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Abstract Motivation Retrosynthesis planning poses a formidable challenge in the organic chemical industry, particularly in pharmaceuticals. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science. Various deep learning-based methods have been proposed for this task in recent years, incorporating diverse levels of additional chemical knowledge dependency. Results This paper introduces UAlign, a template-free graph-to-sequence pipeline for retrosynthesis prediction. By combining graph neural networks and Transformers, our method can more effectively leverage the inherent graph structure of molecules. Based on the fact that the majority of molecule structures remain unchanged during a chemical reaction, we propose a simple yet effective SMILES alignment technique to facilitate the reuse of unchanged structures for reactant generation. Extensive experiments show that our method substantially outperforms state-of-the-art template-free and semi-template-based approaches. Importantly, our template-free method achieves effectiveness comparable to, or even surpasses, established powerful template-based methods. Scientific contribution We present a novel graph-to-sequence template-free retrosynthesis prediction pipeline that overcomes the limitations of Transformer-based methods in molecular representation learning and insufficient utilization of chemical information. We propose an unsupervised learning mechanism for establishing product-atom correspondence with reactant SMILES tokens, achieving even better results than supervised SMILES alignment methods. Extensive experiments demonstrate that UAlign significantly outperforms state-of-the-art template-free methods and rivals or surpasses template-based approaches, with up to 5% (top-5) and 5.4% (top-10) increased accuracy over the strongest baseline.

Presently, ethylene oxide (EtO) is posing a significant threat to both human health and the environment due to occasional or deliberate emissions. However, few works so far have focused on this issue. It is urgent to explore novel and effective technology to protect against the threat of EtO. Herein, a series of AC/ZSM-5 composites were prepared to improve the adsorption performance for EtO, evaluated by dynamic breakthrough experiments. Particularly, the AC/ZSM-20% composite demonstrated a more excellent adsorption capacity of 81.9 mg/g at 25 °C and 50% RH than that of pristine AC and ZSM-5 with 32.5 and 52.3 mg/g, respectively. Moreover, the adsorption capacity of the AC/ZSM-20% composite remained constant even after five adsorption-desorption cycles. The adsorption mechanism of EtO on the composite is further revealed by density functional theory (DFT) calculations.

Vision-based weld seam extraction poses a significant challenge for weldments with complex spatial structures in automated welding. Existing research primarily focuses on identifying weld seams from weldments with given positions and postures, while practical weld path planning requires multiple weld seams identified within arbitrarily placed weldments. This paper proposes a methodology that identifies weld seams from arbitrarily placed spatial planar weldments in a single run. First, by introducing a turntable calibrated with respect to a 3D camera, we perform 3D reconstruction on an arbitrarily placed spatial planar weldment. Second, an improved RANSAC algorithm based on Euclidean clustering is proposed to carry out plane segmentation, focusing on segmentation accuracy. Finally, we present a novel weld seam extraction algorithm leveraging the half-edge data structure to efficiently compute weld seams from the segmented planes. The experiments conducted in this study demonstrate that the average segmentation errors (as an indirect indicator of weld seam extraction error) are reduced by 90.3% to 99.8% over conventional segmentation methods, and the standard deviations are reduced by 64.8% to 97.0%.

Age-related macular degeneration (AMD) is a common disease contributing to vision loss in the elderly. All-trans-retinal (atRAL) is a retinoid in the retina, and its abnormal accumulation exhibits toxicity to the retina and promotes oxidative stress-induced photoreceptor degeneration, which plays a crucial role in AMD progression. Crocin is a natural product extracted from saffron, which displays significant antioxidant and anti-inflammatory effects. The present study elucidates the protective effects of crocin on photoreceptor cell damage by atRAL and its potential mechanisms. The results revealed that crocin significantly attenuated cytotoxicity by repressing oxidative stress, mitochondrial injury, and DNA damage in atRAL-loaded photoreceptor cells. Moreover, crocin visibly inhibited DNA damage-induced apoptosis and gasdermin E (GSDME)-mediated pyroptosis in photoreceptor cells after exposure to atRAL. It was also observed that crocin distinctly prevented an increase in Fe2+ levels and lipid peroxidation caused by atRAL via suppressing the Kelch-like ECH-associated protein 1 (KEAP1)/nuclear factor-erythroid 2-related factor 2 (NRF2)/heme oxygenase-1 (HO-1) signaling pathway, thereby ameliorating photoreceptor cell ferroptosis. In short, these findings provide new insights that crocin mitigates atRAL-induced toxicity to photoreceptor cells by inhibiting oxidative stress, apoptosis, pyroptosis, and ferroptosis.

Accurate rock mass quality classification is crucial for the design and construction of underground projects. Traditional methods often rely on expert experience, introducing subjectivity, and struggle with complex geological conditions. Machine learning algorithms have improved this issue, but obtaining complete rock mass quality datasets is often difficult due to high cost and complex procedures. This study proposed a hybrid XGBoost model for predicting rock mass quality using incomplete datasets. The zebra optimization algorithm (ZOA) and Bayesian optimization (BO) were used to optimize the hyperparameters of the model. Data from various regions and types of underground engineering projects were utilized. Adaptive synthetic (ADASYN) oversampling addressed class imbalance. The model was evaluated using metrics including accuracy, Kappa, precision, recall, and F1-score. The ZOA-XGBoost model achieved an accuracy of 0.923 on the test set, demonstrating the best overall performance. Feature importance analysis and individual conditional expectation (ICE) plots highlighted the roles of RQD and UCS in predicting rock mass quality. The model’s robustness with incomplete data was verified by comparing its performance with other machine learning models on a dataset with missing values. The ZOA-XGBoost model outperformed other models, proving its reliability and effectiveness. This study provides an efficient and objective method for rock mass quality classification, offering significant value for engineering applications.

Perception of digitized depth is a prerequisite for enabling the intelligence of three-dimensional (3D) laparoscopic systems. In this context, stereo matching of laparoscopic stereoscopic images presents a promising solution. However, the current research in this field still faces challenges. First, the acquisition of accurate depth labels in a laparoscopic environment proves to be a difficult task. Second, errors in the correction of laparoscopic images are prevalent. Finally, laparoscopic image registration suffers from ill-posed regions such as specular highlights and textureless areas. In this paper, we make significant contributions by developing (1) a correction compensation module to overcome correction errors; (2) an adaptive cost aggregation module to improve prediction performance in ill-posed regions; (3) a novel self-supervised stereo matching framework based on these two modules. Specifically, our framework rectifies features and images based on learned pixel offsets, and performs differentiated aggregation on cost volumes based on their value. The experimental results demonstrate the effectiveness of the proposed modules. On the SCARED dataset, our model reduces the mean depth error by 12.6% compared to the baseline model and outperforms the state-of-the-art unsupervised methods and well-generalized models.

As a frequent hazard, tropical cyclones have a great impact on the social and economic development of China, which is close to the origin of tropical cyclones in the western Pacific Ocean. The primary objective of this study was to construct a comprehensive risk assessment model for tropical cyclone hazards based on natural influencing factors, informing recommendations for hazard prevention and mitigation in affected regions. This research focused on tropical cyclones that made landfall in mainland China and Hainan from 1949 to 2023, utilizing hazard risk theory and classical extreme value theory. The wind speed and rainfall data during the peak cyclone periods (June to October) from 1997 to 2021 gathered from various meteorological stations, as well as altitude and vegetation cover data, were examined. Hierarchical analysis and ArcGIS spatial analysis methods were employed to study the characteristics of the spatiotemporal distribution of landfalling tropical cyclones and the comprehensive risk of tropical cyclone hazards, and the regions of China were delineated according to these methods. The results showed that, during the period from 1949 to 2023, the overall number of landfalling tropical cyclones decreased in a fluctuating manner, while the intensity of the cyclones increased. Furthermore, severe typhoons tended to occur more frequently in the summer than autumn with time, intensifying the challenge to resist short-term hazards. Moreover, the hazard-causing factors in areas affected by tropical cyclones displayed an increasing trend from north to south and from west to east. In detail, the regions sensitive to natural hazards were primarily located in the central part of Liaoning province, Tianjin, central and eastern Hebei province, Shandong province, eastern Henan province, central and northern Anhui province, Jiangsu province, and Shanghai, which are characterized by flat terrain and relatively low vegetation cover. Overall, the comprehensive risk of tropical cyclone hazards showed a geographical distribution that decreases from south to north and from east to west, with coastal cities in provinces such as Hainan, Guangdong, Guangxi, Fujian, and Zhejiang—including Haikou, Zhanjiang, Xiamen, Beihai, and Taizhou—exhibiting the highest levels of risk.

Multimodal sarcasm detection is a developing research field in social Internet of Things, which is the foundation of artificial intelligence and human psychology research. Sarcastic comments issued on social media often imply people’s real attitudes toward the events they are commenting on, reflecting their current emotional and psychological state. Additionally, the limited memory of Internet of Things mobile devices has posed challenges in deploying sarcastic detection models. An abundance of parameters also leads to an increase in the model’s inference time. Social networking platforms such as Twitter and WeChat have generated a large amount of multimodal data. Compared to unimodal data, multimodal data can provide more comprehensive information. Therefore, when studying sarcasm detection on social Internet of Things, it is necessary to simultaneously consider the inter-modal interaction and the number of model parameters. In this paper, we propose a lightweight multimodal interaction model with knowledge enhancement based on deep learning. By integrating visual commonsense knowledge into the sarcasm detection model, we can enrich the semantic information of image and text modal representation. Additionally, we develop a multi-view interaction method to facilitate the interaction between modalities from different modal perspectives. The experimental results indicate that the model proposed in this paper outperforms the unimodal baselines. Compared to multimodal baselines, it also has similar performance with a small number of parameters.

The present challenge in dental pulp tissue engineering scaffold materials lies in the development of tissue-specific scaffolds that are conducive to an optimal regenerative microenvironment and capable of accommodating intricate root canal systems. This study utilized porcine dental pulp to derive the decellularized extracellular matrix (dECM) via appropriate decellularization protocols. The resultant dECM was dissolved in an acid pepsin solution to form dECM hydrogels. The analysis encompassed evaluating the microstructure and rheological properties of dECM hydrogels and evaluated their biological properties, including in vitro cell viability, proliferation, migration, tube formation, odontogenic, and neurogenic differentiation. Gelatin methacrylate (GelMA) hydrogel served as the control. Subsequently, hydrogels were injected into treated dentin matrix tubes and transplanted subcutaneously into nude mice to regenerate dental pulp tissue in vivo. The results showed that dECM hydrogels exhibited exceptional injectability and responsiveness to physiological temperature. It supported the survival, odontogenic, and neurogenic differentiation of dental pulp stem cells in a 3D culture setting. Moreover, it exhibited a superior ability to promote cell migration and angiogenesis compared to GelMA hydrogel in vitro. Additionally, the dECM hydrogel demonstrated the capability to regenerate pulp-like tissue with abundant blood vessels and a fully formed odontoblast-like cell layer in vivo. These findings highlight the potential of porcine dental pulp dECM hydrogel as a specialized scaffold material for dental pulp regeneration.

Summary: Dry reforming of methane (DRM) is an efficient process to transform methane and carbon dioxide to syngas. Nickel could show good catalytic activity for DRM, whereas the deactivation of nickel surfaces by the formation of inert carbon structures is inevitable. In this study, we carry out a detailed investigation of the evolution and catalytic performance of the carbon-covered surface structure on Ni(100) with a combined density functional theory and microkinetic modeling approach. The results suggest that the pristine Ni(100) surface is prone to carbon deposition and accumulation under reaction conditions. Further studies show that over this carbon-covered reconstructed Ni(100) surface, a carbon-based Mars-van-Krevelen mechanism would be favored, and the activity and coke resistance is promoted. This surface state and reaction mechanism were rarely reported before and would provide more insights into the DRM process under real reaction conditions and would help design more stable Ni catalysts.

Recently, deep learning techniques have been used for low-dose CT (LDCT) reconstruction to reduce the radiation risk for patients. Despite the improvement in performance, the network models used for LDCT reconstruction are becoming increasingly complex and computationally expensive under the mantra of “deeper is better”. However, in clinical settings, lightweight models with a low computational cost and short reconstruction times are more popular. For this reason, this paper proposes a computationally efficient CNN model with a simple structure for sparse-view LDCT reconstruction. Inspired by super-resolution networks for natural images, the proposed model interpolates projection data directly in the sinogram domain with a fully convolutional neural network that consists of only four convolution layers. The proposed model can be used directly for sparse-view CT reconstruction by concatenating the classic filtered back-projection (FBP) module, or it can be incorporated into existing dual-domain reconstruction frameworks as a generic sinogram domain module. The proposed model is validated on both the 2016 NIH-AAPM-Mayo Clinic LDCT Grand Challenge dataset and The Lung Image Database Consortium dataset. It is shown that despite the computational simplicity of the proposed model, its reconstruction performance at lower sparsity levels (1/2 and 1/4 radiation dose) is comparable to that of the sophisticated baseline models and shows some advantages at higher sparsity levels (1/8 and 1/15 radiation dose). Compared to existing sinogram domain baseline models, the proposed model is computationally efficient and easy to train on small training datasets, and is thus well suited for clinical real-time reconstruction tasks.

Long noncoding RNAs (lncRNAs) are transcripts with lengths of more than 200 nt and limited protein-coding potential. They were found to play important roles in plant stress responses. In this study, the maize drought-tolerant inbred line AC7643 and drought-sensitive inbred line AC7729/TZSRW, as well as their recombinant inbred lines (RILs) were selected to identify drought-responsive lncRNAs in roots. Compared with non-responsive lncRNAs, drought-responsive lncRNAs had different sequence characteristics in length of genes and number of exons. The ratio of down-regulated lncRNAs induced by drought was significantly higher than that of coding genes; and lncRNAs were more widespread expressed in recombination sites in the RILs. Additionally, by integration of the modifications of DNA 5-methylcytidine (5mC), histones, and RNA N6-methyladenosine (m6A), it was found that the enrichment of histone modifications associated with transcriptional activation in the genes generated lncRNAs was lower that coding genes. The lncRNAs-mRNAs co-expression network, containing 15,340 coding genes and 953 lncRNAs, was constructed to investigate the molecular functions of lncRNAs. There are 13 modules found to be associated with survival rate under drought. We found nine SNPs located in lncRNAs among the modules associated with plant survival under drought. In conclusion, we revealed the characteristics of lncRNAs responding to drought in maize roots based on multiomics studies. These findings enrich our understanding of lncRNAs under drought and shed light on the complex regulatory networks that are orchestrated by the noncoding RNAs in response to drought stress.

Fault localization, a critical process of software debugging, can be implemented by ranking program statements according to their suspiciousness of being faulty, which, in turn, is calculated based on the execution behaviors of test cases. The performance of fault localization will deteriorate if the actual faulty statement is ranked low in suspiciousness. Intuitively speaking, the quality of the used test cases affects the suspiciousness ranking and thus the efficacy of fault localization. As such, it is necessary to generate test cases with “better” quality to improve the chance of faulty statements being ranked as highly suspicious. In this paper, we propose a software fault localization approach based on feedback ranking information, namely FLFR, according to an improved genetic algorithm. The starting point of the new method is the execution of a set of test cases, which gives a preliminary suspiciousness ranking of program statements. The improved genetic algorithm is iteratively applied to generate new test cases. The new method is evaluated through a series of experiments on four C programs and two Java programs. The experimental results show that the test cases automatically generated by the method can improve the suspiciousness ranking of the faulty statement, and thus enhance the performance of fault localization.

Shortwave infrared polarization imaging can increase the contrast of the target to the background to improve the detection system’s recognition ability. The division of focal plane polarization indium gallium arsenide (InGaAs) focal plane array (FPA) detector is the ideal choice due to the advantages of compact structure, real-time imaging, and high stability. However, because of the mismatch between nanostructures and photosensitive pixels as well as the crosstalk among the different polarization directions, the currently reported extinction ratio (ER) of superpixel-polarization-integrated detectors cannot meet the needs of high-quality imaging. In this paper, a 1024 × 4 InGaAs FPA detector on-chip integrated with a linear polarization grating (LPG) was realized and tested. The detector displayed good performance throughout the 0.9–1.7 um band, and the ERs at 1064 nm, 1310 nm and 1550 nm reached up to 22:1, 29:1 and 46:1, respectively. For the crosstalk investigation, the optical simulation of the grating-integrated InGaAs pixel was carried out, and the limitation of the ER was calculated. The result showed that the scattering of incident light in the InP substrate led to the crosstalk. Moreover, the deviation of the actual grating morphology from the designed structure caused a further reduction in the ER.

Protein modifications such as post-translational modifications (PTMs) and sequence variants (SVs) occur frequently during protein biosynthesis and have received great attention by biopharma industry and regulatory agencies. In this study, an aberrant peak near light chain (LC) was observed in the non-reduced capillary electrophoresis sodium dodecyl sulfate (nrCE-SDS) electrophoretogram during cell line development of one bispecific antibody (BsAb) product, and the detected mass was about 944 Da higher than LC. The corresponding peak was then enriched by denaturing size-exclusion chromatography (SEC-HPLC) and further characterized by nrCE-SDS and peptide mapping analyses. De novo mass spectra/mass spectra (MS/MS) analysis revealed that the aberrant peak was LC related sequence variant, with the truncated C-terminal sequence “SFNR” (“GEC”deleted) linked with downstream SV40 promotor sequence “EAEAASASELFQ”. The unusual sequence was further confirmed by comparing with the direct synthetic peptide “SFNREAEAASASELFQ”. It was demonstrated by mRNA sequencing of the cell pool that the sequence variant was caused by aberrant splicing at the transcription step. The prepared product containing this extension variant maintained well-folded structure and good functional properties though the LC/Heavy chain (HC) inter-chain disulfide was not formed. Several control strategies to mitigate the risk of this LC related sequence variant were also proposed.

Abstract Virtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

Continuous-flow PCR (CF-PCR) can realize rapid DNA amplification because of the high temperature variation rate. However, off-line detection methods for PCR may induce cross contamination. To overcome this problem, we herein fabricated an integrated CF-PCR and electrophoresis microfluidic chip. The optimal voltage applied in the electrophoresis part of the microfluidic chip was achieved by simulation in COMSOL. Coating the inside wall of the microchannel can inhibit electroosmotic flow and improve the resolution for DNA fragments. The temperature distribution of the serpentine part can meet the PCR and has no obvious suppressive effect on sample separation. Finally, we have performed the amplification of target genes for Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola and detected the corresponding PCR products in the microfluidic chip within 11 min. Such work provides a new method for the rapid detection of bacteria.

AI has seen great progress in recent decades, with a rapidly increasing computing capacity and the exponentially growing amount and types of processed data [...]

Recently, the Vision Transformer (ViT) model has been used for various computer vision tasks, due to its advantages to extracting long-range features. To better integrate the long-range features useful for classification, the standard ViT adds a class token, in addition to patch tokens. Despite state-of-the-art results on some traditional vision tasks, the ViT model typically requires large datasets for supervised training, and thus, it still face challenges in areas where it is difficult to build large datasets, such as medical image analysis. In the ViT model, only the output corresponding to the class token is fed to a Multi-Layer Perceptron (MLP) head for classification, and the outputs corresponding to the patch tokens are exposed. In this paper, we propose an improved ViT architecture (called ViT-Patch), which adds a shared MLP head to the output of each patch token to balance the feature learning on the class and patch tokens. In addition to the primary task, which uses the output of the class token to discriminate whether the image is malignant, a secondary task is introduced, which uses the output of each patch token to determine whether the patch overlaps with the tumor area. More interestingly, due to the correlation between the primary and secondary tasks, the supervisory information added to the patch tokens help with improving the performance of the primary task on the class token. The introduction of secondary supervision information also improves the attention interaction among the class and patch tokens. And by this way, ViT reduces the demand on dataset size. The proposed ViT-Patch is validated on a publicly available dataset, and the experimental results show its effectiveness for both malignant identification and tumor localization.

The attitude closed-loop control of the parallel platform in the working space needs to determine the relationship between the pose of the top moving platform and the length of each mechanical arm, that is, the kinematics problem of the parallel platform. In this study, the kinematics model of the six-degree-of-freedom parallel platform was established. The kinematics forward solution algorithm based on Newton–Raphson iteration was studied. The kinematics forward solution method usually adopts a numerical solution, which often needs multiple iterations, and the algorithm has a poor real-time performance. In order to improve the real-time performance of the parallel platform control system, a multivariate polynomial regression kinematics forward solution algorithm is proposed in this paper. Moreover, by combining the multivariate polynomial regression with the Newton iterative method, we obtained an efficient solution algorithm with controllable solution accuracy. The effectiveness of the proposed method was verified by simulation tests and physical tests.

The modeling and simulation of biological tissue is the core part of a virtual surgery system. In this study, the geometric and physical methods related to soft tissue modeling were investigated. Regarding geometric modeling, the problem of repeated inverse calculations of control points in the Bezier method was solved via re-parameterization, which improved the calculation speed. The base surface superposition method based on prior information was proposed to make the deformation model not only have the advantages of the Bezier method but also have the ability to fit local irregular deformation surfaces. Regarding physical modeling, the fitting ability of the particle spring model to the anisotropy of soft tissue was improved by optimizing the topological structure of the particle spring model. Then, the particle spring model had a more extensive nonlinear fitting ability through the dynamic elastic coefficient parameter. Finally, the secondary modeling of the elastic coefficient based on the virtual body spring enabled the model to fit the creep and relaxation characteristics of biological tissue according to the elongation of the virtual body spring.

Conventional Euler deconvolution is widely used for interpreting profile, grid, and ungridded potential field data. The Tensor Euler deconvolution applies additional constraints to the Euler solution using all gravity vectors and the full gravity gradient tensor. These algorithms use a series of different-sized moving windows to yield many solutions that can be employed to estimate the source location from the entire survey area. However, traditional discrimination techniques ignore the interrelation among the Euler solutions, so they cannot be employed to separate adjacent targets. To overcome this difficulty, we introduced multivariate Kernel Density Derivative Estimation (KDDE) as an extension of Kernel Density Estimation, which is a mathematical process to estimate the probability density function of a random variable. The multivariate KDDE was tested on a single cube model, a single cylinder model, and three composite models consisting of two cubes with various separations using gridded data. The probability value calculated by the multivariate KDDE was used to discriminate spurious solutions from the Euler solution dataset and isolate adjacent geological sources. The method was then applied to airborne gravity data from British Columbia, Canada. Then, the results of synthetic models and field data show that the proposed method can successfully locate meaningful geological targets.

Argonautes (AGOs) interact with microRNAs (miRNAs) to form the RNA-induced silencing complex (RISC), which can posttranscriptionally regulate the expression of targeted genes. To date, however, the AGOs and their miRNA triggers remain elusive in rapeseed (Brassica napus). Here, we systematically performed a phylogenetic analysis and examined the collinear relationships of the AGOs among four Brassicaceae species. Their physicochemical properties, gene structures, and expression patterns among 81 tissues from multiple materials and developmental stages were further analyzed. Additionally, their posttranscriptional regulation was analyzed using psRNATarget prediction, miRNA-/mRNA-Seq analyses, and a qRT-PCR verification. We finally identified 10 AtAGOs, 13 BolAGOs, 11 BraAGOs, and 24 BnaAGOs. An expression analysis of the BnaAGOs in the B. napus cultivar ZS11, as well as genotypes with extreme phenotypes in various yield-related traits, revealed the conservation and diversity of these genes. Furthermore, we speculated the posttranscriptional regulation of the B. napus miR168a–AGO1s and miR403–AGO2s modules. Combining miRNA-Seq and mRNA-Seq analyses, we found that the B. napus miR168a–AGO1s module may play an essential role in negatively regulating yield traits, whereas the miR403–AGO2s module positively impacts yield. This is the first attempt to comprehensively analyze the AGOs and their miRNA triggers in B. napus and provides a theoretical basis for breeding high-yielding varieties through the manipulation of the miRNA–AGOs modules.

Cannabidiol (CBD) is one specific kind of the cannabinoid in Cannabis sativa L with a wide range of pharmacological activities. However, the poor water solubility and specificity of CBD limits its application in pharmaceutical field. For solving these problems, in this work, we successfully prepared a targeted carrier by grafting biotin (BIO) onto ethylenediamine-β-Cyclodextrin (EN-CD) in a single step to generate a functionalized supramolecule, named BIO-CD. Subsequently, an amantadine-conjugated cannabinoids (AD-CBD) was prepared and self-assembled with the BIO-CD. A series of methods were used to characterize the inclusion behavior and physicochemical properties of AD-CBD and BIO-CD. The results showed that AD-CBD entered the cavity of BIO-CD and formed a 1:1 host-guest inclusion complex. MTT assay and confocal laser scanning microscopy (CLSM) revealed that the targeting effect and anticancer activity of AD-CBD/BIO-CD inclusion complex against three human cancer cell lines were higher than BIO-CD, AD-CBD and free CBD. Moreover, the inclusion complex could release drugs under weakly acidic conditions. These results demonstrated that AD-CBD/BIO-CD inclusion complex possess excellent targeted and anticancer activity, which is hopeful to be applied in clinic as a new therapeutic approach.

By using three different head types (5 m, 7.5 m and 10 m nose lengths), the CRH3 (China Railway High-speed 3) flow field structure at 15° yaw was studied through wind tunnel experiments and numerical simulations. The modifications of the aerodynamic coefficients were studied using the Improved Delayed Separation Eddy Simulation (IDDES) method. The results show that the longer the nose length of the tail car, the more it is affected by crosswind. However, the increase in turbulence mitigates the risk of overturning the tail car as the pressure distribution between the train side surface and the underbody becomes more disturbed. The nose length of the head car can affect the position and length of the longitudinal vortex core on the leeward side, thus affecting the lift and side force of each section of the train. The location of the time-averaged vortex core for a 15° crosswind is approximately 0.67H~0.7H high from the ground and 0.65H~0.67H wide from the center of the train. The main frequency of the leeward vortex ranges from 0.1 to 0.3. The transient vibration amplitude at the position of the vortex core is the largest, and the main frequency of vibration is 0.18. The tail car nose length should be properly lengthened since increasing the length of the tail car reduces the negative pressure on the surface of the tail car, thus reducing drag and side force. However, the excessive length of the tail car nose increases the risk of overturning under crosswind.

Stereo matching is the operation of obtaining the parallax value between two images by matching all the corresponding image points in the two images, thus obtaining the dense parallax image between the two images. How to obtain accurate disparity images has always been a key point in the field of stereo vision. Presently, in the research of 3D reconstruction technology based on binocular stereo vision, the main research direction of domestic and foreign scholars is to improve the efficiency and accuracy of stereo matching, and there is research literature on soft tissues. This paper proposes an improved stereo matching algorithm based on joint similarity measures and adaptive weights. The algorithm improves the matching cost calculation based on the joint similarity measure to fit the color image of the heart soft tissue. At the same time, the algorithm uses the idea of graph cutting to improve the adaptive weight. The experimental results show that both the improved joint similarity measure and the improved adaptive weight can effectively reduce the mismatch rate. In addition, the corresponding matching effect is better than using only one of the improved joint similarity measures.

Inflammatory bowel disease (IBD) is a worldwide chronic intestinal inflammatory immune-related disease. In this study, mice with dextran sulfate sodium (DSS)-induced colitis were used to evaluate the effect of Lactobacillus acidophilus on colitis. The results revealed that L. acidophilus CCFM137 and FAHWH11L56 show potential for relieving colitis symptoms, while L. acidophilus FGSYC48L79 did not show a protective effect. Moreover, L. acidophilus NCFM and FAHWH11L56 showed similar effects on various indicators of DSS-induced colitis, increasing the IL-10 and IL-17 in the colon, and modifying the CCL2/CCR2 axis and CCL3/CCR1 axis. For L. acidophilus CCFM137, its effects on colitis were different from the above two strains. Moreover, L. acidophilus FGSYC48L79 had negative effects on colitis by increasing the abundance of harmful bacteria in the gut microbiota and may promote the signaling of chemokines and their receptors. This may be related to its special genome compared to the other strains.

Amino acids, which are important compatible solutes, play a significant role in probiotic lyophilization. However, studies on the functions of Bifidobacterium during freeze-drying are limited. Therefore, in this study, we compared the freeze-drying survival rate of Bifidobacterium longum CCFM 1029 cultivated in different media containing different kinds of compatible solutes. We found that the addition of 21 g/L proline to the culture media substantially improved the freeze-drying survival rate of B. longum CCFM 1029 from 18.61 ± 0.42% to 38.74 ± 1.58%. Interestingly, this change has only been observed when the osmotic pressure of the external culture environment is increased. Under these conditions, we found that proline accumulation in this strain increased significantly. This change also helped the strain to maintain its membrane integrity and the activity of some key enzymes during freeze-drying. Overall, these results show that the addition of proline can help the strain resist a tough environment during lyophilization. The findings of this study provide preliminary data for producers of probiotics who wish to achieve higher freeze-drying survival rates during industrial production.

Postbiotics are rich in a variety of bioactive components, which may have beneficial effects in inhibiting hepatic lipid accumulation. In this study, we investigated the preventive effects of postbiotics (POST) prepared from Lactobacillus paracasei on non-alcoholic fatty liver disease (NAFLD). Our results showed that when mice ingested a high-fat diet (HFD) and POST simultaneously, weight gain was slowed, epididymal white fat hypertrophy and insulin resistance were suppressed, serum biochemical indicators related to blood lipid metabolism were improved, and hepatic steatosis and liver inflammation decreased. Bacterial sequencing showed that POST modulated the gut microbiota in HFD mice, increasing the relative abundance of Akkermansia and reducing the relative abundance of Lachnospiraceae NK4A136 group, Ruminiclostridium and Bilophila. Spearman’s correlation analysis revealed significant correlations between lipid metabolism parameters and gut microbes. Functional prediction results showed that the regulation of gut microbiota was associated with the improvement of metabolic status. The metabolomic analysis of the liver revealed that POST-regulated liver metabolic pathways, such as glycerophospholipid and ether lipid metabolism, pantothenate and CoA biosynthesis, some parts of amino acid metabolism, and other metabolic pathways. In addition, POST regulated the gene expression in hepatocytes at the mRNA level, thereby regulating lipid metabolism. These findings suggest that POST plays a protective role against NAFLD and may exert its efficacy by modulating the gut microbiota and liver metabolism, and these findings may be applied to related functional foods.

A new phospholipase D from marine Moritella sp. JT01 (MsPLD) was recombinantly expressed and biochemically characterized. The optimal reaction temperature and pH of MsPLD were determined to be 35 °C and 8.0. MsPLD was stable at a temperature lower than 35 °C, and the t1/2 at 4 °C was 41 days. The crystal structure of apo-MsPLD was resolved and the functions of a unique extra loop segment on the enzyme activity were characterized. The results indicated that a direct deletion or fastening of the extra loop segment by introducing disulfide bonds both resulted in a complete loss of its activity. The results of the maximum insertion pressure indicated that the deletion of the extra loop segment significantly decreased MsPLD’s interfacial binding properties to phospholipid monolayers. Finally, MsPLD was applied to the synthesis of phosphatidic acid by using a biphasic reaction system. Under optimal reaction conditions, the conversion rate of phosphatidic acid reached 86%. The present research provides a foundation for revealing the structural–functional relationship of this enzyme.

At present, machine sense of smell has shown its important role and advantages in many scenarios. The development of machine sense of smell is inseparable from the support of corresponding data and algorithms. However, the process of olfactory data collection is relatively cumbersome, and it is more difficult to collect labeled data. However, in many scenarios, to use a small amount of labeled data to train a good-performing classifier, it is not feasible to rely only on supervised learning algorithms, but semi-supervised learning algorithms can better cope with only a small amount of labeled data and a large amount of unlabeled data. This study combines the new weighted kernel with SKELM and proposes a semi-supervised extreme learning machine algorithm based on the weighted kernel, SELMWK. The experimental results show that the proposed SELMWK algorithm has good classification performance and can solve the semi-supervised gas classification task of the same domain data well on the used dataset.

Automatic Speech Recognition (ASR) provides a new way of human-computer interaction. However, it is vulnerable to adversarial examples, which are obtained by deliberately adding perturbations to the original audios. Thorough studies on the universal feature of adversarial examples are essential to prevent potential attacks. Previous research has shown classic adversarial examples have different logits distribution compared to normal speech. This paper proposes a logit-traction attack to eliminate this difference at the statistical level. Experiments on the LibriSpeech dataset show that the proposed attack reduces the accuracy of the LOGITS NOISE detection to 52.1%. To further verify the effectiveness of this approach in attacking detection based on logits, three different features quantifying the dispersion of logits are constructed in this paper. Furthermore, a richer target sentence is adopted for experiments. The results indicate that these features can detect baseline adversarial examples with an accuracy of about 90% but cannot effectively detect Logits-Traction adversarial examples, proving that Logits-Traction attack can evade the logits-based detection method.

Z-conformation nucleic acid binding protein 1 (ZBP1), a powerful innate immune sensor, has been identified as the important signaling initiation factor in innate immune response and the multiple inflammatory cell death known as PANoptosis. The initiation of ZBP1 signaling requires recognition of left-handed double-helix Z-nucleic acid (includes Z-DNA and Z-RNA) and subsequent signaling transduction depends on the interaction between ZBP1 and its adapter proteins, such as TANK-binding kinase 1 (TBK1), interferon regulatory factor 3 (IRF3), receptor-interacting serine/threonine-protein kinase 1 (RIPK1), and RIPK3. ZBP1 activated innate immunity, including type-I interferon (IFN-I) response and NF-κB signaling, constitutes an important line of defense against pathogenic infection. In addition, ZBP1-mediated PANoptosis is a double-edged sword in anti-infection, auto-inflammatory diseases, and tumor immunity. ZBP1-mediated PANoptosis is beneficial for eliminating infected cells and tumor cells, but abnormal or excessive PANoptosis can lead to a strong inflammatory response that is harmful to the host. Thus, pathogens and host have each developed multiplex tactics targeting ZBP1 signaling to maintain strong virulence or immune homeostasis. In this paper, we reviewed the mechanisms of ZBP1 signaling, the effects of ZBP1 signaling on host immunity and pathogen infection, and various antagonistic strategies of host and pathogen against ZBP1. We also discuss existent gaps regarding ZBP1 signaling and forecast potential directions for future research.

This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm. Usually for this kind of problem, it is difficult to obtain better recognition results by directly using the semi-supervised learning algorithm. For this reason, we propose a domain transformation semi-supervised weighted kernel extreme learning machine (DTSWKELM) algorithm, which converts the data through the domain and uses SWKELM algorithmic classification to transform the semi-supervised classification problem of different domain data into a semi-supervised classification problem of the same domain data.

The key to image-guided surgery (IGS) technology is to find the transformation relationship between preoperative 3D images and intraoperative 2D images, namely, 2D/3D image registration. A feature-based 2D/3D medical image registration algorithm is investigated in this study. We use a two-dimensional weighted spatial histogram of gradient directions to extract statistical features, overcome the algorithm’s limitations, and expand the applicable scenarios under the premise of ensuring accuracy. The proposed algorithm was tested on CT and synthetic X-ray images, and compared with existing algorithms. The results show that the proposed algorithm can improve accuracy and efficiency, and reduce the initial value’s sensitivity.

Fully homomorphic encryption allows users to use the computing resources of cloud servers for the computation of encrypted data without worrying about data leakage. The fully homomorphic encryption approach has problems with excessive noise and the expansion of the ciphertext dimension after the homomorphic evaluation. The key switching technology effectively solves the problem of the ciphertext dimension expansion. The generated evaluation key is a masked secret key that must be shared between the data owner and the computational entity, so the security must be guaranteed. In the RLWE-based FHE scheme, the efficiency improvement of the key switching depends on the circular security assumption, meaning the security needs to be improved. To solve the above problems, we select the secret key from the noise distribution with variable parameters so that the evaluation key and the initial noise of the encryption scheme are smaller. Specifically, the secret key is replaced after each homomorphic evaluation to ensure the security. We use the “modulus scaling” method to control the noise generated by itself, rather than the BitDecomp technology, which is complex when applied to polynomials. Finally, we combine the packing technology that relies on the polynomial CRT (Chinese remainder theorem) to design a batch-leveled fully homomorphic encryption scheme. We analyze the scheme’s noise, security proof, and specific security parameters. Compared with the FV12 scheme, our scheme is more secure. Compared with the MB18 scheme, our evaluation key size is smaller.

The infant gut microbiota is critical for promoting and maintaining early-life health. The study aimed to analyze the composition of sIgA-coated and sIgA-uncoated bacterial communities at genus level and lactobacilli and bifidobacterial communities at species level in human breast milk (HBM) and infant and maternal feces. Eleven pregnant women were recruited successfully. HBM; infant feces during colostrum, transition, and mature stages; and maternal feces within the mature stage were collected. sIgA-coated and sIgA-uncoated bacteria were separated with magnetic-activated cell sorting. Then, 16S rRNA sequencing, bifidobacterial groEL gene sequencing, and lactobacilli groEL gene sequencing were performed to analyze the bacterial community. PCoA revealed that the compositions of sIgA-coated and sIgA-uncoated bacteria were different among HBM and infant and maternal feces. Higher relative abundance of sIgA-uncoated Bifidobacterium was found in the three lactation stages in infant feces compared to the corresponding HBM, and a higher relative abundance of sIgA-uncoated Faecalibacterium was found in maternal feces compared to HBM and infant feces. For bifidobacterial community, sIgA-coated and sIgA-uncoated B. longum subsp. infantis and B. pseudocatenulatum was dominant in infant feces and maternal feces, respectively. The relative abundance of sIgA-uncoated B. longum subsp. infantis was significantly higher in infant feces compared to that in maternal feces. For the Lactobacillus community, L. paragasseri and L. mucosae were dominant in infant and maternal feces, respectively. HBM and infant and maternal feces showed distinct diversity and composition of both sIgA-coated and sIgA-uncoated bacteria at genus level. Infant and maternal feces showed similar composition of Bifidobacterium at species level. The same Bifidobacterium species could be detected both in sIgA-coated and -uncoated form. This article provided deeper understanding on the microbiota profile in HBM and infant and maternal feces.

Summary: The coking issue is the main challenge for dry reforming of methane (DRM) over Ni-based catalysts. Herein, we excavate the reasons for the enhanced coking resistance of the bounded Ni over the free state Ni in Ni/γ-Al2O3 catalysts for DRM. Rational metal-support interaction of the bounded Ni would facilitate desorption of CO, thus suppressing CO disproportionation and decreasing carbon deposition. The higher activity of the bounded Ni is ascribed to better methane cracking ability, stronger adsorption, and activation of CO2 by forming polydentate carbonate. The better activation of CO2 over the bounded Ni would also contribute to the gasification of formed coke. We gain an insight into the anti-coking mechanism of DRM determined by metal-support interaction in Ni/γ-Al2O3 catalysts through mechanistic studies. It is believed that our findings would enlighten the design of more efficient catalysts for DRM.

The breakdown of Cu–cyanide complexes (decomplexation) and recovery of the associated copper was explored using a photoelectrocatalytic treatment incorporating Cl− in the electrolyte (PEC-Cl) and with WO3 as a photoanode. Using the PEC-Cl process, the efficiency of decomplexation improved in the presence of a high concentration of Cl−. However, when the concentration of Cl− exceeded 20 mM, the deposited Cu could be oxidized to copper compounds by free chlorine (Cl2, HOCl, Cl and OCl), resulting in a decline in Cu recovery. The total nitrogen removal increased from 7.9% (PEC without Cl−) to 95.6% for the PEC-Cl process, which can be attributed to the generation of chlorine radicals (OCl and/or Cl). A mechanism for Cu–cyanide decomplexation and Cu recovery during the PEC-Cl process is proposed. Cu–cyanide complexes are destroyed by free chlorine, generating CNO− and Cu2+. The Cu2+ is then reduced onto the cathode and the CNO− is further oxidized into NH4+ and HCOO−. NH4+ is attacked by chlorine radicals and finally converted into N2 while HCOO− is gradually degraded to CO2 and H2O. Keywords: Cu–cyanide complex, Copper recovery, Free chlorine, Photoelectrocatalysis

Chlorobenzene (CB) is a volatile and harmful organic molecule that may result in deformities, cancer, etc. Catalytic oxidization of CB may be a way to manage it. The development of nonprecious catalysts with high catalytic activity is the key but is still a challenge. In this work, a series of Ce-Mn-Ox/TiO2 modified by citric acid monohydrate were developed and exhibited a composite pore structure. This pore structure leads to a large specific surface area, highly exposed activity sites, and excellent catalytic activity. The as-prepared 10C-CM/T exhibited nearly 100% efficiency for CB oxidization in the temperature range of 300–350 °C. The in situ DRIFT measurements demonstrated that the main intermediates at 250 °C are maleate and phenolic acid, whereas when the temperature is 350 °C, the main intermediates are carbonate, bidentate carbonate, and maleate.

As a typical pollutant, methylene blue poses a serious threat to the environment and human health. Oily sludge pyrolysis residue loaded with metal oxides could be used to prepare composite materials, which is not only an effective way to treat oily sludge, but also a possible method to treat methylene blue pollutants. In this paper, composite materials (AC-CuO, AC-ZnO, and AC-TiO2) were prepared by oily sludge pyrolysis residue-loaded CuO, ZnO, and TiO2 directly, and characterized by XRD, SEM, EDS, BET, FT-IR, and XPS, and it was shown that the metal oxides were successfully supported on the pyrolysis residue. Then, the composite materials were applied to the removal of methylene blue solution. The removal effect of composite materials on methylene blue with respect to the impregnation time, impregnation ratio and dosage, and the contact time and number of regenerations were investigated, and the removal parameters were optimized by response surface methodology. The removal process for methylene blue was described by applying Lagergren, McKay, Langmuir isotherm, Freundlish isotherm and intraparticle diffusion models. According to the response surface methodology and the main factors affecting the removal effect of methylene blue, the results indicate that the removal effect of 5 mg/L methylene blue could reach 95.28%, 94.95%, and 96.96%, respectively, and the corresponding removal capacities were 4.76, 4.75, and 4.85 mg/g. In addition, kinetic studies showed that the removal process of methylene blue was mainly constituted by chemical adsorption. The intraparticle diffusion showed that the removal of methylene blue may be controlled by both liquid film diffusion and intraparticle diffusion. The isotherms showed that the adsorption sites of composites for methylene blue were uniformly distributed and had the same affinity. Furthermore, regeneration experiments showed that the composite materials were stable and had relatively reusability.

Acidified hydraulic fracturing technology is a technical measure that uses acid liquid to react with minerals in near wells or reservoirs to enhance permeability, oil well and shale gas extraction, and increase the injection volume of wells. After the acidification construction is completed, the residual acid liquid will be discharged to the ground, which forms acidified waste liquid with low pH and complex composition. Recent studies providing snapshots of processing technology on the acidified waste have highlighted the need to convert the high concentration of Fe(II) in the acidified wastewater into flocculating components through advanced oxidation process. Here we report the discovery of the oxidation kinetics of ferrous ion in simulated acidified waste system, which based on the analysis of major pollutants and high iron content. We show that the change of concentration of ferrous ions was determined by chemiluminescence in tandem with flow injection, and the reaction of hydrogen peroxide with Fe(II) is a pseudo-first-order reaction. The reaction rate constants at Fe(II) of 50, 100 and 150 mg/L are 0.12612, 0.41686, 1.18230 s−1 respectively. In addition, we evaluated the adaptability of the catalytic oxidation system to acidified waste liquid. This offers a theoretical basis for treatment of acid hydraulic fracturing flowback and a series of treatability of tests, which serves to guide the selection of suitable treatment approach.