Evaluation of [18F]fluoroestradiol and ChRERα as a gene expression PET reporter system in rhesus monkey brain.

Positron emission tomography (PET) reporter systems are a valuable means of estimating the level of expression of a transgene in vivo. For example, the safety and efficacy of gene therapy approaches for the treatment of neurological and neuropsychiatric disorders could be enhanced via the monitoring of exogenous gene expression levels in the brain. The present study evaluated the ability of a newly developed PET reporter system, [18F]fluoroestradiol and the estrogen receptor-based PET reporter ChRERα, to monitor expression levels of a shRNA designed to suppress choline acetyltransferase (ChAT) expression in rhesus monkey brain. The ChRERα gene and shRNA were expressed from the same transcript via lentivirus injected into monkey striatum. In two monkeys that received injections of viral vector, [18F]fluoroestradiol binding increased by 70% and 86%, respectively, at the target sites compared to pre-injection, demonstrating that ChRERα expression could be visualized in vivo with PET imaging. Postmortem immunohistochemistry confirmed that ChAT expression was significantly suppressed in regions in which [18F]fluoroestradiol uptake was increased. The consistency between PET imaging and immunohistochemical results suggests that [18F]fluoroestradiol and ChRERα can serve as a PET reporter system in rhesus monkey brain for in vivo evaluation of the expression of potential therapeutic agents, such as shRNAs.

Evaluation of immunoregulation and immunoprotective efficacy of Glaesserella parasuis histidine kinase QseC.

QseC is a membrane sensor kinase that enables bacteria to perceive autoinducers -3, adrenaline, and norepinephrine to initiate downstream gene transcription. In this study, we found that the QseC protein of Glaesserella parasuis can serve as an effective antigen to activate the host's immune response. Therefore, we investigated the immunogenicity and host protective effect of this protein. ELISA and indirect immunofluorescence results showed that QseC protein can induce high titer levels of humoral immunity in mice and regularly generate specific serum antibodies. We used MTS reagents to detect lymphocyte proliferation levels and found that QseC protein can cause splenic lymphocyte proliferation with memory and specificity. Further immunological analysis of the spleen cell supernatant revealed significant upregulation of levels of IL-1ß, IL-4 and IFN-γ in the QseC + adjuvant group. In the mouse challenge experiment, it was found that QseC + adjuvant can provide effective protection. The results of this study demonstrate that QseC protein provides effective protection in a mouse model and has the potential to serve as a candidate antigen for a novel subunit vaccine for further research.

Inflammation and Acinar Cell Dual-Targeting Nanomedicines for Synergistic Treatment of Acute Pancreatitis via Ca2+ Homeostasis Regulation and Pancreas Autodigestion Inhibition.

Severe acute pancreatitis (AP) is a life-threatening pancreatic inflammatory disease with a high mortality rate (∼40%). Existing pharmaceutical therapies in development or in clinical trials showed insufficient treatment efficacy due to their single molecular therapeutic target, poor water solubility, short half-life, limited pancreas-targeting specificity, etc. Herein, acid-responsive hollow mesoporous Prussian blue nanoparticles wrapped with neutrophil membranes and surface modified with the N,N-dimethyl-1,3-propanediamine moiety were developed for codelivering membrane-permeable calcium chelator BAPTA-AM (BA) and trypsin activity inhibitor gabexate mesylate (Ga). In the AP mouse model, the formulation exhibited efficient recruitment at the inflammatory endothelium, trans-endothelial migration, and precise acinar cell targeting, resulting in rapid pancreatic localization and higher accumulation. A single low dose of the formulation (BA: 200 µg kg-1, Ga: 0.75 mg kg-1) significantly reduced pancreas function indicators to close to normal levels at 24 h, effectively restored the cell redox status, reduced apoptotic cell proportion, and blocked the systemic inflammatory amplified cascade, resulting in a dramatic increase in the survival rate from 58.3 to even 100%. Mechanistically, the formulation inhibited endoplasmic reticulum stress (IRE1/XBP1 and ATF4/CHOP axis) and restored impaired autophagy (Beclin-1/p62/LC3 axis), thereby preserving dying acinar cells and restoring the cellular "health status". This formulation provides an upstream therapeutic strategy with clinical translation prospects for AP management through synergistic ion homeostasis regulation and pancreatic autodigestion inhibition.

UCL-Dehaze: Toward Real-World Image Dehazing via Unsupervised Contrastive Learning.

While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain shift problem. From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus alleviating the domain shift problem and enhancing the network's generalization ability in real-world scenarios. We propose an effective unsupervised contrastive learning paradigm for image dehazing, dubbed UCL-Dehaze. Unpaired real-world clean and hazy images are easily captured, and will serve as the important positive and negative samples respectively when training our UCL-Dehaze network. To train the network more effectively, we formulate a new self-contrastive perceptual loss function, which encourages the restored images to approach the positive samples and keep away from the negative samples in the embedding space. Besides the overall network architecture of UCL-Dehaze, adversarial training is utilized to align the distributions between the positive samples and the dehazed images. Compared with recent image dehazing works, UCL-Dehaze does not require paired data during training and utilizes unpaired positive/negative data to better enhance the dehazing performance. We conduct comprehensive experiments to evaluate our UCL-Dehaze and demonstrate its superiority over the state-of-the-arts, even only 1,800 unpaired real-world images are used to train our network. Source code is publicly available at https://github.com/yz-wang/UCL-Dehaze.

N, O-Doped Walnut-Like Porous Carbon Composite Microspheres Loaded with Fe/Co Nanoparticles for Adjustable Electromagnetic Wave Absorption.

This study addresses the challenge of designing simple and environmentally friendly methods for the preparation of effective electromagnetic wave (EMW) absorbing materials with tailored microstructures and multi-component regulation. N, O doped walnut-like porous carbon composite microspheres loaded with FeCo nanoparticles (WPCM/Fe-Co) are synthesized through high-temperature carbonization combined with soap-free emulsion polymerization and hydrothermal methods, avoiding the use of toxic solvents and complex conditions. The incorporation of magnetic components enhances magnetic loss, complementing dielectric loss to optimize EMW attenuation. The unique walnut-like morphology further improves impedance matching. The proportions of Fe and Co components can be adjusted to regulate the material's reflection loss, thickness, and bandwidth, allowing for fine-tuning of absorption performance. At a low filling ratio (16.7%), the optimal WPCM/Fe-Co composites exhibit a minimum reflection loss (RLmin) of -48.34 dB (10.33 GHz, 3.0 mm) and an overall effective absorbing bandwidth (EAB) covering the entire C bands, X bands, and Ku bands. This work introduces a novel approach to composition regulation and presents a green synthesis method for magnetic carbon composite absorbers with high-performance EMW absorption at low loading.

Influence of grasping postures on skin deformation of hand.

To investigate the influence of different grasping postures on the hand's skin deformation, a handheld 3D EVA SCANNER was used to obtain 3D models of 111 women in five postures, including a straight posture and grasping cylinders with various diameters (4/6/8/10 cm). Skin relaxation strain ratio ([Formula: see text]) and surface area skin relaxation strain ratio ([Formula: see text]) were used as measures of skin deformation between two landmarks and multiple landmarks, respectively. The effects of grasping posture on skin deformation in different directions were analyzed. The results revealed significant variations in skin deformation among different grasping postures, except for the width of middle finger metacarpal and the length of middle finger's proximal phalanx. The [Formula: see text] increased with decreasing grasping object diameter, ranging from 5 to 18% on the coronal axis, and from 4 to 20% on the vertical axis. The overall variation of [Formula: see text] ranged from 5 to 37.5%, following the same trend as [Formula: see text] except for the surface area of tiger's mouth, which exhibited a maximum difference of 10.9% with significant differences. These findings have potential applications in improving the design of hand equipment and understanding hand movement characteristics.

PointSee: Image Enhances Point Cloud.

There is a prevailing trend towards fusing multi-modal information for 3D object detection (3OD). However, challenges related to computational efficiency, plug-and-play capabilities, and accurate feature alignment have not been adequately addressed in the design of multi-modal fusion networks. In this paper, we present PointSee, a lightweight, flexible, and effective multi-modal fusion solution to facilitate various 3OD networks by semantic feature enhancement of point clouds (e.g., LiDAR or RGB-D data) assembled with scene images. Beyond the existing wisdom of 3OD, PointSee consists of a hidden module (HM) and a seen module (SM): HM decorates point clouds using 2D image information in an offline fusion manner, leading to minimal or even no adaptations of existing 3OD networks; SM further enriches the point clouds by acquiring point-wise representative semantic features, leading to enhanced performance of existing 3OD networks. Besides the new architecture of PointSee, we propose a simple yet efficient training strategy, to ease the potential inaccurate regressions of 2D object detection networks. Extensive experiments on the popular outdoor/indoor benchmarks show quantitative and qualitative improvements of our PointSee over thirty-five state-of-the-art methods.

Multiple feature fusion transformer for modeling penicillin fermentation process with unequal sampling intervals.

The quality prediction of batch processes is an important task in the field of biological fermentation. However, dynamic nonlinearity, unequal sampling intervals, uneven duration, and multiple features of a batch process make this task challenging. Thus, the multiple-feature fusion transformer (MFFT) model is proposed for the time series quality prediction of a batch process. First, the application of sequence-to-sequence architecture enables MFFT to perform a wide range of sequence prediction tasks. Second, the transformer parallel operation model imposes no rigid requirement for the order of sequence input, allowing the model to deal with problems of unequal interval sampling and utilize the sequence information. Third, MFFT integrates a pretrained ResNet50 as a mycelium status classifier for fusing image information into the features. Moreover, a multiple-feature encoding structure is proposed to integrate sampling time and mycelium status. Finally, multiple tasks in penicillin fermentation have shown that MFFT significantly outperforms existing methods for time series prediction.

Near-Infrared Light Triggered the Shape Memory Behavior of Polydopamine-Nanoparticle-Filled Epoxy Acrylate.

Through the effective combination of photothermal conversion agent polydopamine (PDA) nanoparticles and epoxy acrylate polymer (EA), a new kind of near-infrared (NIR) light-triggered shape memory polymer (PDA/EA) is developed. Due to the outstanding photothermal effect of PDA, even with a very low concentration of PDA (0.1 wt.%), when exposed to an 808 nm NIR light with a power of 1 W/cm2, the temporary shapes can be fully light-responsive, recovered in 60 s. Based on dynamic thermomechanical analysis and thermal gravimetric analysis, it can be seen that the introduction of PDA is beneficial for improving dynamic mechanical properties and thermal resistance compared to EA. As an environmentally friendly and highly efficient photoactive SMP, PDA/EA has a great application prospect.

Correction: DCE-MRI-Derived Parameters in Evaluating Abraxane-Induced Early Vascular Response and the Effectiveness of Its Synergistic Interaction with Cisplatin.

[This corrects the article DOI: 10.1371/journal.pone.0162601.].

Efficient viral expression of a chemogenetic receptor in the old-world monkey amygdala.

Genetically encoded synthetic receptors, such as the chemogenetic and optogenetic proteins, are powerful tools for functional brain studies in animals. In the primate brain, with its comparatively large, intricate anatomical structures, it can be challenging to express transgenes, such as the hM4Di chemogenetic receptor, in a defined anatomical structure with high penetrance. Here, we compare parameters for lentivirus vector injections in the rhesus monkey amygdala. We find that four injections of 20 µl, infused at 0.5 µl/min, can achieve neuronal hM4Di expression in 50-100% of neurons within a 60 mm3 volume, without observable damage from overexpression. Increasing the number of hM4Di_CFP lentivirus injections to up to 12 sites per hemisphere, resulted in 30%-40% neuronal coverage of the overall amygdala volume, with coverage reaching 60% in some subnuclei. Manganese Chloride was mixed with lentivirus and used as an MRI marker to verify targeting accuracy and correct unsuccessful injections in these experiments. In a separate monkey we visualized, in vivo, viral expression of the hM4Di receptor protein in the amygdala, using Positron Emission Tomography. Together, these data show efficient and verifiable expression of a chemogenetic receptor in old-world monkey amygdala.

Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models.

Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ( k cat ${k}_{\mathrm{cat}}$ ) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 k cat ${k}_{\mathrm{cat}}$ c), highly sensitive (1759 k cat ${k}_{\mathrm{cat}}$ ), and nonsensitive (2502 k cat ${k}_{\mathrm{cat}}$ ) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.

In vivo evaluation of a novel 18F-labeled PET radioligand for translocator protein 18 kDa (TSPO) in monkey brain.

PURPOSE: [18F]SF51 was previously found to have high binding affinity and selectivity for 18 kDa translocator protein (TSPO) in mouse brain. This study sought to assess the ability of [18F]SF51 to quantify TSPO in rhesus monkey brain. METHODS: Positron emission tomography (PET) imaging was performed in monkey brain (n = 3) at baseline and after pre-blockade with the TSPO ligands PK11195 and PBR28. TSPO binding was calculated as total distribution volume corrected for free parent fraction in plasma (VT/fP) using a two-tissue compartment model. Receptor occupancy and nondisplaceable uptake were determined via Lassen plot. Binding potential (BPND) was calculated as the ratio of specific binding to nondisplaceable uptake. Time stability of VT was used as an indirect probe to detect radiometabolite accumulation in the brain. In vivo and ex vivo experiments were performed in mice to determine the distribution of the radioligand. RESULTS: After [18F]SF51 injection, the concentration of brain radioactivity peaked at 2.0 standardized uptake value (SUV) at ~ 10 min and declined to 30% of the peak at 180 min. VT/fP at baseline was generally high (203 ± 15 mL· cm-3) and decreased by ~ 90% after blockade with PK11195. BPND of the whole brain was 7.6 ± 4.3. VT values reached levels similar to terminal 180-min values by 100 min and remained relatively stable thereafter with excellent identifiability (standard errors < 5%), suggesting that no significant radiometabolites accumulated in the brain. Ex vivo experiments in mouse brain showed that 96% of radioactivity was parent. No significant uptake was observed in the skull, suggesting a lack of defluorination in vivo. CONCLUSION: The results demonstrate that [18F]SF51 is an excellent radioligand that can quantify TSPO with a good ratio of specific to nondisplaceable uptake and has minimal radiometabolite accumulation in brain. Collectively, the results suggest that [18F]SF51 warrants further evaluation in humans.

High-performance multifunctional porous iron Acetylacetonate/N, O-doped carbon nanospheres for electromagnetic wave absorption at 2-18 GHz and methyl orange absorption.

Nowadays, multifunction is regarded as an advanced development direction of new-generation electromagnetic wave absorption (EMWA) materials to fulfill the ever-growing demands in complex environment and situation. Environmental pollution and electromagnetic pollution are all difficult problems for human beings all the time. Now, there is no multifunctional materials for collaborative treatment of environmental and electromagnetic pollution. Herein, We synthesized nanospheres with divinyl benzene (DVB) and N-[3-(dimethylamino)propyl]methacrylamide (DMAPMA), using a simple one-pot method. After calcination at 800 ℃ in N2, porous N, O-doped porous carbon materials were prepared. By regulating the mole ratio of DVB and DMAPMA, the ratio was 5:1 reached excellent EMWA property. Remarkably, the introduction of iron acetylacetonate into the reaction of DVB and DMAPMA was effective in enhancing the absorption bandwidth to 8.00 GHz at a 3.74 mm thickness, which depended on the synergistic effects from dielectric and magnetic losses. Simultaneously, the Fe-doped carbon materials had a methyl orange adsorption capacity. The adsorption isotherm conformed to the Freundlich model. After methyl orange absorption, the EMWA property did not greatly change. Thus, this research paves the way for the creation of multifunctional materials to solve environmental pollution and electromagnetic pollution together.

AGConv: Adaptive Graph Convolution on 3D Point Clouds.

Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep learning. The traditional wisdom of convolution characterises feature correspondences indistinguishably among 3D points, arising an intrinsic limitation of poor distinctive feature learning. In this article, we propose Adaptive Graph Convolution (AGConv) for wide applications of point cloud analysis. AGConv generates adaptive kernels for points according to their dynamically learned features. Compared with the solution of using fixed/isotropic kernels, AGConv improves the flexibility of point cloud convolutions, effectively and precisely capturing the diverse relations between points from different semantic parts. Unlike the popular attentional weight schemes, AGConv implements the adaptiveness inside the convolution operation instead of simply assigning different weights to the neighboring points. Extensive evaluations clearly show that our method outperforms state-of-the-arts of point cloud classification and segmentation on various benchmark datasets. Meanwhile, AGConv can flexibly serve more point cloud analysis approaches to boost their performance. To validate its flexibility and effectiveness, we explore AGConv-based paradigms of completion, denoising, upsampling, registration and circle extraction, which are comparable or even superior to their competitors.

Discovery of a High-Affinity Fluoromethyl Analog of [11C]5-Cyano-N-(4-(4-methylpiperazin-1-yl)-2-(piperidin-1-yl)phenyl)furan-2-carboxamide ([11C]CPPC) and Their Comparison in Mouse and Monkey as Colony-Stimulating Factor 1 Receptor Positron Emission Tomography Radioligands.

[11C]CPPC has been advocated as a radioligand for colony-stimulating factor 1 receptor (CSF1R) with the potential for imaging neuroinflammation in human subjects with positron emission tomography (PET). This study sought to prepare fluoro analogs of CPPC with higher affinity to provide the potential for labeling with longer-lived fluorine-18 (t 1/2 = 109.8 min) and for delivery of higher CSF1R-specific PET signal in vivo. Seven fluorine-containing analogs of CPPC were prepared and four were found to have high inhibitory potency (IC50 in low to sub-nM range) and selectivity at CSF1R comparable with CPPC itself. One of these, a 4-fluoromethyl analog (Psa374), was investigated more deeply by labeling with carbon-11 (t 1/2 = 20.4 min) for PET studies in mouse and monkey. [11C]Psa374 showed high peak uptake in monkey brain but not in mouse brain. Pharmacological challenges revealed no CSF1R-specific binding in either species at baseline. [11C]CPPC also failed to show specific binding at baseline. Moreover, both [11C]Psa374 and [11C]CPPC showed brain efflux transporter substrate behavior in both species in vivo, although Psa374 did not show liability toward human efflux transporters in vitro. Further development of [11C]Psa374 in non-human primate models of neuroinflammation with demonstration of CSF1R-specific binding would be required to warrant the fluorine-18 labeling of Psa374 with a view to possible application in human subjects.

Rapidly Inhibiting the Inflammatory Cytokine Storms and Restoring Cellular Homeostasis to Alleviate Sepsis by Blocking Pyroptosis and Mitochondrial Apoptosis Pathways.

Pyroptosis, systemic inflammation, and mitochondrial apoptosis are the three primary contributors to sepsis's multiple organ failure, the ultimate cause of high clinical mortality. Currently, the drugs under development only target a single pathogenesis, which is obviously insufficient. In this study, an acid-responsive hollow mesoporous polydopamine (HMPDA) nanocarrier that is highly capable of carrying both the hydrophilic drug NAD+ and the hydrophobic drug BAPTA-AM, with its outer layer being sealed by the inflammatory targeting peptide PEG-LSA, is developed. Once targeted to the region of inflammation, HMPDA begins depolymerization, releasing the drugs NAD+ and BAPTA-AM. Depletion of polydopamine on excessive reactive oxygen species production, promotion of ATP production and anti-inflammation by NAD+ replenishment, and chelation of BAPTA (generated by BA-AM hydrolysis) on overloaded Ca2+ can comprehensively block the three stages of sepsis, i.e., precisely inhibit the activation of pyroptosis pathway (NF-κB-NLRP3-ASC-Casp-1), inflammation pathway (IL-1ß, IL-6, and TNF-α), and mitochondrial apoptosis pathway (Bcl-2/Bax-Cyt-C-Casp-9-Casp-3), thereby restoring intracellular homeostasis, saving the cells in a state of "critical survival," further reducing LPS-induced systemic inflammation, finally restoring the organ functions. In conclusion, the synthesis of this agent provides a simple and effective synergistic drug delivery nanosystem, which demonstrates significant therapeutic potential in a model of LPS-induced sepsis.

Dynamic analysis of rumor propagation model with media report and time delay on social networks.

When a rumor appears on social networks, the media of relevant departments need reaction time to make an authoritative announcement. Considering the effects of the media report and time delay on a rumor spreading, and the different attitudes of individuals towards media reports. We proposed a susceptible-expose-infective-media-remover (SEIMR) rumor propagation model with media reports and time delay. Firstly, the basic reproduction number of the model is obtained. Secondly, the positivity, boundedness and existence of the solutions of the model are analyzed. Then, the local asymptotic stability of the rumor free equilibrium and the boundary equilibriums is proved, and the global asymptotic stability of the equilibriums is proved by constructing Lyapunov function when the time delay is zero. Besides, the prevention and control effects of the media report on rumor spreading and the effect of time delay are analyzed. The shorter time delay in media report and the greater the impact of the media report, the more effective the suppression of rumors will be. Finally, the accuracy of the theoretical results as well as the effects of different parameters of the model have been verified through numerical simulations, and the effectiveness of the SEIMR model has been verified via comparative experiments.

1,2-Bis(2-aminophenoxy)ethane-N,N,N',N'-tetraacetic Acid Acetoxymethyl Ester Loaded Reactive Oxygen Species Responsive Hyaluronic Acid-Bilirubin Nanoparticles for Acute Kidney Injury Therapy via Alleviating Calcium Overload Mediated Endoplasmic Reticulum Stress.

Calcium overload is one of the early determinants of the core cellular events that contribute to the pathogenesis of acute kidney injury (AKI), which include oxidative stress, ATP depletion, calcium overload, and inflammatory response with self-amplifying and interactive feedback loops that ultimately lead to cellular injury and renal failure. Excluding adjuvant therapy, there are currently no approved pharmacotherapies for the treatment of AKI. Using an adipic dihydride linker, we modified the hyaluronic acid polymer chain with a potent antioxidant, bilirubin, to produce an amphiphilic conjugate. Subsequently, we developed a kidney-targeted and reactive oxygen species (ROS)-responsive drug delivery system based on the flash nanocomplexation method to deliver a well-known intracellular calcium chelator, 1,2-bis(2-aminophenoxy)ethane-N,N,N',N'-tetraacetic acid acetoxymethyl ester (BAPTA-AM, BA), with the goal of rescuing renal cell damage via rapidly scavenging of intracellularly overloaded Ca2+. In the ischemia-reperfusion (I/R) induced AKI rat model, a single dose of as-prepared formulation (BA 100 µg·kg-1) 6 h post-reperfusion significantly reduced renal function indicators by more than 60% within 12 h, significantly alleviated tissular pathological changes, ameliorated tissular oxidative damage, significantly inhibited apoptosis of renal tubular cells and the expression of renal tubular marker kidney injury molecule 1, etc., thus greatly reducing the risk of kidney failure. Mechanistically, the treatment with BA-loaded NPs significantly inhibited the activation of the ER stress cascade response (IRE1-TRAF2-JNK, ATF4-CHOP, and ATF6 axis) and regulated the downstream apoptosis-related pathway while also reducing the inflammatory response. The BA-loaded NPs hold great promise as a potential therapy for I/R injury-related diseases.

FindNet: Can You Find Me? Boundary-and-Texture Enhancement Network for Camouflaged Object Detection.

Camouflaged objects share very similar colors but have different semantics with the surroundings. Cognitive scientists observe that both the global contour (i.e., boundary) and the local pattern (i.e., texture) of camouflaged objects are key cues to help humans find them successfully. Inspired by the cognitive scientist's observation, we propose a novel boundary-and-texture enhancement network (FindNet) for camouflaged object detection (COD) from single images. Different from most of existing COD methods, FindNet embeds both the boundary-and-texture information into the camouflaged object features. The boundary enhancement (BE) module is leveraged to focus on the global contour of the camouflaged object, and the texture enhancement (TE) module is utilized to focus on the local pattern. The enhanced features from BE and TE, which complement each other, are combined to obtain the final prediction. FindNet performs competently on various conditions of COD, including slightly clear boundaries but very similar textures, fuzzy boundaries but slightly differentiated textures, and simultaneous fuzzy boundaries and textures. Experimental results exhibit clear improvements of FindNet over fifteen state-of-the-art methods on four benchmark datasets, in terms of detection accuracy and boundary clearness. The code will be publicly released.