring (IBF) process that constitutes the nal step of the segment surface guring. The surface artifacts di ract a signi cant amount of light out past 3.5 arcseconds and generate chromatic e ects that limit segment phasing accuracy. We present on-sky measurements of the di racted intensity. Segment surface roughness measurements are also presented, which show that surface roughness is not the source of the problem. These results have direct relevance to segment manufacturing for the Thirty Meter Telescope (TMT) and other segmented mirror telescopes.
Quantifying the unknown impact of segmentation uncertainty on image-based simulations
Abstract Image-based simulation, the use of 3D images to calculate physical quantities, relies on image segmentation for geometry creation. However, this process introduces image segmentation uncertainty because different segmentation tools (both manual and machine-learning-based) will each produce a unique and valid segmentation. First, we demonstrate that these variations propagate into the physics simulations, compromising the resulting physics quantities. Second, we propose a general framework for rapidly quantifying segmentation uncertainty. Through the creation and sampling of segmentation uncertainty probability maps, we systematically and objectively create uncertainty distributions of the physics quantities. We show that physics quantity uncertainty distributions can follow a Normal distribution, but, in more complicated physics simulations, the resulting uncertainty distribution can be surprisingly nontrivial. We establish that bounding segmentation uncertainty can fail in these nontrivial situations. While our work does not eliminate segmentation uncertainty, it improves simulation credibility by making visible the previously unrecognized segmentation uncertainty plaguing image-based simulation.
The interaction between transport-segment DNA and topoisomerase IA—crystal structure of MtbTOP1 in complex with both G- and T-segments
Abstract Each catalytic cycle of type IA topoisomerases has been proposed to comprise multistep reactions. The capture of the transport-segment DNA (T-segment) into the central cavity of the N-terminal toroidal structure is an important action, which is preceded by transient gate-segment (G-segment) cleavage and succeeded by G-segment religation for the relaxation of negatively supercoiled DNA and decatenation of DNA. The T-segment passage in and out of the central cavity requires significant domain–domain rearrangements, including the movement of D3 relative to D1 and D4 for the opening and closing of the gate towards the central cavity. Here we report a direct observation of the interaction of a duplex DNA in the central cavity of a type IA topoisomerase and its associated domain–domain conformational changes in a crystal structure of a Mycobacterium tuberculosis topoisomerase I complex that also has a bound G-segment. The duplex DNA within the central cavity illustrates the non-sequence-specific interplay between the T-segment DNA and the enzyme. The rich structural information revealed from the novel topoisomerase–DNA complex, in combination with targeted mutagenesis studies, provides new insights into the mechanism of the topoisomerase IA catalytic cycle.
Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images
The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.
Electric Power Distribution System Model Simplification Using Segment Substitution
Quasi-static time-series (QSTS) simulation is used to simulate the behavior of distribution systems over long periods of time (typically hours to years). The technique involves repeatedly solving the load-flow problem for a distribution system model and is useful for distributed energy resource (DER) planning. When a QSTS simulation has a small time step and a long duration, the computational burden of the simulation can be a barrier to integration into utility workflows. One way to relieve the computational burden is to simplify the system model. The segment substitution method of simplifying distribution system models introduced in this paper offers model bus reduction of up to 98% with a simplification error as low as 0.2% (0.002 pu voltage). In contrast to existing methods of distribution system model simplification, which rely on topological inspection and linearization, the segment substitution method uses black-box segment data and an assumed simplified topology.
Electro-Optic Segment-Segment Sensors for Radio and Optical Telescopes
A document discusses an electro-optic sensor that consists of a collimator, attached to one segment, and a quad diode, attached to an adjacent segment. Relative segment-segment motion causes the beam from the collimator to move across the quad diode, thus generating a measureable electric signal. This sensor type, which is relatively inexpensive, can be configured as an edge sensor, or as a remote segment-segment motion sensor.
LUVOIR Primary Mirror Segment Alignment Control With Joint Laser Metrology and Segment Edge Sensing
An approach is developed for the alignment and stability maintenance of the LUVOIR segmented primary mirror using a segment state estimation and wavefront control method based on a hybrid segment motion sensing architecture of laser truss metrology and segment edge sensors. Our current computer model was generated for LUVOIR Architecture Option A with a 15-meter aperture, 120segment primary mirror. The methodology and simulation results will be presented and analyzed. JPL has a long history of technology development in laser metrology and edge sensors, including work in SIM [7], Keck and TMT [8], CCAT [3] and LUVOIR [1]. We will discuss our current efforts of LUVOIR laser metrology and edge-sensor models development, showing sensitivities of sensor measurements to various mirror eigenmodes, removing global modes and strengthening weak modes by performing joint (hybrid) laser-metrology and edge sensing. We will define and derive an important performance metric called wavefront error multiplier (WEM), and show that WEM provides a simple link between sensor errors and the closed-loop (controlled) system wavefront error. We will show WEM values for several hybrid sensor configuration options studied. We will discuss an algorithm for mirror shape control and maintenance through segment state and wavefront estimations using joint edge-metrology sensing. We will compare simulated performance of mirror state estimation, wavefront estimation and wavefront control based on joint edge-metrology sensing among several sensor configurations, and show the impact of sensor error distributions on the segmented mirror alignment performance. Mirror shape control performance will be also be evaluated in terms of imaging contrast between inner working angles (IWA) and outer working angles (OWA) of a LUVOIR coronagraph. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. LUVOIR is a mission concept under study for the next astronomy Decadal Survey—the information presented in this paper is pre-decisional and is provided for planning and discussion purposes only.
Segment 2/3 Hypertrophy is Greater When Right Portal Vein Embolisation is Extended to Segment 4 in Patients with Colorectal Liver Metastases: A Retrospective Cohort Study
Background: In patients with colorectal cancer liver metastases (CRLM), right portal vein embolisation (RPVE) is used to increase the volume of the future remnant liver (FRL) before major hepatic resection. It is not established whether embolisation of segment 4 in addition RPVE (RPVE + 4) induces greater hypertrophy of the FRL. Limitations of prior studies include heterogenous populations and use of hypertrophy metrics sensitive to baseline variables. Methods: From 2010 to 2015, consecutive patients undergoing RPVE or RPVE + 4 for CRLM, who had not undergone prior major hepatic resection and in whom imaging was available, were included in a retrospective study. Data were extracted from hospital electronic records. Volumetric assessments of segments 2–3 were made on cross-sectional imaging before and after embolisation and corrected for standardised liver volume. Results: Ninety-nine patients underwent PVE, and 60 met the inclusion criteria. Thirty-eight patients underwent RPVE, and 22 underwent RPVE + 4. Forty-five patients had undergone median 6 cycles of prior chemotherapy. Eighteen patients had FRL metastases at PVE, and 16 had undergone subsegmental metastasectomy in the FRL. Assessments of the degree of hypertrophy (DH) of segments 2/3 were made at median 35 (interquartile range 30–49) days after PVE. RPVE + 4 resulted in a significantly greater increase in DH than RPVE (7.7 ± 1.8% vs 11.3 ± 2.6%, p = 0.011). No confounding association between baseline variables and the decision to undertake RPVE or RPVE + 4 was identified. Median survival was 2.4 years and was not influenced by segment 4 embolisation. Conclusion: RPVE + 4 results in greater DH of segments 2/3 than RPVE in people with CLRM.
Demonstration of a Segment Alignment Maintenance System on a Seven-Segment Sub-Array of the Hobby-Eberly Telescope
NASA's Marshall Space Flight Center, in collaboration with Blue Line Engineering of Colorado Springs, Colorado, is developing a Segment Alignment Maintenance System (SAMS) for McDonald Observatory's Hobby-Eberly Telescope (HET). The SAMS shall sense motions of the 91 primary mirror segments and send corrections to HET's primary mirror controller as the mirror segments misalign due to thermo-elastic deformations of the mirror support structure. The SAMS consists of inductive edge sensors supplemented by inclinometers for global radius of curvature sensing. All measurements are sent to the SAMS computer where mirror motion corrections are calculated. In October 2000, a prototype SAMS was installed on a seven-segment cluster of the HET. Subsequent testing has shown that the SAMS concept and architecture are a viable practical approach to maintaining HET's primary mirror figure, or the figure of any large segmented telescope. This paper gives a functional description of the SAMS sub-array components and presents test data to characterize the performance of the sub-array SAMS.
Leech segmental repeats develop normally in the absence of signals from either anterior or posterior segments
We have investigated whether the development of segmental repeats is autonomous in the embryo of the leech Helobdella robusta. The segmental tissues of the germinal band arise from progeny of five stem cells called teloblasts. Asymmetric divisions of the teloblasts form chains of segment founder cells (called primary blast cells) that divide in a stereotypical manner to produce differentiated descendants. Using two distinct techniques, we have looked for potential interactions between neighboring blast cell clones along the anterior-posterior axis. In one technique, we prevented the birth of primary blast cells by injection of DNase I into the teloblast, thereby depriving the last blast cell produced before the ablation of its normal posterior neighbors. We also ablated single blast cells with a laser microbeam, which allowed us to assess potential signals acting on either more anterior or more posterior primary blast cell clones. Our results suggest that interactions along the anterior-posterior axis between neighboring primary blast cell clones are not required for development of normal segmental organization within the blast cell clone. We also examined the possibility that blast cells receive redundant signals from both anterior and posterior neighboring clones and that either is sufficient for normal development. Using double blast cell laser ablations to isolate a primary blast cell clone by removal of both its anterior and its posterior neighbor, we found that the isolated clone still develops normally. These results reveal that the fundamental segmental repeat in the leech embryo, the primary blast cell clone, can develop normally in the apparent absence of signals from adjacent repeats along the anterior-posterior axis.
A Data-Driven Customer Segmentation Strategy Based on Contribution to System Peak Demand
Not provided.