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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Suppression of IgE-mediated mast cell activation and mouse anaphylaxis via inhibition of Syk activation by 8-formyl-7-hydroxy-4-methylcoumarin, 4μ8C

Mast cells trigger IgE-mediated allergic reactions by releasing various allergic mediators. 8-Formyl-7-hydroxy-4-methylcoumarin, also called 4μ8C, was originally known as an inositol-requiring enzyme 1 (IRE1) suppressant, but no study has examined its relationship with mast cells and allergic diseases. Therefore, the purpose of this study was to determine whether 4μ8C is effective in suppressing allergic reactions in mast cells and in IgE-mediated allergic animal model. 4μ8C suppressed the degranulation of IgE-mediated mast cells (IC{sub 50} = 3.2 μM) and the production of cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-4 (IL-4) in a dose-dependent manner. 4μ8C also suppressed passive cutaneous anaphylaxis (PCA) in mice (ED{sub 50} = 25.1 mg/kg). In an experiment on mast cell signaling pathways stimulated by antigen, the phosphorylation and activation of Syk was decreased by 4μ8C, and phosphorylation of downstream signaling molecules, such as linker for activated T cells (LAT), Akt, and the three MAP kinases, ERK, p38, and JNK, were suppressed. Mechanistic studies showed that 4μ8C inhibited the activity of Lyn and Fyn in vitro. Based on the results of those experiments, the suppressor mechanism of allergic reaction by 4μ8C involved reduced activity of Lyn and Fyn, which is pivotal in an IgE-mediated signaling pathway. In summary, for the first time, this study shows that 4μ8C inhibits Lyn and Fyn, thus suppressing allergic reaction by reducing the degranulation and the production of inflammatory cytokines. This suggests that 4μ8C can be used as a new medicinal candidate to control allergic diseases such as seasonal allergies and atopic dermatitis. - Highlights: • Anti-allergic effect of 8-formyl-7-hydroxy-4-methylcoumarin, 4μ8C, was investigated in vitro and in vivo. • 4μ8C reversibly inhibited the degranulation of mast cells by antigen. • 4μ8C suppressed passive cutaneous anaphylaxis (PCA) in mice. • The anti-allergic effect of 4μ8C was mediated by inhibition of Lyn and Fyn in mast cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Chemometrics and visible diffuse reflectance spectroscopy to classify plutonium dioxide

Diffuse reflectance (DR) spectra in the Vis-NIR (∼380–1050 nm) region were acquired for a series of PuO 2 samples with a spot size of about 10 × 10 μm. Two batches of six PuO 2 samples, synthesized approximately 7.5 months apart, were prepared using both Pu(III) and Pu(IV) oxalate precursors at three distinct calcination temperatures (450, 650, and 950 °C). This yielded a total of 12 PuO 2 samples and 433 DR spectra. The DR spectrum of PuO 2 contained numerous peaks in the visible region, and characteristic features were identified with respect to calcination temperature and chemistry. A distinct peak multiplet near 615 nm was observed for samples prepared at low calcination temperatures, and a peak near 660 nm was observed for higher calcination temperatures. A multivariate classification strategy based on principal component analysis (PCA) was developed to distinguish PuO 2 calcination temperatures of 450, 650, and 950 °C with 100 % accuracy. Classification results also indicate the potential to distinguish chemical processing history (i.e., Pu(III) or Pu(IV)) based on the spectra with 72 % accuracy based on k-nearest neighbors applied to the PCA scores. Partial least squares discriminant analysis was used to identify variation among batches with 88 % accuracy and found that peaks near 669, 681, 811, and 970 nm were the most useful for predicting the batch identity. Here, this work demonstrates how micro-diffuse reflectance spectroscopy and chemometrics can be used to classify PuO 2 processing history based on Vis-NIR spectral features. Combining the chemometric approach with mapping sequences could provide a rapid, nondestructive approach to classify Pu oxide materials for environmental, forensics, and nonproliferation applications.

Actinide↗
DOE OSTI2015

Performance evaluation of a hybrid-passive landfill leachate treatment system using multivariate statistical techniques

Highlights: • Performance of a hybrid passive landfill leachate treatment system was evaluated. • 33 Water chemistry parameters were sampled for 21 months and statistically analyzed. • Parameters were strongly linked and explained most (>40%) of the variation in data. • Alkalinity, ammonia, COD, heavy metals, and iron were criteria for performance. • Eight other parameters were key in modeling system dynamics and criteria. - Abstract: A pilot-scale hybrid-passive treatment system operated at the Merrick Landfill in North Bay, Ontario, Canada, treats municipal landfill leachate and provides for subsequent natural attenuation. Collected leachate is directed to a hybrid-passive treatment system, followed by controlled release to a natural attenuation zone before entering the nearby Little Sturgeon River. The study presents a comprehensive evaluation of the performance of the system using multivariate statistical techniques to determine the interactions between parameters, major pollutants in the leachate, and the biological and chemical processes occurring in the system. Five parameters (ammonia, alkalinity, chemical oxygen demand (COD), “heavy” metals of interest, with atomic weights above calcium, and iron) were set as criteria for the evaluation of system performance based on their toxicity to aquatic ecosystems and importance in treatment with respect to discharge regulations. System data for a full range of water quality parameters over a 21-month period were analyzed using principal components analysis (PCA), as well as principal components (PC) and partial least squares (PLS) regressions. PCA indicated a high degree of association for most parameters with the first PC, which explained a high percentage (>40%) of the variation in the data, suggesting strong statistical relationships among most of the parameters in the system. Regression analyses identified 8 parameters (set as independent variables) that were most frequently retained for modeling the five criteria parameters (set as dependent variables), on a statistically significant level: conductivity, dissolved oxygen (DO), nitrite (NO{sub 2}{sup −}), organic nitrogen (N), oxidation reduction potential (ORP), pH, sulfate and total volatile solids (TVS). The criteria parameters and the significant explanatory parameters were most important in modeling the dynamics of the passive treatment system during the study period. Such techniques and procedures were found to be highly valuable and could be applied to other sites to determine parameters of interest in similar naturalized engineered systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

In vitro-induced M2 type macrophages induces the resistance of prostate cancer cells to cytotoxic action of NK cells

Highlights: • THP-1CM treatment decreased the susceptibility of CRPC cells to NK cell action. • THP-1 CM treatment increased PD-L1 while decreasing NKG2D ligands in CRPC cells. • IL-6 may be the key cytokine to exert the THP-1 CM effect. • JAK/Stat3 is the critical IL-6 downstream signaling that triggers the THP-1 CM effect. • Targeting the JAK/Stat3 signaling may enhance the NK cell action to CRPC tumors. Previous reports, including our experimental results, showed that macrophages migrate to prostate cancer (PCa) cells. We tested whether the migrated macrophages affect the susceptibility of castration-resistant PCa (CRPC) cells to cytotoxic actions of natural killer (NK) cells. We found treatment of tumor cells with the conditioned media (CM) of the PMA/IL-4 treated THP-1 cells (M2 type macrophages) (THP-1 CM) decreased the susceptibility of tumor cells to NK cell cytotoxicity, as a result of increased programmed death receptor ligand 1 (PD-L1) and decreased NK group 2D (NKG2D) ligands in CRPC cells. Meanwhile, the decreased susceptibility of tumor cells was also detected when NK cells were treated with THP-1 CM and used in NK cell cytotoxicity tests. Therefore, we observed higher resistance of CRPC cells when both tumor and NK cells were treated with THP-1 CM than when tumor cells or NK cells were individually treated. We further discovered that the PMA/IL-4 treated THP-1 cells secrete a high level of IL-6, so blocking the IL-6 action significantly decreased the PD-L1 level while recovering the NKG2D ligands, thus increasing the susceptibility of CRPC cells to NK cell action. Moreover, we discovered that JAK-Stat3 is the most critical IL-6 downstream signaling in triggering the THP-1 CM effect. Consequently, we found the susceptibility of CRPC cells to NK cells was increased when either JAK or Stat 3 inhibitor was added when tumor cells were treated with THP-1 CM, and that the best effect was observed when the JAK inhibitor and PD-L1 Ab were added together.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Characterization of aromatic acid/proton symporters in Pseudomonas putida KT2440 toward efficient microbial conversion of lignin-related aromatics

Pseudomonas putida KT2440 (hereafter KT2440) is a well-studied platform bacterium for the production of industrially valuable chemicals from heterogeneous mixtures of aromatic compounds obtained from lignin depolymerization. KT2440 can grow on lignin-related monomers, such as ferulate (FA), 4-coumarate (4CA), vanillate (VA), 4-hydroxybenzoate (4HBA), and protocatechuate (PCA). Genes associated with their catabolism are known, but knowledge about the uptake systems remains limited. In this work, we studied the KT2440 transporters of lignin-related monomers and their substrate selectivity. Based on the inhibition by protonophores, we focused on five genes encoding aromatic acid/H+ symporter family transporters categorized into major facilitator superfamily that uses the proton motive force. Furthermore, the mutants of PP_1376 (pcaK) and PP_3349 (hcnK) exhibited significantly reduced growth on PCA/4HBA and FA/4CA, respectively, while no change was observed on VA for any of the five gene mutants. At pH 9.0, the conversion of these compounds by hcnK mutant (FA/4CA) and vanK mutant (VA) was dramatically reduced, revealing that these transporters are crucial for the uptake of the anionic substrates at high pH. Uptake assays using 14 C-labeled substrates in Escherichia coli and biosensor-based assays confirmed that PcaK, HcnK, and VanK have ability to take up PCA, FA/4CA, and VA/PCA, respectively. Additionally, analyses of the predicted protein structures suggest that the size and hydropathic properties of the substrate-binding sites of these transporters determine their substrate preferences. Overall, this study reveals that at physiological pH, PcaK and HcnK have a major role in the uptake of PCA/4HBA and FA/4CA, respectively, and VanK is a VA/PCA transporter. This information can contribute to the engineering of strains for the efficient conversion of lignin-related monomers to value-added chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Debottlenecking 4-hydroxybenzoate hydroxylation in Pseudomonas putida KT2440 improves muconate productivity from p -coumarate

The transformation of 4-hydroxybenzoate (4-HBA) to protocatechuate (PCA) is catalyzed by flavoprotein oxygenases known as para-hydroxybenzoate-3-hydroxylases (PHBHs). In Pseudomonas putida KT2440 (P. putida) strains engineered to convert lignin-related aromatic compounds to muconic acid (MA), PHBH activity is rate-limiting, as indicated by the accumulation of 4-HBA, which ultimately limits MA productivity. Here, we hypothesized that replacement of PobA, the native P. putida PHBH, with PraI, a PHBH from Paenibacillus sp. JJ-1b with a broader nicotinamide cofactor preference, could alleviate this bottleneck. Biochemical assays confirmed the strict preference of NADPH for PobA, while PraI can utilize either NADH or NADPH. Kinetic assays demonstrated that both PobA and PraI can utilize NADPH with comparable catalytic efficiency and that PraI also efficiently utilizes NADH at roughly half the catalytic efficiency. The X-ray crystal structure of PraI was solved and revealed absolute conservation of the active site architecture to other PHBH structures despite their differing cofactor preferences. To understand the effect in vivo, we compared three P. putida strains engineered to produce MA from p-coumarate (pCA), showing that expression of praI leads to lower 4-HBA accumulation and decreased NADP+/NADPH ratios relative to strains harboring pobA, indicative of a relieved 4-HBA bottleneck due to increased NADPH availability. In bioreactor cultivations, a strain exclusively expressing praI achieved a titer of 40 g/L MA at 100% molar yield and a productivity of 0.5 g/L/h. Altogether, this study demonstrates the benefit of sampling readily available natural enzyme diversity for debottlenecking metabolic flux in an engineered strain for microbial conversion of lignin-derived compounds to value-added products.

09 BIOMASS FUELS↗
DOE OSTI2018

In Situ Monitoring of Groundwater Contamination Using the Kalman Filter

This study presents a Kalman filter-based framework to establish a real-time in situ monitoring system for groundwater contamination based on in situ measurable water quality variables, such as specific conductance (SC) and pH. First, this framework uses principal component analysis (PCA) to identify correlations between the contaminant concentrations of interest and in situ measurable variables. It then applies the Kalman filter to estimate contaminant concentrations continuously and in real-time by coupling data-driven concentration-decay models with the previously identified data correlations. We demonstrate our approach with historical groundwater data from the Savannah River Site F-Area: We use SC and pH data to estimate tritium and uranium concentrations over time. Results show that the developed method can estimate these contaminant concentrations based on in situ measurable variables. The estimates remain reliable with less frequent or no direct measurements of the contaminant concentrations, while capturing the dynamics of short- and long-term contaminant concentration changes. In addition, we show that data mining, such as PCA, is useful to understand correlations in groundwater data and to design long-term monitoring systems. As a result, the developed in situ monitoring methodology is expected to improve long-term groundwater monitoring by continuously confirming the contaminant plume's stability and by providing an early warning system for unexpected changes in the plume's migration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Extraction of Nitric Acid and Uranium with DEHiBA under High Loading Conditions

We report the mechanism by which high concentrations (1.5 M in n-dodecane) of N,N-di-2-ethylhexylisobutyramide (DEHiBA) extracts HNO 3 and UO 2 (NO 3 ) 2 is under examination. Most prior studies have examined the extractant and the mechanism at a concentration of 1.0 M in n-dodecane, however under the higher loading conditions that can be achieved by a higher concentration of extractant this mechanism could change. Increased extraction of both nitric acid and uranium is observed with an increased concentration of DEHiBA. The mechanisms are examined by thermodynamic modeling of distribution ratios, 15 N NMR, and FTIR spectroscopy coupled with Principal Component Analysis (PCA). Speciation diagrams produced through thermodynamic modeling have been qualitatively reproduced through PCA of the FTIR spectra. The predominant extracted species of HNO 3 (DEHiBA), HNO 3 (DEHiBA) 2 , and UO 2 (NO 3 ) 2 (DEHiBA) 2 are in good agreement with prior literature reports for 1.0 M DEHiBA systems. Evidence for an additional species of either UO 2 (NO 3 ) 2 (DEHiBA) or UO 2 (NO 3 ) 2 (DEHiBA) 2 (HNO 3 ) also contributing to the extraction of uranium species is given.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Lignin Valorization: Two Hybrid Biochemical Routes for the Conversion of Polymeric Lignin into Value-added Chemicals

Naturally, many aerobic organisms degrade lignin-derived aromatics through conserved intermediates including protocatechuate and catechol. Employing this microbial approach offers a potential solution for valorizing lignin into valuable chemicals for a potential lignocellulosic biorefinery and enabling bioeconomy. In this study, two hybrid biochemical routes combining lignin chemical depolymerization, plant metabolic engineering, and synthetic pathway reconstruction were demonstrated for valorizing lignin into value-added products. In the biochemical route 1, alkali lignin was chemically depolymerized into vanillin and syringate as major products, which were further bio-converted into cis, cis-muconic acid (ccMA) and pyrogallol, respectively, using engineered Escherichia coli strains. In the second biochemical route, the shikimate pathway of Tobacco plant was engineered to accumulate protocatechuate (PCA) as a soluble intermediate compound. The PCA extracted from the engineered Tobacco was further converted into ccMA using the engineered E. coli strain. This study reports a direct process for converting lignin into ccMA and pyrogallol as value-added chemicals, and more importantly demonstrates benign methods for valorization of polymeric lignin that is inherently heterogeneous and recalcitrant. Our approach also validates the promising combination of plant engineering with microbial chassis development for the production of value added and speciality chemicals.

Science & Technology - Other Topics↗
DOE OSTI2020

Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device

The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA), partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. Additionally, a boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Erratum: Planck 2018 results: VI. Cosmological parameters

In the original version, the bounds given in Eqs. (87a) and (87b) on the contribution to the early-time optical depth, (15,30), contained a numerical error in deriving the 95th percentile from the Monte Carlo samples. The corrected 95% upper bounds are: τ(15,30) < 0:018 (lowE, flat τ(15, 30), FlexKnot), (1) τ(15, 30) < 0:023 (lowE, flat knot, FlexKnot): (2) These bounds are a factor of 3 larger than the originally reported results. Consequently, the new bounds do not significantly improve upon previous results from Planck data presented in Millea & Bouchet (2018) as was stated, but are instead comparable. Equations (1) and (2) give results that are now similar to those of Heinrich & Hu (2021), who used the same Planck 2018 data to derive a 95% upper bound of 0.020 using the principal component analysis (PCA) model and uniform priors on the PCA mode amplitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

X-ray spectral and flux variability of the microquasar GRS 1758-258 on timescales from weeks to years

We present the spectral and timing evolution of the persistent black hole X-ray binary GRS 1758-258 based on almost 12 years of observations using the Rossi X-ray Timing Explorer Proportional Counter Array. While the source was predominantly found in the hard state during this time, it entered the thermally dominated soft state seven times. In the soft state GRS 1758-258 shows a strong decline in flux above 3 keV rather than the pivoting flux around 10 keV more commonly shown by black hole transients. In its 3–20 keV hardness intensity diagram, GRS 1758-258 shows a hysteresis of hard and soft state fluxes typical for transient sources in outburst. The RXTE-PCA and RXTE-ASM long-term light curves do not show any orbital modulations in the range of 2–30 d. However, in the dynamic power spectra significant peaks drift between 18.47 and 18.04 d for the PCA data, while less significant signatures between 19 d and 20 d are seen for the ASM data as well as for the Swift/BAT data. We discuss different models for the hysteresis behavior during state transitions as well as possibilities for the origin of the long term variation in the context of a warped accretion disk.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The Carnegie Supernova Project I: Spectroscopic analysis of stripped-envelope supernovae

An analysis leveraging 170 optical spectra of 35 stripped-envelope (SE) core-collapse supernovae (SNe) observed by the Carnegie Supernova Project I and published in a companion paper is presented. Mean template spectra were constructed for the SNe IIb, Ib, and Ic subtypes, and parent ions associated with designated spectral features are identified with the aid of the spectral synthesis code SYNAPPS. Our modeled mean spectra suggest the ~6150 Å feature in SNe IIb may have an underlying contribution due to silicon, while the same feature in some SNe Ib may have an underlying contribution due to hydrogen. Standard spectral line diagnostics consisting of pseudo-equivalent widths (pEWs) and blue-shifted Doppler velocity were measured for each of the spectral features. Correlation matrices and rolling mean values of both spectral diagnostics were constructed. A principle component analysis (PCA) was applied to various wavelength ranges of the entire dataset and suggests clear separation among the different SE SN subtypes, which follows from trends previously identified in the literature. In addition, our findings reveal the presence of two SNe IIb subtypes, a select number of SNe Ib displaying signatures of weak, high-velocity hydrogen, and a single SN Ic with evidence of weak helium features. Our PCA results can be leveraged to obtain robust subtyping of SE SNe based on a single spectrum taken during the so-called photospheric phase, separating SNe IIb from SNe Ib with ~80% completion.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Classification of Malaysia aromatic rice using multivariate statistical analysis

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Comparing gas composition from fast pyrolysis of live foliage measured in bench-scale and fire-scale experiments

Background: Fire models have used pyrolysis data from oxidising and non-oxidising environments for flaming combustion. In wildland fires pyrolysis, flaming and smouldering combustion typically occur in an oxidising environment (the atmosphere). Aims: Using compositional data analysis methods, determine if the composition of pyrolysis gases measured in non-oxidising and ambient (oxidising) atmospheric conditions were similar. Methods: Permanent gases and tars were measured in a fuel-rich (non-oxidising) environment in a flat flame burner (FFB). Permanent and light hydrocarbon gases were measured for the same fuels heated by a fire flame in ambient atmospheric conditions (oxidising environment). Log-ratio balances of the measured gases common to both environments (CO, CO 2 , CH 4 , H 2 , C 6 H 6 O (phenol), and other gases) were examined by principal components analysis (PCA), canonical discriminant analysis (CDA) and permutational multivariate analysis of variance (PERMANOVA). Key results: Mean composition changed between the non-oxidising and ambient atmosphere samples. PCA showed that flat flame burner (FFB) samples were tightly clustered and distinct from the ambient atmosphere samples. CDA found that the difference between environments was defined by the CO-CO 2 log-ratio balance. PERMANOVA and pairwise comparisons found FFB samples differed from the ambient atmosphere samples which did not differ from each other. Conclusion: Relative composition of these pyrolysis gases differed between the oxidising and non-oxidising environments. This comparison was one of the first comparisons made between bench-scale and field scale pyrolysis measurements using compositional data analysis. Implications: These results indicate the need for more fundamental research on the early time-dependent pyrolysis of vegetation in the presence of oxygen.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Extensive sampling of basidiomycete genomes demonstrates inadequacy of the white-rot/brown-rot paradigm for wood decay fungi

Basidiomycota (basidiomycetes) make up 32% of the described fungi and include most wood-decaying species, as well as pathogens and mutualistic symbionts. Wood-decaying basidiomycetes have typically been classified as either white rot or brown rot, based on the ability (in white rot only) to degrade lignin along with cellulose and hemicellulose. Prior genomic comparisons suggested that the two decay modes can be distinguished based on the presence or absence of ligninolytic class II peroxidases (PODs), as well as the abundance of enzymes acting directly on crystalline cellulose (reduced in brown rot). To assess the generality of the white-rot/brown-rot classification paradigm, we compared the genomes of 33 basidiomycetes, including four newly sequenced wood decayers, and performed phylogenetically informed principal-components analysis (PCA) of a broad range of gene families encoding plant biomass-degrading enzymes. The newly sequenced Botryobasidium botryosum and Jaapia argillacea genomes lack PODs but possess diverse enzymes acting on crystalline cellulose, and they group close to the model white-rot species Phanerochaete chrysosporium in the PCA. Furthermore, laboratory assays showed that both B. botryosum and J. argillacea can degrade all polymeric components of woody plant cell walls, a characteristic of white rot. We also found expansions in reducing polyketide synthase genes specific to the brown-rot fungi. Our results suggest a continuum rather than a dichotomy between the white-rot and brown-rot modes of wood decay. A more nuanced categorization of rot types is needed, based on an improved understanding of the genomics and biochemistry of wood decay.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing

Big Area Additive Manufacturing (BAAM) of composites requires significant time, energy, and material, so it is critical to reduce production inefficiencies to make functional parts without multiple iterations. Statistical process control coupled with Principal Component Analysis (PCA) is a powerful technique that provides a quick, computationally inexpensive, and intuitive way for operators to detect defects that form in a manufacturing process without massive datasets. Recently, a combined index that is a weighted sum of the Hotelling's T 2 and squared residual error statistics has been proposed that can be monitored in one chart, improving interpretation accuracy and simplicity. However, the literature does not offer a formal method to optimise the weights. Here, we introduce two new approaches to the traditional weight selection approach using simulated and BAAM image data. Approach 1 uses a theoretically motivated optimum inspired by probabilistic principal component analysis. Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. Surprisingly, we also show that choosing a more complex PCA model has a minimal negative impact on anomaly detection performance compared to a more simplistic model.

3-dimensional printing↗
DOE OSTI2012

A DATA-DRIVEN MODEL FOR SPECTRA: FINDING DOUBLE REDSHIFTS IN THE SLOAN DIGITAL SKY SURVEY

We present a data-driven method-heteroscedastic matrix factorization, a kind of probabilistic factor analysis-for modeling or performing dimensionality reduction on observed spectra or other high-dimensional data with known but non-uniform observational uncertainties. The method uses an iterative inverse-variance-weighted least-squares minimization procedure to generate a best set of basis functions. The method is similar to principal components analysis (PCA), but with the substantial advantage that it uses measurement uncertainties in a responsible way and accounts naturally for poorly measured and missing data; it models the variance in the noise-deconvolved data space. A regularization can be applied, in the form of a smoothness prior (inspired by Gaussian processes) or a non-negative constraint, without making the method prohibitively slow. Because the method optimizes a justified scalar (related to the likelihood), the basis provides a better fit to the data in a probabilistic sense than any PCA basis. We test the method on Sloan Digital Sky Survey (SDSS) spectra, concentrating on spectra known to contain two redshift components: these are spectra of gravitational lens candidates and massive black hole binaries. We apply a hypothesis test to compare one-redshift and two-redshift models for these spectra, utilizing the data-driven model trained on a random subset of all SDSS spectra. This test confirms 129 of the 131 lens candidates in our sample and all of the known binary candidates, and turns up very few false positives.

79 ASTRONOMY AND ASTROPHYSICS↗