Method for quantifying nitromethane in blood as a potential biomarker of halonitromethane exposure

The cytotoxicity and genotoxicity of nitromethane and its halogenated analogues in mammals raise concerns about potential toxicity to humans. This study shows that halonitromethanes are not stable in human blood and undergo dehalogenation to form nitromethane. We quantified nitromethane in human blood using solid-phase microextraction (SPME) headspace sampling coupled with gas chromatography (GC) and high resolution mass spectrometry (HRMS). The limit of detection was 0.01 microg/L with a linear calibration curve spanning 3 orders of magnitude. This method employs isotope dilution to precisely quantify trace amounts of nitromethane (coefficient of variation <6%). At three spiked concentrations of nitromethane, method accuracy ranged from 88 to 99%. We applied this method to blood samples collected from 632 people with no known occupational exposure to nitromethane or halonitromethanes. Nitromethane was detected in all blood samples tested (range: 0.28-3.79 microg/L, median: 0.66 microg/L). Time-course experiments with trichloronitromethane- and tribromonitromethane-spiked blood showed that nitromethane was the major product formed (1 nmole tribromonitromethane formed 0.59 nmole of nitromethane, whereas 1 nmole trichloronitromethane formed 0.77 nmole nitromethane). Nitromethane may form endogenously from peroxynitrite: nitromethane concentrations increased proportionately in blood samples spiked with peroxynitrite. Blood nitromethane can be a biomarker of exposure to both nitromethane and halonitromethanes. This sensitive, accurate, and precise analytical method can be used to determine baseline blood nitromethane level in the general population. It can also be used to study the health impact from exposure to nitromethane and halonitromethanes in occupational environments and to assess trichloronitromethane (chloropicrin) exposure in chemical terrorism investigations.     

Multi-objective optimization of green sand mould system using evolutionary algorithms

The quality of cast products in green sand moulds is largely influenced by the mould properties, such as green compression strength, permeability, hardness and others, which depend on the input (process) parameters (that is, grain fineness number, percentage of clay, percentage of water and number of strokes). This paper presents multi-objective optimization of green sand mould system using evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO). In this study, non-linear regression equations developed between the control factors (process parameters) and responses like green compression strength, permeability, hardness and bulk density have been considered for optimization utilizing GA and PSO. As the green sand mould system contains four objectives, an attempt is being made to form a single objective, after considering all the four individual objectives, to obtain a compromise solution, which satisfies all the four objectives. The results of this study show a good agreement with the experimental results.     

Evolutionary Minimization of Network Coding Resources

A method to identify feasible minimal network coding configurations between a source and a set of receivers without altering or modifying the established network infrastructure is proposed. The approach minimizes the resources used for multicast coding while achieving the desired throughput in the multicast scenario. Because the problem of identifying minimal configurations of a graph is known to be NP-hard, our method first identifies candidate minimal configurations and then searches for the optimal ones using a genetic algorithm (GA). Because the optimization process considers the number of coding nodes, the mean number of coding node input links and the sharing of resources by sinks, the problem is thus a multiobjective problem. Two multiobjective algorithms, MOGA and VEGA, are chosen to solve the problem because they are simple enough not to place heavy demands on source nodes when the minimal configuration is sought. The optimization process is investigated by the simulation of a range of randomly generated networks of varying sizes. Performance differences between the multiple-objective GAs are observed, which seem to arise from the difference in their methods of searching. Nevertheless, both methods perform well in terms of identifying feasible minimal configurations with optimized coding resources. The performance is assessed by comparing the optimized solutions with randomly chosen starting configurations. There are always reductions in the number of coding nodes used, typically 50%, and resource sharing is multiplied by several times. Typical mean in-link savings are 10% but may range from zero to close to 30%. We thus show that relatively simple multiple-objective GAs can deliver optimized minimal coding configurations for the network coding multicast problem. Moreover, the approach here offers an improvement over solutions in the literature because our method remains feasible for relatively large networks and its implementation at the source simplifies the functions that must be employed at intermediate nodes.     

Functional,physiological, and metabolic toolbox for clinical magnetic resonance imaging: Integration of acquisition and analysis strategies

Selected neuroimaging strategies have been integrated into a clinical brain imaging protocol to provide quantitative high-resolution functional, physiological, and metabolic maps to complement exquisitely detailed anatomic images without excessively prolonging the conventional clinical examination or analysis time. The physiological maps of blood pool parameters (relative cerebral blood volume, tissue transit time, and arrival time), apparent diffusion coefficient, tissue water content, and functional neuronal activation maps are derived from series of images acquired with echo-planar imaging. The metabolic map reflecting tissue sodium homeostasis (tissue sodium concentration) is acquired using twisted projection imaging and a customized dual-tuned, dual-quadrature 23Na/1H brain radiofrequency coil that ensures coregistration of data and avoids moving the patient. The different types of acquired images are transferred to a common file format with customized file management software and the corresponding maps are derived by applying appropriate fitting algorithms. Customized software allows rapid interrogation and manipulation of all resultant images and maps for detailed but rapid interpretation, printing, and archiving immediately following completion of acquisition. As all acquisitions and processing are performed by the magnetic resonance technologist, the neuroradiologist is able to focus on the interpretation of this immensely rich data set. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 572–581, 1997     

Role of plume dynamics phase in a deepwater oil and gas release model

Offshore exploration and production of oil and gas have increased significantly in the last decade. Computer models are used in emergency response, contingency planning, and impact assessment to simulate the behavior of oil and gas if accidentally released from a well, pipeline, or ship. There are two types of models used for this purpose-models that have both plume dynamics stage and the advection diffusion stage and models that are of simplified nature that has only the advection diffusion stage. This paper compares both types of models and shows what information are similar and what are different and under what conditions. The paper also examines in detail about different criteria that can be used as the transition point (TLPD) from plume dynamics stage to advection diffusion stage. Key findings of the paper are that except for slow leaks the two types of models give different results for surfacing time and location. This is important because sometimes the two models may show profiles that correspond to different times to be similar in shape. The present parametric study suggests that the transition point for TLPD can be based on the buoyant oil droplet velocity corresponding to the median oil droplet size.     

HPAM: Hirshfeld Partitioned Atomic Multipoles

An implementation of the Hirshfeld (HD) and Hirshfeld-Iterated (HD-I) atomic charge density partitioning schemes is described. Atomic charges and atomic multipoles are calculated from the HD and HD-I atomic charge densities for arbitrary atomic multipole rank l(max) on molecules of arbitrary shape and size. The HD and HD-I atomic charges/multipoles are tested by comparing molecular multipole moments and the electrostatic potential (ESP) surrounding a molecule with their reference ab initio values. In general, the HD-I atomic charges/multipoles are found to better reproduce ab initio electrostatic properties over HD atomic charges/multipoles. A systematic increase in precision for reproducing ab initio electrostatic properties is demonstrated by increasing the atomic multipole rank from l(max) = 0 (atomic charges) to l(max) = 4 (atomic hexadecapoles). Both HD and HD-I atomic multipoles up to rank l(max) are shown to exactly reproduce ab initio molecular multipole moments of rank L for L ≤ l(max). In addition, molecular dipole moments calculated by HD, HD-I, and ChelpG atomic charges only (l(max) = 0) are compared with reference ab initio values. Significant errors in reproducing ab initio molecular dipole moments are found if only HD or HD-I atomic charges used.     

基于分裂Bregman方法的加权频差电阻抗成像算法

Sleep apnea (SA) is a common sleep disorder. Identifying patients at risk by means of comprehensive monitoring that requires overnight stay at professional sleep clinics are costly and inconvenient and can lead to unreliable results in view of the unfamiliar sleep environment. Existing wearable devices for sleep monitoring, which can be used in a familiar home environment, do not provide the same comprehensive monitoring as through clinical monitoring. The larger objective of the present work is to develop a sleep monitoring system for home use, which can provide comprehensive monitoring. In the development in this paper, machine learning (ML) models are explored for the classification of SA and sleep stages using multisensory data, without neglecting any of the required signals. The data acquired through the sensors are normalized, their features are extracted using Composite Multiscale Sample Entropy (CMSE) and are standardized using a robust scaling algorithm. Processed features are classified using a Neural Network (NN) and the obtained results for the SA classification are compared with those obtained by using a Support Vector Machine (SVM) approach. The impact of neglecting signals when classifying sleep stages is analyzed as well. The results are presented in the paper and observations are made. The NN model trained with the Bayesian regularization algorithm has provided an overall average accuracy of 94.5% and performed slightly better than when trained using the scaled conjugate gradient backpropagation algorithm (93.2%). The SVMs have yielded lower accuracy levels compared to the NNs (<92%). It is observed that the use of all 14 signals for SS classification yields an overall test accuracy of 72.3%, which is higher than that when one or few signals are used. It is concluded that ML models are effective in classifying sleep data from multiple sensors. Accuracy levels are higher when fused multisensory data are used as inputs. Furthermore, NN models are found to be better suitable in practical application and can be incorporated into an inexpensive and convenient wearable device that can carry out comprehensive monitoring.     

Impacts of high-speed waterjets on web structures

Hydroentangling, where a fabric is formed by striking of fine, closely spaced, high speed waterjets, is one of the fastest growing bonding methods in the nonwoven industry. Softness, drape, conformability, and relatively high strength are the major characteristics that make this bonding technology unique. Despite the method appeal, few understand the impact of waterjet on fabric structures. The primary function of waterjet is to produce fiber entangling, which induces web integrity. In this paper, we have analyzed the interaction of waterjets on web structures to provide a better understanding of the hydroentangling mechanism. We have successfully visualized and analyzed structures of entangled regions through 2D and 3D imaging techniques. The influence of water-jet pressure, jet diameter, and number of jets on hydroentangled web structures is reported.     

Structures and properties of hydroentangled nonwovens: effect of number of manifolds

Hydroentangling is a process in which fibers are entangled by impinging of a curtain of high-speed water jets to form mechanically strong, soft, and textile-like fabrics. Hydroentangled nonwovens are finding a gamut of applications without knowing the entangling mechanisms. In most applications, hydroentangling is carried out using multiple manifolds. This study focuses on the formation of hydroentangled web structures with multiple manifolds and their properties. The 3D analysis revealed the internal structures of hydroentangled nonwovens disclosing formation of fiber loops at jet impact regions. We also report changes of fiber orientations and fiber interlocking within web structures in nonwovens hydroentanged with multiple manifolds.