Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “fitting”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,639 records · Page 91

tRNA functional signatures classify plastids as late-branching cyanobacteria

Eukaryotes acquired the trait of oxygenic photosynthesis through endosymbiosis of the cyanobacterial progenitor of plastid organelles. Despite recent advances in the phylogenomics of Cyanobacteria, the phylogenetic root of plastids remains controversial. Although a single origin of plastids by endosymbiosis is broadly supported, recent phylogenomic studies are contradictory on whether plastids branch early or late within Cyanobacteria. One underlying cause may be poor fit of evolutionary models to complex phylogenomic data. Using Posterior Predictive Analysis, we show that recently applied evolutionary models poorly fit three phylogenomic datasets curated from cyanobacteria and plastid genomes because of heterogeneities in both substitution processes across sites and of compositions across lineages. To circumvent these sources of bias, we developed CYANO-MLP, a machine learning algorithm that consistently and accurately phylogenetically classifies (“phyloclassifies”) cyanobacterial genomes to their clade of origin based on bioinformatically predicted function-informative features in tRNA gene complements. Classification of cyanobacterial genomes with CYANO-MLP is accurate and robust to deletion of clades, unbalanced sampling, and compositional heterogeneity in input tRNA data. CYANO-MLP consistently classifies plastid genomes into a late-branching cyanobacterial sub-clade containing single-cell, starch-producing, nitrogen-fixing ecotypes, consistent with metabolic and gene transfer data. Phylogenomic data of cyanobacteria and plastids exhibit both site-process heterogeneities and compositional heterogeneities across lineages. These aspects of the data require careful modeling to avoid bias in phylogenomic estimation. Furthermore, we show that amino acid recoding strategies may be insufficient to mitigate bias from compositional heterogeneities. However, the combination of our novel tRNA-specific strategy with machine learning in CYANO-MLP appears robust to these sources of bias with high accuracy in phyloclassification of cyanobacterial genomes. CYANO-MLP consistently classifies plastids as late-branching Cyanobacteria, consistent with independent evidence from signature-based approaches and some previous phylogenetic studies.

59 BASIC BIOLOGICAL SCIENCES↗

Complete genome sequence and the expression pattern of plasmids of the model ethanologen Zymomonas mobilis ZM4 and its xylose-utilizing derivatives 8b and 2032

Zymomonas mobilis is a natural ethanologen being developed and deployed as an industrial biofuel producer. To date, eight Z. mobilis strains have been completely sequenced and found to contain 2-8 native plasmids. However, systematic verification of predicted Z. mobilis plasmid genes and their contribution to cell fitness has not been hitherto addressed. Moreover, the precise number and identities of plasmids in Z. mobilis model strain ZM4 have been unclear. The lack of functional information about plasmid genes in ZM4 impedes ongoing studies for this model biofuel-producing strain. In this study, we determined the complete chromosome and plasmid sequences of ZM4 and its engineered xylose-utilizing derivatives 2032 and 8b. Compared to previously published and revised ZM4 chromosome sequences, the ZM4 chromosome sequence reported here contains 65 nucleotide sequence variations as well as a 2400-bp insertion. Four plasmids were identified in all three strains, with 150 plasmid genes predicted in strain ZM4 and 2032, and 153 plasmid genes predicted in strain 8b due to the insertion of heterologous DNA for expanded substrate utilization. Plasmid genes were then annotated using Blast2GO, InterProScan, and systems biology data analyses, and most genes were found to have apparent orthologs in other organisms or identifiable conserved domains. To verify plasmid gene prediction, RNA-Seq was used to map transcripts and also compare relative gene expression under various growth conditions, including anaerobic and aerobic conditions, or growth in different concentrations of biomass hydrolysates. Overall, plasmid genes were more responsive to varying hydrolysate concentrations than to oxygen availability. Additionally, our results indicated that although all plasmids were present in low copy number (about 1-2 per cell), the copy number of some plasmids varied under specific growth conditions or due to heterologous gene insertion. The complete genome of ZM4 and two xylose-utilizing derivatives is reported in this study, with an emphasis on identifying and characterizing plasmid genes. Furthermore, plasmid gene annotation, validation, expression levels at growth conditions of interest, and contribution to host fitness are reported for the first time.

09 BIOMASS FUELS↗

A two-phase substrate model for enzymatic hydrolysis of lignocellulose: application to batch and continuous reactors

Abstract Background Enzymatic hydrolysis continues to have a significant projected production cost for the biological conversion of biomass to fuels and chemicals, motivating research into improved enzyme and reactor technologies in order to reduce enzyme usage and equipment costs. However, technology development is stymied by a lack of accurate and computationally accessible enzymatic-hydrolysis reaction models. Enzymatic deconstruction of cellulosic materials is an exceedingly complex physico-chemical process. Models which elucidate specific mechanisms of deconstruction are often too computationally intensive to be accessible in process or multi-physics simulations, and empirical models are often too inflexible to be effectively applied outside of their batch contexts. In this paper, we employ a phenomenological modeling approach to represent rate slowdown due to substrate structure (implemented as two substrate phases) and feedback inhibition, and apply the model to a continuous reactor system. Results A phenomenological model was developed in order to predict glucose and solids concentrations in batch and continuous enzymatic-hydrolysis reactors from which liquor is continuously removed by ultrafiltration. A series of batch experiments were performed, varying initial conditions (solids, enzyme, and sugar concentrations), and best-fit model parameters were determined using constrained nonlinear least-squares methods. The model achieved a good fit for overall sugar yield and insoluble solids concentration, as well as for the reduced rate of sugar production over time. Additionally, without refitting model coefficients, good quantitative agreement was observed between results from continuous enzymatic-hydrolysis experiments and model predictions. Finally, the sensitivity of the model to its parameters is explored and discussed. Conclusions Although the phenomena represented by the model correspond to behaviors that emerge from clusters of mechanisms, and hence a set of model coefficients are unique to the substrate and the enzyme system, the model is efficient to solve and may be applied to novel reactor schema and implemented in computational fluid dynamics (CFD) simulations. Hence, this modeling approach finds the right balance between model complexity and computational efficiency. These capabilities have broad application to reactor design, scale-up, and process optimization.

09 BIOMASS FUELS↗

The impact of kidney function on Alzheimer’s disease blood biomarkers: implications for predicting amyloid-β positivity

Impaired kidney function has a potential confounding effect on blood biomarker levels, including biomarkers for Alzheimer’s disease (AD). Given the imminent use of certain blood biomarkers in the routine diagnostic work-up of patients with suspected AD, knowledge on the potential impact of comorbidities on the utility of blood biomarkers is important. We aimed to evaluate the association between kidney function, assessed through estimated glomerular filtration rate (eGFR) calculated from plasma creatinine and AD blood biomarkers, as well as their influence over predicting Aβ-positivity. We included 242 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, comprising cognitively unimpaired individuals (CU; n = 124), mild cognitive impairment (MCI; n = 58), AD dementia (n = 34), and non-AD dementia (n = 26) patients all characterized by [ 18 F] AZD-4694. Plasma samples were analyzed for Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau phosphorylated at threonine 181 (p-tau181), 217 (p-tau217), 231 (p-tau231) and N-terminal containing tau fragments (NTA-tau) using Simoa technology. Kidney function was assessed by eGFR in mL/min/1.73 m 2 , based on plasma creatinine levels, age, and sex. Participants were also stratified according to their eGFR-indexed stages of chronic kidney disease (CKD). We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. Biomarker concentrations were highest in individuals with CKD stage 3, followed by stages 2 and 1, but differences were only significant for NfL, Aβ42, and Aβ40 (not Aβ42/Aβ40). All investigated biomarkers showed significant associations with eGFR except plasma NTA-tau, with stronger relationships observed for Aβ40 and NfL. However, after adjusting for either age, sex or Aβ-PET SUVr, the association with eGFR was no longer significant for all biomarkers except Aβ40, Aβ42, NfL, and GFAP. When evaluating whether accounting for kidney function could lead to improved prediction of Aβ-positivity, we observed no improvements in model fit (Akaike Information Criterion, AIC) or in discriminative performance (AUC) by adding eGFR to a base model including each plasma biomarker, age, and sex. While covariates like age and sex improved model fit, eGFR contributed minimally, and there were no significant differences in clinical discrimination based on AUC values. We found that kidney function seems to be associated with AD blood biomarker concentrations. However, these associations did not remain significant after adjusting for age and sex, except for Aβ40, Aβ42, NfL, and GFAP. While covariates such as age and sex improved prediction of Aβ-positivity, including eGFR in the models did not lead to improved prediction for any biomarker. Our findings indicate that renal function, within the normal to mild impairment range, does not seem to have a clinically relevant impact when using highly accurate blood biomarkers, such as p-tau217, in a biomarker-supported diagnosis.

60 APPLIED LIFE SCIENCES↗

Breeding of microbiomes conferring salt tolerance to plants

Microbiome breeding through host-mediated selection is a technique to artificially select for microbiomes conferring beneficial properties to plants. Using a systematic selection protocol that maximises the heritability of microbiome effects, transmission fidelity, and microbiome stability through multiple selection cycles, we previously developed root-associated microbial communities conferring sodium and aluminium tolerance to Brachypodium distachyon, a model for cereal crops. Here, we explore the physiological mechanisms underlying our selected microbiomes’ effect on plant fitness and analyse how our selection protocol shaped the composition and structure of these microbiomes. We analysed the effects of our selected microbiomes on plant fitness and tissue-nutrient concentration, then used 16S rRNA amplicon sequencing to examine microbial community composition and co-occurrence network patterns. Our sodium-selected microbiomes reduced leaf sodium concentration by ~ 50%, whereas the aluminium-selected microbiomes had no effect on leaf-tissue nutrient concentration, suggesting different mechanisms underlying the microbiome-mediated stress tolerance. By testing the selected microbiomes in a cross-fostering experiment, we show that our artificially selected microbiomes attained (a) ecological robustness contributing to transplantability (i.e. inheritance) of microbiome-encoded effects between plants; and (b) network features identifying key bacteria promoting salt-stress tolerance. Combined, these findings elucidate critical mechanisms underlying host-mediated artificial selection as a framework to breed microbiomes with targeted benefits for plants under salt stresses, with significant implications for sustainable agriculture.

59 BASIC BIOLOGICAL SCIENCES↗

Fast approximate STEM image simulations from a machine learning model

Abstract Accurate quantum mechanical scanning transmission electron microscopy image simulation methods such as the multislice method require computation times that are too large to use in applications in high-resolution materials imaging that require very large numbers of simulated images. However, higher-speed simulation methods based on linear imaging models, such as the convolution method, are often not accurate enough for use in these applications. We present a method that generates an image from the convolution of an object function and the probe intensity, and then uses a multivariate polynomial fit to a dataset of corresponding multislice and convolution images to correct it. We develop and validate this method using simulated images of Pt and Pt–Mo nanoparticles and find that for these systems, once the polynomial is fit, the method runs about six orders of magnitude faster than parallelized CPU implementations of the multislice method while achieving a 1 − R 2 error of 0.010–0.015 and root-mean-square error/standard deviation of dataset being predicted of about 0.1 when compared to full multislice simulations.

47 OTHER INSTRUMENTATION↗

An adaptable technique for comparative image assessment: Application to crosswell electromagnetic survey design for fluid monitoring

Reservoir integrity stewardship accompanying carbon capture and sequestration considers fluid extraction and reinjection as a risk-mitigating method against overpressuring that could lead to caprock damage and ensuing [Formula: see text] leakage. Crosswell electromagnetics offers a technically viable monitoring method with the spatial volume coverage necessary for reservoir-encompassing pressure management. However, a certain logistic dilemma for deep gas sequestration into saline and thus electrically conductive aquifers is that crosswell magnetic-field measurements underperform in the imaging of more resistive plume bodies, further exacerbated when vertical arrays intersect, as opposed to surround, plumes. Furthermore, comparative synthetic-data plume imaging of such scenarios rates the information content of magnetic-field versus electric-field 3D crosswell layouts for reservoir and infrastructure conditions of a representative pilot site in a coastal area in Florida. The image quality of the resulting plume replications can be ranked numerically through a newly proposed semblance qualifier, appraising the model goodness of fit to a given reference. In contrast to common least-squares measures for goodness of fit, the semblance formulation uses classifying logistic function types, thus enabling a better distinction of predefined anomaly features.

58 GEOSCIENCES↗

Early arrival waveform inversion using data uncertainties and matching filters with application to near-surface seismic refraction data

We develop an early arrival waveform inversion (EAWI) technique for high-resolution near-surface velocity estimation by iteratively updating the P-wave velocity model to minimize the difference between the observed and calculated seismic refraction data. Traditional EAWI uses a least-squares penalty function and an acoustic forward-modeling engine. Conventional least-squares error is sensitive to data with low signal-to-noise ratio (S/N) and iterations of EAWI stop at a local-minimum data misfit or at the preassigned maximum number of iterations. These stopping criteria can result in overfitting the data. In addition, fitting the elastic field data with an acoustic modeling engine can introduce artifacts in velocity estimation, especially in land data with significant elastic effects. To overcome these challenges, we develop a robust EAWI (REAWI) method by (1) incorporating the data uncertainties into the penalty function and (2) mitigating the elastic effects using a matching filter workflow. The data uncertainties are estimated from waveform reciprocal errors. When full-waveform reciprocity is not available, trace interpolation is applied. The proposed method prevents closely fitting data with low S/N, avoids overall overfitting by stopping the iterations when a normalized chi-square ([Formula: see text]) waveform misfit of one is achieved, and is less affected by elastic effects. Numerical examples and application to near-surface refraction data at a groundwater contamination site suggest that the final REAWI models are more accurate than the corresponding EAWI models, at the same level of misfit. This is the first known application of a matching filter workflow to real land data. The final REAWI models satisfy an appropriate misfit between the real data and predicted elastic P-wave data, making this approach in this respect equivalent to elastic waveform inversion. We also develop a method to analyze model constraint by examining the energy of the wavefield Fréchet derivative thereby avoiding the influence of the data residuals in traditional Fréchet kernels.

Geochemistry & Geophysics↗

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗

Data for "Examining Organic Acid Production Potential and Growth-Coupled Strategies in Issatchenkia orientalis Using Constraint-Based Modeling"

Growth-coupling product formation can facilitate strain stability by aligning industrial objectives with biological fitness. Organic acids make up many building block chemicals that can be produced from sugars obtainable from renewable biomass. Issatchenkia orientalis is a yeast strain tolerant to acidic conditions and is thus a promising host for industrial production of organic acids. Here, we use constraint-based methods to assess the potential of computationally designing growth-coupled production strains for I. orientalis that produce 22 different organic acids under aerobic or microaerobic conditions. We explore native and engineered pathways using glucose or xylose as the carbon substrates as proxy constituents of hydrolyzed biomass. We identified growth-coupled production strategies for 37 of the substrate-product pairs, with 15 pairs achieving production for any growth rate. We systematically assess the strain design solutions and categorize the underlying principles involved.

Bioproducts↗

Using data-science approaches to unravel insights for enhanced transport of lithium ions in single-ion conducting polymer electrolyte

Solid polymer electrolytes have yet to achieve the an ionic conductivity > 1 mS/cm at room temperature for realistic applications. This target implies the need to reduce the effective energy barriers of ion transport in polymer electrolytes to around 20 kJ/mol. In this work, we combine information extracted from existing experimental results with theoretical calculations to provide insights into ion transport in single-ion conductors (SICs) with a focus on lithium ion SICs. Through the analysis of temperature-dependent ionic conductivity data obtained from the literature, we evaluate different methods of extracting energy barriers for lithium transport. The traditional Arrhenius fit to the temperature-dependent ionic conductivity data indicates that the Meyer-Neldel rule holds for SICs. However, the values of the fitting parameters remain unphysical. Our modified approach based on recent work (Macromolecules, 56, 15, 6051(2023)), which incorporates a fixed pre-exponential factor, reveals that the energy barriers exhibit temperature dependence over a wide range of temperatures. Using this approach, we identify a series of anions leading to the energy barriers less than 30 kJ/mol, which include trifluoromethane sulfonimide (TFSI), fluoromethane sulfonimide (FSI), and boron-based organic anions. In our efforts to design the next generation of anions, which can exhibit the energy barriers less than 20 kJ/mol, we focused on boron-containing SICs, and performed density functional theory (DFT) based calculations to connect the chemical structures via the binding energy of cation (lithium)-anion pairs with the experimentally derived effective energy barriers for ion transport. Not only have we identified a correlation between the binding energy and the energy barriers, but we also propose a strategy to design new boron-based anions by using the correlation. This combined approach involving experiments and theoretical calculations is capable of facilitating the identification of promising new anions, which can exhibit ionic conductivity $> 1$ mS/cm near room temperature, thereby expediting the development of novel superionic single-ion conducting polymer electrolytes. The published datasets include all the temperature-dependent ionic conductivity collected from the literature with literature DOIs, DFT calculated binding energies, and python scripts to analyze data, construct statistical models, and generate plots.

36 MATERIALS SCIENCE↗

BEPS Propagation of priors Datasets

BEPS datasets used for loop fitting. Datasets consists of a 50x50 grid BFO (supplied by N. Valanoor), 50x50 grid of PTO (supplied by H. Funakubo), and a 128x128 grid synthetic dataset (generated by N. Creange). This data supplements the publication Propagation of priors for more accurate and efficient spectroscopic functional fits.

36 MATERIALS SCIENCE↗

Two-stage formation-energy correction (NbZr, TaZr, VZr)

This bundle contains the scripts, the raw and corrected per-structure data, and the manuscript plots for the NbZr / TaZr / VZr BCC binary formation energies and the associated RMSDs. Why a two-stage correction is necessary: The "raw" formation energy of every relaxed VASP configuration is computed in the usual way, FE_raw(c) = E_alloy(c) - sum_i x_i * E_pure_i , where E_pure_i are the per-atom total energies of the pure-element reference structures (Nb, Ta, V, Zr in the same BCC supercell, with identical INCAR / KPOINTS / PAW choices). With perfectly consistent reference runs the raw FE should vanish at the two pure-element endpoints (x = 0 and x = 1) by construction. In practice this does not hold for two reasons that are present in our dataset: 1. Reference-energy inconsistency (composition-dependent bias). Even with identical input parameters, the pure-element runs (stored in `corrected_DFT_pure_element_runs/`) differ slightly from the values that would be implied by the alloy runs at near-pure compositions (a few meV/atom). This bias is approximately linear in concentration, because the residual error in E_pure_Nb (or E_pure_Ta / E_pure_V) propagates into FE_raw(c) as (1 - x) * dE_pure_1, and the corresponding error in E_pure_Zr propagates as x * dE_pure_2. Left uncorrected, this produces a non-physical "tilt" of FE_raw(x) and shifts the entire FE-vs-x cloud away from zero at the endpoints. 2. Endpoint anchoring against the audited true endpoints. The strict endpoint values (FE_x0_meVatom, FE_x1_meVatom in `corrected_fe_strict_endpoints_20260518/strict_endpoint_check_20260518.csv`) were re-derived from an independent cross-check of the pure-element runs. After stage 1 removes the linear bias, the near-pure compositions in the alloy dataset still extrapolate to values that differ slightly from these audited endpoints — because stage 1 is fit from a few near-end alloy bins, not from the audited pure-element references themselves. The README.txt file discusses how these issues are addressed by the two-stage correction, and describes folder layout, pipeline summary, and how to re-run.

36 MATERIALS SCIENCE↗

Cross-Section Comparison for Pu-238 Production in the Advanced Test Reactor at Idaho National Laboratory

Qualification of Advanced Test Reactor (ATR) positions for Pu-238 production has been ongoing at Idaho National Laboratory (INL). The ATR qualifications have stretched over multiple years during which new techniques have been developed and made available for ATR experiment neutronic analysis. As part of the transition to newer codes, new cross-section libraries have been evaluated for use in the Pu-238 production experiment analysis. A comparative study was done using the MCNP ORGIEN Activation Analysis (MOAA) tool between ENDF/B-VII.0 and ENDF/B-VIII.0 cross sections to capture the impact of the change in cross-sections on the analysis needed to qualify Pu-238 production targets. All comparisons were done assuming the ATR GEN-I targets were located in the south flux trap of the ATR. Finally, an overview of how this qualification and potential irradiation fits into Pu-238 is discussed.

07 ISOTOPE AND RADIATION SOURCES↗

Two-Exponential Rossi-Alpha Analysis of Copper- and Polyethylene-Reflected Plutonium

The prompt neutron decay constant $\mathcal{α}$, which is used to infer reactivity, is estimated using the Rossi-alpha technique that is predicated on fitting Rossi-alpha histograms. The histograms are traditionally fit with a one-exponential model; however, recent work has proposed a two-exponential model to account for reflector-induced phenomenon. Recent work also validated the two-exponential model for shields, a subset of reflectors, but not for reflectors in general. Ultimately, it is desirable to validate the two-exponential model for the general shield. This work will investigate if previous 3 He measurements of plutonium reflected by copper and polyethylene, and replacement measurements where the plutonium is replaced by 252 Cf are suitable for validating the new model. Since the 3 He detectors ( 3 He gas proportional counters embedded in a polyethylene matrix) inherently contain moderating/reflector material (to increase detection efficiency), this work also investigates the use of organic scintillation detectors that can directly detect fast, fission neutrons.

07 ISOTOPE AND RADIATION SOURCES↗

Combining Spectroscopic Techniques to Determine the Optical Constants of Powdered Lactose

A method for deriving the optical constants (n/k) of organic powdered materials using pressed pellets in the mid-infrared spectral range is introduced that combines variable angle spectroscopic ellipsometry and transmission spectroscopy. The approach is applied to lactose, in which three different forms of pellets were pressed and measured: A pure lactose pellet and a mixed lactose/KBr pellet with a large analyte percentage were used for ellipsometric measurements, and a KBr transmission pellet with only a small analyte percentage were used for transmission measurements. The transmission data provide an initial set of oscillators and improve the spectral fitting of weak absorption features (k<0.01). Ellipsometric data for the pure and mixed pellets are then fit simultaneously to derive the final n/k values for lactose from 6000-400 cm-1. An alternative method just using the ellipsometric data from the mixed pellet and the transmission data is also presented and shows good agreement with the multi-sample analysis, providing a simpler method for powders that do not press easily into pure pellets. Finally, the derived optical constants are used to model the reflectance data, demonstrating a good match with the measured reflectance spectra if non-idealities are included.

Yokosuk, Michael O.↗

Mathematical modeling of hepatitis C RNA replication, exosome secretion and virus release

Hepatitis C virus (HCV) causes acute hepatitis C and can lead to life-threatening complications if it becomes chronic. The HCV genome is a single plus strand of RNA. Its intracellular replication is a spatiotemporally coordinated process of RNA translation upon cell infection, RNA synthesis within a replication compartment, and virus particle production. While HCV is mainly transmitted via mature infectious virus particles, it has also been suggested that HCV-infected cells can secrete HCV RNA carrying exosomes that can infect cells in a receptor independent manner. In order to gain insight into these two routes of transmission, we developed a series of intracellular HCV replication models that include HCV RNA secretion and/or virus assembly and release. Fitting our models to in vitro data, in which cells were infected with HCV, suggests that initially most secreted HCV RNA derives from intracellular cytosolic plus-strand RNA, but subsequently secreted HCV RNA derives equally from the cytoplasm and the replication compartments. Furthermore, our model fits to the data suggest that the rate of virus assembly and release is limited by host cell resources. Including the effects of direct acting antivirals in our models, we found that in spite of decreasing intracellular HCV RNA and extracellular virus concentration, low level HCV RNA secretion may continue as long as intracellular RNA is available. This may possibly explain the presence of detectable levels of plasma HCV RNA at the end of treatment even in patients that ultimately attain a sustained virologic response.

59 BASIC BIOLOGICAL SCIENCES↗

Combining biomarker and virus phylogenetic models improves HIV-1 epidemiological source identification

To identify and stop active HIV transmission chains new epidemiological techniques are needed. Here, we describe the development of a multi-biomarker augmentation to phylogenetic inference of the underlying transmission history in a local population. HIV biomarkers are measurable biological quantities that have some relationship to the amount of time someone has been infected with HIV. To train our model, we used five biomarkers based on real data from serological assays, HIV sequence data, and target cell counts in longitudinally followed, untreated patients with known infection times. The biomarkers were modeled with a mixed effects framework to allow for patient specific variation and general trends, and fit to patient data using Markov Chain Monte Carlo (MCMC) methods. Subsequently, the density of the unobserved infection time conditional on observed biomarkers were obtained by integrating out the random effects from the model fit. This probabilistic information about infection times was incorporated into the likelihood function for the transmission history and phylogenetic tree reconstruction, informed by the HIV sequence data. To critically test our methodology, we developed a coalescent-based simulation framework that generates phylogenies and biomarkers given a specific or general transmission history. Testing on many epidemiological scenarios showed that biomarker augmented phylogenetics can reach 90% accuracy under idealized situations. Under realistic within-host HIV-1 evolution, involving substantial within-host diversification and frequent transmission of multiple lineages, the average accuracy was at about 50% in transmission clusters involving 5–50 hosts. Realistic biomarker data added on average 16 percentage points over using the phylogeny alone. Using more biomarkers improved the performance. Shorter temporal spacing between transmission events and increased transmission heterogeneity reduced reconstruction accuracy, but larger clusters were not harder to get right. More sequence data per infected host also improved accuracy. We show that the method is robust to incomplete sampling and that adding biomarkers improves reconstructions of real HIV-1 transmission histories. The technology presented here could allow for better prevention programs by providing data for locally informed and tailored strategies.

60 APPLIED LIFE SCIENCES↗