Now showing items 1-20 of 50312

• #### Horizontal acquisition of Symbiodiniaceae in the Anemonia viridis (Cnidaria, Anthozoa) species complex

(Molecular Ecology, Wiley, 2020-11-29) [Article]
All metazoans are in fact holobionts, resulting from the association of several organisms, and organismal adaptation is then due to the composite response of this association to the environment. Deciphering the mechanisms of symbiont acquisition in a holobiont is therefore essential to understanding the extent of its adaptive capacities. In cnidarians, some species acquire their photosynthetic symbionts directly from their parents (vertical transmission) but may also acquire symbionts from the environment (horizontal acquisition) at the adult stage. The Mediterranean snakelocks sea anemone, Anemonia viridis (Forskål, 1775), passes down symbionts from one generation to the next by vertical transmission, but the capacity for such horizontal acquisition is still unexplored. To unravel the flexibility of the association between the different host lineages identified in A. viridis and its Symbiodiniaceae, we genotyped both the animal hosts and their symbiont communities in members of host clones in five different locations in the North Western Mediterranean Sea. The composition of within-host symbiont populations was more dependent on the geographical origin of the hosts than their membership to a given lineage or even to a given clone. Additionally, similarities in host symbiont communities were greater among genets (i.e. among different clones) than among ramets (i.e. among members of the same given clonal genotype). Taken together, our results demonstrate that A. viridis may form associations with a range of symbiotic dinoflagellates and suggest a capacity for horizontal acquisition. A mixed-mode transmission strategy in A. viridis, as we posit here, may help explain the large phenotypic plasticity that characterises this anemone.
• #### Computational Design of Cold Bent Glass Façades

(ACM Transactions on Graphics, ACM, 2020-11-27) [Article]
Cold bent glass is a promising and cost-efficient method for realizing doubly curved glass facades. They are produced by attaching planar glass sheets to curved frames and require keeping the occurring stress within safe limits. However, it is very challenging to navigate the design space of cold bent glass panels due to the fragility of the material, which impedes the form-finding for practically feasible and aesthetically pleasing cold bent glass facades. We propose an interactive, data-driven approach for designing cold bent glass facades that can be seamlessly integrated into a typical architectural design pipeline. Our method allows non-expert users to interactively edit a parametric surface while providing real-time feedback on the deformed shape and maximum stress of cold bent glass panels. Designs are automatically refined to minimize several fairness criteria while maximal stresses are kept within glass limits. We achieve interactive frame rates by using a differentiable Mixture Density Network trained from more than a million simulations. Given a curved boundary, our regression model is capable of handling multistable configurations and accurately predicting the equilibrium shape of the panel and its corresponding maximal stress. We show predictions are highly accurate and validate our results with a physical realization of a cold bent glass surface.
• #### Mixed-dimensional MXene-hydrogel heterostructures for electronic skin sensors with ultrabroad working range

(Science Advances, American Association for the Advancement of Science (AAAS), 2020-11-27) [Article]
Skin-mountable microelectronics are garnering substantial interest for various promising applications including human-machine interfaces, biointegrated devices, and personalized medicine. However, it remains a critical challenge to develop e-skins to mimic the human somatosensory system in full working range. Here, we present a multifunctional e-skin system with a heterostructured configuration that couples vinyl-hybrid-silica nanoparticle (VSNP)–modified polyacrylamide (PAM) hydrogel with two-dimensional (2D) MXene through nano-bridging layers of polypyrrole nanowires (PpyNWs) at the interfaces, featuring high toughness and low hysteresis, in tandem with controlled crack generation and distribution. The multidimensional configurations endow the e-skin with an extraordinary working range (2800%), ultrafast responsiveness (90 ms) and resilience (240 ms), good linearity (800%), tunable sensing mechanisms, and excellent reproducibility. In parallel, this e-skin platform is capable of detecting, quantifying, and remotely monitoring stretching motions in multiple dimensions, tactile pressure, proximity sensing, and variations in temperature and light, establishing a promising platform for next-generation smart flexible electronics.
• #### Nocturnal Surface Urban Heat Island over Greater Cairo: Spatial Morphology, Temporal Trends and Links to Land-Atmosphere Influences

(Remote Sensing, MDPI AG, 2020-11-27) [Article]
This study assesses the spatial and temporal characteristics of nighttime surface urban heat island (SUHI) effects over Greater Cairo: the largest metropolitan area in Africa. This study employed nighttime land surface temperature (LST) data at 1 km resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua sensor for the period 2003–2019. We presented a new spatial anomaly algorithm, which allowed to define SUHI using the most anomalous hotspot and cold spot of LST for each time step over Greater Cairo between 2003 and 2019. Results demonstrate that although there is a significant increase in the spatial extent of SUHI over the past two decades, a significant decrease in the mean and maximum intensities of SUHI was noted. Moreover, we examined the dependency between SUHI characteristics and related factors that influence energy and heat fluxes between atmosphere and land in urban environments (e.g., surface albedo, vegetation cover, climate variability, and land cover/use changes). Results demonstrate that the decrease in the intensity of SUHI was mainly guided by a stronger warming in daytime and nighttime LST in the neighborhood of urban localities. This warming was accompanied by a decrease in surface albedo and diurnal temperature range (DTR) over these areas. Results of this study can provide guidance to local urban planners and decision-makers to adopt more effective mitigation strategies to diminish the negative impacts of urban warming on natural and human environments.
• #### Large deep-sea zooplankton biomass mirrors primary production in the global ocean

(Nature Communications, Springer Science and Business Media LLC, 2020-11-27) [Article]
AbstractThe biological pump transports organic carbon produced by photosynthesis to the meso- and bathypelagic zones, the latter removing carbon from exchanging with the atmosphere over centennial time scales. Organisms living in both zones are supported by a passive flux of particles, and carbon transported to the deep-sea through vertical zooplankton migrations. Here we report globally-coherent positive relationships between zooplankton biomass in the epi-, meso-, and bathypelagic layers and average net primary production (NPP). We do so based on a global assessment of available deep-sea zooplankton biomass data and large-scale estimates of average NPP. The relationships obtained imply that increased NPP leads to enhanced transference of organic carbon to the deep ocean. Estimated remineralization from respiration rates by deep-sea zooplankton requires a minimum supply of 0.44 Pg C y$^{−1}$ transported into the bathypelagic ocean, comparable to the passive carbon sequestration. We suggest that the global coupling between NPP and bathypelagic zooplankton biomass must be also supported by an active transport mechanism associated to vertical zooplankton migration.
• #### The equations of polyconvex thermoelasticity

(2020-11-25) [Dissertation]
Committee members: Hoteit, Ibrahim; Markowich, Peter A.; Christoforou, Cleopatra; Dafermos Constantine, M.
In my Dissertation, I consider the system of thermoelasticity endowed with poly- convex energy. I will present the equations in their mathematical and physical con- text, and I will explain the relevant research in the area and the contributions of my work. First, I embed the equations of polyconvex thermoviscoelasticity into an aug- mented, symmetrizable, hyperbolic system which possesses a convex entropy. Using the relative entropy method in the extended variables, I show convergence from ther- moviscoelasticity with Newtonian viscosity and Fourier heat conduction to smooth solutions of the system of adiabatic thermoelasticity as both parameters tend to zero and convergence from thermoviscoelasticity to smooth solutions of thermoelasticity in the zero-viscosity limit. In addition, I establish a weak-strong uniqueness result for the equations of adiabatic thermoelasticity in the class of entropy weak solutions. Then, I prove a measure-valued versus strong uniqueness result for adiabatic poly- convex thermoelasticity in a suitable class of measure-valued solutions, de ned by means of generalized Young measures that describe both oscillatory and concentra- tion e ects. Instead of working directly with the extended variables, I will look at the parent system in the original variables utilizing the weak stability properties of certain transport-stretching identities, which allow to carry out the calculations by placing minimal regularity assumptions in the energy framework. Next, I construct a variational scheme for isentropic processes of adiabatic polyconvex thermoelasticity. I establish existence of minimizers which converge to a measure-valued solution that dissipates the total energy. Also, I prove that the scheme converges when the limit- ing solution is smooth. Finally, for completeness and for the reader's convenience, I present the well-established theory for local existence of classical solutions and how it applies to the equations at hand.
• #### Multiple wheat genomes reveal global variation in modern breeding

(Nature, Springer Science and Business Media LLC, 2020-11-25) [Article]
AbstractAdvances in genomics have expedited the improvement of several agriculturally important crops but similar efforts in wheat (Triticum spp.) have been more challenging. This is largely owing to the size and complexity of the wheat genome$^{1}$, and the lack of genome-assembly data for multiple wheat lines$^{2,3}$. Here we generated ten chromosome pseudomolecule and five scaffold assemblies of hexaploid wheat to explore the genomic diversity among wheat lines from global breeding programs. Comparative analysis revealed extensive structural rearrangements, introgressions from wild relatives and differences in gene content resulting from complex breeding histories aimed at improving adaptation to diverse environments, grain yield and quality, and resistance to stresses$^{4,5}$. We provide examples outlining the utility of these genomes, including a detailed multi-genome-derived nucleotide-binding leucine-rich repeat protein repertoire involved in disease resistance and the characterization of Sm1$^{6}$, a gene associated with insect resistance. These genome assemblies will provide a basis for functional gene discovery and breeding to deliver the next generation of modern wheat cultivars.
• #### Quantile Function Modeling and Analysis for Multivariate Functional Data

(2020-11-25) [Dissertation]
Committee members: Ombao, Hernando; Tester, Mark A.; He, Xuming
Quantile function modeling is a more robust, comprehensive, and flexible method of statistical analysis than the commonly used mean-based methods. More and more data are collected in the form of multivariate, functional, and multivariate functional data, for which many aspects of quantile analysis remain unexplored and challenging. This thesis presents a set of quantile analysis methods for multivariate data and multivariate functional data, with an emphasis on environmental applications, and consists of four significant contributions. Firstly, it proposes bivariate quantile analysis methods that can predict the joint distribution of bivariate response and improve on conventional univariate quantile regression. The proposed robust statistical techniques are applied to examine barley plants grown in saltwater and freshwater conditions providing interesting insights into barley’s responses, informing future crop decisions. Secondly, it proposes modeling and visualization of bivariate functional data to characterize the distribution and detect outliers. The proposed methods provide an informative visualization tool for bivariate functional data and can characterize non-Gaussian, skewed, and heavy-tailed distributions using directional quantile envelopes. The radiosonde wind data application illustrates our proposed quantile analysis methods for visualization, outlier detection, and prediction. However, the directional quantile envelopes are convex by definition. This feature is shared by most existing methods, which is not desirable in nonconvex and multimodal distributions. Thirdly, this challenge is addressed by modeling multivariate functional data for flexible quantile contour estimation and prediction. The estimated contours are flexible in the sense that they can characterize non-Gaussian and nonconvex marginal distributions. The proposed multivariate quantile function enjoys the theoretical properties of monotonicity, uniqueness, and the consistency of its contours. The proposed methods are applied to air pollution data. Finally, we perform quantile spatial prediction for non-Gaussian spatial data, which often emerges in environmental applications. We introduce a copula-based multiple indicator kriging model, which makes no distributional assumptions on the marginal distribution, thus offers more flexibility. The method performs better than the commonly used variogram approach and Gaussian kriging for spatial prediction in simulations and application to precipitation data.
• #### BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data

(2020-11-25) [Thesis]
Committee members: Sun, Ying; Laleg-Kirati, Taous-Meriem; Ting, Chee-Ming
Intrinsic connectivity networks (ICNs) refer to brain functional networks that are consistently found under various conditions, during tasks or at rest. Some studies demonstrated that while some stimuli do not impact intrinsic connectivity, other stimuli actually activate intrinsic connectivity through suppression, excitation, moderation or modi cation. Most analyses of functional magnetic resonance imaging (fMRI) data use ad-hoc methods to estimate the latent structure of ICNs. Modeling the effects on ICNs has also not been fully investigated. Bayesian Intrinsic Connectivity Network (BICNet) captures the ICN structure with We propose a BICNet model, an extended Bayesian dynamic sparse latent factor model, to identify the ICNs and quantify task-related effects on the ICNs. BICNet has the following advantages: (1) It simultaneously identifies the individual and group-level ICNs; (2) It robustly identifies ICNs by jointly modeling resting-state fMRI (rfMRI) and task-related fMRI (tfMRI); (3) Compared to independent component analysis (ICA)-based methods, it can quantify the difference of ICNs amplitudes across different states; (4) The sparsity of ICNs automatically performs feature selection, instead of ad-hoc thresholding. We apply BICNet to the rfMRI and language tfMRI data from the Human Connectome Project (HCP) and identify several ICNs related to distinct language processing functions.
• #### Recessive, Deleterious Variants in SMG8 Expand the Role of Nonsense-Mediated Decay in Developmental Disorders in Humans.

(American journal of human genetics, Elsevier BV, 2020-11-25) [Article]
We have previously described a heart-, eye-, and brain-malformation syndrome caused by homozygous loss-of-function variants in SMG9, which encodes a critical component of the nonsense-mediated decay (NMD) machinery. Here, we describe four consanguineous families with four different likely deleterious homozygous variants in SMG8, encoding a binding partner of SMG9. The observed phenotype greatly resembles that linked to SMG9 and comprises severe global developmental delay, microcephaly, facial dysmorphism, and variable congenital heart and eye malformations. RNA-seq analysis revealed a general increase in mRNA expression levels with significant overrepresentation of core NMD substrates. We also identified increased phosphorylation of UPF1, a key SMG1-dependent step in NMD, which most likely represents the loss of SMG8--mediated inhibition of SMG1 kinase activity. Our data show that SMG8 and SMG9 deficiency results in overlapping developmental disorders that most likely converge mechanistically on impaired NMD.
• #### Host-directed editing of the SARS-CoV-2 genome.

(Biochemical and biophysical research communications, Elsevier BV, 2020-11-25) [Article]
The extensive sequence data generated from SARS-CoV-2 during the 2020 pandemic has facilitated the study of viral genome evolution over a brief period of time. This has highlighted instances of directional mutation pressures exerted on the SARS-CoV-2 genome from host antiviral defense systems. In this brief review we describe three such human defense mechanisms, the apolipoprotein B mRNA editing catalytic polypeptide-like proteins (APOBEC), adenosine deaminase acting on RNA proteins (ADAR), and reactive oxygen species (ROS), and discuss their potential implications on SARS-CoV-2 evolution.
• #### Imitation Learning based on Generative Adversarial Networks for Robot Path Planning

(2020-11-24) [Thesis]
Committee members: Wonka, Peter; Moshkov, Mikhail
Robot path planning and dynamic obstacle avoidance are defined as a problem that robots plan a feasible path from a given starting point to a destination point in a nonlinear dynamic environment, and safely bypass dynamic obstacles to the destination with minimal deviation from the trajectory. Path planning is a typical sequential decision-making problem. Dynamic local observable environment requires real-time and adaptive decision-making systems. It is an innovation for the robot to learn the policy directly from demonstration trajectories to adapt to similar state spaces that may appear in the future. We aim to develop a method for directly learning navigation behavior from demonstration trajectories without defining the environment and attention models, by using the concepts of Generative Adversarial Imitation Learning (GAIL) and Sequence Generative Adversarial Network (SeqGAN). The proposed SeqGAIL model in this thesis allows the robot to reproduce the desired behavior in different situations. In which, an adversarial net is established, and the Feature Counts Errors reduction is utilized as the forcing objective for the Generator. The refinement measure is taken to solve the instability problem. In addition, we proposed to use the Rapidly-exploring Random Tree* (RRT*) with pre-trained weights to generate adequate demonstration trajectories in dynamic environment as the training data, and this idea can effectively overcome the difficulty of acquiring huge training data.
• #### A Multilayer Nonlinear Elimination Preconditioned Inexact Newton Method for Steady-State Incompressible Flow Problems in Three Dimensions

(SIAM Journal on Scientific Computing, Society for Industrial & Applied Mathematics (SIAM), 2020-11-24) [Article]
We develop a multilayer nonlinear elimination preconditioned inexact Newton method for a nonlinear algebraic system of equations, and a target application is the three-dimensional steady-state incompressible Navier--Stokes equations at high Reynolds numbers. Nonlinear steadystate problems are often more difficult to solve than time-dependent problems because the Jacobian matrix is less diagonally dominant, and a good initial guess from the previous time step is not available. For such problems, Newton-like methods may suffer from slow convergence or stagnation even with globalization techniques such as line search. In this paper, we introduce a cascadic multilayer nonlinear elimination approach based on feedback from intermediate solutions to improve the convergence of Newton iteration. Numerical experiments show that the proposed algorithm is superior to the classical inexact Newton method and other single layer nonlinear elimination approaches in terms of the robustness and efficiency. Using the proposed nonlinear preconditioner with a highly parallel domain decomposition framework, we demonstrate that steady solutions of the Navier--Stokes equations with Reynolds numbers as large as 7,500 can be obtained for the lid-driven cavity flow problem in three dimensions without the use of any continuation methods.
• #### FDTD modeling and experiments of microfabricated coplanar waveguide probes for electromagnetic compatibility applications

(Journal of Electromagnetic Waves and Applications, Informa UK Limited, 2020-11-24) [Article]
We present the design, fabrication and experiment of a miniature nonresonant probe based on a coplanar waveguide dedicated to near-field imaging for electromagnetic compatibility applications. Our modeling approach, based on the Finite Difference Time Domain and Finite Element Method, proves that this probe is sensitive to the longitudinal component of the electric field. In addition to its compatibility with integrated circuits, this probe is suitable for wide frequency band applications since it is produced from a micro-metric coplanar line which has no cut-off frequency. We achieved prototypes using the clean room techniques. Using this probe on a precision X-Y scanning table, two-dimensional images of micro-strip line and electric dipole antenna have been successfully constructed. Simulations and measurements results on a microstrip line have shown that the probe is mainly sensitive to the normal electric field. The estimated sensitivity is 30 μV/(V/m).
• #### Seismic Velocities Distribution in a 3D Mantle: Implications for InSight Measurements

(Wiley, 2020-11-24) [Preprint]
• #### Shedding Light on the Interfacial Structure of Low-Coverage Alkanethiol Lattices

(The Journal of Physical Chemistry C, American Chemical Society (ACS), 2020-11-24) [Article]
A comprehensive description of the self-assembly process of alkanethiols on Au(111) is presented, focused on the initial formation of the lying down phases. Low-coverage monolayers are prepared by the disintegration of Au144(RS)60 nanoclusters on the reconstructed (22 × √3)-Au(111) surface. The method provides a limited number of thiols together with a large excess of gold adatoms. Scanning tunneling microscopy and density functional theory calculations were employed to study the transition between low to high thiolate coverage phases. The process involves different lattices and surface transformations, including thiyl radicals on the herringbone reconstruction, radical-induced herringbone lifting, and the formation of energetically similar metastable phases formed by RS-Au-RS moieties. Results also show that the transition is slow, and different surface structures can coexist on the same sample. Along the process, the first source of Au adatoms to form the RS-Au-SR moieties is the lifting of the herringbone reconstruction because of the lower energetic cost to extract the extra Au atom. However, for hexanethiol (and shorter alkanethiols) at low coverage, additional Au adatoms must be taken from terraces leading to vacancy islands. This process can be entirely suppressed by growing the lying down phases in the presence of an excess of Au adatoms. Taken together, our results shed light on the elusive initial steps of thiol adsorption on clean reconstructed Au, showing that the RS-Au-SR staple motif is also present at the interface of low-coverage self-assembled monolayers.
• #### Adversarial Generation of Continuous Images

(arXiv, 2020-11-24) [Preprint]
In most existing learning systems, images are typically viewed as 2D pixel arrays. However, in another paradigm gaining popularity, a 2D image is represented as an implicit neural representation (INR) -- an MLP that predicts an RGB pixel value given its (x,y) coordinate. In this paper, we propose two novel architectural techniques for building INR-based image decoders: factorized multiplicative modulation and multi-scale INRs, and use them to build a state-of-the-art continuous image GAN. Previous attempts to adapt INRs for image generation were limited to MNIST-like datasets and do not scale to complex real-world data. Our proposed architectural design improves the performance of continuous image generators by x6-40 times and reaches FID scores of 6.27 on LSUN bedroom 256x256 and 16.32 on FFHQ 1024x1024, greatly reducing the gap between continuous image GANs and pixel-based ones. To the best of our knowledge, these are the highest reported scores for an image generator, that consists entirely of fully-connected layers. Apart from that, we explore several exciting properties of INR-based decoders, like out-of-the-box superresolution, meaningful image-space interpolation, accelerated inference of low-resolution images, an ability to extrapolate outside of image boundaries and strong geometric prior. The source code is available at https://github.com/universome/inr-gan
• #### Source Apportionment and Elemental Composition of Atmospheric Total Suspended Particulates (TSP) Over the Red Sea Coast of Saudi Arabia

(Earth Systems and Environment, Springer Science and Business Media LLC, 2020-11-24) [Article]
AbstractThis work presents a comprehensive study on concentrations and elemental composition of total suspended atmospheric particulates for a semi-urban site on the Red Sea coast, and on-board a research vessel, which collected off-shore samples along the Red Sea. We conducted one of the most extended measurement campaigns of atmospheric particulates ever for the region, with continuous measurements over 27 months. The overall mean concentrations (± st. dev.) of TSP were 125 ± 197 µg m$^{−3}$ for the permanent semi-urban site, and 108 ± 193 µg m$^{−3}$ for the off-shore mobile site. The region is frequently severely impacted by both localised and widespread dust storms, which on occasion, can increase atmospheric particulate concentrations to levels above mg m$^{−3}$ (> 1000 µg m$^{−3}$). Median concentrations were not as variable between seasons, indicating a stable, permanent presence of atmospheric particulates independent of the time of year. The primary chemical elements contributing to particulate mass were Na, Ca, S, Al and Fe. We employed Positive Matrix Factorisation (EPA PMF v5.0.14) to identify different major sources of particulates, which were crustal, marine, fuel oil combustion/secondary sulphate and mixed anthropogenic. The crustal source was characterised by tracers Al, Fe, K, Mg and Sn, and was present to some extent in the other identified sources due to the permanent presence of dust particles in the atmosphere. The fuel oil combustion/secondary sulphate source was identifiable by the almost exclusive presence of S, and to a lesser extent V, emitted from oil combustion as primary emissions and also secondary sulphate formation following the release of S to the atmosphere. A mixed anthropogenic source was characterised by Zn, Ni, Cr, Cu and Pb, emitted from traffic, industry, power generation and water desalination. This study highlights that the natural sources of particulates in this desert region give rise to frequent episodes of extremely poor air quality, and this problem is compounded by significant emissions of anthropogenic pollution, which has an impact across the entire Red Sea basin. Further stringent measures should be adopted to improve air quality across the region and prevent long-term damage to the health of the local population and ecosystems.
• #### Electrode Protection in High-Efficiency Li–O2 Batteries

(ACS Central Science, American Chemical Society (ACS), 2020-11-24) [Article]
The aprotic Li–O2 battery possessing the highest theoretical energy density, approaching that of gasoline, has been regarded as one of the most promising successors to Li-ion batteries. Before this kind of battery can become a viable technology, a series of critical issues need to be conquered, like low round-trip efficiency and short cycling lifetime, which are closely related to the continuous parasitic processes happening at the cathode and anode during cycling. With an aim to promote the practical application of Li–O2 batteries, great effort has been devoted to identify the reasons for oxygen and lithium electrodes degradation and provide guidelines to overcome them. Thus, the stability of cathode and anode has been improved a lot in the past decade, which in turn significantly boosts the electrochemical performances of Li–O2 batteries. Here, an overlook on the electrode protection in high-efficiency Li–O2 batteries is presented by providing first the challenges of electrodes facing and then the effectiveness of the existing approaches that have been proposed to alleviate these. Moreover, new battery systems and perspectives of the viable near-future strategies for rational configuration and balance of the electrodes are also pointed out. This Outlook deepens our understanding of the electrodes in Li–O2 batteries and offers opportunities for the realization of high performance and long-term durability of Li–O2 batteries.
• #### Energy Stability of Explicit Runge--Kutta Methods for Nonautonomous or Nonlinear Problems

(SIAM Journal on Numerical Analysis, Society for Industrial & Applied Mathematics (SIAM), 2020-11-24) [Article]
Many important initial value problems have the property that energy is nonincreasing in time. Energy stable methods, also referred to as strongly stable methods, guarantee the same property discretely. We investigate requirements for conditional energy stability of explicit Runge--Kutta methods for nonlinear or nonautonomous problems. We provide both necessary and sufficient conditions for energy stability over these classes of problems. Examples of conditionally energy stable schemes are constructed, and an example is given in which unconditional energy stability is obtained with an explicit scheme.