Complex Distribution Research Articles

Generative AI unlocks PET insights: brain amyloid dynamics and quantification.

Studying the spatiotemporal patterns of amyloid accumulation in the brain over time is crucial in understanding Alzheimer's disease (AD). Positron Emission Tomography (PET) imaging plays a pivotal role because it allows for the visualization and quantification of abnormal amyloid beta (Aβ) load in the living brain, providing a powerful tool for tracking disease progression and evaluating the efficacy of anti-amyloid therapies. Generative artificial intelligence (AI) can learn complex data distributions and generate realistic synthetic images. In this study, we demonstrate for the first time the potential of Generative Adversarial Networks (GANs) to build a low-dimensional representation space that effectively describes brain amyloid load and its dynamics. Using a cohort of 1,259 subjects with AV45 PET images from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we develop a 3D GAN model to project images into a latent representation space and generate back synthetic images. Then, we build a progression model on the representation space based on non-parametric ordinary differential equations to study brain amyloid evolution. We found that global SUVR can be accurately predicted with a linear regression model only from the latent representation space (RMSE = 0.08 ± 0.01). We generated synthetic PET trajectories and illustrated predicted Aβ change in four years compared with actual progression. Generative AI can generate rich representations for statistical prediction and progression modeling and simulate evolution in synthetic patients, providing an invaluable tool for understanding AD, assisting in diagnosis, and designing clinical trials. The aim of this study was to illustrate the huge potential that generative AI has in brain amyloid imaging and to encourage its advancement by providing use cases and ideas for future research tracks.

Carbon footprint accounting method based on dynamic allocation of multi-level carbon data from manufacturing organizations to products

In the process of accounting for product carbon footprints, data quality is a key requirement. To mitigate the impacts of uncertainty, dynamism, and coupling in carbon data during product carbon footprint accounting, a method based on multi-level dynamic allocation of carbon data between organizations and products is proposed. First, considering the complex distribution and merging relationships between organizational carbon emissions and product carbon footprints, an AAC (Accounting Allocation Correlation) model between the two is constructed. Then, through EIS (Enterprise Information System), carbon data is obtained, and a multi-level dynamic allocation method for carbon data, as well as a Monte Carlo-based method for merging carbon data, is proposed for the dynamic accounting of product carbon footprints. Finally, using high-voltage switchgear as a case study, the feasibility and effectiveness of the proposed method are validated.

The role of microRNAs in NBS-LRR gene expression and its implications for plant immunity and crop development.

Plants evolved, over millions of years, complex defense systems against pathogens. Once infected, the interaction between pathogen effector molecules and host receptors triggers plant immune responses, which include apoptosis, systemic immune response, among others. An important protein family responsible for pathogen effector recognition is the nucleotide binding site-leucine repeat rich (NBS-LRR) proteins. The NBS-LRR gene family is the largest disease resistance gene class in plants. These proteins are widely distributed in vascular plants and have a complex multigenic cluster distribution in plant genomes. To counteract the genetic load of such a large gene family on fitness cost, plants evolved a mechanism using post transcriptional gene silencing induced by small RNAs, particularly microRNAs. For the NBS-LRR gene family, the small RNAs involved in this silencing mechanism are mainly the microRNA482/2118 superfamily. This suppression mechanism is relieved upon pathogen infection, thus allowing increased NBS-LRR expression and triggering plant immunity. In this review, we will discuss the biogenesis of microRNAs and secondary RNAs involved in this silencing mechanism, biochemical and structural features of NBS-LRR proteins in response to pathogen effectors and the evolution of microRNA-based silencing mechanism with a focus on the miR482/2118 family. Furthermore, the biotechnological manipulation of microRNA expression, using both transgenic or genome editing approaches to improve cultivated plants will be discussed, with a focus on the miR482/2118 family in soybean.

Isogeometric method for buckling prediction and post-buckling analysis of variable stiffness composite underwater pressure shell

Composite cylindrical pressure hulls are thin-walled structures widely used for autonomous underwater vehicles. Buckling failure is one of the most important failure modes for these shells under external pressure. In existing buckling studies of cylindrical pressure hulls, FEM is the most popular analysis method but not efficient enough when dealing with structures with complex material distributions such as the variable stiffness composite shells. Motivated by this, an isogeometric method for buckling prediction and post-buckling analysis of variable stiffness composite underwater pressure shell is proposed in this article. In this method, based on Reissner–Mindlin shell formulas undergoing large deformation, a buckling analysis framework in IGA forms is established. Then a modified arc-length method is proposed based on kinematics used in the shell formulas, and is used to overcome the inaccuracy caused by the 2 degrees of freedom node rotation in the prediction step of arc-length method. In addition, the influence of bifurcation is considered, which may seriously affect the precision of buckling behavior simulation when the limit point is close to a bifurcation point. The computational accuracy of this framework has been validated in a series of constant stiffness composite shell cases involving buckling load prediction and post-buckling behavior simulation. For variable stiffness composite hull cases, this framework achieves the same precision as FEM with fewer elements. Therefore, the proposed framework provides an efficient way for the buckling design of underwater variable stiffness composite pressure hulls.

Exploring microstructures of metal-doped oxides via simulated Raman spectrum

Solid solution of metal-doped oxide has been widely used in material industry and catalysis process. Its performance is highly correlated with the distribution of doped ions. Due to the complex distribution of doped ions in solid solution and its variation with temperatures, to obtain the microstructures of metal-doped ions in solid solution remains a substantial challenge. Taken Ce1-xZrxO2 as a model, the global structure searching, structures proportion with temperature determined by Boltzmann distribution, and the weighted simulation Raman spectra were integrated to explore the microstructures of metal-doped solid solution oxides. It was further verified by application into rutile and anatase TiO2 mixture, indicating that the present method is feasible to deduce the microstructure of metal composite oxides. We anticipate that it provides a powerful solution to explore microstructures of solid solution and complex metal oxides.

Bactericidal effect of tetracycline in E. coli strain ED1a may be associated with ribosome dysfunction

Ribosomes translate the genetic code into proteins. Recent technical advances have facilitated in situ structural analyses of ribosome functional states inside eukaryotic cells and the minimal bacterium Mycoplasma. However, such analyses of Gram-negative bacteria are lacking, despite their ribosomes being major antimicrobial drug targets. Here we compare two E. coli strains, a lab E. coli K-12 and human gut isolate E. coli ED1a, for which tetracycline exhibits bacteriostatic and bactericidal action, respectively. Using our approach for close-to-native E. coli sample preparation, we assess the two strains by cryo-ET and visualize their ribosomes at high resolution in situ. Upon tetracycline treatment, these exhibit virtually identical drug binding sites, yet the conformation distribution of ribosomal complexes differs. While K-12 retains ribosomes in a translation-competent state, tRNAs are lost in the vast majority of ED1a ribosomes. These structural findings together with the proteome-wide abundance and thermal stability assessments indicate that antibiotic responses are complex in cells and can differ between different strains of a single species, thus arguing that all relevant bacterial strains should be analyzed in situ when addressing antibiotic mode of action.

Epitaxial Growth of Hierarchical CuxS Heterostructures for Broadband Dielectric Response

AbstractWhen interacting with an external Electromagnetic (EM) field, symmetric nanostructures, characterized by their periodic crystalline arrangement, typically resonate at specific frequencies. This resonance enhances local electromagnetic fields, leading to strong EM absorption, yet within a narrow absorption range. Conversely, asymmetric nanostructures, distinguished by their complex electric field polarization and distributions, provide broader frequency responses, albeit with generally weaker electromagnetic loss across the broadband. Therefore, striking a balance between wideband and strong absorption using either symmetric or asymmetric nanostructures remains a challenge. Here, a nanostructured epitaxial step‐growth technique is demonstrated that fabricates a CuxS multilevel rod‐like heterostructure with overall axial symmetry. This structure introduces localized asymmetry through variations in a component hierarchy and multilevel rod dimensions during the epitaxial growth process, effectively addressing the aforementioned balance between wideband and strong absorption. Experimental evidence and theoretical simulations confirm that nanostructures possessing these characteristics achieve efficient broadband absorption across a 6.3 GHz bandwidth with just 2.0 mm thickness, owing to the generation of multiple continuous local electric fields and enhanced electric field polarization. It is convincing that this methodology and design concept hold enlightening significance for advancing material and technological innovations in the realm of broadband absorption.

LIBS as diagnostics of analytical chemistry for surface mapping of complex mixed samples simulating debris inside the TEPCO's Fukushima Daiichi Nuclear Power Plant (FDNPS) reactor cores

The Nuclear Energy Agency (NEA) launched the Nuclear Education, Skills and Technology (NEST) Framework to pursue careers in the nuclear field, by exposing researcher working on these topics to international challenging project of real-world issue and by transferring the knowledge and expertise accumulated in the current generation to them through hands-on training.In this framework the 2022 edition of the NEST project offered a training educational period at the Collaborative Laboratories for Advanced Decommissioning (CLADS) located at Tomioka, Futaba District, in the Fukushima prefecture (Japan).Among the research sectors active at CLADS there is the application and development of the LIBS technique as diagnostics of analytical chemistry aiming at characterizing the debris inside the Tokyo Electric Power Company (TEPCO)’s FDNPS reactor cores after the tsunami of March 2011, which destroyed three of the six reactors of the plant. These debris need to be chemically characterized with techniques suitable to be implemented in compact, remote and radiation resistant devices, due to the residual radioactivity of the cores. The present study shows the results in realizing chemical bi-dimensional maps of samples in the form of compressed tablets of mixed oxides, with a complex distribution and concentration of chemicals simulating these debris. The results allowed to create detailed maps of the samples, with spatial resolution down to 0.5 mm and an excellent correspondence between the real spatial distribution of the materials and that reconstructed by LIBS.Moreover, it was found a good correspondence between the nominal concentration of the chemicals and the concentration estimated by using LIBS.This study shows the potentialities of LIBS in the realization of chemical maps on samples of interest for FDNPS, providing multi-elemental images of the samples under analysis.

Strong gravitational lensing’s ‘external shear’ is not shear

ABSTRACT The distribution of mass in galaxy-scale strong gravitational lenses is often modelled as an elliptical power-law plus ‘external shear’, which notionally accounts for neighbouring galaxies and cosmic shear along our line of sight. A small amount of external shear could come from these sources, but we show that the vast majority does not. Except in a handful of rare systems, the best-fitting values do not correlate with independent measurements of line-of-sight shear: from weak lensing in 45 Hubble Space Telescope images, or in 50 mock images of lenses with complex distributions of mass. Instead, the best-fit external shear is aligned with the major or minor axis of 88 per cent of lens galaxies; and the amplitude of the external shear increases if that galaxy is discy. We conclude that ‘external shear’ attached to a power-law model is not physically meaningful, but a fudge to compensate for lack of model complexity. Since it biases other model parameters that are interpreted as physically meaningful in several science analyses (e.g. measuring galaxy evolution, dark matter physics or cosmological parameters), we recommend that future studies of galaxy-scale strong lensing should employ more flexible mass models.

On Expectation-Maximization Algorithm with Split and Merge in R

This paper presents a comprehensive study on the implementation of the Expectation-Maximization (EM) algorithm for a 4-component bivariate Gaussian Mixture Model (GMM) with a focus on incorporating the split and merge techniques. The bivariate GMM is widely utilized in various fields, such as pattern recognition, image processing, and data clustering, due to its flexibility in capturing complex data distributions. Our proposed implementation leverages the versatility of the R programming language to create a robust and efficient framework for modeling and optimizing the parameters of the 4-component GMM. The EM algorithm is employed as a powerful tool for iteratively estimating the model parameters, ensuring convergence to a local maximum of the likelihood function. To enhance the model’s flexibility, we introduce the split and merge strategies, enabling the algorithm to adapt to diverse data structures and efficiently manage the complexity of the mixture components. The split operation allows for the subdivision of components when needed, while the merge operation facilitates the combination of components that represent similar patterns. This adaptive approach contributes to the model’s ability to capture intricate data patterns and improve convergence during the optimization process. The implementation is validated through extensive experimentation on synthetic, demonstrating its effectiveness in accurately estimating the parameters of the 4-component bivariate GMM. The proposed methodology proves to be a valuable addition to the existing tools available for GMM based modeling, providing researchers and practitioners with a flexible and powerful framework for analyzing complex data structures in diverse applications

A stable deep adversarial learning approach for geological facies generation

The simulation of geological facies in an unobservable volume is essential in various geoscience applications. Given the complexity of the problem, deep generative learning is a promising approach to overcome the limitations of traditional geostatistical simulation models, in particular their lack of physical realism. This research aims to investigate the application of generative adversarial networks and deep variational inference for conditionally simulating channelized reservoir in underground volumes. In this paper, we review the generative deep learning approaches, in particular the adversarial ones and the stabilization techniques that aim to facilitate their training. We also study the problem of conditioning deep learning models to observations through a variational Bayes approach, comparing a conditional neural network model to a Gaussian mixture model. The proposed approach is tested on 2D and 3D simulations generated by the stochastic process-based model Flumy. Morphological metrics are utilized to compare our proposed method with earlier iterations of generative adversarial networks. The results indicate that by utilizing recent stabilization techniques, generative adversarial networks can efficiently sample complex target data distributions.

Crustal Structure and Anisotropy Measured by CHINArray and Implications for Complicated Deformation Mechanisms Beneath the Eastern Tibetan Margin

AbstractWe investigated velocity and anisotropic structure of the crust beneath the eastern margin of the Tibetan Plateau to better understand its deformation and evolution mechanism. We performed H‐κ and Pms anisotropy analyses to obtain crustal thickness, Vp/Vs ratio, fast polarization direction, and splitting time from 711 stations, and further conducted quality control using slowness, harmonic and statistical analyses. The Songpan‐Ganzi Block has a large splitting time and a fast polarization direction roughly parallel to the GPS motion and SKS fast direction. It also shows an overall high but complex distribution of Vp/Vs ratio, and large variations in crustal thickness, indicating that crustal deformation is likely caused by crustal shortening and lower crustal flow. The northern Sichuan‐Yunnan Rhombic Block (SYRB) is featured by a thick crust and high Vp/Vs ratio, suggesting that the crust is likely inflated by partial melting lower crustal rocks. The subblock also exhibits a strong azimuthal anisotropy with a splitting time greater than 0.6 s. The fast polarization direction aligns with the nearly N‐S extended direction and rotates clockwise in front of the Emeishan Large Igneous Province (ELIP). The observed anisotropy agrees with aligned amphibole minerals under a simple shear condition, supporting a southward lower crust flow being diverted by the ELIP. Anisotropy measurements on the southern SYRB are less robust and widely scattered, suggesting a deformation mechanism different from the northern SYRB. In addition, the southeastern margin of the Sichuan Basin shows a systematic pattern of crustal anisotropy consistent with a pure shear deformation mechanism.

The various synergistic impacts of precursor emission reduction on PM2.5 and O3 in a typical industrial city with complex distributions of emissions

The synergistic responses of O3 and PM2.5 to their common precursors remain unclear within industrial cities with complex emissions. In this study, hundreds of scenarios of jointly reduced local NOx and VOCs emissions were designed along with the source apportionment techniques embedded in the Comprehensive Air quality Model with extensions (CAMx) system to explore the locally formed O3 and PM2.5 sensitivities to the reduced emissions of NOx and VOCs. The results indicate that locally formed O3 and PM2.5 are more connected to local emissions, resulting in unique formation sensitivities. Local O3 formation is usually in a transitional regime and transferred to VOC-limited condition under O3-polluted conditions due to high VOC emissions. Locally formed O3 and PM2.5 vary largely in different functional regions due to different emission feature and meteorological condition. When reducing VOCs emissions alone, an increase in PM2.5 formation could be observed due to the increase in the formation of nitrate resulting from reduced competition of NOx in O3 formation. To reduce PM2.5 and O3 concentrations simultaneously, specific ratios of NOx reduction percentage to VOC reduction percentage should be considered to different functional regions under different pollution levels. This research highlights the importance to conduct targeted sensitivity tests for emission reduction in different functional zones with complex emission features for the coordinated control of O3 and PM2.5 pollution in typical industrialized cities.

A three-dimensional numerical study on the stability of layered rock spillway tunnels in alpine canyon areas

Rock masses in alpine canyon areas exhibit strong heterogeneity, discontinuity, and are subject to strong tectonic effects and stress unloading, leading to extremely complex distribution of in-situ stress. In addition, the occurrence of layered rock masses makes it more complex, with obvious anisotropic mechanical properties. This study proposes a comprehensive method for evaluating the stability of layered rock spillway tunnels in a hydropower station in an alpine canyon. First, the failure criterion and mechanical model of layered rock masses considering the anisotropy induced by the bedding plane and the true triaxial stress regime were established; an inversion theory and calculation procedure for in-situ stress in alpine canyon areas were then introduced. Finally, by using a self-developed numerical tool, i.e. CASRock, the stability of the layered rock spillway tunnel in a hydropower station was numerically analyzed. The results show that, affected by geological structure and stratigraphic lithology, there is significant differentiation in the in-situ stress in alpine canyons, with horizontal tectonic stress as the main factor. The occurrence of layered rock masses in the region has a significant impact on the stability of surrounding rock, and the angle between the bedding strike and the tunnel axis as well as the bedding dip both exert a significant influence on the failure characteristics of the surrounding rock.

Northeast Atlantic species distribution shifts over the last two decades.

Marine species are widely shifting their distributions in response to global changes and it is commonly expected they will move northward and to greater depths to reach cooler, less disturbed habitats. However, local manifestations of global changes, anthropogenic pressures, and species characteristics may lead to unanticipated and varied responses by individual species. In this regard, the Celtic-Biscay Shelf is a particularly interesting study system because it has historically been heavily fished and occurs at the interface between two distinct biogeographic provinces, its community thus comprised of species with diverse thermal preferenda. In the context of rapidly warming temperatures and intense fishery exploitation, we investigated the distribution shifts of 93 taxa (65 Actinopteri, 10 Elasmobranchii, 11 Cephalopoda, 5 Malacostraca, and 2 Bivalvia), which were sampled annually from 1997 to 2020 during a scientific bottom trawl survey. We used a set of 11 complementary spatial indices to quantify taxon distribution shifts over time. Then, we explored the relative effect of taxon abundance, fishing pressure, and climatic conditions on taxon's distribution shift when a significant shift was detected. We observed that 56% of the taxa significantly shifted. Not all taxa will necessarily shift northward and to deeper areas, as it is often expected. Two opposite patterns were identified: taxa either moving deeper and to the southeast, or moving closer to the surface and to the northwest. The main explanatory factors were climate change (short- and long-term temperatures) and taxon abundance. Fishing pressure was the third, but still significant, explanatory factor of taxa of greater commercial importance. Our research highlights that taxa are displaying complex distribution shifts in response to the combined anthropogenic disturbances and underscores the need to conduct regional studies to better understand these responses at the ecosystem scale to develop more suitable management plans and policies.

Thermal analysis of sinusoidal doubly salient Electro-Magnetic Machine with distributed magnetomotive forces considering complex heat distribution and material thermal characteristics

Sinusoidal doubly salient electro-magnetic machine with distributed magnetomotive forces has DC excitation and AC armature windings in the same stator slot, which leads to the complicated distribution of heat sources and thermal resistances. One of the keys to design this motor is to estimate the temperature rise accurately. In this paper, the thermal performance of the motor materials at different temperatures was tested. According to the complex structure characteristics, the layered and sub-regional equivalent model of the stator slot is proposed. The model not only uses the material thermal data obtained from experiments, but also considers the complex thermal resistances and AC/DC losses distribution in the slots, as well as the influence of winding and insulation processes on the temperature rise of the motor, which is conducive to obtaining the highest point of the motor temperature and ensuring the calculation accuracy. The 3-D global steady-state temperature distribution of sinusoidal doubly salient electro-magnetic machine with distributed magnetomotive forces is studied, and the temperature rise of each part under different current densities is also analyzed. Finally, the temperature experiment of the prototype is carried out. The experiments show that the accurate loss calculation and thermal parameters make the maximum error between the simulation model and the actual measurement is 3.8 °C. The results not only guide the selection of reasonable armature and excitation winding current density of sinusoidal doubly salient electro-magnetic machine with distributed magnetomotive forces, but also provide data and experience support for temperature estimation of motors with complex winding distribution.

Clinical translation of antibody drug conjugate dosing in solid tumors from preclinical mouse data.

Antibody drug conjugates (ADCs) have made impressive strides in the clinic in recent years with 11 Food and Drug Administration approvals, including 6 for the treatment of patients with solid tumors. Despite this success, the development of new agents remains challenging with a high failure rate in the clinic. Here, we show that current approved ADCs for the treatment of patients with solid tumors can all show substantial efficacy in some mouse models when administered at a similar weight-based [milligrams per kilogram (mg/kg)] dosing in mice that is tolerated in the clinic. Mechanistically, equivalent mg/kg dosing results in a similar drug concentration in the tumor and a similar tissue penetration into the tumor due to the unique delivery features of ADCs. Combined with computational approaches, which can account for the complex distribution within the tumor microenvironment, these scaling concepts may aid in the evaluation of new agents and help design therapeutics with maximum clinical efficacy.

Lanthanum-Ferrite based cathode: Impedance data interpretation via complex nonlinear least-squares and distribution of relaxation times analyses

Lanthanum Strontium Cobalt Ferrite Oxide (LaSrCoFeO3) has been widely used as cathode material for intermediate-temperature solid oxide fuel cells at the temperature of 500–800 °C. At this temperature range, understanding the electrochemical behavior which is commonly analyzed by complex nonlinear least-squares (CNLS) analysis is very crucial in improving the cathode's performance. However, this analysis shows some limitations in interpreting the electrochemical processes in detail, particularly at the electrode-electrolyte interface. Hence, this study is conducted to compare the electrochemical impedance data analyses by CNLS and the distribution of relaxation times (DRT) of a fabricated LaSrCoFe|BCZY|LaSrCoFe (LaSrCoFeLa0.6Sr0.4Co0.2Fe0.8O3 and BCZY=BaCe0.54Zr0.36Y0.1O2.95) symmetrical cell. Impedance data of the cell is collected at T = 800 °C at two different stabilization times and to further enhance the analysis process, the impedance data of the cell at measurement temperatures of 700 °C and 750 °C are also included. In a Nyquist plot, the cell exhibits depressed semi-circles that represent a few processes occurring at the interface. The DRT analysis is more precise and easily reveals the semi-circles consisting of four different sub-processes (represented by four peaks) than CNLS (represented by four impedance arcs). The extracted responses from both analyses correspond to the oxygen reduction reactions that follow the Adler-Lane-Steele model. The stabilized symmetrical cells for respective 34 h and 17 h show an area-specific resistance (ASR) of (i) 0.22 Ωcm2 and 0.30 Ωcm2 (by CNLS) and (ii) 0.20 Ωcm2 and 0.26 Ωcm2 (by DRT). In addition, the ASR of the cell at T = 700 °C and T = 750 °C after being stabilized for 34 h is (iii) 0.71 Ωcm2 and 0.40 Ωcm2 (by CNLS) and (iv) 0.70 Ωcm2 and 0.44 Ωcm2 (by DRT), accordingly. Conversely, the cell's microstructure is not affected by the applied stabilization periods as observed by a scanning electron microscope.

ATP6AP2, a regulator of LRP6/β-catenin protein trafficking, promotes Wnt/β-catenin signaling and bone formation in a cell type dependent manner

Wnt/β-catenin signaling is critical for various cellular processes in multiple cell types, including osteoblast (OB) differentiation and function. Exactly how Wnt/β-catenin signaling is regulated in OBs remain elusive. ATP6AP2, an accessory subunit of V-ATPase, plays important roles in multiple cell types/organs and multiple signaling pathways. However, little is known whether and how ATP6AP2 in OBs regulates Wnt/β-catenin signaling and bone formation. Here we provide evidence for ATP6AP2 in the OB-lineage cells to promote OB-mediated bone formation and bone homeostasis selectively in the trabecular bone regions. Conditionally knocking out (CKO) ATP6AP2 in the OB-lineage cells (Atp6ap2Ocn-Cre) reduced trabecular, but not cortical, bone formation and bone mass. Proteomic and cellular biochemical studies revealed that LRP6 and N-cadherin were reduced in ATP6AP2-KO BMSCs and OBs, but not osteocytes. Additional in vitro and in vivo studies revealed impaired β-catenin signaling in ATP6AP2-KO BMSCs and OBs, but not osteocytes, under both basal and Wnt stimulated conditions, although LRP5 was decreased in ATP6AP2-KO osteocytes, but not BMSCs. Further cell biological studies uncovered that osteoblastic ATP6AP2 is not required for Wnt3a suppression of β-catenin phosphorylation, but necessary for LRP6/β-catenin and N-cadherin/β-catenin protein complex distribution at the cell membrane, thus preventing their degradation. Expression of active β-catenin diminished the OB differentiation deficit in ATP6AP2-KO BMSCs. Taken together, these results support the view for ATP6AP2 as a critical regulator of both LRP6 and N-cadherin protein trafficking and stability, and thus regulating β-catenin levels, demonstrating an un-recognized function of osteoblastic ATP6AP2 in promoting Wnt/LRP6/β-catenin signaling and trabecular bone formation.

Transient Modeling of Permafrost Distribution From 1986 to 2016 in Mongolia

ABSTRACTThe distribution of permafrost in the southernmost zones is complicated in both time and space. We assessed this complexity by applying transient heat transfer schemes to model permafrost in Mongolia between 1986 and 2016. The modeling took into account the spatial distribution of soil thermal properties and time series of ground temperature profiles observed at 69 borehole sites. Spatial modeling was forced by downscaled ERA5 air temperatures that were further debiased with local meteorological records. The results indicated that permafrost warmed by 0°C to 0.4°C/decade, most significantly in eastern and northern Hangai and at high elevations in the Altai Mountains. The extent of permafrost remained stable, but the active layer was thickened, especially after the late 1990s. Forested wet soils experienced less warming, while temperatures in soils with low organic matter and low moisture content increased by 0.5°C over the modeling period. The lower limits of mountain permafrost were estimated to be between 1320 and 2030 m a.s.l., depending on the latitude. Our permafrost map allows the delineation of a complex distribution of permafrost in Mongolia, which is strongly controlled by the spatially complex topography, landcovers, and soil wetness.