GTRpmix: A Linked General Time-Reversible Model for Profile Mixture Models.

Profile mixture models capture distinct biochemical constraints on the amino acid substitution process at different sites in proteins. These models feature a mixture of time-reversible models with a common matrix of exchangeabilities and distinct sets of equilibrium amino acid frequencies known as profiles. Combining the exchangeability matrix with each profile generates the matrix of instantaneous rates of amino acid exchange for that profile. Currently, empirically estimated exchangeability matrices (e.g. the LG matrix) are widely used for phylogenetic inference under profile mixture models. However, these were estimated using a single profile and are unlikely optimal for profile mixture models. Here, we describe the GTRpmix model that allows maximum likelihood estimation of a common exchangeability matrix under any profile mixture model. We show that exchangeability matrices estimated under profile mixture models differ from the LG matrix, dramatically improving model fit and topological estimation accuracy for empirical test cases. Because the GTRpmix model is computationally expensive, we provide two exchangeability matrices estimated from large concatenated phylogenomic-supermatrices to be used for phylogenetic analyses. One, called Eukaryotic Linked Mixture (ELM), is designed for phylogenetic analysis of proteins encoded by nuclear genomes of eukaryotes, and the other, Eukaryotic and Archaeal Linked mixture (EAL), for reconstructing relationships between eukaryotes and Archaea. These matrices, combined with profile mixture models, fit data better and have improved topology estimation relative to the LG matrix combined with the same mixture models. Starting with version 2.3.1, IQ-TREE2 allows users to estimate linked exchangeabilities (i.e. amino acid exchange rates) under profile mixture models.

Multiple imputation of more than one environmental exposure with nondifferential measurement error.

Measurement error is common in environmental epidemiologic studies, but methods for correcting measurement error in regression models with multiple environmental exposures as covariates have not been well investigated. We consider a multiple imputation approach, combining external or internal calibration samples that contain information on both true and error-prone exposures with the main study data of multiple exposures measured with error. We propose a constrained chained equations multiple imputation (CEMI) algorithm that places constraints on the imputation model parameters in the chained equations imputation based on the assumptions of strong nondifferential measurement error. We also extend the constrained CEMI method to accommodate nondetects in the error-prone exposures in the main study data. We estimate the variance of the regression coefficients using the bootstrap with two imputations of each bootstrapped sample. The constrained CEMI method is shown by simulations to outperform existing methods, namely the method that ignores measurement error, classical calibration, and regression prediction, yielding estimated regression coefficients with smaller bias and confidence intervals with coverage close to the nominal level. We apply the proposed method to the Neighborhood Asthma and Allergy Study to investigate the associations between the concentrations of multiple indoor allergens and the fractional exhaled nitric oxide level among asthmatic children in New York City. The constrained CEMI method can be implemented by imposing constraints on the imputation matrix using the mice and bootImpute packages in R.

Mass spectrometry of polymers: A tutorial review.

Ever since the inception of synthetic polymeric materials in the late 19th century, the number of studies on polymers as well as the complexity of their structures have only increased. The development and commercialization of new polymers with properties fine-tuned for specific technological, environmental, consumer, or biomedical applications requires powerful analytical techniques that permit the in-depth characterization of these materials. One such method with the ability to provide chemical composition and structure information with high sensitivity, selectivity, specificity, and speed is mass spectrometry (MS). This tutorial review presents and exemplifies the various MS techniques available for the elucidation of specific structural features in a synthetic polymer, including compositional complexity, primary structure, architecture, topology, and surface properties. Key to every MS analysis is sample conversion to gas-phase ions. This review describes the fundamentals of the most suitable ionization methods for synthetic materials and provides relevant sample preparation protocols. Most importantly, structural characterizations via one-step as well as hyphenated or multidimensional approaches are introduced and demonstrated with specific applications, including surface sensitive and imaging techniques. The aim of this tutorial review is to illustrate the capabilities of MS for the characterization of large, complex polymers and emphasize its potential as a powerful compositional and structural elucidation tool in polymer chemistry.

Ion Source Complementarity for Characterization of Complex Organic Mixtures Using Fourier Transform Mass Spectrometry: A Review.

Complex organic mixtures are found in many areas of research, such as energy, environment, health, planetology, and cultural heritage, to name but a few. However, due to their complex chemical composition, which holds an extensive potential of information at the molecular level, their molecular characterization is challenging. In mass spectrometry, the ionization step is the key step, as it determines which species will be detected. This review presents an overview of the main ionization sources employed to characterize these kinds of samples in Fourier transform mass spectrometry (FT-MS), namely electrospray (ESI), atmospheric pressure photoionization (APPI), atmospheric pressure chemical ionization (APCI), atmospheric pressure laser ionization (APLI), and (matrix-assisted) laser desorption ionization ((MA)LDI), and their complementarity in the characterization of complex organic mixtures. First, the ionization techniques are examined in the common direct introduction (DI) usage. Second, these approaches are discussed in the context of coupling chromatographic techniques such as gas chromatography, liquid chromatography, and supercritical fluid chromatography.

Plant diversity decreases greenhouse gas emissions by increasing soil and plant carbon storage in terrestrial ecosystems.

The decline in global plant diversity has raised concerns about its implications for carbon fixation and global greenhouse gas emissions (GGE), including carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4). Therefore, we conducted a comprehensive meta-analysis of 2103 paired observations, examining GGE, soil organic carbon (SOC) and plant carbon in plant mixtures and monocultures. Our findings indicate that plant mixtures decrease soil N2O emissions by 21.4% compared to monocultures. No significant differences occurred between mixtures and monocultures for soil CO2 emissions, CH4 emissions or CH4 uptake. Plant mixtures exhibit higher SOC and plant carbon storage than monocultures. After 10 years of vegetation development, a 40% reduction in species richness decreases SOC content and plant carbon storage by 12.3% and 58.7% respectively. These findings offer insights into the intricate connections between plant diversity, soil and plant carbon storage and GGE-a critical but previously unexamined aspect of biodiversity-ecosystem functioning.

Statistical Thermodynamic Description of Self-Assembly of Large Inclusions in Biological Membranes.

Recent studies revealed anomalous underscreening in concentrated electrolytes, and we suggest that the underscreened electrostatic forces between membrane proteins play a significant role in the process of self-assembly. In this work, we assumed that the underscreened electrostatic forces compete with the thermodynamic Casimir forces induced by concentration fluctuations in the lipid bilayer, and developed a simplified model for a binary mixture of oppositely charged membrane proteins with different preference to liquid-ordered and liquid-disordered domains in the membrane. In the model, like macromolecules interact with short-range Casimir attraction and long-range electrostatic repulsion, and the cross-interaction is of the opposite sign. We determine energetically favored patterns in a system in equilibrium with a bulk reservoir of the macromolecules. Different patterns consisting of clusters and stripes of the two components and of vacancies are energetically favorable for different values of the chemical potentials. Effects of thermal flutuations at low temperature are studied using Monte Carlo simulations in grand canonical and canonical ensembles. For fixed numbers of the macromolecules, a single two-component cluster with a regular pattern coexists with dispersed small one-component clusters, and the number of small clusters depends on the ratio of the numbers of the molecules of the two components. Our results show that the pattern formation is controlled by the shape of the interactions, the density of the proteins, and the proportion of the components.

Integrating Data Across Multiple Sites in the Northeastern United States to Examine Associations Between a Prenatal Metal Mixture and Child Cognition.

We applied a novel hierarchical Bayesian weighted quantile sum (HBWQS) regression to combine data across 3 study sites to examine associations between prenatal exposure to metals and cognitive functioning in childhood. Data from 326 mother-child dyads enrolled in an ongoing cohort study, the Programming of Intergenerational Stress Mechanisms (PRISM) Study, based in New York, New York (recruitment in 2013-2020) and Boston, Massachusetts (recruitment 2011-2013), and the First Thousand Days of Life (FTDL) cohort study (recruitment 2012-2019), based in northern Virginia, were used. Arsenic, cadmium, manganese, lead, and antimony were measured in urine collected during pregnancy. Cognitive functioning was assessed in children aged 3-11 years using the National Institutes of Health Toolbox Cognition Battery. The HBWQS regression showed a negative association between the urinary metal mixture and the Cognition Early Childhood Composite Score in the PRISM New York City (ß = -3.67, 95% credible interval (CrI): -7.61, -0.01) and FTDL (ß = -3.76, 95% CrI: -7.66, -0.24) samples, with a similar trend in the PRISM Boston sample (ß = -3.24, 95% CrI: -6.77, 0.144). We did not detect these associations in traditionally pooled models. HBWQS regression allowed us to account for site heterogeneity and detect associations between prenatal metal-mixture exposure and cognitive outcomes in childhood. Given the ubiquity of metals exposure, interventions aimed at reducing prenatal exposure may improve cognitive outcomes in children. This article is part of a Special Collection on Environmental Epidemiology.

Joint effects of indoor air pollution and maternal psychosocial factors during pregnancy on trajectories of early childhood psychopathology.

Prenatal indoor air pollution and maternal psychosocial factors have been associated with adverse psychopathology. We used environmental-exposure mixture methodology to investigate joint effects of both exposure classes on child behavior trajectories. For 360 children from the South African Drakenstein Child Health Study, we created trajectories of Child Behavior Checklist scores (at 24, 42, and 60 months) using latent-class linear mixed effects models. Indoor air pollutants and psychosocial factors were measured during pregnancy (second trimester). After adjusting for confounding, single-exposure effects (per natural log-1 unit increase) were assessed using polytomous logistic regression models, joint effects using self-organizing maps, and principal component analysis. Three trajectories were chosen for both internalizing and externalizing problems, with "high" (externalizing) or "increasing" (internalizing) being the most adverse trajectories. High externalizing trajectory was associated with increased exposure to particulate matter of ≤ 10 microns in diameter (PM10) (odds ratio [OR] = 1.25; 95% CI, 1.01-1.55) and self-organizing maps exposure profile most associated with smoking (OR = 2.67; 95% CI, 1.14-6.27). Medium internalizing trajectory was associated with increased emotional intimate partner violence (OR = 2.66; 95% CI, 1.17-5.57), increasing trajectory with increased benzene (OR = 1.24; 95% CI, 1.02-1.51) and toluene (1.21; 95% CI, 1.02-1.44) and the principal component most correlated with benzene and toluene (OR = 1.25; 95% CI, 1.02-1.54). Prenatal exposure to environmental pollutants and psychosocial factors was associated with internalizing and externalizing child behavior trajectories. Understanding joint effects of adverse exposure mixtures will facilitate targeted interventions to prevent childhood psychopathology. This article is part of a Special Collection on Mental Health.

A Prospective Cohort Study of Persistent Endocrine Disrupting Chemicals and Perceived Stress.

Persistent endocrine disrupting chemicals (EDCs) can dysregulate the stress response. We evaluated associations between persistent EDCs and perceived stress among participants from the Study of Environment, Lifestyle and Fibroids (n=1,394), a prospective cohort study of Black women. Participants completed the Perceived Stress Scale (PSS-4) at baseline, and every 20 months through 60 months (range of scores: 0-16); higher scores indicated higher stress. EDCs, including per- and polyfluoroalkyl substances (PFAS), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and organochlorine pesticides, were quantified in plasma samples at baseline. We fit Bayesian Kernel Machine Regression (BKMR) and linear mixed effects models to estimate associations of EDCs (as a mixture and individually) with PSS-4 scores at baseline and at each follow-up visit, respectively. Increasing percentiles of the mixture were not strongly associated with PSS-4 scores at baseline, and no interactions were observed among EDCs. Several individual EDCs (e.g., PFDA, PCB 118, PBDE 99) were associated with higher PSS-4 scores at baseline or follow-up, while other EDCs (e.g., PCB 138/158) were associated with lower PSS-4 scores at baseline or follow-up. The directionality of associations for individual EDCs was inconsistent across follow-up visits. In conclusion, specific EDCs may be associated with perceived stress in Black women.

Causal analysis of air pollution mixtures: estimands, positivity, and extrapolation.

Causal inference for air pollution mixtures is an increasingly important issue with appreciable challenges. When the exposure is a multivariate mixture, there are many exposure contrasts that may be of nominal interest for causal effect estimation, but the complex joint mixture distribution often renders observed data extremely limited in their ability to inform estimates of many commonly defined causal effects. We use potential outcomes to (1) define causal effects of air pollution mixtures, (2) formalize the key assumption of mixture positivity required for estimation, and (3) offer diagnostic metrics for positivity violations in the mixture setting that allow researchers to assess the extent to which data can actually support estimation of mixture effects of interest. For settings where there is limited empirical support, we redefine causal estimands that apportion causal effects according to whether they can be directly informed by observed data versus rely entirely on model extrapolation, isolating key sources of information on the causal effect of an air pollution mixture. The ideas are deployed to assess the ability of a national US data set on the chemical components of ambient particulate matter air pollution to support estimation of a variety of causal mixture effects. This article is part of a Special Collection on Environmental Epidemiology.

Multiple prenatal exposures and acute-care clinical encounters for asthma among children born to mothers living near a Superfund site.

Prenatal exposures are associated with childhood asthma, and risk may increase with simultaneous exposures. Pregnant women living in lower-income communities tend to have elevated exposures to a range of potential asthma risk factors, which may interact in complex ways. We examined the association between prenatal exposures and the risk of childhood acute-care clinical encounters for asthma (hospitalizations, emergency department visits, observational stays) using conditional logistic regression with a multivariable smoothing term to model the interaction between continuous variables, adjusted for maternal characteristics and stratified by sex. All births near the New Bedford Harbor (NBH) Superfund site (2000-2006) in New Bedford, Massachusetts, were followed through 2011 using the Massachusetts Pregnancy to Early Life Longitudinal (PELL) Data System to identify children aged 5-11 years with acute-care clinical asthma encounters (265 cases among 7787 children with follow-up). Hazard ratios (HRs) were higher for children living closer to the NBH site with higher umbilical cord blood lead levels than in children living further away from the NBH site with lower lead levels (P <.001). HRs were higher for girls (HR = 4.17; 95% CI, 3.60-4.82) than for boys (HR = 1.72; 95% CI, 1.46-2.02). Our results suggest that prenatal lead exposure in combination with residential proximity to the NBH Superfund site is associated with childhood asthma acute-care clinical encounters. This article is part of a Special Collection on Environmental Epidemiology.

Incorporation of compost and biochar enhances yield and medicinal compounds in seeds of water-stressed Trigonella foenum-graecum L. plants cultivated in saline calcareous soils.

BACKGROUND: The combination of compost and biochar (CB) plays an important role in soil restoration and mitigation strategies against drought stress in plants. In the current study, the impact of CB was determined on the characteristics of saline calcareous soil and the productivity of fenugreek (Trigonella foenum-graecum L.) plants. The field trials examined CB rates (CB0, CB10 and CB20 corresponding to 0, 10, and 20 t ha‒1, respectively) under deficit irrigation [DI0%, DI20%, and DI40% receiving 100, 80, and 60% crop evapotranspiration (ETc), respectively] conditions on growth, seed yield (SY), quality, and water productivity (WP) of fenugreek grown in saline calcareous soils. RESULTS: In general, DI negatively affected the morpho-physio-biochemical responses in plants cultivated in saline calcareous soils. However, amendments of CB10 or CB20 improved soil structure under DI conditions. This was evidenced by the decreased pH, electrical conductivity of soil extract (ECe), and bulk density but increased organic matter, macronutrient (N, P, and K) availability, water retention, and total porosity; thus, maintaining better water and nutritional status. These soil modifications improved chlorophyll, tissue water contents, cell membrane stability, photosystem II photochemical efficiency, photosynthetic performance, and nutritional homeostasis of drought-stressed plants. This was also supported by increased osmolytes, non-enzymatic, and enzymatic activities under DI conditions. Regardless of DI regimes, SY was significantly (P ≤ 0.05) improved by 40.0 and 102.5% when plants were treated with CB10 and CB20, respectively, as similarly observed for seed alkaloids (87.0, and 39.1%), trigonelline content (43.8, and 16.7%) and WP (40.9, and 104.5%) over unamended control plants. CONCLUSIONS: Overall, the application of organic amendments of CB can be a promising sustainable solution for improving saline calcareous soil properties, mitigating the negative effects of DI stress, and enhancing crop productivity in arid and semi-arid agro-climates.

Next-Generation Nanopore Sensors Based on Conductive Pulse Sensing for Enhanced Detection of Nanoparticles.

Nanopore sensing has been successfully used to characterize biological molecules with single-molecule resolution based on the resistive pulse sensing approach. However, its use in nanoparticle characterization has been constrained by the need to tailor the nanopore aperture size to the size of the analyte, precluding the analysis of heterogeneous samples. Additionally, nanopore sensors often require the use of high salt concentrations to improve the signal-to-noise ratio, which further limits their ability to study a wide range of nanoparticles that are unstable at high ionic strength. Here, a new paradigm in nanopore research that takes advantage of a polymer electrolyte system to comprise a conductive pulse sensing approach is presented. A finite element model is developed to explain the conductive pulse signals observed and compare these results with experiments. This system enables the analytical characterization of heterogeneous nanoparticle mixtures at low ionic strength . Furthermore, the wide applicability of the method is demonstrated by characterizing metallic nanospheres of varied sizes, plasmonic nanostars with various degrees of branching, and protein-based spherical nucleic acids with different oligonucleotide loadings. This system will complement the toolbox of nanomaterials characterization techniques to enable real-time optimization workflow for engineering a wide range of nanomaterials.

Stereoisomeric Non-Fullerene Acceptors-Based Organic Solar Cells.

Chiral alkyl chains are ubiquitously observed in organic semiconductor materials and can regulate solution processability and active layer morphology, but the effect of stereoisomers on photovoltaic performance has rarely been investigated. For the racemic Y-type acceptors widely used in organic solar cells, it remains unknown if the individual chiral molecules separate into the conglomerate phase or if racemic phase prevails. Here, the photovoltaic performance of enantiomerically pure Y6 derivatives, (S,S)/(R,R)-BTP-4F, and their chiral mixtures are compared. It is found that (S,S) and (R,R)-BTP-4F molecule in the racemic mixtures tends to interact with its enantiomer. The racemic mixtures enable efficient light harvesting, fast hole transfer, and long polaron lifetime, which is conducive to charge generation and suppresses the recombination losses. Moreover, abundant charge diffusion pathways provided by the racemate contribute to efficient charge transport. As a result, the racemate system maximizes the power output and minimizes losses, leading to a higher efficiency of 18.16% and a reduced energy loss of 0.549 eV, as compared to the enantiomerically pure molecules. This study demonstrates that the chirality of non-fullerene acceptors should receive more attention and be designed rationally to enhance the efficiency of organic solar cells.

The ENDOMIX perspective: how everyday chemical mixtures impact human health and reproduction by targeting the immune system†.

Endocrine disruptor chemicals (EDCs) are natural and synthetic compounds found ubiquitously in the environment that interfere with the hormonal-immune axis, potentially impacting human health and reproduction. Exposure to EDCs has been associated with numerous health risks, such as neurodevelopmental disorders, metabolic syndrome, thyroid dysfunction, infertility, and cancers. Nevertheless, the current approach to establishing causality between EDCs and disease outcomes has limitations. Epidemiological and experimental research on EDCs faces challenges in accurately assessing chemical exposure and interpreting non-monotonic dose response curves. In addition, most studies have focused on single chemicals or simple mixtures, overlooking complex real-life exposures and EDC mechanistic insights, in particular regarding their impact on the immune system. The ENDOMIX project, funded by the EU's Horizon Health Program, addresses these challenges by integrating epidemiological, risk assessment, and immunotoxicology methodologies. This systemic approach comprises the triangulation of human cohort, in vitro, and in vivo data to determine the combined effects of EDC mixtures. The present review presents and discusses current literature regarding human reproduction in the context of immunotolerance and EDC mode of action. It further underscores the ENDOMIX perspective to elucidate the impact of EDCs on immune-reproductive health.

Visualization, benchmarking and characterization of nested single-cell heterogeneity as dynamic forest mixtures.

A major topic of debate in developmental biology centers on whether development is continuous, discontinuous, or a mixture of both. Pseudo-time trajectory models, optimal for visualizing cellular progression, model cell transitions as continuous state manifolds and do not explicitly model real-time, complex, heterogeneous systems and are challenging for benchmarking with temporal models. We present a data-driven framework that addresses these limitations with temporal single-cell data collected at discrete time points as inputs and a mixture of dependent minimum spanning trees (MSTs) as outputs, denoted as dynamic spanning forest mixtures (DSFMix). DSFMix uses decision-tree models to select genes that account for variations in multimodality, skewness and time. The genes are subsequently used to build the forest using tree agglomerative hierarchical clustering and dynamic branch cutting. We first motivate the use of forest-based algorithms compared to single-tree approaches for visualizing and characterizing developmental processes. We next benchmark DSFMix to pseudo-time and temporal approaches in terms of feature selection, time correlation, and network similarity. Finally, we demonstrate how DSFMix can be used to visualize, compare and characterize complex relationships during biological processes such as epithelial-mesenchymal transition, spermatogenesis, stem cell pluripotency, early transcriptional response from hormones and immune response to coronavirus disease. Our results indicate that the expression of genes during normal development exhibits a high proportion of non-uniformly distributed profiles that are mostly right-skewed and multimodal; the latter being a characteristic of major steady states during development. Our study also identifies and validates gene signatures driving complex dynamic processes during somatic or germline differentiation.

Chemical mixture that targets the epidermal growth factor pathway impairs human trophoblast cell functions.

Pregnant women are exposed to complex chemical mixtures, many of which reach the placenta. Some of these chemicals interfere with epidermal growth factor receptor (EGFR) activation, a receptor tyrosine kinase that modulates several placenta cell functions. We hypothesized that a mixture of chemicals (Chem-Mix) known to reduce EGFR activation (polychlorinated biphenyl (PCB)-126, PCB-153, atrazine, trans-nonachlor, niclosamide, and bisphenol S) would interfere with EGFR-mediated trophoblast cell functions. To test this, we determined the chemicals' EGFR binding ability, EGFR and downstream effectors activation, and trophoblast functions (proliferation, invasion, and endovascular differentiation) known to be regulated by EGFR in extravillous trophoblasts (EVTs). The Chem-Mix competed with EGF for EGFR binding, however only PCB-153, niclosamide, trans-nonachlor, and BPS competed for binding as single chemicals. The effects of the Chem-Mix on EGFR phosphorylation were tested by exposing the placental EVT cell line, HTR-8/SVneo to control (0.1% DMSO), Chem-Mix (1, 10, or 100 ng/ml), EGF (30 ng/ml), or Chem-Mix + EGF. The Chem-Mix - but not the individual chemicals - reduced EGF-mediated EGFR phosphorylation in a dose dependent manner, while no effect was observed in its downstream effectors (AKT and STAT3). None of the individual chemicals affected EVT cell invasion, but the Chem-Mix reduced EVT cell invasion independent of EGF. In support of previous studies that have explored chemicals targeting a specific pathway (estrogen/androgen receptor), current findings indicate that exposure to a chemical mixture that targets the EGFR pathway can result in a greater impact compared to individual chemicals in the context of placental cell functions.

A New Look at the Mechanism of Cocrystal Formation and Coformers Exchange in Processes Forced by Mechanical and/or Thermal Stimuli - ex situ and in†situ Studies of Low-Melting Eutectic Mixtures.

Three different devices: ball mill, hot stage melting, and magic angle spinning (MAS) NMR rotor were used for the preparation of ethenzamide (ET) cocrystals with glutaric acid (GLU), ethylmalonic acid (EMA) and maleic acid (MAL) as coformers. In each case, well-defined binary systems (ET:EMA, ET:GLU, ET:MAL) were obtained. The common features of the two solvent free methods of cocrystal formation (grinding, melting) are presented on the basis of arguments obtained by solid state NMR spectroscopy. Thermal analysis (Differential Scanning Calorimetry) proved that the eutectic phase arises over a wide range of molar ratios of components for each of the binary systems. NMR techniques, supported by theoretical calculations, allowed to provide details about the pathway of the reaction mechanism with atomic accuracy. It was found that the formation of ET cocrystals is a complex process that requires five steps. Each step has been recognized and described. Variable temperature 1D and 2D MAS NMR experiments allowed to track physicochemical processes taking place in a molten state. Moreover, it was found that in a multicomponent mixture consisting of all four components, ET, EMA, GLU, and MAL, ET in the molten phase behaves as a specific selector choosing only one partner to form binary cocrystals according to energy preferences. The process of exchange of coformers in binary systems during grinding, melting, and NMR measurements is described. The stabilization energies (Estab ) and molecular electrostatic potential (MEP) maps computed for the cocrystals under discussion and their individual components rationalize the selection rules and explain the relationships between individual species.

Olfactory receptor neurons are sensitive to stimulus onset asynchrony: implications for odor source discrimination.

In insects, olfactory receptor neurons (ORNs) are localized in sensilla. Within a sensillum, different ORN types are typically co-localized and exhibit nonsynaptic reciprocal inhibition through ephaptic coupling. This inhibition is hypothesized to aid odor source discrimination in environments where odor molecules (odorants) are dispersed by wind, resulting in turbulent plumes. Under these conditions, odorants from a single source arrive at the ORNs synchronously, while those from separate sources arrive asynchronously. Ephaptic inhibition is expected to be weaker for asynchronous arriving odorants from separate sources, thereby enhancing their discrimination. Previous studies have focused on ephaptic inhibition of sustained ORN responses to constant odor stimuli. This begs the question of whether ephaptic inhibition also affects transient ORN responses and if this inhibition is modulated by the temporal arrival patterns of different odorants. To address this, we recorded co-localized ORNs in the fruit fly Drosophila melanogaster and exposed them to dynamic odorant mixtures. We found reciprocal inhibition, strongly suggesting the presence of ephaptic coupling. This reciprocal inhibition does indeed modulate transient ORN responses and is sensitive to the relative timing of odor stimuli. Notably, the strength of inhibition decreases as the synchrony and correlation between arriving odorants decrease. These results support the hypothesis that ephaptic inhibition aids odor source discrimination.

Is Over-parameterization a Problem for Profile Mixture Models?

Biochemical constraints on the admissible amino acids at specific sites in proteins lead to heterogeneity of the amino acid substitution process over sites in alignments. It is well known that phylogenetic models of protein sequence evolution that do not account for site heterogeneity are prone to long-branch attraction (LBA) artifacts. Profile mixture models were developed to model heterogeneity of preferred amino acids at sites via a finite distribution of site classes each with a distinct set of equilibrium amino acid frequencies. However, it is unknown whether the large number of parameters in such models associated with the many amino acid frequency vectors can adversely affect tree topology estimates because of over-parameterization. Here we demonstrate theoretically that for long sequences, over-parameterization does not create problems for estimation with profile mixture models. Under mild conditions, tree, amino acid frequencies, and other model parameters converge to true values as sequence length increases, even when there are large numbers of components in the frequency profile distributions. Because large sample theory does not necessarily imply good behavior for shorter alignments we explore the performance of these models with short alignments simulated with tree topologies that are prone to LBA artifacts. We find that over-parameterization is not a problem for complex profile mixture models even when there are many amino acid frequency vectors. In fact, simple models with few site classes behave poorly. Interestingly, we also found that misspecification of the amino acid frequency vectors does not lead to increased LBA artifacts as long as the estimated cumulative distribution function of the amino acid frequencies at sites adequately approximates the true one. In contrast, misspecification of the amino acid exchangeability rates can severely negatively affect parameter estimation. Finally, we explore the effects of including in the profile mixture model an additional 'F-class' representing the overall frequencies of amino acids in the data set. Surprisingly, the F-class does not help parameter estimation significantly and can decrease the probability of correct tree estimation, depending on the scenario, even though it tends to improve likelihood scores.