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At least 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

TropiRoot 1.0: Database of tropical root characteristics across environments

Tropical ecosystems contain the world's largest biodiversity of vascular plants. Yet, our understanding of tropical functional diversity and its contribution to global diversity patterns is constrained by data availability. This discrepancy underscores an urgent need to bridge data gaps by incorporating comprehensive tropical root data into global datasets. Here, we provide a database of tropical root characteristics. This new database, TropiRoot 1.0, will be instrumental in evaluating an array of hypotheses pertaining to root functional ecology and plant biogeography, both within the tropics and relative to other global biomes. The data compilation was conducted by the TropiRoot Initiative, in partnership with the Fine-Root Ecology Database (FRED) and the Global Root Trait (GRooT) database, Colorado State University (CSU) and the Smithsonian Tropical Research Institute (STRI). Literature search and data extraction were conducted between 2020 and 2024. Literature was identified using Web of Science, Scopus, and complemented using the expert knowledge of members of TropiRoot. To provide broad environmental and geographical distributions, literature searches included root characteristics (traits) across global change drivers, natural gradients, and from different continents. We adopted FRED standardized data columns and streamlined the format to enhance accessibility for data extraction across various user groups. This optimized framework resulted in a smaller, yet comprehensive datasheet. To make the database compatible with other global root trait initiatives, column identification was standardized following the codes provided by FRED. These efforts culminated in data extracted from 104 new sources, resulting in more than 8000 rows of data (either species or community data). Most of the data in TropiRoot 1.0 include root characteristics such as root biomass, morphology, root dynamics, mass fraction, architecture, anatomy, physiology, and root chemistry. This initiative represents a 30% increase in the currently available data for tropical roots in FRED. TropiRoot 1.0 contains root characteristics from 25 different countries, where seven are located in Asia, six in South America, five in Central America and the Caribbean, four in Africa, two in North America, and 1 in Oceania. Due to the volume of data, when ancillary data were available, including soil data, these data were either extracted and included in the database or its availability was recorded in an additional column. Multiple contributors checked the entries for outliers during the collation process to ensure data quality. For text-based observations, we examined all cells to ensure that their content relates to their specific categories. For numerical observations, we ordered each numerical value from least to greatest and plotted the values, checking apparent outliers against the data in their respective sources and correcting or removing incorrect or impossible values. Some data (soil and aboveground) have different columns for the same variable presented in different units, including originally published units, but root characteristics data had units converted to match those reported in FRED. By filling a gap from global databases, TropiRoot 1.0 expands our knowledge of otherwise so far underrepresented regions and our ability to assess global trends. This advancement can be used to improve tropical forest representation in vegetation models. The data are freely available and should be cited when used.

FRED↗
DOE OSTI2022

Trace metal contents of autotrophic flagellates from contrasting open‐ocean ecosystems

Abstract Nano‐ and picophytoplankton are a major component of open‐ocean ecosystems and one of the main plankton functional types in biogeochemical models, yet little is known about their trace metal contents. In cultures of the picoeukaryote Ostreococcus lucimarinus , iron limitation reduced iron quotas by 68%, a fraction of the plasticity known in diatoms. In contrast, a commonly co‐occurring cyanobacterium, Prochlorococcus , showed variable iron contents with iron availability in culture. Synchrotron X‐ray fluorescence was used to measure single‐cell metal (Mn, Fe, Co, Ni, Zn) quotas of autotrophic flagellates (1.4–16.8‐ μ m diameter) collected from four ocean regions. Iron quotas were tightly constrained and showed little response to iron availability, similar to cultured Ostreococcus . Zinc quotas also did not vary with zinc availability but appeared to vary with phosphorus availability. These results suggest that macronutrient and metal availability may be equally important for controlling metal contents of small eukaryotic open‐ocean phytoplankton.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Overview of systemic treatment in recurrent and advanced cervical cancer: a primer for radiologists

Imaging has a central role in surveillance of cervical cancer, guiding decision on when to initiate treatment for recurrent disease and to guide management in advanced cervical cancer. Due to the increased availability of pelvic radiation therapy, the rate of atypical presentation of recurrent disease has increased. Simultaneously, the array of systemic therapies now available for advanced cervical cancer has considerably expanded in the last few years, with therapies now available in mid and low-income countries. While pelvic recurrences are amenable of loco-regional treatment, recurrent disease may present with metastases to the thoracoabdominal organs, lymph nodes, bones, skin and brain, for which systemic treatment represent the standard of care. Besides combined chemotherapy regimens, alternative chemotherapies, biosimilars and immune checkpoint inhibitors are now available, each associated with a definite pattern of response and toxicity. In this review, after describing the typical and atypical presentations of recurrent and advanced cervical carcinoma on cross-sectional imaging, we will discuss systemic treatment for recurrent or advanced disease and their associated radiographic sequelae, in light of the newly available therapies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison

We present an overview of results from 11 integrated assessment models (IAMs) that participated in the 33rd study of the Stanford Energy Modeling Forum (EMF-33) on the viability of large-scale deployment of bioenergy for achieving long-run climate goals. The study explores future bioenergy use across models under harmonized scenarios for future climate policies, availability of bioenergy technologies, and constraints on biomass supply. This paper provides a more transparent description of IAMs that span a broad range of assumptions regarding model structures, energy sectors, and bioenergy conversion chains. Without emission constraints, we find vastly different CO 2 emission and bioenergy deployment patterns across models due to differences in competition with fossil fuels, the possibility to produce large-scale bio-liquids, and the flexibility of energy systems. Imposing increasingly stringent carbon budgets mostly increases bioenergy use. A diverse set of available bioenergy technology portfolios provides flexibility to allocate bioenergy to supply different final energy as well as remove carbon dioxide from the atmosphere by combining bioenergy with carbon capture and sequestration (BECCS). Sector and regional bioenergy allocation varies dramatically across models mainly due to bioenergy technology availability and costs, final energy patterns, and availability of alternative decarbonization options. Although much bioenergy is used in combination with CCS, BECCS is not necessarily the driver of bioenergy use. We find that the flexibility to use biomass feedstocks in different energy sub-sectors makes large-scale bioenergy deployment a robust strategy in mitigation scenarios that is surprisingly insensitive with respect to reduced technology availability. However, the achievability of stringent carbon budgets and associated carbon prices is sensitive. Constraints on biomass feedstock supply increase the carbon price less significantly than excluding BECCS because carbon removals are still realized and valued. Incremental sensitivity tests find that delayed readiness of bioenergy technologies until 2050 is more important than potentially higher investment costs.

09 BIOMASS FUELS↗
DOE OSTI2013

The Structural Basis of Cholesterol Activity in Membranes

Although the majority of free cellular cholesterol is present in the plasma membrane, cholesterol homeostasis is principally regulated through sterol-sensing proteins that reside in the cholesterol-poor endoplasmic reticulum (ER). In response to acute cholesterol loading or depletion, there is rapid equilibration between the ER and plasma membrane cholesterol pools, suggesting a biophysical model in which the availability of plasma membrane cholesterol for trafficking to internal membranes modulates ER membrane behavior. Previous studies have predominantly examined cholesterol availability in terms of binding to extramembrane acceptors, but have provided limited insight into the structural changes underlying cholesterol activation. In this study, we use both molecular dynamics simulations and experimental membrane systems to examine the behavior of cholesterol in membrane bilayers. We find that cholesterol depth within the bilayer provides a reasonable structural metric for cholesterol availability and that this is correlated with cholesterol-acceptor binding. Further, the distribution of cholesterol availability in our simulations is continuous rather than divided into distinct available and unavailable pools. This data provide support for a revised cholesterol activation model in which activation is driven not by saturation of membrane-cholesterol interactions but rather by bulk membrane remodeling that reduces membrane-cholesterol affinity.

cholesterol↗
DOE OSTI2018

Soil microbial CNP and respiration responses to organic matter and nutrient additions: Evidence from a tropical soil incubation

Soil nutrient availability has a strong influence on the fate of soil carbon (C) during microbial decomposition, contributing to Earth's C balance. While nutrient availability itself can impact microbial physiology and C partitioning between biomass and respiration during soil organic matter decomposition, the availability of labile C inputs may mediate the response of microorganisms to nutrient additions. As soil organic matter is decomposed, microorganisms retain or release C, nitrogen (N) or phosphorus (P) to maintain a stoichiometric balance. While the concept of a microbial stoichiometric homeostasis has previously been proposed, microbial biomass CNP ratios are not static, and this may have very relevant implications for microbial physiological activities. In this work, we tested the hypothesis that N, P and potassium (K) nutrient additions impact C cycling in a tropical soil due to microbial stoichiometric constraints to growth and respiration, and that the availability of energy-rich labile organic matter in the soil (i.e. leaf litter) mediates the response to nutrient addition. We incubated tropical soil from French Guiana with a 13 C labeled leaf litter addition and with mineral nutrient additions of +K, +N, +NK, +PK and +NPK for 30 days. We found that litter additions led to a ten-fold increase in microbial respiration and a doubling of microbial biomass C, along with greater microbial N and P content. We found some evidence that P additions increased soil CO 2 fluxes. Additionally, we found microbial biomass CP and NP ratios varied more widely than CN in response to nutrient and organic matter additions, with important implications for the role of microorganisms in C cycling. The addition of litter did not prime soil organic matter decomposition, except in combination with +NK fertilization, indicating possible P-mining of soil organic matter in this P-poor tropical soil. Together, these results point toward an ultimate labile organic substrate limitation of soil microorganisms in this tropical soil, but also indicate a complex interaction between C, N, P and K availability. This draws attention to the difference between microbial C cycling responses to N, P, or K additions in the tropics and explains why coupled C, N and P cycle modeling efforts cannot rely on strict microbial stoichiometric homeostasis as an underlying assumption.

13C↗
DOE OSTI2017

Slow pyrolysis enhances the recovery and reuse of phosphorus and reduces metal leaching from biosolids

Highlights: • Waste water biosolids offer a source of P for agricultural applications. • Biosolids with and without alum were converted into biochar. • Conversion of biosolids containing alum into biochar liberated bound P for re-use. • Conversion of biosolids into biochar eliminated metal leaching from biosolids. • Pyrolysis enhances P recycling and reduces metal leaching from municipal waste. - Abstract: In this study, biochar is produced from biosolids with and without alum at a range of temperatures and simulated oxidative aging of the biochars is conducted to quantify the long-term leaching of P and metals. While biosolids containing alum had negligible amounts of plant-available P, after pyrolysis >90% of the P became immediately available for plant growth. When biosolids with no alum were converted into biochar there was a small increase in the availability of P but a larger pool was available after oxidation. Both of the biosolids leached significant amounts of metals after oxidation. In contrast, the biochars had a very low available metal content and this did not increase with oxidation, demonstrating a stable metal content. Pyrolysis is an effective waste management strategy for biosolids that can simultaneously reduce the leaching of metals and increase the efficiency of recycling of P for beneficial re-use.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

The first data release of the KODIAQ survey

We present and make publicly available the first data release (DR1) of the Keck Observatory Database of Ionized Absorption toward Quasars (KODIAQ) survey. The KODIAQ survey is aimed at studying galactic and circumgalactic gas in absorption at high redshift, with a focus on highly ionized gas traced by O vi, using the HIRES spectrograph on the Keck I telescope. KODIAQ DR1 consists of a fully reduced sample of 170 quasars at 0.29<z{sub em}<5.29 observed with HIRES at high resolution (36,000 ≤ R ≤ 103,000) between 2004 and 2012. DR1 contains 247 spectra available in continuum normalized form, representing a sum total exposure time of ∼1.6 megaseconds. These coadded spectra arise from a total of 567 individual exposures of quasars taken from the Keck Observatory Archive (KOA) in raw form and uniformly processed using a publicly available HIRES data reduction package. DR1 is publicly available to the community, housed as a higher level science product at the KOA. We will provide future data releases that make available additional quasars, including those with pre-2004 observations taken with the previous-generation HIRES detectors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Running an open experiment: transparency and reproducibility in soil and ecosystem science

Researchers in soil and ecosystem science, and almost every other field, are being pushed--by funders, journals, governments, and their peers--to increase transparency and reproducibility of their work. A key part of this effort is a move towards open data as a way to fight post-publication data loss, improve data and code quality, enable powerful meta- and cross-disciplinary analyses, and increase trust in, and the efficiency of, publicly-funded research. Many scientists however lack experience in, and may be unsure of the benefits of, making their data and fully-reproducible analyses publicly available. Here we describe a recent "open experiment", in which we documented every aspect of a soil incubation online, making all raw data, scripts, diagnostics, final analyses, and manuscripts available in real time. We found that using tools such as version control, issue tracking, and open-source statistical software improved data integrity, accelerated our team's communication and productivity, and ensured transparency. There are many avenues to improve scientific reproducibility and data availability, of which is this only one example, and it is not an approach suited for every experiment or situation. Nonetheless, we encourage the communities in our respective fields to consider its advantages, and to lead rather than follow with respect to scientific reproducibility, transparency, and data availability.

global change↗
DOE OSTI2021

Optimal Bayesian supervised domain adaptation for RNA sequencing data

Abstract Motivation When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. Results In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. Availability and implementation The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. Supplementary information Supplementary data are available at Bioinformatics online.

Biochemistry & Molecular Biology↗
DOE OSTI2021

CONSTAX2: improved taxonomic classification of environmental DNA markers

Abstract Summary CONSTAX—the CONSensus TAXonomy classifier—was developed for accurate and reproducible taxonomic annotation of fungal rDNA amplicon sequences and is based upon a consensus approach of RDP, SINTAX and UTAX algorithms. CONSTAX2 extends these features to classify prokaryotes as well as eukaryotes and incorporates BLAST-based classifiers to reduce classification errors. Additionally, CONSTAX2 implements a conda-installable command-line tool with improved classification metrics, faster training, multithreading support, capacity to incorporate external taxonomic databases and new isolate matching and high-level taxonomy tools, replete with documentation and example tutorials. Availability and implementation CONSTAX2 is available at https://github.com/liberjul/CONSTAXv2, and is packaged for Linux and MacOS from Bioconda with use under the MIT License. A tutorial and documentation are available at https://constax.readthedocs.io/en/latest/. Data and scripts associated with the manuscript are available at https://github.com/liberjul/CONSTAXv2_ms_code. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

SBbadger: biochemical reaction networks with definable degree distributions

Abstract Motivation An essential step in developing computational tools for the inference, optimization and simulation of biochemical reaction networks is gauging tool performance against earlier efforts using an appropriate set of benchmarks. General strategies for the assembly of benchmark models include collection from the literature, creation via subnetwork extraction and de novo generation. However, with respect to biochemical reaction networks, these approaches and their associated tools are either poorly suited to generate models that reflect the wide range of properties found in natural biochemical networks or to do so in numbers that enable rigorous statistical analysis. Results In this work, we present SBbadger, a python-based software tool for the generation of synthetic biochemical reaction or metabolic networks with user-defined degree distributions, multiple available kinetic formalisms and a host of other definable properties. SBbadger thus enables the creation of benchmark model sets that reflect properties of biological systems and generate the kinetics and model structures typically targeted by computational analysis and inference software. Here, we detail the computational and algorithmic workflow of SBbadger, demonstrate its performance under various settings, provide sample outputs and compare it to currently available biochemical reaction network generation software. Availability and implementation SBbadger is implemented in Python and is freely available at https://github.com/sys-bio/SBbadger and via PyPI at https://pypi.org/project/SBbadger/. Documentation can be found at https://SBbadger.readthedocs.io. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

PIXiE: an algorithm for automated ion mobility arrival time extraction and collision cross section calculation using global data association

Abstract Motivation Drift tube ion mobility spectrometry coupled with mass spectrometry (DTIMS-MS) is increasingly implemented in high throughput omics workflows, and new informatics approaches are necessary for processing the associated data. To automatically extract arrival times for molecules measured by DTIMS at multiple electric fields and compute their associated collisional cross sections (CCS), we created the PNNL Ion Mobility Cross Section Extractor (PIXiE). The primary application presented for this algorithm is the extraction of data that can then be used to create a reference library of experimental CCS values for use in high throughput omics analyses. Results We demonstrate the utility of this approach by automatically extracting arrival times and calculating the associated CCSs for a set of endogenous metabolites and xenobiotics. The PIXiE-generated CCS values were within error of those calculated using commercially available instrument vendor software. Availability and implementation PIXiE is an open-source tool, freely available on Github. The documentation, source code of the software, and a GUI can be found at https://github.com/PNNL-Comp-Mass-Spec/PIXiE and the source code of the backend workflow library used by PIXiE can be found at https://github.com/PNNL-Comp-Mass-Spec/IMS-Informed-Library. Supplementary information Supplementary data are available at Bioinformatics online.

Ma, Jian↗
DOE OSTI2014

Ligand placement based on prior structures: the guided ligand-replacement method

A new module, Guided Ligand Replacement (GLR), has been developed in Phenix to increase the ease and success rate of ligand placement when prior protein-ligand complexes are available. The process of iterative structure-based drug design involves the X-ray crystal structure determination of upwards of 100 ligands with the same general scaffold (i.e. chemotype) complexed with very similar, if not identical, protein targets. In conjunction with insights from computational models and assays, this collection of crystal structures is analyzed to improve potency, to achieve better selectivity and to reduce liabilities such as absorption, distribution, metabolism, excretion and toxicology. Current methods for modeling ligands into electron-density maps typically do not utilize information on how similar ligands bound in related structures. Even if the electron density is of sufficient quality and resolution to allow de novo placement, the process can take considerable time as the size, complexity and torsional degrees of freedom of the ligands increase. A new module, Guided Ligand Replacement (GLR), was developed in Phenix to increase the ease and success rate of ligand placement when prior protein–ligand complexes are available. At the heart of GLR is an algorithm based on graph theory that associates atoms in the target ligand with analogous atoms in the reference ligand. Based on this correspondence, a set of coordinates is generated for the target ligand. GLR is especially useful in two situations: (i) modeling a series of large, flexible, complicated or macrocyclic ligands in successive structures and (ii) modeling ligands as part of a refinement pipeline that can automatically select a reference structure. Even in those cases for which no reference structure is available, if there are multiple copies of the bound ligand per asymmetric unit GLR offers an efficient way to complete the model after the first ligand has been placed. In all of these applications, GLR leverages prior knowledge from earlier structures to facilitate ligand placement in the current structure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2013

The manufacture and use of sludge test materials for R and D purposes in the treatment and processing of magnox based sludge

Among the Intermediate Level Waste materials in store and awaiting treatment and processing in the UK are quantities of magnesium hydroxide sludge. This sludge is a product of radioactive Magnox Swarf which arose from the de-canning of used magnox fuel element rods. As the Swarf was stored underwater, a corrosion reaction occurred over the course of time between the magnox and the water resulting in a magnesium hydroxide based sludge. The differing conditions and materials present in the various storage areas means that the sludge can range in consistency from that of a slurry through to a thick clay. Sludge test materials are required to underpin and validate the research and development equipment and processes that are to be used to treat the waste material. Necessary restrictions imposed on the sampling and testing of the radioactive waste means that the available data on the properties and behaviour of the sludge is limited. The raw materials used to create the sludge test materials are based upon magnesium hydroxide so that as far as possible the chemical behaviour will be similar to that of the waste material. The most representative sludge test material is manufactured by the corrosion of non-radioactive magnox or magnesium. However, time constraints make it impractical to supply this material in sufficient quantities for full scale validation trials. An alternative is to use sludge manufactured from commercially available magnesium hydroxide. The particle shape of commercially available materials differs from corrosion product magnesium hydroxide which means that properties such as the rheological behaviour cannot be replicated. Nevertheless, valuable trial data can be obtained, giving a greater degree of confidence in the waste treatment process than would be possible if only the more representative but less available corrosion product materials were to be used. Key test material parameters used in the trials have been identified as the particle size distribution and the sludge thickness (measured as yield shear strength). Other properties including cohesion, adhesion and rheological behaviour are also considered. The use of different mixers for sludge manufacture has the potential to affect the behavioural properties and a brief description of each of these mixers is included. The scale of mixing has been found to make a significant difference to the ageing. A chemical impurity in the commercially available materials has been successfully exploited, so that sludge mixed at comparatively low yield shear strengths can thicken into the consistency of clay. This aids manufacture and allows large quantities of thick material to be produced relatively easily. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

flowVS: channel-specific variance stabilization in flow cytometry

Abstract Background Comparing phenotypes of heterogeneous cell populations from multiple biological conditions is at the heart of scientific discovery based on flow cytometry (FC). When the biological signal is measured by the average expression of a biomarker, standard statistical methods require that variance be approximately stabilized in populations to be compared. Since the mean and variance of a cell population are often correlated in fluorescence-based FC measurements, a preprocessing step is needed to stabilize the within-population variances. Results We present a variance-stabilization algorithm, called flowVS, that removes the mean-variance correlations from cell populations identified in each fluorescence channel. flowVS transforms each channel from all samples of a data set by the inverse hyperbolic sine (asinh) transformation. For each channel, the parameters of the transformation are optimally selected by Bartlett’s likelihood-ratio test so that the populations attain homogeneous variances. The optimum parameters are then used to transform the corresponding channels in every sample. flowVS is therefore an explicit variance-stabilization method that stabilizes within-population variances in each channel by evaluating the homoskedasticity of clusters with a likelihood-ratio test. With two publicly available datasets, we show that flowVS removes the mean-variance dependence from raw FC data and makes the within-population variance relatively homogeneous. We demonstrate that alternative transformation techniques such as flowTrans, flowScape, logicle, and FCSTrans might not stabilize variance. Besides flow cytometry, flowVS can also be applied to stabilize variance in microarray data. With a publicly available data set we demonstrate that flowVS performs as well as the VSN software, a state-of-the-art approach developed for microarrays. Conclusions The homogeneity of variance in cell populations across FC samples is desirable when extracting features uniformly and comparing cell populations with different levels of marker expressions. The newly developed flowVS algorithm solves the variance-stabilization problem in FC and microarrays by optimally transforming data with the help of Bartlett’s likelihood-ratio test. On two publicly available FC datasets, flowVS stabilizes within-population variances more evenly than the available transformation and normalization techniques. flowVS-based variance stabilization can help in performing comparison and alignment of phenotypically identical cell populations across different samples. flowVS and the datasets used in this paper are publicly available in Bioconductor.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Gap Assessment (FY 13 Update)

To help guide its future data collection efforts, The DOE GTO funded a data gap analysis in FY2012 to identify high potential hydrothermal areas where critical data are needed. This analysis was updated in FY2013 and the resulting datasets are represented by this metadata. The original process was published in FY 2012 and is available here: https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2013/Esposito.pdf Though there are many types of data that can be used for hydrothermal exploration, five types of exploration data were targeted for this analysis. These data types were selected for their regional reconnaissance potential, and include many of the primary exploration techniques currently used by the geothermal industry. The data types include: 1. well data 2. geologic maps 3. fault maps 4. geochemistry data 5. geophysical data To determine data coverage, metadata for exploration data (including data type, data status, and coverage information) were collected and catalogued from nodes on the National Geothermal Data System (NGDS). It is the intention of this analysis that the data be updated from this source in a semi-automated fashion as new datasets are added to the NGDS nodes. In addition to this upload, an online tool was developed to allow all geothermal data providers to access this assessment and to directly add metadata themselves and view the results of the analysis via maps of data coverage in Geothermal Prospector (http://maps.nrel.gov/gt_prospector). A grid of the contiguous U.S. was created with 88,000 10-km by 10-km grid cells, and each cell was populated with the status of data availability corresponding to the five data types. Using these five data coverage maps and the USGS Resource Potential Map, sites were identified for future data collection efforts. These sites signify both that the USGS has indicated high favorability of occurrence of geothermal resources and that data gaps exist. The uploaded data are contained in two data files for each data category. The first file contains the grid and is in the SHP file format (shape file.) Each populated grid cell represents a 10k area within which data is known to exist. The second file is a CSV (comma separated value) file that contains all of the individual layers that intersected with the grid. This CSV can be joined with the map to retrieve a list of datasets that are available at any given site. The attributes in the CSV include: 1. grid_id : The id of the grid cell that the data intersects with 2. title: This represents the name of the WFS service that intersected with this grid cell 3. abstract: This represents the description of the WFS service that intersected with this grid cell 4. gap_type: This represents the category of data availability that these data fall within. As the current processing is pulling data from NGDS, this category universally represents data that are available in the NGDS and are ready for acquisition for analytic purposes. 5. proprietary_type: Whether the data are considered proprietary 6. service_type: The type of service 7. base_url: The service URL

15 GEOTHERMAL ENERGY↗