•We rethink RR task from the perspective of Mask-level Relational Reasoning. It makes the proposed method more explanatory and extensible.•We design two modules: Mask Generate and Mask Transfer. They ...jointly help the model learn more language priors and multimodal information.•We introduce an image-to-text relational reasoning module, which is unsupervised. It improves the generalization ability of the multimodal model.•Our method achieves state-of-the-art accuracy on two challenging datasets, VRD and Visual Genome.
Referring relationship aims at localizing subject and object entities in an image, according to a triple text <subject, predicate, object>. Previous methods use iterative attention to shift between image regions for modeling predicate. However, predicate sometimes is implicit and difficult to be represented in the image domain. Convolution modeling method to express predicate is simple and inappropriate. Besides, relational reasoning information in the text itself is not fully utilized. To this end, we rethink referring relationship from a mask-level relational reasoning perspective to improve model interpretability. For text-to-image reasoning, we design Mask Generate and Mask Transfer modules, so as to fully integrate the text priors into the reasoning and prediction of masks. For image-to-text reasoning, we propose an unsupervised triple reconstruction method to guide text-to-image reasoning and improve multimodal generalization. By bi-directional reasoning between image and text, the proposed method MRR fully conforms to the multimodal relational reasoning process. Experiments show that MRR achieves state-of-the-art performance on two datasets of referring relationships, VRD and Visual Genome.
Global environmental deterioration poses a major risk to ecological security and human health, and emerging technologies are urgently needed to deal with it. Therefore, the exploitation of ...photocatalysts with favorable activity for efficient degradation of pesticide contaminants is one of the strategies to achieve environmental remediation. Herein, oxygen vacancy-rich Bi2WO6 (Ov-BWO) was prepared through a solvothermal method utilizing ethylene glycol (EG), which exhibited excellent photocatalytic efficiency in photodegradation of glyphosate. The formation of oxygen vacancies (Ovs) in Ov-BWO was demonstrated utilizing XPS and EPR. PL, TRPL, photocurrent tests, and EIS analyses revealed that Ovs accelerated effective transfer of photogenerated charge, extended lifetime of charge carriers, promoted production of active species and significantly improved the photocatalytic performance. Compared with the low-activity Bi2WO6 (BWO, 59.6%), Ov-BWO showed outstanding photocatalytic activity, achieving a degradation efficiency of 91% for glyphosate at 120 min of visible light irradiation. Moreover, Ov-BWO also displayed outstanding recyclable stability after four repeated uses. Based on the characterization of photoelectric properties, a feasible photocatalytic reaction was put forth, along with glyphosate degradation pathways. Furthermore, the degradation intermediates of glyphosate were analyzed in detail employing HPLC-MS. The toxicity assessment indicated that degraded products had been proven to be non-toxic to the ecological system. This work presents the potential of photocatalysts with Ovs for the photodegradation of pesticides, providing a viable strategy for environmental renovation.
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•The Ov-BWO was successfully prepared via a simple solvothermal method.•Oxygen vacancies efficiently facilitated the transport efficiency of free electron.•Ov-BWO showed excellent photocatalytic degradation activity at high concentrations.•The photocatalytic pathway and mechanism of degrading glyphosate were proposed.•Biotoxicity of glyphosate was thoroughly photocatalytic eliminated over Ov-BWO.
Global environmental deterioration poses a major risk to ecological security and human health, and emerging technologies are urgently needed to deal with it. Therefore, the exploitation of ...photocatalysts with favorable activity for efficient degradation of pesticide contaminants is one of the strategies to achieve environmental remediation. Herein, oxygen vacancy-rich Bi
WO
(Ov-BWO) was prepared through a solvothermal method utilizing ethylene glycol (EG), which exhibited excellent photocatalytic efficiency in photodegradation of glyphosate. The formation of oxygen vacancies (Ovs) in Ov-BWO was demonstrated utilizing XPS and EPR. PL, TRPL, photocurrent tests, and EIS analyses revealed that Ovs accelerated effective transfer of photogenerated charge, extended lifetime of charge carriers, promoted production of active species and significantly improved the photocatalytic performance. Compared with the low-activity Bi
WO
(BWO, 59.6%), Ov-BWO showed outstanding photocatalytic activity, achieving a degradation efficiency of 91% for glyphosate at 120 min of visible light irradiation. Moreover, Ov-BWO also displayed outstanding recyclable stability after four repeated uses. Based on the characterization of photoelectric properties, a feasible photocatalytic reaction was put forth, along with glyphosate degradation pathways. Furthermore, the degradation intermediates of glyphosate were analyzed in detail employing HPLC-MS. The toxicity assessment indicated that degraded products had been proven to be non-toxic to the ecological system. This work presents the potential of photocatalysts with Ovs for the photodegradation of pesticides, providing a viable strategy for environmental renovation.
Incomplete multi-view learning (IML) is an important and challenging issue. The recent popular matrix factorization methods learn the representation matrix that contains as much complete information ...as possible from incomplete data. However, these works focus more on mining intrinsic information from the remaining views but fail to exploit the latent and connotative consistency, complementarity, and diversity information across views simultaneously. Meanwhile, the commonly used mean completer or deleting incomplete views strategy generates high uncertainty samples. To overcome these limits, this paper presents a Cross-View Multi-Layer Perceptron (CVMLP). CVMLP integrates an auto-encoder module, cross-view classification loss, masked contrastive learning, and variance loss into a unified framework to learn IML problems. The auto-encoder and cross-view modules efficiently express consistency and diversity across views, mining structural information from within views to between views. Masked contrastive loss makes the model robust to missing views by establishing a contrastive relationship between the input and random masked data. The variance loss can reduce the uncertainty of the classification hyperplane. Extensive experiments demonstrate that CVMLP achieves superior performance.
•A Cross-View Multi-Layer Perceptron for incomplete multi-view learning is proposed.•CVMLP utilizes multi-view auto-encoder to complete the difference features.•CVMLP introduces cross-view block to promote the diversity across views.•CVMLP adopts a masked contrastive loss to excavate the complementarity between views.•CVMLP employs the variance loss to increase the contribution of the certain samples.
Abstract
According to the design requirements of pure electric drive modification of a certain A-class car and the vehicle’s fundamental parameters, the longitudinal dynamics theory of the vehicle is ...used to analyze the driving motor, power battery and fixed speed ratio transmission, and other key components. With the help of Cruise simulation software, a pure electric vehicle powertrain model was established and simulation tests were carried out on the New European Driving Cycle, 60km/h isokinetic range cruise, 100km/h acceleration time, and hill-climbing degree and other operating conditions. The results show that the parameters of the power system of pure electric vehicles are reasonably selected, and the power indicators, driving range, and power consumption per 100 kilometers meet the performance design requirements of the whole vehicle, which have theoretical guiding significance for the development of the whole vehicle.
Gaseous molecules, such as hydrogen sulfide (H2S) and nitric oxide (NO), are crucial players in cellular and (patho)physiological processes in biological systems. The biological functions of these ...gaseous molecules, which were first discovered and identified as gasotransmitters in animals, have received unprecedented attention from plant scientists in recent decades. Researchers have arrived at the consensus that H2S is synthesized endogenously and serves as a signaling molecule throughout the plant life cycle. However, the mechanisms of H2S action in redox biology is still largely unexplored. This review highlights what we currently know about the characteristics and biosynthesis of H2S in plants. Additionally, we summarize the role of H2S in plant resistance to abiotic stress. Moreover, we propose and discuss possible redox‐dependent mechanisms by which H2S regulates plant physiology.
The signaling molecule hydrogen sulfide (H2S) modulates plant stress responses. This review summarizes our current understanding of the characteristics and biosynthesis of H2S in plants and highlights recent progress on understanding H2S functions in plant stress physiology, with a particular emphasis on the role of H2S‐triggered protein persulfidation.
Single-arm proof-of-concept (PoC) clinical studies are widely used to accelerate the signal-finding process in oncology drug development before or in lieu of randomized PoC studies. Traditionally the ...primary endpoint for single-arm PoC studies is objective response rate (ORR). However, in cases that ORR is not applicable or not clinically relevant, time-to-event (TTE) endpoint is used instead. One conventional approach is to dichotomize the TTE endpoint as a binary endpoint to assess the survival rate, which may compromise the testing efficiency due to the requirement of minimum follow-up without censoring. Alternatively, we can use the non-parametric one-sample log-rank test (OSLRT) to evaluate the survival curve difference compared with historical control. This approach can incorporate censoring and all time-point information on the survival curve, but the test statistic may be difficult to interpret and quantify the magnitude of treatment effect. Given that clinicians are more interested in the survival rate at a clinically relevant landmark timepoint, we can also use a landmark Kaplan-Meier method (LMKM) to estimate the survival rate at a landmark timepoint for design and analysis of single-arm proof-of-concept oncology trials with TTE endpoint. This non-parametric method is straightforward to clinicians and can be applied to any survival models. Simulation studies show that the LMKM method can improve the efficiency upon the binary survival rate approach and achieve comparable operating characteristics as the one-sample log-rank test. We also develop an R package for the implementation of these mainstream designs, which fills the gap of available software for design and analysis of single-arm studies with TTE endpoint.
Oral squamous cell carcinoma (OSCC), the most common malignancy of the oral cavity, accounts for >90% of all diagnosed oral cancer cases. Baicalein, a naturally derived compound, has been shown to ...alter p65 and the nuclear factor (NF)‑κB pathway, thus exerting cytotoxic effects on various tumor cell types. However, the mechanism of action of baicalein in OSCC has not been fully elucidated. In the present study, the proliferation of OSCC cells treated with baicalein was examined using a CCK‑8 assay. The effects of baicalein on the cell cycle and apoptosis of OSCC cells were determined by flow cytometric analyses. The expression of specificity protein 1 (Sp1), p65 and p50 at the mRNA and protein levels was determined by reverse transcription‑quantitative PCR and western blot analysis, respectively. The results of the present study demonstrated that baicalein suppresses the proliferation of OSCC cell lines in vivo and in vitro. Baicalein also induced apoptosis of OSCC cells and arrested the cell cycle at the G0/G1 phase. Baicalein inhibited the expression of Sp1, p65 and p50 by downregulating the relative mRNA levels. Baicalein reduced the activity of NF‑κB in OSCC cells. Knockdown of Sp1 also resulted in reduced expression of p65 and p50. In addition, Sp1 silencing enhanced the effects of baicalein. In conclusion, the present study demonstrated that baicalein suppresses the growth of OSCC cells through an Sp1/NF‑κB‑dependent mechanism.
RGB-Thermal pedestrian detection has shown many notable advantages in various lighting and weather conditions by combining the information from RGB-T images. Due to distinct imaging principles, RGB-T ...modalities consist of modality-specific and modality-consistent information. However, most existing RGB-T pedestrian detection methods indiscriminately integrate these two types of information, which leads to the pollution of modality information. To address this issue, we propose a novel mask-guided multi-level fusion network (M2FNet) for RGB-T pedestrian detection. M2FNet independently explores consistent and specific features in RGB-T modalities at three different levels, utilizing pixel-level positional information in masks to exclusively focus on pedestrian-related features. Specifically, at the feature extraction level, we selectively embed cross-modality differential compensation (CDC) modules and design the bidirectional multiscale fusion (BMF) module to fully utilize the complementary modality-specific information and enhance the precision of predicted pedestrian masks. At the feature fusion level, the mask-guided global consistency mining (MGCM) module is introduced to capture intra-modal and inter-modal consistent information of pedestrians, which generates highly discriminative RGB-T features. Finally, to further reduce inter-modal differences, we propose a mask-guided pixel-level decision fusion (MPDF) strategy to dynamically weight the RGB-T predictions. Extensive experiments and comparisons demonstrate that our proposed M2FNet, with different backbones, outperforms the state-of-the-art detectors on both publicly available KAIST and CVC-14 RGB-T pedestrian detection datasets.
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