Tracing Model Research Articles

Context-dependent modification of PFKFB3 in hematopoietic stem cells promotes anaerobic glycolysis and ensures stress hematopoiesis.

Metabolic pathways are plastic and rapidly change in response to stress or perturbation. Current metabolic profiling techniques require lysis of many cells, complicating the tracking of metabolic changes over time after stress in rare cells such as hematopoietic stem cells (HSCs). Here, we aimed to identify the key metabolic enzymes that define differences in glycolytic metabolism between steady-state and stress conditions in murine HSCs and elucidate their regulatory mechanisms. Through quantitative 13C metabolic flux analysis of glucose metabolism using high-sensitivity glucose tracing and mathematical modeling, we found that HSCs activate the glycolytic rate-limiting enzyme phosphofructokinase (PFK) during proliferation and oxidative phosphorylation (OXPHOS) inhibition. Real-time measurement of ATP levels in single HSCs demonstrated that proliferative stress or OXPHOS inhibition led to accelerated glycolysis via increased activity of PFKFB3, the enzyme regulating an allosteric PFK activator, within seconds to meet ATP requirements. Furthermore, varying stresses differentially activated PFKFB3 via PRMT1-dependent methylation during proliferative stress and via AMPK-dependent phosphorylation during OXPHOS inhibition. Overexpression of Pfkfb3 induced HSC proliferation and promoted differentiated cell production, whereas inhibition or loss of Pfkfb3 suppressed them. This study reveals the flexible and multilayered regulation of HSC glycolytic metabolism to sustain hematopoiesis under stress and provides techniques to better understand the physiological metabolism of rare hematopoietic cells.

Communication channel parameters estimation using a ray tracing model in heterogeneous networks

Currently, modern networks LTE, Wi-Fi, 5GNR are combined into a single heterogeneous network, which leads to an increase in intra-system interference. According to the integrated optimization method, to distribute data flows in a heterogeneous network, taking into account the intra-system interference influence, the collective dynamic routing method is used. At the analysis stage of this method it is necessary to estimate the channel data rate in all valid routes. To obtain correct channel data rates estimates, it is necessary to reliably estimate the channel matrix. Today, the ray tracing model is widely used to estimate the channel matrix. The verification results of the using ray tracing model given in [1] show the need for additional research to increase the channel matrix estimates reliability. Therefore increasing the reliability of channel matrix transmission coefficients estimates by analyzing the influence of the number of rays taken into account is the actual task. The work analyzes the influence of the number of rays taken into account on the reliability of estimating the complex transmission coefficients of the channel matrix for the cases of the presence and absence of line of sight be-tween the transmitter and the receiver. Expressions are obtained to take into account the influence of diffracted, singly reflected and passed through an obstacle rays. A program has been developed to find all possible rays arriving at the receiver location point and determine their parameters based on a digital 3D map indicating the map objects materials. Using the developed program, it is shown that taking into account the influence of additional rays makes it possible to increase the channel matrix estimation reliability, and therefore, the modified ray tracing model can be used to estimate channel data rates when applying the collective dynamic routing method in heterogeneous net-works.

The Notch-PDGFRβ axis suppresses brown adipocyte progenitor differentiation in early post-natal mice

De novo brown adipogenesis holds potential in combating the epidemics of obesity and diabetes. However, the identity of brown adipocyte progenitor cells (APCs) and their regulation have not been extensively explored. Here, through invivo lineage tracing and mouse modeling, we observed that platelet-derived growth factor receptor beta (PDGFRβ)+ pericytes give rise to developmental brown adipocytes but not to those in adult homeostasis. By contrast, T-box 18 (TBX18)+ pericytes contribute to brown adipogenesis throughout both developmental and adult stages, though in a depot-specific manner. Mechanistically, Notch inhibition in PDGFRβ+ pericytes promotes brown adipogenesis by downregulating PDGFRβ. Furthermore, inhibition of Notch signaling in PDGFRβ+ pericytes mitigates high-fat, high-sucrose (HFHS)-induced glucose and metabolic impairment in mice during their development and juvenile phases. Collectively, these findings show that the Notch/PDGFRβ axis negatively regulates developmental brown adipogenesis, and its repression promotes brown adipose tissue expansion and improves metabolic health.

Defining the contribution of Troy-positive progenitor cells to the mouse esophageal epithelium

Progenitor cells adapt their behavior in response to tissue demands. However, the molecular mechanisms controlling esophageal progenitor decisions remain largely unknown. Here, we demonstrate the presence of a Troy (Tnfrsf19)-expressing progenitor subpopulation localized to defined regions along the mouse esophageal axis. Lineage tracing and mathematical modeling demonstrate that Troy-positive progenitor cells are prone to undergoing symmetrical fate choices and contribute to esophageal tissue homeostasis long term. Functionally, TROY inhibits progenitor proliferation and enables commitment to differentiation without affecting fate symmetry. Whereas Troy expression is stable during esophageal homeostasis, progenitor cells downregulate Troy in response to tissue stress, enabling proliferative expansion of basal cells refractory to differentiation and reestablishment of tissue homeostasis. Our results demonstrate functional, spatially restricted progenitor heterogeneity in the esophageal epithelium and identify how dynamic regulation of Troy coordinates tissue generation.

Abstract 4312: KRAS oncogenic activation in Gramd2 + AT1 cells form lung adenocarcinoma with distinct transcriptomic and tumor microenvironment signatures

Abstract Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer and presents with a high degree of biological heterogeneity and distinct clinical outcomes. The current paradigm of LUAD etiology recognizes alveolar epithelial type II (AT2) cells as the primary cell of origin, while the role of AT1 cells in LUAD oncogenesis remains relatively unknown. Here, we examined transformation via activation of the oncogenic KRASG12D mutation in a Gram-domain containing 2 (Gramd2)+ AT1 cell lineage tracing model system, which resulted in multifocal LUAD, primarily of papillary histology. Spatial transcriptomic sequencing revealed significant differences of transcriptomic signatures and signaling activities between Gramd2+ AT1 cell-derived and Sftpc+ AT2 cell-derived LUAD. Single-cell RNA sequencing of macrodissected LUAD lesions demonstrated heterogeneous cell composition of the tumor microenvironment (TME), with Gramd2+ AT1 cell-derived LUAD fostering a proinflammatory, immunoreactive TME and Sftpc+ AT2-derived LUAD presenting with an immunosuppressive TME. Collectively, our study suggests that cell of origin for LUAD is associated with histologic subtype, transcriptomic aberrations, and immune cell composition within LUAD that may influence response to therapy. Citation Format: Minxiao Yang, Jonathan Castillo, Edgar Gonzalez, Chunli Yan, Evanthia Roussos Torres, Zea Borok, Crystal Marconett. KRAS oncogenic activation in Gramd2 + AT1 cells form lung adenocarcinoma with distinct transcriptomic and tumor microenvironment signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4312.

Abstract 265: Reconstructing the early steps of breast tumor initiation

Abstract Breast cancer screening programs, detect not only the early stages of the disease but also various preneoplastic lesions which are very difficult to predict if and when they may develop into cancer. This uncertainty can lead to over-diagnosis, over-treatment and stress for millions of screened women. As consequence, discovering reliable biomarkers of this evolution could make it possible to monitor these patients appropriately and propose an effective prevention strategy. To meet this challenge, we urgently need to better understand the mechanisms that regulate human mammary homeostasis during early steps of tumor initiation. The hierarchy and the composition of human mammary epithelium, composed of stem, myoepithelial, luminal progenitors and mature luminal cells, are complex. This complexity raises the question of whether a given mammary epithelial cell is equally likely to be the cell-of-origin of breast cancer. Interestingly, increasing evidences show that disruption of lineage integrity can be a precursor of breast tumor initiation, and the basal and the luminal lineages respond differently to a given oncogenic stress and may each follow their own rule towards malignant transformation. These results strongly suggest that each lineage may have its own pliancy to be transformed by a given oncogene, hijacking the inter-tumoral heterogeneity observed in breast cancers. The objectives of this study are to develop a model to reconstruct the early steps of breast tumor initiation and understand the interplay between the initiating oncogenic event and the cell-of-origin. To answer these questions, we used a human mammary epithelial cell lines (HME) that maintains a cellular hierarchy with mammary stem cells (MaSC) and luminal cells (LC). Upon oncogenic expression of HRASG12V or c-Myc, we observed different modifications of cell homeostasis, with altered proportions of the lineages populations in the cell line. Depending on the oncogenic stress induced, we also demonstrated a modification in the morphology and the cellular composition of HME organoids, as well as in the capacity to form tumors. HRASG12V induces increase of MaSC compartment, formation of more branched organoids and generate aggressive basal-like tumors. Conversely, c-MYC leads to an almost loss of MaSC and formation of more proliferative unbranched organoid and generate tumors with limited growth. To address more precisely how cell-of-origin and lineage plasticity influence malignant transformation, we are developing for the first time a lineage tracing model in human mammary epithelium with HME. Moreover, a single cell RNA-seq was performed at different early time points during HME cellular differentiation in homeostasis and upon oncogenic stress to decipher the molecular mechanisms that underpin the very earliest steps of breast tumor initiation. Citation Format: Anaïs Grandon, Shuheng Lin, Martin Castagné, Julien Wicinski, Olivier Rosnet, Christophe Ginestier, Emmanuelle Charafe-Jauffret. Reconstructing the early steps of breast tumor initiation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 265.

Analyses on Characteristics of Spatial Distribution and Matching of the Human–Land–Water–Heat System on the Yunnan Plateau

Water, soil, and heat are strategic supporting elements for human survival and social development. The degree of matching between human-land-water-heat elements directly influences the sustainable development of a region. However, the current evaluation of the matching of human-land-water-heat elements overlooks the influence of elevation factors on the matching results, especially evident in mountainous areas. Taking the Yunnan Plateau with distinctive mountainous features as the research subject, divided into 11 elevation ranges, the Lorenz Gini coefficient, asymmetry coefficient, matching distance, and imbalance index are used to assess the spatial matching and balance of human-land-water-heat elements. A projection tracing model is employed to analyze its water resource carrying capacity. Analyses revealed that the Gini coefficient of monthly precipitation from the 1950s to 2022 on the Yunnan Plateau increases with increasing latitude, whereas the correlation with elevation is notably lower. The asymmetry coefficient increases gradually from west to east with change in longitude. The mismatch of the human–land–water–heat system in regions at different elevations is in the order 1800–2000 m > 2000–2200 m > 1400–1600 m > 800 m > other areas. The matching of the human–land–water–heat system in different wet–dry years and seasons also fluctuates with elevation, resulting in serious seasonal drought and water shortage problems in mountainous areas with elevations of 1200–1600, 1800–2000 m, and >2600 m. The spatial equilibrium of temperature and precipitation in regions of different elevations is best, followed by that of cultivated land, while that of the population is the worst. The Gini coefficients for different water cycle processes of precipitation, surface runoff, and regulating storage capacity for water supply continue to increase. Specifically, the Gini coefficient of industrial water supply is the highest, reaching 0.576, and that of agricultural irrigation is the lowest (0.424). Through artificial regulation of lake and reservoir water, seasonal changes in the demand for agricultural irrigation water are offset to achieve a demand–supply balance and matching of land and water resources. The water resource capacity of different elevation ranges is evenly underloaded. However, the potential of the water resource capacity varies obviously with elevation in the order 2000–2200 m < 1800–2000 m < 1600–8000 m < 1400–1600 m < other areas. It appears that the greater the human–land–water–heat system mismatch, the smaller the regional potential of the water resource capacity.

A deeper knowledge tracking model integrating cognitive theory and learning behavior

Knowledge tracing (KT), which aims to trace human knowledge learning process by using machines, has widely applied in online learning systems. It dynamically models student’s knowledge states in relation to different learning factors through their learning interactions. Recently, KT has attracted many researches attention due to its good performance to using deep learning. Although most of KT models have shown outstanding results, they have limitations: either ignore the human cognitive law and learning behavior, or lack the ability to go deeper modeling to trace knowledge state. In this paper, we propose a deeper knowledge tracking model integrating cognitive theory and learning behavior (CLDKT). It united the advantages of memory network and recurrent neural network of the existing deep learning KT models for modeling student learning. To better implement CLDKT, we add the residual network (ResNet) to realize the deep modeling of learning behaviors. Extensive experiments on three open benchmark datasets to evaluate our model. Experimental results demonstrate that (I) CLDKT outperforms the state-of-the-art KT models on students’ performance prediction. (II) CLDKT can deeper modeling to trace knowledge state owing to the ResNet import. (III) CLDKT has better interpretability and predictability, which proves the effectiveness of the knowledge tracing model integrating cognitive law and learning behavior.

Real-time high-resolution modelling of grid carbon emissions intensity

Reducing greenhouse gas emissions in the energy industry is broadly acknowledged as important with numerous countries implementing targets to reduce emissions. Understanding the carbon emissions intensity of electricity grids is an important step in this process, and higher spatial and temporal granularity estimates are critical for delivering actionable insights. Existing emissions intensity models are limited in terms of their real-time availability and, in particular, their spatial granularity. Here we outline a method for estimating emissions intensity of large-scale interconnected electricity networks in real-time at substation-level resolution. This is realized by integrating nation-wide high-resolution electricity generation and demand data, regional population data, a detailed model of the electricity grid, and power flow simulation and flow tracing models. Results for Australia's National Electricity Market reveal that most capital city locations have higher emissions intensity than neighbouring regional areas, though the variation within a city can also be substantial. In general, emissions intensity is higher in areas closer to, or directly fed by emissions intensive coal and gas generators. These results have important implications for the design of energy efficiency incentives and demand response strategies, in the rollout of distributed renewable generation, and in carbon accounting.

Estimating three-dimensional current fields in the Yeosu Bay using coastal acoustic tomography system

Observation of current speeds in coastal seas is crucial because it can provide useful information for ship operations, fishing activities, and rapid responses to marine disasters. Coastal acoustic tomography (CAT) is a technology that can continuously monitor environmental changes such as current velocity and water temperature using reciprocal acoustic signals between CAT stations in coastal seas. This technology is different from traditional pointwise or intermittent sectional observations in that it can produce time-varying two- or three-dimensional current fields. The results of previous studies using CAT systems have been limited to reproducing horizontal maps of depth-averaged two-dimensional current fields. Utilizing results from a high-resolution coastal ocean model, this study developed a novel technique for estimating three-dimensional (3-D) current fields by combining the inverse method with an artificial intelligence (AI) model. Following three steps are the procedure for the test of estimating the 3-D current fields. First, utilizing the ray tracing model ‘Bellhop,’ reciprocal travel times among five CAT stations using the coastal ocean model outputs are computed. These five stations correspond to the locations where in-situ CAT systems were established for continuous monitoring of current changes in Yeosu Bay, Korea. Subsequently, the range-averaged currents at the five layers were estimated by incorporating this travel time difference data into an AI model trained using the same coastal ocean model outputs. Finally, the inverse method is applied to each layer to estimate the 3-D current fields. The validation results revealed that the newly developed method performed well in both summer and winter. Time-varying two-layer-like current fields were reasonably produced, occasionally revealing an out-of-phase relationship between the upper and lower layers depending on the tidal phases. This method yielded average root-mean-squared errors of less than 4 cm/s on six simulation paths for acoustic signal propagation. Furthermore, when the same method was applied to in-situ CAT observations, the average correlation coefficient (R) of the along-channel current of each layer was found to be approximately 0.9 or higher. These results suggest that this novel method can be effectively applied to the continuous monitoring of 3-D current fields in coastal seas using a CAT system.

Tissue-Resident Alveolar Macrophages Reduce Ozone-induced Inflammation via MerTK-mediated Efferocytosis.

Lung inflammation, caused by acute exposure to ozone (O3), one of the six criteria air pollutants, is a significant source of morbidity in susceptible individuals. Alveolar macrophages (AMØs) are the most abundant immune cells in the normal lung, and their number increases after O3 exposure. However, the role of AMØs in promoting or limiting O3-induced lung inflammation has not been clearly defined. In this study, we used a mouse model of acute O3 exposure, lineage tracing, genetic knockouts, and data from O3-exposed human volunteers to define the role and ontogeny of AMØs during acute O3 exposure. Lineage-tracing experiments showed that 12, 24, and 72 hours after exposure to O3 (2 ppm) for 3 hours, all AMØs were of tissue-resident origin. Similarly, in humans exposed to filtered air and O3 (200 ppb) for 135 minutes, we did not observe at ∼21 hours postexposure an increase in monocyte-derived AMØs by flow cytometry. Highlighting a role for tissue-resident AMØs, we demonstrate that depletion of tissue-resident AMØs with clodronate-loaded liposomes led to persistence of neutrophils in the alveolar space after O3 exposure, suggesting that impaired neutrophil clearance (i.e., efferocytosis) leads to prolonged lung inflammation. Moreover, depletion of tissue-resident AMØs demonstrated reduced clearance of intratracheally instilled apoptotic Jurkat cells, consistent with reduced efferocytosis. Genetic ablation of MerTK (MER proto-oncogene, tyrosine kinase), a key receptor involved in efferocytosis, also resulted in impaired clearance of apoptotic neutrophils after O3 exposure. Overall, these findings underscore the pivotal role of tissue-resident AMØs in resolving O3-induced inflammation via MerTK-mediated efferocytosis.

PRX1-positive mesenchymal stem cells drive molar morphogenesis

Mammalian teeth, developing inseparable from epithelial-mesenchymal interaction, come in many shapes and the key factors governing tooth morphology deserve to be answered. By merging single-cell RNA sequencing analysis with lineage tracing models, we have unearthed a captivating correlation between the contrasting morphology of mouse molars and the specific presence of PRX1+ cells within M1. These PRX1+ cells assume a profound responsibility in shaping tooth morphology through a remarkable divergence in dental mesenchymal cell proliferation. Deeper into the mechanisms, we have discovered that Wnt5a, bestowed by mesenchymal PRX1+ cells, stimulates mesenchymal cell proliferation while orchestrating molar morphogenesis through WNT signaling pathway. The loss of Wnt5a exhibits a defect phenotype similar to that of siPrx1. Exogenous addition of WNT5A can successfully reverse the inhibited cell proliferation and consequent deviant appearance exhibited in Prx1-deficient tooth germs. These findings bestow compelling evidence of PRX1-positive mesenchymal cells to be potential target in regulating tooth morphology.

Source apportionment based on EEM-PARAFAC combined with microbial tracing model and its implication in complex pollution area, Wujin District, China

Further improving the quality of surface water is becoming more difficult after the control of main point-sources, especially in the complex pollution area with mixed industrial and agricultural productions, whereas the pollution source apportionment might be the key to quantify different pollution sources and developing some effective measures. In this study, a technical framework for source apportionment based on three-dimensional fluorescence and microbial traceability model is developed. Based on screening of the main environmental factors and their spatiotemporal characteristics, potential pollution sources have been tentatively identified. Then, the pollution sources are further tested based on the analysis of fluorescence excitation-emission matrix (EEM) and the similarity of fluorescence components in surface water and potential pollution sources. At the same time, the correlation between microbial species and pollution sources is constructed by analyzing the spatiotemporal characteristics of microbial composition and the response of main species to environmental factors. Therefore, pollution source apportionment is quantified using PCA-APCS-MLR, Fast Expectation-maximization for Microbial Source Tracking (FEAST), and Bayesian community-wide culture-independent microbial source tracking (SourceTracker). PCA-APCS-MLR could not effectively distinguish the contributions of different industrial sources in the complex environment of this study, and the contribution of unknown sources was high (average 39.60%). In contrast, the microbial traceability model can accurately identify the contribution of 7 pollution sources and natural sources, effectively reduce the proportion of unknown sources (average of FEAST is 19.81%, SourceTracker is 16.72%), and show better pollution identification and distribution capabilities. FEAST exhibits a more sensitive potential in source apportionment and shorter calculation time than SourceTracker, thus might be used to guide the precise regional pollution control, especially in the complex pollution environments.

Tracing Knowledge States through Student Assessment in a Blended Learning Environment

Blended learning has recently acquired popularity in a variety of educational settings. This approach has the advantage of being able to autonomously monitor students' knowledge states using the collected learning data. Moodle is the most widely used learning management system in blended learning environments. Students can access Moodle to obtain supplementary materials, exercises, and assessments to complement their face-to-face meetings. However, its performance can be improved by more effectively tailoring students' skills and pace of learning. Several studies have been conducted on knowledge tracing; however, we have not discovered any studies that particularly investigate knowledge tracing in a blended learning setting with Moodle as a component. This study proposes a scheme for assessment using the features of the Moodle quiz platform. The assessment data is used to conduct knowledge tracing with the Bayesian Knowledge Tracing (BKT) model, which improves interpretability. The aforementioned data were collected from information engineering undergraduate students who completed 88 exercises that assessed 23 knowledge components within the course. We measure RMSE and MAE to evaluate the performance of the BKT model on our dataset. Furthermore, we compare the knowledge tracing performance to other well-known datasets. Our results show that the BKT model performed better with our dataset, with an RMSE of 0.314 and an MAE of 0.197. Moreover, the BKT model can be used to assess student performance and determine the level of mastery for each knowledge component. Thus, the outcomes can be applied to personalized learning in the future.

Investigation of recent advances in exercise-enhanced knowledge tracing models

Exercising plays a significant role in Knowledge Tracing (KT), however, the majority of KT models struggle to effectively extract the abundant information embedded within students' exercise histories, leading to a prevalent issue of information erosion. Hence, it is vital for exploring methods to mine the data from the exercises to better forecast students' future performance. This paper is a review that aims to discuss recent improvements in KT models on mining information existing in exercises. This paper will briefly discuss DKT, a popular approach to knowledge tracing, and its limitation on mining information from exercises. And then, this review will introduce four exercise-enhanced knowledge tracing models including EERNN, EKT, HGKT and Concept-Aware Deep Knowledge Tracing. EERNN and EKT can track students knowledge acquisition by using vectors to represent knowledge concepts. HGKT goes a step further by examining the interconnectedness among different exercises based on EERNN and EKT. Concept-Aware DKT is an improvement of DKVMN, the other approach of KT, by considering the influence of knowledge concepts and corresponding exercises in more detail. Finally, the applications of exercise-enhanced KT models will be covered.

Evaluating the Effectiveness of Bayesian Knowledge Tracing Model-Based Explainable Recommender

Explainable recommendation, which provides an explanation about why a quiz is recommended, helps to improve transparency, persuasiveness, and trustworthiness. However, little research examined the effectiveness of the explainable recommender, especially on academic performance. To survey its effectiveness, the authors evaluate the math academic performance among middle school students (n=115) by giving pre- and post-test questions based evaluation techniques. During the pre- and post-test periods, students were encouraged to use the Bayesian Knowledge Tracing model based explainable recommendation system. To evaluate how well the students were able to do what they could not do, the authors defined growth rate and found recommended quiz clicked counts had a positive effect on the total number of solved quizzes (R=0.343, P=0.005) and growth rate (R=0.297, P=0.017) despite no correlation between the total number of solved quizzes and growth rate. The results suggest that the use of an explainable recommendation system that learns efficiently will enable students to do what they could not do before.

Temporal Coherence-Based Distributed Ray Tracing of Massive Scenes.

Distributed ray tracing algorithms are widely used when rendering massive scenes, where data utilization and load balancing are the keys to improving performance. One essential observation is that rays are temporally coherent, which indicates that temporal information can be used to improve computational efficiency. In this paper, we use temporal coherence to optimize the performance of distributed ray tracing. First, we propose a temporal coherence-based scheduling algorithm to guide the task/data assignment and scheduling. Then, we propose a virtual portal structure to predict the radiance of rays based on the previous frame, and send the rays with low radiance to a precomputed simplified model for further tracing, which can dramatically reduce the traversal complexity and the overhead of network data transmission. The approach was validated on scenes of sizes up to 355 GB. Our algorithm can achieve a speedup of up to 81% compared to previous algorithms, with a very small mean squared error.

Effects of soil heterogeneity and preferential flow on the water flow and isotope transport in an experimental hillslope

Soil water movement plays vital roles in hillslope runoff generation and groundwater and surface water interaction. However, there are still knowledge gaps about the impacts of soil heterogeneity and preferential flow on the internal water flow and transport process. In this study, the vertical soil heterogeneity focused on the variations in soil retention capacity, and the consideration of lateral preferential flow emphasized the higher hydraulic conductivity. We combined isotopic tracing and numerical modeling in an artificial hillslope, focusing on monitored processes of the artificial rainfall and isotopic tracing experiment. The results showed that the soil moisture quickly accumulated at the bottom of the hillslope during rainfall events, while the 2H enrichment occurred in the topsoil derived from enriched isotope injection in the second artificial rainfall. The evaporation process slowed down the mixing of new water in the topsoil and old water in the lower layer. We found that the vertical soil heterogeneity had significant influences on the internal water and isotope transport paths within the hillslope. The lateral preferential flow played an important role in the water flux and transport time to the seepage face. The coupling of isotopic tracing, which reflects the water transport and mixing with the hillslope, effectively improved the model simulation and mechanism analysis of hillslope water flow. Our findings provide new insights into the mechanisms governing soil water flow and transport dynamics in hillslopes, taking into account vertical soil heterogeneity and lateral preferential flow.

The impact of neutral beam parameters on current drive and neutron yield in DEMO-FNS tokamak

DEMO-FNS (DEMO Fusion Neutron Source) will be a hybrid reactor designed to combine fusion and fission technologies. In a hybrid reactor the power is mainly produced by a fission blanket which is exposed to the neutron flux generated by confined fusion plasma used as a Fusion Neutron Source (FNS). The steady-state operation of the FNS is easier to achieve as compared to pure fusion reactors, allowing the FNS to be more compact. The DEMO-FNS will produce 40 MW of fusion power. The neutral beam injectors (NBI) are designed to provide a steady state plasma heating, rotation, fueling, current drive and neutron generation in DEMO-FNS. Four injector units will deliver 30 MW power in deuterium with energy Eb = 500 keV. The NBI concept and main components are inspired from ITER HNBI design implementing acceleration of negative ions with their neutralization on gas.The high density of injected power in DEMO-FNS leads to a very specific operation scenario with ahigher ratio of high-energy ions in the plasma with respect to conventional tokamaks, where the mainpart of neutron flux will result from fusion between hot-tail ions and relatively coldbackground. The current drive and neutron yield produced by the tangentially injected beam in asteady-state operation are strongly affected by magnetic configuration and plasma kineticprofiles. The fast ions deposition and performance in plasma are limited by the losses associatedwith the neutral beam shape and aiming. The main channels of direct beam losses includeshine-through losses, as well as fast ion orbital losses. The detailed evaluation of NB parametersand geometry effects is a necessary step of scenario optimization.BTR code (Beam Transmission or, in earlier versions, Beam Transmission with Reionization) is intended for injector beamlines study, and BTR detailed beam model can be also applied to perform a detailed analysis of beam losses and performance in plasma. The analysis of beam in plasma shows the ways towards higher values of beam driven current (NBCD) and neutral yield; the beam-in-plasma behaviour can be tuned for a given tokamak configuration. The NB tracing model workflow is implemented in the BTOR suite (Beam in TORoids). The results for DEMO-FNS are compared with the NBI performance in ITER tokamak, as both machines are beam-driven and implement a similar NBI concept.

Gli1+ Periodontal Mesenchymal Stem Cells in Periodontitis

Periodontal mesenchymal stem cells (MSCs) play a crucial role in maintaining periodontium homeostasis and in tissue repair. However, little is known about how periodontal MSCs in vivo respond under periodontal disease conditions, posing a challenge for periodontium tissue regeneration. In this study, Gli1 was used as a periodontal MSC marker and combined with a Gli1-cre ERT2 mouse model for lineage tracing to investigate periodontal MSC fate in an induced periodontitis model. Our findings show significant changes in the number and contribution of Gli1+ MSCs within the inflamed periodontium. The number of Gli1+ MSCs that contributed to periodontal ligament homeostasis decreased in the periodontitis-induced teeth. While the proliferation of Gli1+ MSCs had no significant difference between the periodontitis and the control groups, more Gli1+ MSCs underwent apoptosis in diseased teeth. In addition, the number of Gli1+ MSCs for osteogenic differentiation decreased during the progression of periodontitis. Following tooth extraction, the contribution of Gli1+ MSCs to the tooth socket repair was significantly reduced in the periodontitis-induced teeth. Collectively, these findings indicate that the function of Gli1+ MSCs in periodontitis was compromised, including reduced contribution to periodontium homeostasis and impaired injury response.