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Molybdenum disulfide, as an electronic highly-adjustable catalysts material, tuning its electronic structure is crucial to enhance its intrinsic hydrogen evolution reaction (HER) activity. Nevertheless, there are yet huge challenges to the understanding and regulation of the surface electronic structure of molybdenum disulfide-based catalysts. Here we address these challenges by tuning its electronic structure of phase modulation synergistic with interfacial chemistry and defects from phosphorus or sulfur implantation, and we then successfully design and synthesize electrocatalysts with the multi-heterojunction interfaces (e.g., 1T 0.81 -MoS 2 @Ni 2 P), demonstrating superior HER activities and good stabilities with a small overpotentials of 38.9 and 95 mV at 10 mA/cm 2 , a low Tafel slopes of 41 and 42 mV/dec in acidic as well as alkaline surroundings, outperforming commercial Pt/C catalyst and other reported Mo-based catalysts. Theoretical calculation verified that the incorporation of metallic-phase and intrinsic HER-active Ni-based materials into molybdenum disulfide could effectively regulate its electronic structure for making the bandgap narrower. Additionally, X-ray absorption spectroscopy indicate that reduced nickel possesses empty orbitals, which is helpful for additional H binding ability. All these factors can decrease Mo-H bond strength, greatly improving the HER catalytic activity of these materials. The understanding and regulation of the surface electronic structure of molybdenum disulfide-based catalysts for hydrogen evolution reaction (HER) remains a challenges. Here, the authors design and synthesize electrocatalysts with multi-heterojunction interfaces showing enhanced HER activities and stabilities.

Phylogenetic trees and data are often stored in incompatible and inconsistent formats. The outputs of software tools that contain trees with analysis findings are often not compatible with each other, making it hard to integrate the results of different analyses in a comparative study. The treeio package is designed to connect phylogenetic tree input and output. It supports extracting phylogenetic trees as well as the outputs of commonly used analytical software. It can link external data to phylogenies and merge tree data obtained from different sources, enabling analyses of phylogeny-associated data from different disciplines in an evolutionary context. Treeio also supports export of a phylogenetic tree with heterogeneous-associated data to a single tree file, including BEAST compatible NEXUS and jtree formats; these facilitate data sharing as well as file format conversion for downstream analysis. The treeio package is designed to work with the tidytree and ggtree packages. Tree data can be processed using the tidy interface with tidytree and visualized by ggtree. The treeio package is released within the Bioconductor and rOpenSci projects. It is available at https://www.bioconductor.org/packages/treeio/.

Underwater image enhancement (UIE) is challenging since image degradation in aquatic environments is complicated and changing over time. Existing mainstream methods rely on either physical-model or data-driven, suffering from performance bottlenecks due to changes in imaging conditions or training instability. In this article, we attempt to adapt the diffusion model to the UIE task and propose a Content-Preserving Diffusion Model (CPDM) to address the above challenges. CPDM first leverages a diffusion model as its fundamental model for stable training and then designs a content-preserving framework to deal with changes in imaging conditions. Specifically, we construct a conditional input module by adopting both the raw image and the difference between the raw and noisy images as the input at each time step of the diffusion process, which can enhance the model’s adaptability by considering the changes involving the raw images in underwater environments. To preserve the essential content of the raw images, we construct a content compensation module for content-aware training by extracting low-level image features of the raw images as compensation for each down block. We conducted tests on the LSUI, UIEB, and EUVP datasets, and the results show that CPDM outperforms state-of-the-art methods in both subjective and objective metrics, achieving the best overall performance. The GitHub link for the code is https://github.com/GZHU-DVL/CPDM.

Existing 3D single object tracking methods primarily extract features from the global coordinates of point clouds, overlooking the potential exploitation of their positional information. However, due to the unordered, sparse, and irregular nature of point clouds, effectively exploring their positional information presents a significant challenge. In this letter, the network is explicitly reformulated by introducing a point position embedding module in conjunction with a self‐attention coding module, replacing the use of global coordinate inputs. The proposed reformulation is further integrated into a top‐notch model M2‐Track, called Point Position Embedding (PPE) in this letter. Comprehensive empirical analysis are performed on the KITTI and NuScenes datasets. Experimental results show that the PPE surpasses M2‐Track by a large margin in overall performance. Especially for the challenging NuScenes dataset, the method attains the highest precision and success in all classes compared to state‐of‐the‐art methods. The code is available at https://github.com/GZHU‐DVL/PPE. This letter explicitly reformulates the network as a point position embedding module in conjunction with a self‐attention coding module, and the proposed reformulation is further integrated into a top‐notch model M2‐Track. Experimental results show that our method surpasses M2‐Track by a large margin in overall performance. Especially for the challenging NuScenes dataset, our method attains the highest precision and success in all classes compared to state‐of‐the‐art methods.

The outbreak of coronavirus disease 2019 (COVID-19) is spreading rapidly around the world, resulting in a global pandemic. Imaging techniques such as computed tomography (CT) play an essential role in the diagnosis and treatment of the disease since lung infection or pneumonia is a common complication. However, training a deep network to learn how to diagnose COVID-19 rapidly and accurately in CT images and segment the infected regions like a radiologist is challenging. Since the infectious area is difficult to distinguish manually annotation, the segmentation results are time-consuming. To tackle these problems, we propose an efficient method based on a deep adversarial network to segment the infection regions automatically. Then, the predicted segment results can assist the diagnostic network in identifying the COVID-19 samples from the CT images. On the other hand, a radiologist-like segmentation network provides detailed information of the infectious regions by separating areas of ground-glass, consolidation, and pleural effusion, respectively. Our method can accurately predict the COVID-19 infectious probability and provide lesion regions in CT images with limited training data. Additionally, we have established a public dataset for multitask learning. Extensive experiments on diagnosis and segmentation show superior performance over state-of-the-art methods.

Indocyanine green (ICG) fluorescence imaging-guided lymphadenectomy has been demonstrated to be effective in increasing the number of lymph nodes (LNs) retrieved in laparoscopic gastrectomy for gastric cancer (GC). Previously, we reported the primary outcomes and short-term secondary outcomes of a phase 3, open-label, randomized clinical trial (NCT03050879) investigating the use of ICG for image-guided lymphadenectomy in patients with potentially resectable GC. Patients were randomly (1:1 ratio) assigned to either the ICG or non-ICG group. The primary outcome was the number of LNs retrieved and has been reported. Here, we report the primary outcome and long-term secondary outcomes including three-year overall survival (OS), three-year disease-free survival (DFS), and recurrence patterns. The per-protocol analysis set population is used for all analyses (258 patients, ICG [n = 129] vs. non-ICG group [n = 129]). The mean total LNs retrieved in the ICG group significantly exceeds that in the non-ICG group (50.5 ± 15.9 vs 42.0 ± 10.3, P  < 0.001). Both OS and DFS in the ICG group are significantly better than that in the non-ICG group (log-rank P  = 0.015; log-rank P  = 0.012, respectively). There is a difference in the overall recurrence rates between the ICG and non-ICG groups (17.8% vs 31.0%). Compared with conventional lymphadenectomy, ICG guided laparoscopic lymphadenectomy is safe and effective in prolonging survival among patients with resectable GC. Due to high rate of metastasis, lymphadenectomy is a cornerstone of the surgical treatment of gastric cancer however the accurate dissection of lymph nodes (LN) can be challenging. Here, the authors present the long-term outcomes of a randomised control trial investigating indocyanine green fluorescence image-guided LN retrieval in gastric cancer patients undergoing laparoscopic gastrectomy.

Analysis of pedestrians’ motion is important to real-world applications in public scenes. Due to the complex temporal and spatial factors, trajectory prediction is a challenging task. With the development of attention mechanism recently, transformer network has been successfully applied in natural language processing, computer vision, and audio processing. We propose an end-to-end transformer network embedded with random deviation queries for pedestrian trajectory forecasting. The self-correcting scheme can enhance the robustness of the network. Moreover, we present a co-training strategy to improve the training effect. The whole scheme is trained collaboratively by the original loss and classification loss. Therefore, we also achieve more accurate prediction results. Experimental results on several datasets indicate the validity and robustness of the network. We achieve the best performance in individual forecasting and comparable results in social forecasting. Encouragingly, our approach achieves a new state of the art on the Hotel and Zara2 datasets compared with the social-based and individual-based approaches.

The locomotion mechanisms and structural characteristics of insects in nature offer new perspectives and solutions for designing miniature actuators. Inspired by the underwater movement of aquatic beetles, this paper presents a bidirectional self-propelled linear piezoelectric micro-actuator (SLPMA), whose maximum size in three dimensions is currently recognized as the smallest known of the self-propelled piezoelectric linear micro-actuators. Through the superposition of two bending vibration modes, the proposed actuator generates an elliptical motion trajectory at its driving feet. The size was determined as 15 mm × 12.8 mm × 5 mm after finite element analysis (FEA) through modal and transient simulations. A mathematical model was established to analyze and validate the feasibility of the proposed design. Finally, a prototype was fabricated, and an experimental platform was constructed to test the driving characteristics of the SLPMA. The experimental results showed that the maximum no-load velocity and maximum carrying load of the prototype in the forward motion were 17.3 mm/s and 14.8 mN, respectively, while those in the backward motion were 20.5 mm/s and 15.9 mN, respectively.

The speed and slope of plant self-thinning are all affected by plant–plant interactions across environmental gradients. Possible mechanisms driving the self-thinning dynamics include the relative strength of root versus shoot competition, and the interplay between competition and facilitation. Although these mechanisms often act in concert, their relative importance has not yet been fully explored. We used both a one-layer and a two-layer zone-of-influence (ZOI) model to examine how competition and facilitation drive self-thinning across stress gradients. As a development of the traditional ZOI model, the two-layer version explicitly models shoot and root growth and neighbor interactions, and thus the overall size-symmetry of competition is regulated by the relative strength of root versus shoot competition. One-layer model simulations revealed that increasingly asymmetric competition accelerated thinning, and steepened (slope ranged from about –1 to –4/3) and lowered self-thinning lines. Stress slowed down density-dependent mortality considerably when competition was not completely symmetric. Stress significantly decreased the self-thinning intercept, while facilitation simply counteracted stress effects. Both stress and facilitation showed little effect on the slope. In the two-layer model, both stress and facilitation affected mortality in the same way as in the one-layer version when competition was not completely symmetric. Different from the one-layer model, the two-layer version showed that the effects of stress and facilitation on the self-thinning slope were mediated by the asymmetry of competition. As stress increased, the overall asymmetry of competition shifted from asymmetric to symmetric due to increased relative strength of root competition. High stress thus dramatically flattened self-thinning lines, whereas the inclusion of facilitation counteracted stress and led to steeper selfthinning lines. Our two-layer model is based on the current knowledge of plant–plant interactions, and better represents ecological realities. It can help elaborate experiments for testing the role of competition and facilitation in driving plant population dynamics.

An investigation of terrestrial bryophyte species diversity and community structure along an altitudinal gradient from 2,001 to 4,221 m a.s.l. in Gongga Mountain in Sichuan, China was carried out in June 2010. Factors which might affect bryophyte species composition and diversity, including climate, elevation, slope, depth of litter, vegetation type, soil pH and soil Eh, were examined to understand the altitudinal feature of bryophyte distribution. A total of 14 representative elevations were chosen along an altitudinal gradient, with study sites at each elevation chosen according to habitat type (forests, grasslands) and accessibility. At each elevation, three 100 m × 2 m transects that are 50 m apart were set along the contour line, and three 50 cm × 50 cm quadrats were set along each transect at an interval of 30 m. Species diversity, cover, biomass, and thickness of terrestrial bryophytes were examined. A total of 165 species, including 42 liverworts and 123 mosses, are recorded in Gongga mountain. Ground bryophyte species richness does not show any clear elevation trend. The terrestrial bryophyte cover increases with elevation. The terrestrial bryophyte biomass and thickness display a clear humped relationship with the elevation, with the maximum around 3,758 m. At this altitude, biomass is 700.3 g m(-2) and the maximum thickness is 8 cm. Bryophyte distribution is primarily associated with the depth of litter, the air temperature and the precipitation. Further studies are necessary to include other epiphytes types and vascular vegetation in a larger altitudinal range.