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A process for artificially producing three-dimensional optic tissue has been developed. The optic cells are cultured in a bioreactor at low shear conditions. The tissue forms normal, functional tissue organization and extracellular matrix.
A three-dimensional Navier-Stokes analysis tool has been developed in order to study the effect of film cooling on the flow and heat transfer characteristics of actual turbine airfoils. An existing code (Arnone et al., 1991) has been modified for the purpose. The code is an explicit, multigrid, cell-centered, finite volume code with an algebraic turbulence model. Eigenvalue scaled artificial dissipation and variable-coefficient implicit residual smoothing are used with a full-multigrid technique. Moreover, Mayle's transition criterion (Mayle, 1991) is used. The effects of film cooling have been incorporated into the code in the form of appropriate boundary conditions at the hole locations on the airfoil surface. Each hole exit is represented by several control volumes, thus providing an ability to study the effect of hole shape on the film-cooling characteristics. Comparison is fair with near mid-span experimental data for four and nine rows of cooling holes, five on the shower head, and two rows each on the pressure and suction surfaces. The computations, however, show a strong spanwise variation of the heat transfer coefficient on the airfoil surface, specially with shower-head cooling.
A process for artificially producing three-dimensional optic tissue has been developed. The optic cells are cultured in a bioireactor at low shear conditions. The tissue forms as normal, functional tissue grows with tissue organization and extracellular matrix formation.
The hierarchically structured biominerals with excellent functions are produced by the regulation of organisms via biomineralization. The organic matrix and molecules regulate the inorganic mineralization to fabricate the delicate structural materials with the optimized properties. Inspired by natural biomineralization process, may biomimetic tactics have been developed for applications for materials and biomedicines, such as collagen re-mineralization, tooth and bone repairs. Biomineralization always highlights the control of inorganic materials by using organisms; reversely, the mineralized materials can also regulate or improve the living organisms. By conferring materials on to organism, the rationally designed organism-material hybrids can be created artificially, which are featured by their improved or new functions such as cell protection, bioenergy production, vaccine modification and cell treatment. Such a combination follows a materials-based biological modification, which would contribute a more comprehensive view of biomineralization as well as a new window for biological inorganic chemistry.
The roles of leaf anatomy, moisture and pigment content, and number of leaf layers on spectral reflectance in healthy, pollution-stressed, and water-stressed conifer needles were examined experimentally. Jeffrey pine (Pinus jeffreyi) and giant sequoia (Sequoiadendron gigantea) were exposed to ozone and acid mist treatments in fumigation chambers; red pine (Pinus resinosa) needles were artificially dried. Infrared reflectance from stacked needles rose with free water loss. In an air-drying experiment, cell volume reductions induced by loss of turgor caused near-infrared reflectance (TM band 4) to drop after most free water was lost. Under acid mist fumigation, stunting of tissue development similarly reduced band 4 reflectance. Both artificial drying and pollutant fumigation caused a blue shift of the red edge of spectral reflectance curves in conifers, attributable to chlorophyll denaturation. Thematic mapper band ratio 4/3 fell and 5/4 rose with increasing pollution stress on artificial drying. Loss of water by air-drying, freeze-drying, or oven-drying enhanced spectral features, due in part to greater scattering and reduced water absorption. Grinding of the leaf tissue further enhanced the spectral features by increasing reflecting surfaces and path length. In a leaf-stacking experiment, an asymptote in visible and infrared reflectance was reached at 7-8 needle layers of red pine.
Cut-cell methods for unsteady flow problems can greatly simplify the grid generation process and allow for high-fidelity simulations on complex geometries. However, cut-cell methods have been limited to low orders of accuracy. This is driven, largely, by the variety of procedures typically introduced to evaluate derivatives in a stable manner near the highly irregular embedded geometry. Here, a completely new approach, termed TEMO (truncation error matching and optimization), is taken to solve this problem. The approach is based on two simple and intuitive design principles. These principles directly allow for the construction of stable 8th To the best of the authors' knowledge, these are the highest orders ever achieved for a cut-cell discretization by a significant margin. This is done for both explicit and compact finite differences and is accomplished without any geometric transformations or artificial stabilization procedures.
The isotropic, nonmagnetic doped BaBiO 3 superconductors maintain some similarities to high-T c cuprates, while also providing a cleaner system for isolating charge density wave (CDW) physics that commonly competes with superconductivity. Artificial layered superlattices offer the possibility of engineering the interaction between superconductivity and CDW. Here we stabilize a low-temperature, fluctuating short-range CDW order by using artificially layered epitaxial (BaPbO 3 ) 3m /(BaBiO 3 ) m (m = 1-10 unit cells) superlattices that are not present in the optimally doped BaPb 0.75 Bi 0.25 O 3 alloy with the same overall chemical formula. Charge transfer from BaBiO 3 to BaPbO 3 effectively dopes the former and suppresses the long-range CDW; however, as the short-range CDW fluctuations strengthen at low temperatures charge appears to localize and superconductivity is weakened. The monolayer structural control demonstrated here provides compelling implications to access controllable, local density wave orders absent in bulk alloys and manipulate phase competition in unconventional superconductors.
The last two decades has seen a rapid increase in the application of AIS (Artificial Immune Systems) modeled after the human immune system to a wide range of areas including network intrusion detection, job shop scheduling, classification, pattern recognition, and robot control. JPL (Jet Propulsion Laboratory) has developed an integrated pattern recognition/classification system called AISLE (Artificial Immune System for Learning and Exploration) based on biologically inspired models of B-cell dynamics in the immune system. When used for unsupervised or supervised classification, the method scales linearly with the number of dimensions, has performance that is relatively independent of the total size of the dataset, and has been shown to perform as well as traditional clustering methods. When used for pattern recognition, the method efficiently isolates the appropriate matches in the data set. The paper presents the underlying structure of AISLE and the results from a number of experimental studies.
Solar light absorption and catalysis are physically separated processes in natural photosynthesis. Natural cofactors, such as nicotinamide adenine dinucleotides (NADH), transport electrons and hydrogen to regulate and activate enzymes at remote locations. The physical separation of light absorption from catalysis provides some inspiration for artificial photosynthesis. One rather extreme implementation is to use copper wires to transport carriers from photovoltaic cells to dark electrodes, where catalysis occurs. Indeed, with a futuristic electrical grid powered solely by photovoltaics, solar capture could be separated from catalysis by hundreds of miles. An alternative approach, that bares more similarity to natural photosynthesis, employs mobile NADH/NAD + -like species that shuttle between the light absorber and a proximate, yet unilluminated, location where catalysis occurs. Additionally, such a remote approach to solar photocatalysis was recently proposed for the reduction of carbon oxides, CO 2 and CO, to methanol by cascade catalysis. This developing artificial photosynthetic approach offers the promise of catalytic generation of methanol and oxygen gas with sunlight as the sole energy source and CO 2 and water as the only chemical feedstocks. This Viewpoint evaluates the strengths and weaknesses of this approach with an emphasis on CO reduction catalysis with photorecyclable hydride donors while looking forward to what might reasonably be achieved with continued research.
Real-time monitoring of a research nuclear reactor, a system in which all generated power is dissipated to the environment, can be performed via analysis of the heat rejection from the cooling system. Given an inlet water temperature and flow rate, the reactor power can be well-approximated from the outlet water temperature; however, the instrumentation to measure outlet conditions may not be robust or accurate. If we know how a cooling tower performs from historical data, but cannot measure the outlet temperature, a mathematical representation of the system can be inverted to obtain the outlet water temperature that describes the cooling capacity. Unfortunately, model inversion processes are computationally expensive. To address this, an artificial neural network (ANN) is implemented to assess the performance of a multi-cell cooling tower for a nuclear reactor. This approach leverages the Merkel model to obtain an extensive data set describing performance of the cooling tower cells throughout a wide array of potential operating conditions. The Merkel model is expressed as a function of four parameters: the inlet and outlet water temperatures, inlet air wet bulb temperature, and ratio of liquid-to-gas mass flow rates (L/G), which together provide a non-dimensional number indicative of cooling tower performance, called the Merkel integral. Computing a 4-dimensional data structure that describes finite combinations of the Merkel integral, an inverse model is then generated using an ANN to determine the cell outlet water temperature from the other three model parameters along with the computed Merkel integral. Compared to traditional model inversion methods, the ANN reduces the computational time by approximately 4 orders of magnitude, with effectively no sacrifice to solution accuracy, and could be applied for different cooling towers in the event the performance curve is known. Finally, three use cases of the ANN are then reviewed: (1) determining the cell outlet water temperatures when gas flow at rated conditions (GFRC) is known, (2) performing the prior case without knowledge of the GRFC, and (3) assessing performance differences between the individual tower cells.
Enzyme engineering and metabolic engineering drive innovation in energy biotechnology. In recent years, artificial intelligence (AI) has supported successful applications in designing effective enzymes and productive microbial cell factories. This review summarizes recent advances in enzyme redesign using protein language models, de novo enzyme design with generative models, and AI tools for engineering metabolism and related cellular phenotypes. Across these areas, AI models are shifting from single modality inputs to integrated representations of protein function, metabolic pathways, and cell states. We emphasize that unifying the diverse data representations across scales will be necessary for advancements in energy biotechnology.
The sensitivity and responsiveness of living cells to environmental changes are enabled by dynamic protein structures, inspiring efforts to construct artificial supramolecular protein assemblies. However, despite their sophisticated structures, designed protein assemblies have yet to be incorporated into macroscale devices for real-life applications. We report a 2D crystalline protein assembly of C98/E57/E66 L-rhamnulose-1-phosphate aldolase ( CEE RhuA) that selectively blocks or passes molecular species when exposed to a chemical trigger. CEE RhuA crystals are engineered via cobalt(II) coordination bonds to undergo a coherent conformational change from a closed state (pore dimensions <1 nm) to an ajar state (pore dimensions ~4 nm) when exposed to an HCN(g) trigger. When layered onto a mesoporous silicon (pSi) photonic crystal optical sensor configured to detect HCN (g) , the 2D CEE RhuA crystal layer effectively blocks interferents that would otherwise result in a false positive signal. The 2D CEE RhuA crystal layer opens in selective response to low-ppm levels of HCN (g) , allowing analyte penetration into the pSi sensor layer for detection. These findings illustrate that designed protein assemblies can function as dynamic components of solid-state devices in non-aqueous environments.
The enthalpy change involved in metal oxide reduction is a key quantity in various processes related to energy conversion and storage, and is of particular interest for computational prediction. Often this prediction involves the simulation of a high temperature reduction process with a 0K methodology like density functional theory (DFT), and it is not infrequent for the high temperature and 0K stable crystal structures to differ. This introduces a conundrum with regards to the choice of crystal structure to utilize in the computation, with approaches in the literature varying and experimental validation remaining scarce. In this work we address both the crystal structure conundrum and the experimental validation, and then apply the insights we gain to guide a high-throughput search for new materials for solar thermochemical water-splitting applications. By computing the DFT+U oxygen vacancy formation energy (ΔE vf ) of a selection of ABO 3 compounds and comparing different crystal structures for each composition, we highlight the issues that arise when the structure utilized in the computation is dynamically unstable at 0K, namely the presence of an artificial lowering of ΔE vf , and the lack of convergence of ΔE vf with cell size. We solve these limitations by identifying and employing a suitable surrogate dynamically stable structure. We then validate the predictive power of our calculations against appositely generated experimental measurements of reduction enthalpy for a series of Hubbard U values, finding an accuracy ranging between 0.2-0.6 eV/O. In light of such conclusions, we revise and expand a previous a high-throughput DFT study on ABO 3 perovskite oxides. As a result, we provide a list of candidate STCH materials, highlight trends with redox-active cation and structural distortion, and identify Mn 4+ , Mn 3+ and Co 3+ as the most promising redox-active cations.
Skin is composed of diverse cell populations that cooperatively maintain homeostasis. Up-regulation of the nuclear factor κB (NF-κB) pathway may lead to the development of chronic inflammatory disorders of the skin, but its role during the early events remains unclear. Here, through analysis of single-cell RNA sequencing data via iterative random forest leave one out prediction, an explainable artificial intelligence method, we identified an immunoregulatory role for a unique paired related homeobox-1 (Prx1) + fibroblast subpopulation. Disruption of Ikkb–NF-κB under homeostatic conditions in these fibroblasts paradoxically induced skin inflammation due to the overexpression of C-C motif chemokine ligand 11 (CCL11; or eotaxin-1) characterized by eosinophil infiltration and a subsequent T H 2 immune response. Because the inflammatory phenotype resembled that seen in human atopic dermatitis (AD), we examined human AD skin samples and found that human AD fibroblasts also overexpressed CCL11 and that perturbation of Ikkb–NF-κB in primary human dermal fibroblasts up-regulated CCL11. Monoclonal antibody treatment against CCL11 was effective in reducing the eosinophilia and T H 2 inflammation in a mouse model. Together, the murine model and human AD specimens point to dysregulated Prx1 + fibroblasts as a previously unrecognized etiologic factor that may contribute to the pathogenesis of AD and suggest that targeting CCL11 may be a way to treat AD-like skin lesions.
The Centrifuge Facility is a major element of the biological research facility for the implementation of NASA's Life Science Research Program on Space Station Freedom using nonhuman species (small primates, rodents, plants, insects, cell tissues, etc.). The Centrifuge Facility consists of a variable gravity Centrifuge to provide artificial gravity up to 2 earth G's' a Holding System to maintain specimens at microgravity levels, a Glovebox, and a Service Unit for servicing specimen chambers. The following subject areas are covered: (1) Holding System; (2) Centrifuge System; (3) Glovebox System; (4) Service System; and (5) system study summary.
A logical connection between the survivability of living systems and the complexity of their behavior (equivalently, mental complexity) has been established. This connection is an important intermediate result of continuing research on mathematical models that could constitute a unified representation of the evolution of both living and non-living systems. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the two most relevant being Characteristics of Dynamics of Intelligent Systems (NPO- 21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; and Self-Supervised Dynamical Systems (NPO- 30634) NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72. As used here, living systems is synonymous with active systems and intelligent systems. The quoted terms can signify artificial agents (e.g., suitably programmed computers) or natural biological systems ranging from single-cell organisms at one extreme to the whole of human society at the other extreme. One of the requirements that must be satisfied in mathematical modeling of living systems is reconciliation of evolution of life with the second law of thermodynamics. In the approach followed in this research, this reconciliation is effected by means of a model, inspired partly by quantum mechanics, in which the quantum potential is replaced with an information potential. The model captures the most fundamental property of life - the ability to evolve from disorder to order without any external interference. The model incorporates the equations of classical dynamics, including Newton s equations of motion and equations for random components caused by uncertainties in initial conditions and by Langevin forces. The equations of classical dynamics are coupled with corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces that are gradients of the information potential, which, in turn, is a function of the probability densities. The probability densities are associated with mental images both self-image and nonself images (images of external objects that can include other agents). The evolution of the probability densities represents mental dynamics. Then the interaction between the physical and metal aspects of behavior is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. The interaction of a system with its self and nonself images affords unlimited capacity for increase of complexity. There is a biological basis for this model of mental dynamics in the discovery of mirror neurons that learn by imitation. The levels of complexity attained by use of this model match those observed in living systems. To establish a mechanism for increasing the complexity of dynamics of an active system, the model enables exploitation of a chain of reflections exemplified by questions of the form, "What do you think that I think that you think...?" Mathematically, each level of reflection is represented in the form of an attractor performing the corresponding level of abstraction with more details removed from higher levels. The model can be used to describe the behaviors, not only of biological systems, but also of ecological, social, and economics ones.
Despite considerable efforts to prevent lithium (Li) dendrite growth, stable cycling of Li metal anodes with various structures remains extremely difficult due to the direct contact of the liquid electrolyte with Li. Rational design of solid-electrolyte interphase (SEI) for 3D electrodes is a promising but still challenging strategy for preventing Li dendrite growth and avoiding lithium–electrolyte side reactions in Li-metal batteries. Here, a 3D architecture is constructed with g-C 3 N 4 /graphene/g-C 3 N 4 insulator–metal–insulator sandwiched nanosheets to guide uniform Li plating/stripping in the van der Waals gap between the graphene and the g-C 3 N 4 , and the function of which can be regarded as a 3D artificial SEI. Li deposition on the surface of g-C 3 N 4 is suppressed due to its insulating nature. However, its uniform lithiophilic sites and nanopore channels enable homogeneous lithium plating between the graphene and the g-C 3 N 4 , prohibiting the direct contact of the electrolyte with the Li metal. The use of the g-C 3 N 4 -layer-modified 3D anode enables long-term Li deposition with a high Coulombic efficiency and stable cycling of full cells under high cathode loading, limited Li excess, and lean electrolyte conditions. The concept of a 3D artificial SEI will shed light on developing safe and stable Li-metal anodes.
Standard techniques used to model chemically-reacting flows require an artificial viscosity for stability in the presence of strong shocks. The resulting shock is smeared over at least three computational cells, so that the thickness of the shock is dictated by the structure of the overall mesh and not the shock physics. A gas passing through a strong shock is thrown into a nonequilibrium state and subsequently relaxes down over some finite distance to an equilibrium end state. The artificial smearing of the shock envelops this relaxation zone which causes the chemical kinetics of the flow to be altered. A method is presented which can investigate these issues by following the chemical kinetics and flow kinetics of a gas passing through a fully resolved shock wave at hypersonic Mach numbers. A nonequilibrium chemistry model for air is incorporated into a spectral multidomain Navier-Stokes solution method. Since no artificial viscosity is needed for stability of the multidomain technique, the precise effect of this artifice on the chemical kinetics and relevant flow features can be determined.