Discrimination of internal crack for rice seeds using near infrared spectroscopy.

It is an important thing to identify internal crack in seeds from normal seeds for evaluating the quality of rice seeds (Oryza sativa L.). In this study, non-destructive discrimination of internal crack in rice seeds using near infrared spectroscopy and chemometrics is proposed. Principal component analysis (PCA) was used to analyze the rice seeds spectra. Four supervised classification techniques(partial least squares discriminate analysis (PLS-DA), support vector machines (SVM), k-nearest neighbors (KNN) and random forest (RF)) with four different pre-processing techniques (standard normal variate (SNV), multiplicative scatter correction (MSC), first and second derivative with Savitzky-Golay (SG) smoothing) were applied. The best results (Sn = 0.8824, Sp = 0.9429, Acc = 0.913) were achieved by PLS-DA with the raw spectral data. The performance of the best SVM model was inferior to that of PLS-DA, but superior to that of RF and KNN. Except for PLS-DA, four different preprocessing techniques were improved the performance of the developed models. The important variables for discriminating internal cracks in rice seeds were related to the amylose. Overall, the all results demonstrated the feasibility of non-destructive discrimination of internal crack for rice seeds (Oryza sativa L.) using near infrared spectroscopy and chemometrics.

TNFAIP1 promotes macrophage lipid accumulation and accelerates the development of atherosclerosis through the LEENE/FoxO1/ABCA1 pathway.

Macrophage lipid accumulation is a critical contributor to foam cell formation and atherosclerosis. Tumor necrosis factor-α-induced protein 1 (TNFAIP1) is closely associated with cardiovascular disease. However, its role and molecular mechanisms in atherogenesis remain unclear. TNFAIP1 was knocked down in THP-1 macrophage-derived foam cells and apolipoprotein-deficient (apoE-/-) mice using lentiviral vector. The expression of lncRNA enhancing endothelial nitric oxide synthase expression (LEENE), Forkhead box O1 (FoxO1) and ATP binding cassette transporter A1 (ABCA1) was evaluated by qRT-PCR and/or western blot. Lipid accumulation in macrophage was assessed by high-performance liquid chromatography and Oil red O staining. RNA immunoprecipitation and RNA pull-down assay were performed to verify the interaction between LEENE and FoxO1 protein. Atherosclerotic lesions were analyzed using HE, Oil red O and Masson staining. Our results showed that TNFAIP1 was significantly increased in THP-1 macrophages loaded with oxidized low-density lipoprotein. Knockdown of TNFAIP1 enhanced LEENE expression, promoted the direct interaction of LEENE with FoxO1 protein, stimulated FoxO1 protein degradation through the proteasome pathway, induced ABCA1 transcription, and finally suppressed lipid accumulation in THP-1 macrophage-derived foam cells. TNFAIP1 knockdown also up-regulated ABCA1 expression, improved plasma lipid profiles, enhanced the efficiency of reverse cholesterol transport and attenuated lesion area in apoE-/- mice. Taken together, these results provide the first direct evidence that TNFAIP1 aggravates atherosclerosis by promoting macrophage lipid accumulation via the LEENE/FoxO1/ABCA1 signaling pathway. TNFAIP1 may represent a promising therapeutic target for atherosclerotic cardiovascular disease.

TSAF-Net: a rotated two-stage Cnaphalocrocis medinalis damage detection method based on anchor-free arbitrary-oriented proposal network.

BACKGROUND: Cnaphalocrocis medinalis (C.medinalis) is an agricultural pest with recurrent outbreaks. The investigation into automated pest and disease detection technology holds significant value for in-field surveys. Current generic detection methods are inadequate due to arbitrary orientations and a wide range of aspect ratios in damage symptoms. To tackle these issues, we put forward a rotated two-stage detection method for in-field C.medinalis surveys. This method relies on an anchor-free rotated region proposal network (AF-R2PN), bypassing the need for hyper-parameter optimization induced by predefined anchor boxes. An in-field C.medinalis dataset is constructed during on-site pest surveys to validate the effectiveness of our method. RESULTS: The experimental results show that our method can accomplish 80% average precision (AP), surpassing the corresponding horizontal detector by 2.3%. The visualization results of our work showcase its exceptional localization capability over generic detection methods, facilitating inspection by plant protectors. Meanwhile, our proposed method outperforms other state-of-the-art rotated detection algorithms. The AF-R2PN module can generate superior arbitrary-oriented proposals even with a decreased number of proposals, balancing inference speed and detection performance among other rotated two-stage methods. CONCLUSION: The proposed method exhibits superiority in detecting C. medinalis damage under complex field conditions. It provides greater practical applicability during in-field surveys, enhancing their efficiency and coverage. The findings hold significance for pest and disease monitoring, providing important technical support for agricultural production. © 2024 Society of Chemical Industry.

A lava-inspired proteolytic enzyme therapy on cancer with a PEG-based hydrogel enhances tumor distribution and penetration of liposomes.

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.

Underwater image enhancement using Divide-and-Conquer network.

Underwater image enhancement has become the requirement for more people to have a better visual experience or to extract information. However, underwater images often suffer from the mixture of color distortion and blurred quality degradation due to the external environment (light attenuation, background noise and the type of water). To solve the above problem, we design a Divide-and-Conquer network (DC-net) for enhancing underwater image, which mainly consists of a texture network, a color network and a refinement network. Specifically, the multi-axis attention block is presented in the texture network, which combine different region/channel features into a single stream structure. And the color network employs an adaptive 3D look-up table method to obtain the color enhanced results. Meanwhile, the refinement network is presented to focus on image features of ground truth. Compared to state-of-the-art (SOTA) underwater image enhance methods, our proposed method can obtain the better visual quality of underwater images and better qualitative and quantitative performance. The code is publicly available at https://github.com/zhengshijian1993/DC-Net.

Analysis of pollutant dispersion patterns in rivers under different rainfall based on an integrated water-land model.

In the context of rapid urban expansion, the interaction between humanity and nature has become more prominent. Urban land and rivers often exist as distinct entities with limited material exchange. However, during rainfall, these two systems interconnect, resulting in the transfer of land-derived pollutants into rivers. Such transfer significantly increases river pollutant levels, adversely affecting water quality. Therefore, developing a water quality simulation and prediction model is crucial. This model should effectively illustrate pollutant movement and dispersion during rain events. This study proposes a comprehensive model that merges the Storm Water Management Model (SWMM) with the Environmental Fluid Dynamics Code (EFDC). This integrated model assesses the spread and dispersion of pollutants, including Ammonia Nitrogen (NH3-N), Total Phosphorus (TP), Total Nitrogen (TN), and Chemical Oxygen Demand (COD), within urban water cycles for various rainfall conditions, thus offering critical theoretical support for managing the water environment. The application of this model under different rainfall intensities (light, moderate and heavy) provides vital insights. During light rainfall, the river's natural purification process can sustain surface water quality at Class IV. Moderate rainfall causes accumulation of pollutants, reducing water quality to Class V. Conversely, heavy rainfall rapidly increases pollutant concentrations due to higher inflow, pushing the river to a degraded Class V status, which is beyond its natural purification capacity, necessitating engineering solutions to reattain Class IV quality. Furthermore, pollutant accumulation in downstream river sections is more influenced by flow rate than by rainfall intensity. In summary, the SWMM-EFDC integrated model proves highly effective in predicting river water quality, thereby significantly aiding urban water pollution control.

Wearable biosensors for human sweat glucose detection based on carbon black nanoparticles.

Wearable glucose biosensors enable noninvasive glucose monitoring, thereby enhancing blood glucose management. In this work, we present a wearable biosensor based on carbon black nanoparticles (CBNPs) for glucose detection in human sweat. The biosensor consists of CBNPs, Prussian blue (PB), glucose oxidase, chitosan, and Nafion. The fabricated biosensor has a linear range of 5 µM to 1250 µM, sensitivity of 14.64 µA mM-1 cm-2, and a low detection potential (-0.05 V, vs. Ag/AgCl). The detection limit for glucose was calculated as 4.83 µM. This reusable biosensor has good selectivity and stability and exhibits a good response to glucose in real sweat. These results demonstrate the potential of our CBNP-based biosensor for monitoring blood glucose in human sweat.

COVID-19 cooling: Nanostrategies targeting cytokine storm for controlling severe and critical symptoms.

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to wreak havoc worldwide, the "Cytokine Storm" (CS, also known as the inflammatory storm) or Cytokine Release Syndrome has reemerged in the public consciousness. CS is a significant contributor to the deterioration of infected individuals. Therefore, CS control is of great significance for the treatment of critically ill patients and the reduction of mortality rates. With the occurrence of variants, concerns regarding the efficacy of vaccines and antiviral drugs with a broad spectrum have grown. We should make an effort to modernize treatment strategies to address the challenges posed by mutations. Thus, in addition to the requirement for additional clinical data to monitor the long-term effects of vaccines and broad-spectrum antiviral drugs, we can use CS as an entry point and therapeutic target to alleviate the severity of the disease in patients. To effectively combat the mutation, new technologies for neutralizing or controlling CS must be developed. In recent years, nanotechnology has been widely applied in the biomedical field, opening up a plethora of opportunities for CS. Here, we put forward the view of cytokine storm as a therapeutic target can be used to treat critically ill patients by expounding the relationship between coronavirus disease 2019 (COVID-19) and CS and the mechanisms associated with CS. We pay special attention to the representative strategies of nanomaterials in current neutral and CS research, as well as their potential chemical design and principles. We hope that the nanostrategies described in this review provide attractive treatment options for severe and critical COVID-19 caused by CS.

MGDUN: An interpretable network for multi-contrast MRI image super-resolution reconstruction.

Magnetic resonance imaging (MRI) Super-Resolution (SR) aims to obtain high resolution (HR) images with more detailed information for precise diagnosis and quantitative image analysis. Deep unfolding networks outperform general MRI SR reconstruction methods by providing better performance and improved interpretability, which enhance the trustworthiness required in clinical practice. Additionally, current SR reconstruction techniques often rely on a single contrast or a simple multi-contrast fusion mechanism, ignoring the complex relationships between different contrasts. To address these issues, in this paper, we propose a Model-Guided multi-contrast interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction, which explicitly incorporates the well-studied multi-contrast MRI observation model into an unfolding iterative network. Specifically, we manually design an objective function for MGDUN that can be iteratively computed by the half-quadratic splitting algorithm. The iterative MGDUN algorithm is unfolded into a special model-guided deep unfolding network that explicitly takes into account both the multi-contrast relationship matrix and the MRI observation matrix during the end-to-end optimization process. Extensive experimental results on the multi-contrast IXI dataset and the BraTs 2019 dataset demonstrate the superiority of our proposed model, with PSNR reaching 37.3366 and 35.9690 respectively. Our proposed MGDUN provides a promising solution for multi-contrast MR image super-resolution reconstruction. Code is available at https://github.com/yggame/MGDUN.

Feature Refinement Method Based on the Two-Stage Detection Framework for Similar Pest Detection in the Field.

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.

A normalized differential sequence feature encoding method based on amino acid sequences.

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.

Interplay between G protein-coupled receptors and nanotechnology.

G protein-coupled receptors (GPCRs), as the largest family of membrane receptors, actively modulate plasma membrane and endosomal signalling. Importantly, GPCRs are naturally nanosized, and spontaneously formed nanoaggregates of GPCRs (natural nano-GPCRs) may enhance GPCR-related signalling and functions. Although GPCRs are the molecular targets of the majority of marketed drugs, the poor pharmacokinetics and physicochemical properties of GPCR ligands greatly limit their clinical applicability. Nanotechnology, as versatile techniques, can encapsulate GPCR ligands to assemble synthetic nano-GPCRs to overcome their obstacles, robustly elevating drug efficacy and safety. Moreover, endosomal delivery of GPCR ligands by nanoparticles can precisely initiate sustained endosomal signal transduction, while nanotechnology has been widely utilized for isolation, diagnosis, and detection of GPCRs. In turn, due to overexpression of GPCRs on the surface of various types of cells, GPCR ligands can endow nanoparticles with active targeting capacity for specific cells via ligand-receptor binding and mediate receptor-dependent endocytosis of nanoparticles. This significantly enhances the potency of nanoparticle delivery systems. Therefore, emerging evidence has revealed the interplay between GPCRs and nanoparticles, although investigations into their relationship have been inadequate. This review aims to summarize the interaction between GPCRs and nanotechnology for understanding their mutual influences and utilizing their interplay for biomedical applications. It will provide a fundamental platform for developing powerful and safe GPCR-targeted drugs and nanoparticle systems. STATEMENT OF SIGNIFICANCE: GPCRs as molecular targets for the majority of marketed drugs are naturally nanosized, and even spontaneously form nano aggregations (nano-GPCRs). Nanotechnology has also been applied to construct synthetic nano-GPCRs or detect GPCRs, while endosomal delivery of GPCR ligands by nanoparticles can magnify endosomal signalling. Meanwhile, molecular engineering of nanoparticles with GPCRs or their ligands can modulate membrane binding and endocytosis, powerfully improving the efficacy of nanoparticle system. However, there are rare summaries on the interaction between GPCRs and nanoparticles. This review will not only provide a versatile platform for utilizing nanoparticles to modulate or detect GPCRs, but also facilitate better understanding of the designated value of GPCRs for molecular engineering of biomaterials with GPCRs in therapeutical application.

CMRD-Net: a deep learning-based Cnaphalocrocis medinalis damage symptom rotated detection framework for in-field survey.

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.

PLGA nanoparticles loaded with curcumin produced luminescence for cell bioimaging.

To revise the emission of curcumin (Cur) from "off" to "on", poly (D, L-lactide-co-glycolide) acid (PLGA) nanoparticles loaded with Cur were embedded in a polyvinyl alcohol (PVA) emulsifier (named Cur@PLGA-NPs). First, the emission intensities of different nanoformulations, including liposomes, bovine serum albumin (BSA) nanoparticles, and PLGA nanoparticles, were examined to discover the most effective carriers for Cur luminescence. As a result, Cur@PLGA-NPs exhibited the highest fluorescence intensity due to aggregation-induced emission (AIE), with quantum yields of 23.78% in aqueous solution and 21.52% in the solid state. According to X-ray diffraction (XRD) data, Cur@PLGA-NPs existed in the amorphous state, with a size of 217.2 ± 5.2 nm, an encapsulation efficiency (EE) of 69.98%, and a drug loading efficiency (LE) of 1.37%. The intramolecular interactions, which included hydrophobic interactions, electrostatic interactions, π-π interactions and solvatochromic effects, stabilized the chromophore cluster of Cur@PLGA-NPs in terms of nanoparticle formulation. Compared with free Cur, Cur@PLGA-NPs sensitized CT26 cells more efficiently with an IC50 value of 16.9 µmol/L and an apoptotic rate of 17.20% at 10 µmol/L Cur. Because of the robust fluorescence emission based on AIE, Cur@PLGA-NPs were utilized as a nano-AIE probe for cell bioimaging, and many red fluorescent signals were observed in CT26 cells after treatment. These results suggest that Cur@PLGA-NPs provide a novel amorphous AIE formulation with imaging and bioactive capabilities.

Postsurgical wound management and prevention of triple-negative breast cancer recurrence with a pryoptosis-inducing, photopolymerizable hydrogel.

Surgical removal remains the predominant treatment strategy for triple-negative breast cancer (TNBC). However, risks that include high locoregional recurrence and remote metastasis threaten patient survival and quality of life after surgery. In this study, a hydrogel based on poly (ethylene glycol) dimethacrylate and sericin methacryloyl was fabricated by photopolymerization to fill the resection cavity and prevent recurrence. The obtained hydrogel exhibited mechanical properties compatible with breast tissue and facilitated postsurgical wound management by promoting tissue regeneration. The DNA methylation inhibitor decitabine (DEC) and poly (lactic-co-glycolic acid)-encapsulated phytochemical gambogic acid (GA) were loaded into the hydrogel. The as-prepared hydrogel promoted fast release of DEC and sustained release of GA, leading to gasdermin E-mediated tumor cell pyroptosis and activating antitumor immune responses. Inducing postsurgical tumor cell pyroptosis inhibited local tumor recurrence and lung metastasis. While the dual-drug-loaded hydrogel system cured less than half of tumor-bearing mice, the cured mice survived for over half a year. These findings indicate that our hydrogel system is an excellent biocompatible platform for postsurgical TNBC therapy.

Nucleic acid nanoassembly-enhanced RNA therapeutics and diagnosis.

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.

Class-attention-based lesion proposal convolutional neural network for strawberry diseases identification.

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.