PIG: Prompt Images Guidance for Night-Time Scene Parsing.

Night-time scene parsing aims to extract pixel-level semantic information in night images, aiding downstream tasks in understanding scene object distribution. Due to limited labeled night image datasets, unsupervised domain adaptation (UDA) has become the predominant method for studying night scenes. UDA typically relies on paired day-night image pairs to guide adaptation, but this approach hampers dataset construction and restricts generalization across night scenes in different datasets. Moreover, UDA, focusing on network architecture and training strategies, faces difficulties in handling classes with few domain similarities. In this paper, we leverage Prompt Images Guidance (PIG) to enhance UDA with supplementary night knowledge. We propose a Night-Focused Network (NFNet) to learn night-specific features from both target domain images and prompt images. To generate high-quality pseudo-labels, we propose Pseudo-label Fusion via Domain Similarity Guidance (FDSG). Classes with fewer domain similarities are predicted by NFNet, which excels in parsing night features, while classes with more domain similarities are predicted by UDA, which has rich labeled semantics. Additionally, we propose two data augmentation strategies: the Prompt Mixture Strategy (PMS) and the Alternate Mask Strategy (AMS), aimed at mitigating the overfitting of the NFNet to a few prompt images. We conduct extensive experiments on four night-time datasets: NightCity, NightCity+, Dark Zurich, and ACDC. The results indicate that utilizing PIG can enhance the parsing accuracy of UDA. The code is available at https://github.com/qiurui4shu/PIG.

CSFwinformer: Cross-Space-Frequency Window Transformer for Mirror Detection.

Mirror detection is a challenging task since mirrors do not possess a consistent visual appearance. Even the Segment Anything Model (SAM), which boasts superior zero-shot performance, cannot accurately detect the position of mirrors. Existing methods determine the position of the mirror under hypothetical conditions, such as the correspondence between objects inside and outside the mirror, and the semantic association between the mirror and surrounding objects. However, these assumptions do not apply to all scenarios. For instance, there may be no corresponding real objects to the reflected objects in the scene, or it may be challenging to extract meaningful semantic associations in complex scenes. On the other hand, humans can easily recognize mirrors through the specular texture caused by materials. To mine mirror features in more general scenes, we propose a Cross-Space-Frequency Window Transformer (CSFwinformer) to extract spatial and frequency features for texture analysis. Specifically, we design a Spatial-Frequency Window Alignment module (SFWA) to calculate spatial-frequency feature affinities and learn the difference between mirror and non-mirror textures. We then propose a Dilated Window Attention (DWA) to extract global features to complement the limitation of window alignment. Besides, we propose a Cross-Modality Context Contrast module (CMCC) to fuse cross-modality features and global features, which enables information flow between different windows to take full advantage of cross-modality information. Extensive experiments show that our method performs favorably against state-of-the-art methods on three mirror detection benchmarks and significantly improved SAM performance on mirror detection. The code is available at https://github.com/wangsen99/CSFwinformer.

Influence of dynamic load and water on energy accumulation and dissipation in sandstone.

In various engineering projects such as mineral extraction, hydropower resource utilization, railway construction, and geological hazard mitigation, rock engineering is often encountered. Furthermore, dynamic loads and moisture content exert notable influence on the energy transformation processes within rocks. Yet, the specific interplay of dynamic loading and water's impact on the energy conversion mechanism within the sandstone remains unexplored. To address this gap, this study conducted impact loading experiments on sandstone, elucidating the rock's mechanical response under these conditions and unraveling the underlying energy conversion mechanisms. It was observed that the strength of sandstone exhibits a direct correlation with impact velocity. Moreover, employing energy calculation principles, we established a connection between moisture content and the sandstone's internal energy conversion properties. The study also delved into the microscopic fracture mechanisms within the sandstone, ultimately concluding that both water content and dynamic loading have a significant impact on these microscopic fracture mechanisms.

Boosting Night-Time Scene Parsing With Learnable Frequency.

Night-Time Scene Parsing (NTSP) is essential to many vision applications, especially for autonomous driving. Most of the existing methods are proposed for day-time scene parsing. They rely on modeling pixel intensity-based spatial contextual cues under even illumination. Hence, these methods do not perform well in night-time scenes as such spatial contextual cues are buried in the over-/under-exposed regions in night-time scenes. In this paper, we first conduct an image frequency-based statistical experiment to interpret the day-time and night-time scene discrepancies. We find that image frequency distributions differ significantly between day-time and night-time scenes, and understanding such frequency distributions is critical to NTSP problem. Based on this, we propose to exploit the image frequency distributions for night-time scene parsing. First, we propose a Learnable Frequency Encoder (LFE) to model the relationship between different frequency coefficients to measure all frequency components dynamically. Second, we propose a Spatial Frequency Fusion module (SFF) that fuses both spatial and frequency information to guide the extraction of spatial context features. Extensive experiments show that our method performs favorably against the state-of-the-art methods on the NightCity, NightCity+ and BDD100K-night datasets. In addition, we demonstrate that our method can be applied to existing day-time scene parsing methods and boost their performance on night-time scenes. The code is available at https://github.com/wangsen99/FDLNet.

Large-Scale Simulation of Full Three-Dimensional Flow and Combustion of an Aero-Turbofan Engine on Sunway TaihuLight Supercomputer.

Computational fluid dynamics- (CFD-) based component-level numerical simulation technology has been widely used in the design of aeroengines. However, due to the strong coupling effects between components, the numerical simulation of the whole engine considering the full three-dimensional flow and multi-component chemical reaction is still very difficult at present. Aimed at this problem, an efficient implicit solver, 'sprayDyMFoam' for an unstructured mesh, is developed in this paper based on the Sunway TaihuLight supercomputer. This sprayDyMFoam solver improves the PIMPLE algorithm in the solution of aerodynamic force and adjusts the existing droplet atomization model in the solution of the combustion process so as to meet the matching situation between components and the combustion chamber in the solution process. Meanwhile, the parallel communication mechanism of AMI boundary processing is optimized based on the hardware environment of the Sunway supercomputer. The sprayDyMFoam solver is used to simulate a typical double-rotor turbofan engine: the calculation capacity and efficiency meet the use requirements, and the obtained compressor performance can form a good match with the test. The research proposed in this paper has strong application value in high-confidence computing, complex phenomenon capturing, and time and cost reduction for aeroengine development.

Effects of plasma-derived exosomes from the normal and thin Bactrian camels on hepatocellular carcinoma and their differences at transcriptome and proteomics levels.

Background: Hepatocellular carcinoma (HCC) is a common malignant primary tumor. Bactrian camels have high economic and social values, but their potential medical value has not been studied. This study aimed to investigate the effects of Bactrian camel plasma-derived exosomes on HCC. Methods: Plasma was obtained from thin and normal Bactrian camels, and used to isolate exosomes by ultracentrifugation. The exosomes were then characterized by transmission electron microscopy and Nano particle tracking analyzer. In vivo imaging of nude mice and hematoxylin eosin (HE) staining of liver tissues were used to explore the effects of the exosomes on tumor growth. Finally, the differences of the two exosomes were further analyzed using small RNA sequencing and proteomics. Results: In vivo imaging and HE staining showed that no significant differences were found in fluorescence value and liver tissue morphology between the control mice and the mice treated with the exosomes from thin Bactrian camels; while the fluorescence value and the live histology changes were alleviated in the mice with the exosomes from normal Bactrian camels. After sequencing and proteomic analysis, 40 differentially expressed miRNAs (DE-miRNAs, 15 down-regulated and 25 up-regulated) and 172 differentially expressed proteins (DEPs, 77 up-regulated and 95 down-regulated) were identified in the plasma-derived exosomes from normal Bactrian camels. These identified DE-miRNAs and DEPs were significantly enriched in many signaling pathways. Conclusions: Normal Bactrian camel plasma-derived exosomes may inhibit the growth of HCC cells through regulating pathways of Ras, Ras-Association Proximate 1 (Rap1), phosphoinositide 3-kinase-protein kinase B (PI3K-Akt), mitogen-activated protein kinase (MAPK), adenosine monophosphate-activated protein kinase (AMPK), and canonical Wnt signaling pathways.

BaGFN: Broad Attentive Graph Fusion Network for High-Order Feature Interactions.

Modeling feature interactions is of crucial significance to high-quality feature engineering on multifiled sparse data. At present, a series of state-of-the-art methods extract cross features in a rather implicit bitwise fashion and lack enough comprehensive and flexible competence of learning sophisticated interactions among different feature fields. In this article, we propose a new broad attentive graph fusion network (BaGFN) to better model high-order feature interactions in a flexible and explicit manner. On the one hand, we design an attentive graph fusion module to strengthen high-order feature representation under graph structure. The graph-based module develops a new bilinear-cross aggregation function to aggregate the graph node information, employs the self-attention mechanism to learn the impact of neighborhood nodes, and updates the high-order representation of features by multihop fusion steps. On the other hand, we further construct a broad attentive cross module to refine high-order feature interactions at a bitwise level. The optimized module designs a new broad attention mechanism to dynamically learn the importance weights of cross features and efficiently conduct the sophisticated high-order feature interactions at the granularity of feature dimensions. The final experimental results demonstrate the effectiveness of our proposed model.

Acute toxicity of eucalyptus leachate tannins to zebrafish and the mitigation effect of Fe3+ on tannin toxicity.

Fish ponds polluted by the black water of eucalyptus forests (formed by the complexation of eucalyptus tannins with Fe3+) have experienced fish deaths. However, the toxicity of the components of black water is still unclear. To study the acute toxicities of eucalyptus leachate tannins to fish, their changes in the presence of Fe3+, and the underlying mechanisms, the static bioassay test method was adopted for acute exposure testing of zebrafish. Zebrafish were exposed to three kinds of tannins, namely, tannic acid (TA), epigallocatechin gallate (EGCG) and tannins from fresh eucalyptus leaf leacheate (TFL), and to solutions of these tannins with different molar ratios of Fe3+, under both no-aeration and aeration conditions. The results showed that the 48 h LC50 values of TA, EGCG and TFL were respectively 92, 47, and 186 mg·L-1, under no aeration, and 171, 86, and 452 mg·L-1 under aeration. When Fe3+ at 2, 1, and 6 times the molar amount of tannin was added to LC100 solutions of TA, EGCG and TFL, zebrafish mortality in 24 h was reduced to 0-33%. Acute fish death in eucalyptus plantation areas is related to high concentrations of eucalyptus tannins in the water. However, with increasing dissolved oxygen and Fe3+ levels, the acute toxicity of tannins to fish can be reduced. Thus, the black water in eucalyptus plantation areas reflects a water quality phenomenon that reduces the acute toxicity of eucalyptus tannins to fish. The mechanism of tannin toxicity to fish may be related to the impairment of oxygen delivery by fish blood, but the mechanism needs further study. These results provide a scientific basis for the prevention and control of fish suffering from acute eucalyptus tannin poisoning in eucalyptus plantation areas and for the protection of water resources.

Novel Autotaxin Inhibitor for the Treatment of Idiopathic Pulmonary Fibrosis: A Clinical Candidate Discovered Using DNA-Encoded Chemistry.

The activity of the secreted phosphodiesterase autotaxin produces the inflammatory signaling molecule LPA and has been associated with a number of human diseases including idiopathic pulmonary fibrosis (IPF). We screened a single DNA-encoded chemical library (DECL) of 225 million compounds and identified a series of potent inhibitors. Optimization of this series led to the discovery of compound 1 (X-165), a highly potent, selective, and bioavailable small molecule. Cocrystallization of compound 1 with human autotaxin demonstrated that it has a novel binding mode occupying both the hydrophobic pocket and a channel near the autotaxin active site. Compound 1 inhibited the production of LPA in human and mouse plasma at nanomolar levels and showed efficacy in a mouse model of human lung fibrosis. After successfully completing IND-enabling studies, compound 1 was approved by the FDA for a Phase I clinical trial. These results demonstrate that DECL hits can be readily optimized into clinical candidates.

Carbon dots with efficient solid-state red-light emission through the step-by-step surface modification towards light-emitting diodes.

Carbon dots (CDs) have attracted extensive attention over the past decade due to their excellent advantages. However, few attempts have been reported for realizing the long-wavelength emission, especially for obtaining efficient solid-state red emission with high photoluminescence quantum yield (QY). Herein, we developed highly red light emitting CDs by the step-by-step surface modifications of the nitrogen-doped CDs. By introducing hexadecyltrimethyl ammonium bromide to modulate a red CD aqueous solution, the photoluminescence QY increases from 23.2% to 43.6%. Furthermore, we dispersed CDs in a PVP matrix for a solid-state film, where the solid-state quenching was effectively suppressed, and high QY (41.3%) of red light emission was achieved. Taking advantage of the as-prepared red light emitting CDs combining with the reported high quantum yield blue and green light emitting CDs, we realized UV-pumped WLEDs with tunable correlated color temperature from 7879 to 2961 K. Moreover, a high color rendering index (CRI) of 93 for WLEDs was realized, which is superior than the best records for the semiconductor quantum dot based WLEDs. Finally, the red light emitting CDs were demonstrated to have promising application as a red color converter in traditional YAG-based WLEDs to improve their CCT and CRI.

Synthesis and anticonvulsant activity of 7-alkoxyl-4,5-dihydro-[1,2,4]triazolo[4,3-a]quinolines.

A series of 7-alkoxyl-4,5-dihydro-[1,2,4]triazolo[4,3-a]quinoline derivatives was synthesized using 6-hydroxy-3,4-dihydro-1H-quinolin-2-one as a starting material. Their anticonvulsant activities were evaluated by the maximal electroshock test (MES test) and the subcutaneous (s.c.) pentylenetetrazol test (scMet test), and their neurotoxicity was evaluated by the rotarod neurotoxicity test (Tox). MES and scMet tests show that 7-(4-fluorobenzyloxy)-4,5-dihydro-[1,2,4]triazolo[4,3-a]quinoline 4l was found to be the most potent with ED50 value of 11.8 and 6.7 mg kg(-1) and protective index (PI = TD50/ED50) value of 4.6 and 8.1, respectively.

Synthesis and anticonvulsant activity of 1-substituted-7-benzyloxy-4,5-dihydro-[1,2,4]triazolo[4,3-a]quinoline.

Starting from 6-hydroxy-3,4-dihydro-1H-quinoline-2-one, a series of 1-substituted-7-benzyloxy-4,5-dihydro-[1,2,4]triazolo[4,3-a]quinolines was synthesized and their structures were characterized using IR, 1H-NMR, MS, and elemental analysis techniques. Anticonvulsant activity was evaluated in the maximal electroshock (MES) test, subcutaneous pentylenetetrazol (scMet) test, and rotarod neurotoxicity test. The most active compound was 7-benzyloxy-4,5-dihydro-[1,2,4]triazolo[4,3-a]quinoline 4a. Its ED50 in the MES and scMet tests was 17.3 and 24 mg.kg-1, respectively. The safest compound was 4 g, 1-phenyl-7-benzyloxy-4,5-dihydro-[1,2,4]triazolo[4,3-a]quinoline, with TD50 and protective index (PI) (PI=TD50/ED50) values of greater than 300 mg.kg-1 and 13, respectively. The PI value of compound 4 g was better than that of most marketed drugs. Structure-activity relationships are also described in this paper.