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This investigation delves into the development of cupric oxide functionalized activated carbon fabrics (ACF@CuO) as a filter material for self-decontaminating protective clothing designed to counter chemical warfare agents (CWAs). Three variants of samples were developed by controlling functionalization levels (7.5%, 13.0%, 16.0% w/w) through optimized precursor concentrations, integrating CuO particles onto ACF surfaces. Comprehensive analysis using techniques like FTIR, BET surface area, SEM, EDX, STEM, XRD, TGA, and XPS explored the material's properties. The studies concentrated on evaluating the performance through kinetic studies of self-detoxification of CWA Sarin (GB) using developed ACF@CuO materials analyzed by Gas Chromatography-Mass Spectrometry (GC-MS). ACF@CuO with 16% w/w functionalization exhibited superior self-decontamination against GB, achieving 92.87% efficiency within 18 hours, in contrast to 26.55% for ACF alone. Additionally, materials were tested for tensile strength, air-permeability, and Sulfur Mustard breakthrough time (HDBTT) as per IS 17377:2020 standard. The material with 13% w/w functionalization emerged as the recommended filter layer for CWA protective clothing designed for defense and civilian safety applications. The significant improvements in the self-decontamination efficiency of the material were attributed to the synergistic effects of ACF's adsorption capabilities combined with the decontamination properties of CuO rod-shaped crystals embedded in the surface of ACF. [ABSTRACT FROM AUTHOR]

Chemical warfare agents (CWAs) have become a pivotal concern for the global community and spurred a wide spectrum of research for the development of new generation protective materials. Herein, a highly effective self-detoxifying filter consisting of in-situ immobilized Zirconium hydroxide [Zr(OH)4] over woven activated carbon fabric [Zr(OH)4@W-ACF] is presented for the removal of CWAs. It was prepared to harness the synergistic effect of high surface area of W-ACF, leads to high dispersion of CWAs and high phosphilicity and reactivity of [Zr(OH)4]. The synthesized materials were characterized by ATR-FTIR, EDX, SEM, TEM, XPS, TGA, and BET surface area analyzer. The kinetics of in-situ degradation of CWAs over Zr(OH)4@W-ACF were studied and found to be following the first-order reaction kinetics. The rate constant was found to be 0.244 min−1 and 2.31 × 10−2 min−1 for sarin and soman, respectively over Zr(OH)4@W-ACF. The potential practical applicability of this work was established by fabricating Zr(OH)4@W-ACF as reactive adsorbent layer for protective suit, and found to be meeting the specified criteria in terms of air permeability, tearing strength and nerve agent permeation as per TOP-08-2-501A:2013 and IS-17380:2020. The degradation products of CWAs were analyzed with NMR and GC–MS. The combined properties of dual functional textile with reactive material are expected to open up new exciting avenues in the field of CWAs protective clothing and thus find diverse application in defence and environmental sector. [ABSTRACT FROM AUTHOR]

A new analytical method based on liquid chromatography/electrospray ionization tandem mass spectrometry (LC/ESI-MS/MS) is proposed and validated for the identification and quantification of alkyl alkylphosphonic acids (AAPAs) in aqueous matrices. Retrospective detection and identification of degradation products of chemical warfare agents is important as an indicator of possible use of chemical warfare agents or of environmental contamination. A commercially available solution of 1,9-nonanediyl-bis-(3-methylimidazolium)bisfluoride (NBMI) allowed detection of AAPAs by positive mode electrospray ionization mass spectrometry by forming an adduct with AAPAs. MS/MS experiments using an ion trap analyzer were carried out for unambiguous identification of AAPAs. Different parameters were optimized in order to obtain both an adequate chromatographic separation and a high sensitivity using experimental design methodology. Quantification was done with matrix-matched calibration standards of AAPAs. The method was validated in terms of linearity (r2 >0.982), intra- and inter-day precisions (RSD below 15%), and robustness. The method is sensitive enough for the determination of AAPAs in aqueous matrices, with limits of detection in the 1-5 ng mL-1 range and limits of quantification in the 5-20 ng mL-1 range. Finally, the method was successfully applied to determine these AAPAs in aqueous samples provided by the Organization for the Prohibition of Chemical Weapons during 26th and 29th official proficiency tests. The added advantage of this method is identification of low mass range analyte at high mass range, which obviates the background noise at low mass range. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The effect of Type 2 Diabetes (T2D) on brain health is poorly understood. This study aims to quantify the association between T2D and perfusion in the brain. T2D is a very common metabolic disorder that can cause long term damage to the renal and cardiovascular systems. Previous research has discovered the shape, volume and white matter microstructures in the brain to be significantly impacted by T2D. We propose a fully-connected deep neural network to classify the regional Cerebral Blood Flow into low or high levels, given 16 clinical measures as predictors. The clinical measures include diabetes, renal, cardiovascular and demographics measures. Our model enables us to discover any nonlinear association which might exist between the input features and target. Moreover, our end-to-end architecture automatically learns the most relevant features and combines them without the need for applying a feature selection method. We achieved promising classification performance. Furthermore, in comparison with six (6) classical machine learning algorithms and six (6) alternative deep neural networks similarly tuned for the task, our proposed model outperformed all of them.

Magnetoencephelography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from muscle activity often corrupts the data. Eye-blinks are one of the most common types of muscle artifact. They can be recorded by affixing eye proximal electrodes, as in electrooculography (EOG), however this complicates patient preparation and decreases comfort. Moreover, it can induce further muscular artifacts from facial twitching. We propose an EOG free, data driven approach. We begin with Independent Component Analysis (ICA), a well-known preprocessing approach that factors observed signal into statistically independent components. When applied to MEG, ICA can help separate neuronal components from non-neuronal ones, however, the components are randomly ordered. Thus, we develop a method to assign one of two labels, non-eye-blink or eye-blink, to each component. Our contributions are two-fold. First, we develop a 10-layer Convolutional Neural Network (CNN), which directly labels eye-blink artifacts. Second, we visualize the learned spatial features using attention mapping, to reveal what it has learned and bolster confidence in the method's ability to generalize to unseen data. We acquired 8-min, eyes open, resting state MEG from 44 subjects. We trained our method on the spatial maps from ICA of 14 subjects selected randomly with expertly labeled ground truth. We then tested on the remaining 30 subjects. Our approach achieves a test classification accuracy of 99.67%, sensitivity: 97.62%, specificity: 99.77%, and ROC AUC: 98.69%. We also show the learned spatial features correspond to those human experts typically use which corroborates our model's validity. This work (1) facilitates creation of fully automated processing pipelines in MEG that need to remove motion artifacts related to eye blinks, and (2) potentially obviates the use of additional EOG electrodes for the recording of eye-blinks in MEG studies.

Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by electrical neuronal activity; however, signal from non-neuronal sources can corrupt the data. Eye-Blinks (EB) and Cardiac Activity (CA) are two of the most common types of non-neuronal artifacts. They can be measured by affixing eye proximal electrodes, as in electrooculography (EOG) and chest electrodes, as in electrocardiography (EKG), however this complicates imaging setup, decreases patient comfort, and often induces further artifacts from facial twitching and postural muscle movement. We propose an EOG- and EKG-free approach to identify eye-blink, cardiac, or neuronal signals for automated artifact suppression. Our contributions are two-fold. First, we combine a data driven, multivariate decomposition approach based on Independent Component Analysis (ICA) and a highly accurate classifier constructed as a deep 1-D Convolutional Neural Network. Second, we visualize the features learned to reveal what features the model uses and to bolster user confidence in our model's training and potential for generalization. We train and test three variants of our method on resting state MEG data from 49 subjects. Our cardiac model achieves a 96% sensitivity and 99% specificity on the set-aside test-set. Our eye-blink model achieves a sensitivity of 85% and specificity of 97%. This work facilitates automated MEG processing for both, clinical and research use, and can obviate the need for EOG or EKG electrodes.

We describe the gas chromatography/mass spectrometric (GC/MS) analysis of O-alkyl methylphosphinates (AMPs), which are included in schedule 2B4 chemicals in the Chemical Weapons Convention (CWC). GC/MS analysis of variety of AMPs and their deuterated analogues revealed that their fragmentations were determined by alpha-cleavages, McLafferty +1 and hydrogen rearrangements. Based on the obtained electron ionization mass spectra of AMPs the fragmentation routes were rationalized, which were substantiated by the GC/MS analysis of deuterated analogues.