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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Adaptive Activation Functions Accelerate Convergence in Deep and Physics-informed Neural Networks

We employ adaptive activation functions for regression in deep and physics-informed neural networks (PINNs) to approximate smooth and discontinuous functions as well as solutions of linear and nonlinear partial differential equations. In particular, we solve the nonlinear Klein-Gordon equation, which has smooth solutions, the nonlinear Burgers equation, which can admit high gradient solutions, and the Helmholtz equation. We introduce a scalable hyper-parameter in the activation function, which can be optimized to achieve best performance of the network as it changes dynamically the topology of the loss function involved in the optimization process. The adaptive activation function has better learning capabilities than the traditional one (fixed activation) as it improves greatly the convergence rate, especially at early training, as well as the solution accuracy. To better understand the learning process, we plot the neural network solution in the frequency domain to examine how the network captures successively different frequency bands present in the solution. We consider both forward problems, where the approximate solutions are obtained, as well as inverse problems, where parameters involved in the governing equation are identified. Our simulation results show that the proposed method is a very simple and effective approach to increase the efficiency, robustness and accuracy of the neural network approximation of nonlinear functions as well as solutions of partial differential equations, especially for forward problems. We theoretically prove that in the proposed method, gradient descent algorithms are not attracted to suboptimal critical points or local minima.

machine leaning, Bad minima, Inverse problems, Phy↗
DOE OSTI2020

Online multimedia retrieval on CPU–GPU platforms with adaptive work partition

Nearest neighbors search is a core operation found in several online multimedia services. These services have to handle very large databases, while, at the same time, they must minimize the query response times observed by users. This is specially complex because those services deal with fluctuating query workloads (rates). Consequently, they must adapt at run-time to minimize the response times as the load varies. In this paper, we address the aforementioned challenges with a distributed memory parallelization of the product quantization nearest neighbor search, also known as IVFADC, for hybrid CPU–GPU machines. Overall, our parallel IVFADC implements an out-of-GPU memory execution scheme to use the GPU for databases in which the index does not fit in its memory, which is crucial for searching in very large databases. The careful use of CPU and GPU with work stealing led to an average response time reduction of 2.4 as compared to using the GPU only. Also, our approach to adapt the system to fluctuating loads, called Dynamic Query Processing Policy (DQPP), attained a response time reduction of up to 5 vs. the best static (BS) policy for moderate loads. The system has attained high query processing rates and near-linear scalability in all experiments. We have evaluated our system on a machine with up to 256 NVIDIA V100 GPUs processing a database of 256 billion SIFT features vectors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Photosynthetic adaptation to polar life: Energy balance, photoprotection and genetic redundancy

The persistent low temperature that characterize polar habitats combined with the requirement for light for all photoautotrophs creates a conundrum. The absorption of too much light at low temperature can cause an energy imbalance that decreases photosynthetic performance that has a negative impact on growth and can affect long-term survival. The goal of this review is to survey the mechanism(s) by which polar photoautotrophs maintain cellular energy balance, that is, photostasis to overcome the potential for cellular energy imbalance in their low temperature environments. Photopsychrophiles are photosynthetic organisms that are obligately adapted to low temperature (0⁰- 15 °C) but usually die at higher temperatures (≥20 °C). In contrast, photopsychrotolerant species can usually tolerate and survive a broad range of temperatures (5⁰- 40 °C). First, we summarize the basic concepts of excess excitation energy, energy balance, photoprotection and photostasis and their importance to survival in polar habitats. Second, we compare the photoprotective mechanisms that underlie photostasis and survival in aquatic cyanobacteria and green algae as well as terrestrial Antarctic and Arctic plants. We show that polar photopsychrophilic and photopsychrotolerant organisms attain energy balance at low temperature either through a regulated reduction in the efficiency of light absorption or through enhanced capacity to consume photosynthetic electrons by the induction of O 2 as an alternative electron acceptor. Finally, we compare the published genomes of three photopsychrophilic and one photopsychrotolerant alga with five mesophilic green algae including the model green alga, Chlamydomonas reinhardtii. We relate our genomic analyses to photoprotective mechanisms that contribute to the potential attainment of photostasis. Finally, we discuss how the observed genomic redundancy in photopsychrophilic genomes may confer energy balance, photoprotection and resilience to their harsh polar environment. Primary production in aquatic, Antarctic and Arctic environments is dependent on diverse algal and cyanobacterial communities. Although mosses and lichens dominate the Antarctic terrestrial landscape, only two extant angiosperms exist in the Antarctic. Furthermore, the identification of a single ‘molecular key’ to unravel adaptation of photopsychrophily and photopsychrotolerance remains elusive. Since these photoautotrophs represent excellent biomarkers to assess the impact of global warming on polar ecosystems, increased study of these polar photoautotrophs remains essential.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts

Reducing human reliance on inefficient energy systems and fossil fuels has become more urgent due to the consequences of global climate change. However, traditional trial-and-error approaches have hampered our ability to accelerate the discovery and implementation of functional materials for efficient energy conversion devices, such as polymer electrolyte fuel cells (PEFCs). To address this, we develop an adaptive learning framework that integrates machine learning and state-of-the-art capabilities in high-throughput synthesis to achieve expedited optimization of iron-nitrogen-carbon PEFC oxygen reduction reaction (ORR) electrocatalysts. We use statistical inference, uncertainty quantification, and global optimization to build a computational design-of-experiment tool that identifies the optimum compositions to be investigated next to reduce the demands placed on experimental materials discovery. We benchmark the ability of the proposed strategy to discover optimum catalyst synthesis conditions in a six-dimensional search space when starting with a thirty-six-sample database. By following the adaptive learning strategy, we synthesize fourteen new catalysts from approximately ten billion unique compositions and discover four catalysts that outperform all original samples. The best machine learning-optimized catalyst is 33% more active than the highest-performing one in the initial database, showing an ORR activity seven times larger than those typically reported for the same class of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A comparison between radiation therapists and medical specialists in the use of kilovoltage cone-beam computed tomography scans for potential lung cancer radiotherapy target verification and adaptation

Target volume matching using cone-beam computed tomography (CBCT) is the preferred treatment verification method for lung cancer in many centers. However, radiation therapists (RTs) are trained in bony matching and not soft tissue matching. The purpose of this study was to determine whether RTs were equivalent to radiation oncologists (ROs) and radiologists (RDs) in alignment of the treatment CBCT with the gross tumor volume (GTV) defined at planning and in delineating the GTV on the treatment CBCT, as may be necessary for adaptive radiotherapy. In this study, 10 RTs, 1 RO, and 1 RD performed a manual tumor alignment and correction of the planning GTV to a treatment CBCT to generate an isocenter correction distance for 15 patient data sets. Participants also contoured the GTV on the same data sets. The isocenter correction distance and the contoured GTVs from the RTs were compared with the RD and RO. The mean difference in isocenter correction distances was 0.40 cm between the RO and RD, 0.51 cm between the RTs, and RO and 0.42 cm between the RTs and RD. The 95% CIs were smaller than the equivalence limit of 0.5 cm, indicating that the RTs were equivalent to the RO and RD. For GTV delineation comparisons, the RTs were not found to be equivalent to the RD or RO. The alignment of the planning defined GTV and treatment CBCT using soft tissue matching by the RTs has been shown to be equivalent to those by the RO and RD. However, tumor delineation by the RTs on the treatment CBCT was not equivalent to that of the RO and RD. Thus, it may be appropriate for RTs to undertake soft tissue alignment based on CBCT; however, further investigation may be necessary before RTs undertake delineation for adaptive radiotherapy purposes.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2026

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled target data to align the learned feature representations between the source and target domains. We compare a range of different UDA techniques, finding that minimizing the maximum mean discrepancy (MMD) between source and target feature vectors yields the most consistent improvement to testing scores. For instance, using a custom transformer-based neural network, we achieved a testing accuracy of $0.904 \pm 0.022$ on an experimental LaBr test set after performing unsupervised feature alignment via MMD minimization, compared to $0.754 \pm 0.014$ before alignment. Overall, our results highlight the potential of using UDA to adapt a radioisotope classifier trained on synthetic data for real-world deployment.

Lalor, Peter W.↗
DOE OSTI2016

ASA-FTL: An adaptive separation aware flash translation layer for solid state drives

Here, the flash-memory based Solid State Drive (SSD) presents a promising storage solution for increasingly critical data-intensive applications due to its low latency (high throughput), high bandwidth, and low power consumption. Within an SSD, its Flash Translation Layer (FTL) is responsible for exposing the SSD’s flash memory storage to the computer system as a simple block device. The FTL design is one of the dominant factors determining an SSD’s lifespan and performance. To reduce the garbage collection overhead and deliver better performance, we propose a new, low-cost, adaptive separation-aware flash translation layer (ASA-FTL) that combines sampling, data clustering and selective caching of recency information to accurately identify and separate hot/cold data while incurring minimal overhead. We use sampling for light-weight identification of separation criteria, and our dedicated selective caching mechanism is designed to save the limited RAM resource in contemporary SSDs. Using simulations of ASA-FTL with both real-world and synthetic workloads, we have shown that our proposed approach reduces the garbage collection overhead by up to 28% and the overall response time by 15% compared to one of the most advanced existing FTLs. We find that the data clustering using a small sample size provides significant performance benefit while only incurring a very small computation and memory cost. In addition, our evaluation shows that ASA-FTL is able to adapt to the changes in the access pattern of workloads, which is a major advantage comparing to existing fixed data separation methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Automated and efficient local adaptive regression for principal component-based reduced-order modeling of turbulent reacting flows

Principal Component Analysis can be used to reduce the cost of Computational Fluid Dynamics simulations of turbulent reacting flows by reducing the dimensionality of the transported variables through projection of the thermochemical state onto a lower-dimensional manifold. However, because of the nonlinearity of the principal component source terms, nonlinear regression techniques must be utilized for the source terms in terms of the principal components. Unfortunately, widely available and utilized nonlinear regression techniques can have prohibitive computational requirements and/or accuracy that is highly dependent on user experience in ad hoc tuning of model architecture and hyperparameters. Here, in this work, a new nonlinear regression approach is proposed that is both computationally efficient and automated so does not require any user input. The approach is evaluated through a priori prediction of principal component source terms using data from a Direct Numerical Simulation of a turbulent nonpremixed n-heptane/air jet flame. In particular, the proposed framework consists of local regressions whose complexity is adapted according to the local nonlinearity of the data: local linear regression when accurate enough and local Artificial Neural Networks when nonlinear regression is required. The number of local clusters for local regression is determined automatically using the Davies-Bouldin index. In addition, Bayesian optimization is utilized for model training (i.e., to select the best architectures and hyperparameters of the nonlinear regressions in an unsupervised fashion), eliminating ad hoc hand-tuning and/or expensive grid searches. Overall, compared to a single, global neural network, the new local adaptive regression approach is shown to have comparable accuracy but 69% less training time due to the utilization of local linear regression and faster training of local neural networks.

42 ENGINEERING↗
DOE OSTI2023

An adaptive synchronous extraction (ASE) method for estimating intensity and footprint of surface urban heat islands: A case study of 254 North American cities

The urban heat island (UHI) effect has attracted great attention due to its potential impacts on rapidly growing urban areas. Using remotely sensed estimates of land surface temperature (LST), a large number of studies have focused on the surface UHI (SUHI) effect, which can be characterized by its two fundamental properties: intensity and footprint. The SUHI intensity reflects the LST difference between the urban area and the background reference area (BRA), and the SUHI footprint indicates the spatial extent influenced by the heat island. Currently, numerous methods have been developed to estimate the SUHI intensity and footprint, but are still greatly challenged by three main issues. Namely, the discrepancy in BRA selection criterion brings great uncertainty to the estimated SUHI intensity, the estimation of SUHI footprint is largely constrained by the predefined models, and the quantification of SUHI effect is potentially influenced by several confounding factors. Here, we proposed an adaptive synchronous extraction (ASE) method, which is capable of adaptively selecting the most optimal BRA while removing the influence of confounding factors, and achieving synchronous estimation of SUHI intensity and footprint. We applied the ASE method to 254 North American cities and conducted an in-depth comparative analysis to discuss its applicability and benefits. The main results include: (1) The ASE method avoids the limitations of existing methods in BRA selection and model presetting, and shows resilience to parameter variations. This makes the ASE method highly applicable to quantify the SUHI intensity and footprint in cities with various thermal characteristics. (2) The ASE method can better highlight the spatial, seasonal and day-night contrasts in the estimated SUHI intensity. This superiority is particularly evident when comparing it to methods based on the equal-area buffer or the simplified urban-extent algorithm. (3) Confounding factors pose non-negligible impacts on the quantification of the SUHI effect. Typically, ignoring the influence of topographic relief or missing LST data can lead to an overall overestimation of the SUHI intensity, while not removing surrounding urban areas will cause some underestimation of the SUHI intensity. In conclusion, overall, the proposed ASE method provides a new generalizable tool for quantifying the SUHI effect, which has great potentials for future studies and urban climate assessments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

New adaptive method to optimize the secondary reflector of linear Fresnel collectors

Performance of linear Fresnel collectors may largely depend on the secondary-reflector profile design when small-aperture absorbers are used. Optimization of the secondary-reflector profile is an extremely challenging task because there is no established theory to ensure superior performance of derived profiles. In this work, an innovative optimization method is proposed to optimize the secondary-reflector profile of a generic linear Fresnel configuration. The method correctly and accurately captures impacts of both geometric and optical aspects of a linear Fresnel collector to secondary-reflector design. The proposed method is an adaptive approach that does not assume a secondary shape of any particular form, but rather, starts at a single edge point and adaptively constructs the next surface point to maximize the reflected power to be reflected to absorber(s). As a test case, the proposed optimization method is applied to an industrial linear Fresnel configuration, and the results show that the derived optimal secondary reflector is able to redirect more than 90% of the power to the absorber in a wide range of incidence angles. Here, the proposed method can be naturally extended to other types of solar collectors as well, and it will be a valuable tool for solar-collector designs with a secondary reflector.

14 SOLAR ENERGY↗
DOE OSTI2026

A bioinspired approach for adaptive solid-solid phase change material coatings with optimized surface features for passive thermal regulation

The necessity to reduce global energy consumption calls for innovative strategies in building thermal management. Passive thermal regulation, particularly through bio-inspired designs, offers a promising avenue by mimicking nature's efficient control of optical properties. This research introduces a novel, climate-responsive coating that integrates optimized bio-inspired surface features with a solid-solid phase change material (SS-PCM) to dynamically manage solar absorptivity without adding additional thickness, enabling both heating and cooling as needed. Drawing on the photonic architectures of the Saharan silver ant and Morpho Didius butterfly, we employed a modeling and multi-objective optimization framework to tailor these surface features. Simulations reveal that surface texture, rather than the intrinsic phase transition of the SS PCM, dominates optical control. Relative to a flat SS PCM coating, optimized isotropic random roughness and broader range features yielded the highest passive heating power increase of about 144 % and 319 % respectively suitable for cold climates. Saharan ant-inspired features enhanced passive cooling for hot climates, achieving a 21.8 % improvement. For moderate climates, Butterfly-wing-inspired surface features provided a balanced enhancement of 19 % for heating and 7 % for cooling. Across all cases, the optimized surface features reduced combined heating and cooling energy demand more effectively than the baseline coating, while preserving material thickness. These findings demonstrate that climate-adaptive, optimized bio-inspired surface features can unlock the full potential of SS PCM coatings, providing a versatile pathway to significant energy savings in buildings and other applications. The methodology establishes a framework for designing next-generation adaptive envelopes that leverage natural photonic principles for high-impact, low-cost thermal regulation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Covariance operator estimation via adaptive thresholding

This paper studies sparse covariance operator estimation for nonstationary processes with sharply varying marginal variance and small correlation lengthscale. We introduce a covariance operator estimator that adaptively thresholds the sample covariance function using an estimate of the variance component. Building on recent results from empirical process theory, we derive an operator norm bound on the estimation error in terms of the sparsity level of the covariance and the expected supremum of a normalized process. Furthermore, our theory and numerical simulations demonstrate the advantage of adaptive threshold estimators over universal threshold and sample covariance estimators in nonstationary settings.

Al-Ghattas, Omar [University of Chicago, IL (Unite↗
DOE OSTI2015

Thiol dependent NF-κB suppression and inhibition of T-cell mediated adaptive immune responses by a naturally occurring steroidal lactone Withaferin A

Withaferin A (WA), a steroidal lactone isolated from ayurvedic medicinal plant Withania somnifera, was shown to inhibit tumor growth by inducing oxidative stress and suppressing NF-κB pathway. However, its effect on T-cell mediated adaptive immune responses and the underlying mechanism has not been investigated. Since both T-cell responses and NF-κB pathway are known to be redox sensitive, the present study was undertaken to elucidate the effect of WA on adaptive immune responses in vitro and in vivo. WA inhibited mitogen induced T-cell and B-cell proliferation in vitro without inducing any cell death. It inhibited upregulation of T-cell (CD25, CD69, CD71 and CD54) and B-cell (CD80, CD86 and MHC-II) activation markers and secretion of Th1 and Th2 cytokines. WA induced oxidative stress by increasing the basal ROS levels and the immunosuppressive effects of WA were abrogated only by thiol anti-oxidants. The redox modulatory effects of WA in T-cells were attributed to its ability to directly interact with free thiols. WA inhibited NF-κB nuclear translocation in lymphocytes and prevented the direct binding of nuclear NF-κB to its consensus sequence. MALDI-TOF analysis using a synthetic NF-κB-p50 peptide containing Cys-62 residue suggested that WA can modify the cysteine residue of NF-κB. The pharmacokinetic studies for WA were also carried out and in vivo efficacy of WA was studied using mouse model of Graft-versus-host disease. In conclusion, WA is a potent inhibitor of T-cell responses and acts via a novel thiol dependent mechanism and inhibition of NF-κB pathway. - Highlights:: • Withaferin A (WA) inhibited T-cell and B-cell mediated immune responses. • WA increased basal ROS levels in lymphocytes. • WA directly interacted with GSH as studied using spectrophotometry and HPLC. • WA inhibited NF-κB nuclear translocation and binding of nuclear NF-κB to DNA. • WA inhibited induction of the graft-versus-host disease in mice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Adaptation of HepG2 cells to a steady-state reduction in the content of protein phosphatase 6 (PP6) catalytic subunit

Protein phosphatase 6 (PP6) is a ubiquitous Ser/Thr phosphatase involved in an array of cellular processes. To assess the potential of PP6 as a therapeutic target in liver disorders, we attenuated expression of the PP6 catalytic subunit in HepG2 cells using lentiviral-transduced shRNA. Two PP6 knock-down (PP6KD) cell lines (90% reduction of PP6-C protein content) were studied in depth. Both proliferated at a rate similar to control cells. However, flow cytometry indicated G2/M cell cycle arrest that was accounted for by a shift of the cells from a diploid to tetraploid state. PP6KD cells did not show an increase in apoptosis, nor did they exhibit reduced viability in the presence of bleomycin or taxol. Gene expression analysis by microarray showed attenuated anti-inflammatory signaling. Genes associated with DNA replication were downregulated. Mass spectrometry-based phosphoproteomic analysis yielded 80 phosphopeptides representing 56 proteins that were significantly affected by a stable reduction in PP6-C. Proteins involved in DNA replication, DNA damage repair and pre-mRNA splicing were overrepresented among these. PP6KD cells showed intact mTOR signaling. Our studies demonstrated involvement of PP6 in a diverse set of biological pathways and an adaptive response that may limit the effectiveness of targeting PP6 in liver disorders. - Highlights: • Lentiviral-transduced shRNA was used to generate a stable knockdown of PP6 in HepG2 cells. • Cells adapted to reduced PP6; cell proliferation was unaffected, and cell survival was normal. • However, PP6 knockdown was associated with a transition to a tetraploid state. • Genomic profiling showed downregulated anti-inflammatory signaling and DNA replication. • Phosphoproteomic profiling showed changes in proteins associated with DNA replication and repair.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Nrf2 mediates redox adaptation in NOX4-overexpressed non-small cell lung cancer cells

The redox adaptation mechanisms in cancer cells are very complex and remain largely unclear. Our previous studies have confirmed that NADPH oxidase 4 (NOX4) is abundantly expressed in non-small cell lung cancer (NSCLC) and confers apoptosis resistance on NSCLC cells. However, the comprehensive mechanisms for NOX4-mediated oxidative resistance of cancer cells remain still undentified. The present study found that NOX4-derived H{sub 2}O{sub 2} enhanced the nuclear factor erythroid 2-related factor 2 (Nrf2) stability via disruption of redox-dependent proteasomal degradation and stimulated its activity through activation of PI3K signaling. Specifically, the results showed that ectopic NOX4 expression did not induce apoptosis of A549 cells; however, inhibition of Nrf2 resulted in obvious apoptotic death of NOX4-overexpressed A549 cells, accompanied by a significant increase in H{sub 2}O{sub 2} level and decrease in GSH content. Besides, inhibition of Nrf2 could suppress cell growth and efficiently reverse the enhancement effect of NOX4 on cell growth. The in vivo data confirmed that inhibition of Nrf2 could interfere apoptosis resistance in NOX4-overexpressed A549 tumors and led to cell growth inhibition. In conclusion, these results reveal that Nrf2 is critically involved in redox adaptation regulation in NOX4-overexpressed NSCLC cells. Therefore, NOX4 and Nrf2 may be promising combination targets against malignant progression of NSCLC. - Highlights: • NOX4-derived H{sub 2}O{sub 2} upregulates Nrf2 expression and activity in NSCLC. • Nrf2 confers apoptosis resistance in NOX4-overexpressed NSCLC cells. • Inhibition of Nrf2 reverses the enhancement effect of NOX4 on cell growth.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Tight junction-associated protein GEF-H1 in the neighbours of dividing epithelial cells is essential for adaptation of cell-cell membrane during cytokinesis

Highlights: • GEF-H1 regulates cell-cell contacts deformation during epithelial cell cytokinesis. • GEF-H1 depletion in neighbouring cells induces lateral contacts deformation. • The exocyst complex is involved in the cell-cell contacts geometry regulation. Animal cells divide by a process called cytokinesis which relies on the constriction of a contractile actomyosin ring leading to the production of two daughter cells. Cytokinesis is an intrinsic property of cells which occurs even for artificially isolated cells. During division, isolated cells undergo dramatic changes in shape such as rounding and membrane deformation as the division furrow ingresses. However, cells are often embedded in tissues and thus are surrounded by neighbouring cells. How these neighbours might influence, or might themselves be influenced by, the shape changes of cytokinesis is poorly understood in vertebrates. Here, we show that during cytokinesis of epithelial cells in the Xenopus embryo, lateral cell-cell contacts remain almost perpendicular to the epithelial plane. Depletion of the tight junction-associated protein GEF-H1 leads to a transient and stereotyped deformation of cell-cell contacts. Although, this deformation occurs only during cytokinesis, we show that it originates from immediate neighbours of the dividing cell. Moreover, we show that exocyst and recycling endosome regulation by GEF-H1 are involved in adaptation of cell-cell contacts to deformation. Our results highlight the crucial role of tight junctions and GEF-H1 in cell-cell contact adaptation when cells are exposed to a mechanical stress such as cytokinesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Acquisition of STEM Images by Adaptive Compressive Sensing

Compressive Sensing (CS) allows a signal to be sparsely measured first and accurately recovered later in software [1]. In scanning transmission electron microscopy (STEM), it is possible to compress an image spatially by reducing the number of measured pixels, which decreases electron dose and increases sensing speed [2,3,4]. The two requirements for CS to work are: (1) sparsity of basis coefficients and (2) incoherence of the sensing system and the representation system. However, when pixels are missing from the image, it is difficult to have an incoherent sensing matrix. Nevertheless, dictionary learning techniques such as Beta-Process Factor Analysis (BPFA) [5] are able to simultaneously discover a basis and the sparse coefficients in the case of missing pixels. On top of CS, we would like to apply active learning [6,7] to further reduce the proportion of pixels being measured, while maintaining image reconstruction quality. Suppose we initially sample 10% of random pixels. We wish to select the next 1% of pixels that are most useful in recovering the image. Now, we have 11% of pixels, and we want to decide the next 1% of “most informative” pixels. Active learning methods are online and sequential in nature. Our goal is to adaptively discover the best sensing mask during acquisition using feedback about the structures in the image. In the end, we hope to recover a high quality reconstruction with a dose reduction relative to the non-adaptive (random) sensing scheme. In doing this, we try three metrics applied to the partial reconstructions for selecting the new set of pixels: (1) variance, (2) Kullback-Leibler (KL) divergence using a Radial Basis Function (RBF) kernel, and (3) entropy. Figs. 1 and 2 display the comparison of Peak Signal-to-Noise (PSNR) using these three different active learning methods at different percentages of sampled pixels. At 20% level, all the three active learning methods underperform the original CS without active learning. However, they all beat the original CS as more of the “most informative” pixels are sampled. One can also argue that CS equipped with active learning requires less sampled pixels to achieve the same value of PSNR than CS with pixels randomly sampled, since all the three PSNR curves with active learning grow at a faster pace than that without active learning. For this particular STEM image, by observing the reconstructed images and the sensing masks, we find that while the method based on RBF kernel acquires samples more uniformly, the one on entropy samples more areas of significant change, thus less uniformly. The KL-divergence method performs the best in terms of reconstruction error (PSNR) for this example [8].

Xie, Weiyi↗
DOE OSTI2021

Heterogeneous Composition of Oxygen-Evolving Complexes in Crystal Structures of Dark-Adapted Photosystem II

Photosystem II (PSII) is a homodimeric protein complex that catalyzes water oxidation at the oxygen-evolving complex (OEC), a heterocubanoid calcium-tetra-manganese cluster. Here, we analyze the omit electron-density peaks of the OEC’s metal ions in five X-ray free-electron laser PSII structures at a resolution between 2.15 Å and 1.95 Å. The omit peaks can be described by the total number of electrons and approximated by the variance of electron density distribution when the distributions are spherically symmetric. We show that the number of electrons of metal centers are different in the two OECs of PSII dimers, implying that either the oxidation states and/or occupancies of individual metal ions are different in the two monomers. In either case, we find that the two OECs of dark-adapted PSII dimers in crystals are not fully synchronized in the same expected S 1 state. Differences in redox states of the OEC in PSII only partially account for the observation that the electron densities integrate to a smaller number of electrons than expected. Differences between the determined and expected relative electron numbers are much larger than the estimated errors, indicating heterogeneity in the OEC composition. Here, the reported findings suggest that two OECs of dark-adapted PSII crystals are typically not fully synchronized in the S 1 state and likely generate heterogeneity in the crystallographical data of PSII intermediate structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗