Dual modality prompt learning for visual question-grounded answering in robotic surgery.

With recent advancements in robotic surgery, notable strides have been made in visual question answering (VQA). Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the image. This limitation restricts the interpretative capacity of the VQA models and their ability to explore specific image regions. To address this issue, this study proposes a grounded VQA model for robotic surgery, capable of localizing a specific region during answer prediction. Drawing inspiration from prompt learning in language models, a dual-modality prompt model was developed to enhance precise multimodal information interactions. Specifically, two complementary prompters were introduced to effectively integrate visual and textual prompts into the encoding process of the model. A visual complementary prompter merges visual prompt knowledge with visual information features to guide accurate localization. The textual complementary prompter aligns visual information with textual prompt knowledge and textual information, guiding textual information towards a more accurate inference of the answer. Additionally, a multiple iterative fusion strategy was adopted for comprehensive answer reasoning, to ensure high-quality generation of textual and grounded answers. The experimental results validate the effectiveness of the model, demonstrating its superiority over existing methods on the EndoVis-18 and EndoVis-17 datasets.

Active substance and mechanisms of Actinidia chinensis Planch for the treatment of breast cancer was explored based on network pharmacology and in silico study.

In this paper, our objective was to investigate the potential mechanisms of Actinidia chinensis Planch (ACP) for breast cancer treatment with the application of network pharmacology, molecular docking, and molecular dynamics. "Mihoutaogen" was used as a key word to query the Traditional Chinese Medicine Systems Pharmacology database for putative ingredients of ACP and its related targets. DrugBank, GeneCards, Online Mendelian Inheritance in Man, and therapeutic target databases were used to search for genes associated with "breast cancer." Using Cytoscape 3.9.0 we then constructed the protein-protein interaction and drug-ingredient-target-disease networks. An enrichment analysis of Kyoto encyclopedia of genes and genomes pathway and gene ontology were performed to exploration of the signaling pathways associated with ACP for breast cancer treatment. Discovery Studio software was applied to molecular docking. Finally, the ligand-receptor complex was subjected to a 50-ns molecular dynamics simulation using the Desmond_2020.4 tools. Six main active ingredients and 176 targets of ACP and 2243 targets of breast cancer were screened. There were 118 intersections of targets for both active ingredients and diseases. Tumor protein P53 (TP53), AKT serine/threonine kinase 1 (AKT1), estrogen receptor 1 (ESR1), Erb-B2 receptor tyrosine kinase 2 (ERBB2), epidermal growth factor receptor (EGFR), Jun Proto-Oncogene (JUN), and Heat Shock Protein 90 Alpha Family Class A Member 1 (HSP90AA1) selected as the most important genes were used for verification by molecular docking and molecular dynamics simulation. The primary active compounds of ACP against breast cancer were predicted preliminarily, and its mechanism was studied, thereby providing a theoretical basis for future clinical studies.

A signal transmission strategy driven by gap-regulated exonuclease hydrolysis for hierarchical molecular networks.

Exonucleases serve as efficient tools for signal processing and play an important role in biochemical reactions. Here, we identify the mechanism of cooperative exonuclease hydrolysis, offering a method to regulate the cooperative hydrolysis driven by exonucleases through the modulation of the number of bases in gap region. A signal transmission strategy capable of producing amplified orthogonal DNA signal is proposed to resolve the polarity of signals and byproducts, which provides a solution to overcome the signal attenuation. The gap-regulated mechanism combined with DNA strand displacement (DSD) reduces the unpredictable secondary structures, allowing for the coexistence of similar structures in hierarchical molecular networks. For the application of the strategy, a molecular computing model is constructed to solve the maximum weight clique problems (MWCP). This work enhances for our knowledge of these important enzymes and promises application prospects in molecular computing, signal detection, and nanomachines.

Efficient data reconstruction: The bottleneck of large-scale application of DNA storage.

Over the past decade, the rapid development of DNA synthesis and sequencing technologies has enabled preliminary use of DNA molecules for digital data storage, overcoming the capacity and persistence bottlenecks of silicon-based storage media. DNA storage has now been fully accomplished in the laboratory through existing biotechnology, which again demonstrates the viability of carbon-based storage media. However, the high cost and latency of data reconstruction pose challenges that hinder the practical implementation of DNA storage beyond the laboratory. In this article, we review existing advanced DNA storage methods, analyze the characteristics and performance of biotechnological approaches at various stages of data writing and reading, and discuss potential factors influencing DNA storage from the perspective of data reconstruction.

Bubble DNA tweezer: A triple-conformation sensor responsive to concentration-ratios.

DNA tweezers, with their elegant simplicity and flexibility, have been pivotal in biosensing and DNA computing. However, conventional tweezers are confined to a binary transformation pre/post target signal recognition, limiting them to presence/absence judgments. This study introduces bubble DNA tweezers (BDT), capable of three distinct conformations based on variable target signal ratios. In contrast to traditional compact tweezers, BDT features a looser structure centered around a non-complementary bubble domain located between the tweezer arms' connecting axis and target signal recognition jaws. This bubble triggers toehold-free DNA strand displacement, leading to three conformational changes at different target signal concentrations. BDT detects presence/absence and true concentration with remarkable specificity and sensitivity. This adaptability is not confined to ideal scenarios, proving valuable in complex, noisy environments. Our method facilitates target DNA/miRNA signal quantification within a specific length range, promising applications in clinical research and environmental detection, while inspiring future biological assay innovations.

KAMPNet: multi-source medical knowledge augmented medication prediction network with multi-level graph contrastive learning.

BACKGROUNDS: Predicting medications is a crucial task in intelligent healthcare systems, aiding doctors in making informed decisions based on electronic medical records (EMR). However, medication prediction faces challenges due to complex relations within heterogeneous medical data. Existing studies primarily focus on the supervised mining of hierarchical relations between homogeneous codes in medical ontology graphs, such as diagnosis codes. Few studies consider the valuable relations, including synergistic relations between medications, concurrent relations between diseases, and therapeutic relations between medications and diseases from historical EMR. This limitation restricts prediction performance and application scenarios. METHODS: To address these limitations, we propose KAMPNet, a multi-sourced medical knowledge augmented medication prediction network. KAMPNet captures diverse relations between medical codes using a multi-level graph contrastive learning framework. Firstly, unsupervised graph contrastive learning with a graph attention network encoder captures implicit relations within homogeneous medical codes from the medical ontology graph, generating knowledge augmented medical code embedding vectors. Then, unsupervised graph contrastive learning with a weighted graph convolutional network encoder captures correlative relations between homogeneous or heterogeneous medical codes from the constructed medical codes relation graph, producing relation augmented medical code embedding vectors. Finally, the augmented medical code embedding vectors, along with supervised medical code embedding vectors, are fed into a sequential learning network to capture temporal relations of medical codes and predict medications for patients. RESULTS: Experimental results on the public MIMIC-III dataset demonstrate the superior performance of our KAMPNet model over several baseline models, as measured by Jaccard, F1 score, and PR-AUC for medication prediction. CONCLUSIONS: Our KAMPNet model can effectively capture the valuable relations between medical codes inherent in multi-sourced medical knowledge using the proposed multi-level graph contrastive learning framework. Moreover, The multi-channel sequence learning network facilitates capturing temporal relations between medical codes, enabling comprehensive patient representations for downstream tasks such as medication prediction.

Distractor-Aware Event-Based Tracking.

Event cameras, or dynamic vision sensors, have recently achieved success from fundamental vision tasks to high-level vision researches. Due to its ability to asynchronously capture light intensity changes, event camera has an inherent advantage to capture moving objects in challenging scenarios including objects under low light, high dynamic range, or fast moving objects. Thus event camera are natural for visual object tracking. However, the current event-based trackers derived from RGB trackers simply modify the input images to event frames and still follow conventional tracking pipeline that mainly focus on object texture for target distinction. As a result, the trackers may not be robust dealing with challenging scenarios such as moving cameras and cluttered foreground. In this paper, we propose a distractor-aware event-based tracker that introduces transformer modules into Siamese network architecture (named DANet). Specifically, our model is mainly composed of a motion-aware network and a target-aware network, which simultaneously exploits both motion cues and object contours from event data, so as to discover motion objects and identify the target object by removing dynamic distractors. Our DANet can be trained in an end-to-end manner without any post-processing and can run at over 80 FPS on a single V100. We conduct comprehensive experiments on two large event tracking datasets to validate the proposed model. We demonstrate that our tracker has superior performance against the state-of-the-art trackers in terms of both accuracy and efficiency.

Design, synthesis, and biological evaluation of THIQ as antidepressive agents.

7-substituted tetrahydroisoquinolines derivatives were designed, synthesized, and evaluated for neuroprotective properties. We summarized the preliminary structure activity relationships (SAR). Compound 3i was screened as a hit compound and its antidepressant activity was evaluated by employing the forced swimming test, tail suspension test. Additionally, ADMET profile (absorption, distribution, metabolism, excretion and toxicity properties) of the compound 3i was predicted in order to evaluate their lead-like properties and safety. The interaction of compound 3i bound to MAO-A was explored using molecular docking and molecular dynamics simulation. Results of biological studies revealed that the compound 3i exhibited almost equal antidepressant activity compared with magnoflorine. Compound 3i is predicted to possess good drug like properties and safety based on ADMET profile predictions. This work provides ideas for the drugs discovery of antidepressant agents.

Temporal logic circuits implementation using a dual cross-inhibition mechanism based on DNA strand displacement.

Molecular circuits crafted from DNA molecules harness the inherent programmability and biocompatibility of DNA to intelligently steer molecular machines in the execution of microscopic tasks. In comparison to combinational circuits, DNA-based temporal circuits boast supplementary capabilities, allowing them to proficiently handle the omnipresent temporal information within biochemical systems and life sciences. However, the lack of temporal mechanisms and components proficient in comprehending and processing temporal information presents challenges in advancing DNA circuits that excel in complex tasks requiring temporal control and time perception. In this study, we engineered temporal logic circuits through the design and implementation of a dual cross-inhibition mechanism, which enables the acceptance and processing of temporal information, serving as a fundamental building block for constructing temporal circuits. By incorporating the dual cross-inhibition mechanism, the temporal logic gates are endowed with cascading capabilities, significantly enhancing the inhibitory effect compared to a cross-inhibitor. Furthermore, we have introduced the annihilation mechanism into the circuit to further augment the inhibition effect. As a result, the circuit demonstrates sensitive time response characteristics, leading to a fundamental improvement in circuit performance. This architecture provides a means to efficiently process temporal signals in DNA strand displacement circuits. We anticipate that our findings will contribute to the design of complex temporal logic circuits and the advancement of molecular programming.

Wounding induces suberin deposition, relevant gene expressions and changes of endogenous phytohormones in Chinese yam (Dioscorea opposita) tubers.

Wounds on Chinese yam (Dioscorea opposita ) tubers can ocurr during harvest and handling, and rapid suberisation of the wound is required to prevent pathogenic infection and desiccation. However, little is known about the causal relationship among suberin deposition, relevant gene expressions and endogenous phytohormones levels in response to wounding. In this study, the effect of wounding on phytohormones levels and the expression profiles of specific genes involved in wound-induced suberisation were determined. Wounding rapidly increased the expression levels of genes, including PAL , C4H , 4CL , POD , KCSs , FARs , CYP86A1 , CYP86B1 , GPATs , ABCGs and GELPs , which likely involved in the biosynthesis, transport and polymerisation of suberin monomers, ultimately leading to suberin deposition. Wounding induced phenolics biosynthesis and being polymerised into suberin poly(phenolics) (SPP) in advance of suberin poly(aliphatics) (SPA) accumulation. Specifically, rapid expression of genes (e.g. PAL , C4H , 4CL , POD ) associated with the biosynthesis and polymerisation of phenolics, in consistent with SPP accumulation 3days after wounding, followed by the massive accumulation of SPA and relevant gene expressions (e.g. KCSs , FARs , CYP86A1 /B1 , GPATs , ABCGs , GELPs ). Additionally, wound-induced abscisic acid (ABA) and jasmonic acid (JA) consistently correlated with suberin deposition and relevant gene expressions indicating that they might play a central role in regulating wound suberisation in yam tubers.

Development of a multispectral fluorescence LiDAR for point cloud segmentation of plants.

The accelerating development of high-throughput plant phenotyping demands a LiDAR system to achieve spectral point cloud, which will significantly improve the accuracy and efficiency of segmentation based on its intrinsic fusion of spectral and spatial data. Meanwhile, a relatively longer detection range is required for platforms e.g., unmanned aerial vehicles (UAV) and poles. Towards the aims above, what we believe to be, a novel multispectral fluorescence LiDAR, featuring compact volume, light weight, and low cost, has been proposed and designed. A 405 nm laser diode was employed to excite the fluorescence of plants, and the point cloud attached with both the elastic and inelastic signal intensities that was obtained through the R-, G-, B-channels of a color image sensor. A new position retrieval method has been developed to evaluate far field echo signals, from which the spectral point cloud can be obtained. Experiments were designed to validate the spectral/spatial accuracy and the segmentation performance. It has been found out that the values obtained through the R-, G-, B-channels are consistent with the emission spectrum measured by a spectrometer, achieving a maximum R2 of 0.97. The theoretical spatial resolution can reach up to 47 mm and 0.7 mm in the x- and y-direction at a distance of around 30 m, respectively. The values of recall, precision, and F score for the segmentation of the fluorescence point cloud were all beyond 0.97. Besides, a field test has been carried out on plants at a distance of about 26 m, which further demonstrated that the multispectral fluorescence data can significantly facilitate the segmentation process in a complex scene. These promising results prove that the proposed multispectral fluorescence LiDAR has great potential in applications of digital forestry inventory and intelligent agriculture.

Participation of FaTRAB1 Transcription Factor in the Regulation of FaMADS1 Involved in ABA-Dependent Ripening of Strawberry Fruit.

Abscisic acid (ABA) plays a crucial role in regulating the ripening of non-climacteric strawberry fruit. In the present study, ABA was confirmed to promote strawberry ripening and induce the down-regulation of FaMADS1. The transient silence of FaMADS1 in strawberries promoted fruit ripening and induced the content of anthocyanin and soluble pectin but reduced firmness and protopectin through a tobacco rattle virus-induced gene silencing technique. In parallel with the accelerated ripening, the genes were significantly induced in the transiently modified fruit, including anthocyanin-related PAL6, C4H, 4CL, DFR, and UFGT, softening-related PL and XTH, and aroma-related QR and AAT2. In addition, the interaction between FaMADS1 and ABA-related transcription factors was researched. Yeast one-hybrid analysis indicated that the FaMADS1 promoter could interact with FaABI5-5, FaTRAB1, and FaABI5. Furthermore, dual-luciferase assay suggested that FaTRAB1 could actively bind with the FaMADS1 promoter, resulting in the decreased expression of FaMADS1. In brief, these results suggest that the ABA-dependent ripening of strawberry fruit was probably inhibited through inhibiting FaMADS1 expression by the active binding of transcript FaTRAB1 with the FaMADS1 promoter.

Large-Field Contextual Feature Learning for Glass Detection.

Glass is very common in our daily life. Existing computer vision systems neglect it and thus may have severe consequences, e.g., a robot may crash into a glass wall. However, sensing the presence of glass is not straightforward. The key challenge is that arbitrary objects/scenes can appear behind the glass. In this paper, we propose an important problem of detecting glass surfaces from a single RGB image. To address this problem, we construct the first large-scale glass detection dataset (GDD) and propose a novel glass detection network, called GDNet-B, which explores abundant contextual cues in a large field-of-view via a novel large-field contextual feature integration (LCFI) module and integrates both high-level and low-level boundary features with a boundary feature enhancement (BFE) module. Extensive experiments demonstrate that our GDNet-B achieves satisfying glass detection results on the images within and beyond the GDD testing set. We further validate the effectiveness and generalization capability of our proposed GDNet-B by applying it to other vision tasks, including mirror segmentation and salient object detection. Finally, we show the potential applications of glass detection and discuss possible future research directions.

Semisupervised Semantic Segmentation with Mutual Correction Learning.

The semisupervised semantic segmentation method uses unlabeled data to effectively reduce the required labeled data, and the pseudo supervision performance is greatly influenced by pseudo labels. Therefore, we propose a semisupervised semantic segmentation method based on mutual correction learning, which effectively corrects the wrong convergence direction of pseudo supervision. The well-calibrated segmentation confidence maps are generated through the multiscale feature fusion attention mechanism module. More importantly, using internal knowledge, a mutual correction mechanism based on consistency regularization is proposed to correct the convergence direction of pseudo labels during cross pseudo supervision. The multiscale feature fusion attention mechanism module and mutual correction learning improve the accuracy of the entire learning process. Experiments show that the MIoU (mean intersection over union) reaches 75.32%, 77.80%, 78.95%, and 79.16% using 1/16, 1/8, 1/4, and 1/2 labeled data on PASCAL VOC 2012. The results show that the new approach achieves an advanced level.

From Pixels to Semantics: Self-Supervised Video Object Segmentation With Multiperspective Feature Mining.

Existing self-supervised methods pose one-shot video object segmentation (O-VOS) as pixel-level matching to enable segmentation mask propagation across frames. However, the two tasks are not fully equivalent since O-VOS is more reliant on semantic correspondence rather than accurate pixel matching. To remedy this issue, we explore a new self-supervised framework that integrates pixel-level correspondence learning with semantic-level adaptation. The pixel-level correspondence learning is performed through photometric reconstruction of adjacent RGB frames during offline training, while semantic-level adaption operates at test-time by enforcing a bi-directional agreement of the predicted segmentation masks. In addition, we further propose a new network architecture with multi-perspective feature mining mechanism which can not only enhance reliable features but also suppress noisy ones to facilitate more robust image matching. By training the network using the proposed self-supervised framework, we achieve state-of-the-art performance on widely adopted datasets, further closing up the gap between self-supervised learning methods and their fully supervised counterparts.

Modeling and Analysis of Radial Electromagnetic Force and Vibration Characteristics Based on Deflection Dual-Stator Switched Reluctance Generator.

In this paper, a mechanical model of the deflection dual-stator switched reluctance generator (DDSRG) is developed, and the advantages of the dual-stator structure for the deflecting motion are analyzed. Secondly, the spatio-temporal and spatial distribution characteristics of the inhomogeneous electromagnetic force are derived analytically and further verified by fast Fourier transform (FFT).Thirdly, the spatial and temporal distributions of electromagnetic forces of DDSRG are calculated based on finite element software, and the distributions of electromagnetic forces under different motion states are analyzed. By combining the analysis of modal analysis and harmonic response analysis, the free mode and vibration response acceleration variation laws of the internal and external stator are determined. The results show that the order of electromagnetic forces on the stator at rated speed is mainly 8 times the fundamental frequency, and the modal vibration order is more violent in the order of 2-7. Finally, the experimental platform of DDSRG is built, and the vibration characteristics are tested to verify the validity and accuracy of the proposed simulation results.

Performance Evaluation of Ultra-Thin Wearing Course with Different Polymer Modified Asphalt Binders.

Ultra-thin wearing course (UTWC) as an asphalt overlay is widely used in pavement maintenance for extending pavement service life. Researchers focused on improving and evaluating its performance, yet few researchers compare the performance of typical UTWCs. Moreover, some traditional asphalt mixture tests are improper for UTWC due to the thicknesses of UTWC, which is thinner than the traditional asphalt overlay. This study further evaluated the advantages and disadvantages of typical UTWCs. A series of tests were conducted to compare the comprehensive performance of three typical UWTC products, including SMA-10, Novachip-B, and GT-10. Moreover, this study improved the rutting test to evaluate its rutting performance more accurately. Rutting specimens of 20 mm thick and 50 mm thick composite specimens (20 mm UTWC + 30 mm Portland cement concrete slabs) were prepared. Two types of PCC slabs were used, including unprocessed PCC slabs and PCC slabs with preset cracks. The test results showed that Novachip-B showed the best water stability and weakest raveling resistance, while GT-10 showed the best fatigue and anti-skid performance. The rutting performance of UTWCs was reduced because of the influence of preset cracks. The rutting depth of GT-10 was only 60-90% of that of others, showing the comprehensive performance of GT-10 was better than that of others. These results provide a significant reference for the research and application of UTWC.

STTG-net: a Spatio-temporal network for human motion prediction based on transformer and graph convolution network.

In recent years, human motion prediction has become an active research topic in computer vision. However, owing to the complexity and stochastic nature of human motion, it remains a challenging problem. In previous works, human motion prediction has always been treated as a typical inter-sequence problem, and most works have aimed to capture the temporal dependence between successive frames. However, although these approaches focused on the effects of the temporal dimension, they rarely considered the correlation between different joints in space. Thus, the spatio-temporal coupling of human joints is considered, to propose a novel spatio-temporal network based on a transformer and a gragh convolutional network (GCN) (STTG-Net). The temporal transformer is used to capture the global temporal dependencies, and the spatial GCN module is used to establish local spatial correlations between the joints for each frame. To overcome the problems of error accumulation and discontinuity in the motion prediction, a revision method based on fusion strategy is also proposed, in which the current prediction frame is fused with the previous frame. The experimental results show that the proposed prediction method has less prediction error and the prediction motion is smoother than previous prediction methods. The effectiveness of the proposed method is also demonstrated comparing it with the state-of-the-art method on the Human3.6 M dataset.

Removal of Alternaria mycotoxins exposed to different food components by cold plasma.

Alternariol (AOH) and alternariol monomethyl ether (AME), the two Alternaria mycotoxins with the highest outbreak rates in food systems, could be effectively reduced by cold plasma. This research evaluated the impact of food components on the plasma removal of AOH and AME. The results showed that 6% whey protein or ovalbumin almost completely inhibited the reduction of AOH or AME. Polyphenols inhibited the removal of AOH and AME by up to 90.8% and 83.4%, respectively. Organic acids and Vc reduced AME removal by up to 43.4% and 31.9%, respectively, but had little effect on AOH removal. Sugars and amino acids could decrease both toxin removal by less than 10%. Proteins exhibited the most inhibitory effect on plasma removal of AOH and AME, followed by polyphenols, while the effect of other components was relatively small. AOH and AME removal by cold plasma was highly related to H2O2 produced during plasma discharge.

Effects of Lactobacillus plantarum and Saccharomyces cerevisiae co-fermentation on the structure and flavor of wheat noodles.

BACKGROUND: Although traditional fermented noodles possess high eating quality, it is difficult to realize large-scale industrialization as a result of the complexity of spontaneous fermentation. In present study, commercial Lactobacillus plantarum and Saccharomyces cerevisiae were applied in the preparation of fermented noodles. RESULTS: The changes in the structural characteristics and aroma components of noodles after fermentation were investigated via scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), low-field magenetic resonance imaging, electronic nose, and simultaneous distillation and extraction/gas chromatography-mass spectrometry (GC-MS) analysis. SEM images revealed that co-fermentation of the L. plantarum and S. cerevisiae for 10-40 min enhanced the continuity of the gluten network and promoted the formation of pores. FTIR spectra analysis showed that the co-fermentation increased significantly (P < 0.05) the proportion of α-helices of noodles gluten protein, enhancing the orderliness of the molecular structure of protein. After fermentation for 10-40 min, the signal density of hydrogen protons increased from the surface to the core, indicating that the water in the noodles migrated inward during a short fermentation process. The results of multivariate statistical analysis demonstrated that the main aroma differences between unfermented and fermented noodles were mainly in hydrocarbons, aromatic compounds and inorganic sulfides. GC-MS analysis indicated that the main volatile compounds detected were 2, 4-di-tert-butylphenol, bis (2-ethylhexyl) adipate, butyl acetate, dibutyl phthalate, dioctyl terephthalate, bis (2-ethylhexyl) phthalate, pentanol and 2-pentylfuran, etc. CONCLUSION: Co-fermentation with L. plantarum and S. cerevisiae improved the structure of gluten network and imparted more desirable volatile components to wheat noodles. © 2022 Society of Chemical Industry.