Reference curves for pediatric endocrinology: leveraging biomarker z-scores for clinical classifications

Context: Hormone reference intervals in pediatric endocrinology are traditionally partitioned by age and lack the framework for benchmarking individual blood test results as normalized z-scores and plotting sequential measurements onto a chart. Reference curve modeling is applicable to endocrine variables and represents a standardized method to account for variation with gender and age. Objective: We aimed to establish gender-specifc biomarker reference curves for clinical use and benchmark associations between hormones, pubertal phenotype, and body mass index (BMI). Methods: Using cross-sectional population sample data from 2139 healthy Norwegian children and adolescents, we analyzed the pubertal status, ultrasound measures of glandular breast tissue (girls) and testicular volume (boys), BMI, and laboratory measurements of 17 clinical biomarkers modeled using the established “LMS” growth chart algorithm in R. Results: Reference curves for puberty hormones and pertinent biomarkers were modeled to adjust for age and gender. Z-score equivalents of biomarker levels and anthropometric measurements were compiled in a comprehensive beta coeffcient matrix for each gender. Excerpted from this analysis and independently of age, BMI was positively associated with female glandular breast volume (β = 0.5, P < 0.001) and leptin (β = 0.6, P < 0.001), and inversely correlated with serum levels of sex hormone-binding globulin (SHBG) (β = −0.4, P < 0.001). Biomarker z-score profles differed signifcantly between cohort subgroups stratifed by puberty phenotype and BMI weight class. <p<Conclusion: Biomarker reference curves and corresponding z-scores provide an intuitive framework for clinical implementation in pediatric endocrinology and facilitate the application of machine learning classifcation and covariate precision medicine for pediatric patients

HYPSO-1 CubeSat: First Images and In-Orbit Characterization

The HYPSO-1 satellite, a 6U CubeSat carrying a hyperspectral imager, was launched on 13 January 2022, with the Goal of imaging ocean color in support of marine research. This article describes the development and current status of the mission and payload operations, including examples of agile planning, captures with low revisit time and time series acquired during a campaign. The in-orbit performance of the hyperspectral instrument is also characterized. The usable spectral range of the instrument is in the range of 430 nm to 800 nm over 120 bands after binning during nominal captures. The spatial resolvability is found empirically to be below 2.2 pixels in terms of Full-Width at Half-Maximum (FWHM) at 565 nm. This measure corresponds to an inherent ground resolvable resolution of 142 m across-track for close to nadir capture. In the across-track direction, there are 1216 pixels available, which gives a swath width of 70 km. However, the 684 center pixels are used for nominal captures. With the nominal pixels used in the across-track direction, the nadir swath-width is 40 km. The spectral resolution in terms of FWHM is estimated to be close to 5 nm at the center wavelength of 600 nm, and the Signal-to-Noise Ratio (SNR) is evaluated to be greater than 300 at 450 nm to 500 nm for Top-of-Atmosphere (ToA) signals. Examples of images from the first months of operations are also shown.publishedVersio

Dimensionality Reduction and Target Detection in Hyperspectral Remote Sensing

The work presented here is originally motivated by the low transfer rate possible for earth orbiting small satellites. Through different methods for dimensionality reduction, the effects of such representation on data exploration with respect to target detection have been investigated. The theory suggests that through using these methods for dimensionality reduction a considerable compression ratio could be achieved whiteout losing desired information. How these reduced space representations of the data then would affect the exploratory analysis, here represented by target detection with known signatures, was used as an example. The different dimensionality reduction methods were PCA, MNF and JADE ICA, and the different target detection algorithms were ACE, CEM, OSP, and SAM. Target detection performance has been measured using the $F_1$-score and Matthews correlation coefficient for binary classification performance, and visibility for robustness. When looking purely at the compression of synthetic data all dimensionality transforms performed as expected. The MNF transform was able to achieve a higher score of restoration than the others for all cases. It should be noted that the synthetic scenes fit well with some of the assumptions made in the MNF implementation. On data from the HICO mission the MNF representation was not able to produce as good results as when performed on synthetic data or data with ground truth. It is speculated that this might be due to the limitations found in the raw data of the HICO mission and how the restoration was measured, whilst the synthetic scenes and the scenes with known ground truth were able to give consistent results for all dimensionality reduction methods, and especially for the MNF transform. In the simulations performed MNF was in most cases able to represent the data with a high level of visibility and good results wrt. both the $F_1$-score and the Matthews correlation coefficient, across all detection algorithms. It was demonstrated that the MNF transform will be dependent on the estimated noise model, but that even with a lesser noise model the MNF transform was able to perform well, when compared to PCA and ICA. These results were trending across all target detection algorithms. The different Target detection algorithms were able to produce good results, both wrt. binary classification and robustness. The ACE and SAM detection algorithms showed great promise wrt. both their ability to detect targets and their apparent robustness. The CEM algorithm often had a high detection ability, but had a tendency to be less robust. Lastly, the OSP detector did not perform as well as the others, for any of the scenes tested. The combination of dimensionality reduction and target detection displayed positive results wrt. both detection rate and robustness. This suggests that combining the two in the proposed pipeline will not undermine the performance of the system as a whole, and potentially even strengthen the operation performed by a target detection system

The effect of dimensionality reduction on signature-based target detection for hyperspectral imaging

Target detection is one of the more popular applications of hyperspectral remote sensing. To enhance the detection rate, it is common to do preprocessing to reduce the effects of noise and other forms of undesired interference with the observed spectral signatures. In current earth observing systems, in particular small satellite systems, data rate limitations can make the utilization of sensors with high spectral dimensionality undesirable and even unobtainable. In this paper, the effect of different methods for dimensionality reduction and noise removal has been observed on multiple classical methods for signature matched target detection often used in hyperspectral imaging. The dimensionality reduction differs from resampling in the sense that the original spectral range and resolution can be restored via a linear transformation. This paper suggests that by combining dimensionality reduction and target detection, the resulting data cube has a reduced dimensionality and suppressed undesired effects. The ability to correctly detect spectral phenomena has improved while also achieving reduce data volume. Combining dimensionality reduction and target detection can also reduce the number of computational operations needed in later stages of processing, when operating on the projected space. The observed effects are demonstrated by using simulated and real-world hyperspectral scenes. The real-world scenes are from well-calibrated sensors e.g. AVIRIS, ROSIS, and Hyperion, of classified agricultural and urban areas. The simulated scene is generated using the ASTER library

Digital Engineering Management in an Academic CubeSat Project

Digital engineering is increasingly introduced for managing and supporting the development of systems for space. However, few academic teams have the competency needed to manage projects using digital engineering and systems engineering. The subject of this paper is an academic CubeSat project in which a variety of digital engineering techniques are used. The tailoring that has been applied to fit the academic environment including students from different disciplines and levels of maturity is described. This paper shows how a customized Scrum methodology for hardware and software integrated with a workflow in a digital tool environment has given positive results for both the team and the system development. This paper also discusses how to introduce new members to the team and how to train them to work with digital engineering as a multidisciplinary team. This paper presents how the systems engineering and project management activities have been integrated into the academic CubeSat project, evaluate how well this fusion worked, and estimate its potential to be used as a guide for other digital engineering projects

Simulation tool for hyper-spectral imaging from a satellite

This paper presents a tool created for simulating the expected radiance from oceanic geophysical parameters through the atmosphere as perceived by a hyperspectral remote sensing satellite. The effects of varying off-nadir viewing angles, solar zenith angles and the presence of water clouds on the resulting radiance has been analyzed, and simulated top-of-atmosphere (TOA) radiance values coincide well with experimental data

Pre-Launch Assembly, Integration, and Testing Strategy of a Hyperspectral Imaging CubeSat, HYPSO-1

Assembly, Integration, and Verification/Testing (AIV or AIT) is a standardized guideline for projects to ensure consistency throughout spacecraft development phases. The goal of establishing such a guideline is to assist in planning and executing a successful mission. While AIV campaigns can help reduce risk, they can also take years to complete and be prohibitively costly for smaller new space programs, such as university CubeSat teams. This manuscript outlines a strategic approach to the traditional space industry AIV campaign through demonstration with a 6U CubeSat mission. The HYPerspectral Smallsat for Ocean observation (HYPSO-1) mission was developed by the Norwegian University of Science and Technology’s (NTNU) SmallSatellite Laboratory in conjunction with NanoAvionics (the platform provider). The approach retains critical milestones of traditional AIV, outlines tailored testing procedures for the custom-built hyperspectral imager, and provides suggestions for faster development. A critical discussion of de-risking and design-driving decisions, such as imager configuration and machining custom parts, highlights the consequences that helped, or alternatively hindered, development timelines. This AIV approach has proven key for HYPSO-1’s success, defining further development within the lab (e.g., already with the second-generation, HYPSO-2), and can be scaled to other small spacecraft programs throughout the new space industry

Reference curves for pediatric endocrinology: leveraging biomarker z-scores for clinical classifications

Context Hormone reference intervals in pediatric endocrinology are traditionally partitioned by age and lack the framework for benchmarking individual blood test results as normalized z-scores and plotting sequential measurements onto a chart. Reference curve modeling is applicable to endocrine variables and represents a standardized method to account for variation with gender and age. Objective We aimed to establish gender-specific biomarker reference curves for clinical use and benchmark associations between hormones, pubertal phenotype, and body mass index (BMI). Methods Using cross-sectional population sample data from 2139 healthy Norwegian children and adolescents, we analyzed the pubertal status, ultrasound measures of glandular breast tissue (girls) and testicular volume (boys), BMI, and laboratory measurements of 17 clinical biomarkers modeled using the established “LMS” growth chart algorithm in R. Results Reference curves for puberty hormones and pertinent biomarkers were modeled to adjust for age and gender. Z-score equivalents of biomarker levels and anthropometric measurements were compiled in a comprehensive beta coefficient matrix for each gender. Excerpted from this analysis and independently of age, BMI was positively associated with female glandular breast volume (β = 0.5, P < 0.001) and leptin (β = 0.6, P < 0.001), and inversely correlated with serum levels of sex hormone-binding globulin (SHBG) (β = −0.4, P < 0.001). Biomarker z-score profiles differed significantly between cohort subgroups stratified by puberty phenotype and BMI weight class. Conclusion Biomarker reference curves and corresponding z-scores provide an intuitive framework for clinical implementation in pediatric endocrinology and facilitate the application of machine learning classification and covariate precision medicine for pediatric patients