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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 253 records · Page 14

Utah FORGE: Well 16B(78)-32 Drill Core Fracture Analysis Images and Data

This dataset contains drilling core data from well 16B(78)-32, including PDF documents with flattened core images annotated by feature type and core interval, as well as spreadsheets detailing feature morphologies by depth, planar feature measurements, and planar feature orientations rotated to in situ conditions. Core was recovered from three intervals, one per stimulation stage, in the crystalline rocks affected by the stimulation of well 16A(78)-32. Seven core runs were conducted, yielding 135.8 feet of recovered core. Features in the core were categorized into planar fractures, semi-planar fractures, unbroken mineralized fractures, rough fractures, curviplanar fractures, concave-convex surfaces, and planar compositional features such as mylonite or dike-like structures. Planar features were measured while the core was positioned horizontally, with the core axis aligned to a downhole azimuth of 42 degrees. Planar core measurements from stimulations 2 and 3 that could be confidently correlated with FMI data were rotated to in situ orientations. This was done by rotating the planes along vertical and horizontal axes to match the azimuth and inclination data recorded in the directional survey of well 16B(78)-32, as well as applying an axial rotation to resemble the fracture orientations observed in the FMI log at corresponding depths. Coherent sets of planar fracture measurements were made by aligning the core within each 3-foot section of the dissected core barrel, and between adjacent 3-foot sections within a core run by matching rock fabrics, saw cuts and/or tool marks. Where coherent fracture measurements could not be made within a core run, data sets are denoted by a subscript (i.e. 2-Ta and 2-Tb both come from tangent core run number 2).

15 GEOTHERMAL ENERGY↗

RNAcentral: a hub of information for non-coding RNA sequences

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences, collating information on ncRNA sequences of all types from a broad range of organisms. We have recently added a new genome mapping pipeline that identifies genomic locations for ncRNA sequences in 296 species. We have also added several new types of functional annotations, such as tRNA secondary structures, Gene Ontology annotations, and miRNA-target interactions. A new quality control mechanism based on Rfam family assignments identifies potential contamination, incomplete sequences, and more. The RNAcentral database has become a vital component of many workflows in the RNA community, serving as both the primary source of sequence data for academic and commercial groups, as well as a source of stable accessions for the annotation of genomic and functional features. These examples are facilitated by an improved RNAcentral web interface, which features an updated genome browser, a new sequence feature viewer, and improved text search functionality.

Sweeney, Blake A.↗

Progress and Challenges in Ocean Metaproteomics and Proposed Best Practices for Data Sharing

Ocean metaproteomics is an emerging field enabling discoveries about marine microbial communities and their impact on global biogeochemical processes. Recent ocean metaproteomic studies have provided insight into microbial nutrient transport, colimitation of carbon fixation, the metabolism of microbial biofilms, and dynamics of carbon flux in marine ecosystems. Future methodological developments could provide new capabilities such as characterizing long-term ecosystem changes, biogeochemical reaction rates, and in situ stoichiometries. Yet challenges remain for ocean metaproteomics due to the great biological diversity that produces highly complex mass spectra, as well as the difficulty in obtaining and working with environmental samples. This review summarizes the progress and challenges facing ocean metaproteomic scientists and proposes best practices for data sharing of ocean metaproteomic data sets, including the data types and metadata needed to enable intercomparisons of protein distributions and annotations that could foster global ocean metaproteomic capabilities.

54 ENVIRONMENTAL SCIENCES↗

A Two-Tier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery

Deep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionally, performing these tasks simultaneously in 3D has proven to be computationally expensive. Currently available methodologies thus largely work to segment 3D objects from 2D images (without context from captured 3D volumes). In this work, we present a novel approach to perform fast and accurate localization, detection, and segmentation of volumes containing cells. Specifically, in our method, we modify and tune two state-of-the-art CNNs, namely 2D YOLOv2 and 3D U-Net, and combine them with a new fusion and image processing algorithms. Annotated volumes in this space are limited, and we have created synthetic data that mimics actual structures for training and testing our proposed approach. Promising results on this test data demonstrate the value of the technique and offers a methodology for 3D cell analysis in real microscopy imagery.

Ziabari, Amir↗

16s Amplicon Analysis of Soil Data for Interactive effects of depth and differential irrigation on soil microbiome composition and functioning

Genomic DNA was isolated from soil and rhizosphere samples using the Zymo Quick-DNA fecal/soil microbe miniprep kit (catalog no. D6010) according to the manufacturer’s instructions (Zymo Research; Irvine, CA) with the modification of eluting in 100 uL elution buffer. Sample concentrations were quantified using the Qubit dsDNA HS assay kit (Thermo Fisher). For rhizosphere samples only, DNA was subsequently purified using Zymo’s ZR-96 DNA Clean & Concentrator kit (catalog no. D4024) to account for low DNA concentrations of these samples. . In each replicate block, there were five drip irrigation treatments (T1 = 100% normal irrigation, T2 = 56.25%, T3 = 37.5%, T4 = 18.75% and T5=no irrigation. On July 20, the strength of the drought treatments was increased: T1 remained at 100%, whereas T2 changed from 75% to 56.25%, T2 changed from 50% to 37.5%, and T4 changed from 25% to 18.75%. Sequencing was performed as described previously (Naylor, Fansler, et al. 2020). Sequences were amplified on the MiSeq platform (Illumina, San Diego, CA) using 16S primers (515F and 806R) specific to the V4 region. Raw sequence data was processed with the pipeline Hundo for amplicon quality control and annotation. Downstream statistical analyses on 16S datasets were performed using the program R and the packages ‘phyloseq’ and ‘vegan’.

Soil microbiome, metatranscriptomics↗

PPI DataHub Project Data Package: High-density Lipoprotein (HDL) Structure and Function Proteomics

The purpose of this experiment was to investigate how the interactions between APOA1 and APOA2 on the surface of high-density lipoproteins (HDL) impact particle function. Interactions were investigated on HDL isolated from human blood plasma using structural proteomics tools such as chemical cross-linking and limited proteolysis (LiP). The structural proteomics data was acquired using a Q-Exactive HF-X mass spectrometer and data was processed and compiled using MaxQuant sofware (v.1.6.17.0). Processed datasets are openly accessible from the download button (~2.8 GB) and contain secondary processed LiP and global proteomic results files and supporting metadata materials. Processed data downloads include a sample naming key, processed MaxQuant results/parameters, and protein annotated relative abundance files.

59 BASIC BIOLOGICAL SCIENCES↗

Optimizing inference of segmentation on high-resolution images in MLExchange

MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically segment high-resolution scientific images with minimal manual annotation effort. In this work, we share code optimizations that significantly speed up the segmentation inference workflow of large data in short time. By optimizing the sequence of CPU-GPU data transfers and introducing CPU parallelization to key operations, we improve the per-device, per-image frame computational efficiency and observe close to 3×$$\times$$ speedup over the original segmentation inference workflow run time when utilizing a single GPU. Further adaptations enabling multi-GPU inference yield more than 40×$$\times$$ speedup with 100 GPUs compared to the optimized single GPU inference workflow. This acceleration of the segmentation inference workflow will provide MLExchange users with easy access to segmentation results with little wait time.

Lu, Shizhao↗

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. Furthermore, we observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

97 MATHEMATICS AND COMPUTING↗

A pre-training and self-training approach for biomedical named entity recognition

Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training data in order to be effective. This severely limits the effectiveness of NER models in applications where expert annotations are difficult and expensive to obtain. In this work, we explore the effectiveness of transfer learning and semi-supervised self-training to improve the performance of NER models in biomedical settings with very limited labeled data (250-2000 labeled samples). We first pre-train a BiLSTM-CRF and a BERT model on a very large general biomedical NER corpus such as MedMentions or Semantic Medline, and then we fine-tune the model on a more specific target NER task that has very limited training data; finally, we apply semi-supervised self-training using unlabeled data to further boost model performance. We show that in NER tasks that focus on common biomedical entity types such as those in the Unified Medical Language System (UMLS), combining transfer learning with self-training enables a NER model such as a BiLSTM-CRF or BERT to obtain similar performance with the same model trained on 3x-8x the amount of labeled data. We further show that our approach can also boost performance in a low-resource application where entities types are more rare and not specifically covered in UMLS.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

54 ENVIRONMENTAL SCIENCES↗

An expanded registry of candidate cis -regulatory elements

Mammalian genomes contain millions of regulatory elements that control the complex patterns of gene expression. Previously, the ENCODE consortium mapped biochemical signals across hundreds of cell types and tissues and integrated these data to develop a registry containing 0.9 million human and 300,000 mouse candidate cis-regulatory elements (cCREs) annotated with potential functions. Here we have expanded the registry to include 2.37 million human and 967,000 mouse cCREs, leveraging new ENCODE datasets and enhanced computational methods. This expanded registry covers hundreds of unique cell and tissue types, providing a comprehensive understanding of gene regulation. Functional characterization data from assays such as STARR-seq, massively parallel reporter assay, CRISPR perturbation and transgenic mouse assays have profiled more than 90% of human cCREs, revealing complex regulatory functions. We identified thousands of novel silencer cCREs and demonstrated their dual enhancer and silencer roles in different cellular contexts. Integrating the registry with other ENCODE annotations facilitates genetic variation interpretation and trait-associated gene identification, exemplified by the identification of KLF1 as a novel causal gene for red blood cell traits. This expanded registry is a valuable resource for studying the regulatory genome and its impact on health and disease.

Moore, Jill E. [Univ. of Massachusetts, Worchester↗

RNAseq-based transcriptome assembly of Clostridium acetobutylicum for functional genome annotation and discovery

Accurate genome annotations are essential in modern biology and biotechnology, yet they are still largely based on genome sequencing and comparative analyses. We show that the Clostridium acetobutylicum genome annotation can be markedly improved by integrating bioinformatic predictions with RNA sequencing (RNAseq) data. Samples were acquired under butanol, butyrate, and unstressed treatments across various growth conditions. Analysis of an initial assembly revealed errors due to background signals and limitations of assembly algorithms. Hurdles for RNAseq transcriptome mapping include optimizing library complexity and sequencing depth, yet most studies report low sequencing depth and ignore the effect of ribosomal RNA abundance. An integrative analysis was developed to combine motif predictions, single-nucleotide resolution sequencing depth, and library complexity to resolve difficulties in assembly curation. This minimized false positive error and determined gene boundaries, in some cases, to the exact base-pair of prior studies. This will be the first strand-specific transcriptome assembly in a Clostridium organism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems such as cell signaling and epigenetic regulation. Precisely defining the stoichiometry of PTMs has been challenging because, in the widely used bottom-up proteomics methods, the detection occurs at the peptide level and thus the link between peptides and their specific modification site is lost, resulting in proteoform ambiguity. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Proteins with positional isomers refers to proteoforms with identical total mass and set of modifications but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for relative quantification of PI within a single tool. We benchmarked IsoForma’s performance against two existing workflows and highlight the similarity of the results and improvements in speed. Overall, IsoForma provides a streamlined process, reduces the time of conducting isoform-based analyses, and offers an essential framework for developing customized proteoform analysis workflows. Finally, the software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

59 BASIC BIOLOGICAL SCIENCES↗

Identification of a Fusarium ear rot resistance gene in maize by QTL mapping and RNA sequencing

Fusarium ear rot (FER) caused by Fusarium verticillioides is a prevalent maize disease. To comprehensively characterize the genetic basis of the natural variation in FER resistance, a recombinant inbred line (RIL) population was used to map quantitative trait loci (QTL) for FER resistance. A total of 17 QTL were identified by linkage mapping in eight environments. These QTL were located on six chromosomes and explained 3.88–15.62% of the total phenotypic variation. Moreover, qFER1.03 had the strongest effect and accounted for 4.98–15.62% of the phenotypic variation according to analyses of multiple environments involving best linear unbiased predictions. The chromosome segment substitution lines (CSSLs) derived from a cross between Qi319 (donor parent) and Ye478 (recurrent parent) were used to verify the contribution of qFER1.03 to FER resistance. The line CL171, which harbored an introgressed qFER1.03 , was significantly resistant to FER. Further fine mapping of qFER1.03 revealed that the resistance QTL was linked to insertion/deletion markers InDel 8 and InDel 2, with physical distances of 43.55 Mb and 43.76 Mb, respectively. Additionally, qFER1.03 differed from the previous resistance QTL on chromosome 1. There were three annotated genes in this region. On the basis of the RNA-seq data, which revealed the genes differentially expressed between the FER-resistant Qi319 and susceptible Ye478, GRMZM2G017792 (MPK3) was preliminarily identified as a candidate gene in the qFER1.03 region. The Pr-CMV-VIGS system was used to decrease the GRMZM2G017792 expression level in CL171 by 34–57%, which led to a significant decrease in FER resistance. Using RIL and CSSL populations combined with RNA-seq and Pr-CMV-VIGS, the candidate gene can be dissected effectively, which provided important gene resource for breeding FER-resistant varieties.

Xia, Yusheng↗

Combining active learning suggestions

We study the problem of combining active learning suggestions to identify informative training examples by empirically comparing methods on benchmark datasets. Many active learning heuristics for classification problems have been proposed to help us pick which instance to annotate next. But what is the optimal heuristic for a particular source of data? Motivated by the success of methods that combine predictors, we combine active learners with bandit algorithms and rank aggregation methods. We demonstrate that a combination of active learners outperforms passive learning in large benchmark datasets and removes the need to pick a particular active learner a priori. We discuss challenges to finding good rewards for bandit approaches and show that rank aggregation performs well.

97 MATHEMATICS AND COMPUTING↗

TranSyT , an innovative framework for identifying transport systems

The importance and rate of development of genome-scale metabolic models have been growing for the last few years, increasing the demand for software solutions that automate several steps of this process. However, since TRIAGE’s release, software development for the automatic integration of transport reactions into models has stalled. Here, in this paper, we present the Transport Systems Tracker (TranSyT). Unlike other transport systems annotation software, TranSyT does not rely on manual curation to expand its internal database, which is derived from highly curated records retrieved from the Transporters Classification Database and complemented with information from other data sources. TranSyT compiles information regarding transporter families and proteins, and derives reactions into its internal database, making it available for rapid annotation of complete genomes. All transport reactions have GPR associations and can be exported with identifiers from four different metabolite databases. TranSyT is currently available as a plugin for merlin v4.0 and an app for KBase.

59 BASIC BIOLOGICAL SCIENCES↗

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗

Protein remote homology detection and structural alignment using deep learning

Exploiting sequence–structure–function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec and DeepBLAST. TM-Vec allows searching for structure–structure similarities in large sequence databases. It is trained to accurately predict TM-scores as a metric of structural similarity directly from sequence pairs without the need for intermediate computation or solution of structures. Once structurally similar proteins have been identified, DeepBLAST can structurally align proteins using only sequence information by identifying structurally homologous regions between proteins. It outperforms traditional sequence alignment methods and performs similarly to structure-based alignment methods. We show the merits of TM-Vec and DeepBLAST on a variety of datasets, including better identification of remotely homologous proteins compared with state-of-the-art sequence alignment and structure prediction methods.

59 BASIC BIOLOGICAL SCIENCES↗