A scientific environmental investment prediction plays a crucial role in controlling environmental pollution and avoiding the blind investment of environmental management. However effective environmental investment prediction usually has to fact three challenges about diversiform indicators, insufficient data, and the reliability of prediction models. In the present study, a new prediction model is proposed using the extended belief rule-based system (EBRBS) and evidential reasoning (ER) rule, called ensemble EBRBS model, with the aim to overcome the above challenges for better environmental investment prediction. The proposed ensemble EBRBS model consists of two components: 1) multiple EBRBSs, which are constructed on the basis of not only using various feature selection methods to select representative indicators but also data increment transformation to enrich the training data; 2) an ER rule-based combination method, which utilizes the ER rule to accommodate the weights and reliabilities of different EBRBSs with the predicted outputs of these EBRBSs to have an integrated environmental investment prediction. A detailed case study is then provided for validating the proposed model via extensive experimental and comparison analysis based on the real-world environmental data about 25 environmental indicators for 31 provinces in China ranged from 2005 to 2018. The results demonstrate that the ensemble EBRBS model can be used as an effective model to accurately predict environmental investments. More importantly, the ensemble EBRBS model not only obtains a high accuracy better than some existing prediction models, but also has an excellent robustness compared with others under the situations of excessive indicators and insufficient data. (c) 2020 Elsevier Ltd. All rights reserved.

Aims: Gastric cancer stem cells (GCSCs) have been used as a therapeutic target. This study aims to estimate the role of miR-98-5p (termed miR-98) in the development of GCSCs. Main methods: The expression of miR-98 in CD44(+) GCSCs was verified by RT-PCR. The miR-98 was overexpressed in CD44(+) GCSCs by Lentivirus. The ability of self-renewal, invasion, chemoresistance and tumorigenicity was detected in vitro or in vivo after overexpression of miR-98. The target genes of miR-98 were predicted and verified by luciferase reporter assays. The effects miR-98/BCAT1 signaling on the chemoresistance and tumorigenicity of CD44(+) GCSCs were investigated in a xenograft model by rescue experiments. Key findings: We have shown that miR-98 was decreased in CD44(+) GCSCs. The overexpression of miR-98 could inhibit the expression of stem-related genes and the ability of self-renewal, invasion, and tumorigenicity of GCSCs. Also, we found that miR-98 overexpression enhances the sensitivity to cisplatin treatment in vitro. Using a xenograft model, we showed that miR-98 overexpression reversed paclitaxel resistance to CD44(+) GCSCs. Finally, we found that branched-chain aminotransferases 1 (BCAT1) is a target gene of miR-98. Overexpressed BCAT1 reversed xenograft tumor formation ability and attenuated the paclitaxel chemosensitivity induced by miR-98 downregulation. Furthermore, BCAT1 restoration affected the expression of invasion and drug resistance-related genes. Significance: This study revealed miR-98 inhibits gastric cancer cell stemness and chemoresistance by targeting BCAT1, suggesting that this miR-98/BCAT1 axis represents a potential therapeutic target in gastric cancer.

In this work, we studied how the oxidic CoMoOx/Al2O3 and sulphurated CoMoSx/Al2O3 interfacial structures determine catalytic efficiency in selective hydrodesulfurization (HDS) of dibenzothiophene (DBT). To achieve this goal, we first synthesized the gamma-Al2O3 materials of unique morphologies through the ionic liquid-assisted hydrothermal process under different conditions. The resulting gamma-Al2O3 hexagonal plates (HP) and stacked hexagonal plates (SHP) are single crystallites with the dominant (110) and (11 1)/(1 0 0) facet structures, respectively; whereas the Al2O3 rods show polycrystalline feature. Through impregnation of Mo/Co salts of different loadings, the various CoMoOx precursors were generated on the different Al2O3 facets owing to the distinct interaction: mostly the two-dimensional (2D) poly-/mono-meric MoOx species together with the threedimensional (3D) MoO3 on the Al2O3 (110) facet vs. the quasi 2D polymeric MoOx species on the Al2O3 polycrystalline facet. Such structural difference further affected the sulfidation of the MoCoOx as well as the quantity of active MoCoSx entities on different gamma-Al2O3 supports: 10Mo3CoSx/Al2O3-HP had the highest level of MoCoOx sulfidation thus a much higher fraction of MoS2 plus Co9S8/CoMoS species whereas less quantity of partially sulphurated CoMoOxS2/CoOx together with SOx2-species. All the distinct interfacial domains of the sulphurated catalysts reasonably account for significantly different efficiency in HDS of DBT. Surface MoS2/CoMoS/Co9S8 species of the highest content over the Al2O3 (1 1 0) facet correspond to the highest DBT areal conversion rate and TOF value, owing to the enhanced MoS2-Co9S8 and MoS2-CoMoS conjunction and synergism.

Automatic segmentation of lung opacification from computed tomography (CT) images shows excellent potential for quickly and accurately quantifying the infection of Coronavirus disease 2019 (COVID-19) and judging the disease development and treatment response. However, some challenges still exist, including the complexity and variability features of the opacity regions, the small difference between the infected and healthy tissues, and the noise of CT images. Due to limited medical resources, it is impractical to obtain a large amount of data in a short time, which further hinders the training of deep learning models. To answer these challenges, we proposed a novel spatial-and channel-wise coarse-to-fine attention network (SCOAT-Net), inspired by the biological vision mechanism, for the segmentation of COVID-19 lung opacification from CT images. With the UNet++ as basic structure, our SCOAT-Net introduces the specially designed spatial-wise and channel-wise attention modules, which serve to collaboratively boost the attention learning of the network and extract the efficient features of the infected opacification regions at the pixel and channel levels. Experiments show that our proposed SCOAT-Net achieves better results compared to several state-of-the-art image segmentation networks and has acceptable generalization ability. (c) 2021 Elsevier Ltd. All rights reserved.

Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M2UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M2UNet consists of a patch level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods. (c) 2021 Published by Elsevier Ltd.

Deep learning techniques have shown their superior performance in dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty of incorporating the useful dermatologist clinical knowledge into the learning process. In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically, to exploit the knowledge of morphological expressions of the lesion region and also the periphery region for melanoma identification, a lesion-based pooling and shape extraction (LPSE) scheme is designed, which transfers the structure information obtained from skin lesion segmentation into melanoma recognition. Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma recognition to skin lesion segmentation, an effective diagnosis guided feature fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual learning mechanism that further promotes the inter task cooperation, and thus iteratively improves the joint learning capability of the model for both skin lesion segmentation and melanoma recognition. Experimental results on two publicly available skin lesion datasets show the effectiveness of the proposed method for melanoma analysis. (c) 2021 Elsevier Ltd. All rights reserved.