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Tungsten is both naturally occurring and an anthropogenically released contaminant metal in soils, sediments and water systems that typically exits as the soluble tungstate oxyanions, W(VI)O4 2− . Tungsten mobility and fate are strongly dependent on the adsorption of tungstate to mineral surfaces. However, environmental mineral surfaces are commonly coated with natural organic matter (NOM), and the role of this coating in the tungsten adsorption process, and thus in controlling tungsten reactivity and transport, is unclear. This study investigates W(VI) adsorption to ferrihydrite (Fh), a ubiquitous iron (hydr)oxide in soils and sediments, both in the absence and presence of humic acid (HA), a widely occurring type of NOM, using batch experiments coupled with spectroscopic and thermodynamic techniques. Kinetic results indicate that access to the adsorption sites for W(VI) on the organomineral surfaces is limited when Fh is coprecipitated with HA. Commensurate with this observation, batch experiments show that HA decreases W(VI) adsorption to Fh over a wide pH range (4-11), and this inhibitory effect is more pronounced at higher HA concentration. X-ray photoelectron spectroscopy (XPS) measurements demonstrate the formation of inner-sphere type W complexes on both the Fh and HA fraction of the Fh-HA binary composite. In particular, ~40% of the adsorbed W(VI) species is reduced to W(V) in the presence of HA. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) results show the presence of poly tungstate species on Fh, particularly at lower pH and in the presence of HA. Isothermal titration calorimetry shows that W(VI) adsorption to Fh is an exothermic process both in the presence and absence of HA, and that process is accompanied by a positive entropy. The findings of this work suggest that NOM not only mobilizes tungstate but also reduces tungstate from W(VI) to W(V) at environmental iron (hydr)oxide-water interfaces, which is of significance for evaluating the migration and bioavailability of tungsten in both natural and contaminated environments.

Due to the outbreak of lung infections caused by the coronavirus disease (COVID-19), humans have to face an unprecedented and devastating global health crisis. Since chest computed tomography (CT) images of COVID-19 patients contain abundant pathological features closely related to this disease, rapid detection and diagnosis based on CT images is of great significance for the treatment of patients and blocking the spread of the disease. In particular, the segmentation of the COVID-19 CT lung-infected area can quantify and evaluate the severity of the disease. However, due to the blurred boundaries and low contrast between the infected and the non-infected areas in COVID-19 CT images, the manual segmentation of the COVID-19 lesion is laborious and places high demands on the operator. Quick and accurate segmentation of COVID-19 lesions from CT images based on deep learning has drawn increasing attention. To effectively improve the segmentation effect of COVID-19 lung infection, a modified UNet network that combines the squeeze-and-attention (SA) and dense atrous spatial pyramid pooling (Dense ASPP) modules) (SD-UNet) is proposed, fusing global context and multi-scale information. Specifically, the SA module is introduced to strengthen the attention of pixel grouping and fully exploit the global context information, allowing the network to better mine the differences and connections between pixels. The Dense ASPP module is utilized to capture multi-scale information of COVID-19 lesions. Moreover, to eliminate the interference of background noise outside the lungs and highlight the texture features of the lung lesion area, we extract in advance the lung area from the CT images in the pre-processing stage. Finally, we evaluate our method using the binary-class and multi-class COVID-19 lung infection segmentation datasets. The experimental results show that the metrics of Sensitivity, Dice Similarity Coefficient, Accuracy, Specificity, and Jaccard Similarity are 0.8988 (0.6169), 0.8696 (0.5936), 0.9906 (0.9821), 0.9932 (0.9907), and 0.7702 (0.4788), respectively, for the binary-class (multi-class) segmentation task in the proposed SD-UNet. The result of the COVID-19 lung infection area segmented by SD-UNet is closer to the ground truth compared to several existing models such as CE-Net, DeepLab v3+, UNet++, and other models, which further proves that a more accurate segmentation effect can be achieved by our method. It has the potential to assist doctors in making more accurate and rapid diagnosis and quantitative assessment of COVID-19.

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In the medical field, Alzheimer’s disease (AD), as a neurodegenerative brain disease which is very difficult to diagnose, can cause cognitive impairment and memory decline. Many existing works include a variety of clinical neurological and psychological examinations, especially computer-aided diagnosis (CAD) methods based on electroencephalographic (EEG) recording or MRI images by using machine learning (ML) combined with different preprocessing steps such as hippocampus shape analysis, fusion of embedded features, and so on, where EEG dataset used for AD diagnosis is usually is large and complex, requiring extraction of a series of features like entropy features, spectral feature, etc., and it has seldom been applied in the AD detection based on deep learning (DL), while MRI images were suitable for both ML and DL. In terms of the structural MRI brain images, few differences could be found in brain atrophy among the three situations: AD, mild cognitive impairment (MCI), and Normal Control (NC). On the other hand, DL methods have been used to diagnose AD incorporating MRI images in recent years, but there have not yet been many selective models with very deep layers. In this article, the Gray Matter (GM) Magnetic Resonance Imaging (MRI) is automatically extracted, which could better distinguish among the three types of situations like AD, MCI, and NC, compared with Cerebro Spinal Fluid (CSF) and White Matter (WM). Firstly, FMRIB Software Library (FSL) software is utilized for batch processing to remove the skull, cerebellum and register the heterogeneous images, and the SPM + cat12 tool kits in MATLAB is used to segment MRI images for obtaining the standard GM MRI images. Next, the GM MRI images are trained by some new neural networks. The characteristics of the training process are as follows: (1) The Tresnet, as the network that achieves the best classification effect among several new networks in the experiment, is selected as the basic network. (2) A multi-receptive-field mechanism is integrated into the network, which is inspired by neurons that can dynamically adjust the receptive fields according to different stimuli. (3) The whole network is realized by adding multiple channels to the convolutional layer, and the size of the convolution kernel of each channel can be dynamically adjusted. (4) Transfer learning method is used to train the model for speeding up the learning and optimizing the learning efficiency. Finally, we achieve the accuracies of 86.9% for AD vs. NC, 63.2% for AD vs. MCI vs. NC respectively, which outperform the previous approaches. The results demonstrate the effectiveness of our approach.

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Tungsten (W) is an emerging contaminant whose environmental behaviors remain rather sketchy, narrow, and fragmentary. The mobility and fate of W in the aquatic environments may be influenced by naturally dissolved organic matter (DOM), nevertheless, no studies have addressed how W is bound to DOM. In this study, complexation behaviors and mechanisms of W(VI) with representative DOM, humic acid (HA) and fulvic acid (FA), were examined by batch adsorption, spectrometry, and isothermal titration calorimetry (ITC) under environmentally-relevant conditions. A higher W(VI) binding was observed at a lower pH. Compared to HA, FA showed a higher W(VI) complexing capability owing to the presence of more carboxylic groups. As shown in ITC, the carboxylic–W interaction was an endothermic process and driven by entropy, whereas the phenolic–W association was exothermic and driven by both entropy and enthalpy. The redox-active moieties within HA/FA molecules could reduce W(VI) to lower valence states species, predominantly W(V). The presence of Ca2+ not only promoted W–HA/FA complexation but also hindered W(VI) reduction. All in all, the role of dissolved organic matter in the complexation of W(VI) in the aquatic environments merits close attention. Graphical Abstract