Optical Nonlinear Properties of Gold Nanoparticles Synthesized by Laser Ablation in Polymer Solution

In the present study, gold nanoparticles were synthesized in various polymer solutions by means of employing laser ablation technique at the same ablation time. Specifically, gold nanoparticles were synthesized in polyethylene glycol and chitosan solutions, in order to compare the effects of the liquid media which served as stabilizers for particle size and volume fraction of nanoparticles. In addition, this experiment was repeated in distilled water for reference purposes. As the findings indicated, the particle size which was obtained in polyethylene glycol was about 7.49 nm, that is, smaller than those of chitosan solution and distilled water, respectively. In contrast, it was observed that the volume fraction of gold nanoparticles increased in polyethylene glycol in comparison with the other media which indicated an effect on the formation of NPs. On the other hand, Z-scan technique was employed to measure the nonlinear refractive index and nonlinear absorption coefficient of nanofluids containing gold nanoparticles. Consequently, the nonlinear properties of nanofluids pointed to a significant contribution with the number of nanoparticles observed in fluids and both optical nonlinear parameters were observed to increase by means of a prior increase in the volume fraction of Au-NPs in polyethylene glycol solution

Effect of Silicon Content on Carbide Precipitation and Low-Temperature Toughness of Pressure Vessel Steels

Cr – Mn – Mo – Ni pressure vessel steels containing 0.54 and 1.55% Si are studied. Metallographic and fractographic analyses of the steels after tempering at 650 and 700°C are performed. The impact toughness at – 30°C and the hardness of the steels are determined. The mass fraction of the carbide phase in the steels is computed with the help of the J-MatPro 4.0 software

Effect of Cu addition on microstructure and impact toughness in the simulated coarse-grained heat-affected zone of high-strength low-alloy steels

The effects of Cu content on microstructure and impact toughness in the simulated coarse-grained heat-affected zone (CGHAZ) of high-strength low-alloy steels were investigated. It has been observed that the microstructure in the simulated CGHAZ of Cu-free steel is dominated by a small proportion of acicular ferrite and predominantly bainite with martensite–austenite constituent. Whereas, in the 0.45 and 1.01% Cu-containing steels, the acicular ferrite increased significantly due to the effective nucleation on intragranular inclusions with outer layer of MnS and CuS. The formation of acicular ferrite is attributed to superior high heat-affected zone impact toughness in the 0.45% Cu-containing steel. Furthermore, the increasing martensite–austenite constituent and ε-Cu precipitates in the simulated CGHAZ of 1.01% Cu-containing steel caused degradation in impact toughness

Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery

ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice

Location of pathogenic variants in PSEN1 impacts progression of cognitive, clinical, and neurodegenerative measures in autosomal-dominant Alzheimer's disease

Although pathogenic variants in PSEN1 leading to autosomal-dominant Alzheimer disease (ADAD) are highly penetrant, substantial interindividual variability in the rates of cognitive decline and biomarker change are observed in ADAD. We hypothesized that this interindividual variability may be associated with the location of the pathogenic variant within PSEN1. PSEN1 pathogenic variant carriers participating in the Dominantly Inherited Alzheimer Network (DIAN) observational study were grouped based on whether the underlying variant affects a transmembrane (TM) or cytoplasmic (CY) protein domain within PSEN1. CY and TM carriers and variant non-carriers (NC) who completed clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF) as part of their participation in DIAN were included in this study. Linear mixed effects models were used to determine differences in clinical, cognitive, and biomarker measures between the NC, TM, and CY groups. While both the CY and TM groups were found to have similarly elevated Aβ compared to NC, TM carriers had greater cognitive impairment, smaller hippocampal volume, and elevated phosphorylated tau levels across the spectrum of pre-symptomatic and symptomatic phases of disease as compared to CY, using both cross-sectional and longitudinal data. As distinct portions of PSEN1 are differentially involved in APP processing by γ-secretase and the generation of toxic β-amyloid species, these results have important implications for understanding the pathobiology of ADAD and accounting for a substantial portion of the interindividual heterogeneity in ongoing ADAD clinical trials

Crystallographic reconstruction study of the effects of finish rolling temperature on the variant selection during bainite transformation in C-Mn high-strength steels

The effect of finish rolling temperature (FRT) on the austenite- () to-bainite () phase transformation is quantitatively investigated in high-strength C-Mn steels. In particular, the present study aims to clarify the respective contributions of the conditioning during the hot rolling and the variant selection (VS) during the phase transformation to the inherited texture. To this end, an alternative crystallographic reconstruction procedure, which can be directly applied to experimental electron backscatter diffraction (EBSD) mappings, is developed by combining the best features of the existing models: the orientation relationship (OR) refinement, the local pixel-by-pixel analysis and the nuclei identification and spreading strategy. The applicability of this method is demonstrated on both quenching and partitioning (Q&P) and as-quenched lath-martensite steels. The results obtained on the C-Mn steels confirm that the sample finish rolled at the lowest temperature (829{\deg}C) exhibits the sharpest transformation texture. It is shown that this sharp texture is exclusively due to a strong VS from parent brass {110}, S {213} and Goss {110} grains, whereas the VS from the copper {112} grains is insensitive to the FRT. In addition, a statistical VS analysis proves that the habit planes of the selected variants do not systematically correspond to the predicted active slip planes using the Taylor model. In contrast, a correlation between the Bain group to which the selected variants belong and the FRT is clearly revealed, regardless of the parent orientation. These results are discussed in terms of polygranular accommodation mechanisms, especially in view of the observed development in the hot-rolled samples of high-angle grain boundaries with misorientation axes between and

Cerebral perfusion changes in presymptomatic genetic frontotemporal dementia: a GENFI study

Genetic forms of frontotemporal dementia are most commonly due to mutations in three genes, C9orf72, GRN or MAPT, with presymptomatic carriers from families representing those at risk. While cerebral blood flow shows differences between frontotemporal dementia and other forms of dementia, there is limited evidence of its utility in presymptomatic stages of frontotemporal dementia. This study aimed to delineate the cerebral blood flow signature of presymptomatic, genetic frontotemporal dementia using a voxel-based approach. In the multicentre GENetic Frontotemporal dementia Initiative (GENFI) study, we investigated cross-sectional differences in arterial spin labelling MRI-based cerebral blood flow between presymptomatic C9orf72, GRN or MAPT mutation carriers (n = 107) and non-carriers (n = 113), using general linear mixed-effects models and voxel-based analyses. Cerebral blood flow within regions of interest derived from this model was then explored to identify differences between individual gene carrier groups and to estimate a timeframe for the expression of these differences. The voxel-based analysis revealed a significant inverse association between cerebral blood flow and the expected age of symptom onset in carriers, but not non-carriers. Regions included the bilateral insulae/orbitofrontal cortices, anterior cingulate/paracingulate gyri, and inferior parietal cortices, as well as the left middle temporal gyrus. For all bilateral regions, associations were greater on the right side. After correction for partial volume effects in a region of interest analysis, the results were found to be largely driven by the C9orf72 genetic subgroup. These cerebral blood flow differences first appeared approximately 12.5 years before the expected symptom onset determined on an individual basis. Cerebral blood flow was lower in presymptomatic mutation carriers closer to and beyond their expected age of symptom onset in key frontotemporal dementia signature regions. These results suggest that arterial spin labelling MRI may be a promising non-invasive imaging biomarker for the presymptomatic stages of genetic frontotemporal dementia

Cerebral perfusion changes in presymptomatic genetic frontotemporal dementia: a GENFI study

Genetic forms of frontotemporal dementia are most commonly due to mutations in three genes, C9orf72, GRN or MAPT, with presymptomatic carriers from families representing those at risk. While cerebral blood flow shows differences between frontotemporal dementia and other forms of dementia, there is limited evidence of its utility in presymptomatic stages of frontotemporal dementia. This study aimed to delineate the cerebral blood flow signature of presymptomatic, genetic frontotemporal dementia using a voxel-based approach. In the multicentre GENetic Frontotemporal dementia Initiative (GENFI) study, we investigated cross-secti