Model System Research Articles

The Impact of Body Image on Physical Function and Return to Work After Burn: A Burn Model System Study

Abstract Burn injury can have a lasting impact on quality of life beyond the initial injury. The aim of this study was to examine the recovery process through analyzing the relationship between body image, physical function, and return to work. This study uses data from the Burn Model System National Longitudinal Database and includes 1001 participants injured between 2015 and 2023 who were measured using Patient-Reported Outcomes Measurement Information System-29 Physical Function, Employment Status, and the Body Image subscale. Associations were explored using multivariate linear and logistic regression analyses. Physical function was positively associated with body image and negatively associated with burn size, age, and amputation. Employment was positively correlated with body image and employment at injury, while negatively correlated with age. Other variables, including burn etiology, burn center site, race, and ethnicity, were significant at different time points. By understanding how these factors change and are associated with outcomes across recovery, the healthcare team can make more tailored efforts to improve the psychosocial and physical well-being of burn survivors.

Data-driven discovery of dynamics from time-resolved coherent scattering

Coherent X-ray scattering (CXS) techniques are capable of interrogating dynamics of nano- to mesoscale materials systems at time scales spanning several orders of magnitude. However, obtaining accurate theoretical descriptions of complex dynamics is often limited by one or more factors—the ability to visualize dynamics in real space, computational cost of high-fidelity simulations, and effectiveness of approximate or phenomenological models. In this work, we develop a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved CXS measurements without solving the phase reconstruction problem for the entire time series of diffraction patterns. Our approach uses neural differential equations to parameterize unknown real-space dynamics and implements a computational scattering forward model to relate real-space predictions to reciprocal-space observations. This method is shown to recover the dynamics of several computational model systems under various simulated conditions of measurement resolution and noise. Moreover, the trained model enables estimation of long-term dynamics well beyond the maximum observation time, which can be used to inform and refine experimental parameters in practice. Finally, we demonstrate an experimental proof-of-concept by applying our framework to recover the probe trajectory from a ptychographic scan. Our proposed framework bridges the wide existing gap between approximate models and complex data.

Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community

Abstract. The Community Multiscale Air Quality Model (CMAQ) is a local- to hemispheric-scale numerical air quality modeling system developed by the U.S. Environmental Protection Agency (USEPA) and supported by the Community Modeling and Analysis System (CMAS) center. CMAQ is used for regulatory purposes by the USEPA program offices and state and local air agencies and is also widely used by the broader global research community to simulate and understand complex air quality processes and for computational environmental fate and transport and climate and health impact studies. Leveraging state-of-the-science cloud computing resources for high-performance computing (HPC) applications, CMAQ is now available as a fully tested, publicly available technology stack (HPC cluster and software stack) for two major cloud service providers (CSPs). Specifically, CMAQ configurations and supporting materials have been developed for use on their HPC clusters, including extensive online documentation, tutorials and guidelines to scale and optimize air quality simulations using their services. These resources allow modelers to rapidly bring together CMAQ, cloud-hosted datasets, and visualization and evaluation tools on ephemeral clusters that can be deployed quickly and reliably worldwide. Described here are considerations in CMAQ version 5.3.3 cloud use and the supported resources for each CSP, presented through a benchmark application suite that was developed as an example of a typical simulation for testing and verifying components of the modeling system. The outcomes of this effort are to provide findings from performing CMAQ simulations on the cloud using popular vendor-provided resources, to enable the user community to adapt this for their own needs, and to identify specific areas of potential optimization with respect to storage and compute architectures.

Scalable Self-Assembly of Composite Nanofibers into High-Energy-Density Li-Ion Battery Electrodes.

The application of nanosized active particles in Li-ion batteries has been the subject of intense investigation, yielding mixed results in terms of overall benefits. While nanoparticles have shown promise in improving rate performance and reducing issues related to cracking, they have also faced criticism due to side reactions, low packing density, and consequent subpar volumetric battery performance. Interesting processes such as self-assembly have been proposed to increase packing density, but these tend to be incompatible with scalable processes such as roll-to-roll coating, which are essential to manufacture electrodes at scale. Addressing these challenges, this research demonstrates the long-range self-assembly of carbon-decorated V2O5 nanofiber cathodes as a model system. These nanorods are closely packed into thick electrode films, exhibiting a high volumetric capacity of 205 mA h cm-3at 0.2 C. This surpasses the volumetric capacity of unaligned V2O5 nanofiber electrodes (82 mA h cm-3) under the same cycling conditions. We also demonstrate that these energy-dense electrodes retain an excellent capacity of up to 190.4 mA h cm-3(<2% loss) over 500 cycles without needing binders. Finally, we demonstrate that the proposed self-assembly process is compatible with roll-to-roll coating. This work contributes to the development of energy-dense coatings for next-generation battery electrodes with high volumetric energy density.

The chemical composition of secondary organic aerosols regulates transcriptomic and metabolomic signaling in an epithelial-endothelial in vitro coculture

BackgroundThe formation of secondary organic aerosols (SOA) by atmospheric oxidation reactions substantially contributes to the burden of fine particulate matter (PM2.5), which has been associated with adverse health effects (e.g., cardiovascular diseases). However, the molecular and cellular effects of atmospheric aging on aerosol toxicity have not been fully elucidated, especially in model systems that enable cell-to-cell signaling.MethodsIn this study, we aimed to elucidate the complexity of atmospheric aerosol toxicology by exposing a coculture model system consisting of an alveolar (A549) and an endothelial (EA.hy926) cell line seeded in a 3D orientation at the air‒liquid interface for 4 h to model aerosols. Simulation of atmospheric aging was performed on volatile biogenic (β-pinene) or anthropogenic (naphthalene) precursors of SOA condensing on soot particles. The similar physical properties for both SOA, but distinct differences in chemical composition (e.g., aromatic compounds, oxidation state, unsaturated carbonyls) enabled to determine specifically induced toxic effects of SOA.ResultsIn A549 cells, exposure to naphthalene-derived SOA induced stress-related airway remodeling and an early type I immune response to a greater extent. Transcriptomic analysis of EA.hy926 cells not directly exposed to aerosol and integration with metabolome data indicated generalized systemic effects resulting from the activation of early response genes and the involvement of cardiovascular disease (CVD) -related pathways, such as the intracellular signal transduction pathway (PI3K/AKT) and pathways associated with endothelial dysfunction (iNOS; PDGF). Greater induction following anthropogenic SOA exposure might be causative for the observed secondary genotoxicity.ConclusionOur findings revealed that the specific effects of SOA on directly exposed epithelial cells are highly dependent on the chemical identity, whereas non directly exposed endothelial cells exhibit more generalized systemic effects with the activation of early stress response genes and the involvement of CVD-related pathways. However, a greater correlation was made between the exposure to the anthropogenic SOA compared to the biogenic SOA. In summary, our study highlights the importance of chemical aerosol composition and the use of cell systems with cell-to-cell interplay on toxicological outcomes.Graphical

GSMS: a goaf scanning and modeling system for underground mines based on a quadruped robot

Abstract Underground mining creates goafs, impacting resource extraction and safety. To address the issues of low efficiency and high reliance on manual operations in traditional techniques, this paper proposes a goaf scanning and modeling system (GSMS) for underground mine based on a quadruped robot. This system performs three-dimensional modeling of goafs through two main modules: frontend mapping and backend optimization, and evaluates the modeling accuracy using a multi-dimensional precision evaluation module. To validate the effectiveness of this system, both simulation and field experiments were conducted. The experimental results indicate that the goaf models established using GSMS achieved a level of accuracy comparable to traditional methods: the volume error of the constructed goaf models compared to the reference models was less than 3%, and the average profile contour similarity exceeded 98%.

Multi‐response kinetic study of Maillard reaction hazards in the glucose‐lysine model system

AbstractBACKGROUNDNε‐carboxymethyllysine (CML), Nε‐carboxyethyllysine (CEL) and α‐aminoadipic acid (AAA) are important foodborne hazards and their intake can cause a variety of diseases in humans. It is extremely important to investigate the formation mechanism of CML, CEL and AAA, as well as their association with each other when aiming to control their production.RESULTSA multi‐response kinetic model was developed within the glucose‐lysine Maillard reaction model system. The concentrations of glucose, lysine, glyoxal (GO), methylglyoxal (MGO), CML, CEL and AAA were quantified at different temperature (100–160 °C) and at different intervals (0–60 min). The experimental data were fitted to the proposed model to calculate kinetic parameters for the corresponding steps. The results indicated that the production of CML was primarily relied on the direct oxidative cleavage of the Amadori product, rather than the reaction between GO and Lys, whereas CEL and AAA were generated through the reaction of MGO with Lys. Significantly, the reaction between α‐dicarbonyl compounds and Lys preferentially generated CML and CEL, resulting in the lower concentrations of AAA compared to CML and CEL.CONCLUSIONThe multi‐response kinetic model developed in the present study can be applied well to the Maillard reaction. The relationship between the formation mechanisms of CML, CEL and AAA is also explained. © 2024 Society of Chemical Industry.

Measurement report: Source attribution and estimation of black carbon levels in an urban hotspot of the central Po Valley – an integrated approach combining high-resolution dispersion modelling and micro-aethalometers

Abstract. Understanding black carbon (BC) levels and its sources in urban environments is of paramount importance due to the far-reaching health, climate, and air quality implications. While several recent studies have assessed BC concentrations at specific fixed urban locations, there is a notable lack of knowledge in the existing literature on spatially resolved data alongside source estimation methods. This study aims to fill this gap by conducting a comprehensive investigation of BC levels and sources in Modena (Po Valley, Italy), which serves as a representative example of a medium-sized urban area in Europe. Using a combination of multi-wavelength micro-aethalometer measurements and a hybrid Eulerian–Lagrangian modelling system, we studied two consecutive winter seasons (February–March 2020 and December 2020–January 2021). Leveraging the multi-wavelength absorption analyser (MWAA) model, we differentiate sources (fossil fuel combustion, FF, and biomass burning, BB) and components (BC vs. brown carbon, BrC) from micro-aethalometer measurements. The analysis reveals consistent, minimal diurnal variability in BrC absorption, in contrast to FF-related sources that exhibit distinctive diurnal peaks during rush hours, while BB sources show less diurnal variation. The city itself contributes significantly to BC concentrations (52 ± 16 %), with BB and FF playing a prominent role (35 ± 15 % and 9 ± 4 %, respectively). Long-distance transport also influences BC concentrations, especially in the case of BB and FF emissions, with 28 ± 1 % and 15 ± 2 %, respectively. When analysing the traffic-related concentrations, Euro 4 diesel passenger cars considerably contribute to the exhaust emissions. These results provide valuable insights for policy makers and urban planners to manage BC levels in medium-sized urban areas, taking into account local and long-distance sources.

Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal

Accurate simulation and stream flow prediction are essential for effective water resources planning and management. Hydrological models have long been used for rainfall-runoff modeling; in recent years, with the availability of large datasets, machine learning algorithms, and deep learning techniques have become powerful tools to explore. This study compares rainfall-runoff simulation between the Long and Short-Term Memory (LSTM) (a deep learning model) and the Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS) (a conceptual model) for a steep mountainous catchment in Nepal. During calibration (1989–2006) and validation (2007–2016), the HEC-HMS model demonstrated a root mean square error (RMSE) and Nash Sutcliffe Efficiency (NSE) of 18.3 and 0.73 and 22.93 and 0.66, respectively, for the calibration and validation periods. During the same periods, the LSTM model showed values of 7.25 and 0.87 and 19.59 and 0.64, respectively, for the calibration and validation periods. The performance indices show that the LSTM model has performed better than the HEC-HMS model. The outcomes of this study suggest that the LSTM deep learning model can simulate the discharge successfully across steep mountainous catchments characterized by monsoon and snowmelt runoff, even with a wide range of precipitation variation, making it an asset for hydrological modeling and water resources management in the data-scarce region.

Laminar specificity and coverage of viral-mediated gene expression restricted to GABAergic interneurons and their parvalbumin subclass in marmoset primary visual cortex.

In the mammalian neocortex, inhibition is important for dynamically balancing excitation and shaping the response properties of cells and circuits. The various computational functions of inhibition are thought to be mediated by different inhibitory neuron types, of which a large diversity exists in several species. Current understanding of the function and connectivity of distinct inhibitory neuron types has mainly derived from studies in transgenic mice. However, it is unknown whether knowledge gained from mouse studies applies to the non-human primate, the model system closest to humans. The lack of viral tools to selectively access inhibitory neuron types has been a major impediment to studying their function in the primate. Here, we have thoroughly validated and characterized several recently developed viral vectors designed to restrict transgene expression to GABAergic cells or their parvalbumin (PV) subtype, and identified two types that show high specificity and efficiency in marmoset V1. We show that in marmoset V1, AAV-h56D induces transgene expression in GABAergic cells with up to 91-94% specificity and 79% efficiency, but this depends on viral serotype and cortical layer. AAV-PHP.eB-S5E2 induces transgene expression in PV cells across all cortical layers with up to 98% specificity and 86-90% efficiency, depending on layer. Thus, these viral vectors are promising tools for studying GABA and PV cell function and connectivity in the primate cortex.

Design and performance of ELSA v2.0: an isochronal model for ice-sheet layer tracing

Abstract. We provide a detailed description of the ice-sheet layer age tracer Englacial Layer Simulation Architecture (ELSA) – a model that uses a straightforward method to simulate the englacial stratification of large ice sheets – as an alternative to Eulerian or Lagrangian tracer schemes. ELSA's vertical axis is time, and individual layers of accumulation are modeled explicitly and are isochronal. ELSA is not a standalone ice-sheet model but requires unidirectional coupling to another model providing ice physics and dynamics (the “host model”). Via ELSA's layer tracing, the host model’s output can be evaluated throughout the interior using ice core or radiostratigraphy data. We describe the stability and resolution dependence of this coupled modeling system using simulations of the last glacial cycle of the Greenland ice sheet using one specific host model. Key questions concern ELSA's design to maximize usability, which includes making it computationally efficient enough for ensemble runs, as well as exploring the requirements for offline forcing of ELSA with output from a range of existing ice-sheet models. ELSA is an open source and collaborative project, and this work provides the foundation for a well-documented, flexible, and easily adaptable model code to effectively force ELSA with (any) existing full ice-sheet model via a clear interface.

Laminar specificity and coverage of viral-mediated gene expression restricted to GABAergic interneurons and their parvalbumin subclass in marmoset primary visual cortex

In the mammalian neocortex, inhibition is important for dynamically balancing excitation and shaping the response properties of cells and circuits. The various computational functions of inhibition are thought to be mediated by different inhibitory neuron types, of which a large diversity exists in several species. Current understanding of the function and connectivity of distinct inhibitory neuron types has mainly derived from studies in transgenic mice. However, it is unknown whether knowledge gained from mouse studies applies to the non-human primate, the model system closest to humans. The lack of viral tools to selectively access inhibitory neuron types has been a major impediment to studying their function in the primate. Here, we have thoroughly validated and characterized several recently developed viral vectors designed to restrict transgene expression to GABAergic cells or their parvalbumin (PV) subtype, and identified two types that show high specificity and efficiency in marmoset V1. We show that in marmoset V1, AAV-h56D induces transgene expression in GABAergic cells with up to 91–94% specificity and 79% efficiency, but this depends on viral serotype and cortical layer. AAV-PHP.eB-S5E2 induces transgene expression in PV cells across all cortical layers with up to 98% specificity and 86–90% efficiency, depending on layer. Thus, these viral vectors are promising tools for studying GABA and PV cell function and connectivity in the primate cortex.

A Study on the Impact of Vertical Grid Parameter Perturbations in the Regional Ocean Modeling System

In this study, the Regional Ocean Modeling System (ROMS) is employed to construct a three-dimensional barotropic ocean model with a monodirectional upper boundary and homogeneous and steady wind covering the entire computation area. Eight perturbation experiments are designed to determine the vertical grid distribution difference with high resolution at the surface and bottom. Two types are considered in the model, including removing the Coriolis force (type 1) and employing a different Coriolis force (type 2). According to the experiments, the velocity of the current in type 1 yields uncertainty, and wind energy could penetrate the upper ocean and reach the abyss. The surface velocity in type 2 is fundamentally compatible with the empirical relationship constructed by Ekman, and the curved lines of the vertical distribution of horizontal currents nearly match. For type 1, the velocity is very strong from the sea surface to the bottom. When comparing type 1 and type 2 cases, the Coriolis force obstructs the wind energy transfer into the deep ocean. In addition, the European Centre for Medium-Range Weather Forecasts (ECMWF)’s global surface wind distribution indicates that the realistic ocean upper wind boundary is similar to the numerical experiment in the Pacific and Atlantic oceans, where the wind direction is along the latitude line at the equator. In order to make the experimental situation as close as possible to the real ocean, validation experiments are conducted in this study to consider the uncertainty in the current profile at the equator. The simulation results of type 1 differ significantly from the data obtained from the real ocean. This uncertainty may transfer the signal to higher latitudes, causing incorrect simulation results, especially in the critical region. Overall, this research not only makes discoveries in physical ocean theory but also guides predictive and forecasting techniques for ocean modeling.

Synergy Effect of High K-Low Ca-High Si Biomass Ash Model System on Syngas Production and Reactivity Characteristics during Petroleum Coke Steam Gasification

The synergy effect of high K-low Ca-high Si biomass ash-based model system (BAMS) on the synthesis gas output and reaction characteristics of petroleum coke (PC) steam gasification process was studied using three biomass ash (BA) components, KCl, SiO2, and CaCO3, which were used as the model compounds. In the ternary model system, the steam gasification experiment of PC was conducted using a fixed bed reactor and gas phase chromatography. The synergistic effects of binary and ternary components in the ternary model system on the gasification of PC were obtained. These investigations were based on the data from the gas analysis and examined the gasification reaction process, syngas release behavior, and reaction characteristics. This study examined the effects of binary and ternary components in the ternary model system on the evolution of semi-char structure during PC gasification. This correlation revealed the synergistic effect of the model system on PC gasification. Scanning electron microscope (SEM) and Raman spectroscopy were used to characterize the structure and surface microstructure of the gasification semi-char. The results showed that the yields of different gases in the ternary model system were in H2 &gt; CO &gt; CO2. Compared with single PC gasification, the yields of H2, CO, syngas, and carbon conversion were increased by 29.42 mmol/g, 20.40 mmol/g, 56.68 mmol/g, and 0.35, respectively. All other components in the ternary model system with high K-low Ca-high Si demonstrated catalytic effect, except for SiO2 and the Ca-Si system, which showed inhibitory effects on syngas release and reaction features. Integrating SEM and Raman spectroscopic analyses, it was elucidated that CaCO3 and KCl diminished the degree of graphitization in semi-char through interactions with the carbonaceous matrix. This phenomenon facilitated the gasification process and exhibited a synergistic effect. Secondly, SiO2 will react with CaCO3 and KCl, producing inert silicates and inactivating these compounds, leading to the decline of catalysis.

Optimizing Numerical Weather Prediction Utility of the Maryland Mesonet with Observing System Simulation Experiments

Abstract The Maryland Mesonet Project will construct a network of 75 surface observing stations with aims that include mitigating the statewide impact of severe convective storms and improving analyses of record. The spatial configuration of mesonet stations is expected to affect the utility newly provided observations will have via data assimilation, making it desirable to study the effects of mesonet configuration. Furthermore, the impact associated with any observing system configuration is constrained by errors inherent to the prediction systems used to generate forecasts, which may change with future advances in data assimilation methodology, physical parameterization schemes, and resource availability. To address such possibilities, we perform sets of observing system simulation experiments using a high-resolution regional modeling system to assess the expected impact of four candidate mesonet configurations. Experiments cover seven 18-hour case-study events featuring moist convective regimes associated with severe weather over the state of Maryland and are performed using two versions of our experimental modeling system: a ’standard-uncertainty’ configuration tuned to be representative of existing convective-allowing prediction systems, and a ’constrained-uncertainty’ configuration with reduced boundary condition and model error that reflects a possible trajectory for future prediction systems. We find that the assimilation of mesonet data produces definitive improvements to analysis fields below 1000 m that are mediated by modeling system uncertainty. Conversely, mesonet impact on forecast verification is inconclusive and strongly variable across verification metrics. The impact of mesonet configuration appears limited by a saturation effect that caps local analysis improvements past a minimal density of observing stations.

A network pharmacology-based approach to understand the mechanism of action of anti-mycobacterial activity of Acacia nilotica: a modelling and experimental study.

The rapid rise in drug-resistant tuberculosis poses a serious threat to public health and demands the discovery of new anti-mycobacterial agents. Medicinal plants are a proven potential source of bioactive compounds; however, identifying those responsible for the putative anti-mycobacterial action still remains a challenging task. In this study, we undertook a systematic network pharmacology approach to identify and evaluate anti-mycobacterial compounds from a traditional plant, Acacia nilotica, as a model system. The protein-protein interaction network revealed 17 key pathways in M. tuberculosis encompassing 40 unique druggable targets that are necessary for its growth and survival. The phytochemicals of A. nilotica were preferentially found to interfere with the cell division and cell wall biogenesis proteins, especially FtsZ and Mur. Notably, the compounds epigallocatechin, ellagic acid, chlorogenic acid, and D-pinitol were found to exhibit a potential polypharmacological effect against multiple proteins. Further, in vitro studies confirmed that the selected candidates, chlorogenic acid, and ellagic acid exhibited potent anti-mycobacterial activity (against M. smegmatis) with specific inhibition of purified M.tb FtsZ enzyme. Taken together, the present study demonstrates that network pharmacology combined with molecular docking can be utilized as an efficient approach to identify potential bioactive phytochemicals from natural products along with their mechanism of action. Hence, the compounds identified in this study can be potential lead candidates for developing novel anti-mycobacterial drugs, while the key proteins identified here can be potential drug targets.

Catalyzing Knoevenagel Condensation and Radioiodine Sequestration with Tuned Porous Organic Polymers to Decipher the Role of Surface Area, Pore Volume, and Heteroatom.

The impact of surface area, pore volume, and heteroatom type on the performance of porous organic polymers (POPs) in various applications remains unclear. To investigate this, three isoreticular POPs were employed having one common building block, resulting in varying surface areas, pore volumes, and heteroatom compositions. This study aimed to establish a correlation between the structural features of POPs (surface area, pore volume, and heteroatom type) with their adsorption capacity, and catalytic efficiency. To explore this relationship, the Knoevenagel condensation reaction was used as a model system, testing various substituted aldehydes to further validate our findings. Additionally, the capture of radioactive iodine vapor at 75°C was simulated to examine the correlation with adsorption capacity, comparing the gravimetric iodine uptake capacity of each POP to gain insights into this relationship.

Exploring the Relationship Between COVID-19 Transmission and Population Mobility over Time

This study explores the dynamic relationship between COVID-19 transmission and transportation mobility, with an emphasis on understanding the time-varying bidirectional interplay across the different phases of the pandemic. To gain insight into this relationship, we analyzed county-level data on transmission and mobility patterns from the United States over a 74-week period using a comprehensive list of factors including: temporal factors, socio-demographics, health indicators, health care infrastructure attributes, and spatial factors. For our analysis, we proposed a simultaneous econometric model system that explicitly accounts for the bidirectional relationship between COVID-19 transmission and mobility patterns while also accounting for the influence of common unobserved factors on the two variables. The model results strongly support our hypothesis that COVID-19 transmission and mobility patterns are interconnected. Further, our findings show distinct phases of the bidirectional relationship influenced by behavior changes, vaccine availability, and the emergence of new variants. Additionally, we conducted a validation exercise on a hold-out sample to assess the robustness of our model. The results confirm the superiority of the simultaneous model system with enhanced interpretability and prediction capability. By analyzing data from several weeks for the COVID-19 pandemic, our study provides valuable insights into the evolving dynamics and potential strategies for future pandemics.

Modeling X chromosome inactivation using t5iLA naive human pluripotent stem cells

BackgroundX chromosome inactivation (XCI) is a critical epigenetic event for dosage compensation of X-linked genes in female mammals, ensuring developmental stability. A robust in vitro model is required for mimicking XCI during the early stages of embryonic development. This methodology article introduces an advanced framework for the in-depth study of XCI using human pluripotent stem cells (hPSCs). By focusing on the transition between naive and primed pluripotent states, we highlight the role of long non-coding RNA X-inactive specific transcript (XIST) and epigenetic alterations in mediating XCI.ResultsOur methodology enables the distinction between naive and primed hESCs based on XIST expression and the activity of X-linked reporters, facilitating the investigation of XCI initiation and maintenance. Through detailed experimental procedures, we demonstrate the utility of our hESC lines in modeling the process of human XCI, including the establishment of conditions for random XCI induction and the analysis of X chromosome reactivation.MethodsThe study outlines a comprehensive approach for characterizing the X chromosome status in hPSCs, employing dual fluorescent reporter hESC lines. These reporter lines enable real-time tracking of XCI dynamics through differentiation processes. We detailed protocols for the induction of X chromosome reactivation and inactivation, as well as the X status characterization methods including cultivation of hESCs, flow cytometric analysis, RNA fluorescence in situ hybridization (FISH), and transcriptome sequencing, providing a step-by-step guide for researchers to investigate XCI mechanisms in vitro.ConclusionsThis article provides a detailed, reproducible methodology for studying XCI mechanisms in vitro, employing hPSCs as a model system. It presents a significant advance in our ability to investigate XCI, offering potential applications in developmental biology, disease modeling, and regenerative medicine. By facilitating the study of XCI dynamics, this methodological framework paves the way for deeper understanding and manipulation of this fundamental biological process.

Parameterization of a biomechanical collision model for safe human‐robot collaboration

AbstractThe Fraunhofer IFF has developed a patented model (Fast Response Model = FRM) for safe Human‐Robot Collaboration (HRC), enabling the calculation of reaction forces in the event of a collision between a contact body of any shape and a selected human body part. This paper specifically focuses on adapting the model for five selected body locations on the hand‐arm system. A comparative analysis is conducted, contrasting the results obtained from the FRM with those derived from existing finite element reference models to assess the plausibility of the model's system response. Additionally, computational times of the model are compared with those of established models for simulation‐based risk evaluation of robot collisions. The model's quality is validated using a validation geometry. Results indicate that depending on the selected body part, end effector geometry, and given displacement, similar results to a finite element model can be obtained. However, simulations with the FRM are less time‐consuming. In the future, the data‐driven model could be transformed into a real‐time model using machine learning techniques.