Higher Order Mixtures Research Articles

GLYCOSAMINOGLYCANS AND GLYCOPROTEINS INFLUENCE THE ELASTIC RESPONSE OF SYNOVIAL FLUID NANOFILMS ON MODEL OXIDE SURFACES

Purpose: Many SF studies have focused on the lubricity and wear protection properties of isolated components, such as lubricin, hyaluronan, or lipids. Higher-order mixtures studies, such as hyaluronan and lubricin, hyaluronan and phospholipids, lubricin and galectins, or hyaluronan and aggrecans, combined with a top-down approach in which different components have been enzymatically digested, emphasize that it is the synergistic interactions that provide SF's unique load-bearing, wear protecting, and lubricating.

Effect of enzymatic treatment and dilution of synovial fluid on thin film compliance and load-bearing forces under confinement

Many synovial fluid (SF) studies have focused on the lubricity and wear protection properties of isolated components, such as lubricin, hyaluronan, or lipids. Higher-order mixtures studies, such as hyaluronan and lubricin, hyaluronan and phospholipids, lubricin and galectins, or hyaluronan and aggrecans, combined with a top-down approach in which different components have been enzymatically digested, emphasize that it is the synergistic interactions that provide SF’s unique load-bearing, lubricating, and wear protecting properties.

Total allele count distribution (TAC curves) improves number of contributor estimation for complex DNA mixtures

As the forensic community is transitioning to probabilistic genotyping and the use of likelihood ratios to assign probative weight to DNA mixtures, the assessment of the number of contributors (NOC) needs to be more robust for mixture interpretation. However, NOC assessment can be challenging for low-template and/or high order mixtures. Here, we present a quick and easy-to-use tool to help with NOC estimation: total allele count curves (TAC curves). TAC curves for two to seven contributors were generated using sets of 20,000 in silico mixtures, for five populations (African American, Caucasian, Asian, Apache and Native Alaska) and for commonly used commercial STR kits (GlobalFilerTM, PowerPlex® Fusion, PowerPlex® ESX 17 and IdentifilerTM). To assess the performance of TAC curves, the NOC was evaluated for 80 mixtures, with and without use of the curves. Results show that TAC curves allow for a better NOC assessment as correct evaluations rose from 10% when using maximal allele count (MAC) to 65% when also using TAC for four to six contributor mixtures. Supplemental data for this article is available online at http://dx.doi.org/10.1080/00085030.2022.2028359 .

Experimental study of impurity freeze-out in ternary methane + ethane + benzene mixtures with applications to LNG production

The freeze-out of impurities in LNG production can pose significant operational risks and lead to costly blockage-induced plant shutdowns. Study of ternary and higher-order mixtures, which are more analogous to LNG, present the critical tests of thermodynamic models in terms of their utility and predictive accuracy. In this work, a visual CryoSolids apparatus was upgraded to allow analytical measurements of solvent composition in multi-component systems where a solid phase is present at equilibrium. The analytical system, which included a ROLSI sampling valve and capillary together with a gas chromatograph, was successfully commissioned and used to measure melting temperatures and solvent compositions of a ternary mixture containing methane, ethane, and benzene at temperatures down to 125 K and pressures up to 6 MPa. The effect on the solubility of benzene by adding ethane to the solvent was investigated by varying the ethane mole fraction from 0 to 0.96. The resulting temperature at which benzene melted into the liquid solvent decreased from 246 K at 4.7 MPa to 125 K at 5.7 MPa. The comparison of results with the ThermoFAST model showed that it could describe the new data with a deviation less than 1 K for ethane liquid phase mole fractions from 0 <xC2< 0.5, which increased to less than 4 K for 0.85 <xC2< 0.97. This result indicates that ThermoFAST satisfactorily represents SFE conditions in LNG mixtures of industrial relevance but needs improvement to cover wider ranges of composition.

Identification of synergistic and antagonistic actions of environmental pollutants: Bisphenols A, S and F in the presence of DEP, DBP, BADGE and BADGE·2HCl in three component mixtures

Ecosystems are facing increased pressure due to the emission of many classes of emerging contaminants. However, very little is known about the interactions of these pollutants, such as bisphenols (BPs), plasticizers or pharmaceuticals. By employing bioluminescent bacteria (Microtox assay), we were able to define interactions between selected emerging pollutants (namely BPA, BPS, BPF, BADGE, BADGE·2HCl, DEP, DBP) in ternary mixtures, at environmentally relevant concentration levels (down to as low as 1.89, 1.42, 3.08, and 0.326 μM for, respectively, BPA, BPF, BPS and BADGE·2HCl). We provide the first systematic analysis of bisphenols and phthalates in three component mixtures. Using this system, we performed toxicity modelling with concentration addition (CA) and independent action (IA) approaches, followed by data interpretation using Model Deviation Ratio (MDR) evaluation. Interestingly, we mathematically and experimentally confirmed a novel synergy between BPA, BADGE and BADGE·2HCl. The synergy of BPA, BADGE and BADGE·2HCl is distinct, with both models suggesting these analytes have a similar mode of action (MOA). Moreover, we unexpectedly found a strong antagonistic impact with DEP, in mixtures containing BPA and BADGE analogues, which is confirmed with both mathematical models. Our study also shows that the impact of BPS and BPF in many mixtures is highly concentration dependent, justifying the necessity to perform mixture studies using wide concentration ranges. Overall, this study demonstrates that bioluminescent bacteria are a relevant model for detecting the synergistic and antagonist actions of environmental pollutants in mixtures, and highlights the importance of analyzing combinations of pollutants in higher order mixtures.

A DFT accurate machine learning description of molten ZnCl2 and its mixtures: 1. Potential development and properties prediction of molten ZnCl2

Molten eutectic salts consisting of ZnCl2 and other alkali chlorides are promising thermal storage and heat transfer fluid materials in the next generation concentrated solar thermal power. To go deep into the thermal and transport properties for a high order mixture, the microstructure information, as well as thermodynamics properties of individual components, have to be identified first. This work develops interatomic potentials of molten ZnCl2 based on neural-network machine learning approach for the first time. The machine learning potential is trained by fitting to the energies and forces of liquid structures ab initio molecular dynamics calculations. The developed machine learning potential is validated by comparing partial radial distribution functions, coordination numbers, and partial structure factors with AIMD and PIM potential. The machine learning potential yields a more precise description of the microstructures than the PIM potential which suffers from the analytical form. Furthermore, structural and thermophysical evolution with temperature are studied and the results are in good agreement with experimental values. The efficient machine learning potential with DFT accuracy from our study will provide a promising scheme for accurate molecular simulations of structures and dynamics of molten ZnCl2 mixtures.

Performance of a method for weighting a range in the number of contributors in probabilistic genotyping

Uncertainty in the assignment of the number of contributors (NoC) can be encountered, particularly in higher-order mixtures, where alleles may be shared between contributors, may have dropped out, or may be masked by the stutter artefacts or allelic peaks of a more dominant contributor. Most probabilistic genotyping software requires the assignment of NoC prior to interpretation. NoC has been described as a nuisance parameter. Taylor et al. [1] describe a method to weigh the probability of the profile under different values of N and incorporate this into a likelihood ratio (LR). Within this paper we explore the performance of this variable number of contributors (varNoC) method programmed within the probabilistic genotyping software STRmix™. The desired combination of performance and runtime was obtained using the default STRmix™ version 2.7 MCMC settings in conjunction with a 2.5 % hyper-rectangle range, at least 10,000 naïve MC iterations and 8 MCMC chains. The varNoC LR demonstrated the typical sensitivity and specificity behaviour seen in previous studies, with a high level of reproducibility given repeat analyses. Profiles previously demonstrating ambiguity in the NoC assigned using conventional estimation methods, were able to be reliably interpreted and a varNoC LR assigned.

COMBSecretomics: A pragmatic methodological framework for higher-order drug combination analysis using secretomics

Multi drug treatments are increasingly used in the clinic to combat complex and co-occurring diseases. However, most drug combination discovery efforts today are mainly focused on anticancer therapy and rarely examine the potential of using more than two drugs simultaneously. Moreover, there is currently no reported methodology for performing second- and higher-order drug combination analysis of secretomic patterns, meaning protein concentration profiles released by the cells. Here, we introduce COMBSecretomics (https://github.com/EffieChantzi/COMBSecretomics.git), the first pragmatic methodological framework designed to search exhaustively for second- and higher-order mixtures of candidate treatments that can modify, or even reverse malfunctioning secretomic patterns of human cells. This framework comes with two novel model-free combination analysis methods; a tailor-made generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering. Quality control procedures to eliminate outliers and non-parametric statistics to quantify uncertainty in the results obtained are also included. COMBSecretomics is based on a standardized reproducible format and could be employed with any experimental platform that provides the required protein release data. Its practical use and functionality are demonstrated by means of a proof-of-principle pharmacological study related to cartilage degradation. COMBSecretomics is the first methodological framework reported to enable secretome-related second- and higher-order drug combination analysis. It could be used in drug discovery and development projects, clinical practice, as well as basic biological understanding of the largely unexplored changes in cell-cell communication that occurs due to disease and/or associated pharmacological treatment conditions.

Model-based rationalization of mixture toxicity and accumulation in Triticum aestivum upon concurrent exposure to yttrium, lanthanum, and cerium

Rare earth elements (REEs) often co-exist in the environment, but predicting their ‘cocktail effects’ is still challenging, especially for high-order mixtures with more than two components. Here, we systematically investigated the toxicity and accumulation of yttrium, lanthanum, and cerium mixtures in Triticum aestivum following a standardized bioassay. Toxic effects of mixtures were predicted using the reference model of Concentration Addition (CA), Ternary model, and Ternary-Plus model. Interactions between the REEs in binary and ternary mixtures were determined based on external and internal concentrations, and their magnitude estimated from the parameters deviated from CA. Strong antagonistic interactions were found in the ternary mixtures even though there were no significant interactions in the binary mixtures. Predictive ability increased when using the CA model, Ternary model, and Ternary-Plus model, with R2= 0.78, 0.80, and 0.87 based on external exposure concentrations, and R2= 0.72, 0.73, and 0.79, respectively based on internal concentrations. The bioavailability-based model WHAM-FTOX explained more than 88 % and 85 % of the toxicity of binary and ternary REE treatments, respectively. Our result showed that the Ternary-Plus model and WHAM-FTOX model are promising tools to account for the interaction of REEs in mixtures and could be used for their risk assessment.

Interpreting a major component from a mixed DNA profile with an unknown number of minor contributors

Modern interpretation strategies typically require an assignment of the number of contributors (N) to a DNA profile. This can prove to be a difficult task, particularly when dealing with higher order mixtures or mixtures where one or more contributors have donated low amounts of DNA. Differences in the assigned N at interpretation can lead to differences in the likelihood ration (LR). If the number of contributors cannot reasonably be assigned, then an interpretation of the profile may not be able to be progressed.In this study, we investigate mixed DNA profiles of varying complexity and interpret them altering the assigned N. We assign LRs for true- and non- contributors and compare the results given different assignments of N over a range of mixture proportions. When a component of a mixture had a proportion of at least 10%, a ratio of at least 1.5:1 to the next highest component, and a DNA amount (as determined by STRmix™) of at least 50 rfu, the LR of the component for a true contributor was not significantly affected by varying N and was therefore suitable for interpretation and the assignment of an LR. LRs produced for minor contributors were found to vary significantly as the assigned N was changed. These heuristics may be used to identify profiles suitable for interpretation.

Estimating number of contributors in massively parallel sequencing data of STR loci

In recent years a number of computer-based algorithms have been developed for the deconvolution of complex DNA mixtures in forensic science. These procedures utilize likelihood ratios that quantify the evidence for a hypothesis for the presence of a person of interest in a DNA profile compared to an alternative hypothesis. Proper operation of these software systems requires an assumption regarding the total number of contributors present in the mixture. Unfortunately, estimates based on counting the number of alleles at a locus can be inaccurate due to the sharing and masking of alleles at individual loci. The effects of allele masking become increasingly severe as the number of contributors increases, rendering estimates about high-order mixtures uncertain. The accuracy of these estimates can be improved by increasing the number of STR markers in panels, and by using highly polymorphic markers. Increasing the number of STR markers from 13 to 20 (expanded CODIS panel) improves the accuracy of allele count-based estimation methods for low-order mixtures, but accuracy for high-order mixtures (> 3 contributors) remains poor due to allele masking. An alternative technique, massively parallel sequencing, holds great potential to improve the accuracy of the estimate of number of contributors due to its ability to detect sequence polymorphisms within alleles. This process results in an expansion of the number of alleles when compared to that obtained using capillary electrophoresis. Here, we show that the detection of these additional sequence-defined alleles in 22-marker panels improves number of contributor estimates in conceptual mixtures of 4 and 5 contributors.

Separation/extraction, detection, and interpretation of DNA mixtures in forensic science (review).

Interpreting mixed DNA samples containing material from multiple contributors has long been considered a major challenge in forensic casework, especially when encountering low-template DNA (LT-DNA) or high-order mixtures that may involve missing alleles (dropout) and unrelated alleles (drop-in), among others. In the last decades, extraordinary progress has been made in the analysis of mixed DNA samples, which has led to increasing attention to this research field. The advent of new methods for the separation and extraction of DNA from mixtures, novel or jointly applied genetic markers for detection and reliable interpretation approaches for estimating the weight of evidence, as well as the powerful massively parallel sequencing (MPS) technology, has greatly extended the range of mixed samples that can be correctly analyzed. Here, we summarized the investigative approaches and progress in the field of forensic DNA mixture analysis, hoping to provide some assistance to forensic practitioners and to promote further development involving this issue.

Facilitating complex DNA mixture interpretation by sequencing highly polymorphic haplotypes

Interpretation of complex DNA mixtures is an ongoing challenge in the field of forensic genetics. Commonly used STR markers are quite polymorphic, enabling very high statistical association between a single source DNA profile from a crime scene and a matching suspect. STR typing of low order mixtures with two and three contributors also commonly produces high statistical association for a contributor, using current interpretation software. However higher order mixtures, with four contributors or more, are more challenging. Shared alleles among the many contributors may complicate the correct assessment of the number of contributors to a mixture and decreases the statistical support for inclusion of contributors. Recently, there is a rising use of massively parallel sequencing for forensic applications, and markers such as SNPs and short haplotypes are receiving more attention. However, these markers are even less polymorphic than autosomal STRs and are not suitable for complex mixture interpretation. We propose to use a different panel of haplotype markers, which contain many SNPs and are very polymorphic. These markers can be sequenced either by standard sequencing technologies or by a new instrument called MinION that sequences DNA as it passes through a nanopore. This instrument is very small and can sequence long stretches of DNA, but suffers from high error rate. We present a method for calling haplotype alleles facing high error rate, and use simulation to test its robustness. We also calculate likelihood ratio (LR) between propositions of contribution and non-contribution for individuals that do, and do not, contribute to various complex DNA mixtures. Our results indicate that the correct alleles can be identified in a mixture, despite the high sequencing error rate, and that contributors get high LR (>109) even in complex mixtures with up to five contributors. Non-contributors receive a very small LR, below 1 in most cases (>98%), which support their exclusion as possible donors to the complex DNA mixtures.

Determining the number of contributors to DNA mixtures in the low-template regime: Exploring the impacts of sampling and detection effects.

The interpretation of DNA evidence may rely upon the assumption that the forensic short tandem repeat (STR) profile is composed of multiple genotypes, or partial genotypes, originating from n contributors. In cases where the number of contributors (NOC) is in dispute, it may be justifiable to compute likelihood ratios that utilize different NOC parameters in the numerator and denominator, or present different likelihoods separately. Therefore, in this work, we evaluate the impact of allele dropout on estimating the NOC for simulated mixtures with up to six contributors in the presence or absence of a major contributor. These simulations demonstrate that in the presence of dropout, or with the application of an analytical threshold (AT), estimating the NOC using counting methods was unreliable for mixtures containing one or more minor contributors present at low levels. The number of misidentifications was only slightly reduced when we expand the number of STR loci from 16 to 21. In many of the simulations tested herein, the minimum and actual NOC differed by more than two, suggesting that low-template, high-order mixtures with allele counts fewer than six may be originating from as many as four-, five-, or six-persons. Thus, there is justification for the use of differing or multiple assumptions on the NOC when computing the weight of DNA evidence for low-template mixtures, particularly when the peak heights are in the vicinity of the signal threshold or allele counting methods are the mechanism by which the NOC is assessed.

Widom Lines in Binary Mixtures of Supercritical Fluids

Recent experiments on pure fluids have identified distinct liquid-like and gas-like regimes even under supercritical conditions. The supercritical liquid-gas transition is marked by maxima in response functions that define a line emanating from the critical point, referred to as Widom line. However, the structure of analogous state transitions in mixtures of supercritical fluids has not been determined, and it is not clear whether a Widom line can be identified for binary mixtures. Here, we present first evidence for the existence of multiple Widom lines in binary mixtures from molecular dynamics simulations. By considering mixtures of noble gases, we show that, depending on the phase behavior, mixtures transition from a liquid-like to a gas-like regime via distinctly different pathways, leading to phase relationships of surprising complexity and variety. Specifically, we show that miscible binary mixtures have behavior analogous to a pure fluid and the supercritical state space is characterized by a single liquid-gas transition. In contrast, immiscible binary mixture undergo a phase separation in which the clusters transition separately at different temperatures, resulting in multiple distinct Widom lines. The presence of this unique transition behavior emphasizes the complexity of the supercritical state to be expected in high-order mixtures of practical relevance.

The effect of varying the number of contributors on likelihood ratios for complex DNA mixtures

Interpretation of DNA mixtures with three or more contributors, defined here as high order mixtures, is difficult because of the inevitability of allele sharing. Allele sharing complicates the estimation of the number of contributors, which is an important parameter to assess the probative value. Consequently, these mixtures may not be deemed suitable for interpretation and reporting. In this study, we generated three-, four- and five-person mixtures with little or no drop-out and with varying levels of allele sharing. For these DNA mixtures we computed likelihood ratios (LRs) using the LRmix model, and always using persons of interest that are true contributors. We assessed the influence of different scenarios on the LR, and used (1) the true or an incorrect number of contributors, (2) zero, one or two anchored individuals and (3) an equal number of contributors under Hp and Hd or an extra contributor under Hd. It was shown that the LR varied considerably when the hypotheses used an incorrect number of contributors, especially when individuals were anchored under the hypotheses.Overall, when analysing high order mixtures, there may occur a transition from LR greater than one to less than one if an incorrect number of contributors is conditioned. This is a result of allele sharing among the multiple contributors rather than allele drop-out, since this study only utilised samples with little or no drop-out.

Complex DNA mixture analysis in a forensic context: Evaluating the probative value using a likelihood ratio model

The interpretation of mixed DNA profiles obtained from low template DNA samples has proven to be a particularly difficult task in forensic casework. Newly developed likelihood ratio (LR) models that account for PCR-related stochastic effects, such as allelic drop-out, drop-in and stutters, have enabled the analysis of complex cases that would otherwise have been reported as inconclusive. In such samples, there are uncertainties about the number of contributors, and the correct sets of propositions to consider. Using experimental samples, where the genotypes of the donors are known, we evaluated the feasibility and the relevance of the interpretation of high order mixtures, of three, four and five donors.The relative risks of analyzing high order mixtures of three, four, and five donors, were established by comparison of a ‘gold standard’ LR, to the LR that would be obtained in casework. The ‘gold standard’ LR is the ideal LR: since the genotypes and number of contributors are known, it follows that the parameters needed to compute the LR can be determined per contributor. The ‘casework LR’ was calculated as used in standard practice, where unknown donors are assumed; the parameters were estimated from the available data. Both LRs were calculated using the basic standard model, also termed the drop-out/drop-in model, implemented in the LRmix module of the R package Forensim.We show how our results furthered the understanding of the relevance of analyzing high order mixtures in a forensic context. Limitations are highlighted, and it is illustrated how our study serves as a guide to implement likelihood ratio interpretation of complex DNA profiles in forensic casework.

Active wavelength selection for mixture analysis with tunable infrared detectors

This article presents an active wavelength selection algorithm for multicomponent analysis with tunable infrared sensors. Traditional techniques for wavelength selection operate off-line; as a result, the resulting feature subset is fixed and only optimal for the specific mixtures and noise levels in the training set. To address this limitation, the proposed algorithm interleaves the wavelength-selection and sensing steps so that the feature subset adapts to information from previous measurements. At each point in the process, the algorithm maintains a pool of candidate solutions (i.e., mixtures) consistent with all past measurements, then selects the wavelength that maximizes discrimination across the pool. The algorithm uses a weighting function based on Akaike information criterion to promote parsimonious solutions and balance exploration vs. exploitation strategies. The algorithm is validated experimentally on binary mixture problems with a tunable infrared detector (Fabry-Perot interferometer), and its performance on higher-order mixtures characterized in simulation with a large spectral library. Active wavelength selection outperforms passive strategies, particularly at low signal-to-noise and foreground-background ratios, and when mixture components are similar, in which case the problem becomes ill conditioned.

In-tube condensation of zeotropic fluid mixtures: A review

The condensation of multi-component mixtures in tubes is critical to the function of many energy conversion processes. This paper reviews experimental and analytical studies of the coupled heat and mass transfer phenomena during condensation of different fluid mixtures in different in-tube geometries. Experimental studies on condensation of binary and higher order mixtures in vertical and horizontal smooth and enhanced tubes are discussed. For zeotropic mixtures the heat and mass transfer resistances in the vapor and liquid phases increase the overall heat transfer resistance beyond what would be expected from a weighted average of corresponding resistances for the pure components. There are significant opportunities to improve the quality of available data for new fluid combinations in smaller channels and at fine mass quality increments. Four categories of models are reviewed (1) Conservation equation (2) Non-equilibrium film theory (3) Equilibrium and (4) Empirical models.

The Role of Odour Quality in the Perception of Binary and Higher-Order Mixtures

Twenty participants scaled similarities in odour quality, odour intensity and pleasantness/ unpleasantness of 10 binary and 5 higher-order mixtures of 5 odorous degradation products from the polymer Polyamide 6.6. The perceived odour qualities of all binary mixtures were represented well as intermediary vectors relative to their component-odour vectors in a three-component principal components analysis. The odour qualities of the "floral/fruity" 2-pentylcyclopentan-1-one and the "sharp/cheese-like" pentanoic acid contributed profoundly to their binary mixtures, as did the "minty" cyclopentanone, but in fewer cases. Conversely, the "ether-like" 2-methyl pyridine and "nutty" butanamide did not contribute much. Odour similarity was shown to be caused by odour quality, rather than odour intensity. Three out of five degradation products formed distinct clusters of odours and were therefore interpreted to be profound contributors to the odour quality of the binary mixtures. The higher-order mixtures created new odour qualities which were completely different and untraceable to their various parts as perceived alone. These results demonstrate that it is critical to research the perception of natural mixtures in order to be able to understand the human olfactory code.