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Abstract Background Dendrobium catenatum is a perennial herb of the genus Dendrobium orchidaceae. It has been known as “Golden Grass, Soft Gold” since ancient times with effects of strengthening the body, benefiting the stomach, generating body fluid, nourishing Yin and clearing internal heat. The flowers of D. catenatum have anti-oxidation, immune regulation and other biological activities. The composition analysis of flowers showed that flavonoid glycosides were significantly accumulated in floral tissue. However, in the flowers of D. catenatum, there was only one case of the UDP-glycosyltransferase (UGT) responsible for the glycosylation of flavonoids has been reported. Result In this study, a new UGT (named UGT708S6) was cloned from D. catenatum flowers rich in O-glycosides and C-glycosides, and its function and biochemical properties were characterized. Through homology comparison and molecular docking, we identified the key amino acid residues affecting the catalytic function of UGT708S6. The glycosyltransferase UGT708S6 was characterized and demonstrated C-glycosyltransferase (CGT) activity in vitro assay using phloretin and 2-hydroxynaringenin as sugar acceptors. The catalytic promiscuity assay revealed that UGT708S6 has a clear sugar donor preference, and displayed O-glycosyltransferase (OGT) activity towards luteolin, naringenin and liquiritigenin. Furthermore, the catalytic characteristics of UGT708S6 were explored, shedding light on the structural basis of substrate promiscuity and the catalytic mechanism involved in the formation of flavonoid C-glycosides. R271 was a key amino acid residue site that sustained the catalytic reaction. The smaller binding pocket resulted in the production of new O-glycosides and the reduction of C-glycosides. This highlighted the importance of the binding pocket in determining whether C-glycosides or O-glycosides were produced. Conclusions The findings suggest that UGT708S6 holds promise as a new glycosyltransferase for synthesizing flavonoid glycosides and offer valuable insights for further understanding the catalytic mechanisms of flavonoid glycosyltransferases.

Abstract Timely detection of hard exudates in fundus images can effectively avoid the severity of the disease, but the labelling of small and numerous lesion areas requires a lot of labour costs. This paper proposes a semi‐supervised segmentation network, which integrates pseudo‐labels and multi‐level features consistency correction. It achieves accurate segmentation of hard exudates by making full use of a small amount of labelled data and a large amount of unlabelled data. The network effectively extracts features from the unlabelled data through knowledge transfer of the teacher‐student model, and incorporates a Transformer network for auxiliary training to promote the quality of transfer. In addition, three unsupervised losses are introduced to improve the performance: the perturbation loss improves the robustness of the model to noise by adding different noises to the same input; the multi‐level feature consistency correction loss ensures the consistency of features of the student model at different scales; and the pseudo‐labelling cross‐supervision loss utilizes the generated pseudo‐labels for supervision between CNN and Transformer. By comparing the segmentation results with different proportion of the labelled data, it has better segmentation performance compared to other methods. The proposed methods can totally increase dice by 16.56% and mean intersection over union (MIoU) by 25.11%.

Thermal management of nanoscale quantum dots (QDs) in light-emitting devices is a long-lasting challenge. The existing heat transfer reinforcement solutions for QDs-polymer composite mainly rely on thermal-conductive fillers. However, this strategy failed to deliver the QDs’ heat generation across a long distance, and the accumulated heat still causes considerable temperature rise of QDs-polymer composite, which eventually menaces the performance and reliability of light-emitting devices. Inspired by the radially aligned fruit fibers in oranges, we proposed to eliminate this heat dissipation challenge by establishing long-range ordered heat transfer pathways within the QDs-polymer composite. Ultrahigh molecular weight polyethylene fibers (UPEF) were radially aligned throughout the polymer matrix, thus facilitating massive efficient heat dissipation of the QDs. Under a UPEF filling fraction of 24.46 vol%, the in-plane thermal conductivity of QDs-radially aligned UPEF composite (QDs-RAPE) could reach 10.45 W m−1 K−1, which is the highest value of QDs-polymer composite reported so far. As a proof of concept, the QDs’ working temperature can be reduced by 342.5 °C when illuminated by a highly concentrated laser diode (LD) under driving current of 1000 mA, thus improving their optical performance. This work may pave a new way for next generation high-power QDs lighting applications.

Abstract Combining the extracted tongue features with other medical indicators can effectively judge the diseases of patients. The previous work usually only analyzes a certain feature of the tongue body and is unable to extract multiple features simultaneously. In this study, a multi‐label classification network named TIM‐Net is proposed, which integrates global and local features to achieve multi‐label intelligent diagnosis of Chinese medicine tongue images. First, a feature extraction network based on ResNet is proposed to capture the features of tongue images more sufficiently. Then, a multi‐label classification algorithm fusing global and local features is proposed, and targeted screening operations are carried out on the class‐related feature maps based on global confidence. In addition, a logical masking algorithm is proposed to ensure that the local features can only correct the feature labels they represent, and do not interfere with other feature labels. The classification accuracy is further improved by using local feature confidence and correcting the global classification results. Finally, the experimental results indicate that the classification accuracy of the tongue images is gradually improved through optimizing the feature extraction network and fusing local features, and it exceeds other state‐of‐the‐art multi‐label classification networks.

Objective To observe the expression of serum interleukin(IL)-17 and procalcitonin(PCT) in elderly patients with severe chronic obstructive pulmonary disease(COPD) accompanied by pulmonary infection,and to explore their predictive value for the prognosis of patients. Methods A total of 120 elderly patients with severe COPD complicated with pulmonary infection admitted to Beijing Rehabilitation Hospital Affiliated with Capital Medical University from March 2021 to May 2022 were selected as the study subjects.All patients were followed up for 6 months. Serum IL-17 and PCT levels were measured,general data were collected,and the above data were statistically analyzed. Results At the end of follow-up,80 patients survived with a survival rate of 66.67% and 40 patients died with a mortality rate of 33.33%.There was no significant difference in gender,age,combined underlying diseases,smoking history and other data between the death group and the survival group(P>0.05);the serum IL-17 and PCT levels in the death group were higher than those in the survival group,and the duration of COPD and pulmonary infection was longer than those in the survival group,and the differences were statistically significant(P0.90. Conclusion Serum IL-17 and PCT levels are associated with prognosis in elderly patients with severe COPD with pulmonary infection and may be risk factors for increased risk of death.

With the increasing demand for the performance and design refinement of steel structures (including houses, bridges, and infrastructure), many structures have adopted ultimate bearing capacity in service. The design service lives of steel building structures are generally more than 50 years, and most of them contain bolted connections, which suffer from extreme conditions such as fire (high temperature) during service. When the structure contains defects or cracks and bolt holes, it is easy to produce stress concentration at the defect location, which leads to crack nucleation and crack propagation, reduces the bearing capacity of the structure, and causes the collapse of the structure and causes disasters. In the process of structural damage and crack propagation, the traditional method has some disadvantages, such as stress singularity, the mesh needing to be redivided, and the crack being restricted to mesh; however, the integral method of peridynamics (PD) can completely avoid these problems. Therefore, in this paper, the constitutive equation of PD in high temperature is derived according to the variation law of steel material properties when changed by temperature increase and peridynamics parameters; the damage and crack expansion characteristics of Q345 steel specimens with bolt holes and a central double-crack at 20 °C, 200 °C, 400 °C, and 600 °C were analyzed to clarify the structural damage and failure mechanism. This study is helpful for providing theoretical support for the design of high-temperature steel structures, improving the stability of the structure, and ensuring the bearing capacity of the structure and the safety of people’s lives and property.

Abstract Chronic inflammation contributes to various chronic diseases in humans. Currently used anti‐inflammatory drugs have several limitations and are not suitable for long‐term use. Anti‐inflammatory foods or extracts can prevent inflammation‐related diseases, offering a promising approach for the management of these pathologies. In vitro anti‐inflammatory experiments have the advantages of short experimental period and low cost for screening anti‐inflammatory foods or drugs. They measure pro‐inflammatory enzyme activity such as lipoxygenase (LOX) and cyclooxygenase (COX), and the production of pro‐inflammatory cytokines such as tumor necrosis factor (TNF) and interleukins (IL). This article reviews in vitro assays used to evaluate the anti‐inflammatory effect, examining the effects of different inflammatory factors and the employed methods, and analyzing their principles, advantages, and disadvantages. Thus, it aims to provide guidelines for screening natural foods with anti‐inflammatory potential.

Currently, many scholars have investigated reversible data hiding (RDH) based on automatic contrast enhancement (ACE). For RDH based on ACE (ACERDH), various methods have been proposed on how to better improve image quality. Preserving brightness can prevent the image from being over-enhanced. However, some ACERDH methods that can preserve brightness well cannot sufficiently enhance low-light images. Although some ACERDH methods that cannot preserve brightness well can more sufficiently enhance low-light color images, they lack the consideration of the local region, which is not conducive to enhancing the contrast of the local region. Therefore, a novel RDH method based on global ACE and local ACE is proposed. The global contrast is first improved by equalizing the global histogram. Then the high complexity region is optimized by the proposed RDH method based on double-layer ACE to further enhance the local contrast. The low-light color image can be well enhanced by applying the proposed method to enhance the R, G, and B channels sequentially. Experimental results demonstrated that the proposed method is superior to some existing advanced methods in obtaining better image quality and hiding more secret data.

Abstract The emergence of dockless shared bikes (DSB) has led to their use as an important transfer mode to urban rail transit (URT) stations. However, in highly populated areas such as subway stations in peak hours, there is increasing concern about the imbalance between the demand and supply of shared bikes. To promote smoother subway transfer trips using shared bikes, it is very important to estimate the DSB demand, especially the disparity in the volume of bike pick-up and drop-off demand around subway stations. This research first utilizes the Shenzhen metro usage data and DSB usage data, analyzes data regarding subway and shared bike usage, discusses their potential transfer uses, and finds great disparity in DSB demand between different subway stations. The catchment area method is used to estimate bike usage as a potential transfer mode to the subway, where the catchment area is defined as a radius of 150 m from the subway station center. The DSB trip demand is categorized into two types: pick-up and drop-off. The most recent deep learning method, adaptive graph convolutional recurrent network (AGCRN), is used to predict the DSB demand more accurately because of its ability in enabling the modeling of relationships between entities in a self-adapted graph, and the prediction is compared with long short-term memory (LSTM), spatiotemporal neural network (STNN), diffusion convolutional recurrent neural network (DCRNN), and Graph WaveNet. Results show that methods with graphs (STNN, DCRNN, Graph WaveNet, and AGCRN) perform better than LSTM, and methods with adaptive graphs (Graph WaveNet and AGCRN) outperform methods with static graphs in terms of mean absolute error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE). DSB prediction results show that AGCRN performs the best in this study. More data, particularly land use data and URT station volume data, are expected to improve the predictive accuracy of the method due to potentially improved graph representation of station characteristics and subway station volume correlations. And with more accurate prediction results, it will be possible to achieve a better balancing strategy for bike operation optimization for better bike usage, and thus for a higher transfer rate of DSB to subway.

Abstract In recent years, the speaker-independent, single-channel speech separation problem has made significant progress with the development of deep neural networks (DNNs). However, separating the speech of each interested speaker from an environment that includes the speech of other speakers, background noise, and room reverberation remains challenging. In order to solve this problem, a speech separation method for a noisy reverberation environment is proposed. Firstly, the time-domain end-to-end network structure of a deep encoder/decoder dual-path neural network is introduced in this paper for speech separation. Secondly, to make the model not fall into local optimum during training, a loss function stretched optimal scale-invariant signal-to-noise ratio (SOSISNR) was proposed, inspired by the scale-invariant signal-to-noise ratio (SISNR). At the same time, in order to make the training more appropriate to the human auditory system, the joint loss function is extended based on short-time objective intelligibility (STOI). Thirdly, an alignment operation is proposed to reduce the influence of time delay caused by reverberation on separation performance. Combining the above methods, the subjective and objective evaluation metrics show that this study has better separation performance in complex sound field environments compared to the baseline methods.

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview of these approaches, this paper provides a comprehensive survey of both subjective and objective HDR IQA methods. Specifically, we review 7 subjective HDR IQA databases and 12 objective HDR IQA metrics. In addition, we conduct a statistical analysis of 9 IQA algorithms, incorporating 3 perceptual mapping functions. Our findings highlight two main areas for improvement. Firstly, the size and diversity of HDR IQA subjective databases should be significantly increased, encompassing a broader range of distortion types. Secondly, objective quality assessment algorithms need to identify more generalizable perceptual mapping approaches and feature extraction methods to enhance their robustness and applicability. Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the limitations of current methodologies and potential research directions in the future.

Abstract Diet‐related chronic diseases are becoming more common where many consumers and nutritionists are seeking food and lifestyle interventions to improve health and standard of living. To supplement regular dietary intake, scientists are seeking to develop new crops that are productive and nutritious, which can improve health outcomes. This review focused on the history of Huangjing as a medicine, healthy food as part of a diverse diet and as an input into pharmacology uses. The sustainable production of Huangjing and the development of health‐promoting Huangjing products are reviewed through consulting the “Chinese Pharmacopoeia” along with other herbal textual research and modern research publications. Hence, the potential development of Huangjing as a regular part of a healthy diet is discussed. Huangjing has been used as medicine and food in China for over 2000 years. While it contains no starch, it is rich in fructan, saponins, and flavonoids, which have been reported to prevent and treat chronic diseases such as diabetes and obesity. As part of a healthy diet, Huangjing has been developed into innovative products used in meals, snacks, drinks, health foods, and drugs. Furthermore, Huangjing is a perennial plant, is suitable for cultivation in forests, and does not occupy fertile farmland. It is proposed to use the achievements of genomics and targeted precision breeding to make in‐forest cultivation of Huangjing more productive, comprehensively nutritious, and environmentally friendly and obtain high‐quality food from forests and mountains, providing Chinese wisdom for the global food and nutrition security issue.

Abstract Accurate segmentation of hard exudates in early non‐proliferative diabetic retinopathy can assist physicians in taking appropriate treatment in a more targeted manner, in order to avoid more serious damage to vision caused by the deterioration of the disease in the later stages. Here, an Adaptive Learning Unet‐based adversarial network with Convolutional neural network and Transformer (CT‐ALUnet) is proposed for automatic segmentation of hard exudates, combining the excellent local modelling ability of Unet with the global attention mechanism of transformer. Firstly, multi‐scale features are extracted through a CNN dual‐branch encoder. Then, the information fusion of features at adjacent scale is realized and the fused features are selected adaptively to maintain the overall consistency of features by attention‐guided multi‐scale fusion blocks (AGMFB). After that, the high‐level encoded features are input to transformer blocks to extract global contexts. Finally, these features are fused layer‐by‐layer to achieve accurate segmentation of hard exudates. In addition, adversarial training is incorporated into the above segmentation model, which improves Dice scores and MIoU scores by 7.5% and 3%, respectively. Experiments demonstrate that CT‐ALUnet shows more reliable segmentation and stronger generalization ability than other SOTA methods, which lays a good foundation for computer‐assisted diagnosis and assessment of efficacy.

Abstract Background Collecting duct carcinoma (CDC) is a rare renal tumor, originating from the distal collecting duct. CDC rarely presents as a primary tumor outside the renal system. Case presentation In this study, we report a rare case of collecting duct carcinoma, with an initial presentation of retroperitoneal lymph node metastasis, and no identifiable primary renal tumor on CT, at the time of diagnosis. The patient was a 64-year-old man presenting with lower back pain. Preoperative CT showed a round, soft tissue mass, measuring 6.7 × 4.4 × 3.3 cm, in the left retroperitoneum with no exact occupying lesion in the left kidney. Clinically, ectopic pheochromocytoma was considered to be a differential diagnosis, and tumor resection was performed. Postoperative pathological results demonstrated that the mass was a fused lymph node, and the tumor cells were destroying the structure. The final diagnosis was lymph node metastatic collecting duct carcinoma, by histology and immunohistochemistry. No further treatment was performed as no space occupying lesion was found in the kidney. Three months later, CT was reexamined, and a mass of 3.6 cm in diameter, was found in the lower left kidney, along with multiple soft tissue masses, in the left renal hilum. Considering recurrence or metastasis, the patient was recommended to undergo surgical treatment, but the patient refused. Four months later, CT was re-examined. The tumor had rapidly progressed but the patient refused treatment again. As per the author’s press release (eleven months after the first discovery), the patient is still alive. Conclusion CDC is a rare malignant renal carcinoma, with a high chance of rapid progress, regional lymph nodes involvement and metastasis. It presents diagnostic challenges to clinicians and pathologists, particularly, in the absence of radiographically detectable intrarenal lesions. Definite diagnosis is based on pathological examination combined with immunohistochemical staining.

Background and aims: Tyrosine kinase inhibitors (TKIs) combined with programmed cell death protein-1 (PD-1) have significantly improved survival in patients with unresectable hepatocellular carcinoma (uHCC), but effective biomarkers to predict treatment efficacy are lacking. Peripheral blood bile acids (BAs) are associated with tumor response to therapy, but their roles in HCC remain unclear.Methods: This retrospective study included HCC patients who received first-line TKIs combined with PD-1 inhibitors treatment (combination therapy) in our clinical center from November 2020 to June 2022. The aim of this study was to analyze the changes in plasma BA profiles before and after treatment in both the responding group (Res group) and the non-responding group (Non-Res group). We aimed to explore the potential role of BAs in predicting the response to combination therapy in HCC patients.Results: Fifty-six patients with HCC who underwent combination therapy were included in this study, with 28 designated as responders (Res group) and 28 as non-responders (Non-Res group). There were differences in plasma BA concentrations between the two groups before systemic therapy. Plasma taurohyocholic acid (THCA) levels in the Res group were significantly lower than those in the Non-Res group. Patients with low levels of THCA exhibited superior median progression-free survival (7.6 vs. 4.9 months, p = 0.027) and median overall survival (23.7 vs. 11.6 months, p = 0.006) compared to those of patients with high levels of THCA.Conclusion: Peripheral blood BA metabolism is significantly correlated with combination therapy response and survival in patients with HCC. Our findings emphasize the potential of plasma BAs as biomarkers for predicting combination therapy outcomes and offering novel therapeutic targets for modulating responses to systemic cancer therapy.

Since breast cancer is a heterogeneous disease, there are currently a variety of treatment methods available, including chemotherapy, endocrine therapy, molecular targeted therapy, immunotherapy, radiation therapy, etc. Breast cancer recurrence and metastasis, despite many treatment modalities, constitute a considerable threat to patients’ survival time and pose a clinical challenge that is difficult to tackle precisely. Exosomes have a very special and crucial role in the treatment of drug resistance in breast cancer as a carrier of intercellular communication in the tumor microenvironment. Exosomes and breast cancer treatment resistance have been linked in a growing number of clinical investigations in recent years. This paper covers the status of research on exosomes in the treatment of breast cancer drug resistance and offers theoretical guidance for investigating new strategies to treat breast cancer drug resistance.

The electric vertical take-off and landing (eVTOL) aircraft offers the advantages of vertical take-off and landing, environmental cleanliness, and automated control, making it a crucial component of future urban air traffic. As competition intensifies, demands for aircraft performance are escalating, including forward flight speed and payload capacity. The article presents a novel eVTOL design with propulsive wings and establishes methodologies for propulsive wing unsteady numerical simulation and wind tunnel experiments, analyzing its aerodynamic characteristics and lift enhancement mechanism. The results indicate that the cross-flow fan (CFF) provides unique airflow control capabilities, enabling the propulsive wing to achieve remarkably high lift coefficients (exceeding 7.6 in experiments) and propulsion coefficients (exceeding 7.1 in experiments) at extreme angles of attack (30°~40°) and low airspeeds. On the one hand, the CFF effectively controls boundary layer flow, delaying airflow separation at high angles of attack; on the other hand, the rotation of the CFF induces two eccentric vortices, generating vortex-induced lift and propulsion. The aerodynamic performance of the propulsive wing depends on the advance ratio and angle of attack. Typically, both lift and propulsion coefficients increase with the advance ratio, while lift and drag coefficients increase with the angle of attack. The propulsive wing shows significant advantages and prospects for eVTOL aircrafts in the low flight velocity range (0–30 m/s).

Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price among different varieties. Therefore, achieving an efficient classification of Dendrobium is crucial. However, most of the existing identification methods for Dendrobium make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial production. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendrobium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constructed fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100%. Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperforms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10%, 10%, and 17%. This fully validates the excellent performance of our classification method. Finally, visualization analysis of the entire research process based on t-distributed Stochastic Neighbor Embedding (t-SNE) technology further enhances the interpretability of the model. This study, by combining LIBS and machine learning technologies, achieves efficient classification of Dendrobium, providing a feasible solution for the identification of Dendrobium and even traditional Chinese medicinal herbs.

Polyoxymethylene dimethyl ethers (PODE) are newly appeared promising oxygenated additive which can significantly reduce soot emissions of diesel engines. In this study, the spray and combustion characteristics of the blends of diesel with different PODE blending ratio of 0%, 20%, 50% and 100% by volume are investigated in a constant volume chamber (CVC) under 100 MPa, 120 MPa and 140 MPa injection pressures. The spray results show that liquid spray tip penetration (STP), average spray angle (ASA) and spray projected area (SPA) all decrease with PODE blending ratio under 4 MPa ambient pressure and 573 K ambient temperature. The addition of PODE in diesel can significantly reduce the soot formation and the integrated natural flame luminosity (INFL). Under 100 MPa injection pressure, the time integral sum KL in whole combustion duration of D100, P20, P50 and P100 are namely 107.35, 2.66, 0.27 and 0.08. When the PODE blending ratio are 50% and 100%, nearly no yellow flame can be observed in the combustion duration. The chemical structure of PODE plays an important role in reducing soot formation.

Filler-reinforced polymer composites demonstrate pervasive applications due to their strengthened performances, multi-degree tunability, and ease of manufacturing. In thermal management field, polymer composites reinforced with thermally conductive fillers are widely adopted as thermal interface materials (TIMs). However, the three dimensional (3D)-stacked heterogenous integration of electronic devices has posed the problem that high-density heat sources are spatially distributed in the package. This situation puts forward new requirements for TIMs, where efficient heat dissipation channels must be established according to the specific distribution of discrete heat sources. To address this challenge, a 3D printing-assisted streamline orientation (3D-PSO) method was proposed to fabricate composite thermal materials with 3D programmable microstructures and orientations of fillers, which combines the shape-design capability of 3D printing and oriented control ability of fluid. The mechanism of fluid-based filler orientation control along streamlines was revealed by mechanical analysis of fillers in matrix. Thanks to the designed heat dissipation channels, composites showed better thermal and mechanical properties in comparison to random composites. Specifically, the thermal conductivity of 3D mesh-shape polydimethylsiloxane/liquid metal (PDMS/LM) composite was 5.8 times that of random PDMS/LM composite under filler loading of 34.8 vol%. The thermal conductivity enhancement efficiency of 3D mesh-shape PDMS/carbon fibers composite reached 101.05% under filler loading of 5.2 vol%. In the heat dissipation application of 3D-stacked chips, the highest chip temperature with 3D-PSO composite was 42.14 °C lower than that with random composites. This is mainly attributed to the locally aggregated and oriented fillers’ microstructure in fluid channels, which contributes to thermal percolation phenomena. The 3D-PSO method exhibits excellent programmable design capabilities to adopt versatile distributions of heat sources, paving a new way to solve the complicated heat dissipation issue in 3D-stacked chips integration application.

Panicle development is crucial to increase the grain yield of rice (Oryza sativa). The molecular mechanisms of the control of panicle development in rice remain unclear. In this study, we identified a mutant with abnormal panicles, termed branch one seed 1-1 (bos1-1). The bos1-1 mutant showed pleiotropic defects in panicle development, such as the abortion of lateral spikelets and the decreased number of primary panicle branches and secondary panicle branches. A combined map-based cloning and MutMap approach was used to clone BOS1 gene. The bos1-1 mutation was located in chromosome 1. A T-to-A mutation in BOS1 was identified, which changed the codon from TAC to AAC, resulting in the amino acid change from tyrosine to asparagine. BOS1 gene encoded a grass-specific basic helix–loop–helix transcription factor, which is a novel allele of the previously cloned LAX PANICLE 1 (LAX1) gene. Spatial and temporal expression profile analyses showed that BOS1 was expressed in young panicles and was induced by phytohormones. BOS1 protein was mainly localized in the nucleus. The expression of panicle development-related genes, such as OsPIN2, OsPIN3, APO1, and FZP, was changed by bos1-1 mutation, suggesting that the genes may be the direct or indirect targets of BOS1 to regulate panicle development. The analysis of BOS1 genomic variation, haplotype, and haplotype network showed that BOS1 gene had several genomic variations and haplotypes. These results laid the foundation for us to further dissect the functions of BOS1.

Abstract Lipid peroxidation (LPO), an oxidative chain reaction often occurring in the human body and food, is harmful to human health. Due to its convenience and low cost, this guideline highlights the benefits and drawbacks of LPO models by spectrophotometer in vitro and in vivo. The preparation process and reaction mechanism of lipid substrates, oxidation triggers and detection methods are discussed in this paper to better screen antioxidants. The lipid substrates mostly consist of rat liver/brain homogenate, microsomal, low‐density lipoprotein (LDL), and linoleic acid (LA), are appropriate for modeling oxidative damage in a variety of situations. Transition metals, ascorbic acid, 2,2′‐azobis (2‐amidinopropane) hydrochloride (AAPH), sodium Nitroprusside (SNP), 3‐morpholinosydnonimine (SIN‐1) and nicotinamide adenine dinucleotide phosphate (NADPH) make up the majority of the oxidizing triggers. The following LPO quantification techniques have been reviewed: (a) thiobarbituric acid reactive substances (TBARS) assay, (b) measurement of conjugated‐diene (CV), (c) β‐carrot bleaching assay, (d) thiocyanate method, (e) ferrous oxidation‐xylenol orange (FOX‐2) assay. It can be concluded that more than one test system should be applied to obtain comprehensive results. In a word, this guideline provides a scientific basis for future research on antioxidants screening in food and body systems.

Abstract Tongue image segmentation is the key step of traditional Chinese medicine (TCM) intelligent tongue image analysis. The subsequent tongue image quality analysis is directly affected by the precision of segmentation. Deeplabv3+ network has become an excellent algorithm in the field of tongue images segmentation by virtue of its ability to extract multi‐scale information and its codec structure. However, there are unclear edge segmentation of the tongue body and missegmentation of small areas in some tongue images. In view of the above phenomenon, an improved algorithm is proposed. Firstly, the network structure is optimized, so that the ability of the network to extract multi‐scale information and low‐level information is improved. Secondly, a loss function based on edge information is proposed, which makes the network pay more attention to the separation of tongue edges in the process of training. Finally, the segmentation results are post‐processed by using the prior knowledge of tongue image, so as to eliminate the phenomenon of misjudgement. The experimental results show that the algorithm significantly improves the ambiguity of image segmentation, and the MIOU value is still increased to 99.13% when the MIOU value has reached 98.77%.

This paper proposes an energy-saving lane-changing trajectory model for intelligent electric vehicles at high speeds that considers different driving needs under multiple constraints to address the issues of simple lane-changing considerations and poor safety. An economic index is added to construct a multi-objective optimization function based on the comfort, safety, and efficiency of lane changing. The particle swarm optimization algorithm is used to solve the optimal lane-changing time. The weight of the index is obtained by analyzing different driving needs using the Analytic Hierarchy Process in different scenarios. The multi-objective function is adjusted to plan the optimal lane-changing trajectory that meets driving needs. The simulation shows that the proposed model can generate smooth and feasible lane-changing trajectories that meet different driving needs. The energy consumption analysis results indicate that the construction of economic indicators can effectively reduce the energy consumption of vehicles driving on highways. The tracking analysis results indicate that the target vehicle can smoothly and safely change lanes on the planned trajectory, verifying the effectiveness and rationality of the planned trajectory.

Abstract Anthocyanins, as a kind of natural pigment, have a broad development prospect in the field of human production and life due to its safety, nontoxic, rich resources, and rich pharmacological effects. However, due to the structure instability of anthocyanins, anthocyanins are susceptible to physical and chemical factors during food processing, such as light, temperature, pH, metal ions, food additives, oxidation reductants, and cochromatic factors. This review summarized the experimental methods for these factors affecting anthocyanin content, structural transformation, and degradation dynamics, which lays the foundation for further studies of anthocyanins in plants. Meanwhile, we expounded the methods for obtaining accurate quantitative and qualitative data of anthocyanin by UV‐vis, HPLC, LC‐MS, and NMR, which can provide a basis for further development and utilization of anthocyanin resources.

The ducted coaxial propeller (DCP) is highly advantageous in the design of eVTOL aircraft due to its safety, compactness, and low noise levels. To study the aerodynamic characteristics of DCP in hovering, a novel eVTOL was used, and a slip grid model was established to solve the three-dimensional unsteady N-S equation. The aerodynamic characteristics of DCP were compared to those of the free coaxial propeller (FCP) and ducted single propeller (DSP) to reveal the interaction mechanism of unsteady flow between the duct and propellers. The results indicate that the duct significantly mitigates the intensity of tip vortexes by changing the characteristics of propeller tip winding, which reduces the corresponding energy loss. Additionally, the static pressure loss is decreased by the reduced radical-induced velocity in the slipstream area. Finally, the induced power loss is reduced by the decreased axial-induced velocity and suppressed wake contraction, resulting in DCP having 39% higher aerodynamic efficiency than FCP and the duct accounting for 41.7% of the total lift. Although DCP generates 1.77 times more lift than DSP, its aerodynamic efficiency is only 91.08% of DSP.

Fructans, fructose polymers, are one of the three major reserve carbohydrate in plants. The nutritional and therapeutic benefits of natural fructans in plants have attracted increasing interest by consumers and food industry. In the course of evolution, many plants have developed the ability of regulating plant fructans metabolism to produce fructans with different structures and chain lengths, which are strongly correlated with their survival in harsh environments. Exploring these evolution-related genes in fructans biosynthesis and de novo domestication of fructans-rich plants based on genome editing is a viable and promising approach to improve human dietary quality and reduce the risk of chronic disease. These advances will greatly facilitate breeding and production of tailor-made fructans as a healthy food ingredient from wild plants such as huangjing (Polygonatum cyrtonema). The purpose of this review is to broaden our knowledge on plant fructans biosynthesis, evolution and benefits to human health.

Background: Tyrosine kinase inhibitors (TKI) in combination with programmed cell death-1 (PD-1) inhibitors become the potential treatment modality for patients undergoing unresectable hepatocellular carcinoma (uHCC) in the first-line setting. However, the efficacy and safety of this combination regimen in patients after sorafenib failure remains unclear.Methods: Participants in this study included patients with uHCC after sorafenib failure who received TKI monotherapy (TKI group) or TKI combined with PD-1 inhibitors therapy (combination group) in our center from July 2018 to July 2021. The overall survival (OS) was used to be the primary efficacy endpoint, while progression-free survival (PFS), objective response rate (ORR), and disease control rate (DCR) were applied to be secondary endpoints. In addition, the adverse events are recorded and evaluated.Results: Among the 92 patients contained in this work, 50 patients were categorized into the TKI group, while 42 patients were in the combination group. There existed no evident differences between the two groups concerning the ORR (8.0% vs. 9.5%, p = 1.000). However, the DCR in the combined group was better in relative to that in the TKI group (71.4% vs. 50.0%, p = 0.037). In comparison with the TKI group, it was found that the combination group presented notably better median PFS (8.1 months vs. 4.7 months, p = 0.005) and median OS (21.9 months vs. 16.6 months, p = 0.042). According to multivariate analysis, PFS (HR 0.5, 95% CI: 0.3–0.8, p = 0.005) and OS (HR 0.5, 95% CI: 0.3–1.0, p = 0.051) were improved in the combination group in relative to the TKI group after the adjustment for some risk factors. Additionally, the incidence rates of grade ≥1 adverse event in the TKI group and the combination group were 96.0% and 97.6%, respectively. The most normal adverse event in the TKI group was neutropenia (n = 24,48.0%) and the combination group was hypoalbuminemia (n = 23,54.8%). All of these adverse events improved after symptomatic treatment, and no new toxic events were found to occur.Conclusion: TKI combined with PD-1 inhibitors showed better prognosis with manageable toxicity in uHCC patients after sorafenib failure compared with TKI monotherapy.

Predicting lithium-ion batteries’ state of charge (SOC) is essential to electric vehicle battery management systems. Traditional lithium-ion battery models mainly include equivalent circuit models (ECMs) and electrochemical models (EMs). ECMs are based on integer-order component modeling, which cannot characterize the internal electrochemical reaction mechanism of the battery, resulting in lower SOC prediction accuracy. In contrast, due to their complex structure, EMs are limited in their application. This study takes lithium batteries as the research object and proposes a fractional-order impedance model (FOIM) that characterizes the dynamic properties of the internal behavior of lithium-ion batteries using fractional-order elements. Considering the highly nonlinear characteristics of lithium-ion batteries, this study introduces the theory of fractional-order calculus into the extended Kalman filter (EKF) algorithm, and proposes the fractional-order extended Kalman filter (FEKF) algorithm applied to the prediction of battery charge state. Comparative analysis of simulation and experimental results shows that the accuracy of the FOIM, compared to ECMs, is significantly improved. The FEKF algorithm has good robustness in estimating the SOC, and the SOC prediction accuracy achieved with the algorithm is also improved compared with that obtained using the EKF algorithm of the integer-order model.

The transverse momentum distributions of deuterons and anti-deuterons in Au + Au collisions at sNN = 14.5, 62.4 and 200 GeV with different centralities are studied within the framework of the UrQMD model combined with the conventional phase-space coalescence model. A strong reversed correlation between R0 (the maximal relative distances between hadrons) and P0 (the maximal relative momentum between hadrons) can be seen. It is also time-dependent. The number of particles generated are inconsistent with experimental data for 40, −,60% and 60, −,80% centralities because deuterons have plenty of time to react with other particles, this effect becomes more obvious with the decrease of beam energy. Our results can quantitatively describe the STAR data for 0, −,10%, 10, −,20% and 20, −,40% centralities.

In practice, objective condition may impose constraints on design region, which make it difficult to find the exact D-optimal design. In this paper, we propose a Multi-stage Differential Evolution (MDE) algorithm to find the global approximated D-optimal design in an experimental region with linear or nonlinear constraints. MDE algorithm is approved from Differential Evolution (DE) algorithm. It has low requirements for both feasible regions and initial values. In iteration, MDE algorithm pursues evolutionary equilibrium rather than convergence speed, so it can stably converge to the global D-optimal design instead of the local ones. The advantages of MDE algorithm in finding D-optimal design will be illustrated by examples.

Abstract Triple-negative breast cancer (TNBC) is associated with a high rate of early recurrence and distant metastasis, frequent development of therapeutic resistance, and a poor prognosis. There is a lack of targeted therapies for this aggressive subtype of breast cancer. Identifying novel effective treatment modalities for TNBC remains an urgent and unmet clinical need. In this study, we investigated the anti-cancer effect of triptonide, a natural compound derived from the traditional Chinese medicinal herb Tripterygium wilfordii Hook F, in TNBC. We found that triptonide inhibits human TNBC cell growth in vitro and growth of TNBC xenograft mammary tumors. It induces apoptosis and suppresses stem-like properties as indicated by reduced mammosphere formation and aldehyde dehydrogenase activity in TNBC cells. We show that triptonide downregulates multiple cancer stem cell-associated genes but upregulates SNAI1 gene expression. In support of SNAI1 induction as a negative feedback response to triptonide treatment, in vitro-derived triptonide-resistant HCC1806 cells display a markedly higher expression of SNAI1 compared with parental cells. Mechanistically, the increase of SNAI1 expression is mediated by the activation of JNK signaling, but not by ERK and AKT, two well-established SNAI1 regulators. Furthermore, knockdown of SNAI1 in the triptonide-resistant HCC1806 cells increases sensitivity to triptonide and reduces mammosphere formation. These results indicate that triptonide holds promise as a novel anti-tumor agent for TNBC treatment. Our study also reveals a SNAI1-associated feedback mechanism which may lead to acquired resistance to triptonide.

The total water-soluble polysaccharide (TP) of Dendrobium catenatum is composed of starch and active non-starch polysaccharides (NSPs) with glucomannan as the main structural type. Although the TP content has been used as a quality assessment indicator for many years, the NSPs content in samples from different environments and growth seasons have not been reported. In this study, we found that NSPs had stronger antioxidant activity than TP. The NSPs content was higher in wild-simulated environments including rocks and trees compared to plantings grown in greenhouse. The culture mode and growth period affected the ratio of NSPs and starch. Facility cultivation provided optimal growth conditions but produced more starch, whereas wild-simulated cultivation resulted in a higher ratio of NSPs, particularly in September. Therefore, cultivation by lithophytation and epiphytation may be preferable to facility plantings, which is expected to be enormously useful for the current production and quality control of D. catenatum.

In the recent past, the Classifiers are based on genetic signatures in which many microarray studies are analyzed to predict medical results for cancer patients. However, the Signatures from different studies have been benefitted with low-intensity ratio during the classification of individual datasets has been considered as a significant point of research in the present scenario. Hence to overcome the above-discussed issue, this paper provides a Deep Learning Framework that combines an algorithm of necessary processing of Linear Discriminant Analysis (LDA) and Auto Encoder (AE) Neural Network to classify different features within the profile of gene expression. Hence, an advanced ensemble classification has been developed based on the Deep Learning (DL) algorithm to assess the clinical outcome of breast cancer. Furthermore, numerous independent breast cancer datasets and representations of the signature gene, including the primary method, have been evaluated for the optimization parameters. Finally, the experiment results show that the suggested deep learning frameworks achieve 98.27% accuracy than many other techniques such as genomic data and pathological images with multiple kernel learning (GPMKL), Multi-Layer Perception (MLP), Deep Learning Diagnosis (DLD), and Spatiotemporal Wavelet Kinetics (SWK).

The rate-distortion optimized quantization (RDOQ) provides an excellent trade-off between rate and distortion in High Efficiency Video Coding (HEVC), leading to inspiring improvement in terms of rate-distortion performance. However, its heavy use imposes high complexity on the encoder in real-world video compression applications. In this paper, we provide a comprehensive review on low complexity quantization techniques in HEVC, including both fast RDOQ and all-zero block detection. In particular, the fast RDOQ relies on rate and distortion models for rate-distortion cost estimation, such that the most appropriate quantized coefficient is selected in a low complexity way. All-zero block detection infers the all-zero block by skipping transform and quantization, in an effort to further reduce the complexity. The relationship between the two techniques is also discussed, and moreover, we also envision the future design of low complexity quantization in the upcoming Versatile Video Coding (VVC) standard.

Anomaly detection in video surveillance is challenging due to the variety of anomaly types and definitions, which limit the use of supervised techniques. As such, auto-encoder structures, a type of classical unsupervised method, have recently been utilized in this field. These structures consist of an encoder followed by a decoder and are typically adopted to restructure a current input frame or predict a future frame. However, regardless of whether a 2D or 3D autoencoder structure is adopted, only single-scale information from the previous layer is typically used in the decoding process. This can result in a loss of detail that could potentially be used to predict or reconstruct video frames. As such, this study proposes a novel spatio-temporal U-Net for frame prediction using normal events and abnormality detection using prediction error. This framework combines the benefits of U-Nets in representing spatial information with the capabilities of ConvLSTM for modeling temporal motion data. In addition, we propose a new regular score function, consisting of a prediction error for not only the current frame but also future frames, to further improve the accuracy of anomaly detection. Extensive experiments on common anomaly datasets, including UCSD (98 video clips in total) and CUHK Avenue (30 video clips in total), validated the performance of the proposed technique and we achieved 96.5% AUC for the Ped2 dataset, which is much better than existing autoencoder-based and U-Net-based methods.

In recent years, terahertz waves have attracted significant attention for their promising applications. Due to a broadband optical response, an ultra-fast relaxation time, a high nonlinear coefficient of graphene, and the flexible and controllable physical characteristics of its meta-structure, graphene metamaterial has been widely explored in interdisciplinary frontier research, especially in the technologically important terahertz (THz) frequency range. Here, graphene’s linear and nonlinear properties and typical applications of graphene metamaterial are reviewed. Specifically, the discussion focuses on applications in optically and electrically actuated terahertz amplitude, phase, and harmonic generation. The review concludes with a brief examination of potential prospects and trends in graphene metamaterial.

Introduction Recent studies have proved the diverse roles of miRs in different cancer-related processes. This study was undertaken to determine the therapeutic implications of miR-325-3p in breast cancer. Material and methods Expression analysis was carried out by qRT-PCR. Transfections were performed by Lipofectamine 2000 reagent. MTT assay was used for cell viability. Transwell assays were used for cell migration and invasion. Western blot analysis was used for protein expression analysis. Results Gene expression analysis revealed miR-325 to be significantly suppressed in breast cancer tissues and cell lines. Nonetheless, ectopic expression of miR-325 resulted in suppression of the growth and colony development potential of the SK-BR-3 and CAMA-1 cells. Transwell assays showed that miR-325 overexpression also resulted in the decline of the migration and invasion of the SK-BR-3 and CAMA-1 cells. Bioinformatic analysis showed that miR-325 targets lipocalin 15 (LNC15) in breast cancer cells. LNC15 was also overexpressed in the breast cancer tissues and cell lines. However, overexpression of miR-325 caused a significant decline in the LNC15 expression in SK-BR-3 cells. Additionally, silencing of LNC15 resulted in inhibition of the growth, migration and invasion of the SK-BR-3 cells. Rescue assay showed that overexpression of LNC15 could promote the growth, migration and invasion of the miR-325 overexpressing effects. Conclusions Taken together, the evidence shows that miR-325 acts as a tumor suppressor in breast cancer and may be used in the treatment of breast cancer.