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All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Deep-learning↗
NASA NTRS2020

Amino Acid Encoding for Deep Learning Applications

Background: The number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction – a process called ‘end-to-end learning’ – has led to state-ofthe-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN. Results: By using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension. Conclusion: Our study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.

Hesham ElAbd↗
NASA NTRS1997

The Path Resistance Method for Bounding the Smallest Nontrivial Eigenvalue of a Laplacian

We introduce the path resistance method for lower bounds on the smallest nontrivial eigenvalue of the Laplacian matrix of a graph. The method is based on viewing the graph in terms of electrical circuits; it uses clique embeddings to produce lower bounds on lambda(sub 2) and star embeddings to produce lower bounds on the smallest Rayleigh quotient when there is a zero Dirichlet boundary condition. The method assigns priorities to the paths in the embedding; we show that, for an unweighted tree T, using uniform priorities for a clique embedding produces a lower bound on lambda(sub 2) that is off by at most an 0(log diameter(T)) factor. We show that the best bounds this method can produce for clique embeddings are the same as for a related method that uses clique embeddings and edge lengths to produce bounds.

Guattery, Stephen↗
NASA NTRS1998

Cell Kinetic and Histomorphometric Analysis of Microgravitational Osteopenia: PARE.03B

Previous methods of identifying cells undergoing DNA synthesis (S-phase) utilized H-3 thymidine (3HT) autoradiography. 5-Bromo-2'-deoxyuridine (BrdU) immunohistochemistry is a nonradioactive alternative method. This experiment compared the two methods using the nuclear volume model for osteoblast histogenesis in two different embedding media. Twenty Sprague-Dawley rats were used, with half receiving 3HT (1 micro Ci/g) and the other half BrdU (50 microgram/g). Condyies were embedded (one side in paraffin, the other in plastic) and S-phase nuclei were identified using either autoradiography or immunohistochemistry. The fractional distribution of preosteoblast cell types and the percentage of labeled cells (within each cell fraction and label index) were calculated and expressed as mean q standard error. Chi-Square analysis showed only a minor difference in the fractional distribution of cell types. However, there were significant differences (p less than 0.05) by ANOVA, in the nuclear labeling of specific cell types. With the exception of the less-differentiated A+A'cells, more BrdU label was consistently detected in paraffin than in plastic-embedded sections. In general, more nuclei were labeled with 3H-thymidine than with BrdU in both types of embedding media. Labeling index data (labeled cells/total cells sampled x 100) indicated that BrdU in paraffin, but not plastic gave the same results as 3HT in either embedding method. Thus, we conclude that the two labeling methods do not yield the same results for the nuclear volume model and that embedding media is an important factor whenusing BrdU. As a result of this work, 3HT was chosen for used in the PARE.03 flight experiments.

Roberts, W. Eugene↗
NASA NTRS2009

Reconfigurable, Bi-Directional Flexfet Level Shifter for Low-Power, Rad-Hard Integration

Two prototype Reconfigurable, Bi-directional Flexfet Level Shifters (ReBiLS) have been developed, where one version is a stand-alone component designed to interface between external low voltage and high voltage, and the other version is an embedded integrated circuit (IC) for interface between internal low-voltage logic and external high-voltage components. Targeting stand-alone and embedded circuits separately allows optimization for these distinct applications. Both ReBiLS designs use the commercially available 180-nm Flex fet Independently Double-Gated (IDG) SOI CMOS (silicon on insulator, complementary metal oxide semiconductor) technology. Embedded ReBiLS circuits were integrated with a Reed-Solomon (RS) encoder using CMOS Ultra-Low-Power Radiation Tolerant (CULPRiT) double-gated digital logic circuits. The scope of the project includes: creation of a new high-voltage process, development of ReBiLS circuit designs, and adjustment of the designs to maximize performance through simulation, layout, and manufacture of prototypes. The primary technical objectives were to develop a high-voltage, thick oxide option for the 180-nm Flexfet process, and to develop a stand-alone ReBiLS IC with two 8-channel I/O busses, 1.8 2.5 I/O on the low-voltage pins, 5.0-V-tolerant input and 3.3-V output I/O on the high-voltage pins, and 100-MHz minimum operation with 10-pF external loads. Another objective was to develop an embedded, rad-hard ReBiLS I/O cell with 0.5-V low-voltage operation for interface with core logic, 5.0-V-tolerant input and 3.3-V output I/O pins, and 100-MHz minimum operation with 10- pF external loads. A third objective was to develop a 0.5- V Reed-Solomon Encoder with embedded ReBilS I/O: Transfer the existing CULPRiT RS encoder from a 0.35-micron bulk-CMOS process to the ASI 180-nm Flexfet, rad-hard SOI Process. 0.5-V low-voltage core logic. 5.0-V-tolerant input and 3.3-V output I/O pins. 100-MHz minimum operation with 10- pF external loads. The stand-alone ReBiLS chip will allow system designers to provide efficient bi-directional communication between components operating at different voltages. Embedding the ReBiLS cells into the proven Reed-Solomon encoder will demonstrate the ability to support new product development in a commercially viable, rad-hard, scalable 180-nm SOI CMOS process.

DeGregorio, Kelly↗
NASA NTRS1998

Cell Kinetic and Histomorphometric Analysis of Microgravitational Osteopenia: PARE.03B

Previous methods of identifying cells undergoing DNA synthesis (S-phase) utilized 3H-thymidine (3HT) autoradiography. 5-Bromo-2'-deoxyuridine (BrdU) immunohistochemistry is a nonradioactive alternative method. This experiment compared the two methods using the nuclear volume model for osteoblast histogenesis in two different embedding media. Twenty Sprague-Dawley rats were used, with half receiving 3HT (1 micro-Ci/g) and the other half BrdU (50 micro-g/g). Condyles were embedded (one side in paraffin, the other in plastic) and S-phase nuclei were identified using either autoradiography or immunohistochemistry. The fractional distribution of preosteoblast cell types and the percentage of labeled cells (within each cell fraction and label index) were calculated and expressed as mean +/- standard error. Chi-Square analysis showed only a minor difference in the fractional distribution of cell types. However, there were,significant differences (p less than 0.05) by ANOVA, in the nuclear labeling of specific cell types. With the exception of the less-differentiated A+A' cells, more BrdU label was consistently detected in paraffin than in plastic-embedded sections. In general, more nuclei were labeled with 3H-thymidine than with BrdU in both types of embedding media (Fig 2.). Labeling index data (labeled cells/total cells sampled x 100) indicated that BrdU in paraffin, but not plastic gave the same results as 3HT in either embedding method. Thus, we conclude that the two labeling methods do not yield the same results.

Roberts, W. Eugene↗
NASA NTRS2004

Practical Pocket PC Application w/Biometric Security

I work in the Flight Software Engineering Branch, where we provide design and development of embedded real-time software applications for flight and supporting ground systems to support the NASA Aeronautics and Space Programs. In addition, this branch evaluates, develops and implements new technologies for embedded real-time systems, and maintains a laboratory for applications of embedded technology. The majority of microchips that are used in modern society have been programmed using embedded technology. These small chips can be found in microwaves, calculators, home security systems, cell phones and more. My assignment this summer entails working with an iPAQ HP 5500 Pocket PC. This top-of-the-line hand-held device is one of the first mobile PC's to introduce biometric security capabilities. Biometric security, in this case a fingerprint authentication system, is on the edge of technology as far as securing information. The benefits of fingerprint authentication are enormous. The most significant of them are that it is extremely difficult to reproduce someone else's fingerprint, and it is equally difficult to lose or forget your own fingerprint as opposed to a password or pin number. One of my goals for this summer is to integrate this technology with another Pocket PC application. The second task for the summer is to develop a simple application that provides an Astronaut EVA (Extravehicular Activity) Log Book capability. The Astronaut EVA Log Book is what an astronaut would use to report the status of field missions, crew physical health, successes, future plans, etc. My goal is to develop a user interface into which these data fields can be entered and stored. The applications that I am developing are created using eMbedded Visual C++ 4.0 with the Pocket PC 2003 Software Development Kit provided by Microsoft.

Logan, Julian↗
NASA NTRS2015

Variable-Domain Displacement Transfer Functions for Converting Surface Strains into Deflections for Structural Deformed Shape Predictions

Variable-Domain Displacement Transfer Functions were formulated for shape predictions of complex wing structures, for which surface strain-sensing stations must be properly distributed to avoid jointed junctures, and must be increased in the high strain gradient region. Each embedded beam (depth-wise cross section of structure along a surface strain-sensing line) was discretized into small variable domains. Thus, the surface strain distribution can be described with a piecewise linear or a piecewise nonlinear function. Through discretization, the embedded beam curvature equation can be piece-wisely integrated to obtain the Variable-Domain Displacement Transfer Functions (for each embedded beam), which are expressed in terms of geometrical parameters of the embedded beam and the surface strains along the strain-sensing line. By inputting the surface strain data into the Displacement Transfer Functions, slopes and deflections along each embedded beam can be calculated for mapping out overall structural deformed shapes. A long tapered cantilever tubular beam was chosen for shape prediction analysis. The input surface strains were analytically generated from finite-element analysis. The shape prediction accuracies of the Variable- Domain Displacement Transfer Functions were then determined in light of the finite-element generated slopes and deflections, and were fofound to be comparable to the accuracies of the constant-domain Displacement Transfer Functions

displacement transfer functions↗
NASA NTRS2016

Internship Abstract and Final Reflection

The primary objective for this internship is the evaluation of an embedded natural language processor (NLP) as a way to introduce voice control into future space suits. An embedded natural language processor would provide an astronaut hands-free control for making adjustments to the environment of the space suit and checking status of consumables procedures and navigation. Additionally, the use of an embedded NLP could potentially reduce crew fatigue, increase the crewmember's situational awareness during extravehicular activity (EVA) and improve the ability to focus on mission critical details. The use of an embedded NLP may be valuable for other human spaceflight applications desiring hands-free control as well. An embedded NLP is unique because it is a small device that performs language tasks, including speech recognition, which normally require powerful processors. The dedicated device could perform speech recognition locally with a smaller form-factor and lower power consumption than traditional methods.

Sandor, Edward↗
NASA NTRS2017

Characterization of a Method for Inverse Heat Conduction Using Real and Simulated Thermocouple Data

It is often impractical to instrument the external surface of high-speed vehicles due to the aerothermodynamic heating. Temperatures can instead be measured internal to the structure using embedded thermocouples, and direct and inverse methods can then be used to estimate temperature and heat flux on the external surface. Two thermocouples embedded at different depths are required to solve direct and inverse problems, and filtering schemes are used to reduce noise in the measured data. Accuracy in the estimated surface temperature and heat flux is dependent on several factors. Factors include the thermocouple location through the thickness of a material, the sensitivity of the surface solution to the error in the specified location of the embedded thermocouples, and the sensitivity to the error in thermocouple data. The effect of these factors on solution accuracy is studied using the methodology discussed in the work of Pizzo, et. al.1 A numerical study is performed to determine if there is an optimal depth at which to embed one thermocouple through the thickness of a material assuming that a second thermocouple is installed on the back face. Solution accuracy will be discussed for a range of embedded thermocouple depths. Moreover, the sensitivity of the surface solution to (a) the error in the specified location of the embedded thermocouple and to (b) the error in the thermocouple data are quantified using numerical simulation, and the results are discussed.

Pizzo, Michelle E.↗
NASA NTRS1991

Intelligent structures technology

Viewgraphs on intelligent structures technology are presented. Topics covered include: embedding electronics; electrical and mechanical compatibility; integrated circuit chip packaged for embedding; embedding devices within composite structures; test of embedded circuit in G/E coupon; temperature/humidity/bias test; single-chip microcomputer control experiment; and structural shape determination.

Crawley, Edward F.↗