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Cadmium (Cd) and arsenic (As) are commonly co-adsorbed onto iron oxides in the soil environment, especially in south China. This study aimed to elucidate the regulatory mechanisms in determining the As(Ⅴ)-Cd(Ⅱ) interactions on a goethite interface after excluding pH interference. At pH 6.0, the results obtained illustrated that As(Ⅴ) and Cd(Ⅱ) adsorbed onto goethite obeyed the pseudo-second-order kinetic model, and the adsorption processes were mainly chemical adsorption. Furthermore, As(Ⅴ) adsorbed onto goethite were mainly inner ring adsorption and monolayer adsorption both in the single adsorption process and in co-adsorption processes with Cd(Ⅱ). For comparison, Cd(Ⅱ) adsorbed onto goethite also demonstrated inner ring adsorption and monolayer adsorption during single adsorption, and As(Ⅴ)-Cd(Ⅱ) co-adsorption were transformed to outer ring adsorption and multilayer adsorption processes. Through analysis by Zeta potential, X-ray diffraction, and X-ray photoelectron spectroscopy, electrostatic adsorption and formation of ternary complexes (Fe-As-Cd) were proven to be the critical mechanisms in determining the interactions between As(Ⅴ) and Cd(Ⅱ) during their co-adsorption processes. The results obtained in this study should help us to further understand the micro-chemical interaction processes of heavy metals on the soil environment.

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Unsupervised cross-modality medical image adaptation aims to alleviate the severe domain gap between different imaging modalities without using the target domain label. A key in this campaign relies upon aligning the distributions of source and target domain. One common attempt is to enforce the global alignment between two domains, which, however, ignores the fatal local-imbalance domain gap problem, i.e., some local features with larger domain gap are harder to transfer. Recently, some methods conduct alignment focusing on local regions to improve the efficiency of model learning. While this operation may cause a deficiency of critical information from contexts. To tackle this limitation, we propose a novel strategy to alleviate the domain gap imbalance considering the characteristics of medical images, namely Global-Local Union Alignment. Specifically, a feature-disentanglement style-transfer module first synthesizes the target-like source images to reduce the global domain gap. Then, a local feature mask is integrated to reduce the 'inter-gap' for local features by prioritizing those discriminative features with larger domain gap. This combination of global and local alignment can precisely localize the crucial regions in segmentation target while preserving the overall semantic consistency. We conduct a series of experiments with two cross-modality adaptation tasks, i,e. cardiac substructure and abdominal multi-organ segmentation. Experimental results indicate that our method achieves state-of-the-art performance in both tasks.

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Domain Adaptation (DA) has recently been of strong interest in the medical imaging community. While a large variety of DA techniques have been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality Domain Adaptation. The goal of the challenge is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are commonly performed using contrast-enhanced T1 (ceT1) MR imaging. However, there is growing interest in using non-contrast imaging sequences such as high-resolution T2 (hrT2) imaging. For this reason, we established an unsupervised cross-modality segmentation benchmark. The training dataset provides annotated ceT1 scans (N=105) and unpaired non-annotated hrT2 scans (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 scans as provided in the testing set (N=137). This problem is particularly challenging given the large intensity distribution gap across the modalities and the small volume of the structures. A total of 55 teams from 16 countries submitted predictions to the validation leaderboard. Among them, 16 teams from 9 different countries submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice score - VS: 88.4%; Cochleas: 85.7%) and close to full supervision (median Dice score - VS: 92.5%; Cochleas: 87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.

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We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-modality medical image segmentation, aiming to perform segmentation on the unannotated target domain (e.g. MRI) with the help of labeled source domain (e.g. CT). Previous UDA methods in medical image analysis usually suffer from two challenges: 1) they focus on processing and analyzing data at 2D level only, thus missing semantic information from the depth level; 2) one-to-one mapping is adopted during the style-transfer process, leading to insufficient alignment in the target domain. Different from the existing methods, in our work, we conduct a first of its kind investigation on multi-style image translation for complete image alignment to alleviate the domain shift problem, and also introduce 3D segmentation in domain adaptation tasks to maintain semantic consistency at the depth level. In particular, we develop an unsupervised domain adaptation framework incorporating a novel quartet self-attention module to efficiently enhance relationships between widely separated features in spatial regions on a higher dimension, leading to a substantial improvement in segmentation accuracy in the unlabeled target domain. In two challenging cross-modality tasks, specifically brain structures and multi-organ abdominal segmentation, our model is shown to outperform current state-of-the-art methods by a significant margin, demonstrating its potential as a benchmark resource for the biomedical and health informatics research community.

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Cadmium (Cd) and arsenic (As), two common heavy metals that are toxic to living bodies, are often commonly coadsorbed onto minerals in the soil environment and influenced by many surrounding factors. Among them, pH is the critical factor in determining the As(V)-Cd(II) interaction during coadsorption processes; hence, this study aimed to elucidate the regulatory mechanisms in determining the As(V)-Cd(II) interactions on γ-Al2O3 interface after excluding pH interference. At pH 6.0, Cd(II) adsorption sharply increased at first and then decreased with increasing As(V) concentrations, and the turning point of As(V)/Cd(II) molar ratios was approximately 5. For comparison, As(V) adsorption remained stable at the beginning and then sharply increased with increasing Cd(II) concentrations, with the turning point at Cd(II)/As(V) molar ratios = 1. Through analysis by zeta potential, X-ray diffraction and high resolution transmission electron microscope, electrostatic adsorption and formation of ternary complexes were proven to be the critical mechanisms in deciding the reactivity of Cd(II), whereas formation of ternary complexes and surface precipitation were the dominant mechanisms controlling the stability of As(V). The results in this study allowed us to infer that the mechanism for the coadsorption of Cd(II) and As(V) at stable pH conditions included both competitive and synergistic effects.

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In this work, a sensitive and quantitative immunochromatographic assay (ICA) detection method for avian influenza viruses (AIVs) of the H7 hemagglutinin (HA) antigen was established based on highly chromatic red silica nanoparticles (SiNPs). It can detect two H7 subtypes of influenza viruses, H7N2 and H7N9. The highly chromatic red SiNPs were prepared by adsorbing C.I. Direct Red 224 on the surface of the SiNPs for multiple times using the layer by layer (LbL) self-assembly method under the electrostatic action of ethylene imine polymer (PEI) and poly(sodium-p-styrenesulfonate) (PSS). The highly chromatic red silica nanoparticles modified with anti-H7 HA mAb1 were used as immunodetection probes. The accumulated highly chromatic red SiNPs on the T-line can be observed by the naked eye to qualitatively detect the H7 HA antigen. The quantitative analysis is carried out by using a camera and Image J software. Within the range of 0.1-10 ng mL-1, the linear equation between the H7 HA antigen concentration and the peak area of the T-line gray value was y = 868.9722 + 435.4836X (R2 = 0.9716), and the limit of detection (LOD) of this method was 0.08 pg mL-1 (S/N = 3). The highly chromatic red SiNP based ICA for the detection of H7 HA has no cross activity with other subtypes of influenza viruses. This method of combining highly chromatic colored markers with ICA has great potential in practical applications for the rapid and quantitative detection of other types of AIVs.

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Dehydration-responsive element binding proteins (DREBs) are an important class of transcription factors related to plant stress tolerance. Ammopiptanthus mongolicus is an evergreen broadleaf shrub endemic to desert areas of northwest China, and it has a very high tolerance to harsh environments. In order to reveal the functions and mechanisms of the AmDREB1F gene from this species in enduring abiotic stresses, we performed subcellular localization test, expression pattern analysis, and stress tolerance evaluation of transgenic Arabidopsis harboring this gene. The protein encoded by AmDREB1F was localized in the nucleus. In laboratory-cultured A. mongolicus seedlings, the expression of AmDREB1F was induced significantly by cold and drought but very slightly by salt and heat stresses, and undetectable upon ABA treatment. In leaves of naturally growing shrubs in the wild, the expression levels of the AmDREB1F gene were much higher during the late autumn, winter and early spring than in other seasons. Moreover, the expression was abundant in roots and immature pods rather than other organs of the shrubs. Constitutive expression of AmDREB1F in Arabidopsis induced the expression of several DREB-regulated stress-responsive genes and improved the tolerance of transgenic lines to drought, high salinity and low temperature as well as oxidative stress. The constitutive expression also caused growth retardation of the transgenics, which could be eliminated by the application of gibberellin 3. Stress-inducible expression of AmDREB1F also enhanced the tolerance of transgenic Arabidopsis to all of the four stresses mentioned above, without affecting its growth and development. These results suggest that AmDREB1F gene may play positive regulatory roles in response to abiotic stresses through the ABA-independent signaling pathways.

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Aflatoxin M1 (AFM1) residues in milk pose a major threat to human health, so there is an urgent need for a simple, rapid, and sensitive method for the determination of trace AFM1 in milk. In this study, a competitive immunochromatographic assay (ICA), using visual (PEI/PSS)4 red silica nanoparticles (SiNPs) as signal amplification probes, was used for the highly sensitive detection of AFM1. The (PEI/PSS)4 red SiNPs were used to label AFM1 monoclonal antibody (mAb) to prepare ICA for the detection of AFM1. After exploring the optimal conditions of mAb and immunoprobe dosage conditions, the lowest visual detection limit (VDL) of AFM1 in phosphate-buffered saline with Tween 20 (PBST, 10 mM, pH 7.4, containing 1% BSA, 3% sucrose, 1% trehalose, and 0.5% Tween 20) can reach 0.1 pg/mL. The intuitive visually visible value of AFM1 in both PBST and milk was 10 pg/mL. The results showed that the immunochromatographic system based on high chroma color (PEI/PSS)4 red SiNPs has high sensitivity and broad application prospects for the detection of trace AFM1 residues in milk. The high chroma (PEI/PSS)4 red SiNPs are expected to be a convenient biomarker for improving the sensitivity of immune chromatography bands. Graphical abstract The schematic diagram shows the detection principle. In this work, in the competitive experiment, (PEI/PSS)4 red SiNPs were selected as visual labeling materials, and the specific antibody combined with the labeled material was selected as an immune probe. The AFM1-BSA antigen coupled with the macromolecular BSA was fixed on the T line of the nitrocellulose (NC) membrane. The AFM1 in sample solution competes with AFM1-BSA for the specific binding site of immune probe. The detection sensitivity of this method for AFM1 is obtained by judging the change of the red signal intensity produced by the positive sample, compared with the color at the T line of the negative sample.

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