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ABSTRACTThe present study investigated the neuromodulatory substrates of salience processing and its impact on memory encoding and behaviour, with a specific focus on two distinct types of salience: reward and contextual unexpectedness. 46 Participants performed a novel task paradigm modulating these two aspects independently and allowing for investigating their distinct and interactive effects on memory encoding while undergoing high‐resolution fMRI. By using advanced image processing techniques tailored to examine midbrain and brainstem nuclei with high precision, our study additionally aimed to elucidate differential activation patterns in subcortical nuclei in response to reward‐associated and contextually unexpected stimuli, including distinct pathways involving in particular dopaminergic modulation. We observed a differential involvement of the ventral striatum, substantia nigra (SN) and caudate nucleus, as well as a functional specialisation within the subregions of the cingulate cortex for the two salience types. Moreover, distinct subregions within the SN in processing salience could be identified. Dorsal areas preferentially processed salience related to stimulus processing (of both reward and contextual unexpectedness), and ventral areas were involved in salience‐related memory encoding (for contextual unexpectedness only). These functional specialisations within SN are in line with different projection patterns of dorsal and ventral SN to brain areas supporting attention and memory, respectively. By disentangling stimulus processing and memory encoding related to two salience types, we hope to further consolidate our understanding of neuromodulatory structures' differential as well as interactive roles in modulating behavioural responses to salient events.- Book : 46(1)
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Correction for ‘H2O assisted in improving the electrochemical performance of a deep eutectic electrolyte formed by choline chloride and magnesium chloride hexahydrate’ by Kaixiang Zou et al., J. Mater. Chem. A, 2024, 12, 33257–33267, https://doi.org/10.1039/D4TA05504G.- Book : ()
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2025
ABSTRACTEnvironmental DNA (eDNA) analysis has become a popular conservation tool for detecting rare and elusive species. eDNA assays typically target mitochondrial DNA (mtDNA) due to its high copy number per cell and its ability to persist in the environment longer than nuclear DNA. Consequently, the development of eDNA assays has relied on mitochondrial reference sequences available in online databases, or in cases where such data are unavailable, de novo DNA extraction and sequencing of mtDNA. In this study, we designed eDNA primers for the critically endangered Bellinger River turtle (Myuchelys georgesi) using a bioinformatically assembled mitochondrial genome (mitogenome) derived from a reference genome. We confirmed the accuracy of this assembled mitogenome by comparing it to a Sanger‐sequenced mitogenome of the same species, and no base pair mismatches were detected. Using the bioinformatically extracted mitogenome, we designed two 20 bp primers that target a 152‐base‐pair‐long fragment of the cytochrome oxidase 1 (CO1) gene and a 186‐base‐pair‐long fragment of the cytochrome B (CytB) gene. Both primers were successfully validated in silico, in vitro, and in situ.- Book : 15(1)
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2025
AbstractImages can be corrupted during capture or transmission due to clouds, overlaps, and other interferences, deviating from their original state. Image inpainting techniques restore such images, but different types—Synthetic Aperture Radar (SAR), RGB, and infrared—require varying field‐of‐view sizes. SAR and infrared images, with less information, need a larger field of view, leading to uncorrelated interference in distant areas. RGB images, richer in information, are constrained by a limited local field of view, hindering access to full semantic details. To address these challenges, an aggregated convolution progressive network is proposed. This model employs a coarse‐grained inpainting module for initial restoration, enhanced by an aggregated convolution module to capture contextual information. Local and global details are then used to refine the output, improving restoration quality. Additionally, existing datasets predominantly focus on RGB images, lacking diversity. To bridge this gap, a comprehensive dataset covering SAR, RGB, and infrared images under cloud, overlap, and corruption conditions is constructed. This method achieves superior performance, with MAE of 0.05, SSIM of 0.95, and PSNR of 36.68 within a 20–30% mask size range, outperforming state‐of‐the‐art techniques across diverse image types and size ranges. Experimental results validate its effectiveness in advancing image inpainting.- Book : 19(1)
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