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One of the most important food crops is rice. For this reason, the accurate identification of rice pests is a critical foundation for rice pest control. In this study, we propose an algorithm for automatic rice pest identification and classification based on fully convolutional networks (FCNs) and select 10 rice pests for experiments. First, we introduce a new encoder–decoder in the FCN and a series of sub-networks connected by jump paths that combine long jumps and shortcut connections for accurate and fine-grained insect boundary detection. Secondly, the network also integrates a conditional random field (CRF) module for insect contour refinement and boundary localization, and finally, a novel DenseNet framework that introduces an attention mechanism (ECA) is proposed to focus on extracting insect edge features for effective rice pest classification. The proposed model was tested on the data set collected in this paper, and the final recognition accuracy was 98.28%. Compared with the other four models in the paper, the proposed model in this paper is more accurate, faster, and has good robustness; meanwhile, it can be demonstrated from our results that effective segmentation of insect images before classification can improve the detection performance of deep-learning-based classification systems. [ABSTRACT FROM AUTHOR]

In the field of computer vision,3D face reconstruction is a valuable research direction.High quality reconstruction of 3D faces can find applications in face recognition,anti-proofing,animation and medical cosmetology.In the last two decades,although great progress has been made 3D face reconstruction based on a single image,the results of reconstruction using traditionalalgorithms are still facing the challenge of facial expression,occlusion and ambient light,and there will be problems such as poor reconstruction accuracy and robustness.With the rapid development of deep learning in 3D face reconstruction,various methods which are superior to traditional reconstruction algorithms have emerged.Firstly,this paper focuses on deep-learning-based reconstruction algorithms.The algorithms are divided into four categories according to different network architecture,and the most popular methods are described in detail.Then commonly used 3D face data sets are introduced,and performance of representative methods are evaluated.Finally,conclusions and prospects of the single-image-based 3D face reconstruction are given.

Background Necroptosis is a necrotic programmed cell death with potent immunogenicity. Due to the dual effects of necroptosis on tumor growth, metastasis and immunosuppression, we evaluated the prognostic value of necroptosis-related genes (NRGs) in hepatocellular carcinoma (HCC). Methods We first analyzed RNA sequencing and clinical HCC patient data obtained to develop an NRG prognostic signature based on the TCGA dataset. Differentially expressed NRGs were further evaluated by GO and KEGG pathway analyses. Next, we conducted univariate and multivariate Cox regression analyses to build a prognostic model. We also used the dataset obtained from the International Cancer Genome Consortium (ICGC) database to verify the signature. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to investigate the immunotherapy response. Furthermore, we investigated the relationship between the prediction signature and chemotherapy treatment response in HCC. Results We first identified 36 differentially expressed genes out of 159 NRGs in hepatocellular carcinoma. Enrichment analysis showed that they were mainly enriched in the necroptosis pathway. Four NRGs were screened by Cox regression analysis to establish a prognostic model. The survival analysis revealed that the overall survival of patients with high-risk scores was significantly shorter than that of patients with low-risk scores. The nomogram demonstrated satisfactory discrimination and calibration. The calibration curves validated a fine concordance between the nomogram prediction and actual observation. The efficacy of the necroptosis-related signature was also validated by an independent dataset and immunohistochemistry experiments. TIDE analysis revealed that patients in the high-risk group were possibly more susceptible to immunotherapy. Furthermore, high-risk patients were found to be more sensitive to conventional chemotherapeutic medicines such as bleomycin, bortezomib, and imatinib. Conclusion We identified 4 necroptosis-related genes and established a prognostic risk model that could potentially predict prognosis and response to chemotherapy and immunotherapy in HCC patients in the future.

With the gradual increase of the scale of the breeding industry in recent years, the intelligence level of livestock breeding is also improving. Intelligent breeding is of great significance to the identification of livestock individuals. In this paper, the cattle face images are obtained from different angles to generate the cow face dataset, and a cow face recognition model based on SK_ResNet is proposed. Based on ResNet-50 and using a different number of sk_Bottleneck, this model integrates multiple receptive fields of information to extract facial features at multiple scales. The shortcut connection part connects to the maximum pooling layer to reduce information loss; the ELU activation function is used to reduce the vanishing gradient, prevent overfitting, accelerate the convergence speed, and improve the generalization ability of the model. The constructed bovine face dataset was used to train the SK-ResNet-based bovine face recognition model, and the accuracy rate was 98.42%. The method was tested on the public dataset and the self-built dataset. The accuracy rate of the model was 98.57% on the self-built pig face dataset and the public sheep face dataset. The accuracy rate was 97.02%. The experimental results verify the superiority of this method in practical application, which is helpful for the practical application of animal facial recognition technology in livestock. [ABSTRACT FROM AUTHOR]

Individual cow identification is a prerequisite for intelligent dairy farming management, and is important for achieving accurate and informative dairy farming. Computer vision-based approaches are widely considered because of their non-contact and practical advantages. In this study, a method based on the combination of Ghost and attention mechanism is proposed to improve ReNet50 to achieve non-contact individual recognition of cows. In the model, coarse-grained features of cows are extracted using a large sensory field of cavity convolution, while reducing the number of model parameters to some extent. ResNet50 consists of two Bottlenecks with different structures, and a plug-and-play Ghost module is inserted between the two Bottlenecks to reduce the number of parameters and computation of the model using common linear operations without reducing the feature map. In addition, the convolutional block attention module (CBAM) is introduced after each stage of the model to help the model to give different weights to each part of the input and extract the more critical and important information. In our experiments, a total of 13 cows' side view images were collected to train the model, and the final recognition accuracy of the model was 98.58%, which was 4.8 percentage points better than the recognition accuracy of the original ResNet50, the number of model parameters was reduced by 24.85 times, and the model size was only 3.61 MB. In addition, to verify the validity of the model, it is compared with other networks and the results show that our model has good robustness. This research overcomes the shortcomings of traditional recognition methods that require human extraction of features, and provides theoretical references for further animal recognition. [ABSTRACT FROM AUTHOR]

Crystalline/crystalline blends of polymer have shown advantages in the preparation of new polymeric materials. However, the regulation of co-crystallization in a blend is still full of challenges due to the preferential self-crystallization driven by thermodynamics. Here, an inclusion complex approach is proposed to facilitate the co-crystallization between crystalline polymers, because the crystallization process displays a prominent kinetics advantage when polymer chains are released from the inclusion complex. Poly(butylene succinate) (PBS), poly(butylene adipate) (PBA) and urea are chosen to form co-inclusion complexes, where PBS and PBA chains play as isolated guest molecules and urea molecules construct the host channel framework. The coalesced PBS/PBA blends are obtained by fast removing the urea framework and systematically investigated by differential scanning calorimetry, X-ray diffraction, proton nuclear magnetic resonance and Fourier transformation infrared spectrometry. It is demonstrated that PBA chains are co-crystallized into PBS extended-chain crystals in the coalesced blends, while such a phenomenon has not been detected in simply co-solution-blended samples. Though PBA chains could not be totally accommodated in the PBS extended-chain crystals, their co-crystallized content increases with the initial feeding ratio of PBA. Consequently, the melting point of the PBS extended-chain crystal gradually declines from 134.3 °C to 124.2 °C with an increasing PBA content. The PBA chains playing as defects mainly induce lattice expansion along the a-axis. In addition, when the co-crystals are soaked in tetrahydrofuran, some of the PBA chains are extracted out, leading to damage to the correlative PBS extended-chain crystals. This study shows that co-inclusion complexation with small molecules could be an effective way to promote co-crystallization behavior in polymer blends.

An affiliation is missing for the first author. Shijun Li is affiliated with #1 and #2. The correct affiliations are:1 College of Information Technology, Jilin Agricultural University, Changchun, China, 2 College of Electronic and Information Engineering, Wuzhou University, Wuzhou, China.By Shijun Li; Lili Fu; Yu Sun; Ye Mu; Lin Chen; Ji Li and He GongReported by Author; Author; Author; Author; Author; Author; Author [Extracted from the article]

Research on the rheological performance and mechanism of polymer nanocomposites (PNCs), mainly focuses on non-polar polymer matrices, but rarely on strongly polar ones. To fill this gap, this paper explores the influence of nanofillers on the rheological properties of poly (vinylidene difluoride) (PVDF). The effects of particle diameter and content on the microstructure, rheology, crystallization, and mechanical properties of PVDF/SiO2 were analyzed, by TEM, DLS, DMA, and DSC. The results show that nanoparticles can greatly reduce the entanglement degree and viscosity of PVDF (up to 76%), without affecting the hydrogen bonds of the matrix, which can be explained by selective adsorption theory. Moreover, uniformly dispersed nanoparticles can promote the crystallization and mechanical properties of PVDF. In summary, the viscosity regulation mechanism of nanoparticles for non-polar polymers, is also applicable to PVDF, with strong polarity, which is of great value for exploring the rheological behavior of PNCs and guiding the process of polymers.

In actual farms, individual livestock identification technology relies on large models with slow recognition speeds, which seriously restricts its practical application. In this study, we use deep learning to recognize the features of individual cows. Alexnet is used as a skeleton network for a lightweight convolutional neural network that can recognise individual cows in images with complex backgrounds. The model is improved for multiple multiscale convolutions of Alexnet using the short-circuit connected BasicBlock to fit the desired values and avoid gradient disappearance or explosion. An improved inception module and attention mechanism are added to extract features at multiple scales to enhance the detection of feature points. In experiments, side-view images of 13 cows were collected. The proposed method achieved 97.95% accuracy in cow identification with a single training time of only 6 s, which is one-sixth that of the original Alexnet. To verify the validity of the model, the dataset and experimental parameters were kept constant and compared with the results of Vgg16, Resnet50, Mobilnet V2 and GoogLenet. The proposed model ensured high accuracy while having the smallest parameter size of 6.51 MB, which is 1.3 times less than that of the Mobilnet V2 network, which is famous for its light weight. This method overcomes the defects of traditional methods, which require artificial extraction of features, are often not robust enough, have slow recognition speeds, and require large numbers of parameters in the recognition model. The proposed method works with images with complex backgrounds, making it suitable for actual farming environments. It also provides a reference for the identification of individual cows in images with complex backgrounds. [ABSTRACT FROM AUTHOR]

Co-crystallization plays a crucial role in the integration and regulation of thermal and mechanical properties in polymer blends, but the poor compatibility of the components in the crystal phase has always been a major obstacle to co-crystallization, which puts forward stricter requests for linkage and interaction between different entities. On the basis of the hydrogen-bonding interaction that can promote chain stacking and thus improve miscibility, we propose that crystalline/crystalline blends of 2-ureido-4[1H]-pyrimidinone (UPy)-functionalized poly(butylene succinate) and poly(butylene fumarate) (PBS-UPy/PBF-UPy) where UPy groups with quadruple hydrogen-bonding interaction are employed to connect different chain ends, could inhibit phase separation and improve co-crystallization. PBS-UPy/PBF-UPy blends exhibit complex component-dependent and cooling-rate-dependent co-crystallization behavior. A high level of co-crystallization occurs in the range of PBS-UPy-rich fractions, and the proportion could approach over 98% under optimized conditions with the aid of UPy quadruple hydrogen bonds interaction. This work enriches the understanding of co-crystallization in crystalline/crystalline polymer blends and provides more possibility for the design of structures and properties of polymer materials.

Plant viruses can modify the performance and behavior of insect vectors to maximize their transmission rates. Whether viruses can evolve to mediate their insect vectors' interspecific competition is poorly understood. Here, we used two vectors of tomato zonate spot virus (TZSV), one native species (Thrips palmi) and one exotic species (Frankliniella occidentalis), to investigate this in laboratory and field experiments. TZSV infection increased the population growth of its insect vectors when the thrips species were isolated from one another. When both species coexisted, F. occidentalis displaced T. palmi on both TZSV-infected and uninfected plants. However, on TZSV-infected plant, F. occidentalis completely displaced T. palmi more rapidly (within 14 days) than on uninfected plants. Our results also revealed that reproductive interference occurred between the two thrips species, whereas TZSV-infected pepper significantly decreased the reproductive interference of T. palmi to F. occidentalis. When F. occidentalis and T. palmi co-occurred in Jinning County, Yunnan Province, we found a rising trend of natural proportions of F. occidentalis in the TZSV-infected pepper fields, suggesting that TZSV-infected pepper enhanced the competitive advantage of F. occidentalis over T. palmi in the field. Together, our findings demonstrate a plant virus that plays an important role in the competitive displacement of its insect vectors. [ABSTRACT FROM AUTHOR]

Metal oxide coupling with carbon materials holds great promise for lithium storage. Herein, multilevel coupled cobalt oxide–graphene (CoO/CO3O4–G) hybrids were fabricated by in situ assembly of Co hydroxide precursors and a calcination process. The oxygen‐containing functional groups on the graphene surface act as bridging sites and tend to bond with Co2+ ions, effectively modifying the morphology and structure of the Co species. The as‐obtained CoO/CO3O4–G hybrids are composed of unique CoO/CO3O4 porous nanoparticles uniformly anchored on graphene sheets, as confirmed by a series of characterization analyses. Benefiting from these structural characteristics, the CoO/CO3O4–G hybrids used as an anode can deliver a high capacity of about 1080 mA h g−1 reversibly at 0.1 Ag−1 in the voltage range between 3.0 and 0.01 V, which is remarkably superior to that of the CoO hexagonal sheets in the absence of graphene. The high reversible capacity of the CoO/CO3O4–G hybrids is retained at elevated current densities, for example, a capacity of approximately 455 mA h g−1 can be achieved at a current rate as high as 4 A g−1, indicative of its potential for high‐performance lithium‐ion batteries. Changing morphology: Cobalt oxide/graphene hybrids were fabricated by an in situ assembly strategy. The oxygen species on graphene were responsible for the morphology change of cobalt oxide from hexagonal sheets to porous spheres, which resulted in an improved performance for lithium storage (see figure). [ABSTRACT FROM AUTHOR]

Big spherulite structure and high crystallinity are the two main drawbacks of poly(butylene succinate) (PBS) and hinder its application. In this work, a new type of copolyester poly(butylene succinate-co-butylene acetylenedicarboxylate) (PBSAD) is synthesized. With the incorporation of acetylenedicarboxylate (AD) units into PBS chains, the crystallization temperature and crystallinity are depressed by excluding AD units to the amorphous region. In contrast, the primary nucleation capability is significantly strengthened, without changing the crystal modification or crystallization kinetics, leading to the recovery of total crystallization rate of PBSAD under the same supercooling condition. The existence of specific interaction among AD units is found to be crucial. Although it is too weak to contribute to the melt memory effect at elevated temperature, the interaction continuously strengthens as the temperature falls down, and the heterogeneous aggregation of AD units keeps growing. When the aggregating process reaches a certain extent, it will induce the formation of a significant amount of crystal nuclei. The unveiled nucleation mechanism helps to design PBS copolymer with good performance.

• Ultra-broadband light source output directly using fiber amplifier with bandwidth covering erbium and thulium gain bands. • Construction of ultra-broadband light source with switchable seed laser, experimental comparison to find narrow pulses easier to achieve a broadband light source with high flatness. Soliton and dissipative soliton have different pulse width, pulse energy, and propagation behaviors. The results are quite different for the generation of broadband photosource. In this work, broadband photosource with a wide spectral range based on two pulsed seed lasers is investigated. A high flatness spectrum with a bandwidth greater than 300 nm and predicted spectrum up to 2000 nm covering Er and Tm emission regime has been obtained by amplifying soliton pulsed laser. 370-mW higher average power broadband photosource has been obtained based on the amplification of dissipative soliton lasers. The proposed broadband photosource does not need to add further nonlinear medium in case of introducing unnecessary losses. These results pave the way for the realization of broadband, high flatness spectrum, compact light sources, and the use of all-fiber optic structure to lay the foundation for integration and detection applications, etc. [ABSTRACT FROM AUTHOR]

Acquisition and metabolism of carbohydrates are essential for host colonization and pathogenesis of bacterial pathogens. Different bacteria can uptake different lines of carbohydrates via ABC transporters, in which ATPase subunits energize the transport though ATP hydrolysis. Some ABC transporters possess their own ATPases, while some share a common ATPase. Here we identified MsmK, an ATPase from Streptococcus suis, an emerging zoonotic bacterium causing dead infections in pigs and humans. Genetic and biochemistry studies revealed that the MsmK was responsible for the utilization of raffinose, melibiose, maltotetraose, glycogen and maltotriose. In infected mice, the msmK-deletion mutant showed significant defects of survival and colonization when compared with its parental and complementary strains. Taken together, MsmK is an ATPase that contributes to multiple carbohydrates utilization and host colonization of S. suis. This study gives new insight into our understanding of the carbohydrates utilization and its relationship to the pathogenesis of this zoonotic pathogen.

When NH 3 in the environment exceeds a certain concentration, it may have adverse effects on human health. Ammonia gas sensors currently on the market usually work under high temperatures and are not only expensive but also have poor performance in terms of selectivity. Therefore, the preparation of an ammonia gas sensor that works at room temperature, is low cost, and has high sensitivity and selectivity is particularly important. This paper introduces a room temperature ammonia gas sensor based on a Ca-doped CNFs/Al 2 O 3 nanocomposite material, prepared using electrospinning, pre-oxidation, and carbonization processes. The surface morphology, microstructure, and chemical composition of the materials have been characterized by scanning electron microscopy, Raman, and X-ray photoelectron spectroscopy. The Ca-doped CNFs/Al 2 O 3 gas sensor has excellent selectivity for ammonia at room temperature and low sensitivity to other volatile gases such as ethanol, dimethylformamide, HCl, and methanol. At 100 ppm of NH 3 , the response value of the Ca-doped CNFs/Al 2 O 3 gas sensor can reach 22.73, demonstrating excellent repeatability and long-term stability. Its performance is not affected by environmental temperature and humidity, providing great convenience for practical applications. In addition, we also discuss the sensing mechanism of the Ca-doped CNFs/Al 2 O 3 gas sensor. This paper not only provides effective materials and methods for the development of high-performance room temperature ammonia gas sensors but is also expected to play a role in the field of environmental monitoring., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

In modern animal husbandry, intelligent digital farming has become the key to improve production efficiency. This paper introduces a model based on improved YOLOv8, Cattle Behavior Recognition-YOLO (CBR-YOLO), which aims to accurately identify the behavior of cattle. We not only generate a variety of weather conditions, but also introduce multi-target detection technology to achieve comprehensive monitoring of cattle and their status. We introduce Inner-MPDIoU Loss and we have innovatively designed the Multi-Convolutional Focused Pyramid module to explore and learn in depth the detailed features of cattle in different states. Meanwhile, the Lightweight Multi-Scale Feature Fusion Detection Head module is proposed to take advantage of deep convolution, achieving a lightweight network architecture and effectively reducing redundant information. Experimental results prove that our method achieves an average accuracy of 90.2% with a reduction of 3.9 G floating-point numbers, an increase of 7.4%, significantly better than 12 kinds of SOTA object detection models. By deploying our approach on monitoring computers on farms, we expect to advance the development of automated cattle monitoring systems to improve animal welfare and farm management.

As the sika deer breeding industry flourishes on a large scale, accurately assessing the health of these animals is of paramount importance. Implementing posture recognition through target detection serves as a vital method for monitoring the well-being of sika deer. This approach allows for a more nuanced understanding of their physical condition, ensuring the industry can maintain high standards of animal welfare and productivity. In order to achieve remote monitoring of sika deer without interfering with the natural behavior of the animals, and to enhance animal welfare, this paper proposes a sika deer individual posture recognition detection algorithm GFI-YOLOv8 based on YOLOv8. Firstly, this paper proposes to add the iAFF iterative attention feature fusion module to the C2f of the backbone network module, replace the original SPPF module with AIFI module, and use the attention mechanism to adjust the feature channel adaptively. This aims to enhance granularity, improve the model's recognition, and enhance understanding of sika deer behavior in complex scenes. Secondly, a novel convolutional neural network module is introduced to improve the efficiency and accuracy of feature extraction, while preserving the model's depth and diversity. In addition, a new attention mechanism module is proposed to expand the receptive field and simplify the model. Furthermore, a new pyramid network and an optimized detection head module are presented to improve the recognition and interpretation of sika deer postures in intricate environments. The experimental results demonstrate that the model achieves 91.6% accuracy in recognizing the posture of sika deer, with a 6% improvement in accuracy and a 4.6% increase in mAP50 compared to YOLOv8n. Compared to other models in the YOLO series, such as YOLOv5n, YOLOv7-tiny, YOLOv8n, YOLOv8s, YOLOv9, and YOLOv10, this model exhibits higher accuracy, and improved mAP50 and mAP50-95 values. The overall performance is commendable, meeting the requirements for accurate and rapid identification of the posture of sika deer. This model proves beneficial for the precise and real-time monitoring of sika deer posture in complex breeding environments and under all-weather conditions.

Background: Increasing evidence suggests that immune modulation contributes to the pathogenesis and progression of diabetic nephropathy (DN). However, the role of immune modulation in DN has not been elucidated. The purpose of this study was to search for potential immune-related therapeutic targets and molecular mechanisms of DN., Methods: Gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. A total of 1793 immune-related genes were acquired from the Immunology Database and Analysis Portal (ImmPort). Weighted gene co-expression network analysis (WGCNA) was performed for GSE142025, and the red and turquoise co-expression modules were found to be key for DN progression. We utilized four machine learning algorithms, namely, random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), and k-nearest neighbor (KNN), to evaluate the diagnostic value of hub genes. Immune infiltration patterns were analyzed using the CIBERSORT algorithm, and the correlation between immune cell type abundance and hub gene expression was also investigated., Results: A total of 77 immune-related genes of advanced DN were selected for subsequent analyzes. Functional enrichment analysis showed that the regulation of cytokine-cytokine receptor interactions and immune cell function play a corresponding role in the progression of DN. The final 10 hub genes were identified through multiple datasets. In addition, the expression levels of the identified hub genes were corroborated through a rat model. The RF model exhibited the highest AUC. CIBERSORT analysis and single-cell sequencing analysis revealed changes in immune infiltration patterns between control subjects and DN patients. Several potential drugs to reverse the altered hub genes were identified through the Drug-Gene Interaction database (DGIdb)., Conclusion: This pioneering work provided a novel immunological perspective on the progression of DN, identifying key immune-related genes and potential drug targets, thus stimulating future mechanistic research and therapeutic target identification for DN., Competing Interests: All the authors have declared that they have no conflicts of interest regarding this article., (© 2023 Peng et al.)

Background: Necroptosis is a necrotic programmed cell death with potent immunogenicity. Due to the dual effects of necroptosis on tumor growth, metastasis and immunosuppression, we evaluated the prognostic value of necroptosis-related genes (NRGs) in hepatocellular carcinoma (HCC)., Methods: We first analyzed RNA sequencing and clinical HCC patient data obtained to develop an NRG prognostic signature based on the TCGA dataset. Differentially expressed NRGs were further evaluated by GO and KEGG pathway analyses. Next, we conducted univariate and multivariate Cox regression analyses to build a prognostic model. We also used the dataset obtained from the International Cancer Genome Consortium (ICGC) database to verify the signature. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to investigate the immunotherapy response. Furthermore, we investigated the relationship between the prediction signature and chemotherapy treatment response in HCC., Results: We first identified 36 differentially expressed genes out of 159 NRGs in hepatocellular carcinoma. Enrichment analysis showed that they were mainly enriched in the necroptosis pathway. Four NRGs were screened by Cox regression analysis to establish a prognostic model. The survival analysis revealed that the overall survival of patients with high-risk scores was significantly shorter than that of patients with low-risk scores. The nomogram demonstrated satisfactory discrimination and calibration. The calibration curves validated a fine concordance between the nomogram prediction and actual observation. The efficacy of the necroptosis-related signature was also validated by an independent dataset and immunohistochemistry experiments. TIDE analysis revealed that patients in the high-risk group were possibly more susceptible to immunotherapy. Furthermore, high-risk patients were found to be more sensitive to conventional chemotherapeutic medicines such as bleomycin, bortezomib, and imatinib., Conclusion: We identified 4 necroptosis-related genes and established a prognostic risk model that could potentially predict prognosis and response to chemotherapy and immunotherapy in HCC patients in the future., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Peng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: The purpose of this paper is to effectively and accurately identify weed species in crop fields in complex environments. There are many kinds of weeds in the detection area, which are densely distributed., Methods: The paper proposes the use of local variance pre-processing method for background segmentation and data enhancement, which effectively removes the complex background and redundant information from the data, and prevents the experiment from overfitting, which can improve the accuracy rate significantly. Then, based on the optimization improvement of DenseNet network, Efficient Channel Attention (ECA) mechanism is introduced after the convolutional layer to increase the weight of important features, strengthen the weed features and suppress the background features., Results: Using the processed images to train the model, the accuracy rate reaches 97.98%, which is a great improvement, and the comprehensive performance is higher than that of DenseNet, VGGNet-16, VGGNet-19, ResNet-50, DANet, DNANet, and U-Net models., Discussion: The experimental data show that the model and method we designed are well suited to solve the problem of accurate identification of crop and weed species in complex environments, laying a solid technical foundation for the development of intelligent weeding robots., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mu, Ni, Fu, Luo, Feng, Li, Pan, Wang, Sun, Gong, Guo, Hu, Bao and Li.)

Objective: To prepare cell membrane nanovesicles (NVs) derived from breast cancer cells, to explore their basic characteristics, tumor cell endocytosis, and in vivo distribution in a tumor-bearing mouse model, and to investigate their tumor targeting properties., Methods: 4T1 breast cancer cells were cultured in vitro . The cell membrane of 4T1 cells was isolated through ultracentrifugation and NVs were formulated with a liposome extruder. The size distribution of NVs was determined by way of dynamic light scattering, and the morphology properties of the NVs were examined with transmission electron microscope. The stability of NVs was analyzed by measuring the diameter changes of NVs submerged in phosphate-buffered saline (PBS). The biocompatibility of NVs was investigated by measuring the viability of dendritic cells treated with NVs at different concentrations (5, 10, 20, 50, and 100 mg·L -1 ) by CCK-8 assay. Fluorescence microscopy was used to analyze the cellular uptake of NVs by breast cancer cells. A mice model of breast cancer model was established with mice bearing subcutaneous xenograft of 4T1 cells. The mice were treated with Cy5.5-labeled NVs injected via the tail vein and the in vivo distribution of NVs was analyzed with an imaging system for small live animals., Results: The results showed that NVs derived from 4T1 breast cancer cells were successfully prepared. The NVs had a mean diameter of 123.2 nm and exhibited a hollow spherical structure under transmission electron microscope. No obvious change in the size of the NVs was observed after 7 days of incubation in PBS solution. CCK-8 assay results showed that the viability of dendritic cells treated with NVs at different concentrations was always higher than 90%. Fluorescence microscopic imaging showed that NVs could be efficiently internalized into breast cancer cells. in vivo biodistribution analysis revealed that breast cancer cell-derived NVs showed higher distribution in tumor tissue than the NVs prepared with normal cells did., Conclusion: We successfully prepared cell membrane NVs derived from 4T1 breast cancer cells. These NVs had efficient cellular uptake by breast cancer cells and sound tumor targeting properties., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

As a widely consumed fruit worldwide, it is extremely important to prevent and control disease in apple trees. In this research, we designed convolutional neural networks (CNNs) for five diseases that affect apple tree leaves based on the AlexNet model. First, the coarse-grained features of the disease are extracted in the model using dilated convolution, which helps to maintain a large receptive field while reducing the number of parameters. The parallel convolution module is added to extract leaf disease features at multiple scales. Subsequently, the series 3 × 3 convolutions shortcut connection allows the model to deal with additional nonlinearities. Further, the attention mechanism is added to all aggregated output modules to better fit channel features and reduce the impact of a complex background on the model performance. Finally, the two fully connected layers are replaced by global pooling to reduce the number of model parameters, to ensure that the features are not lost. The final recognition accuracy of the model is 97.36%, and the size of the model is 5.87 MB. In comparison with five other models, our model design is reasonable and has good robustness; further, the results show that the proposed model is lightweight and can identify apple leaf diseases with high accuracy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Fu, Li, Sun, Mu, Hu and Gong.)

Highlights: Chlorine and PAA spray reduced MNV and L. monocytogenes from raspberries by <1.0 log. Residual PAA on raspberries further reduced MNV and Listeria during postspray frozen storage. PAA decayed more slowly than active chlorine on raspberry surfaces. The data suggest that PAA could aid in risk reduction of pathogens on raspberries.

Aims: To determine whether use of radiofrequency catheter ablation (RFCA) combined with intravenously administered liposomal doxorubicin (L-DOX) facilitates a reduction in the recovery of post-ablation electrical conduction., Methods: Circumferential ablation was performed on the epicardial surface of the left atrial appendage (LAA) in New Zealand White rabbits, and L-DOX was then administered intravenously. Fluorescence spectrophotometry was used to assess reagent bio-distribution, while Western blots and immunohistochemistry were used to assess the localization of the apoptotic markers Bcl-2, Bax, and cleaved CASP3 in the LAA. Liver, kidney, and cardiac functions were also measured to evaluate the safety of this approach., Results: At 1 week and 1 month after RFCA, a pacing electrocardiogram could not be detected in most of the rabbits that had received the combined RFCA and L-DOX therapy. L-DOX began to target the LAA on the second day after RFCA. L-DOX treatment increased the apoptosis of cardiomyocytes in the regions peripheral to the necrotic area induced by RFCA. Doxorubicin had some effect on liver and kidney function, but these effects were reversible and did not affect survival., Conclusion: The present results provide evidence that L-DOX treatment can reduce the recovery of electrical conduction after RFCA therapy owing to L-DOX-induced apoptosis of cardiomyocytes in the ablated area and the proximal transition zone of the LAA., Competing Interests: Disclosure The authors report no conflicts of interest in this work.

Objective: The rostral anterior cingulate cortex (rACC) is implicated in processing the emotional component of pain. N-methyl-D-aspartate receptors (NMDARs) are highly expressed in the rACC and mediate pain-related affect by activating a signaling pathway that involves cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) and/or extracellular regulated kinase (ERK)/cAMP-response element-binding protein (CREB). The present study investigated the contributions of the NMDAR glycine site and GluN2B subunit to the activation of ERK and CREB both in vitro and in vivo in rat rACC., Methods: Immunohistochemistry and Western blot analysis were used to separately assess the expression of phospho-ERK (pERK) and phospho-CREB (pCREB) in vitro and in vivo. Double immunostaining was also used to determine the colocalization of pERK and pCREB., Results: Both bath application of NMDA in brain slices in vitro and intraplantar injection of formalin into the rat hindpaw in vivo induced significant up-regulation of pERK and pCREB in the rACC, which was inhibited by the NMDAR antagonist DL-2-amino-5-phospho-novaleric acid. Selective blockade of the NMDAR GluN2B subunit and the glycine-binding site, or degradation of endogenous D-serine, a co-agonist for the glycine site, significantly decreased the up-regulation of pERK and pCREB expression in the rACC. Further, the activated ERK predominantly colocalized with CREB., Conclusion: Either the glycine site or the GluN2B subunit of NMDARs participates in the phosphorylation of ERK and CREB induced by bath application of NMDA in brain slices or hindpaw injection of 5% formalin in rats, and these might be fundamental molecular mechanisms underlying pain affect.

The binding of quercetin to lysozyme (LYSO) in aqueous solution was investigated by fluorescence spectroscopy, UV-vis absorption spectroscopy and molecular simulation at pH 7.4. The fluorescence quenching of LYSO by addition of quercetin is due to static quenching, the binding constants, K ( a ), were 3.63 × 10(4), 3.31 × 10(4) and 2.85 × 10(4) L·mol(-1) at 288, 298 and 308 K, respectively. The thermodynamic parameters, enthalpy change, ∆H, and entropy change, ∆S, were noted to be -7.56 kJ·mol(-1) and 61.07 J·mol(-1)·K(-1). The results indicated that hydrophobic interaction may play a major role in the binding process. The distance r between the donor (LYSO) and acceptor (quercetin) was determined as 3.34 nm by the fluorescence resonance energy transfer. The synchronous fluorescence spectroscopy showed the polarity around the tryptophan residues increased and the hydrophobicity decreased. Furthermore, the study of molecular simulation indicated that quercetin could bind to the active site (a pocket made up of 24 amino-acid residues) of LYSO mainly via hydrophobic interactions and that there were hydrogen interactions between the residues (Gln 57, Ile 98) of LYSO and quercetin. The accessible surface area (ASA) calculation verified the important roles of tryptophan (Trp) residues during the binding process.

This study was designed to examine the interaction of 4'-O-(a-L-Cladinosyl) daunorubicin (DNR-D5), a disaccharide anthracycline with calf thymus deoxyribonucleic acid (ctDNA) by UV/Vis in combination with fluorescence spectroscopy and molecular modeling techniques under physiological conditions (Britton-Robinson buffer solutions, pH = 7.4). By the analysis of UV/Vis spectrum, it was observed that upon binding to ctDNA the anthraquinone chromophore of DNR-D5 could slide into the base pairs. Moreover, the large binding constant indicated DNR-D5 had a high affinity with ctDNA. At the same time, fluorescence spectra suggested that the quenching mechanism of the interaction of DNR-D5 to ctDNA was a static quenching type. The binding constants between DNR-D5 and ctDNA were calculated based on fluorescence quenching data at different temperatures. The negative ∆G implied that the binding process was spontaneous, and negative ∆H and negative ΔS suggested that hydrogen bonding force most likely played a major role in the binding of DNR-D5 to ctDNA. Moreover, the results obtained from molecular docking corroborate the experimental results obtained from spectroscopic investigations.

The molecular mechanism of the binding of norfloxacin (NRF) to trypsin was investigated by fluorescence, synchronous fluorescence and UV-vis absorbance spectroscopy and molecular modeling at physiological conditions. The quenching mechanism and the binding mode were investigated in terms of the association constants and basic thermodynamic parameters. The results of spectroscopic measurements suggested that NRF have a strong ability to quench the intrinsic fluorescence of trypsin through static quenching procedure. Moreover, fluorescence experiments were also performed at different values of pH to elucidate the effect of pH on the binding. The NRF-trypsin complex was stabilized by hydrophobic forces and hydrogen bonding, via tryptophan residue as indicated from the thermodynamic parameters, which was consistent with the results of molecular docking and accessible surface area calculations., (Copyright 2009 Elsevier B.V. All rights reserved.)