Search NASA⌕ Search

SEARCH · Search NASA

Results for “Differentiability”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11

Role of α-Dystrobrevin in the differentiation process of HL-60 cells

Highlights: • α-Db is downexpressed in HL-60 cells induced into neutrophils with DMSO. • α-Db-1 knockdown prevented nuclei lobulation, increased lamin A/C, and syne1 expression. • α-Db-1 depletion augmented actin-F and the roughness of derived neutrophil membrane. The α-Dystrobrevin gene encodes at least five different protein isoforms, expressed in diverse tissues. The α-Dystrobrevin-1 isoform (α-Db-1) is a member of the cytoplasmic dystrophin-associated protein complex, which has a C-terminal extension comprising at least three tyrosine residues susceptible to phosphorylation in vivo. We previously described α-Db in stem-progenitor cells and blood neutrophils as playing a scaffolding role and, in association with kinesin and microtubules, α-Db promotes platelet-granule trafficking. Additionally, the microtubules must establish a balanced interaction with the lamina A/C network for appropriate nuclear morphology. Considering that the most outstanding feature during neutrophil differentiation is nuclei lobulation, we hypothesized that α-Db might possess a pivotal function during the neutrophil differentiation process. Western Blot (WB) and confocal microscope assays evidenced a differential pattern expression and a subcellular redistribution of α-Db in neutrophils derived from HL-60 cells. At the end of the differentiation process, we detected an important diminution in the expression of tubulin, kinesin, and α-Db-1. Knockdown of α-Db prevented nuclei lobulation, increased Lamin A/C and syne1 expression and augmented the roughness of derived neutrophil membrane and disturbed filopodia assembly. Our results suggest that HL-60 cells undergo extensive cytoskeletal reorganization including α-Db in order to possess lobulated nuclei when they further differentiate into neutrophils.

60 APPLIED LIFE SCIENCES↗

N6-methyladenine demethylase ALKBH1 inhibits the differentiation of skeletal muscle

Highlights: • The DNA N6-mA modification increased during skeletal muscle development, accompanied by a strong decrease of ALKBH1. • ALKBH1 enhanced proliferation and inhibited differentiation of C2C12 cells. • ALKBH1 impaired multiple genes and signaling pathways to promote proliferation and inhibit differentiation of C2C12 cells. DNA N{sup 6}-methyladenine (N6-mA) was recently recognized as a new epigenetic modification in mammalian genome, and ALKBH1 was discovered as its demethylase. Knock-out mice studies revealed that ALKBH1 was indispensable for normal embryonic development. However, the function of ALKBH1 in myogenesis is largely unknown. In this study, we found that N6-mA showed a steady increase, going along with a strong decrease of ALKBH1 during skeletal muscle development. Our results also showed that ALKBH1 enhanced proliferation and inhibited differentiation of C2C12 cells. Genome-wide transcriptome analysis and reporter assays further revealed that ALKBH1 accomplished the differentiation inhibiting function by regulating a core set of genes and multiple signaling pathways, including increasing chemokine (C-X-C motif) ligand 14 (CXCL14) and activating ERK signaling. Taken together, our results demonstrated that ALKBH1 is critical for the myogenic differentiation of C2C12 cells, and suggested that N6-mA might be a new epigenetic mechanism for the regulation of myogenesis.

60 APPLIED LIFE SCIENCES↗

Long noncoding RNA MEG3 expressed in human dental pulp regulates LPS-Induced inflammation and odontogenic differentiation in pulpitis

Highlights: • LncRNA MEG3, upregulated in inflamed dental pulp, is involved in the inflammation and regeneration in pulpitis. • The expression of lncRNA MEG3 was semiquantitatively detected in the pulp tissue by the innovative RNAscope® technique. • MEG3 downregulation can inhibit inflammatory cytokines secretion and also promote the odontogenic differentiation of hDPCs. • LncRNA MEG3 can be a potential therapeutic target in the inflammation and regeneration of dentin-pulp complex in pulpitis. Pulpitis refers to inflammation of the inner pulp by invading microbes, and tissue repair occurs due to odontogenic differentiation of human dental pulp cells (hDPCs) with multidifferentiation potential. Long noncoding RNAs (lncRNAs) can modulate numerous pathological and biological processes; however, the role of lncRNAs in the inflammation and regeneration of the dentin-pulp complex in pulpitis is unclear. Here, we performed high-throughput sequencing to identify differentially expressed lncRNAs between human normal and inflamed pulp and concluded that lncMEG3 (lncRNA maternally expressed gene 3, MEG3) was signicantly upregulated in both inflamed pulp and LPS-treated hDPCs. MEG3 expression in the pulp tissue was detected using the RNAscope® technique. RNA pulldown assays identified the MEG3-interacting proteins and the potential mechanisms. With MEG3 knockdown, we investigated the role of MEG3 in the secretion of inflammatory cytokines in LPS-treated hDPCs and odontogenic differentiation of hDPCs. MEG3 downregulation inhibited the secretion of TNF-α, IL-1β and IL-6 in LPS-treated hDPCs, and the p38/MAPK signaling pathway may be related to this effect. MEG3 knockdown promoted odontogenic differentiation of hDPCs by regulating the Wnt/β-catenin signaling pathway. Our study suggested that MEG3 has a negative effect on inflammation and regeneration of the dentin-pulp complex in pulpitis.

60 APPLIED LIFE SCIENCES↗

Necrosulfonamide reverses pyroptosis-induced inhibition of proliferation and differentiation of osteoblasts through the NLRP3/caspase-1/GSDMD pathway

The acute inflammatory stimulation occurring after a bone fracture regulates the repair and healing of local bone injury; however, under certain conditions, pyroptosis may occur in osteoblasts, which affects osteoblast proliferation and differentiation, thereby affecting the growth, development and morphological changes of bone tissue. The aim of the present study was to examine the effect of the pyroptosis inhibitor necrosulfonamide (NSA) on the proliferation and differentiation of osteoblasts and elucidate the underlying mechanism. The results revealed that NSA reversed the effects of ATP/lipopolysaccharide (LPS) on cell viability and pyroptosis, and on the mRNA and protein expression of pyroptosis-related genes. It also suppressed the secretion of IL-6, TNF-α and IL-1β and reversed the effects of ATP/LPS on the activity of ALP and the mRNA expression of differentiation-related genes in osteoblasts. The fact that overexpression of caspase-1, gasdermin D (GSDMD) and NLRP3 abolished the effects of NSA on the viability and pyroptosis of osteoblasts, as well as the mRNA expression of differentiation-related genes and the activity of ALP in osteoblasts, indicated that NSA promoted the proliferation and differentiation of osteoblasts by inhibiting the NLRP3/caspase-1/GSDMD pyroptosis pathway. The present study provides proof supporting the potential application of NSA for improving the function of osteoblasts in fracture repair and indicates the value of the NLRP3/caspase-1/GSDMD pyroptosis pathway as a pharmaceutical target.

60 APPLIED LIFE SCIENCES↗

Quantum machine learning with differential privacy

Abstract Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer. Many datasets used in machine learning are crowd sourced or contain some private information, but to the best of our knowledge, no current QML models are equipped with privacy-preserving features. This raises concerns as it is paramount that models do not expose sensitive information. Thus, privacy-preserving algorithms need to be implemented with QML. One solution is to make the machine learning algorithm differentially private, meaning the effect of a single data point on the training dataset is minimized. Differentially private machine learning models have been investigated, but differential privacy has not been thoroughly studied in the context of QML. In this study, we develop a hybrid quantum-classical model that is trained to preserve privacy using differentially private optimization algorithm. This marks the first proof-of-principle demonstration of privacy-preserving QML. The experiments demonstrate that differentially private QML can protect user-sensitive information without signficiantly diminishing model accuracy. Although the quantum model is simulated and tested on a classical computer, it demonstrates potential to be efficiently implemented on near-term quantum devices [noisy intermediate-scale quantum (NISQ)]. The approach’s success is illustrated via the classification of spatially classed two-dimensional datasets and a binary MNIST classification. This implementation of privacy-preserving QML will ensure confidentiality and accurate learning on NISQ technology.

97 MATHEMATICS AND COMPUTING↗

Nucleoporin downregulation modulates progenitor differentiation independent of nuclear pore numbers

Nucleoporins (NUPs) comprise nuclear pore complexes, gateways for nucleocytoplasmic transport. As primary human keratinocytes switch from the progenitor state towards differentiation, most NUPs are strongly downregulated, with NUP93 being the most downregulated NUP in this process. To determine if this NUP downregulation is accompanied by a reduction in nuclear pore numbers, we leveraged Stochastic Optical Reconstruction Microscopy. No significant changes in nuclear pore numbers were detected using three independent NUP antibodies; however, NUP reduction in other subcellular compartments such as the cytoplasm was identified. To investigate how NUP reduction influences keratinocyte differentiation, we knocked down NUP93 in keratinocytes in the progenitor-state culture condition. NUP93 knockdown diminished keratinocytes’ clonogenicity and epidermal regenerative capacity, without drastically affecting nuclear pore numbers or permeability. Using transcriptome profiling, we identified that NUP93 knockdown induces differentiation genes related to both mechanical and immune barrier functions, including the activation of known NF-κB target genes. Consistently, keratinocytes with NUP93 knockdown exhibited increased nuclear localization of the NF-κB p65/p50 transcription factors, and increased NF-κB reporter activity. Taken together, these findings highlight the gene regulatory roles contributed by differential NUP expression levels in keratinocyte differentiation, independent of nuclear pore numbers.

54 ENVIRONMENTAL SCIENCES↗

A unified view of hierarchy approach and formula of differentiation

The stochastic differential equation is a powerful tool for describing the dynamics of a dissipative system in which noise characterizes the influence of the environment. For the Ornstein-Uhlenbeck noise, both the formula of differentiation and the hierarchy approach provide efficient numerical simulations, with the stochastic differential equation transformed into a set of coupled, linear ordinary differential equations. We show that while these two deterministic schemes result in different sets of equations, they can be regarded as two representations of an underlying linear-dynamics. Moreover, by manipulating the involved Ornstein-Uhlenbeck noise, we propose a unified algorithm that may reduce to the hierarchy approach or the formula of differentiation in different limits. We further analyze the numerical performance of this algorithm and find that the hierarchy approach appears to be more efficient for our numerical model studies.

Yan, Yun-An (ORCID:000000031943459X)↗

Can we detect the stellar differential rotation of WASP-7 through the Rossiter–McLaughlin observations?

ABSTRACT The Rossiter–McLaughlin (RM) effect is the radial velocity signal generated when an object transits a rotating star. Stars rotate differentially and this affects the shape and amplitude of this signal, on a level that can no longer be ignored with precise spectrographs. Highly misaligned planets provide a unique opportunity to probe stellar differential rotation via the RM effect, as they cross several stellar latitudes. In this sense, WASP-7, and its hot Jupiter with a projected misalignment of ∼90°, is one of the most promising targets. The aim of this work is to understand if the stellar differential rotation is measurable through the RM signal for systems with a geometry similar to WASP-7. In this sense, we use a modified version of soap3.0 to explore the main hurdles that prevented the precise determination of the differential rotation of WASP-7. We also investigate whether the adoption of the next generation spectrographs, like ESPRESSO, would solve these issues. Additionally, we assess how instrumental and stellar noise influence this effect and the derived geometry of the system. We found that, for WASP-7, the white noise represents an important hurdle in the detection of the stellar differential rotation, and that a precision of at least 2 m s−1 or better is essential.

Serrano, L. M.↗

Differential Activity-Driven Instabilities in Biphasic Active Matter

Active stresses can cause instabilities in contractile gels and living tissues. Here we provide a generic hydrodynamic theory that treats these systems as a mixture of two phases of varying activity and different mechanical properties. We find that differential activity between the phases causes a uniform mixture to undergo a demixing instability. We follow the nonlinear evolution of the instability and characterize a phase diagram of the resulting patterns. Our study complements other instability mechanisms in mixtures driven by differential adhesion, differential diffusion, differential growth, and differential motion.

59 BASIC BIOLOGICAL SCIENCES↗

Decoupled Active and Reactive Power Control without PLL Requirement for Differential Buck Converter

This paper presents a decoupled active and reactive power control for the differential buck converter. This control does not require phase locked loop (PLL) for synchronization. Also, it achieves active and reactive power control in a simple single control loop structure. The differential buck converter has the capability to compensate the double line frequency power ripple without relying on additional hardware. Particularly, the Common Mode (CM) operation is used for active double line frequency power ripple absorption. Whereas, the Differential Mode (DM) operation is used as an active and reactive power exchanging port between the converter and the grid at the point of common coupling (PCC). The motivation behind this paper proposal are: (i) to avoid PLL induced instabilities, especially, when connecting the differential buck converter with weak grid, and (ii) direct active and reactive power control for the differential buck in a single loop control. The theoretical analyses are verified with a 5 kW converter with only 50 μF decoupling capacitor connected to a weak grid.

Khan, Ahmad↗

An Optical Differentiator Based on a Three-Layer Structure with a W-Shaped Refractive Index Profile

An optical differentiator based on a three-layer resonant structure with a W-shaped refractive index profile is proposed. The differentiation operation is performed in reflection and is associated with the resonant excitation of an eigenmode of the structure localized in the central layer. The presented results of numerical simulations demonstrate the possibility of spatial differentiation of the transverse profile of an incident optical beam, temporal differentiation of the envelope of an incident optical beam, and simultaneous spatial and temporal differentiation with a high quality. The proposed structure can find applications in designing analog optical computing and optical information processing systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Differentiable Quantum Programming with Unbounded Loops

The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Existing work has formulated differentiable quantum programming with bounded loops, providing a framework for scalable gradient calculation by quantum means for training quantum variational applications. However, promising parameterized quantum applications, e.g., quantum walk and unitary implementation, cannot be trained in the existing framework due to the natural involvement of unbounded loops. To fill in the gap, we provide the first differentiable quantum programming framework with unbounded loops, including a newly designed differentiation rule, code transformation, and their correctness proof. Technically, we introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops, whose applicability in classical and probabilistic programming is also discussed. We implement our framework with Python and Q# and demonstrate a reasonable sample efficiency. Through extensive case studies, we showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.

Computer Science↗

Analysis of Differential Financial Impacts of LWR Load-Following Operations. Revision 1

This document reports the application of HERON (Holistic Energy Resource Optimization Network), a newly-developed RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization, to a case study conducted in collaboration with Arizona Public Service (APS). The study is a work in progress; this report presents a description of current models and corresponding results for a particular consideration. APS is anticipating several operational challenges. The first is rapid growth of Variable Renewable Energy (VRE) sources such as wind and solar power providing energy to the grid in the APS service region. This creates challenges for baseload operation of the Palo Verde Generating Station (PVGS), a nuclear power plant co-owned and operated by APS. The second challenge for APS is a change in the cooling water acquisition contract with the Sub Regional Operating Group (SROG). The existing contract is expiring soon, and a renewal is only available for a significantly higher price of water. These two challenges represent a difficulty in maintaining profitability for PVGS as local area demand is highly fluctuant and water prices may increase. Furthermore, excess production at PVGS is currently traded on the Palo Verde energy Hub (PVH), which occasionally offers very low or negative locational marginal prices (LMP) for electricity. This analysis considers the potential benefits of occasional curtailment of PVGS as a method to mitigate unprofitable production hours. Similar to load following, optional daily curtailment would allow APS to elect curtailment of PVGS as low as 70% of its nominal operating level between the hours of 08:00 and 16:00 provided local area demand is met and it is profitable to do so. This document reports a stochastic differential analysis of the Net Present Value (NPV) of PVGS as operated by NPS as a result of optional curtailment operation. To model this differential case, HERON has been designed from previous efforts in this study as a generic tool to accelerate the design and execution of RAVEN workflows for technoeconomic analysis of grid energy systems. HERON leverages both RAVEN and the CashFlow plugin for RAVEN to stochastically explore or optimize a response surface with energy grid system production capacities as inputs and mean NPV as a goal function. HERON performs these evaluations by stochastically sampling market, weather, and demand events using RAVEN’s ARMA reduced-order model capability to generate boundary conditions for dispatch optimization. The algorithm performs the dispatch optimization for each of the synthetic scenarios. For this case, HERON designs workflows to simulate behavior of the PVH, wind, solar, and APS local area demand, and use these synthetic scenarios to stochastically evaluate the differential benefit of optional curtailment for each scenario. While this study finds optional curtailment to be a potential source of revenue, the magnitude of the differential NPV is sufficiently small that it is unlikely a priority for increasing the profitability of PVGS. Further, the differential NPV shrinks throughout the simulated years, suggesting the long-term benefit of such operation given current assumptions is negligible.

14 SOLAR ENERGY↗

Double-differential Measurements of Mesonless Charged-current Muon Neutrino Interactions on Argon with Final-State Protons using the MicroBooNE Detector

This note reports the first double-differential measurements of charged-current $ν_µ$ scattering on argon leading to final states containing zero mesons and one or more protons. This event topology (hereafter abbreviated as CC0$πNp$) is the most common at the neutrino energies typically produced by the Fermilab Booster Neutrino Beam. A detailed understanding of neutrino-argon scattering in the CC0$πNp$ channel is therefore crucial for the success of the precision neutrino oscillation analyses planned for the Short-Baseline Neutrino (SBN) program. This remains true for the upcoming Deep Underground Neutrino Experiment (DUNE), but the higher mean neutrino energy used there will ensure that more inelastic reaction modes, such as single pion production, will also play a major role. The analysis described in this note builds on a previous MicroBooNE study of the CC0$πNp$ channel which obtained the first single-differential cross-section measurements on an argon target. Since that foundational work, significant improvements have been made to MicroBooNE’s simulation software, event reconstruction algorithms, and procedure for calculating systematic uncertainties. When combined with a larger dataset (corresponding to a beam exposure of 6.79 × 10 20 protons-on-target versus 1.60 × 10 20 in Ref. [3]), these enhancements allow the important CC0$πNp$ channel to be studied in more detail. This note begins with a description of the data and simulation samples used as input to the analysis. Section 3 then defines the CC0$πNp$ signal event topology, and Section 4 describes a set of selection criteria designed to identify these events in MicroBooNE data. Binning schemes are then defined in Section 5 for two double-differential measurements of event rates. The first of these considers the momentum and scattering cosine of the outgoing muon, while the second reports the same observables for the leading proton, i.e., the final-state proton with the largest momentum. After a discussion of systematic uncertainties in Section 6, the note concludes by comparing the predictions of MicroBooNE Monte Carlo (MC) simulations to the measured double-differential distributions. These results will form the basis for a future extraction of flux-averaged double-differential CC0$πNp$ cross sections that will be immediately comparable to the theoretical predictions of multiple neutrino event generators. The selection described herein may also be used to study various other observables in CC0$πNp$ events, including those which are sensitive to correlations between leptonic and hadronic kinematics in the final state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling

Recent advances in differentiable modeling, a genre of physics-informed machine learning that trains neural networks (NNs) together with process-based equations, have shown promise in enhancing hydrological models' accuracy, interpretability, and knowledge-discovery potential. Current differentiable models are efficient for NN-based parameter regionalization, but the simple explicit numerical schemes paired with sequential calculations (operator splitting) can incur numerical errors whose impacts on models' representation power and learned parameters are not clear. Implicit schemes, however, cannot rely on automatic differentiation to calculate gradients due to potential issues of gradient vanishing and memory demand. Here we propose a “discretize-then-optimize” adjoint method to enable differentiable implicit numerical schemes for the first time for large-scale hydrological modeling. The adjoint model demonstrates comprehensively improved performance, with Kling–Gupta efficiency coefficients, peak-flow and low-flow metrics, and evapotranspiration that moderately surpass the already-competitive explicit model. Therefore, the previous sequential-calculation approach had a detrimental impact on the model's ability to represent hydrological dynamics. Furthermore, with a structural update that describes capillary rise, the adjoint model can better describe baseflow in arid regions and also produce low flows that outperform even pure machine learning methods such as long short-term memory networks. The adjoint model rectified some parameter distortions but did not alter spatial parameter distributions, demonstrating the robustness of regionalized parameterization. Despite higher computational expenses and modest improvements, the adjoint model's success removes the barrier for complex implicit schemes to enrich differentiable modeling in hydrology.

58 GEOSCIENCES↗

On the detection of differentially encoded polyphase signals.

Consideration of the transmission and detection of differentially encoded multiple phase-shift-keyed (MPSK) signals, paying particular attention to the ambiguity resolution problem resulting from suppression of the transmitted carrier. A study is made of the coherent detection of differentially encoded MPSK signals, and the performance of a differentially encoded MPSK system is compared with that of a system which transmits absolutely encoded polyphase signals and performs perfect ambiguity resolution. Both the perfect and noisy reference signal cases are treated. Also, the performance of coherent detection of differentially encoded MPSK signals is compared with that of differentially coherent reception of the same signal set.

Lindsey, W. C.↗

Classical seventh-, sixth-, and fifth-order Runge-Kutta-Nystrom formulas with stepsize control for general second-order differential equations

Runge-Kutta-Nystrom formulas of the seventh, sixth, and fifth order were derived for the general second order (vector) differential equation written as the second derivative of x = f(t, x, the first derivative of x). The formulas include a stepsize control procedure, based on a complete coverage of the leading term of the local truncation error in x, and they require no more evaluations per step than the earlier Runge-Kutta formulas for the first derivative of x = f(t, x). The developed formulas are expected to be time saving in comparison to the Runge-Kutta formulas for first-order differential equations, since it is not necessary to convert the second-order differential equations into twice as many first-order differential equations. The examples shown saved from 25 percent to 60 percent more computer time than the earlier formulas for first-order differential equations, and are comparable in accuracy.

Fehlberg, E.↗

Legendre-Tau approximations for functional differential equations

The numerical approximation of solutions to linear functional differential equations are considered using the so called Legendre tau method. The functional differential equation is first reformulated as a partial differential equation with a nonlocal boundary condition involving time differentiation. The approximate solution is then represented as a truncated Legendre series with time varying coefficients which satisfy a certain system of ordinary differential equations. The method is very easy to code and yields very accurate approximations. Convergence is established, various numerical examples are presented, and comparison between the latter and cubic spline approximations is made.

Ito, K.↗