Multiple Mixture Components Research Articles

Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data.

With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy, which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.

Multiple factors contribute to flight behaviors during fear conditioning

Shifting defensive mode from one to another by the imminence of threat is crucial for survival. The transition of defensive mode from freezing to flight is observed during the modified fear conditioning, however, the flight during fear conditioning is not well characterized. To characterize the flight behaviors during the fear conditioning, we conducted experiments in male mice focusing on the influence of the context, the intensity of the unconditioned stimulus and conditioned stimulus (CS), the schedule of conditioning, and the state of the subject. Flight behaviors triggered by salient CS showed characteristics of fear-potentiated defensive behaviors depending on the conditioned context, while repetitive conditioning enhanced the expression of the flight and developed an association between the CS and the flight. The salient auditory stimulus was the primary factor to trigger flight behaviors. Also, the spaced conditioning increased the expression of flight behaviors. Taken together, the flight behavior during fear conditioning is not a simple conditioned response nor simple fear-potentiated behavior, but a complicated mixture of multiple components of defensive behaviors. The transition of defensive mode could be induced by the integration of multiple innate and learned components of fear or anxiety.

Deep Unsupervised Hashing with Latent Semantic Components

Deep unsupervised hashing has been appreciated in the regime of image retrieval. However, most prior arts failed to detect the semantic components and their relationships behind the images, which makes them lack discriminative power. To make up the defect, we propose a novel Deep Semantic Components Hashing (DSCH), which involves a common sense that an image normally contains a bunch of semantic components with homology and co-occurrence relationships. Based on this prior, DSCH regards the semantic components as latent variables under the Expectation-Maximization framework and designs a two-step iterative algorithm with the objective of maximum likelihood of training data. Firstly, DSCH constructs a semantic component structure by uncovering the fine-grained semantics components of images with a Gaussian Mixture Modal~(GMM), where an image is represented as a mixture of multiple components, and the semantics co-occurrence are exploited. Besides, coarse-grained semantics components, are discovered by considering the homology relationships between fine-grained components, and the hierarchy organization is then constructed. Secondly, DSCH makes the images close to their semantic component centers at both fine-grained and coarse-grained levels, and also makes the images share similar semantic components close to each other. Extensive experiments on three benchmark datasets demonstrate that the proposed hierarchical semantic components indeed facilitate the hashing model to achieve superior performance.

Implementing machine learning for the identification and classification of compound and mixtures in portable Raman instruments

Portable Raman instruments provide quick, nondestructive analysis of organic and inorganic compounds, making it widely applicable in various disciplines. However, the instrument’s accuracy when analyzing pure, or multiple component mixtures is still an aspect that needs improvement. This study explored machine learning algorithms to classify single compounds, binary, ternary, and quaternary mixtures by the compound name, and the compound’s class, using seized drugs and common diluents as a model. The accuracies were ≥ 93% for most pure, binary mixtures, and quaternary mixtures algorithms. Therefore, incorporating machine learning algorithms in portable instruments, can improve the detection of unknown substances with high accuracies.

Systematic investigation for the mechanisms and the substance basis of Yang-Xin-Ding-Ji capsule based on the metabolite profile and network pharmacology.

Because traditional Chinese medicine (TCM) is a complex mixture of multiple components, the application of methodologies for evaluating single-component Western medicine in TCM studies may have certain limitations. Appropriate strategies that recognize the integrality of TCM and connect to TCM theories remain to be developed. Yang-Xin-Ding-Ji (YXDJ) capsule is originally from a classical TCM formula used for the treatment of arrhythmia. In this study, we used UPLC-Q-TOF-MS detection method, coupled with the metabolic research and network pharmacology analysis, to study the scientific connotation of the YXDJ capsule. A total of 33 absorbed constituents and 23 metabolites were identified or tentatively characterized in dosed plasma and urine, and the possible metabolic pathways were mainly methylation, oxidation, sulfation, glucuronidation, and deglucosylation. We optimized the conventional process ways of network pharmacology by collecting targets based on absorbed constituents into the blood. The constituents-target disease and Kyoto Encyclopedia of Genes pathway analysis revealed that 24 absorbed constituents, 32 target genes, and 10 key pathways were probably related to the efficacy of the YXDJ capsule against arrhythmia. The results provided a scientific basis for understanding the bioactive compounds and the pharmacological mechanism of the YXDJ capsule.

Acoustic analysis of gas compositions based on molecular relaxation features

The quantitative detection of gas compositions in mixture has been a challenge in the field of acoustic sensing. In this paper, we propose an acoustic gas sensing method using the molecular relaxation features to identify gas composition and determine composition concentration in excitable gases. The proposed method is based on the fact that the relaxation strength and relaxation time constitute relaxation absorption spectrum which characterizes the acoustic molecular relaxation process, and they can be synthesized only using two-frequency acoustic measurements. Then the applications of the method in the simple gas mixtures CO2-air and CH4-air, and the multiple component mixtures CO2-CH4-air demonstrate the feasibility, and the discussion of the sensing performance verifies the robustness. This work provides a more exact, feasible and practical method for the gas sensing applications.

Study on the pyrolysis behaviors of mixed waste plastics

Pyrolysis is considered as a promising technology to recover valuable hydrocarbons from waste streams. Even though some studies exist on waste plastics pyrolysis, it still lacks of in-depth knowledge on multiple components plastics pyrolysis. This study explores three types of waste plastics and their mixed forms to obtain thermal cracking features, products distribution, and kinetic features. TG-FTIR was used to observe the decomposition characteristics and functional groups in the volatile fractions of products. The interaction effects of the plastics components during the co-pyrolysis process were compared, analyzed, and reported. For a more precise knowledge of the reaction procedure, a thermodynamic study was conducted. Results showed that TG and DTG curves of PS and PE were similar to each other but different from those of PVC. The main products of PE and PS pyrolysis were alkanes and alkenes, while the major products of PVC pyrolysis were HCl, alkenes, and a small number of aromatic compounds. During the co-pyrolysis process of PS with PVC and PE with PVC, the thermal decomposition rates of PVC were accelerated due to synergistic effect. Implied by the kinetic parameters, pyrolysis of PS, PE, and PVC exhibited one-order reaction from pure component to multiple component mixtures.

Mixture of Graphene Oxide/Phosphoric Acid/Melamine as Coating for Improved Fire Protective Performance and Enhancement of Surface Electrical Properties on Wood Chipboard.

Looking for multifunctional materials, an assessment of the performances both as fire retardant and generator of electrically conductive surfaces for a three component mixture of graphene oxide, phosphoric acid and melamine applied on wood chipboard was performed. A simple approach was used to investigate the intumescent char formation and quantify the loss mass during vertical burning tests, in which the prepared samples were exposed for a certain time interval to a flame generated by an ethanol lamp in ambient conditions. Moreover, mass loss evolution and structural changes that occur during the burning process were more comprehensive investigated by differential thermal and thermogravimetric (DTA/TGA) techniques. By comparing the performances between the wood chipboard samples without any coverage and those covered with one or multiple component mixture, an increase of protection against the fire action was noticed when the three component mixture was used. Also, an improvement of the electrical properties was observed, after flame exposure of the samples covered with multiple layers (i.e., two and three), when the three component mixture was used. Morphological and structural investigations by microscopy (optical and electronic-SEMEDX), X-ray diffraction (XRD) and spectral (Raman, FTIR) methods are described. An assessment of market potential is also discussed.

Synthesis framework for distillation sequence with sidestream columns: Application in reaction-separation-recycle system

In this work, a comprehensive design method for three-section sidestream columns along with a novel State Task Network representation including both conventional and sidestream distillation sequence configurations is present. The distillation network is embedded in the superstructure-based framework for reaction-separation-recycle process synthesis to explore the mass interaction in different sub-systems. In the superstructure, complete interconnections among reactor modules as well as necessary connections between reactor modules and distillation columns are involved to guarantee opportune coupled degree. Based on this, component distribution in distillation through Underwood equations is introduced to provide desired products with required purity and multiple component mixtures as feedback to the reactor network, achieving the optimization of mass interaction in the whole flowsheet. Furthermore, Fenske-Underwood-Gilliland shortcut method to design columns with and without sidestream, and finite element with orthogonal collocation approach to design PFRs are employed, so that reasonable tradeoff between model accuracy and solution difficulty is achieved. Finally, two literature examples are performed to demonstrate the feasibility and validity of the proposed methodology.

Eutectic Mixing As a Knob for Next-Generation Flow Batteries

Flow batteries can offer scalability, long cycle-life, and power-to-energy tunability, however, they have low energy density. An under-explored “knob” for increasing the energy density of flow batteries is eutectic mixing for the depression of melting points. This knob could enable high energy-density flow batteries by achieving solvent-free electrolytes. We demonstrate the ability of eutectic mixing to decrease melting points in the case of n-component mixtures of benzoquinone derivatives, which are redox-active molecules. Predicting the properties of mixtures of more than two components is not commonly done, but we show that, in this case, a regular solution model has predictive power for finding the eutectic melting temperatures and compositions for mixtures of multiple components.

Compositional inhomogeneity of drug delivery liposomes quantified at the single liposome level

Liposomes are the most used drug delivery vehicle and their therapeutic function is closely linked to their lipid composition. Since most liposome characterization is done using bulk techniques, providing only ensemble averages, the lipid composition of all liposomes within the same formulation are typically assumed to be identical. Here we image individual liposomes using confocal microscopy to quantify that liposomal drug delivery formulations, including multiple component mixtures mimicking Doxil, display more than 10-fold variation in their relative lipid composition. Since liposome function is tightly regulated by the physicochemical properties bestowed by the lipid composition, such significant variations could render only a fraction of liposomes therapeutically active. Additionally, we quantified how this degree of compositional inhomogeneity was modulated by liposome preparation method, the saturation state of the membrane lipid, and whether anti-fouling polyethylene glycol (PEG) conjugated lipids were added to the initial lipid mix or inserted after liposome formation. We believe the insights into the factors governing the degree of inhomogeneity offers the possibility for producing more uniform liposomal drug delivery systems, potentially increasing their therapeutic efficacy.

Using Orbitrap mass spectrometry to assess the isotopic compositions of individual compounds in mixtures

The isotopic compositions of individual chemical species are routinely used by the geochemical, environmental, forensic, anthropological, chemical, and biomedical communities to elucidate the conditions, sources, and reaction pathways of the molecules in question. Mass spectrometric methods of measuring isotopic compositions of individual compounds generally require that analytes be pure to yield precise, accurate results, yet most applications examine materials that are mixtures of multiple components. Various methods of chemical purification, e.g., chromatography, are used to isolate analytes from mixtures prior to mass spectrometric analysis. However, these techniques take time and specialized instrumentation, both of which could potentially be obviated via the use of ultra-high-resolution mass spectrometry. Here we report on the use of Orbitrap™-based Fourier-transform mass spectrometry to perform isotope ratio measurements of single species within mixtures delivered to the mass spectrometer (MS) without prior chromatographic separation. We demonstrate that instrument biases (attributed here to space charge effects) within the Orbitrap mass analyzer can cause the measured 13C/12C ratio of a molecular ion in the presence of non-analyte-derived ‘contaminating’ species to spuriously decrease relative to the 13C/12C ratio measured for the same ion in a pure analyte. We observe that the decrease in 13C/12C is proportional to the relative concentrations of the additional ‘contaminating’ components. We then recommend several strategies by which this effect can be mediated such that accurate isotope ratios can be obtained.

Development and validation of the eutectic reaction model in JUPITER code

The eutectic reaction model in JUPITER code was validated against two series of experimental tests that performed by JAEA. An experiment that aimed to evaluate the eutectic reaction between Zircaloy and Stainless steel, was simulated by JUPITER code to validate its reliability on predicting the binary eutectic reaction phenomenon. A comparison of the simulation and experimental results demonstrates good agreement on the increase rate of the solution depth at various temperature environments. Another series of tests which aimed to predict the eutectic reaction between the control rod blade and channel box in BWR, were simulated by JUPITER code to test its applicability on predicting the eutectic reaction between multiple mixture components. Although the deviation could not be completely eliminated, the reaction performance in the experiment was reasonably reproduced. As a result, it could be concluded that JUPITER code is feasible to predict the eutectic reaction behavior in nuclear severe accident.

Transmetalation and Demetallization for Open-Oyster-like Non-Ionic Cd(II) Macrocycles.

This work designed a nonionic extended dialdehyde 6,6'-(phenylazanediyl)dipicolinaldehyde (PDPA) for constructing Schiff-base macrocyclic complexes with weaker metal-ligand interactions, so as to solve the long-standing challenges of transmetalation and demetallization in macrocyclic complexes. An enantiomeric pair of open-oyster-like 26-membered [2 + 2] Schiff-base macrocyclic dinuclear Cd(II) complexes (S,S-1a, R,R-1b) could be obtained, having S,S/R,R-1,2-diaminocyclohexane (S,S/R,R-DACH) precursors, while Cu(II) ion template only resulted in a mononuclear Schiff-base Cu(II) acyclic complex (S,S-2) accompanied by the half-oxidation of PDPA instead of expected [2 + 2] Cu(II) macrocyclic complexes. It is suggested that the weak oxidization capability of Cu(II) ion is responsible for the formation of S,S-2 because X-ray photoelectron spectroscopy (XPS) for the solid powder of reaction mixture of direct Cu(II) ion template synthesis implies that both Cu(I) and Cu(II) species are present. In fact, corresponding [2 + 2] dinuclear Cu(II) macrocycles and even metal-free macrocycles unsuitable for direct synthesis can be obtained via Cd(II) → Cu(II) transmetalation and Na2S demetalation verified by ESI-MS and UV-vis spectra. In addition, control experiments indicate that the synthesis of metal-free macrocycles via the direct nontemplate method merely results in the mixture of multiple components of [1 + 1], [2 + 2], and [3 + 3] Schiff-base macrocycles, and they are difficult to isolate.

Optimal filtering of angle-time cyclostationary signals: Application to vibrations recorded under nonstationary regimes

Mechanical signals are often a mixture of multiple components produced by different sources. The separation of these components is beneficial for differential diagnosis, fault severity assessment and prognosis. An optimal way to accomplish this task is to apply a linear periodically time-variant filter, also known as the cyclic Wiener filter (CWF), assuming the signal to be cyclostationary and assuming that the source periodicity is a priori known. For the first assumption to be valid, the machine must operate under a stationary regime which is generally a restrictive condition. This paper addresses this issue by proposing a formal extension of the CWF to the nonstationary regime case within the angle-time cyclostationary framework. This framework was specifically designed to describe and process machine signals recorded under variable speed conditions. In addition to the theoretical formalisation of the so-called angle-time CWF, a simple and fast algorithm based on the Welch estimator is proposed. The efficiency of the new filter is demonstrated, and compared to the classical one, on synthetic and real vibration signals.

Automated, context-free assignment of asymmetric rotor microwave spectra.

We present a new algorithm, Robust Automated Assignment of Rigid Rotors (RAARR), for assigning rotational spectra of asymmetric tops. The RAARR algorithm can automatically assign experimental spectra under a broad range of conditions, including spectra comprised of multiple mixture components, in ≲100 s. The RAARR algorithm exploits constraints placed by the conservation of energy to find sets of connected lines in an unassigned spectrum. The highly constrained structure of these sets eliminates all but a handful of plausible assignments for a given set, greatly reducing the number of potential assignments that must be evaluated. We successfully apply our algorithm to automatically assign 15 experimental spectra, including 5 previously unassigned species, without prior estimation of molecular rotational constants. In 9 of the 15 cases, the RAARR algorithm successfully assigns two or more mixture components.

Association of particulate matter air pollution and hospital visits for respiratory diseases: a time-series study from China.

Fine particulate matter (PM2.5) is a mixture of multiple components, which is associated with several chronic diseases, including respiratory and cardiovascular diseases. We evaluated the association between daily PM2.5 and PM2.5-10 exposure and hospital visits for respiratory diseases. Hospital visits for respiratory diseases were collected from Yinzhou Health Information System database. We used generalized additive models to examine the excess relative risk (ERR) and 95% confidence interval for hospital visits for respiratory diseases associated with each 10-μg/m3 increase in PM2.5 and PM2.5-10 concentration. Non-linear exposure-response relationship between PM exposure and hospital visits for respiratory diseases was evaluated by a smooth spline. The ERRs for hospital visits for respiratory diseases associated with a 10-μg/m3 increase in the 6-day cumulative average concentration of PM2.5 and PM2.5-10 were 5.40 (95% CI 2.32, 8.57) and 6.37% (95% CI 1.84, 11.10), respectively. The findings remained stable when we adjusted other gaseous air pollution. PM2.5 and PM2.5-10 were associated with the increased visits for the acute upper respiratory infection, pneumonia, asthma, and COPD. In this time-series study, we found a positive association between daily particulate matter exposure and hospital visits for respiratory diseases.

Volatile profiles of three tree species in the northeastern China and associated effects on Sirex noctilio activity

ABSTRACT The wood-boring wasp, Sirex noctilio, is an invasive pest of numerous species of pine trees worldwide. The female S. noctilio wasps selectively lay eggs on different pine trees. The relationship between host volatile organic compounds and the wood wasps remains elusive. Here, the behaviors of the wood towards the host volatiles wasps were investigated. In Junde Forest Farm, three tree species, Pinus sylvestris var. mongolica, Larix gmelinii (Rupr.) Kuzen., and P. koraiensis Sieb.et Zucc., were girdled. Four treatments of trees, un-girdled, girdled, dying, and dead tree, were randomly selected as sample trees. Lindgren 12-funnel trap was placed on sample tree. The traps were checked once a week and the number of S. noctilio was recorded. The relationship between volatiles and S. noctilio attraction was analyzed by collecting volatiles from different tree species and treatments. trans-β-Ocimene, terpinolene, α-pinene oxide, and longifolene were present only in girdled P. sylvestris var. mongolica. Girdling the host tree can make released more volatiles, which was conducive to attracting S. noctilio. Effect of a single compound on S. noctilio is limited, but a mixture of multiple components formed a specific chemical signature to female S. noctilio. It could be useful in the monitoring and effective management of wood wasps.

Distributive structure-properties relationship for flash point of multiple components mixture

Flash point (FP) is an important reference property for characterizing the fire hazards of any combustible material in the field of petro-chemical processes and functional materials designing, whereas, obtaining FP data for flammable liquids is difficult owing to several limitations. Herein, the distributive structure-properties relationship (DSPR) method for simultaneously investigating the relationship between the structure and the FP for multiple-component mixtures (mono/binary/ternary component) was proposed and was composed of a DSPR model and hybrid norm indexes. Serial of validation metrics prove that this model exhibited good predictive and generalization abilities in prediction of FP for multiple-component mixtures. Thus, this new method and hybrid indexes proposed in this work is effective for the characteristic of the relationship between structure and FP and might have larger applications in chemical process safety.

Investigating the effect of artists\u2019 paint formulation on degradation rates of TiO2-based oil paints

This study reports on the effect of artists’ paint formulation on degradation rates of TiO2-based oil paints. Titanium white oil paint exists in a multitude of different recipes, and the effect of the formulation on photocatalytic binder degradation kinetics is unknown. These formulations contain, among others, one or both titanium dioxide polymorphs, zinc oxide, the extenders barium sulfate or calcium carbonate and various additives. Most research performed on the photocatalytic degradation process focusses on pure titanium white-binder mixtures and thus does not take into account the complete paint system. Since photocatalytic oil degradation is a process initiated by the absorption of UV light, any ingredient or combination of ingredients influencing the light scattering and absorption properties of the paint films may affect the degradation rate. In this study three sets of experiments are conducted, designed using the design of experiments (DoE) approach, to screen for the most important formulation factors influencing the degradation rate. The benefits of using DoE, compared to a more traditional ‘one factor at a time approach’ are robustness, sample efficiency, the ability of evaluate mixtures of multiple components as well as the ability to evaluate factor interactions. The three sets of experiments investigate (1) the influence of the TiO2 type, (2) the impact of different mixtures of two types of TiO2, ZnO and the additive aluminum stearate and (3) the influence of common extenders in combination with photocatalytic TiO2, on the photocatalytic degradation of the oil binder. The impact of the formulation on the degradation rate became apparent, indicating the shortcoming of oversimplified studies. The protective effect of photostable TiO2 pigments, even in a mixture with photocatalytic TiO2 pigments, as well as the negative effect of extenders was demonstrated. Furthermore, the ambiguous role of ZnO (photocatalytic or not) and aluminum stearate is highlighted. Neither can be ignored in a study of degradation behavior of modern oil paints and require further investigation.