Software Approach Research Articles

Detection of bifurcation in phase-space perturbative structures across transient wave–particle interaction in laboratory plasmas

A key ingredient for realizing a magnetically confined tritium-deuterium plasma fusion reactor is plasma heating by fusion-born high-energy helium ions, as a chained cycle of "nuclear burning." Efficient collisionless plasma heating by high-energy particles is anticipated when their energy is directly transferred to the plasma through waves. Those processes often involve nonlinear structure formations in phase-space, spanned by real-space and velocity-space coordinates, that significantly influence heating efficiency. To date, experimental knowledge of phase-space structure formation physics is severely limited, due to lack of experimental diagnostic schemes. Here, state-of-the-art hardware and software approaches for phase-space perturbative structure detection are introduced. A bifurcation in the phase-space structure formation is found, which affects the plasma heating efficiency. A confinement magnetic field structure is shown to be a potential knob for nuclear-burning control through phase-space manipulation.

Optimising acceptability using a user-centred design approach of a robotic software in Industry 4.0: A case study

While robotics and collaborative robotics are becoming increasingly commonplace in industry, operators do not always have full control over these tools. The aim of our robotics software is to give operators back the power to act. A user-centered design approach (UCD, ISO 9241-210, 2019), through four steps, has therefore been put in place within Industry 4.0. The first study aims to understand and specify the context of use through three objectives: to understand the organizational context in which the robotic software is to be designed and implemented, to characterize the end users via primary personas, and to take an interest in the operators' work in order to predict likely future situations and guide the development of the new work tool. The second study aims to specify user requirements using a card sort based on different criteria (ergonomic, UX). The third study presents the design solutions for version 2 of the robotics software. The fourth study evaluates and compares the practical acceptability (UX, usefulness, usability, mental load) and social acceptability (intent to use) of the first and second versions of the robotic software. Our results show, among other things, a significant improvement in acceptability for the second version of the software, suggesting greater acceptability of this technology thanks to the UCD approach.

Nucleo-SAFARI: Automated Identification of Fragment Ions in Top-Down MS/MS Spectra of Nucleic Acids.

Recent progress in top-down mass spectrometry analysis of progressively larger nucleic acids has enabled in-depth characterization of intact, modified RNA molecules. Development of methods for desalting and MS/MS fragmentation allows rapid acquisition of high-quality top-down MS/MS spectra of nucleic acids up to 100 nt, which has spurred the need for development of software approaches to identify and validate nucleic acid fragment ions. We have implemented an R-based approach to aid in analysis of MS/MS spectra of nucleic acids based on fragment ions observed directly in the m/z domain. This program, entitled Shiny Application for Fragment Assignment by Relative Isotopes (Nucleo-SAFARI), utilizes the Shiny HTML framework for deployment of a user-friendly application for automated annotation of top-down MS/MS spectra of nucleic acids recorded on Orbitrap mass spectrometer platforms. This approach proceeds through in silico generation of fragment ions and their isotopic distributions, followed by algorithmic assessment of the experimental isotopic distributions. Nucleo-SAFARI is available for download at https://github.com/mblanzillotti/Nucleo-SAFARI.

Advanced breast diffusion-weighted imaging: what are the next steps? A proposal from the EUSOBI International Breast Diffusion-weighted Imaging working group.

This study by the EUSOBI International Breast Diffusion-weighted Imaging (DWI) working group aimed to evaluate the current and future applications of advanced DWI in breast imaging. A literature search and a comprehensive survey of EUSOBI members to explore the clinical use and potential of advanced DWI techniques and a literature search were involved. Advanced DWI approaches such as intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), and diffusion tensor imaging (DTI) were assessed for their current status and challenges in clinical implementation. Although a literature search revealed an increasing number of publications and growing academic interest in advanced DWI, the survey revealed limited adoption of advanced DWI techniques among EUSOBI members, with 32% using IVIM models, 17% using non-Gaussian diffusion techniques for kurtosis analysis, and only 8% using DTI. A variety of DWI techniques are used, with IVIM being the most popular, but less than half use it, suggesting that the study identified a gap between the potential benefits of advanced DWI and its actual use in clinical practice. The findings highlight the need for further research, standardization and simplification to transition advanced DWI from a research tool to regular practice in breast imaging. The study concludes with guidelines and recommendations for future research directions and clinical implementation, emphasizing the importance of interdisciplinary collaboration in this field to improve breast cancer diagnosis and treatment. Advanced DWI in breast imaging, while currently in limited clinical use, offers promising improvements in diagnosis, staging, and treatment monitoring, highlighting the need for standardized protocols, accessible software, and collaborative approaches to promote its broader integration into routine clinical practice. Increasing number of publications on advanced DWI over the last decade indicates growing research interest. EUSOBI survey shows that advanced DWI is used primarily in research, not extensively in clinical practice. More research and standardization are needed to integrate advanced DWI into routine breast imaging practice.

ExaFEL: extreme-scale real-time data processing for X-ray free electron laser science

ExaFEL is an HPC-capable X-ray Free Electron Laser (XFEL) data analysis software suite for both Serial Femtosecond Crystallography (SFX) and Single Particle Imaging (SPI) developed in collaboration with the Linac Coherent Lightsource (LCLS), Lawrence Berkeley National Laboratory (LBNL) and Los Alamos National Laboratory. ExaFEL supports real-time data analysis via a cross-facility workflow spanning LCLS and HPC centers such as NERSC and OLCF. Our work therefore constitutes initial path-finding for the US Department of Energy's (DOE) Integrated Research Infrastructure (IRI) program. We present the ExaFEL team's 7 years of experience in developing real-time XFEL data analysis software for the DOE's exascale supercomputers. We present our experiences and lessons learned with the Perlmutter and Frontier supercomputers. Furthermore we outline essential data center services (and the implications for institutional policy) required for real-time data analysis. Finally we summarize our software and performance engineering approaches and our experiences with NERSC's Perlmutter and OLCF's Frontier systems. This work is intended to be a practical blueprint for similar efforts in integrating exascale compute resources into other cross-facility workflows.

AAPM Task Group Report 299: Quality control in multi-energy computed tomography.

Multi-energy computed tomography (MECT) offers the opportunity for advanced visualization, detection, and quantification of select elements (e.g., iodine) or materials (e.g., fat) beyond the capability of standard single-energy computed tomography (CT). However, the use of MECT requires careful consideration as substantially different hardware and software approaches have been used by manufacturers, including different sets of user-selected or hidden parameters that affect the performance and radiation dose of MECT. Another important consideration when designing MECT protocols is appreciation of the specific tasks being performed; for instance, differentiating between two different materials or quantifying a specific element. For a given task, it is imperative to consider both the radiation dose and task-specific image quality requirements. Development of a quality control (QC) program is essential to ensure the accuracy and reproducibility of these MECT applications. Although standard QC procedures have been well established for conventional single-energy CT, the substantial differences between single-energy CT and MECT in terms of system implementations, imaging protocols, and clinical tasks warrant QC tests specific to MECT. This task group was therefore charged with developing a systematic QC program designed to meet the needs of MECT applications. In this report, we review the various MECT approaches that are commercially available, including information about hardware implementation, MECT image types, image reconstruction, and postprocessing techniques that are unique to MECT. We address the requirements for MECT phantoms, review representative commercial MECT phantoms, and offer guidance regarding homemade MECT phantoms. We discuss the development of MECT protocols, which must be designed carefully with proper consideration of MECT technology, imaging task, and radiation dose. We then outline specific recommended QC tests in terms of general image quality, radiation dose, differentiation and quantification tasks, and diagnostic and therapeutic applications.

Blind deblurring of astronomical images using a SCGTV-based single-frame method

Atmospheric turbulence often distorts space target imaging, leading to degraded image quality. To address the issue of image quality degradation, various hardware and software approaches have been proposed, including adaptive optics, lucky imaging, and blind deconvolution. Traditional astronomical image deblurring algorithms rely on information from multiple frames, requiring extensive processing time and computational resources. This study introduces the saturation-corrected graph total variation (SCGTV) method to address low signal-to-noise ratio and pixel saturation in single-frame astronomical image deblurring. This method effectively minimizes the influence of atmospheric turbulence and noise by leveraging a reweighted graph total variation prior. Saturation correction and dark channel information are integrated in SCGTV to enhance resolution and reduce artifacts. The SCGTV method is well-designed for astronomical scenarios, successfully deblurring real-world astronomical images and demonstrating superior performance on thirty simulated dataset compared to other algorithms. Quantitative evaluations on artificially blurred datasets demonstrate that SCGTV outperforms existing methods. These enhancements provide significant benefits for astronomical observation and analysis.

The Maintenance Factor as a Necessary Parameter for Sustainable Artificial Lighting in Engineering Production—A Software Approach

The Maintenance Factor as a Necessary Parameter for Sustainable Artificial Lighting in Engineering Production—A Software Approach

Machine Learning System Aware Optimization

ABSTRACT New computer systems have emerged in response to the increasing size and complexity of modern data sets. In order to ensure optimum performance, software approaches have to be closely matched with the basic features of systems. This research demonstrates the impact of system-sensitive machine learning using an optimizer lens, a crucial design and solution factor in the majority of machine learning problems. The exactness and convergence rates are traditionally measured for the optimization method. In contrast, a number of system-related variables are crucial to modern computing systems' overall efficiency. Specifications such as data or parameters for the device and higher-level meanings, such as communication and computer interconnections may be included. We propose CoCoA, an overall learning method that closely reviews and incorporates device parameters into the process and theory. We have shown the impact of CoCoA on two conventional distributed systems, that being the traditional cluster environment and the increasingly (founded) machine learning environment. Our results show that we get orders of magnitude quick, by combining system parameters and optimization techniques, to solve current machine learning difficulties. These empirical findings support the assumption that device parameters give more knowledge about the scientific performance.

Generalized model for eigenfrequency analysis of bolted variable-stiffness flanged-cylindrical shells

A general semi-analytical modeling method for the bolted variable stiffness composite cylindrical shell, incorporating precise modeling of the boundary and connecting flanges, is proposed in this paper. The coupling between shells and flanges is achieved through a spring-based penalty function method. A novel bolted joint model for flange connections is introduced, enabling the simulation of non-uniform pressure distributions at the joint interface. The joint model features a stiffness characterization technique that effectively improves adjustability and computational efficiency. The correctness and accuracy of the proposed modeling method are systematically validated through finite element analysis based on the ANSYS commercial software and experimental testing approaches. The series of parameter influence analyses on the bolted variable stiffness composite cylindrical shell with flanges, conducted using the proposed modeling method, accurately demonstrate the influence of geometric dimensions, fiber layup parameters, and bolt parameters on the free vibration behavior of such structures. Furthermore, these analyses aid in a deeper understanding of the dynamic characteristics of such structures, partially highlighting key design considerations and offering valuable guidance for their dynamic optimization design.

Life Cyle Assessment of Activated Carbon from Waste Materials as an Adsorbent in Wastewater Treatment

Activated Carbon (AC) has been a great alternative to reduce the cost of the process in wastewater treatment plants (WWTP) but they also have several hidden impacts on the environment. The impact assessment on the waste materials from coconut shells and wood will be identified using the Life Cycle Assessment (LCA) software approach. Through the “Cradle-to-gate” approach, activated carbon made from waste materials is produced and consumed, and its eighteen environmental effects which are fine particulate matter formation, fossil resource scarcity, freshwater ecotoxicity, freshwater eutrophication, global warming, human carcinogenic toxicity, human non-carcinogenic toxicity, ionizing radiation, land use, marine ecotoxicity, marine eutrophication, mineral resource scarcity, human health, terrestrial ecosystem, stratospheric ozone depletion, terrestrial acidification, terrestrial ecotoxicity and water consumption are assessed using the LCA software. This study aims to discover whether the choice of waste material precursors from the Activated Carbon (AC) can help to minimise environmental impacts. The study evaluates the potential benefits of using waste-derived activated carbon in wastewater treatment by comparing the environmental performance of activated carbon obtained from coconut, wood, and coal. This study is based on past studies all around the world. In thirteen of the eighteen impact categories, wood has the greatest environmental impact. Coconut shells on the other hand, has the lowest total environmental impacts, ranking first or second in fifteen among the eighteen environmental categories. The findings help in making choices for environmentally friendly wastewater treatment methods by illuminating the effects of employing waste products as an alternative source of adsorbents.

How does organizational support for innovation influence job crafting and knowledge sharing behaviors? A comparison between teleworkers and office workers

PurposeScholars highlight the lack of research that explains the mechanisms leading to knowledge sharing, which appears complex and involves many variables. The primary aim of this study is to investigate the direct effect of organizational support for innovation on job crafting behaviors and knowledge sharing. The second objective is to assess the mediating role of job crafting in the relationship between organizational support for innovation and knowledge sharing. The third aim is to compare the direct effects of organizational support for innovation on job crafting behaviors and knowledge sharing between teleworkers and office workers.Design/methodology/approachBased on an empirical study involving 193 teleworkers and 191 office workers, the structural equation modeling method was employed to examine the direct and indirect effects of organizational support for innovation on knowledge sharing via job crafting behaviors. The comparison between teleworkers and office workers was investigated using a multigroup approach in AMOS software. This research is grounded in the conservation of resources theory and social exchange theory to elucidate these relationships.FindingsThe results indicate that organizational support for innovation has a positive influence on job crafting activities, manifested by the increase in structural and social resources, as well as the amplification of work-related challenges. The results also indicate that organizational support for innovation directly promotes knowledge sharing behavior and indirectly through job crafting. Furthermore, the findings reveal that these effects on job crafting and knowledge sharing are stronger among teleworkers compared to office workers.Research limitations/implicationsThe study has limitations. Its cross-sectional design does not establish causality, potentially leading to common method variance. However, after implementing many procedural and performing statistical tests, common method variance was not significant in this research. Replicating the study longitudinally would be valuable. Additionally, considering personality traits and technology characteristics in job crafting behaviors would be beneficial. Lastly, the study focuses only on accountants and predates COVID-19, which may impact its findings and generalizability.Practical implicationsThe study’s findings underscore the practical significance of supporting innovation and fostering job crafting to enhance knowledge sharing, particularly for remote workers. It highlights that the extent of employees’ engagement in job crafting depends on the level of innovation support provided in their workplace. To mitigate potential negative outcomes such as increased absenteeism, reduced productivity and retention challenges, organizations could benefit from training supervisors to prioritize and encourage job crafting and knowledge sharing behaviors among employees, especially in telework settings. Ensuring alignment between organizational messaging and managerial attitudes is crucial. Without autonomy or flexibility for job crafting, the positive effects of organizational innovation support may be limited.Originality/valueThis study contributes to the literature by demonstrating that job crafting behaviors serves as mechanisms between organizational support for innovation and knowledge sharing. The findings further advance the literature by revealing three psychological and motivational processes that may explain this relationship, particularly when comparing teleworkers to office workers. Our results reveal that the effect of organizational support for innovation on job crafting and knowledge sharing is stronger among workers who telework compared to office workers. This advances the theory of conservation of resources, especially the significance of resource gains, particularly in contexts where employees need resources, such as in telework.

A multi-objective path computation approach for software defined internet of underwater things

A multi-objective path computation approach for software defined internet of underwater things

An explicit finite element discrete crack analysis of open hole tension failure in composites

Developing efficient, robust methods for predicting the progressive failure of carbon fiber composites under tension is essential for optimal design, balancing weight reduction, damage tolerance, and service life. Implicit finite element (FE) methods struggle with the complex interplay of failure mechanisms, leading to a heavy reliance on costly, time-consuming experimental testing. This study introduces a novel explicit algorithm designed for laminate-level mesh models, capable of simulating cohesive cracks without predefined paths. Utilizing an explicit finite element software approach, our method effectively addresses convergence issues associated with the non-linear and unstable nature of composite failures during quasi-static loading. Contrasting with implicit FE method, this mesh-independent approach is both practical and numerically robust. The algorithm’s performance, tested on carbon-fiber reinforced composites with varying ply configurations [452/-452]s, [45n/90n/-45n/0n]s and [45/90/-45/0]ns (n=1/2/4/8), demonstrates improved simulation accuracy and efficiency over implicit FE methods, aligning well with prior experimental and simulation data. This advancement offers a promising solution for accurately simulating and understanding the complex failure behavior of these composites.

Rapid assay development for low input targeted proteomics using a versatile linear ion trap.

Advances in proteomics and mass spectrometry enable the study of limited cell populations, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive low-mass accuracy measurements, these instruments are effectively restricted to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. Here, we describe a workflow using a new hybrid quadrupole-LIT instrument that rapidly develops targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Using an automated software approach for scheduling parallel reaction monitoring assays (PRM), we show consistent quantification across three orders of magnitude in a matched-matrix background. We demonstrate measuring low-level proteins such as transcription factors and cytokines with quantitative linearity below two orders of magnitude in a 1 ng background proteome without requiring stable isotope-labeled standards. From a 1 ng sample, we found clear consistency between proteins in subsets of CD4 + and CD8 + T cells measured using high dimensional flow cytometry and LIT-based proteomics. Based on these results, we believe hybrid quadrupole-LIT instruments represent an economical solution to democratizing mass spectrometry in a wide variety of laboratory settings.

Liquid chromatography-mass spectrometry quantification of phytochemicals in Withania somnifera using data-dependent acquisition, multiple-reaction-monitoring, and parallel-reaction-monitoring with an inclusion list.

Characterization of botanical extracts by mass spectrometry-based metabolomics analysis helps in determining the phytochemical composition that underlies their bioactivity and potential health benefits, while also supporting reproducibility of effects in clinical trials. The quantification of seven withanolides in Withania somnifera using three mass-spectrometry methods was evaluated using Deming regression. Two high-resolution time-of-flight mass spectrometry methods were used, one operating in data-dependent acquisition mode and the other in parallel-reaction-monitoring mode with an inclusion list. The two high-resolution time-of-flight mass spectrometry methods were compared to a multiple-reaction-monitoring method. We evaluated in-source fragmentation of steroidal glycosides and optimized the methods accordingly. A novel software approach to integrating parallel-reaction-monitoring data acquired with an inclusion list was developed. Combining and comparing quantitative results allowed for quantitative specificity, good precision, and adjustment of instrument source conditions for optimal quantification by multiple-reaction-monitoring mass spectrometry, an analytical method that is widely accessible in analytical and phytochemical laboratories.

Effects of camera calibration on the accuracy of Unmanned Aerial Vehicle sensor products

Utilization of Unmanned Aerial Vehicles (UAV) mounted with non-metric consumer grade digital cameras is on the rise globally due to their affordability and ease of operation. For high-accuracy UAV products, there is a need for accurate camera parameters determined through camera calibration. Camera calibration can be performed before (pre-calibration) or during the bundle block adjustment (self-calibration). This study aims to analyze the effect of camera calibration parameters on the accuracy of UAV products namely Digital Elevation Model (DEM) and orthoimage. Camera calibration parameters are estimated using two approaches namely self-calibration which deploys 3D image information of the scene in a bundle adjustment and a 2D reference object-based approach known as Zhang’s technique which requires image information of a planar pattern. A DJI FC220 camera mounted on a DJI Mavic Pro UAV was used. Self-calibration was deployed in Agisoft Metashape software based on Brown’s method and Zhang’s technique was deployed in MATLAB and OpenCV. Based on internal measures of accuracy, OpenCV yields the least reprojection error of 0.14 followed by MATLAB (0.79) and self-calibration (1.21). Processing without calibration yields the highest reprojection error of 2.18. Based on external measures of accuracy, that is the geometric accuracy of UAV products, self-calibration yields the least RMSE of 8.2 and 1.4 cm, for the horizontal and vertical, respectively, followed by Zhang’s technique with 9.6 and 2.3 cm in MATLAB and 13.5 and 4.3 cm in OpenCV. Processing without calibration yields the highest vertical RMSE of 20.0 and 22.9 cm for the horizontal and vertical, respectively. Comparison of accuracy of UAV mapping products computed with and without calibration emphasizes the need for camera calibration to optimize accuracy of UAV products. This study recommends assessing other photogrammetric mapping software and camera calibration approaches and the effect of flying heights on calibration parameters and mapping accuracy.

Characterization of the in vitro metabolic profile of nazartinib in HLMs using UPLC-MS/MS method: In silico metabolic lability and DEREK structural alerts screening using StarDrop software

The orally given, irreversible, third-generation inhibitor of the epidermal growth factor receptor (EGFR), known as Nazartinib (EGF816), is now undergoing investigation in Phase II clinical trials conducted by Novartis for Non-Small Cell Lung Cancer. The primary aim of the current research was to establish a rapid, specific, environmentally friendly, and highly versatile UPLC-MS/MS methodology for the determination of nazartinib (NZT) levels in human liver microsomes (HLMs). Subsequently, same approach was used to examine the metabolic stability of NZT. The UPLC-MS/MS method employed in HLMs was validated as stated in the bioanalytical method validation criteria outlined by the US- FDA. The evaluation of the metabolic stability of NZT and the identification of potentially structural alarms were performed using the StarDrop software package that includes the P450 and DEREK software. The calibration curve for NZT showed a linearity in the range from 1 to 3000 ng/mL. The inter-day accuracy and precision exhibited a range of values between −4.33 % and 4.43 %, whereas the intra-day accuracy and precision shown a range of values between −2.78 % and 7.10 %. The sensitivity of the developed approach was verified through the determination of a LLOQ of 0.39 ng/mL. The intrinsic clearance and in vitro half-life of NZT were assessed to be 46.48 mL/min/kg and 17.44 min, respectively. In our preceding inquiry, we have effectively discerned the bioactivation center, denoted by the carbon atom between the unsaturated conjugated system and aliphatic linear tertiary amine. In the context of computational software, making minor adjustments or substituting the dimethylamino-butenoyl moiety throughout the drug design process may increase the metabolic stability and safety properties of new synthesized derivatives. The efficiency of utilizing different in silico software approaches to conserve resources and reduce effort was proved by the outcomes attained from in vitro incubation experiments and the use of NZT in silico software.

Homogeneous Equilibrium Modeling for Subcritical Flows in Liquid Rocket Engine Cooling Systems

During off-design operations of liquid rocket engines, the coolant operating conditions can easily extend from the subcritical to the supercritical regime. As a matter of fact, it is very useful to have a single software able to study the flow in this range of operating conditions, providing reliable simulations that are reasonably quick and able to accurately estimate and assess the increase in coolant temperature along the channel and the wall temperature field. This objective is pursued by extending an established approach for the study of supercritical flows to the case of subcritical heating, where a two-phase flow may occur. This is done by exploiting the so-called homogeneous equilibrium model, which has been shown to be sufficiently predictive and accurate for specific applications. With the aim of demonstrating and discussing the potential and limitations of such a single software approach, analyses are conducted on different test cases where two-phase flow is induced by cavitation or flow boiling, and results are compared with those of analytical models and experimental data. It is found that, in addition to some intrinsic limitations in the analysis of subcooled boiling flows, satisfactory agreement with experimental data is obtained in the post-critical-heat-flux regime.

CViewer: a Java-based statistical framework for integration of shotgun metagenomics with other omics datasets

BackgroundShotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, the latter of which then helps retrieve their function based on annotated gene products, mRNA, protein, and metabolites. Within the context of a specific hypothesis, additional modalities are often included, to give host-microbiome interaction. For example, in human-associated microbiome projects, it has become increasingly common to include host immunology through flow cytometry. Whilst there are plenty of software approaches available, some that utilize marker-based and assembly-based approaches, for downstream statistical analyses, there is still a dearth of statistical tools that help consolidate all such information in a single platform. By virtue of stringent computational requirements, the statistical workflow is often passive with limited visual exploration.ResultsIn this study, we have developed a Java-based statistical framework (https://github.com/KociOrges/cviewer) to explore shotgun metagenomics data, which integrates seamlessly with conventional pipelines and offers exploratory as well as hypothesis-driven analyses. The end product is a highly interactive toolkit with a multiple document interface, which makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant patterns. We have designed algorithms based on frequently used numerical ecology and machine learning principles, with value-driven from integrated omics tools which not only find correlations amongst different datasets but also provide discrimination based on case–control relationships.ConclusionsCViewer was used to analyse two distinct metagenomic datasets with varying complexities. These include a dietary intervention study to understand Crohn’s disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean. Complete analyses of both studies in CViewer then provide very powerful mechanistic insights that corroborate with the published literature and demonstrate its full potential.EDLr-6-tH-9CdVCxoa6z_8Video