Tracing Model Research Articles

The lung is a megakaryocyte outpost that can defend against thrombocytopenic attack.

Lung megakaryocytes (Mks) are a unique subset of Mks that are distinct from their bone marrow counterparts. Recent evidence suggests that lung Mks favor an immune phenotype, but have unclear contributions to the total platelet mass. In this issue of the JCI, Livada et al. used an array of complementary in vivo labeling and tracing models in mice to investigate a longstanding question of where lung Mks are derived. By combining these models with stressed conditions, the authors assessed the contribution of lung Mks to total platelet counts in a homeostatic and thrombocytopenic state. Mks were minor contributors to the circulating pool of platelets during homeostasis but increased output during thrombocytopenia. These findings add critical understanding to the development of lung Mks and demonstrate the dynamic potential of these specialized cells to respond to thrombocytopenia.

Monocytes give rise to Langerhans cells that preferentially migrate to lymph nodes at steady state

Current evidence suggests that ontogeny may account for the functional heterogeneity of some tissue macrophages, but not others. Here, we asked whether developmental origin drives different functions of skin Langerhans cells (LCs), an embryo-derived mononuclear phagocyte with features of both tissue macrophages and dendritic cells. Using time-course analyses, bone marrow chimeras, and fate tracing models, we found that the complete elimination of embryo-derived LCs at steady state results in their repopulation from circulating monocytes. However, monocyte-derived LCs inefficiently replenished the epidermal niche. Instead, these cells preferentially migrated to skin-draining lymph nodes. Mechanistically, we show that the enhanced migratory capability of monocyte-derived LCs is associated with higher expression of CD207/Langerin, a C-type lectin involved in the capture of skin microbes. Our data demonstrate that ontogeny plays a role in the migratory behavior of epidermal LCs.

Edge Computing for Smart-City Human Habitat: A Pandemic-Resilient, AI-Powered Framework

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge and cloud computing frameworks, and a suite of DL tools. The framework uses a composite attention-driven framework incorporating various DL pre-trained models (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies.

Unifying Model Execution and Deductive Verification with Interaction Trees in Isabelle/HOL

Model execution allows us to prototype and analyse software engineering models by stepping through their possible behaviours, using techniques like animation and simulation. On the other hand, deductive verification allows us to construct formal proofs demonstrating satisfaction of certain critical properties in support of high-assurance software engineering. To ensure coherent results between execution and proof, we need unifying semantics and automation. In this paper, we mechanise Interaction Trees (ITrees) in Isabelle/HOL to produce an execution and verification framework. ITrees are coinductive structures that allow us to encode infinite labelled transition systems, yet they are inherently executable. We use ITrees to create verification tools for stateful imperative programs, concurrent programs with message passing in the form of the CSP and Circus languages, and abstract system models in the style of the Z and B methods. We demonstrate how ITrees can account for diverse semantic presentations, such as structural operational semantics, a relational program model, and CSP's failures-divergences trace model. Finally, we demonstrate how ITrees can be executed using the Isabelle code generator to support the animation of models.

XKT: Toward Explainable Knowledge Tracing Model With Cognitive Learning Theories for Questions of Multiple Knowledge Concepts

XKT: Toward Explainable Knowledge Tracing Model With Cognitive Learning Theories for Questions of Multiple Knowledge Concepts

Lyman line opacities in tokamak divertor plasmas under high-recycling and detached conditions

Radiation transport simulations of the deuterium Lyman series using EIRENE predict a factor of 2 increase of Balmer-α emission in the JET-ILW low confinement divertor plasmas in high-recycling and detached conditions caused by the re-absorption of Lyman-β emission. The photon tracing model in the EIRENE code is applied to SOLPS-ITER pure deuterium plasma solutions of JET-ILW to evaluate the contribution of Lyman-β capture towards the population density of D(p=3) and consequently the Balmer-α emission. The local population escape factors of Lyman-α and Lyman-β lines in the JET-ILW divertor show steep spatial gradients and indicate local regions with high opacity in high-recycling and detached conditions. The relative contribution of Lyman-β capture towards the population density of D(p=3) is significant in high-recycling and detached conditions. This observation is further corroborated by line-integrated calculations using Balmer-α diagnostic line-of-sights.

A novel method for tracing gasoline using GC-IRMS and Relief-Stacking fusion model

Due to China’s vast geographical expanse and the complexity of its natural environment, traditional methods are not suitable for tracing the origins of gasoline samples from different regions of China. The aim of this study is to trace the origins of gasoline samples through the analysis of carbon stable isotopic characteristics from 60 gasoline samples collected across four major regions in China. In this study, a novel method that combines gas chromatography-isotope ratio mass spectrometry (GC-IRMS) with Relief-Stacking was proposed for the first time, significantly enhancing the precision of the provenance results. Firstly, eight characteristic components were identified by Relief feature selection model for the first time and applied to the assessment of carbon stable isotope ratios across different regions. The discovery of these components effectively reduces data redundancy, thereby enhancing the accuracy of provenance analysis. Subsequently, five effective source tracing models (XGBoost, Random Forest, CatBoost, LightGBM, and KNN) were selected based on the CEI values. To further enhance the learning efficiency of the sourcing models, this study integrated the five selected source tracing models using three different methods(voting, stacking, and blending) and validated the performance of the optimal sourcing model. The results indicated that the Stacking fusion model achieves a CEI value of 0.923, significantly outperforming other fusion models. This advantage is attributed to the Stacking fusion model combining the s trengths of Boosting and Bagging ensemble methods and leveraging the advantages of individual classifiers within the model. The method proposed in this study can effectively address the limitations of previous tracing methods and offer valuable insights for the development of platforms related to stable isotope source tracing. In future research, through appropriate parameter adjustments and feature adaptations, the Relief-Stacking model can likewise be applied to source tracing studies in other countries characterized by extensive geographic ranges and variable environmental conditions.

DKVMN&MRI: A new deep knowledge tracing model based on DKVMN incorporating multi-relational information.

Knowledge tracing is a technology that models students' changing knowledge state over learning time based on their historical answer records, thus predicting their learning ability. It is the core module that supports the intelligent education system. To address the problems of sparse input data, lack of interpretability and weak capacity to capture the relationship between exercises in the existing models, this paper build a deep knowledge tracing model DKVMN&MRI based on the Dynamic Key-Value Memory Network (DKVMN) that incorporates multiple relationship information including exercise-knowledge point relations, exercise-exercise relations, and learning-forgetting relations. In the model, firstly, the Q-matrix is utilized to map the link between knowledge points and exercises to the input layer; secondly, improved DKVMN and LSTM are used to model the learning process of learners, then the Ebbinghaus forgetting curve function is introduced to simulate the process of memory forgetting in learners, and finally, the prediction strategies of Item Response Theory (IRT) and attention mechanism are used to combine the similarity relationship between learners' knowledge state and exercises to calculate the probability that learners would correctly respond during the subsequent time step. Through extensive experiments on three real-world datasets, we demonstrate that DKVMN&MRI has significant improvements in both AUC and ACC metrics contrast with the latest models. Furthermore, the study provides explanations at both the exercise level and learner knowledge state level, demonstrating the interpretability and efficacy of the proposed model.

The Parameterized Oceanic Front-Guided PIX2PIX Model: A Limited Data-Driven Approach to Oceanic Front Sound Speed Reconstruction

In response to the demand for high-precision acoustic support under the condition of limited data, this study utilized high-resolution reanalysis data and in situ observation data to extract the Kuroshio Extension Front (KEF) section through front-line identification methods. By combining the parameterized oceanic front model and the statistical features of big data, the parameterized oceanic front was reconstructed. A proxy dataset was generated using the Latin hypercube sampling method, and the sound speed reconstruction model based on the PIX2PIX model was trained and validated using single sound speed profiles at different positions of the oceanic front, combined with the parameterized oceanic front model. The experimental results show that the proposed sound speed reconstruction model can significantly improve the reconstruction accuracy by introducing the parameterized front model as an additional input, especially in the shallow-water area. The mean absolute error (MAE) of the full-depth sound speed reconstruction for this model is 0.63~0.95 m·s−1, and the structural similarity index (SSIM) is 0.76~0.78. The MAE of the sound speed section within a 1000 m depth is reduced by 6.50~37.62%, reaching 1.95~3.31 m·s−1. In addition, the acoustic support capabilities and generalization of the model were verified through ray tracing models and in situ data. This study contributes to advancing high-precision acoustic support in data-limited oceanic environments, laying a solid groundwork for future innovations in marine acoustics.

Inflammation impacts androgen receptor signaling in basal prostate stem cells through interleukin 1 receptor antagonist

Chronic prostate inflammation in patients with benign prostate hyperplasia (BPH) correlates with the severity of symptoms. How inflammation contributes to prostate enlargement and/or BPH symptoms and the underlying mechanisms remain unclear. In this study, we utilize a unique transgenic mouse model that mimics chronic non-bacterial prostatitis in men and investigate the impact of inflammation on androgen receptor (AR) in basal prostate stem cells (bPSC) and their differentiation in vivo. We find that inflammation significantly enhances AR levels and activity in bPSC. More importantly, we identify interleukin 1 receptor antagonist (IL-1RA) as a crucial regulator of AR in bPSC during inflammation. IL-1RA is one of the top molecules upregulated by inflammation, and inhibiting IL-1RA reverses the enhanced AR activity in organoids derived from inflamed bPSC. Additionally, IL-1RA appears to activate AR by counteracting IL-1α's inhibitory effect. Furthermore, using a lineage tracing model, we observe that inflammation induces bPSC proliferation and differentiation into luminal cells even under castrate conditions, indicating that AR activation driven by inflammation is sufficient to promote bPSC proliferation and differentiation. Taken together, our study uncovers mechanisms through which inflammation modulates AR signaling in bPSC and induces bPSC luminal differentiation that may contribute to prostate hyperplasia.

DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model.

Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multichain protein complexes. However, modeling a large complex structure, such as those with more than ten chains, is challenging, particularly when the map resolution decreases. Here we present DiffModeler, a fully automated method for modeling large protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. DiffModeler showed an average template modeling score of 0.88 and 0.91 for two datasets of cryo-EM maps of 0-5 Å resolution and 0.92 for intermediate resolution maps (5-10 Å), substantially outperforming existing methodologies. Further benchmarking at low resolutions (10-20 Å) confirms its versatility, demonstrating plausible performance.

Precomputed low-frequency lighting in cinematic volume rendering.

Cinematic Rendering (CR) employs physical models such as ray tracing and global illumination to simulate real-world light phenomena, producing high-quality images with rich details. In the medical field, CR can significantly aid doctors in accurate diagnosis and preoperative planning. However, doctors require efficient real-time rendering when using CR, which presents a challenge due to the substantial computing resources demanded by CR's ray tracing and global illumination models. Precomputed lighting can enhance the efficiency of real-time rendering by freezing certain scene variables. Typically, precomputed methods freeze geometry and materials. However, since the physical rendering of medical images relies on volume data rendering of transfer functions, the CR algorithm cannot utilize precomputed methods directly. To improve the rendering efficiency of the CR algorithm, we propose a precomputed low-frequency lighting method. By simulating the lighting pattern of shadowless surgical lamps, we adopt a spherical distribution of multiple light sources, with each source capable of illuminating the entire volume of data. Under the influence of these large-area multi-light sources, the precomputed lighting adheres to physical principles, resulting in shadow-free and uniformly distributed illumination. We integrated this precomputed method into the ray-casting algorithm, creating an accelerated CR algorithm that achieves more than twice the rendering efficiency of traditional CR rendering.

Developing a Novel Contact Tracing Mobile Application for Coronavirus Disease 2019 (COVID-19) for Improving Testing Capacity and Controlling the Spread of COVID-19 in Nigeria

In this study, a novel contact tracing model that leverages smartphone technology to enhance efficiency, reduce costs, and extend the duration of contact tracing efforts is developed. This model utilizes smartphones as identification systems, collecting data on the proximity of other smartphone users through integrated Bluetooth and GPS technology. The study examines the frequency, duration, and proximity of interactions between smartphone devices in a clinical setting, highlighting potential implications for infectious disease transmission to pilot the mobile application developed. Contact data from six pairs of devices were analyzed, focusing on metrics such as total contacts, total contact time, average contact time, average distance, and the percentage of contacts occurring within 1.5 meters. The results showed varying levels of interaction across device pairs, with Devices 1 & 3 showing the highest number of contacts (175), and Devices 3 & 4 displaying the longest average contact time (20,133,193.01 seconds). Correlation analysis revealed weak and statistically insignificant relationships between total contacts and average distance (r = 0.13, p = 0.81), contact time and the percentage of close contacts (r = -0.15, p = 0.78). These findings suggest that while there are observable trends in contact patterns, the statistical insignificance highlights the need for further investigation to establish stronger associations that could inform infection control practices in healthcare settings.

Ketogenesis protects against MASLD-MASH progression through fat oxidation-independent mechanisms.

The progression of metabolic-dysfunction-associated steatotic liver disease (MASLD) to metabolic-dysfunction-associated steatohepatitis (MASH) involves complex alterations in both liver-autonomous and systemic metabolism that influence the liver's balance of fat accretion and disposal. Here, we quantify the relative contribution of hepatic oxidative pathways to liver injury in MASLD-MASH. Using NMR spectroscopy, UHPLC-MS, and GC-MS, we performed stable-isotope tracing and formal flux modeling to quantify hepatic oxidative fluxes in humans across the spectrum of MASLD-MASH, and in mouse models of impaired ketogenesis. We found in humans with MASH, that liver injury correlated positively with ketogenesis and total fat oxidation, but not with turnover of the tricarboxylic acid cycle. The use of loss-of-function mouse models demonstrated that disruption of mitochondrial HMG-CoA synthase (HMGCS2), the rate-limiting step of ketogenesis, impairs overall hepatic fat oxidation and induces a MASLD-MASH-like phenotype. Furthermore, disruption of mitochondrial β-hydroxybutyrate dehydrogenase (BDH1), the terminal step of ketogenesis, also impaired fat oxidation, but surprisingly did not exacerbate steatotic liver injury. Taken together, these findings suggest that quantifiable variations in overall hepatic fat oxidation may not be a primary determinant of MASLD-to-MASH progression, but rather, that maintenance of hepatic ketogenesis could serve a protective role through alternative fat oxidation-independent mechanisms.

Critical Impact of Working Distance on OCT Imaging: Correction of Optical Distortion and Its Effects on Measuring Retinal Curvature.

To assess the impact of working distance (WD) on optical distortion in optical coherence tomography (OCT) imaging and to evaluate the effectiveness of optical distortion correction in achieving consistent retinal Bruch's membrane (BM) layer curvature, regardless of variations in WD. Ten subjects underwent OCT imaging with four serial macular volume scans, each employing distinct WD settings adjusted by balancing the sample and reference arm of the OCT interferometer (eye length settings changed). Either of two types of 30° standard objectives (SOs) was used. A ray tracing model was used to correct optical distortion, and BM layer curvature (represented as the second derivative of the curve) was measured. Linear mixed effects (LME) modeling was used to analyze factors associated with BM layer curvature, both before and after distortion correction. WD exhibited significant associations with axial length (β = -1.35, P < 0.001), SO type (P < 0.001), and eye length settings (P < 0.001). After optical distortion correction, the mean ± SD BM layer curvature significantly increased from 16.80 ± 10.08µm-1 to 49.31 ± 7.50µm-1 (P < 0.001). The LME model showed a significant positive association between BM layer curvature and WD (β = 1.94, P < 0.001). After distortion correction, the percentage change in BM layer curvature due to a 1-mm WD alteration decreased from 9.75% to 0.25%. Correcting optical distortion in OCT imaging significantly mitigates the influence of WD on BM layer curvature, enabling a more accurate analysis of posterior eye morphology, especially when variations in WD are unavoidable.

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.

Abstract C067: Tuft cells transdifferentiate to neural-like progenitor cells in the progression of pancreatic cancer

Abstract Pancreatic ductal adenocarcinoma (PDA) is partly initiated through the transdifferentiation of acinar cells to metaplastic ducts that act as precursors of neoplasia and cancer. Tuft cells are solitary chemosensory cells not found in the normal pancreas but arise in metaplasia and neoplasia, diminishing as neoplastic lesions progress to carcinoma. Metaplastic tuft cells (mTCs) function to suppress tumor progression through communication with the tumor microenvironment, but their fate during progression is unknown. To determine the fate of mTCs during PDA progression, we have created a lineage tracing model that uses a tamoxifen-inducible tuft-cell specific Pou2f3CreERT/+ driver to induce transgene expression, including the lineage tracer tdTomato or the oncogene Myc. mTC lineage trace models of pancreatic neoplasia and carcinoma were used to follow mTC fate. We found that mTCs, in the carcinoma model, transdifferentiate into neural-like progenitor cells (NRPs), a cell type associated with poor survival in PDA patients. Using conditional knock-out and overexpression systems, we found that Myc activity in mTCs is necessary and sufficient to induce this Tuft-to- Neuroendocrine-Transition (TNT). Citation Format: Daniel J. Salas-Escabillas, Megan T. Hoffman, Sydney M. Brender, Jacee S. Moore, Hui-Ju Wen, Simone Benitz, Erick T. Davis, Daniel Long, Allison Wombwell, Nina G. Steele, Brittany Allen-Petersen, Rosalie C. Sears, Ichiro Matsumoto, Kathleen E. DelGiorno, Howard C. Crawford. Tuft cells transdifferentiate to neural-like progenitor cells in the progression of pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C067.

Smooth muscle cells clonally expand in a murine carotid allograft model complicated by immune reactions to reporter transgenes

Background and aimsMost experimental studies of allograft vasculopathy (AV) have relied on transplantation between major histocompatibility complex-mismatched inbred mouse strains, but this leads to the complete eradication of donor smooth muscle cells (SMCs) and lesions formed by recipient cells. This is unlike human AV which is thought to form mainly by donor SMCs. Here, we studied sources of neointimal cells in a minor histocompatibility antigen-mismatched AV model by combining male-to-female orthotopic carotid transplantations and lineage tracing by SMC-specific expression of fluorescent proteins. MethodsTo track SMC-derived cells in allograft vasculopathy, we used male donor mice with SMC-restricted Cre recombination of the mT/mG reporter transgene, which switches expression of membrane-bound red fluorescent protein (RFP) to green fluorescent protein (GFP), or the stochastically recombining Confetti reporter transgene, which yields a mosaic expression of four fluorescent proteins. Donor carotid segments were harvested and orthotopically allografted to female recipients that were wildtype or had non-recombined reporter transgenes. Inhibition of T cell responses by CTLA4Ig was used in some experiments. Sections of lesions harvested after 4 weeks were analyzed by fluorescence microscopy. ResultsDonor-derived SMCs survived and gave rise to part of the neointimal cells in experiments where carotid segments from recombined mT/mG male mice were transplanted into wild-type or non-recombined mT/mG female mice. Sex-mismatched transplants developed significant lesions, increasing the intimal and medial area 4.6-fold (p = 0.038) and 2.0-fold (p = 0.024) compared to sex- and fluorescence-matched controls, respectively. Interestingly, sex-matched fluorescence-positive transplants developed intimal lesions in 50% of fluorescence-naïve recipient controls. To study the clonal structure of the neointimal donor-derived SMC lineage cells, we then transplanted male carotids with heterozygous or homozygous recombined Confetti transgenes into female recipients. These transplants developed lesions with few surviving donor SMCs, indicating that expression of the Confetti reporter increased rejection and donor-specific SMC death. Some of the few remaining donor SMCs underwent clonal expansion. CTLA4Ig administration at the time of surgery did not improve SMC survival in mT/mG or Confetti transplants. ConclusionMale-to-female transplant models feature donor-derived SMCs, some of which undergo clonal expansion, but immune rejection to fluorescence reporters appears to bias results in lineage tracing models. Overcoming these challenges with alternative reporter transgenes or tolerant recipients is necessary to study the mechanisms by which donor SMCs contribute to allograft vasculopathy.

Three-Tier Indirect Tracing Model for Enhancing Epidemic Surveillance

Three-Tier Indirect Tracing Model for Enhancing Epidemic Surveillance

An inter-satellite laser occultation method profiling atmospheric temperature and pressure from troposphere to lower mesosphere

We propose a laser occultation method for simultaneous profiling atmospheric temperature and pressure. Measurements can be performed on the optical link between two low-orbit satellites, where frequency-stepwise laser pulses are transmitted from one to the other. These pulses, covering several oxygen absorption lines in the wavelength domain, measure the broadened atmospheric absorption optical depth along the transmission path with a spectral resolution of tens of megahertz. In this way, atmospheric temperature and pressure are obtained by analysing the retrieved shape and intensity of the spectral lines. With the motion of the two satellites, the inter-satellite optical link penetrates different atmospheric layers at various altitudes, enabling the measurement of the vertical structure of atmospheric thermodynamic parameters from the troposphere to the lower thermosphere. This paper presents an end-to-end simulation of the proposed method, including models for laser occultation beam tracing, radiative transfer, and data inversion. The simulation results reveal that with minimal satellite payload resources, this method can accurately measure temperature and pressure at a vertical resolution of 100 m from 5 km to 90 km altitude with accuracies of ±1.5 K and 5 %, respectively. As the proposed differential absorption laser occultation method is independent of the hydrostatic equilibrium hypothesis for data inversion, it can eliminate errors associated with prior data at reference altitudes. It is believed that our method has provided a promising approach to laser satellite constellation for atmospheric observation.