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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2021

Structural evolution of a DNA repair self-resistance mechanism targeting genotoxic secondary metabolites

Abstract Microbes produce a broad spectrum of antibiotic natural products, including many DNA-damaging genotoxins. Among the most potent of these are DNA alkylating agents in the spirocyclopropylcyclohexadienone (SCPCHD) family, which includes the duocarmycins, CC-1065, gilvusmycin, and yatakemycin. The yatakemycin biosynthesis cluster in Streptomyces sp. TP-A0356 contains an AlkD-related DNA glycosylase, YtkR2, that serves as a self-resistance mechanism against yatakemycin toxicity. We previously reported that AlkD, which is not present in an SCPCHD producer, provides only limited resistance against yatakemycin. We now show that YtkR2 and C10R5, a previously uncharacterized homolog found in the CC-1065 biosynthetic gene cluster of Streptomyces zelensis , confer far greater resistance against their respective SCPCHD natural products. We identify a structural basis for substrate specificity across gene clusters and show a correlation between in vivo resistance and in vitro enzymatic activity indicating that reduced product affinity—not enhanced substrate recognition—is the evolutionary outcome of selective pressure to provide self-resistance against yatakemycin and CC-1065.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

How well do self-interaction corrections repair the overestimation of static polarizabilities in density functional calculations?

Herein, we examine the effect of removing self-interaction error (SIE) on the calculation of molecular polarizabilities in the local spin density (LSDA) and generalized gradient approximations (GGA). To this end, we utilize a database of 132 molecules taken from a recent benchmark study [Hait and Head-Gordon, Phys. Chem. Chem. Phys. , 2018, 20 , 19800] to assess the influence of SIE on polarizabilities by comparing results with accurate reference data. Our results confirm that the general overestimation of molecular polarizabilities by these density functional approximations can be attributed to SIE. However, removing SIE using the Perdew–Zunger self-interaction-correction (PZ-SIC) method, implemented using the Fermi–Löwdin Orbital SIC approach, leads to an underestimation of molecular polarizabilities, showing that PZ-SIC overcorrects when combined with LSDA or GGA. Application of a recently proposed locally scaled SIC [Zope, et al., J. Chem. Phys. , 2019, 151 , 214108] is found to provide more accurate polarizabilities. We attribute this to the ability of the local scaling scheme to selectively correct for SIE in the regions of space where the correction is needed most.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Identification of genetic interactions with priB links the PriA/PriB DNA replication restart pathway to double-strand DNA break repair in Escherichia coli

Collisions between DNA replication complexes (replisomes) and impediments such as damaged DNA or proteins tightly bound to the chromosome lead to premature dissociation of replisomes at least once per cell cycle in Escherichia coli. Left unrepaired, these events produce incompletely replicated chromosomes that cannot be properly partitioned into daughter cells. DNA replication restart, the process that reloads replisomes at prematurely terminated sites, is therefore essential in E. coli and other bacteria. Three replication restart pathways have been identified in E. coli: PriA/PriB, PriA/PriC, and PriC/Rep. A limited number of genetic interactions between replication restart and other genome maintenance pathways have been defined, but a systematic study placing replication restart reactions in a broader cellular context has not been performed. We have utilized transposon-insertion sequencing to identify new genetic interactions between DNA replication restart pathways and other cellular systems. Known genetic interactors with the priB replication restart gene (uniquely involved in the PriA/PriB pathway) were confirmed and several novel priB interactions were discovered. Targeted genetic and imaging-based experiments with priB and its genetic partners revealed significant double-strand DNA break accumulation in strains with mutations in dam, rep, rdgC, lexA, or polA. Modulating the activity of the RecA recombinase partially suppressed the detrimental effects of rdgC or lexA mutations in ΔpriB cells. Taken together, our results highlight roles for several genes in double-strand DNA break homeostasis and define a genetic network that facilitates DNA repair/processing upstream of PriA/PriB-mediated DNA replication restart in E. coli.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Mechanism of efficient double-strand break repair by a long non-coding RNA

The Authors wish to correct an error in Figure 6. In Figure 6B the panel label should read ‘Ku + DNA-PKcs + LINP1, 6 kbp bridge’ instead of ‘Ku + DNA-PKcs + PAXX, 6 kbp bridge’. The published article has been updated. This error does not affect the conclusions of the article.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Repair strategy for fused silica wedged focus lens with high-density filamentary damage

The National Ignition Facility (NIF) is intentionally operated with the final fused silica glass (SiO2) optics exposed to fluences and intensities with the potential to induce damage that will grow with additional laser exposure. Therefore, the NIF operates a recycle loop to refurbish final optics by mitigating any initiated surface damage to arrest its growth. However, the morphology of filamentary damage, caused by local self-focusing in the bulk of a silica optic, adds complexity to optics mitigation and provides a limitation to optic reusability. Here, we evaluate techniques for mitigating isolated and clustered filamentary damage. Optical microscopy before and after installation on NIF was used to determine the efficacy of filamentary mitigation after a series of high fluence and intensity laser exposures. The challenges and success rate of the methods are compared for various filamentary damage mitigation strategies.

National Ignition Facility↗
DOE OSTI2020

Exosomes From Induced Pluripotent Stem Cell–Derived Cardiomyocytes Promote Autophagy for Myocardial Repair

Background. Induced pluripotent stem cells and their differentiated cardiomyocytes (iCMs) have tremendous potential as patient-specific therapy for ischemic cardiomyopathy following myocardial infarctions, but difficulties in viable transplantation limit clinical translation. Exosomes secreted from iCMs (iCM-Ex) can be robustly collected in vitro and injected in lieu of live iCMs as a cell-free therapy for myocardial infarction. Methods and Results. iCM-Ex were precipitated from iCM supernatant and characterized by protein marker expression, nanoparticle tracking analysis, and functionalized nanogold transmission electron microscopy. iCM-Ex were then used in in vitro and in vivo models of ischemic injuries. Cardiac function in vivo was evaluated by left ventricular ejection fraction and myocardial viability measurements by magnetic resonance imaging. Cardioprotective mechanisms were studied by JC-1 (tetraethylbenzimidazolylcarbocyanine iodide) assay, immunohistochemistry, quantitative real-time polymerase chain reaction, transmission electron microscopy, and immunoblotting. iCM-Ex measured ≈140 nm and expressed CD63 and CD9. iCM and iCM-Ex microRNA profiles had significant overlap, indicating that exosomal content was reflective of the parent cell. Mice treated with iCM-Ex demonstrated significant cardiac improvement post–myocardial infarction, with significantly reduced apoptosis and fibrosis. In vitro iCM apoptosis was significantly reduced by hypoxia and exosome biogenesis inhibition and restored by treatment with iCM-Ex or rapamycin. Autophagosome production and autophagy flux was upregulated in iCM-Ex groups in vivo and in vitro. Conclusions. iCM-Ex improve post–myocardial infarction cardiac function by regulating autophagy in hypoxic cardiomyoytes, enabling a cell-free, patient-specific therapy for ischemic cardiomyopathy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Photodamage and Repair in Higher Plant Photosynthesis (Final Technical Report)

Elucidation of the functional properties and structural organization of membrane protein complexes is one of the central objectives of current biochemical investigation. One of the most intriguing aspects of membrane protein function is its role in the mediation of energy transduction in photosynthetic organisms. Light energy, which is the product of a most violent physical process, fusion, is transformed into biological energy equivalents utilized by the photosynthetic cell. This photosynthetic process provides both the carbohydrate that lies at the base of virtually all food chains and, as a byproduct, all of the atmospheric oxygen utilized by heterotrophic organisms. The photosynthetic apparatus, however, is quite susceptible to damage by Reactive Oxygen Species (ROS). These species include O2 ·– , ·OH, H 2 O 2 and 1 O 2 . The production of ROS is an unavoidable byproduct of electron transport under aerobic conditions and these can damage lipids, nucleic acids and proteins. Our research has centered on the identification of amino acid oxidative damage which occurs in the vicinity of photosynthetic energy transfer cofactors. These cofactors participate in excitation energy transfer and linear electron transport within Photosystem II, the Cytochrome b 6 ƒ complex and Photosystem I. Additionally, we have participated in a number of productive collaborations leveraging our expertise in photosynthetic measurements and mass spectrometry to assist other investigators in their research.

59 BASIC BIOLOGICAL SCIENCES↗