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Abstract The enhanced permeability and retention (EPR) effect has become the guiding principle for nanomedicine against cancer for a long time. However, several biological barriers severely resist therapeutic agents’ penetration and retention into the deep tumor tissues, resulting in poor EPR effect and high tumor mortality. Inspired by lava, we proposed a proteolytic enzyme therapy to improve the tumor distribution and penetration of nanomedicine. A trypsin-crosslinked hydrogel (Trypsin@PSA Gel) was developed to maintain trypsin’s activity. The hydrogel postponed trypsin’s self-degradation and sustained the release. Trypsin promoted the cellular uptake of nanoformulations in breast cancer cells, enhanced the penetration through endothelial cells, and degraded total and membrane proteins. Proteomic analysis reveals that trypsin affected ECM components and down-regulated multiple pathways associated with cancer progression. Intratumoral injection of Trypsin@PSA Gel significantly increased the distribution of liposomes in tumors and reduced tumor vasculature. Combination treatment with intravenous injection of gambogic acid-loaded liposomes and intratumoral injection of Trypsin@PSA Gel inhibited tumor growth. The current study provides one of the first investigations into the enhanced tumor distribution of liposomes induced by a novel proteolytic enzyme therapy. Graphic Abstract

Weeds in rapeseed field are an important factor in crop yield reduction and economic loss. Thus, Precision Agriculture is an important task for sustainable agriculture and weed management. At present, deep learning techniques have shown great potential for image-based detection and classification in various crops and weeds. However, the inherent limitations of traditional convolutional neural networks pose significant challenges due to the locally similarity of weeds and crops in color, shape and texture. To address this issue, we introduce SWFormer, a scale-wise hybrid CNN-Transformer network. SWFormer leverages the distinct strengths of both convolutional and transformer architectures. Convolutional structures excel at extracting short-range dependency information among pixels, whereas transformer structures are adept at capturing global dependency relationships. Additionally, we propose two innovative modules. Firstly, the Scale-wise Cascade Convolution (SWCC) module is designed to capture multiscale features and expand the receptive field. Secondly, the Adaptive Semantic Aggregation (ASA) module facilitates adaptive and effective information fusion across two distinct feature maps. Our experiments were conducted on the publicly available cropandweed dataset and SB20 dataset. it yields improved performance over other mainstream segmentation models. Specifically, SWFormer with 52.33M/527.51GFLOPs achieves an mAP of 76.54% and an accuracy of 83.95% on the cropandweed dataset. For the SB20 dataset, it attains an mAP of 61.24% and an accuracy of 79.47%. Overall, the evaluation clearly demonstrates our proposed SWFormer is conducive to promoting further research in the area of Precision Agriculture.

Accurate assessment of high-density underwater fish resources is vital to the aquaculture industry. It is directly related to the formulation of fishery insurance strategies and the implementation of breeding plans. However, accurately counting fish in high-density environments becomes challenging due to the uneven distribution of fish density and individual fish’s different sizes and postures. To break through this technical bottleneck, we developed an advanced adaptive density-guided high-density fish counting network. In detail, first of all, the network adopts a multi-layer feature fusion structure similar to UNet, which significantly enhances the matching between fish targets of different scales and feature pyramid levels, effectively alleviating the problems caused by scale changes and morphological deformations. Secondly, the network also introduces a density-guided adaptive selection module, which can intelligently judge the applicability of Convolutional Neural Network and Transformer blocks in different density areas, thereby achieving robust information transfer and interaction between blocks. Finally, to verify the effectiveness of this method, we also specially constructed two high-density data sets: a simulated high-density underwater fish image data set (SHUFD) and a real high-density underwater fish image data set (RHUFD). The proposed method has significant improvements over the state-of-the-art method (CUT) on SHUFD and RHUFD datasets, with the mean absolute error, mean square error, background region bias, foreground region bias and density map bias indicators improving by 3.44% and 6.47%, 11.43% and 4.41%, 23.91% and 29.48%, 4.43% and 10.33%, 8.3% and 13.14%, respectively.

Underwater applications present unique challenges such as color deviation, noise, and low contrast, which can degrade image quality. Addressing these issues, we propose a novel approach called the learnable full-frequency transformer dual generative adversarial network (LFT-DGAN). Our method comprises several key innovations. Firstly, we introduce a reversible convolution-based image decomposition technique. This method effectively separates underwater image information into low-, medium-, and high-frequency domains, enabling more thorough feature extraction. Secondly, we employ image channels and spatial similarity to construct a learnable full-frequency domain transformer. This transformer facilitates interaction between different branches of information, enhancing the overall image processing capabilities. Finally, we develop a robust dual-domain discriminator capable of learning spatial and frequency domain characteristics of underwater images. Extensive experimentation demonstrates the superiority of the LFT-DGAN method over state-of-the-art techniques across multiple underwater datasets. Our approach achieves significantly improved quality and evaluation metrics, showcasing its effectiveness in addressing the challenges posed by underwater imaging. The code can be found at https://github.com/zhengshijian1993/LFT-DGAN.

Abstract Short‐term interval estimation can effectively and precisely quantify the uncertainties of renewable energy, accurately represent the range of fluctuations of uncertain variables in robust optimisation of electricity‐heating integrated energy system (EHIES) and it is getting crucial for reliable and flexible operation of renewable dominated new energy systems. The authors present a multivariate data‐driven short‐term PV power interval prediction model that consists of multiple layers, including one‐dimensional convolutional layer, ultra‐lightweight subspace attention mechanism (ULSAM), bidirectional long and short‐term memory (BiLSTM), quantile regression (QR) and kernel density estimation (KDE). The one‐dimensional convolutional layer and ULSAM can extract sequential features and highlight key information from the data; the BiLSTM processes time series data in both directions and conveys historical information; the QR and KDE models generate interval prediction with a given confidence level. Based on the proposed interval estimation, a refined PV uncertainty set can be established and adopted by robust optimal scheduling of EHIES utilising min‐max‐min algorithm. The simulation results have demonstrated the estimation accuracy and adaptability to various weather scenarios.

Many strategies have been proposed for underwater image enhancement. However, they are often incompatible with underwater remotely operated vehicle platforms with low memory and computational power. In this paper, we address this problem by proposing a simple yet effective multi-scale feature modulation network to achieve a better trade-off between the efficiency of the model and the performance of the reconstruction. Specifically, we develop a multi-scale modulation module on top of a Visual Transformer (VIT)-like block. Within this module, we extract the features of the input image using a Multi-scale Spatial Feature Module (MSFM) to dynamically select representative features in the feature space of the image. Since the MSFM block handles the image features from a spatial point of view of the image, we further introduce the channel mixing module to perform the channel mixing. Extensive experimental results show that the proposed method is 8.5 times smaller in terms of network parameters than the state-of-the-art underwater image enhancement methods (e.g., Semi-uir), and achieves essentially the same performance with less computational cost. The code is available at https://github.com/zhengshijian1993/MFMN.

Protein interactions are the foundation of all metabolic activities of cells, such as apoptosis, the immune response, and metabolic pathways. In order to optimize the performance of protein interaction prediction, a coding method based on normalized difference sequence characteristics (NDSF) of amino acid sequences is proposed. By using the positional relationships between amino acids in the sequences and the correlation characteristics between sequence pairs, NDSF is jointly encoded. Using principal component analysis (PCA) and local linear embedding (LLE) dimensionality reduction methods, the coded 174-dimensional human protein sequence vector is extracted using sequence features. This study compares the classification performance of four ensemble learning methods (AdaBoost, Extra trees, LightGBM, XGBoost) applied to PCA and LLE features. Cross-validation and grid search methods are used to find the best combination of parameters. The results show that the accuracy of NDSF is generally higher than that of the sequence matrix-based coding method (MOS) coding method, and the loss and coding time can be greatly reduced. The bar chart of feature extraction shows that the classification accuracy is significantly higher when using the linear dimensionality reduction method, PCA, compared to the nonlinear dimensionality reduction method, LLE. After classification with XGBoost, the model accuracy reaches 99.2%, which provides the best performance among all models. This study suggests that NDSF combined with PCA and XGBoost may be an effective strategy for classifying different human protein interactions.

RNAs are involved in the crucial processes of disease progression and have emerged as powerful therapeutic targets and diagnostic biomarkers. However, efficient delivery of therapeutic RNA to the targeted location and precise detection of RNA markers remains challenging. Recently, more and more attention has been paid to applying nucleic acid nanoassemblies in diagnosing and treating. Due to the flexibility and deformability of nucleic acids, the nanoassemblies could be fabricated with different shapes and structures. With hybridization, nucleic acid nanoassemblies, including DNA and RNA nanostructures, can be applied to enhance RNA therapeutics and diagnosis. This review briefly introduces the construction and properties of different nucleic acid nanoassemblies and their applications for RNA therapy and diagnosis and makes further prospects for their development.

For the problem that the traceability parameters of sudden water pollution are difficult to determine, a fast traceability model based on a simplified mechanistic model coupled with an optimization algorithm is proposed to improve the accuracy of sudden water pollution traceability. In this paper, according to the diffusion law of pollutants, a quantitative formula of pollutant diffusion is proposed, and the differential calculation process of the pollutant convection equation is optimized. The Dynamic Programming and Beetle Antennae Search algorithm (DP-BAS) with dynamic step size is used in the reverse optimization process, which can avoid the problem of entering the local optimal solution in the calculation process. The DP-BAS is used to inverse solve the quantization equation to realize the decoupling of pollutant traceability parameters, transforming the multi-parameter coupled solution into a single-parameter solution, reducing the solution dimension, and optimizing the difficulty and solution complexity of pollutant traceability. The proposed traceability model is applied to the simulation case, the results show that the mean square errors of pollutant placement mass, location, and time are 2.39, 1.16, and 1.19 percent, respectively. To further verify the model reliability, the Differential Evolution and Markov Chain Monte Carlo simulation method (DE-MCMC) as well as Genetic Algorithms (GA) were introduced to compare with the proposed model to prove that the model has certain reliability and accuracy.

The damage symptoms of Cnaphalocrocis medinalis (C.medinalis) is an important evaluation index for pest prevention and control. However, due to various shapes, arbitrary-oriented directions and heavy overlaps of C.medinalis damage symptoms under complex field conditions, generic object detection methods based on horizontal bounding box cannot achieve satisfactory results. To address this problem, we develop a Cnaphalocrocis medinalis damage symptom rotated detection framework called CMRD-Net. It mainly consists of a Horizontal-to-Rotated region proposal network (H2R-RPN) and a Rotated-to-Rotated region convolutional neural network (R2R-RCNN). First, the H2R-RPN is utilized to extract rotated region proposals, combined with adaptive positive sample selection that solves the hard definition of positive samples caused by oriented instances. Second, the R2R-RCNN performs feature alignment based on rotated proposals, and exploits oriented-aligned features to detect the damage symptoms. The experimental results on our constructed dataset show that our proposed method outperforms those state-of-the-art rotated object detection algorithms achieving 73.7% average precision (AP). Additionally, the results demonstrate that our method is more suitable than horizontal detection methods for in-field survey of C.medinalis.

The rapid quantification of nitrate nitrogen concentration plays a pivotal role in monitoring soil nutrient content. Nevertheless, the low detection efficiency limits the application of traditional methods in rapid testing. For this investigation, we utilized a digital microfluidic platform and 3D-printed microfluidics to accomplish automated detection of soil nitrate nitrogen with high sensitivity across numerous samples. The system combines digital microfluidics (DMF), 3D-printed microfluidics, a peristaltic pump, and a spectrometer. The soil solution, obtained after extraction, was dispensed onto the digital microfluidic platform using a micropipette. The digital microfluidic platform regulated the movement of droplets until they reached the injection area, where they were then aspirated into the 3D-printed microfluidic device for absorbance detection. Implementing this approach allows for the convenient sequential testing of multi-samples, thereby enhancing the efficiency of nitrate nitrogen detection. The results demonstrate that the device exhibits rapid detection (200 s for three samples), low reagent consumption (40 µL per sample), and low detection limit (95 µg/L). In addition, the relative error between the detected concentration and the concentration measured by ultraviolet spectrophotometry is kept within 20%, and the relative standard deviation (RSD) of the measured soil samples is between 0.9% and 4.7%. In the foreseeable future, this device will play a significant role in improving the efficiency of soil nutrient detection and guiding fertilization practices.

Efficient pest identification and control is critical for ensuring food safety. Therefore, automatic detection of pests has high practical value for Integrated Pest Management (IPM). However, complex field environments and the similarity in appearance among pests can pose a significant challenge to the accurate identification of pests. In this paper, a feature refinement method designed for similar pest detection in the field based on the two-stage detection framework is proposed. Firstly, we designed a context feature enhancement module to enhance the feature expression ability of the network for different pests. Secondly, the adaptive feature fusion network was proposed to avoid the suboptimal problem of feature selection on a single scale. Finally, we designed a novel task separation network with different fusion features constructed for the classification task and the localization task. Our method was evaluated on the proposed dataset of similar pests named SimilarPest5 and achieved a mean average precision (mAP) of 72.7%, which was better than other advanced object detection methods.

Diseases have a great impact on the quality and yield of strawberries, an accurate and timely field disease identification method is urgently needed. However, identifying diseases of strawberries in field is challenging due to the complex background interference and subtle inter-class differences. A feasible method to address the challenges is to segment strawberry lesions from the background and learn fine-grained features of the lesions. Following this idea, we present a novel Class-Attention-based Lesion Proposal Convolutional Neural Network (CALP-CNN), which utilizes a class response map to locate the main lesion object and propose discriminative lesion details. Specifically, the CALP-CNN firstly locates the main lesion object from the complex background through a class object location module (COLM) and then applies a lesion part proposal module (LPPM) to propose the discriminative lesion details. With a cascade architecture, the CALP-CNN can simultaneously address the interference from the complex background and the misclassification of similar diseases. A series of experiments on a self-built dataset of field strawberry diseases is conducted to testify the effectiveness of the proposed CALP-CNN. The classification results of the CALP-CNN are 92.56%, 92.55%, 91.80% and 91.96% on the metrics of accuracy, precision, recall and F1-score, respectively. Compared with six state-of-the-art attention-based fine-grained image recognition methods, the CALP-CNN achieves 6.52% higher (on F1-score) than the sub-optimal baseline MMAL-Net, suggesting that the proposed methods are effective in identifying strawberry diseases in the field.

Wheat spike detection has important research significance for production estimation and crop field management. With the development of deep learning-based algorithms, researchers tend to solve the detection task by convolutional neural networks (CNNs). However, traditional CNNs equip with the inductive bias of locality and scale-invariance, which makes it hard to extract global and long-range dependency. In this paper, we propose a Transformer-based network named Multi-Window Swin Transformer (MW-Swin Transformer). Technically, MW-Swin Transformer introduces the ability of feature pyramid network to extract multi-scale features and inherits the characteristic of Swin Transformer that performs self-attention mechanism by window strategy. Moreover, bounding box regression is a crucial step in detection. We propose a Wheat Intersection over Union loss by incorporating the Euclidean distance, area overlapping, and aspect ratio, thereby leading to better detection accuracy. We merge the proposed network and regression loss into a popular detection architecture, fully convolutional one-stage object detection, and name the unified model WheatFormer. Finally, we construct a wheat spike detection dataset (WSD-2022) to evaluate the performance of the proposed methods. The experimental results show that the proposed network outperforms those state-of-the-art algorithms with 0.459 mAP (mean average precision) and 0.918 AP50. It has been proved that our Transformer-based method is effective to handle wheat spike detection under complex field conditions.

ABSTRACT The gut microbiota is closely associated with the health and production performance of livestock. Partial studies on ruminant microbiota are already in progress in the Qinghai-Tibetan Plateau Area (QTPA) in China, but large-scale and representative profiles for the QTPA are still lacking. Here, 16S rRNA sequencing was used to analyze 340 samples from yak, cattle, yak-cattle hybrids, and Tibetan sheep, which lived in a shared environment from 4 eco-regions of the QTPA during the same season, and aimed to investigate the fecal microbiota community composition, diversity, and potential function. All samples were clustered into 2 enterotypes, which were derived from the genera Ruminococcaceae UCG-005 and Acinetobacter, respectively. Environment, human activity, species, and parasitization all affected the fecal microbiota. By assessing the relationship between the fecal microbiota and the above variables, we identified a scattered pattern of fecal microbiota dissimilarity based more significantly on diet over other factors. Additionally, gastrointestinal nematode infection could reduce the capacity of the bacterial community for biosynthesis of other secondary metabolites, carbohydrate metabolism, and nucleotide metabolism. Ultimately, this study provided a fecal microbiota profile for ruminants living in 4 eco-regions of the QTPA and its potential future applications in developing animal husbandry regimes. IMPORTANCE Cattle, yak, and sheep reside as the main ruminants distributed throughout most regions of Qinghai-Tibetan Plateau Area (QTPA) in China. However, there is a lack of large-scale research in the QTPA on their fecal microbiota, which can regulate and reflect host health as an internalized “microbial organ.” Our study depicted the fecal microbiota community composition and diversity of yak, cattle, yak-cattle hybrids, and Tibetan sheep from 4 eco-regions of the QTPA. Additionally, our results demonstrated here that the ruminant samples could be clustered into 2 enterotypes and that diet outweighed other factors in shaping fecal microbiota in the QTPA. This study provided a basis for understanding the microbiota characteristic of ruminants and its possible applications for livestock production in the QTPA.

The cartilage has poor ability to mount a sufficient healing response. Herein, kartogenin (KGN), an emerging stable non-protein compound with the ability to recruit bone marrow mesenchyme stem cells (BMSCs) to promote chondrogenic differentiation, was grafted onto dopamine-Fe(III) chelating nanoparticles, followed by involving a gelatin- and dextran-based injectable hydrogel to mimic the extracellular matrix to promote cartilage repair. The in vitro results demonstrated that KGN underwent long-term sustained release behavior and availably promoted the deep migration of BMSC cells in yielding hydrogels. Furthermore, in vivo New Zealand white rabbits’ cartilage defect model repairing results showed that cartilage defect obtained significant regeneration post operation in the 12th week, and the defect edge almost disappeared compared to adjacent normal cartilage tissue. Meanwhile, the T2-weighted magnetic resonance imaging (MRI) property resulting from dissociative Fe (III) can significantly monitor the degradation degree of the implanted hydrogels in the defect site. This integrated diagnosis and treatment system gives insight into cartilage regeneration.

An accurate and robust pest detection and recognition scheme is an important step to enable the high quality and yield of agricultural products according to integrated pest management (IPM). Due to pose-variant, serious overlap, dense distribution, and interclass similarity of agricultural pests, the precise detection of multi-classes pest faces great challenges. In this study, an end-to-end pest detection algorithm has been proposed on the basis of deep convolutional neural networks. The detection method adopts a deformable residual network to extract pest features and a global context-aware module for obtaining region-of-interests of agricultural pests. The detection results of the proposed method are compared with the detection results of other state-of-the-art methods, for example, RetinaNet, YOLO, SSD, FPN, and Cascade RCNN modules. The experimental results show that our method can achieve an average accuracy of 77.8% on 21 categories of agricultural pests. The proposed detection algorithm can achieve 20.9 frames per second, which can satisfy real-time pest detection.

Wheat stripe rusts are responsible for the major reduction in production and economic losses in the wheat industry. Thus, accurate detection of wheat stripe rust is critical to improving wheat quality and the agricultural economy. At present, the results of existing wheat stripe rust detection methods based on convolutional neural network (CNN) are not satisfactory due to the arbitrary orientation of wheat stripe rust, with a large aspect ratio. To address these problems, a WSRD-Net method based on CNN for detecting wheat stripe rust is developed in this study. The model is a refined single-stage rotation detector based on the RetinaNet, by adding the feature refinement module (FRM) into the rotation RetinaNet network to solve the problem of feature misalignment of wheat stripe rust with a large aspect ratio. Furthermore, we have built an oriented annotation dataset of in-field wheat stripe rust images, called the wheat stripe rust dataset 2021 (WSRD2021). The performance of WSRD-Net is compared to that of the state-of-the-art oriented object detection models, and results show that WSRD-Net can obtain 60.8% AP and 73.8% Recall on the wheat stripe rust dataset, higher than the other four oriented object detection models. Furthermore, through the comparison with horizontal object detection models, it is found that WSRD-Net outperforms horizontal object detection models on localization for corresponding disease areas.