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DOE OSTI2020

Modeling Stochastic Variability in Multiband Time-series Data

In preparation for the era of time-domain astronomy with upcoming large-scale surveys, we propose a state-space representation of a multivariate damped random walk process as a tool to analyze irregularly-spaced multifilter light curves with heteroscedastic measurement errors. We adopt a computationally efficient and scalable Kalman filtering approach to evaluate the likelihood function, leading to maximum O(k 3 n) complexity, where k is the number of available bands and n is the number of unique observation times across the k bands. This is a significant computational advantage over a commonly used univariate Gaussian process that can stack up all multiband light curves in one vector with maximum O(k 3 n 3 ) complexity. Using such efficient likelihood computation, we provide both maximum likelihood estimates and Bayesian posterior samples of the model parameters. Three numerical illustrations are presented: (i) analyzing simulated five-band light curves for a comparison with independent single-band fits; (ii) analyzing five-band light curves of a quasar obtained from the Sloan Digital Sky Survey Stripe 82 to estimate short-term variability and timescale; (iii) analyzing gravitationally lensed g- and r-band light curves of Q0957+561 to infer the time delay. Two R packages, Rdrw and timedelay, are publicly available to fit the proposed models.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Differentiating spatial from environmental effects on foliar fungal communities of Populus trichocarpa

Abstract Aim Foliar fungi – pathogens, endophytes, epiphytes – form taxonomically diverse communities that affect plant health and productivity. The composition of foliar fungal communities is variable at spatial scales both small (e.g. individual plants) and large (e.g. continents), yet few studies have attempted to tease apart spatial from climatic factors influencing these communities. Moreover, few studies have sampled in more than 1 year to gauge interannual variation in community structure. Location The Pacific Northwest of western North America. Taxon Foliar fungi associated with the deciduous tree Populus trichocarpa . Methods In two consecutive years, we used DNA metabarcoding to characterize foliar fungal communities of Populus trichocarpa across its geographic range, which encompasses a sharp climatic transition as it crosses the Cascade Mountain Range. We used multivariate analyses to (a) test for and differentiate spatial and environmental factors affecting community composition and (b) test for temporal variation in community composition across spatial and environmental gradients. Results In both study years, we found that foliar fungal community composition varied among sites and between regions (east vs. west of the Cascades). We found that climate explained more variation in community composition than geographic distance, although the majority of variation explained by each was shared. We also found that interannual variation in community composition depended on environmental context: communities located in the dry, eastern portion of the tree's geographic range varied more between study years than those located in the wet, western portion of the tree's range. Main conclusions Our results suggest that the environment plays a greater role in structuring foliar fungal communities than dispersal limitation.

Barge, Edward G.↗

DOE OSTI2025

Monitoring Sulfuric Acid and Temperature Using Raman Spectroscopy and Multivariate Chemometrics

Multivariate regression models were optimized for the quantification of sulfuric acid (H 2 SO 4 ) [0–8 M] and temperature (20 °C–80 °C) in the presence of ammonium sulfate ((NH 4 ) 2 SO 4 [0–0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H 2 SO 4 . The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, in this study, partial least squares regression models were optimized for the quantification of H 2 SO 4 and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H 2 SO 4 . The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

D-optimal design↗

DOE OSTI2023

Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS

The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking. Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited. This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender. The volatile organic compound (VOC) signatures from subjects’ hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects. The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model. The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects. The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Beyond leaf habit: generalities in plant function across 97 tropical dry forest tree species

Leaf habit has been hypothesized to define a linkage between the slow-fast plant economic spectrum and the drought resistance-avoidance trade-off in tropical forests (‘slow-safe vs fast-risky’). However, variation in hydraulic traits as a function of leaf habit has rarely been explored for a large number of species. We sampled leaf and branch functional traits of 97 tropical dry forest tree species from four sites to investigate whether patterns of trait variation varied consistently in relation to leaf habit along the ‘slow-safe vs fast-risky’ trade-off. Leaf habit explained from 0% to 43.69% of individual trait variation. We found that evergreen and semi-deciduous species differed in their location along the multivariate trait ordination when compared to deciduous species. While deciduous species showed consistent trait values, evergreen species trait values varied as a function of the site. Last, trait values varied in relation to the proportion of deciduous species in the plant community. Here We found that leaf habit describes the strategies that define drought avoidance and plant economics in tropical trees. However, leaf habit alone does not explain patterns of trait variation, which suggests quantifying site-specific or species-specific uncertainty in trait variation as the way forward.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

National serosurvey and risk mapping reveal widespread distribution of Coxiella burnetii in Kenya

Coxiella burnetii, the causative agent of Q fever, is an emerging pathogen that has the potential to cause severe chronic infections in animals and humans worldwide. The detrimental impact on public health is projected to be higher in the low- and middle-income countries given their lower capacity to sustain effective surveillance and response measures. We implemented a national serosurvey of cattle in Kenya to map the spatial distribution of the pathogen. The study used serum samples that were collected from randomly selected cattle in different ago-ecological zones across the country. These samples were screened for the pathogen using PrioCHECK Ruminant Q Fever AB Plate ELISA kit. The laboratory findings were analyzed using INLA package to identify risk factors for C. burnetii exposure from herd- and animal-level factors, area, and bioclimatic datasets accessed from online databases. A total of 6,593 cattle were recruited for the study; of these, 7.9% (95% CI; 7.2–8.5) were seropositive. Outputs from the multivariable analysis revealed that the animal age and some of the geographical variables including wind speed, area under shrubs and “petric calcisols” type of soil were significantly associated with C. burnetii seropositivity. Being a calf, weaner or subadult was associated with lower odds of exposure compared to being an adult by 0.24 (credibility interval: 2.5% and 97.5%), 0.41 (0.30–0.55) and 0.51 (0.38–0.69), respectively. In addition, a unit increase in the wind speed increased the odds of C. burnetii seropositivity by 1.27 (1.05–1.52) while an increase on the land area under shrubs was associated with lower odds of exposure (0.67 [0.47–0.69]). The effect of petric calcisols was non-linear; an increase of the land area with this soil type was associated with an exponential increase in C. burnetii seropositivity. This study provides new data on C. burnetii seroprevalence, information of its risk factors and a prevalence map that can be used for C. burnetii risk surveillance and control. The identification of environmental risk factors for C. burnetii exposure, and the increasing awareness of the zoonotic potential of the pathogen, calls for the need to enhance the existing collaborations for the surveillance and control of C. burnetii in line with the One Health framework. The evidence generated on the potential role of environmental factors can also be used to design nature-based interventions, such as replacement of vegetation in denuded areas, to reduce potential for the aerosolization of the pathogen. Livestock vaccination in the hotspots would also reduce animal infections and hence the contamination of the environment.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Measurements of $\textrm{t}\overline{\textrm{t}}\textrm{W}$ differential cross sections and the leptonic charge asymmetry at $\sqrt{s}=13$ TeV

Measurements of properties of top quark-antiquark pair production in association with a W boson in proton-proton collisions at a center-of-mass energy of 13 TeV are presented, using a data sample corresponding to an integrated luminosity of 138 fb −1 , recorded by the CMS experiment at the CERN LHC. Events are selected based on the presence of either two leptons with the same electric charge or three leptons, and multiple jets and b-tagged jets. We present measurements of differential production cross sections as a function of kinematic variables sensitive to different aspects of the process modeling, using a multivariate discriminator in the two-lepton selection region and a simple selection-based method in the three-lepton region. The normalized cross section measurements are generally consistent with the standard model expectations, while we observe larger values compared to the expectations in the absolute cross section measurements, consistent with previous inclusive cross section measurements. In addition, we measure the leptonic charge asymmetry of this process, obtaining an observed value of ${A}_c^{\ell }=-{0.19}_{-0.18}^{+0.16}$, consistent with the expectation of −0.085 ± 0.006 predicted by next-to-leading order simulations.

Hadron-Hadron Scattering↗

DOE OSTI2023

COVID-19 prevention at institutions of higher education, United States, 2020–2021: implementation of nonpharmaceutical interventions

Background, In early 2020, following the start of the coronavirus disease 2019 (COVID-19) pandemic, institutions of higher education (IHEs) across the United States rapidly pivoted to online learning to reduce the risk of on-campus virus transmission. We explored IHEs’ use of this and other nonpharmaceutical interventions (NPIs) during the subsequent pandemic-affected academic year 2020–2021. Methods, From December 2020 to June 2021, we collected publicly available data from official webpages of 847 IHEs, including all public (n = 547) and a stratified random sample of private four-year institutions (n = 300). Abstracted data included NPIs deployed during the academic year such as changes to the calendar, learning environment, housing, common areas, and dining; COVID-19 testing; and facemask protocols. We performed weighted analysis to assess congruence with the October 29, 2020, US Centers for Disease Control and Prevention (CDC) guidance for IHEs. For IHEs offering ≥50% of courses in person, we used weighted multivariable linear regression to explore the association between IHE characteristics and the summated number of implemented NPIs. Results, Overall, 20% of IHEs implemented all CDC-recommended NPIs. The most frequently utilized NPI was learning environment changes (91%), practiced as one or more of the following modalities: distance or hybrid learning opportunities (98%), 6-ft spacing (60%), and reduced class sizes (51%). Additionally, 88% of IHEs specified facemask protocols, 78% physically changed common areas, and 67% offered COVID-19 testing. Among the 33% of IHEs offering ≥50% of courses in person, having < 1000 students was associated with having implemented fewer NPIs than IHEs with ≥ 1000 students. Conclusions, Only 1 in 5 IHEs implemented all CDC recommendations, while a majority implemented a subset, most commonly changes to the classroom, facemask protocols, and COVID-19 testing. IHE enrollment size and location were associated with degree of NPI implementation. Additional research is needed to assess adherence to NPI implementation in IHE settings.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Deciphering phase evolution in complex metal oxide thin films via high-throughput materials synthesis and characterization

Discovery of structure-property relationships in thin film alloys of complex metal oxides enabled by high-throughput materials synthesis and characterization facilities is demonstrated here with a case-study. In this study, thin films of binary transition metal oxides (Ti–Zn) are prepared by pulsed laser deposition with continuously varying Ti:Zn ratio, creating combinatorial samples for exploration of the properties of this material family. The atomic structure and electronic properties are probed by spatially resolved techniques including x-ray absorption near edge structures (XANES) and x-ray fluorescence (XRF) at the Ti and Zn K-edge, x-ray diffraction, and spectroscopic ellipsometry. The observed properties as a function of Ti:Zn ratio are resolved into mixtures of five distinguishable phases by deploying multivariate curve resolution analysis on the XANES spectral series, under constraints set by results from the other characterization techniques. First-principles computations based on density function theory connect the observed properties of each distinct phase with structural and spectral characteristics of crystalline polymorphs of Ti–Zn oxide. Continuous tuning of the optical absorption edge as a function of Ti:Zn ratio, including the unusual observation of negative optical bowing, exemplifies a functional property of the film correlated to the phase evolution.

36 MATERIALS SCIENCE↗

DOE OSTI2022

HSC-XXL: Baryon budget of the 136 XXL groups and clusters

Abstract We present our determination of the baryon budget for an X-ray-selected XXL sample of 136 galaxy groups and clusters spanning nearly two orders of magnitude in mass (M500 ∼ 1013–1015 M⊙) and the redshift range 0 ≲ z ≲ 1. Our joint analysis is based on the combination of Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) weak-lensing mass measurements, XXL X-ray gas mass measurements, and HSC and Sloan Digital Sky Survey multiband photometry. We carry out a Bayesian analysis of multivariate mass-scaling relations of gas mass, galaxy stellar mass, stellar mass of brightest cluster galaxies (BCGs), and soft-band X-ray luminosity, by taking into account the intrinsic covariance between cluster properties, selection effect, weak-lensing mass calibration, and observational error covariance matrix. The mass-dependent slope of the gas mass–total mass (M500) relation is found to be $1.29_{-0.10}^{+0.16}$, which is steeper than the self-similar prediction of unity, whereas the slope of the stellar mass–total mass relation is shallower than unity; $0.85_{-0.09}^{+0.12}$. The BCG stellar mass weakly depends on cluster mass with a slope of $0.49_{-0.10}^{+0.11}$. The baryon, gas mass, and stellar mass fractions as a function of M500 agree with the results from numerical simulations and previous observations. We successfully constrain the full intrinsic covariance of the baryonic contents. The BCG stellar mass shows the larger intrinsic scatter at a given halo total mass, followed in order by stellar mass and gas mass. We find a significant positive intrinsic correlation coefficient between total (and satellite) stellar mass and BCG stellar mass and no evidence for intrinsic correlation between gas mass and stellar mass. All the baryonic components show no redshift evolution.

Akino, Daichi↗

DOE OSTI2025

Multiclass Classification Using Bayesian Multivariate Adaptive Regression Splines

We present a new Bayesian model for the problem of multiclass classification. In this model, the probabilities of class membership of a given observation are determined by the mean of a latent Gaussian distribution. The mean functions of this latent distribution consist of combinations of highly flexible basis functions of the inputs: multivariate adaptive regression splines (MARS), first developed for multiple regression. We use reversible jump Markov chain Monte Carlo to make inference on the classification model, including the number of basis functions. We compare the probabilistic classification performance of our proposed approach to existing methods on simulated and benchmark data, and compare uncertainty estimates on simulated data. Our proposed method compares favorably with existing Bayesian and frequentist multiclass classification methods in out-of-sample probabilistic classification, and uncertainty estimation of these probabilistic classifications. We examine the fit of the proposed method to a data set of hurricane storm surge levels near Delaware Bay, US, and conclude that sea level rise is a key contributor to damage delivered by storm surge.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Is Breast-Conserving Therapy Appropriate for Male Breast Cancer Patients? A National Cancer Database Analysis

Background: Current treatment guidelines for male breast cancer are predominantly guided by female-only clinical trials. With scarce research, it is unclear whether breast-conserving therapy (BCT) is equivalent to mastectomy in men. We sought to compare overall survival (OS) among male breast cancer patients who underwent BCT versus mastectomy. Methods: We performed a retrospective analysis of 8445 stage I–II (T1–2 N0–1 M0) male breast cancer patients from the National Cancer Database (2004–2014). Patients were grouped according to surgical and radiation therapy (RT). BCT was defined as partial mastectomy followed by RT. Multivariable and inverse probability of treatment-weighted (IPTW) Cox proportional hazards models were used to compare OS between treatment groups, controlling for demographic and clinicopathologic characteristics. Results: Most patients underwent total mastectomy (61.2%), whereas 18.2% underwent BCT, 12.4% underwent total mastectomy with RT, and 8.2% underwent partial mastectomy alone. In multivariable and IPTW models, partial mastectomy alone, total mastectomy alone, and total mastectomy with RT were associated with worse OS compared with BCT (p < 0.001 all). Ten-year OS was 73.8% for BCT and 56.3, 58.0 and 56.3% for other treatment approaches. Older age, higher T/N stage, histological grade, and triple-negative receptor status were associated with poorer OS (p < 0.05). Subgroup analysis by stage demonstrated similar results. Conclusions: In this national sample of male breast cancer patients, BCT was associated with greater survival. The underlying mechanisms of this association warrant further study, because more routine adoption of BCT in male breast cancer appears to translate into clinically meaningful improvements in survival.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

Surface-Enhanced Raman Spectroscopy Combined with Multivariate Analysis for Fingerprinting Clinically Similar Fibromyalgia and Long COVID Syndromes

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10–20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Increased Expression of DNAJC12 is Associated with Aggressive Phenotype of Gastric Cancer

Background: Identification of gastric cancer-related molecules is necessary to elucidate the pathological mechanisms of this heterogeneous disease. The purpose of this study was to identify novel genes associated with aggressive phenotypes of gastric cancer. Methods: Global expression profiling was conducted using tissues from four patients with metastatic gastric cancer to identify genes overexpressed in gastric cancer. Fifteen gastric cell lines and 262 pairs of surgically resected gastric tissues were subjected to mRNA expression analysis. The contribution of the candidate gene on gastric cancer cell proliferation, invasion, adhesion, and migration were evaluated using small interfering RNA. Results: DnaJ heat shock protein family (Hsp40) member C12 (DNAJC12) was identified as a candidate gene by transcriptome analysis. In clinical samples, DNAJC12 mRNA levels were higher in gastric cancer tissues compared with normal adjacent tissues. Patients with high DNAJC12 expression showed significantly shorter overall survival in our cohort and in the extra-validation cohort analyzed by a published microarray dataset. High DNAJC12 expression in gastric cancer tissues was significantly associated with lymphatic involvement, infiltrative growth type, lymph node metastasis, and advanced stage and was identified as an independent prognostic factor for overall survival in multivariable analysis. Increased expression of DNAJC12 was found in 12 of 14 examined gastric cancer cell lines. Knockdown of DNAJC12 expression significantly decreased the proliferation and invasion abilities of gastric cancer cells. Conclusions: Our findings support DNAJC12 as a candidate gene associated with aggressive phenotypes of gastric cancer.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Comparison of temperature adaptive calibration methods for laser induced fluorescence based fuel-in-oil instrument

Fuel dilution of engine oil (or fuel-in-oil, FiO) is an important issue as multiple and late-cycle fuel injection, integral to many combustion efficiency and emissions improvements (e.g., downsized boosted gasoline engines and catalyst thermal management) increases FiO rate. In addition to causing general wear and corrosion in engine due to decreased oil viscosity and pH buffering, FiO is also believed to cause destructive low speed pre-ignition (or super knock) in boosted SI engines. To understand the effects of engine operating conditions on the FiO rate, an optical diagnostic capable of measuring transient FiO on minute timescales was recently developed and demonstrated on a modified GM Ecotech engine system (Neupane et al., Applied Spectroscopy 2021). The measurement is based on adding a dye to the fuel and monitoring for its presence in oil via laser-induced fluorescence (LIF). The measured LIF signal is related to FiO concentration via pre-determined calibration factors using a multivariate classical least square (CLS) method.Since fluorescence quantum yield is a function of temperature, measured LIF intensity not only depends on FiO concentration but also oil temperature. To expand the applicability of the FiO diagnostic to transient oil-temperature conditions (e.g., cold start in practical engines), this study develops a method to account for oil-temperature variations. The effect of oil temperature (20°C - 95°C) on the LIF spectra of eight FiO samples ranging from ~0.8-15% was investigated. LIF intensities of the FiO samples decreased linearly with increasing temperature; the reductions being more significant at dye peaks. We develop a new calibration model (T-adaptive CLS) incorporating the temperature (T) effects on LIF intensity that enables simultaneous calculation of FiO and oil temperature. The improved FiO diagnostic with T-adaptive calibration is more robust, and applicable to varying oil-temperature conditions. For example, when strategies such as multiple/late fuel injections are applied to overcome cold-start instability due to use of low vaporization bio-based fuels such as ethanol, FiO rate is expected to be very high; the improved T-adaptive FiO diagnostic is hence relevant for engine- and fuel-system calibration and optimization. The diagnostic could also provide validation data for flow-field and spray interaction CFD models, further broadening the diagnostic’s utility for advancing engine technology and efficiency.

Neupane, Sneha↗

NASA NTRS1977

Alteration mapping at Goldfield, Nevada, by cluster and discriminant analysis of LANDSAT digital data

The ability of Landsat multispectral digital data to differentiate among 62 combinations of rock and alteration types at the Goldfield mining district of Western Nevada was investigated by using statistical techniques of cluster and discriminant analysis. Multivariate discriminant analysis was not effective in classifying each of the 62 groups, with classification results essentially the same whether data of four channels alone or combined with six ratios of channels were used. Bivariate plots of group means revealed a cluster of three groups including mill tailings, basalt and all other rock and alteration types. Automatic hierarchical clustering based on the fourth dimensional Mahalanobis distance between group means of 30 groups having five or more samples was performed. The results of the cluster analysis revealed hierarchies of mill tailings vs. natural materials, basalt vs. non-basalt, highly reflectant rocks vs. other rocks and exclusively unaltered rocks vs. predominantly altered rocks. The hierarchies were used to determine the order in which sets of multiple discriminant analyses were to be performed and the resulting discriminant functions were used to produce a map of geology and alteration which has an overall accuracy of 70 percent for discriminating exclusively altered rocks from predominantly altered rocks.

Ballew, G.↗

NASA NTRS1977

Alteration mapping at Goldfield, Nevada, by cluster and discriminant analysis of Landsat digital data

The ability of Landsat multispectral digital data to differentiate among 62 combinations of rock and alteration types at the Goldfield mining district of Western Nevada was investigated by using statistical techniques of cluster and discriminant analysis. Multivariate discriminant analysis was not effective in classifying each of the 62 groups, with classification results essentially the same whether data of four channels alone or combined with six ratios of channels were used. Bivariate plots of group means revealed a cluster of three groups including mill tailings, basalt and all other rock and alteration types. Automatic hierarchical clustering based on the fourth dimensional Mahalanobis distance between group means of 30 groups having five or more samples was performed using Johnson's HICLUS program. The results of the cluster analysis revealed hierarchies of mill tailings vs. natural materials, basalt vs. non-basalt, highly reflectant rocks vs. other rocks and exclusively unaltered rocks vs. predominantly altered rocks. The hierarchies were used to determine the order in which sets of multiple discriminant analyses were to be performed and the resulting discriminant functions were used to produce a map of geology and alteration which has an overall accuracy of 70 percent for discriminating exclusively altered rocks from predominantly altered rocks.

Ballew, G.↗

NASA NTRS1998

Experiments to Determine Whether Recursive Partitioning (CART) or an Artificial Neural Network Overcomes Theoretical Limitations of Cox Proportional Hazards Regression

New computationally intensive tools for medical survival analyses include recursive partitioning (also called CART) and artificial neural networks. A challenge that remains is to better understand the behavior of these techniques in effort to know when they will be effective tools. Theoretically they may overcome limitations of the traditional multivariable survival technique, the Cox proportional hazards regression model. Experiments were designed to test whether the new tools would, in practice, overcome these limitations. Two datasets in which theory suggests CART and the neural network should outperform the Cox model were selected. The first was a published leukemia dataset manipulated to have a strong interaction that CART should detect. The second was a published cirrhosis dataset with pronounced nonlinear effects that a neural network should fit. Repeated sampling of 50 training and testing subsets was applied to each technique. The concordance index C was calculated as a measure of predictive accuracy by each technique on the testing dataset. In the interaction dataset, CART outperformed Cox (P less than 0.05) with a C improvement of 0.1 (95% Cl, 0.08 to 0.12). In the nonlinear dataset, the neural network outperformed the Cox model (P less than 0.05), but by a very slight amount (0.015). As predicted by theory, CART and the neural network were able to overcome limitations of the Cox model. Experiments like these are important to increase our understanding of when one of these new techniques will outperform the standard Cox model. Further research is necessary to predict which technique will do best a priori and to assess the magnitude of superiority.

Kattan, Michael W.↗