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Doping, which is the intentional introduction of impurities into a material, can improve the metal-semiconductor interface by reducing Schottky barrier width. Here, we present high-quality two-dimensional SnS 2 nanosheets with well-controlled Sb doping concentration via direct vapor growth approach and following micromechanical cleavage process. X-ray photoelectron spectroscopy (XPS) measurement demonstrates that Sb contents of the doped samples are approximately 0.22%, 0.34% and 1.21%, respectively, and doping induces the upward shift of the Fermi level with respect to the pristine SnS 2. Transmission electron microscopy (TEM) characterization exhibits that Sb-doped SnS 2 nanosheets have a high-quality hexagonal symmetry structure and Sb element is uniformly distributed in the nanosheets. The phototransistors based on the Sb-doped SnS 2 monolayers show n-type behavior with high mobility which is one order of magnitude higher than that of pristine SnS 2 phototransistors. The photoresponsivity and external quantum efficiency (EQE) of Sb-SnS 2 monolayers phototransistors are approximately three orders of magnitude higher than that of pristine SnS 2 phototransistor. The results suggest that the method of reducing Shottky barrier width to achieve high mobility and photoresponsivity is effective, and Sb-doped SnS 2 monolayer has significant potential in future nanoelectronic and optoelectronic applications.

Purpose Post‐reconstruction filtering is often applied for noise suppression due to limited data counts in myocardial perfusion imaging (MPI) with single‐photon emission computed tomography (SPECT). We study a deep learning (DL) approach for denoising in conventional SPECT‐MPI acquisitions, and investigate whether it can be more effective for improving the detectability of perfusion defects compared to traditional postfiltering. Methods Owing to the lack of ground truth in clinical studies, we adopt a noise‐to‐noise (N2N) training approach for denoising in SPECT‐MPI images. We consider a coupled U‐Net (CU‐Net) structure which is designed to improve learning efficiency through feature map reuse. For network training we employ a bootstrap procedure to generate multiple noise realizations from list‐mode clinical acquisitions. In the experiments we demonstrated the proposed approach on a set of 895 clinical studies, where the iterative OSEM algorithm with three‐dimensional (3D) Gaussian postfiltering was used to reconstruct the images. We investigated the detection performance of perfusion defects in the reconstructed images using the non‐prewhitening matched filter (NPWMF), evaluated the uniformity of left ventricular (LV) wall in terms of image intensity, and quantified the effect of smoothing on the spatial resolution of the reconstructed LV wall by using its full‐width at half‐maximum (FWHM). Results Compared to OSEM with Gaussian postfiltering, the DL denoised images with CU‐Net significantly improved the detection performance of perfusion defects at all contrast levels (65%, 50%, 35%, and 20%). The signal‐to‐noise ratio (SNRD) in the NPWMF output was increased on average by 8% over optimal Gaussian smoothing (P < 10−4, paired t‐test), while the inter‐subject variability was greatly reduced. The CU‐Net also outperformed a 3D nonlocal means (NLM) filter and a convolutional autoencoder (CAE) denoising network in terms of SNRD. In addition, the FWHM of the LV wall in the reconstructed images was varied by less than 1%. Furthermore, CU‐Net also improved the detection performance when the images were processed with less post‐reconstruction smoothing (a trade‐off of increased noise for better LV resolution), with SNRD improved on average by 23%. Conclusions The proposed DL with N2N training approach can yield additional noise suppression in SPECT‐MPI images over conventional postfiltering. For perfusion defect detection, DL with CU‐Net could outperform conventional 3D Gaussian filtering with optimal setting as well as NLM and CAE.

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Climate change poses a serious threat to biodiversity. Predicting the effects of climate change on the distribution of a species' habitat can help humans address the potential threats which may change the scope and distribution of species. Pterocarya stenoptera is a common fast‐growing tree species often used in the ecological restoration of riverbanks and alpine forests in central and eastern China. Until now, the characteristics of the distribution of this species' habitat are poorly known as are the environmental factors that influence its preferred habitat. In the present study, the Maximum Entropy Modeling (Maxent) algorithm and the Genetic Algorithm for Ruleset Production (GARP) were used to establish the models for the potential distribution of this species by selecting 236 sites with known occurrences and 14 environmental variables. The results indicate that both models have good predictive power. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. According to the models, the temperate and subtropical regions of eastern China had high environmental suitability for this species, where the species had been recorded. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically to the north and higher elevation. GARP predicted a more conservative expansion. The projected spatial and temporal patterns of P. stenoptera can provide reference for the development of forest management and protection strategies.

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Purpose The accurate segmentation of liver and liver tumors from CT images can assist radiologists in decision‐making and treatment planning. The contours of liver and liver tumors are currently obtained by manual labeling, which is time‐consuming and subjective. Computer‐aided segmentation methods have been widely used in the segmentation of liver and liver tumors. However, due to the diversity of shape, volume, and image intensity, the segmentation is still a difficult task. In this study, we present a Spatial Feature Fusion Convolutional Network (SFF‐Net) to automatically segment liver and liver tumors from CT images. Methods First, we extract side‐outputs at each convolutional block in SFF‐Net to make full use of multiscale features. Second, skip‐connections are added in the down‐sampling phase, therefore, the spatial information can be efficiently transferred to later layers. Third, we present feature fusion blocks (FFBs) to merge spatial features and high‐level semantic features from early layers and later layers, respectively. Finally, a fully connected 3D conditional random fields (CRFs) is applied to refine the liver and liver tumor segmentation results. Results We test our method on the MICCAI 2017 Liver Tumor Segmentation (LiTS) challenge dataset. The Dice Global (DG) score, Dice per case (DC) score, Volume Overlap Error (VOE), Average Symmetric Surface Distance (ASSD), and tumor precision score are calculated to evaluate the liver and liver tumor segmentation accuracies. For the liver segmentation, DG is 0.955; DC is 0.937; VOE is 0.106; and ASSD is 3.678. For the tumor segmentation, DG is 0.746; DC is 0.592; VOE is 0.416; ASSD is 1.585 and the tumor precision score is 0.369. Conclusions The SFF‐Net learns more spatial information by adding skip‐connections and feature fusion blocks. The experiments validate that our method can accurately segment liver and liver tumors from CT images.