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At least 55 records · Page 3

Expression of extracellular calcium-sensing receptor in human osteoblastic MG-63 cell line

We have previously shown the expression of the extracellular calcium (Ca2+o)-sensing receptor (CaR) in osteoblast-like cell lines, and others have documented its expression in sections of murine, bovine, and rat bone. The existence of the CaR in osteoblasts remains controversial, however, since some studies have failed to document its expression in the same osteoblast-like cell lines. The goals of the present study were twofold. 1) We sought to determine whether the CaR is expressed in the human osteoblast-like cell line, MG-63, which has recently been reported by others not to express this receptor. 2) We investigated whether the CaR, if present in MG-63 cells, is functionally active, since most previous studies have not proven the role of the CaR in mediating known actions of Ca2+o on osteoblast-like cells. We used immunocytochemistry and Western blotting with the specific, affinity-purified anti-CaR antiserum 4637 as well as Northern blot analysis and RT-PCR using a riboprobe and PCR primers specific for the human CaR, respectively, to show readily detectable CaR protein and mRNA expression in MG-63 cells. Finally, we employed the patch-clamp technique to show that an elevation in Ca2+o as well as the specific, allosteric CaR activator NPS R-467 (0.5 microM), but not its less active stereoisomer NPS S-467 (0.5 microM), activate an outward K+ channel in MG-63 cells, strongly suggesting that the CaR in MG-63 cells is not only expressed but is functionally active.

NASA Discipline Regulatory Physiology↗

Uncertainty propagation in feed-forward neural network models

We develop new uncertainty propagation methods for feed-forward neural network architectures with leaky ReLU activation functions subject to random perturbations in the input vectors. In particular, we derive analytical expressions for the probability density function (PDF) of the neural network output and its statistical moments as a function of the input uncertainty and the parameters of the network, i.e., weights and biases. A key finding is that an appropriate linearization of the leaky ReLU activation function yields accurate statistical results even for large perturbations in the input vectors. This can be attributed to the way information propagates through the network. We also propose new analytically tractable Gaussian copula surrogate models to approximate the full joint PDF of the neural network output. To validate our theoretical results, we conduct Monte Carlo simulations and a thorough error analysis on a multi-layer neural network representing a nonlinear integro-differential operator between two polynomial function spaces. Our findings demonstrate excellent agreement between the theoretical predictions and Monte Carlo simulations.

MLP networks↗

Extending Parsimonious Bayesian Inference

Parsimonious Bayesian inference is a theoretical framework for efficient data assimilation that seeks to balance increased consistency between predictions and training data against corresponding increases in model complexity. Within this framework, over-training is understood as optimization that encodes excessive information within model parameters while only achieving small improvements between predictions and training data. This project aims to develop practical methods of limiting excess model information during optimization. One key observation is that practical heuristics for parsimonious learning in high-dimensions must balance expressivity, i.e. the ability of the model to capture diverse predictions with only a few non-zero parameters, against discoverability, i.e. the ability to train the model with gradient-based optimization and drive parameters to low information states. As such, we developed logical activation functions that are able to adaptively approximate arbitrary truth tables that define Boolean logic operations within a probabilistic framework. These functions have demonstrated the ability to learn exclusive disjunction (XOR) and conditioned disjunction (if [condition] then [result_if_true] else [result_if_false]) within a single layer of a neural network. To efficiently exploit these activation functions to drive parsimonious learning required several other advances within the domain of variational inference. The most efficient form of complexity suppression is structured sparsification, driving most model parameters to zero while achieving the structural coherence among nonzeros needed for bandwidth reduction. Such models are not only far more efficient at suppressing information-theoretic complexity, they also reduce the other forms of complexity (computations, communication, storage, and the number of dependencies needed to evaluate predictions). Aiming to support enhanced sparsification, this project examined new approaches to high-dimensional variational inference that allow us to calibrate and control parameter uncertainty during optimization. By identifying which parameters can sustain sparsifying perturbations with little impact on prediction quality, we can develop better pruning strategies by framing them as approximate Bayesian inference. These advances also open paths to mitigate concerns with deploying advanced learning methods in resource-constrained environments, such as running models on power-limited or communication-limited devices.

97 MATHEMATICS AND COMPUTING↗

Comparative Activity and Functional Ecology of Permafrost Soils and Lithic Niches in a Hyper-Arid Polar Desert

This study represents the first metagenomic interrogation of Antarctic permafrost and polar cryptoendolithic microbial communities. The results underlie two different habitability conditions in the same location under extreme cold and dryness: the permafrost habitat where viable microbial life and activity is questionable, and the cryptoendolithic habitat which contains organisms capable of growth under the extreme conditions of the Antarctic Dry Valleys.

Antarctic permafrost↗

Deep Learning without Global Optimization by Random Fourier Neural Networks

Here we introduce a new training algorithm for deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov chain Monte Carlo sampling procedure to iteratively train network layers, avoiding global and gradient-based optimization while maintaining error control. It consistently attains the theoretical approximation rate for residual networks with complex exponential activation functions, determined by network complexity. Additionally, it enables efficient learning of multiscale and high-frequency features, producing interpretable parameter distributions. Despite using sinusoidal basis functions, we do not observe Gibbs phenomena in approximating discontinuous target functions.

97 MATHEMATICS AND COMPUTING↗

Functional group activation and coupling agent migration induced by plasma treatment in adhesive for enhanced toughness of metal-composite joints

Commercial adhesive paste was additionally activated using low-power oxygen plasma with different plasma exposure durations to investigate its interfacial bonding contribution in adhesively-bonded Aural-5/CFRP-PA6 double cantilever beam (DCB) joints with plasma-treated adherends. The plasma-treated adhesive showed enhanced functional peak intensities in Fourier transfer infrared spectroscopy (FTIR) as plasma exposure time was increased, and some degree of oxidation was also detected via X-ray photoelectron spectroscopy (XPS). A small molecule, dicyandiamde (coupling agent) in adhesive was migrated to adhesive layer, where double concentration of coupling agent was detected after 10 minutes of plasma treatment. As a result, characterization of the adhesive/CFRP-PA6 interface after DCB fracture showed additional chemical bonds formed through amine-initiated epoxy ring opening polymerization, as well as amide bonds and ester bonds. In conclusion, these additional bonds at the adhesive/CFRP-PA6 interfaces led to significantly increased failure extensions and fracture energies of the joints as the plasma exposure time on the adhesive increased, compared to the joints with plasma-treated adherends but non-plasma-treated adhesive as commonly seen in the literature.

36 MATERIALS SCIENCE↗

Physics-informed neural networks for heterogeneous poroelastic media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.

42 ENGINEERING↗

Discrete event simulation tool for analysis of qualitative models of continuous processing systems

An artificial intelligence design and qualitative modeling tool is disclosed for creating computer models and simulating continuous activities, functions, and/or behavior using developed discrete event techniques. Conveniently, the tool is organized in four modules: library design module, model construction module, simulation module, and experimentation and analysis. The library design module supports the building of library knowledge including component classes and elements pertinent to a particular domain of continuous activities, functions, and behavior being modeled. The continuous behavior is defined discretely with respect to invocation statements, effect statements, and time delays. The functionality of the components is defined in terms of variable cluster instances, independent processes, and modes, further defined in terms of mode transition processes and mode dependent processes. Model construction utilizes the hierarchy of libraries and connects them with appropriate relations. The simulation executes a specialized initialization routine and executes events in a manner that includes selective inherency of characteristics through a time and event schema until the event queue in the simulator is emptied. The experimentation and analysis module supports analysis through the generation of appropriate log files and graphics developments and includes the ability of log file comparisons.

Malin, Jane T.↗

Linear and nonlinear ARMA model parameter estimation using an artificial neural network

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.

NASA Discipline Regulatory Physiology↗

The effect of anisotropic emission from thick accretion disks on the luminosity functions of active galactic nuclei

High-luminosity active galactic nuclei (AGNs) powered by accretion onto a massive black hole (or other compact object) may have bolometric luminosities dominated by thermal emission from a geometrically thick accretion disk. Radiation from these disks is strongly anisotropic, which has important consequences for the observed luminosity distribution, and therefore for systematic biases in flux-limited samples. The effect of anisotropic emission from an ensemble of AGNs with random oriented thick disks radiating at or near the Eddington limit is calculated. Because of their higher luminosities, it is predicted face-on disks should constitute an increasing fraction of observed high-redshift, high-luminosity AGNs. Comparison of the results with observed quasar luminosity functions suggests a narrow mass distribution with an upper limit of about a billion solar masses for high-redshift quasars.

Urry, C. M.↗

Restricting electron flow at cytochrome b6f when downstream electron acceptors are severely limited

Abstract Photosynthetic organisms frequently experience abiotic stress that restricts their growth and development. Under such circumstances, most absorbed solar energy cannot be used for CO2 fixation and can cause the photoproduction of reactive oxygen species (ROS) that can damage the photosynthetic reaction centers of PSI and PSII, resulting in a decline in primary productivity. This work describes a biological “switch” in the green alga Chlamydomonas reinhardtii that reversibly restricts photosynthetic electron transport (PET) at the cytochrome b6f (Cyt b6f) complex when the capacity for accepting electrons downstream of PSI is severely limited. We specifically show this restriction in STARCHLESS6 (sta6) mutant cells, which cannot synthesize starch when they are limited for nitrogen (growth inhibition) and subjected to a dark-to-light transition. This restriction represents a form of photosynthetic control that causes diminished electron flow to PSI and thereby prevents PSI photodamage but does not appear to rely on a ΔpH. Furthermore, when electron flow is restricted, the plastid alternative oxidase (PTOX) becomes active, functioning as an electron valve that dissipates some excitation energy absorbed by PSII and allows the formation of a proton motive force (PMF) that would drive some ATP production (potentially sustaining PSII repair and nonphotochemical quenching [NPQ]). The restriction at the Cyt b6f complex can be gradually relieved with continued illumination. This study provides insights into how PET responds to a marked reduction in availability of downstream electron acceptors and the protective mechanisms involved.

Plant Sciences↗

Functions and activities of the Arizona Regional Ecological Test Site

The Arizona Regional Ecological Test Site (ARETS) project and its efforts to coordinate and transfer satellite remote sensing techniques to solving earth resources and environmental problems in the Arizona area are discussed. Project objectives include: (1) establishing a multidiscipline resources and environment team in Arizona capable of applying satellite and supporting aircraft data to expedite remote sensing technology transfer to user agencies; (2) determine future resources and environment information needs for Arizona; (3) conduct coordinated multidiscipline experiments in the use of ERTS data; and (4) provide and recommend facilities, instruments, and training necessary to support the practical uses of remote sensing data.

Lepley, L. K.↗

Simplifying activations with linear approximations in neural networks

A key step in Neural Networks is activation. Among the different types of activation functions, sigmoid, tanh, and others involve the usage of exponents for calculation. From a hardware perspective, exponential implementation implies the usage of Taylor series or repeated methods involving many addition, multiplication, and division steps, and as a result are power-hungry and consume many clock cycles. We implement a piecewise linear approximation of the sigmoid function as a replacement for standard sigmoid activation libraries. This approach provides a practical alternative by leveraging piecewise segmentation, which simplifies hardware implementation and improves computational efficiency. In this paper, we detail piecewise functions that can be implemented using linear approximations and their implications for overall model accuracy and performance gain. Our results show that for the DenseNet, ResNet, and GoogLeNet architectures, the piecewise linear approximation of the sigmoid function provides faster execution times compared to the standard TensorFlow sigmoid implementation while maintaining comparable accuracy. Specifically, for MNIST with DenseNet, accuracy reaches 99.91% (Piecewise) vs. 99.97% (Base) with up to 1.31x speedup in execution time. For CIFAR-10 with DenseNet, accuracy improves to 98.97% (Piecewise) vs. 99.40% (Base) while achieving 1.24x faster execution. Similarly, for CIFAR-100 with DenseNet, the accuracy is 97.93% (Piecewise) vs. 98.39% (Base), with a 1.18x execution time reduction. These results confirm the proposed method’s capability to efficiently process large-scale datasets and computationally demanding tasks, offering a practical means to accelerate deep learning models, including LSTMs, without compromising accuracy.

Activation function↗

Multifunctional and biologically active matrices from multicomponent polymeric solutions

The present invention relates to a biologically active functionalized electrospun matrix to permit immobilization and long-term delivery of biologically active agents. In particular the invention relates to a functionalized polymer matrix comprising a matrix polymer, a compatibilizing polymer and a biomolecule or other small functioning molecule. In certain aspects the electrospun polymer fibers comprise at least one biologically active molecule functionalized with low molecular weight heparin. Examples of active molecules that may be used with the multicomponent polymer of the invention include, for example, a drug, a biopolymer, for example a growth factor, a protein, a peptide, a nucleotide, a polysaccharide, a biological macromolecule or the like. The invention is further directed to the formation of functionalized crosslinked matrices, such as hydrogels, that include at least one functionalized compatibilizing polymer capable of assembly.

Kiick, Kristi L.↗

A new minimax algorithm

The representation min t s.t. F(I)(x). - t less than or equal to 0 for all i is examined. An active set strategy is designed of functions: active, semi-active, and non-active. This technique will help in preventing zigzagging which often occurs when an active set strategy is used. Some of the inequality constraints are handled with slack variables. Also a trust region strategy is used in which at each iteration there is a sphere around the current point in which the local approximation of the function is trusted. The algorithm is implemented into a successful computer program. Numerical results are provided.

Vardi, A.↗

A human antibody epitope map of Pfs230D1 derived from analysis of individuals vaccinated with a malaria transmission-blocking vaccine

Pfs230 domain 1 (Pfs230D1) is an advanced malaria transmission-blocking vaccine antigen demonstrating high functional activity in clinical trials. However, the structural and functional correlates of transmission-blocking activity are not defined. Here, we characterized a panel of human monoclonal antibodies (hmAbs) elicited in vaccinees immunized with Pfs230D1. These hmAbs exhibited diverse transmission-reducing activity, yet all bound to Pfs230D1 with nanomolar affinity. We compiled epitope-binning data for seventeen hmAbs and structures of nine hmAbs complexes to construct a high-resolution epitope map and revealed that potent transmission-reducing hmAbs bound to one face of Pfs230D1, while non-potent hmAbs bound to the opposing side. The structure of Pfs230D1D2 revealed that non-potent transmission-reducing epitopes were occluded by the second domain. The hmAb epitope map delineated binary hmAb combinations that synergized for extremely high-potency, transmission-reducing activity. Finally, this work provides a high-resolution guide for structure-based design of enhanced immunogens and informs diagnostics that measure the transmission-reducing response.

60 APPLIED LIFE SCIENCES↗

The crystal structure of Grindelia robusta 7,13-copalyl diphosphate synthase reveals active site features controlling catalytic specificity

Diterpenoid natural products serve critical functions in plant development and ecological adaptation and many diterpenoids have economic value as bioproducts. The family of class II diterpene synthases catalyzes the committed reactions in diterpenoid biosynthesis, converting a common geranylgeranyl diphosphate precursor into different bicyclic prenyl diphosphate scaffolds. Enzymatic rearrangement and modification of these precursors generate the diversity of bioactive diterpenoids. We report the crystal structure of Grindelia robusta 7,13-copalyl diphosphate synthase, GrTPS2, at 2.1 Å of resolution. GrTPS2 catalyzes the committed reaction in the biosynthesis of grindelic acid, which represents the signature metabolite in species of gumweed (Grindelia spp., Asteraceae). Grindelic acid has been explored as a potential source for drug leads and biofuel production. The GrTPS2 crystal structure adopts the conserved three-domain fold of class II diterpene synthases featuring a functional active site in the γβ-domain and a vestigial α-domain. Substrate docking into the active site of the GrTPS2 apo protein structure predicted catalytic amino acids. Biochemical characterization of protein variants identified residues with impact on enzyme activity and catalytic specificity. Specifically, mutagenesis of Y457 provided mechanistic insight into the position-specific deprotonation of the intermediary carbocation to form the characteristic 7,13 double bond of 7,13-copalyl diphosphate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗