Aquatic Vegetation, an Understudied Depot for PFAS.

Per- and polyfluoroalkyl substances (PFAS) are a class of manufactured chemicals that have been extensively utilized worldwide. We hypothesize that the presence, uptake, and accumulation of PFAS in aquatic vegetation (AV) is dependent upon several factors, such as the physiochemical properties of PFAS and proximity to potential sources. In this study, AV was collected from eight locations in Florida to investigate the PFAS presence, accumulation, and spatiotemporal distribution. PFAS were detected in AV at all sampling locations, with a range from 0.18 to 55 ng/g sum (∑)PFAS. Individual PFAS and their concentrations varied by sampling location, time, and AV species. A total of 12 PFAS were identified, with the greatest concentrations measured in macroalgae. The average bioconcentration factor (BCF) among all samples was 1225, indicating high PFAS accumulation in AV from surface water. The highest concentrations, across all AV types, were recorded in the Indian River Lagoon (IRL), a location with a history of elevated PFAS burdens. The present study represents the first investigation of PFAS in naturally existing estuarine AV, filling an important gap on PFAS partitioning within the environment, as well as providing insights into exposure pathways for aquatic herbivores. Examining the presence, fate, and transport of these persistent chemicals in Florida's waterways is critical for understanding their effect on environmental, wildlife, and human health.

PFAS surveillance in abiotic matrices within vital aquatic habitats throughout Florida.

Per- and polyfluoroalkyl substances (PFAS) are a group of manufactured chemicals that are resistant to degradation and thus persistent in the environment. The presence, uptake, and accumulation of PFAS is dependent upon the physiochemical properties of the PFAS and matrix, as well as the environmental conditions since the time of release. The objective of this study was to measure the extent of PFAS contamination in surface water and sediment from nine vulnerable aquatic systems throughout Florida. PFAS were detected at all sampling locations with sediment exhibiting greater PFAS concentrations when compared to surface water. At most locations, elevated concentrations of PFAS were identified around areas of increased human activity, such as airports, military bases, and wastewater effluents. The results from the present study highlight the ubiquitous presence of PFAS in vital Florida waterways and filled an important gap in understanding the distribution of PFAS in dynamic, yet vulnerable, aquatic environments.

Transposition of Both Oblique Muscles Combined With Lateral Rectus Botulinum Toxin Injection and Globe Fixation Suture in the Treatment of Congenital Cranial Nerve III Palsy.

The authors report a new technique to treat complete cranial nerve III palsy. A 15-year-old girl underwent botulinum toxin injection into the lateral rectus muscle, nasal transposition of both the superior and inferior oblique muscles to the medial rectus insertion, and absorbable suture globe fixation to the nasal orbital periosteum. Six months postoperatively, her primary position eye deviation was within 12 prism diopters of orthotropia with limitation of ductions in all directions. [J Pediatr Ophthalmol Strabismus. 2017;54:e13-e17].

Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors.

We seek to optimize Ionic liquids (ILs) for application to redox flow batteries. As part of this effort, we have developed a computational method for suggesting ILs with high conductivity and low viscosity. Since ILs consist of cation-anion pairs, we consider a method for treating ILs as pairs using product descriptors for QSPRs, a concept borrowed from the prediction of protein-protein interactions in bioinformatics. We demonstrate the method by predicting electrical conductivity, viscosity, and melting point on a dataset taken from the ILThermo database on June 18th , 2014. The dataset consists of 4,329 measurements taken from 165 ILs made up of 72 cations and 34 anions. We benchmark our QSPRs on the known values in the dataset then extend our predictions to screen all 2,448 possible cation-anion pairs in the dataset.

Sparse methods in spectroscopy: an introduction, overview, and perspective.

Multivariate calibration methods such as partial least-squares build calibration models that are not parsimonious: all variables (either wavelengths or samples) are used to define a calibration model. In high-dimensional or large sample size settings, interpretable analysis aims to reduce model complexity by finding a small subset of variables that significantly influences the model. The term "sparsity", as used here, refers to calibration models having many zero-valued regression coefficients. Only the variables associated with non-zero coefficients influence the model. In this paper, we briefly review the regression problems associated with sparse models and discuss their spectroscopic applications. We also discuss how one can re-appropriate sparse modeling algorithms that perform wavelength selection for purposes of sample selection. In particular, we highlight specific sparse modeling algorithms that are easy to use and understand for the spectroscopist, as opposed to the overly complex "black-box" algorithms that dominate much of the statistical learning literature. We apply these sparse modeling approaches to three spectroscopic data sets.

A survey of machine learning methods for secondary and supersecondary protein structure prediction.

In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to ß-hairpin and ß-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible.

Lattice enumeration for inverse molecular design using the signature descriptor.

We describe an inverse quantitative structure-activity relationship (QSAR) framework developed for the design of molecular structures with desired properties. This framework uses chemical fragments encoded with a molecular descriptor known as a signature. It solves a system of linear constrained Diophantine equations to reorganize the fragments into novel molecular structures. The method has been previously applied to problems in drug and materials design but has inherent computational limitations due to the necessity of solving the Diophantine constraints. We propose a new approach to overcome these limitations using the Fincke-Pohst algorithm for lattice enumeration. We benchmark the new approach against previous results on LFA-1/ICAM-1 inhibitory peptides, linear homopolymers, and hydrofluoroether foam blowing agents. Software implementing the new approach is available at www.cs.otago.ac.nz/homepages/smartin.

'Just send them all to a burn centre': managing burn resources in a mass casualty incident.

Burn experts estimate that 20-30 per cent of injuries from mass casualty events result in serious burns, many requiring specialised care only available at burn centres. Yet, in the USA there are less then 1,850 burn beds available to provide such a level and quality of care. To address this concern, burn centres are beginning to put into practice new mass casualty triage and transport guidelines that must coordinate with local, regional and federal response plans, while still adhering to an accepted standard of care. This presentation describes how one US burn centre developed and implemented a Homeland Security Exercise and Evaluation Program (HSEEP) designed mass casualty incident (MCI) exercise focused on coordinating 'the right patient to the right facility at the right time', based upon acuity and bed availability. Discussion will enable planners to identify methodologies adaptable for incorporation into catastrophic emergency management operations within their regions.

Topology of cyclo-octane energy landscape.

Understanding energy landscapes is a major challenge in chemistry and biology. Although a wide variety of methods have been invented and applied to this problem, very little is understood about the actual mathematical structures underlying such landscapes. Perhaps the most general assumption is the idea that energy landscapes are low-dimensional manifolds embedded in high-dimensional Euclidean space. While this is a very mild assumption, we have discovered an example of an energy landscape which is nonmanifold, demonstrating previously unknown mathematical complexity. The example occurs in the energy landscape of cyclo-octane, which was found to have the structure of a reducible algebraic variety, composed of the union of a sphere and a Klein bottle, intersecting in two rings.

Algorithmic dimensionality reduction for molecular structure analysis.

Dimensionality reduction approaches have been used to exploit the redundancy in a Cartesian coordinate representation of molecular motion by producing low-dimensional representations of molecular motion. This has been used to help visualize complex energy landscapes, to extend the time scales of simulation, and to improve the efficiency of optimization. Until recently, linear approaches for dimensionality reduction have been employed. Here, we investigate the efficacy of several automated algorithms for nonlinear dimensionality reduction for representation of trans, trans-1,2,4-trifluorocyclo-octane conformation--a molecule whose structure can be described on a 2-manifold in a Cartesian coordinate phase space. We describe an efficient approach for a deterministic enumeration of ring conformations. We demonstrate a drastic improvement in dimensionality reduction with the use of nonlinear methods. We discuss the use of dimensionality reduction algorithms for estimating intrinsic dimensionality and the relationship to the Whitney embedding theorem. Additionally, we investigate the influence of the choice of high-dimensional encoding on the reduction. We show for the case studied that, in terms of reconstruction error root mean square deviation, Cartesian coordinate representations and encodings based on interatom distances provide better performance than encodings based on a dihedral angle representation.

Efficient calculation of molecular properties from simulation using kernel molecular dynamics.

Understanding the relationship between chemical structure and function is a ubiquitous problem within the fields of chemistry and biology. Simulation approaches attack the problem utilizing physics to understand a given process at the particle level. Unfortunately, these approaches are often too expensive for many problems of interest. Informatics approaches attack the problem with empirical analysis of descriptions of chemical structure. The issue in these methods is how to describe molecules in a manner that facilitates accurate and general calculation of molecular properties. Here, we present a novel approach that utilizes aspects of simulation and informatics in order to formulate structure-property relationships. We show how supervised learning can be utilized to overcome the sampling problem in simulation approaches. Likewise, we show how learning can be achieved based on molecular descriptions that are rooted in the physics of dynamic intermolecular forces. We apply the approach to three problems including the analysis of corticosteroid binding globulin ligand binding affinity, identification of formylpeptide receptor ligands, and identification of resveratrol analogues capable of inhibiting activation of transcription factor nuclear factor kappaB.

Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor.

MOTIVATION: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. RESULTS: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.

Using product kernels to predict protein interactions.

There is a wide variety of experimental methods for the identification of protein interactions. This variety has in turn spurred the development of numerous different computational approaches for modeling and predicting protein interactions. These methods range from detailed structure-based methods capable of operating on only a single pair of proteins at a time to approximate statistical methods capable of making predictions on multiple proteomes simultaneously. In this chapter, we provide a brief discussion of the relative merits of different experimental and computational methods available for identifying protein interactions. Then we focus on the application of our particular (computational) method using Support Vector Machine product kernels. We describe our method in detail and discuss the application of the method for predicting protein-protein interactions, beta-strand interactions, and protein-chemical interactions.

Inferring protein-protein interaction networks from protein complex data.

Present day approaches for the determination of protein-protein interaction networks are usually based on two hybrid experimental measurements. Here we consider a computational method that uses another type of experimental data: instead of direct information about protein-protein interactions, we consider data in the form of protein complexes. We propose a method for using these complexes to provide predictions of protein-protein interactions. When applied to a dataset obtained from a cat melanoma cell line we find that we are able to predict when a protein pair belongs to a complex with approximately 96% accuracy. Further, we are able to extrapolate the experimentally identified interaction pairs to the entire cat proteome.

Sensitivity analysis of a computational model of the IKK NF-kappaB IkappaBalpha A20 signal transduction network.

The NF-kappaB signaling network plays an important role in many different compartments of the immune system during immune activation. Using a computational model of the NF-kappaB signaling network involving two negative regulators, IkappaBalpha and A20, we performed sensitivity analyses with three different sampling methods and present a ranking of the kinetic rate variables by the strength of their influence on the NF-kappaB signaling response. We also present a classification of temporal-response profiles of nuclear NF-kappaB concentration into six clusters, which can be regrouped to three biologically relevant clusters. Last, we constructed a reduced network of the IKK-NF-kappaB-IkappaBalpha-A20 signal transduction based on the ranking.

Boolean dynamics of genetic regulatory networks inferred from microarray time series data.

MOTIVATION: Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this article we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. RESULTS: We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our method first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation-inhibition networks to match the discretized data. Finally, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene expression profiling of adult acute myeloid leukemia identifies novel biologic clusters for risk classification and outcome prediction.

To determine whether gene expression profiling could improve risk classification and outcome prediction in older acute myeloid leukemia (AML) patients, expression profiles were obtained in pretreatment leukemic samples from 170 patients whose median age was 65 years. Unsupervised clustering methods were used to classify patients into 6 cluster groups (designated A to F) that varied significantly in rates of resistant disease (RD; P < .001), complete response (CR; P = .023), and disease-free survival (DFS; P = .023). Cluster A (n = 24), dominated by NPM1 mutations (78%), normal karyotypes (75%), and genes associated with signaling and apoptosis, had the best DFS (27%) and overall survival (OS; 25% at 5 years). Patients in clusters B (n = 22) and C (n = 31) had the worst OS (5% and 6%, respectively); cluster B was distinguished by the highest rate of RD (77%) and multidrug resistant gene expression (ABCG2, MDR1). Cluster D was characterized by a "proliferative" gene signature with the highest proportion of detectable cytogenetic abnormalities (76%; including 83% of all favorable and 34% of unfavorable karyotypes). Cluster F (n = 33) was dominated by monocytic leukemias (97% of cases), also showing increased NPM1 mutations (61%). These gene expression signatures provide insights into novel groups of AML not predicted by traditional studies that impact prognosis and potential therapy.

Designing novel polymers with targeted properties using the signature molecular descriptor.

A method for solving the inverse quantitative structure-property relationship (QSPR) problem is presented which facilitates the design of novel polymers with targeted properties. Here, we demonstrate the efficacy of the approach using the targeted design of polymers exhibiting a desired glass transition temperature, heat capacity, and density. We present novel QSPRs based on the signature molecular descriptor capable of predicting glass transition temperature, heat capacity, density, molar volume, and cohesive energies of linear homopolymers with cross-validation squared correlation coefficients ranging between 0.81 and 0.95. Using these QSPRs, we show how the inverse problem can be solved to design poly(N-methyl hexamethylene sebacamide) despite the fact that the polymer was used not used in the training of this model.

Prediction of beta-strand packing interactions using the signature product.

The prediction of beta-sheet topology requires the consideration of long-range interactions between beta-strands that are not necessarily consecutive in sequence. Since these interactions are difficult to simulate using ab initio methods, we propose a supplementary method able to assign beta-sheet topology using only sequence information. We envision using the results of our method to reduce the three-dimensional search space of ab initio methods. Our method is based on the signature molecular descriptor, which has been used previously to predict protein-protein interactions successfully, and to develop quantitative structure-activity relationships for small organic drugs and peptide inhibitors. Here, we show how the signature descriptor can be used in a Support Vector Machine to predict whether or not two beta-strands will pack adjacently within a protein. We then show how these predictions can be used to order beta-strands within beta-sheets. Using the entire PDB database with ten-fold cross-validation, we have achieved 74.0% accuracy in packing prediction and 75.6% accuracy in the prediction of edge strands. For the case of beta-strand ordering, we are able to predict the correct ordering accurately for 51.3% of the beta-sheets. Furthermore, using a simple confidence metric, we can determine those sheets for which accurate predictions can be obtained. For the top 25% highest confidence predictions, we are able to achieve 95.7% accuracy in beta-strand ordering. [Figure: see text].