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Results for “Reverse Engineering”

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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The F-box protein gene exo-1 is a target for reverse engineering enzyme hypersecretion in filamentous fungi

Carbohydrate active enzymes (CAZymes) are vital for the lignocellulose-based biorefinery. The development of hypersecreting fungal protein production hosts is therefore a major aim for both academia and industry. However, despite advances in our understanding of their regulation, the number of promising candidate genes for targeted strain engineering remains limited. Here, we resequenced the genome of the classical hypersecreting Neurospora crassa mutant exo-1 and identified the causative point of mutation to reside in the F-box protein–encoding gene, NCU09899. The corresponding deletion strain displayed amylase and invertase activities exceeding those of the carbon catabolite derepressed strain ?cre-1, while glucose repression was still mostly functional in ?exo-1. Surprisingly, RNA sequencing revealed that while plant cell wall degradation genes are broadly misexpressed in ?exo-1, only a small fraction of CAZyme genes and sugar transporters are up-regulated, indicating that EXO-1 affects specific regulatory factors. Aiming to elucidate the underlying mechanism of enzyme hypersecretion, we found the high secretion of amylases and invertase in ?exo-1 to be completely dependent on the transcriptional regulator COL-26. Furthermore, misregulation of COL-26, CRE-1, and cellular carbon and nitrogen metabolism was confirmed by proteomics. Finally, we successfully transferred the hypersecretion trait of the exo-1 disruption by reverse engineering into the industrially deployed fungus Myceliophthora thermophila using CRISPR-Cas9. Our identification of an important F-box protein demonstrates the strength of classical mutants combined with next-generation sequencing to uncover unanticipated candidates for engineering. These data contribute to a more complete understanding of CAZyme regulation and will facilitate targeted engineering of hypersecretion in further organisms of interest.

Gabriel, Raphael↗
DOE OSTI2023

Using Eye-Tracking to Quantify Reverse Engineering Expertise

Software reverse engineering (RE) requires analysts to closely read and make decisions about code. Little is known about what makes an analyst successful, making it difficult to train new analysts or design tools to augment existing ones. The goal of this project was to quantify the eye movement behaviors supporting RE and code comprehension more generally. We applied eye-tracking methods from the language comprehension literature to understand where analysts direct their attention over time when completing tasks (e.g., function identification, bug detection). Across three studies, we manipulated aspects of code hypothesized to impact comprehension (e.g., variable name meaningfulness, code complexity) and presentation methods (e.g., line-by-line, free viewing, gaze-contingent moving window) to understand effects on accuracy and gaze patterns. Results showed clear benefits of meaningful variable names, and effects of expertise on global and line-specific viewing patterns. Findings could inspire empirically-supported tool or analytic adaptations that help to reduce analyst workload.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Perfect quantum state engineering by the combination of the counterdiabatic driving and the reverse-engineering technique

We propose a method to design shortcuts to adiabaticity for implementing perfect quantum state engineering by the combination of the counterdiabatic driving and the reverse engineering technique. Based on the method, we can design simple schemes to realize the intended dynamics. For the sake of clearness, we apply this method to several examples including two-level, three-level and four-level system. We show that fast quantum state engineering can be realized by utilizing simply-designed auxiliary Hamiltonian. Furthermore, a suitable choice of the control parameters can eliminate the additional couplings in the introduced auxiliary Hamiltonian. Numerical simulation reveals that the constructed scheme is reliable and robust against various dissipation effects and the fluctuations of control parameters in current technology. - Highlights: • Fast and feasible quantum state engineering can be realized by using the method. • The method allows one to control accurately the evolution of the system. • The scheme is fast and robust against dissipation effects and parameter errors. • The scheme can be generalized to realize N-dimensional quantum state engineering.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Preventing Reverse Engineering of Critical Industrial Data with DIOD

Business analytics augmented by artificial intelligence and machine learning (AI/ML) have revolutionized the role of data in the modern world. In recent years, businesses have incorporated data into their decision-making process for better prediction, risk-assessment, content creation, etc. While such businesses often seek to leverage the full use of their data through third-party AI/ML services, they are often hampered by the risks of data leaks, reverse-engineering, stolen technology, etc. that often have disastrous consequences for businesses and their stakeholders alike. Thus, there arises a need for data masking prior to its transmission that obfuscates proprietary information while preserving the information relevant for AI/ML applications. In order to meet the needs of industrial data which are significantly different from those of data warehouses, previous work proposed an efficient time and space-scalable data masking paradigm known as the deceptive infusion of data (DIOD) methodology. The present work expands upon this work by leveraging existing reverse-engineering capabilities to facilitate the decomposition of industrial data into its proprietary and AI/ML-relevant parts, referred to as fundamental and inference metadata respectively. Both sets of metadata are further obfuscated in accordance with the DIOD methodology to create the DIOD rendition of the industrial data, which is rendered immune to reverse-engineering by discarding proprietary information and only preserving AI/ML-relevant information. Additionally, constraints of the original DIOD manuscript are relaxed using mutual information by configuring the methodology to the target AI/ML application to unlock the full potential of the DIOD methodology. As an example, data from a nuclear reactor is transformed into that from a nonlinear spring-mass system with different levels of data masking as required by the generic system and the target application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS

A difficult problem that is currently growing rapidly due to the sharp increase in the amount of high-throughput data available for many systems is that of determining useful and informative causative influence networks. These networks can be used to predict behavior given observation of a small number of components, predict behavior at a future time point, or identify components that are critical to the functioning of the system under particular conditions. In these endeavors incorporating observations of systems from a wide variety of viewpoints can be particularly beneficial, but has often been undertaken with the objective of inferring networks that are generally applicable. The focus of the current work is to integrate both general observations and measurements taken for a particular pathology, that of ischemic stroke, to provide improved ability to produce useful predictions of systems behavior. A number of hybrid approaches have recently been proposed for network generation in which the Gene Ontology is used to filter or enrich network links inferred from gene expression data through reverse engineering methods. These approaches have been shown to improve the biological plausibility of the inferred relationships determined, but still treat knowledge-based and machine-learning inferences as incommensurable inputs. In this paper, we explore how further improvements may be achieved through a full integration of network inference insights achieved through application of the Gene Ontology and reverse engineering methods with specific reference to the construction of dynamic models of transcriptional regulatory networks. We show that integrating two approaches to network construction, one based on reverse-engineering from conditional transcriptional data, one based on reverse-engineering from in situ hybridization data, and another based on functional associations derived from Gene Ontology, using probabilities can improve results of clustering as evaluated by a predictive model of transcriptional expression levels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Privacy policy robustness to reverse engineering

Differential privacy policies allow one to preserve data privacy while sharing and analyzing data. However, these policies are susceptible to an array of attacks. In particular, often a portion of the data desired to be privacy protected is exposed online. Access to these pre-privacy protected data samples can then be used to reverse engineer the privacy policy. With knowledge of the generating privacy policy, an attacker can use machine learning to approximate the full set of originating data. Bayesian inference is one method for reverse engineering both model and model parameters. We present a methodology for evaluating and ranking privacy policy robustness to Bayesian inference-based reverse engineering, and demonstrated this method across data with a variety of temporal trends.

Kusne, Aaron Gilad↗
DOE OSTI2013

Non-Destructive State Machine Reverse Engineering

Most of the integrated circuits (ICs) that are in electronic systems today are based on state machines. We are taking advantage of this to develop a hardware reverse engineering method that discovers the IC’s underlying state machine, rather than its transistors and gates. While there are other methods for destructively reverse engineering ICs or for non-destructively characterizing ICs, our method offers a fast and accurate analysis while remaining non-destructive. To do this, we present an intelligent brute-force method of exploring the logic of the IC using only the input and outputs designed into the IC - the I/O pins. From this exploration, we can apply a folding algorithm to discover the designed state machine.

counterfeit↗
DOE OSTI2016

Investigation on Reaction Probabilities for Cyclic Voltammetry of Zirconium in LiCl-KCl Eutectic Molten Salt via Reverse-Engineering Method

This work focuses on a reverse-engineering program design for the cyclic voltammetry (CV) method to help illustrating, enhancing, and providing robustness in detection probability of the reactions during an electrorefining process. Analyzing the reactions from CV plots for 1.07, 2.49, and 4.98 wt% of zirconium chloride (ZrCl{sub 4}) in a LiCl-KCl molten salt at 773 K under different scan rates from Hoover's study has been accomplished. The results provide the current (Amp) versus potential (V) to investigate the optimum probability of the reactions at each peak. All simulations have been done through the commercial software MATLAB by using various experimental values existing in the literature as a first guess (e.g., the diffusion coefficients, and formal potentials). Preliminary results indicate that the zirconium reactions at each peak from cathodic to anodic side for 1.07 wt% are as followed: combination of Zr{sup +2}/Zr and Zr{sup +4}/ Zr{sup +2} at approximately -1.15 V, Zr{sup +2}/Zr at -1.5 V, Zr{sup +4}/Zr at -1.85 V, 70% Zr/Zr{sup +4} and 30% Zr/Zr{sup +2} at -0.5 V, and Zr{sup +2}/ Zr{sup +4} at - 0.25 V. These mentioned peaks are similar for 2.49 wt% at different voltages. In addition, the highest anodic peak for 4.98 wt% is found to be a combination of 30% Zr/Zr{sup +4} and 70% Zr/Zr{sup +2}. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

revengc: An R package to Reverse Engineer Summarized Data

Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations, such as the World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), and World Bank. The challenge therefore is to infer what the original data could have been given summarized information. We present an R package that reverse engineers censored and/or decoupled data with two main functions. The cnbinom.pars() function estimates the average and dispersion parameter of a censored univariate frequency table. The rec() function reverse engineers summarized data into an uncensored bivariate table of probabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Reverse engineering systems models of regulation: discovery, prediction and mechanisms

Biological systems can now be understood in comprehensive and quantitative detail using systems biology approaches. Putative genome-scale models can be built rapidly based upon biological inventories and strategic system-wide molecular measurements. Furthermore, current models combine statistical associations, causative abstractions, and known molecular mechanisms to explain and predict quantitative and complex phenotypes. This top-down ‘reverse engineering’ approach generates useful organism-scale models despite noise and incompleteness in data and knowledge. Here we review and discuss the reverse engineering of biological systems using top-down data-driven approaches, in order to improve discovery, hypothesis generation, and the inference of biological properties.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Reverse Engineering Adverse Outcome Pathways

The toxicological effects of many stressors are mediated through unknown, or poorly characterized, mechanisms of action. We describe the application of reverse engineering complex interaction networks from high dimensional omics data (gene, protein, metabolic, signaling) to characterize adverse outcome pathways (AOPs) for chemicals that disrupt the hypothalamus-pituitary-gonadal endocrine axis in fathead minnows. Gene expression changes in fathead minnow ovaries in response to 7 different chemicals, over different times, doses, and in vivo versus in vitro conditions were captured in a large data set of 868 arrays. We examined potential AOPs of the antiandrogen flutamide using two mutual information theory methods, ARACNE and CLR to infer gene regulatory networks and potential adverse outcome pathways. Representative networks from these studies were used to predict a network path from stressor to adverse outcome as a candidate AOP. The relationship of individual chemicals to an adverse outcome can be determined by following perturbations through the network in response to chemical treatment leading to the nodes associated with the adverse outcome. Identification of candidate pathways allows for formation of testable hypotheses about key biologic processes, biomarkers or alternative endpoints, which could be used to monitor an adverse outcome pathway. Finally, we identify the unique challenges facing the application of this approach in ecotoxicology, and attempt to provide a road map for the utilization of these tools. Key Words: mechanism of action, toxicology, microarray, network inference

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Brain in situ hybridization maps as a source for reverse-engineering transcriptional regulatory networks: Alzheimer's disease insights

Microarray data have been a valuable resource for identifying transcriptional regulatory relationships among genes. As an example, brain region-specific transcriptional regulatory events have the potential of providing etiological insights into Alzheimer Disease (AD). However, there is often a paucity of suitable brain-region specific expression data obtained via microarrays or other high throughput means. The Allen Brain Atlas in situ hybridization (ISH) data sets (Jones et al., 2009) represent a potentially valuable alternative source of high-throughput brain region-specific gene expression data for such purposes. In this study, Allen BrainAtlasmouse ISH data in the hippocampal fields were extracted, focusing on 508 genes relevant to neurodegeneration. Transcriptional regulatory networkswere learned using three high-performing network inference algorithms. Only 17% of regulatory edges from a network reverse-engineered based on brain region-specific ISH data were also found in a network constructed upon gene expression correlations inmousewhole brain microarrays, thus showing the specificity of gene expression within brain sub-regions. Furthermore, the ISH data-based networks were used to identify instructive transcriptional regulatory relationships. Ncor2, Sp3 and Usf2 form a unique three-party regulatory motif, potentially affecting memory formation pathways. Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, andSyn2). Further, Nfe2l1, Egr1 and Usf2 are sensitive to dietary factors and could be among links between dietary influences and genes in the AD etiology. Thus, this approach of harnessing brain region-specific ISH data represents a rare opportunity for gleaning unique etiological insights for diseases such as AD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Reverse engineering nuclear properties from rare earth abundances in the r process

The bulk of the rare earth elements are believed to be synthesized in the rapid neutron capture process or r process of nucleosynthesis. The solar r-process residuals show a small peak in the rare earths around $A\sim 160$, which is proposed to be formed dynamically during the end phase of the r process by a pileup of material. This abundance feature is of particular importance as it is sensitive to both the nuclear physics inputs and the astrophysical conditions of the main r process. Here, we explore the formation of the rare earth peak from the perspective of an inverse problem, using Monte Carlo studies of nuclear masses to investigate the unknown nuclear properties required to best match rare earth abundance sector of the solar isotopic residuals. When nuclear masses are changed, we recalculate the relevant β-decay properties and neutron capture rates in the rare earth region. The feedback provided by this observational constraint allows for the reverse engineering of nuclear properties far from stability where no experimental information exists. We investigate a range of astrophysical conditions with this method and show how these lead to different predictions in the nuclear properties influential to the formation of the rare earth peak. Finally, we conclude that targeted experimental campaigns in this region will help to resolve the type of conditions responsible for the production of the rare earth nuclei, and will provide new insights into the longstanding problem of the astrophysical site(s) of the r process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Reverse-Engineering Strain in Nanocrystallites by Tracking Trimerons

Although strain underpins the behavior of many transition-oxide-based magnetic nanomaterials, it is elusive to quantify. Since the formation of orbital molecules is sensitive to strain, a metal–insulator transition should be a window into nanocrystallite strain. Using three sizes of differently strained Fe 3 O 4 polycrystalline nanorods, the impact of strain on the Verwey transition and the associated formation and dissolution processes of quasiparticle trimerons is tracked. In 40 and 50 nm long nanorods, increasing isotropic strain results in Verwey transitions going from T V ≈ 60 K to 20 K. By contrast, 700 nm long nanorods with uniaxial strain along the (110) direction have T V ≈ 150 K—the highest value reported thus far. A metal–insulator transition, like T V in Fe 3 O 4 , can be used to determine the effective strain within nanocrystallites, thus providing new insights into nanoparticle properties and nanomagnetism.

36 MATERIALS SCIENCE↗