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The traditional design strategy for constructing highly bright solid‐state luminescent materials relies on incorporating aggregation‐induced emission (AIE) scaffolds, molecular rotors, or bulky substituents to prevent close cofacial packing, which limits the strategy diversity in developing new materials. Herein, a strategy of tuning molecular packing by twisting molecular structure of conventional aggregation‐caused quenching fluorophore is proposed to endow materials with AIE effect and enhance the solid‐state fluorescence. Accordingly, a series of 1,4‐bis(diphenylamino)‐2,5‐disubstituted benzene fluorophores exhibiting AIE characteristics, high solid‐state fluorescence efficiency (up to 0.99), wide emission color tunability, and good near‐infrared (NIR) two‐photon absorption is facilely developed. All these luminogens can specifically stain intracellular lipid droplets with a high signal‐to‐noise ratio, high biocompatibility, and good photostability. Besides, the luminogens exhibit effective reactive oxygen species (ROS) generation capability upon low‐power white light irradiation. Among them, the AIE luminogen, named BDBDC, integrating the NIR emission, good NIR two‐photon absorption and strongest ROS generation demonstrates superior performances in two‐photon fluorescence imaging of various tissues and photodynamic cancer therapy. This molecular design philosophy provides a new way of designing highly bright solid‐state fluorophores for practical applications. [ABSTRACT FROM AUTHOR]

Accessing high-order multiphoton excited fluorescence (H-MPEF) materials is challenging yet and needs complicated synthesis procedures. In this study, we successfully assembled plasmonic Au nanorods (Au NRs) with multiphoton responsive metal-organic frameworks (MOFs), resulting in a significant several-fold enhancement of H-MPEF. The extent of multiphoton enhancement was found to be strongly dependent on the degree of overlap between the multiphoton excitation wavelength of MOFs and the localized surface plasmon resonance absorbance of Au NRs, indicating the importance of plasmon-induced resonance energy transfer. Besides, plasmon-induced hot electron transfer played a vital role in enhanced multiphoton activity as well. Notably, the optimum H-MPEF enhancement occurs at the second near-infrared (NIR-II) region, which provides a promising platform for fluorescent bioimaging. Our findings provide a feasible and practical method to fabricate optimized H-MPEF materials for biological imaging using tissue-penetrating NIR-II light. [ABSTRACT FROM AUTHOR]

Commercial photosensitizers (PSs) present great potential in the field of clinical photodynamic therapy (PDT) yet face a formidable challenge owing to the extremely short lifespan of singlet oxygen (1O2). Herein, the 1O2 slow‐release "platform" is proposed based on an anthracene‐groups functionalized D‐π‐A system (AIC), which can capture and slowly release 1O2 produced by commercial PSs. Meanwhile, the reversible chemical process between AIC and 1O2 is demonstrated by a series of optical experiments and high‐resolution mass spectrum. Remarkably, by virtue of 1O2 slow‐release "platform", the sustaining mitochondrial autophagy and cell apoptosis still take place when the laser is cut off with a short period of irradiation, reducing the side effect of sustained irradiation. In general, the construction of 1O2 slow‐release platform opens up exciting opportunities for improving the PDT effect of commercial PSs. [ABSTRACT FROM AUTHOR]

Consumer trust in fruit industry is partly depended on the labeling of its geographical origin. The accurate label is not only of great significance to consumers and retailers, but also for their brands shaping. This work evaluated the utility of Fourier transform near-infrared spectroscopy (FT-NIRS) in identifying the geographical origins of Chinese pears including Laiyang pear (Pyrus bretschneideri cv. Laiyang), Dangshan pear (Pyrus bretschneideri Rehd. cv. Dangshansuli), and Korla pear (Pyrus sinkiangensis T. T. Yu). FT-NIR spectra of 316 pears from three different geographical origins were obtained in the spectral range of 10,000–4000 cm−1. Preliminary investigation was first conducted based on principal component analysis (PCA) to confirm the similar spectral features of samples within one class, and the results showed that PC1 and PC2 were effective for the identification. Partial least square discriminant analysis (PLS-DA) models based on full spectra indicated that the optimal classification based on second-order derivative (Der2) preprocessed spectra achieved 100% correct classification rates (CCRs) in all datasets. After that, the multivariate selection strategies including two-dimensional correlation spectroscopy (2D-COS), competitive adaptive reweighted sampling (CARS) combined with successive projection algorithm (SPA), and uninformative variable elimination (UVE) combined with SPA were individually optimized for selecting the characteristic wavenumbers from full FT-NIR spectra. On the basis of simplified PLS-DA models establishment, the CCRs in calibration, cross-validation, and prediction sets of the selected CARS-SPA-PLS-DA model achieved 98.58%, 98.11%, and 98.08%, respectively. This research demonstrated that FT-NIRS combined with multivariate analysis can be successfully employed to identify the geographical origins of Chinese pears. [ABSTRACT FROM AUTHOR]

In order to detect and evaluate the moisture content of chestnuts with shells non-destructively and rapidly, the quantitative prediction methods based on visible/near-infrared, and mid- and short-wave near-infrared spectroscopy techniques were explored and compared. First, different preprocessings were applied to full spectra in wavebands of I and II to establish PLSR models. Results show that SNV preprocessed spectra in waveband I and normalization preprocessed spectra in waveband II were individually optimized. The Rp and RMSEP in prediction set achieved 0.882 and 0.856, and 1.389% and 1.665%, respectively. In further steps, iPLSR, BiPLSR and SiPLSR modeling methods based on full spectra with the optimal preprocessing methods were also employed and compared. The models' performance indicates that waveband I is generally superior to waveband II, and the iPLSR model performed best with Rp of 0.910 and RMSEP of 1.180%. In this study, the optimal waveband, preprocessing method, and modeling method for detecting the moisture content of chestnuts with shells using near-infrared spectroscopy were confirmed. These results provide important technical and methodological support for high-throughput online rapid detection of chestnut moisture content in the future. [ABSTRACT FROM AUTHOR]

Tea cultivation holds significant economic value, yet the leaves of tea plants are frequently susceptible to various pest and disease infestations. Consequently, there is a critical need for research focused on precisely and efficiently detecting these threats to tea crops. The investigation of a model capable of effectively identifying pests and diseases in tea plants is often hindered by challenges, such as limited datasets of pest and disease samples and the small size of detection targets. To address these issues, this study has chosen TLB, a common pest and disease in tea plants, as the primary research subject. The approach involves the application of transfer learning in conjunction with data augmentation as a fundamental methodology. This technique entails transferring knowledge acquired from a comprehensive source data domain to the model, aiming to mitigate the constraints of limited sample sizes. Additionally, to tackle the challenge of detecting small targets, this study incorporates the decoupling detection head TSCODE and integrates the Triplet Attention mechanism into the E-ELAN structure within the backbone to enhance the model's focus on the TLB's small targets and optimize detection accuracy. Furthermore, the model's loss function is optimized based on the Wasserstein distance measure to mitigate issues related to sensitivity in localizing small targets. Experimental results demonstrate that, in comparison to the conventional YOLOv7 tiny model, the proposed model exhibits superior performance on the TLB small sample dataset, with precision increasing by 6.5% to 92.2%, recall by 4.5% to 86.6%, and average precision by 5.8% to 91.5%. This research offers an effective solution for identifying tea pests and diseases, presenting a novel approach to developing a model for detecting such threats in tea cultivation. [ABSTRACT FROM AUTHOR]

Bamboo is one of the green building materials that have been used for centuries. Combining with metal materials will endow bamboo with new functions, such as weather resistance, anti-corrosion, conductivity, and electromagnetic shielding. However, there is a natural barrier between bamboo and metal materials. This paper proposes fabrication of novel bamboo-based metal composites (BMC) which are composed of bamboo substrate and metal coating, without any adhesive, using an efficient and sustainable arc thermal spraying technique. In this method, the metal wire is melted and deposited on the bamboo substrate through a high-temperature heat source. In the feasibility experiment, arc spraying using aluminum as the wire electrode was selected. Scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) were used to observe and test the fabricated specimens. It was demonstrated that when the spraying voltage was 40 V, the deposition rate was as high as 5.8 g/min with the average thickness of the metal coating exceeding 400 μm. The surface of the BMC aluminum coating was flat, continuous, and compact with an average roughness of about Ra 3.0 μm. Based on experimental results, the integrity of bamboo substrate in thermal spraying was discussed. Results from SEM–EDS test showed that there are crack areas and adhesion areas between bamboo and metal coatings, and the highest bonding strength exhibited over 1.0 MPa. This work provides a new practice of fabricating novel BMC through a green manufacturing method with high efficiency. The findings of this study may be useful in understanding the preparation of BMC and can help find their suitability for a wide range of applications. [ABSTRACT FROM AUTHOR]

The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model's learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model's ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities. [ABSTRACT FROM AUTHOR]

As a key factor in determining the optimal harvest period of Camellia oleifera fruit maturity is significant in increasing camellia oil yield. Due to the interference of environmental factors, capturing hyperspectral images of Camellia oleifera fruit on-site often suffers from a low spectral signal-to-noise ratio. In this study, the multi-head attention parallel residual block-cycle generative adversarial network (MAPRB-CycleGAN) model was used to reconstruct on-site spectral data of Camellia oleifera fruit. Meanwhile, the characteristics and quality of the reconstructed spectra were analyzed through subjective visual inspection of the curve effect and the principal component analysis (PCA) algorithm. Then, the modeling effect of the reconstructed on-site spectral data after being reconstructed by wavelet transform, PCA, and kernel principal component analysis was compared. After the conventional pretreatment method in chemometrics, the partial least squares discriminant analysis (PLS-DA) model established with the field original spectra of Camellia oleifera fruit achieved the highest classification accuracy of 93.82%. The maturity classification model of Camellia oleifera fruit established by the PLS-DA method using the reconstructed spectra from this paper achieved 99.54% accuracy, and the classification accuracy of this data set in multiple models was higher than that of other reconstructed data sets. The results indicate that hyperspectral imaging technique combined with MAPRB-CycleGAN can realize high-accuracy maturity classification of Camellia oleifera fruit, and the spectral reconstruction can be exploited as a preprocessing method for on-site spectral data collection, which provides important technical support for obtaining high-quality spectral data of Camellia oleifera fruit in field environments. The randomness and unpredictability of outdoor environmental factors lead to the diversity in data collection, and enhancing the robustness of generative models to cope with environmental influences is the most significant challenge in practical applications. It will also be the focal point of our future research. [ABSTRACT FROM AUTHOR]

With the intensification of global climate change and the frequent occurrence of forest fires, the development of efficient and precise forest fire monitoring and image segmentation technologies has become increasingly important. In dealing with challenges such as the irregular shapes, sizes, and blurred boundaries of flames and smoke, traditional convolutional neural networks (CNNs) face limitations in forest fire image segmentation, including flame edge recognition, class imbalance issues, and adapting to complex scenarios. This study aims to enhance the accuracy and efficiency of flame recognition in forest fire images by introducing a backbone network based on the Swin Transformer and combined with an adaptive multi-scale attention mechanism and focal loss function. By utilizing a rich and diverse pre-training dataset, our model can more effectively capture and understand key features of forest fire images. Through experimentation, our model achieved an intersection over union (IoU) of 86.73% and a precision of 91.23%. This indicates that the performance of our proposed wildfire segmentation model has been effectively enhanced. A series of ablation experiments validate the importance of these technological improvements in enhancing model performance. The results show that our approach achieves significant performance improvements in forest fire image segmentation tasks compared to traditional models. The Swin Transformer provides more refined feature extraction capabilities, the adaptive multi-scale attention mechanism helps the model focus better on key areas, and the focal loss function effectively addresses the issue of class imbalance. These innovations make the model more precise and robust in handling forest fire image segmentation tasks, providing strong technical support for future forest fire monitoring and prevention. [ABSTRACT FROM AUTHOR]

The timely and effective detection of forest fires is crucial for environmental and socio-economic protection. Existing deep learning models struggle to balance accuracy and a lightweight design. We introduce SWVR, a new lightweight deep learning algorithm. Utilizing the Reparameterization Vision Transformer (RepViT) and Simple Parameter-Free Attention Module (SimAM), SWVR efficiently extracts fire-related features with reduced computational complexity. It features a bi-directional fusion network combining top-down and bottom-up approaches, incorporates lightweight Ghost Shuffle Convolution (GSConv), and uses the Wise Intersection over Union (WIoU) loss function. SWVR achieves 79.6% accuracy in detecting forest fires, which is a 5.9% improvement over the baseline, and operates at 42.7 frames per second. It also reduces the model parameters by 11.8% and the computational cost by 36.5%. Our results demonstrate SWVR's effectiveness in achieving high accuracy with fewer computational resources, offering practical value for forest fire detection. [ABSTRACT FROM AUTHOR]

An accurate simulation model is crucial for the analysis of the correct modal information of the ginkgo seed–stem system (ginkgo subsystem). This underpins the provision of technical rationale for efficient and low-damage precision vibrational harvesting operations in ginkgo cultivation. In this study, based on the modal parameters of the ginkgo subsystem, a finite element model updating method is proposed to correct the elastic modulus of the stem with the natural frequency of the first bending mode. The large difference in the modal results calculated before and after model updating reveals that model updating is a critical step in the finite element analysis of crop subsystems. Then, an uncertainty parameter modeling method is proposed to investigate the modal variability of the ginkgo subsystem by finite element analysis. The results show that the stem length is a key parameter affecting the variability of natural frequencies, and the seed weight is a minor parameter. The variability of the ginkgo seed's gravity center offset has a negligible effect on the natural frequencies of the system. The first natural frequency of the ginkgo subsystem can be utilized for vibrational harvesting. In addition, since the difference between the upper and lower limits of the first natural frequency of the ginkgo subsystem does not exceed 1 Hz, a specific excitation frequency can cause most ginkgo subsystems to resonate. This result facilitates the determination of precise excitation frequencies for efficient and low-damage ginkgo vibrational harvesting, ensuring both economic and ecological benefits in the management of ginkgo plantations. [ABSTRACT FROM AUTHOR]

To reduce the cutting force and cutting energy consumption during the operation of tree-climbing pruning machines for poplar trees, a linear cutting test bench device for branches was designed according to the growth characteristics of poplar branches and pruning forestry requirements in this study. Firstly, the cutting mechanical analysis of poplar branches was carried out to explore the significance parameters affecting the cutting force, and then the cutting performance test and parameter optimization of poplar branches was carried out through the response surface method (RSM). The test results indicated that cutting speed, tool edge angle and tool back angle had significant effects on the ultimate shear stress, cutting power consumption per unit area, and the branch damage rate of poplar branches, and the established regression equation demonstrated high goodness of fit. Meanwhile, a second-order regression mathematical model was developed between ultimate shear stress, cutting the power consumption per unit area of the cut and the branch damage rate, and the significance parameter. The non-dominated Sorting Genetic Algorithm II (NSGA-II) was used for multi-objective optimization computation to obtain the optimal combination of cutting parameters as 3.02 m/s for cutting speed, 15° for tool edge angle, and 3° for tool back angle. In this case, the ultimate shear stress, cutting power consumption per unit area, and branch damage rate of poplar branches were small, which were 346.63 kPa, 9.35 mJ/mm2, and 12.36%, respectively. Through the test verification, it can be seen that the relative error between the verification test and the predicted value of model was less than 7%. Moreover, under a cutting tool edge angle of 15°, the ultimate shear stress, cutting power consumption per unit area, and branch damage rate were, respectively, reduced by 17.29%, 14.98%, and 34.21% compared with those under a cutting tool edge angle of 20°, which verifies the validity and reliability of the test results and the research method. This study can provide data support and reference for the research and development of energy-efficient poplar tree-climbing pruning equipment and related branch-cutting performance tests. [ABSTRACT FROM AUTHOR]

Spin-dependent nonlinear harmonic generations from nanostructures may provide a new class of design for future applications such as nonlinear optical switching and modulation and nonlinear information processing. Most reported works are based on the utilization of metasurfaces with specific symmetry (such as C3 and C4) and the application of the Pancharatnam–Berry phase approach, making them sensitive to local defects. In this work, we investigated a controllable second harmonic generation (SHG) based on the topological spin-dependent edge state, which is robust to a broad class of perturbations. Simulation results demonstrated that spin-dependent edge states with strong field localization can be obtained through spin–orbit coupling, resulting in SHG with increased efficiency. In addition, the propagation direction of the enhanced SHG could be dynamically controlled using an excitation source with left/right circular polarizations, exhibiting negligible backscattering and topological protection against local defects. The proposed approach may open a new avenue for manipulating light–light interactions. [ABSTRACT FROM AUTHOR]

Engineering high‐order multiphoton excited fluorescent (H‐MPEF) materials is of significant importance for sensing, 3D optical data storage and bioimaging. However, it remains a challenging endeavor due to a lack of appropriate construction strategies. This study demonstrates that incorporating octupolar modules within metal‐organic framework (MOF) offers new possibilities for the design of highly attractive H‐MPEF materials. Constructed from a typical MOF (UiO(bpdc)), the in situ formation of multibranched octupolar cyclometallated iridium(III) modules via post‐ligand coordination modification endowed the framework with enlarged static hyperpolarizabilities, an extended conjugated system, and enhanced charge transfer, ultimately unlocking its second near‐infrared (NIR‐II, 1000–1700 nm) light activated H‐MPEF performance including three‐ and four‐photon activity. Moreover, these exciting features, combined with subsequent orotic acid‐capping, enabled its application in cancer cell‐specific targeting oncotherapy using tissue‐penetrating NIR‐II light. This finding highlights the vital role of octupoles in H‐MPEF performance and sets a benchmark for unlocking the multiphoton activity of MOFs at the molecular level for deep‐seated tumor fluorescence imaging and therapy. [ABSTRACT FROM AUTHOR]

Multivariate statistical analysis approaches are extensively employed in process monitoring because they can effectively detect abnormal conditions in industrial processes. However, both Gaussian and non-Gaussian variables are often present in industrial processes. A single multivariate statistical process monitoring method often has difficulty simultaneously dealing with variable information of mixed distribution characteristics. This paper proposes a multivariate quality-related process monitoring method based on a Bayesian classifier to address this issue. The proposed method separates the variables into Gaussian and non-Gaussian parts using a Jarque–Bera test. Then, Gaussian and non-Gaussian properties are extracted through modified kernel partial least squares and kernel independent component analysis. After feature extraction, a Bayesian-based classifier relevance vector machine is constructed to monitor quality-related information of the process, which avoids the construction of a threshold in conventional methods and offsets the drawbacks of insufficient single statistic information. A numerical simulation and the Tennessee-Eastman process verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]

Research into vinyl-linked covalent organic frameworks (COFs) has grown significantly in recent years due to various attractive properties. Herein, we design and synthesize two highly crystalline and stable 2,4,6-collidine-derived vinylene-linked 2D COFs. Both COFs can act as efficient photocatalysts to facilitate visible-light-driven aerobic oxidation. The TM-TBT-COF was observed to exhibit superior activity and recyclability owing to its excellent semiconducting properties. [ABSTRACT FROM AUTHOR]

The long non‐coding RNA (lncRNA)–microRNA (miRNA) interaction network plays a crucial part in the pathogenesis of nasopharyngeal carcinoma (NPC). Here, we discovered a relationship between LINC01376 and miR‐4757 in NPC tumor development. First, LINC01376 was abnormally overexpressed in NPC tissues and cells, and its elevated expression was associated with advanced clinical stage and shorter distant metastasis‐free survival time. Moreover, biological experiments showed that LINC01376 facilitated the proliferative, invasive, and migratory abilities of NPC cells in vitro and in vivo. Mechanistically, bioinformatics and RT‐qPCR assays revealed that LINC01376 knockdown upregulated the expression level of downstream miR‐4757, including miR‐4757 primary transcript (pri‐miR‐4757) and mature miR‐4757. Furthermore, LINC01376 competitively sponged the transcription factor SP1 and reduced its enrichment in the upstream promoter region of miR‐4757 to repress miR‐4757 expression. Finally, insulin‐like growth factor 1(IGF1) was identified as the target of miR‐4757. Rescue experiments indicated that LINC01376 accelerated NPC cell proliferation, migration, and invasion through the miR‐4757‐5p/IGF1 axis. In conclusion, the SP1/miR‐4757/IGF1 axis, which is regulated by LINC01376 in NPC deterioration and metastasis, is expected to provide new insights into the molecular mechanism of NPC carcinogenesis. [ABSTRACT FROM AUTHOR]

Background: Automatic and precise jujube yield prediction is important for the management of orchards and the allocation of resources. Traditional yield prediction techniques are based on object detection, which predicts a box to achieve target statistics, but are often used in sparse target settings. Those techniques, however, are challenging to use in real-world situations with particularly dense jujubes. The box labeling is labor- and time-intensive, and the robustness of the system is adversely impacted by severe occlusions. Therefore, there is an urgent need to develop a robust method for predicting jujube yield based on images. But in addition to the extreme occlusions, it is also challenging due to varying scales, complex backgrounds, and illumination variations. Results: In this work, we developed a simple and effective feature enhancement guided network for yield estimation of high-density jujube. It has two key designs: Firstly, we proposed a novel label representation method based on uniform distribution, which provides a better characterization of object appearance compared to the Gaussian-kernel-based method. This new method is simpler to implement and has shown greater success. Secondly, we introduced a feature enhancement guided network for jujube counting, comprising three main components: backbone, density regression module, and feature enhancement module. The feature enhancement module plays a crucial role in perceiving the target of interest effectively and guiding the density regression module to make accurate predictions. Notably, our method takes advantage of this module to improve the overall performance of our network. To validate the effectiveness of our method, we conducted experiments on a collected dataset consisting of 692 images containing a total of 40,344 jujubes. The results demonstrate the high accuracy of our method in estimating the number of jujubes, with a mean absolute error (MAE) of 9.62 and a mean squared error (MSE) of 22.47. Importantly, our method outperforms other state-of-the-art methods by a significant margin, highlighting its superiority in jujube yield estimation. Conclusions: The proposed method provides an efficient image-based technique for predicting the yield of jujubes. The study will advance the application of artificial intelligence for high-density target recognition in agriculture and forestry. By leveraging this technique, we aim to enhance the level of planting automation and optimize resource allocation. [ABSTRACT FROM AUTHOR]

Organic luminogens (OLs) that emit strong fluorescence in both solution and the aggregated state, referred to as dual-state emission luminogens (DSEgens), are highly desirable because of their capability to achieve multiple functions within onefold materials. The fluorescence of OLs, including DSEgens, with intramolecular charge transfer characteristics, often decreases in solution as the solvent polarity increases, namely the positive solvatokinetic effect, resulting in inferior environmental stability. In this work, fluorination to naphthalimide (NI)–cyanostilbene (CS) derivatives was adopted to construct novel DSEgens (NICSF-X, X = B, P, M, and T, respectively). Steady-state and transient spectroscopies were utilized to study their photophysical properties, evidencing their DSE properties with fluorescence quantum yields (ϕ) ∼0.2–0.4 in solution and ∼0.5–0.9 as solids. In particular, strong fluorescence emission in highly polar solvents i.e., ϕ up to ∼0.4–0.5 in ethanol, was sustained for NICSF-Xs, possibly assisted by hydrogen bonding (H-bonding) formation. Theoretical calculations and single-crystal structure analysis rationalized the intense photoluminescence (PL) emission of NICSF-Xs in the solid state. In addition, NICSF-Xs showed two-photon absorption (2PA) behaviors in dual states and were successfully applied for HepG2 cell imaging with one-photon and 2PA excitation, with lipid droplet targeting. Our study suggests that functionalization of molecules by fluorination to introduce H-bonding is a promising strategy to enhance the environmental stability of fluorescence in solution and realize strong PL emission in highly polar solvents, which could be favorable for bioimaging. [ABSTRACT FROM AUTHOR]

The objective of this study was to evaluate the performance of near-infrared spectroscopy (NIRS) systems operated in dual band for the non-destructive measurement of the fat, protein, collagen, ash, and Na contents of soy sauce stewed meat (SSSM). Spectra in the waveband ranges of 650–950 nm and 960–1660 nm were acquired from vacuum-packed ready-to-eat samples that were purchased from 97 different brands. Partial least squares regression (PLSR) was employed to develop models predicting the five critical quality parameters. The results showed the best predictions were for the fat (Rp = 0.808; RMSEP = 2.013 g/kg; RPD = 1.666) and protein (Rp = 0.863; RMSEP = 3.372 g/kg; RPD = 1.863) contents, while barely sufficient performances were found for the collagen (Rp = 0.524; RMSEP = 1.970 g/kg; RPD = 0.936), ash (Rp = 0.384; RMSEP = 0.524 g/kg; RPD = 0.953), and Na (Rp = 0.242; RMSEP = 2.097 g/kg; RPD = 1.042) contents of the SSSM. The quality of the content predicted by the spectrum of 960–1660 nm was generally better than that for the 650–950 nm range, which was retained in the further prediction of fat and protein. To simplify the models and make them practical, regression models were established using a few wavelengths selected by the random frog (RF) or regression coefficients (RCs) method. Consequently, ten wavelengths (1048 nm, 1051 nm, 1184 nm, 1191 nm, 1222 nm, 1225 nm, 1228 nm, 1450 nm, 1456 nm, 1510 nm) selected by RF and eight wavelengths (1019 nm, 1097 nm, 1160 nm, 1194 nm, 1245 nm, 1413 nm, 1441 nm, 1489 nm) selected by RCs were individually chosen for the fat and protein contents to build multi-spectral PLSR models. New models led to the best predictive ability of Rp, RMSEP, and RPD of 0.812 and 0.855, 1.930 g/kg and 3.367 g/kg, and 1.737 and 1.866, respectively. These two simplified models both yielded comparable performances to their corresponding full-spectra models, demonstrating the effectiveness of these selected variables. The overall results indicate that NIRS, especially in the spectral range of 960–1660 nm, is a potential tool in the rapid estimation of the fat and protein contents of SSSM, while not providing particularly good prediction statistics for collagen, ash, and Na contents. [ABSTRACT FROM AUTHOR]

Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constructs a three-stream pan-sharpening network based on the Swin transformer and a multi-scale feature extraction module. Unlike the traditional convolutional neural network (CNN) pan-sharpening model, we use the Swin transformer to establish global connections with the image and combine it with a multi-scale feature extraction module to extract local features of different sizes. The model combines the advantages of the Swin transformer and CNN, enabling fused images to maintain good local detail and global linkage by mitigating distortion in hyperspectral images. In order to verify the effectiveness of the method, this paper evaluates fused images with subjective visual and quantitative indicators. Experimental results show that the method proposed in this paper can better preserve the spatial and spectral information of images compared to the classical and latest models. [ABSTRACT FROM AUTHOR]

LiDAR technology has been widely used in forest survey and research, but the high-resolution point cloud data generated by LiDAR equipment also pose challenges in storage and computing. To address this problem, we propose a point cloud simplification method for trees, which considers both higher similarity to the original point cloud and the area of the tree point cloud. The method first determines the optimal search neighborhood using the standard deviation of FPFH information entropy. Based on FPFH information entropy and Poisson disc sampling theory, the point cloud is partitioned and sampled. By optimizing the separation thresholds of significant feature points and less significant feature points using a genetic algorithm with the Hausdorff distance and point cloud area as the objective function, the final simplified point cloud is obtained. Validation with two point cloud data sets shows that the proposed method achieves good retention of the area information of the original point cloud while ensuring point cloud quality. The research provides new approaches and techniques for processing large-scale forest LiDAR scan point clouds, reducing storage and computing requirements. This can improve the efficiency of forest surveys and monitoring. [ABSTRACT FROM AUTHOR]

Exploring vinylene-linked nanoporous covalent organic framework (COF) materials has become a valuable research focus due to their various outstanding properties. However, only a few examples have been reported to construct vinylene-linked COFs, which seriously restricted their applications. Herein, two COFs linked by olefins TC-PT and TC-PB via the Knoevenagel condensation of 1,3,5-tris-(4-cyanomethylphenyl) benzene and 2,4,6-tris (4-formylphenyl)-1,3,5-triazine or 1,3,5-tris (4-formylphenyl) benzene were designed and constructed, which possessed high crystallinity, remarkable porosity, and high structural, chemical, and thermal stability. The two nanoporous organic framework materials prepared can be used for catalytic organic reaction of phenylboric acid and its derivatives driven efficiently by visible light. It is noted that TC-PT with a donor–acceptor structure presented excellent activity, yield, and recyclability. We believed that this work would provide some precious insights for developing functional vinylene-linked COFs. [ABSTRACT FROM AUTHOR]

Tomato cultivation in China is concentrated in Xinjiang, Henan, and Shandong provinces, and the quality of tomatoes produced in different regions shows huge differences. Xinjiang tomatoes have extraordinary nutritional compositions and sensory qualities, and counterfeit Xinjiang tomato products are proliferating in the market. This study aimed to investigate the feasibility of identifying the geographical origin of Chinese tomatoes by using long-wave Fourier-Transform near-infrared spectroscopy (FT-NIR, 10,000–4000 cm−1). First, principal component analysis (PCA) was conducted on the raw spectra, and it was found that the first two PCs effectively identified the geographical origin of the tomatoes. Meanwhile, the results of partial least squares discriminant analysis (PLS-DA) combined with different preprocessing methods showed that the PLS-DA model based on the raw spectra achieved the best performance. The optimal PLS-DA model achieved the correct classification rate (CCR) of 97.8% in an external prediction set, and showed that raw spectra contained sufficient valid information. Besides, six algorithms, grid search (GS), genetic algorithm (GA), particle swarm algorithm (PSO), grey wolf algorithm (GWO), improved grey wolf algorithm (IGWO), and sparrow search algorithm (SSA) were employed to optimize the parameters of support vector machine (SVM). The SSA-SVM model exhibited the best performance with a CCR of 97.8% in the prediction set. Afterward, 40 and 8 spectral features were extracted from the raw full spectra using stacked autoencoder (SAE) and PC loading, respectively. Finally, the qualitative analysis model based on the feature variables was further investigated, and the SAE-SSA-SVM simplified model showed the best performance with a CCR of 98.7%, 95.6%, and 95.6% in the calibration, cross-validation, and prediction set, respectively. The results of this study provide theoretical support for applying long-wave FT-NIRS combined with machine learning and deep learning to tomato origin identification. [ABSTRACT FROM AUTHOR]

Two-photon excited phototherapy has attracted considerable attention due to its advantages such as deeper penetration depth and higher spatial resolution. The lack of a high-performance photosensitizer with large two-photon absorption cross-sections and specific targeting ability makes the efficacy of phototherapy in the treatment of cancer unsatisfactory. Here, a new BODIPY-derived photosensitizer 6DBF2 is designed with two-photon photosensitization for two-photon excited photodynamic therapy in vivo. 6DBF2 possesses good two-photon absorption and efficient 1O2 generation upon near-infrared laser excitation. Excellent targeting specificities to lipid droplets of 6DBF2 without any encapsulation or modification at a low working concentration of 0.1 μM is in favor of efficient photodynamic therapy. In vitro cancer cell ablation and in vivo tumor ablation inside mice models upon two-photon irradiation in NIR demonstrate the outstanding therapeutic performance of 6DBF2 in two-photon excited photodynamic therapy. This work thus discusses a rare example of lipid droplets targeting two-photon excited photodynamic therapy for deep cancer tissue imaging and treatment under near-infrared light irradiation. [ABSTRACT FROM AUTHOR]

Multi-rotor plant protection Unmanned Aerial Vehicles (UAVs) have suitable terrain adaptability and efficient ultra-low altitude spraying capacity, which is a significant development direction in efficient plant protection equipment. The interaction mechanisms of the wind field, droplet, and crop are unclear, and have become the bottleneck factor restricting the improvement of the deposition quality. This paper suggests a method to study the influence of the pesticide load on the detailed distribution law of downwash for a six-rotor UAV. Based on a hexahedral structured mesh, a 3D numerical calculation model was established. Analysis showed that the relative errors between the simulated and measured velocities in the z-axis were less than 11% when the downwash air flow was stable. Numerical simulations were carried out for downwash in hover under 0, 1, 2, 3, 4, and 5 kg loads. The effect of load on the airflow was evident, and the greater the load was, the higher the wind speed of downwash would be. Then, the influence of wing interference on the distribution of airflow would be more pronounced. Furthermore, under the rotation of the rotor and the extrusion of external atmospheric pressure, the "trumpet" phenomenon appeared in the downwash airflow area. As an extension, the phenomenon of the "shrinkage–expansion" was shown in the longitudinal section under heavy load, while the phenomenon of "shrinkage–expansion–shrinkage" was present under light load. After that, based on the detailed analysis of the downwash wind field, the spray height of this multi-rotor UAV was suggested to be 2.5 m or higher, and the nozzle was recommended to be mounted directly under the rotor and to have the same rotation direction as the rotor. The research in this paper lays a solid foundation for the proposal of the three-zone overlapping matching theory of wind field, droplet settlement, and canopy shaking. [ABSTRACT FROM AUTHOR]

The maturity of Camellia oleifera fruit is one of the most important indicators to optimize the harvest day, which, in turn, results in a high yield and good quality of the produced Camellia oil. A hyperspectral imaging (HSI) system in the range of visible and near-infrared (400–1000 nm) was employed to assess the maturity stages of Camellia oleifera fruit. Hyperspectral images of 1000 samples, which were collected at five different maturity stages, were acquired. The spectrum of each sample was extracted from the identified region of interest (ROI) in each hyperspectral image. Spectral principal component analysis (PCA) revealed that the first three PCs showed potential for discriminating samples at different maturity stages. Two classification models, including partial least-squares discriminant analysis (PLS-DA) and principal component analysis discriminant analysis (PCA-DA), based on the raw or pre-processed full spectra, were developed, and performances were compared. Using a PLS-DA model, based on second-order (2nd) derivative pre-processed spectra, achieved the highest results of correct classification rates (CCRs) of 99.2%, 98.4%, and 97.6% in the calibration, cross-validation, and prediction sets, respectively. Key wavelengths selected by PC loadings, two-dimensional correlation spectroscopy (2D-COS), and the uninformative variable elimination and successive projections algorithm (UVE+SPA) were applied as inputs of the PLS-DA model, while UVE-SPA-PLS-DA built the optimal model with the highest CCR of 81.2% in terms of the prediction set. In a confusion matrix of the optimal simplified model, satisfactory sensitivity, specificity, and precision were acquired. Misclassification was likely to occur between samples at maturity stages two, three, and four. Overall, an HSI with effective selected variables, coupled with PLS-DA, could provide an accurate method and a reference simple system by which to rapidly discriminate the maturity stages of Camellia oleifera fruit samples. [ABSTRACT FROM AUTHOR]

Tetradentate N2S2 coordination platforms are widespread in biological systems and have endowed metalloenzymes and metalloproteins with abundant reactivities and functions. However, there are only three types of N2S2 scaffolds respectively based on the bipyridine, aryl and alkyl amine derivatives, which are significantly underdeveloped for coordination chemistry. With the objective of developing a new N2S2 coordination platform to assemble a series of first-row transition metal complexes, we have designed a novel tetradentate N2S2 ligand containing a central dipyrrin donor functionalized with two thioether-substituted aryl units. Interestingly, complexation of the N2S2 ligand with the chloride salts of Ni(II), Cu(II) and Zn(II) yields various geometries with various coordination numbers. The reaction between the ligand and NiCl2 readily forms two chloride-bridged centrosymmetric dinickel complexes in which the nickel centers are hexacoordinated by an N2S2Cl2 coordination environment in distorted octahedron geometry. In contrast, metalation of the ligand with CuCl2 gives a mononuclear copper complex consisting of a pentacoordinated copper center in a trigonal bipyramidal geometry with an N2S2Cl coordination sphere. Unexpectedly, the complexation of the ligand with ZnCl2 forms a homoleptic zinc complex in which the zinc center is surrounded by an N4 coordination sphere from two dipyrrin units in a non-planar pseudo-tetrahedral geometry despite the steric hindrance of two bulk thioether-substituted aryl units. These various geometries illustrate the potential structural flexibility of this new ligand. In addition, the optical properties of these compounds were also examined. This work thus provides a new N2S2 coordination platform with geometric flexibility. [ABSTRACT FROM AUTHOR]

In this article, the motion control problem of hydraulic lifting systems subject to parametric uncertainties, unmodeled disturbances, and a valve dead-zone is studied. To surmount the problem, an active disturbance rejection adaptive controller was developed for hydraulic lifting systems. Firstly, the dynamics, including both mechanical dynamics and hydraulic actuator dynamics with a valve dead-zone of the hydraulic lifting system, were modeled. Then, by adopting the system model and a backstepping technique, a composite parameter adaptation law and extended state disturbance observer were successfully combined, which were employed to dispose of the parametric uncertainties and unmodeled disturbances, respectively. This much decreased the learning burden of the extended state disturbance observer, and the high-gain feedback issue could be shunned. An ultimately bounded tracking performance can be assured with the developed control method based on the Lyapunov theory. A simulation example of a hydraulic lifting system was carried out to demonstrate the validity of the proposed controller. [ABSTRACT FROM AUTHOR]

Exploration of fully conjugated covalent organic framework (COF)-based photocatalysts has become valuable research owing to their excellent stability, outstanding semiconducting properties and fascinating properties. However, the design and construction of olefin-linked COF-based photocatalysts are still in an infant stage. Herein, three olefin-linked 2D COFs (F-TBT-COF, TBT-COF and MeO-TBT-COF) with high crystallinity and preeminent stability were designed and synthesized via the Knoevenagel condensation of aryl aldehyde monomers (F-TBT, H-TBT and MeO-TBT) and 2,4,6-trimethyl-1,3,5-triazine (TMT). Significantly, the pore wall surface of these COFs was docked with a single atom or unit to induce distinct inductive and conjugative effects. Then these effects transmitted to the COF's backbone, so that the absorption range, band gap and charge transfer efficiency of the COFs can be regulated via wall surface engineering. The MeO-TBT-COF with a surface methoxy site showed the highest photocatalytic activity and recyclability in the visible-light-driven C-3 thiocyanation reaction of indole derivatives and aerobic transformation of arylboronic acids to phenols. Therefore, we present a facile strategy to design and construct olefin-linked COF based photocatalysts via tuning the groups of the framework's pore wall surface. [ABSTRACT FROM AUTHOR]

High-range sprayers (HRSs) are widely used in tree pest control. However, the design of the HRS is complex and time-consuming. This paper proposed a case study of developing a design platform for the HRS based on a virtual reality (VR) visualization. Firstly, the function of the HRS design platform was introduced. And then, an architecture of the HRS and the compositions of the HRS design platform, including calculation, optimization, assembly, and verification, were illustrated. Meanwhile, development methods and related theories of building the HRS design platform were presented in detail. The VR-based HRS design platform proposed in this paper has a significant advantage in reducing the design period and costs, which can also lower the experimental threshold and improve the training of new designers. [ABSTRACT FROM AUTHOR]

Background: The outbreak of Coronavirus Disease 2019(COVID-19) caused psychological stress in Chinese adults population. But we are unaware of whether the pandemic causes psychological stress on children. Methods: We used the Children's Impact of Event Scale questionnaire (CRIES-13) to investigate the degree of Post-traumatic Stress (PTSD) symptoms caused by the pandemic in students selected from schools in Sichuan, Jiangsu, Henan, Yunnan, and Chongqing provinces of China. Results: A total of 7769 students(3692 male and 4077 female), aged 8–18 years, were enrolled in the study, comprising 1214 in primary schools, 2799 in junior high schools and 3756 in senior high schools. A total of 1639 students (21.1%) had severe psychological stress reactions. A large proportion of senior high school students (23.3%) experienced severe psychological stress, and they had the highest median total CRIES-13 score. Female students were more likely to experience severe psychological stress and had higher median CRIES-13 total scores than males. Conclusion: COVID-19 has placed psychological stresses on primary and secondary school students in China. These stresses are more likely to reach severe levels among female students and senior high school students. [ABSTRACT FROM AUTHOR]

Particleboards are widely used in the artificial board market, which can be constructed from a variety of raw materials and require small amounts of energy to be produced. In the particleboard production process, forming machines play an important role as the key equipment for achieving continuous production. In recent years, airflow forming machines have received increasing attention in particleboard production lines because of their strong separation ability and low price. However, the internal flow field is complex and difficult to control, which affects the surface quality and strength of the particleboard. The most pressing technical difficulty is controlling the flow field characteristics of the airflow paver. At present, the research on this subject is conducted primarily through repeated experiments, which entail long research periods and high processing costs. To reduce human and financial costs, in this study, Computational Fluid Dynamics (CFD) is employed to investigate the flow field and the gas-solid two-phase flow field coupled with particle movement of an airflow forming machine. The accuracy of the calculation model is verified by comparing characteristic point velocities obtained from experimental analysis and a simulation. The simulation results show that in practical production, the frequency of a negative pressure fan should be greater than 27 Hz. It is necessary to set the shoulder properly, and the slab smoothness can be improved by moving the shoulder back on the premise of meeting the strength requirements of the box. The distance between the shoulders of the box body should be less than 2570 mm, and particles with uniform diameter should be added to the paving box to reduce the turbulence effect, improve the quality of particle forming and provide actual particleboard production with a solid theoretical foundation. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed an improved convolutional neural network model based on the ShuffleNet V2 network for recognizing the orbital angular momentum (OAM) modes for the OAM based free space optical communication systems in the environments of atmospheric turbulence (AT). The network is trained by inputting the intensity images of the Laguerre Gaussian beams, which can effectively finish the training process due to its special designs, and can recognize the OAM modes with high accuracy. Compared with previous works for the single and multiplexing OAM modes, the proposed network model has high-precision and high-efficiency characteristics. Especially for the multiplexing OAM modes, our proposed system can achieve the recognition accuracy of 99.5% under strong AT and long-distance transmission. In addition, in order to prove that our system has good generalization ability and strong robustness, we used the trained model to test several groups of data obtained under untrained AT intensities, and the results showed that our model could still maintain high accuracy under the untrained AT intensities, which is very important to the realization of high-capacity optical communication technologies based on OAM in the future [ABSTRACT FROM AUTHOR]

While immunotherapy has dramatically revolutionized the treatment of non‐small cell lung cancer (NSCLC), it still faces challenges such as low therapeutic efficacy and immune‐related adverse events, indicating that safe and effective approaches to enhance NSCLC immunotherapy are still highly demanded. Epidermal growth factor receptor (EGFR) is an established target for molecularly targeted therapy of NSCLC. Overexpression and activating mutations of EGFR can promote the resistance of NSCLC to immunotherapy via upregulating inhibitory immune checkpoints such as programmed death receptor ligand 1 (PD‐L1) and indoleamine‐2,3‐dioxygenase‐1 (IDO1). Thus, therapeutic inhibition of EGFR also holds promise in modulating the immune microenvironment to advance NSCLC immunotherapy. In this study, we employed a proteolysis targeting chimera (PROTAC) that degrades EGFRL858R to investigate its potential in dually inhibiting PD‐L1 and IDO1 to potentiate the anti‐tumor immunity in NSCLC. We demonstrated that PROTAC significantly downregulated the protein levels of both PD‐L1 and IDO1 in NSCLC H3255 cell and tumor. We also confirmed that PROTAC significantly suppressed the H3255 tumor growth and enhanced the anti‐tumor immune response in H3255 tumor. Overall, our study provides a novel strategy for future NSCLC therapy. [ABSTRACT FROM AUTHOR]

Sweetness is one of the most important quality traits for fruit which directly affects consumers' acceptance. The aim of this study was to detect and grade sweetness levels of intact peaches and nectarines by combining short-wave and long-wave Fourier-transform near-infrared spectroscopy (FT-NIRS). FT-NIR spectra of 720 samples (360 peaches and 360 nectarines) were collected using a system operated in range from 15,700 to 4000 cm−1. Soluble solids contents (SSC) were measured as indicators and samples were individually grouped into three sweetness levels based on predetermined thresholds. Partial least squares regression (PLSR) and principal component regression (PCR) were compared, and results showed that PLSR models developed using raw full spectra performed best with correlation coefficient (Rp) of 0.7747 and 0.8473, and root mean square error (RMSEP) of 0.6915% and 0.7228% in prediction. Furthermore, effective wavelengths were individually selected using regression coefficients (RC) and competitive adaptive reweighted sampling (CARS) to simplify the PLSR models. Through comparison, RC-PLSR resulted in comparable performance with original models showing Rp = 0.7716 and 0.8157, and RMSEP = 0.6943% and 0.7945%. To grade different sweetness levels, two-dimensional correlation spectroscopy (2D-COS) was innovatively conducted, and autopeaks characterizing the differences among different groups were retained. Partial least square discriminant analysis (PLS-DA) classification models based on autopeaks achieved a total correct classification rate (CCR) of 66.7% and 86.6% for peaches and nectarines, respectively. The overall results indicated the combination of short-wave and long-wave FT-NIRS was potentially useful in detecting and grading sweetness for these fruit varieties. [ABSTRACT FROM AUTHOR]

Bamboo scrimber, a bio-material which is made of bamboo, has been considered as a substitute material in place of wood in China under the situation of increasing contradiction between supply and demand of wood resources. In the last decades, the fabrication technology of bamboo scrimber has been improved, which promotes the applications of this material. Especially in China where the bamboo scrimber was developed originally, blowout-like growth has emerged in related research in the recent 3–5 years. It is urgent to summarize the relevant research and present it to more people who are concerned with the aspects of bamboo scrimber as a bio-material. This paper presents a comprehensive introduction to bamboo scrimber including fabrication, properties, heat treatment, anti-mildew and anti-fungi, surface coating, machining, applications, etc. based on a review of literature from Chinese National Knowledge Infrastructure (CNKI) and Web of Science. This work not only establishes fabrication parameters, surface proof technologies and application samples for the industry practice but also summarizes detailed inspection standards, mechanical and thermal properties regarding bamboo scrimber for the reference of scientific research. [ABSTRACT FROM AUTHOR]

Recently, phototherapy has attracted much attention due to its negligible invasiveness, insignificant toxicity and excellent applicability. The construction of a newly proposed nanosystem with synergistic photothermal and photodynamic tumor-eliminating properties requires a delicate structure design. In this work, a novel therapeutic nanoplatform (denoted as BCS-Ce6) based on defective cobalt hydroxide nanosheets was developed, which realized hypoxia-relieved photothermal-enhanced photodynamic therapy against cancer. Defective cobalt hydroxide exhibited high photothermal conversion efficacy at the near-infrared region (49.49% at 808 nm) as well as enhanced catalase-like activity to produce oxygen and greatly boost the singlet oxygen generation by a photosensitizer, Ce6, realizing efficacious dual-modal phototherapy. In vivo and in vitro experiments revealed that BCS-Ce6 can almost completely extinguish implanted tumors in a mouse model and present satisfactory biocompatibility during the treatment. This work sets a new angle of preparing photothermal agents and constructing comprehensive therapeutic nanosystems with the ability to modulate the hypoxic tumor microenvironment for efficient cancer therapy. [ABSTRACT FROM AUTHOR]

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Exploiting two-dimensional nanomaterials as photo-based theranostic agents is promising for the highly efficient ablation of deep-tissue-buried tumors. However, they are limited by their poor absorption in the second near-infrared-light (NIR-II) bio-window (1000–1300 nm) and intrinsic nonbiodegradability. Herein, defect-rich sulfur-doped Ni(OH)2 (S-Ni(OH)2) nanosheets decorated with bovine serum albumin (BSA) as a novel theranostic agent is developed, which can accomplish multimodal-imaging-guided photothermal ablation of mouse cancers in the NIR-II bio-window. Sulfur doping extends the absorption spectra of Ni(OH)2 nanosheets from the visible to NIR-II bio-window, affording highly efficient photothermal conversion (58.20% for 1064 nm), entailing it to become an excellent contrast agent for photoacoustic imaging. Further, because of their intrinsic paramagnetic property, they can be applied for magnetic resonance imaging. Owing to the abundant defective sites in S-Ni(OH)2 nanosheets, they exhibit response to the tumor microenvironment, resulting in effective biodegradation and excretion from the body. In vivo toxicity experiments indicated that S-Ni(OH)2-BSA NSs delivered no appreciable toxicity and good biocompatibility. This work provides an avenue for the rational design of effective theranostics agents. [ABSTRACT FROM AUTHOR]