Search

Abstract This study addresses the intricate challenge of circuit layout optimization central to integrated circuit (IC) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as PPA optimization). The complexity of this task, evolving into a multidimensional problem under multiple constraints, necessitates the exploration of advanced methodologies. In response to these challenges, our research introduces deep learning technology as an innovative strategy to revolutionize circuit layout optimization. Specifically, we employ Convolutional Neural Networks (CNNs) in developing an optimized layout strategy, a performance prediction model, and a system for fault detection and real-time monitoring. These methodologies leverage the capacity of deep learning models to learn from high-dimensional data representations and handle multiple constraints effectively. Extensive case studies and rigorous experimental validations demonstrate the efficacy of our proposed deep learning-driven approaches. The results highlight significant enhancements in optimization efficiency, with an average power consumption reduction of 120% and latency decrease by 1.5%. Furthermore, the predictive capabilities are markedly improved, evidenced by a reduction in the average absolute error for power predictions to 3%. Comparative analyses conclusively illustrate the superiority of deep learning methodologies over conventional techniques across several dimensions. Our findings underscore the potential of deep learning in achieving higher accuracy in predictions, demonstrating stronger generalization abilities, facilitating superior design quality, and ultimately enhancing user satisfaction. These advancements not only validate the applicability of deep learning in IC design optimization but also pave the way for future advancements in addressing the multidimensional challenges inherent to circuit layout optimization.

Abstract In practical engineering, the working conditions of gearbox are complex and variable. In varying working conditions, the performance of intelligent fault diagnosis model is degraded because of limited valid samples and large data distribution differences of gearbox signals. Based on these issues, this research proposes a gearbox fault diagnosis method integrated with lightweight channel attention mechanism, and further realizes the cross-component transfer learning. First, time–frequency distribution of original signals is obtained by wavelet transform. It could intuitively reflect local characteristics of signals. Secondly, based on a local cross-channel interaction strategy, a lightweight efficient channel attention mechanism (LECA) is designed. The kernel size of 1D convolution is affected by channel number and coefficients. Multi-scale feature input is used to retain more detailed features of different dimensions. A lightweight convolutional neural network is constructed. Finally, a transfer learning method is applied to freeze lower structures of the network and fine-tune higher structures of the model using small samples. Through experimental verification, the proposed model could effectively utilize samples. The application of transfer learning could realize accurate and fast fault classification of small samples, and achieve good gearbox fault diagnosis effect under varying working conditions and cross-component conditions.

Monitoring and regulating the marine environment and reasonably developing and utilizing marine natural resources require advanced observation methods. Marine optical technology, as a mature information perception method with high spatio-temporal resolution and high-throughput data collection capabilities, plays an important role in marine scientific research. The research on polarization optics has expanded the information modality of traditional optical imaging and measurement technologies, and a large number of research and applications in ocean observation and its derivative fields represented by ecology have emerged. This paper reviewed the research progress of polarization optics technology in the field of marine environmental observation and marine biological observation. On the one hand, polarization optics assists in the in-depth analysis of complex physical processes and environmental physicochemical properties. On the other hand, polarization optics achieves incremental functions in the field of biological observation, such as expanding imaging dimensions, detecting specific targets, and recognizing biophysical and chemical characteristics. This paper also systematically introduced the models of polarization optical imaging and measurement schemes. In addition, combined with a series of works carried out by the research team in the field of underwater in-situ biological monitoring, the paper discussed the prospects of introducing polarization optics to achieve a new generation of real-time on-site monitoring systems.

Zooplankton size is a crucial indicator in marine ecosystems, reflecting demographic structure, species diversity and trophic status. Traditional methods for measuring zooplankton size, which involve direct sampling and microscopic analysis, are laborious and time-consuming. In situ imaging systems are useful sampling tools; however, the variation in angles, orientations, and image qualities presented considerable challenges to early machine learning models tasked with measuring sizes.. Our study introduces a novel, efficient, and precise deep learning-based method for zooplankton size measurement. This method employs a deep residual network with an adaptation: replacing the fully connected layer with a convolutional layer. This modification allows for the generation of an accurate predictive heat map for size determination. We validated this automated approach against manual sizing using ImageJ, employing in-situ images from the PlanktonScope. The focus was on three zooplankton groups: copepods, appendicularians, and shrimps. An analysis was conducted on 200 individuals from each of the three groups. Our automated method's performance was closely aligned with the manual process, demonstrating a minimal average discrepancy of just 1.84%. This significant advancement presents a rapid and reliable tool for zooplankton size measurement. By enhancing the capacity for immediate and informed ecosystem-based management decisions, our deep learning-based method addresses previous challenges and opens new avenues for research and monitoring in zooplankton.

Plankton is critical for the structure and function of marine ecosystems. In the past three decades, various underwater imaging systems have been developed to collect in-situ plankton images and image processing has been a major bottleneck that hinders the deployment of plankton imaging systems. In recent years, deep learning methods have greatly enhanced our ability of processing in-situ plankton images, but high-computational demands and longtime consumption still remain problematic. In this study, we used knowledge distillation as a framework for model compression and improved computing efficiency while maintaining original high accuracy. A novel inter-class similarity distillation algorithm based on feature prototypes was proposed and enabled the student network (small scale) to acquire excellent ability for plankton recognition after being guided by the teacher network (large scale). To identify the suitable teacher network, we compared emerging Transformer neural networks and convolution neural networks (CNNs), and the best performing deep learning model, Swin-B, was selected. Utilizing the proposed knowledge distillation algorithm, the feature extraction ability of Swin-B was transferred to five more lightweight networks, and the results had been evaluated in taxonomic dataset of in-situ plankton images. Subsequently, the chosen lightweight model and the Bilateral–Sobel edge enhancement were tested to process in-situ images with high level of noises captured from coastal waters of Guangdong, China and achieved an overall recall rate of 91.73%. Our work contributes to effective deep learning models and facilitates the deployment of underwater plankton imaging systems by promoting both accuracy and speed in recognition of plankton targets.

Under different working conditions of gearbox, the feature extraction of fault signals is difficult, and large difference in data distribution affects the fault diagnosis results. Based on the problems, the research proposes a method based on improved MobileNetV3 network and transfer learning (TL-Pro-MobilenetV3 network). Three time-frequency analysis methods are used to obtain time-frequency distribution. Among them, short time Fourier transform (STFT) combined with Pro-MobilenetV3 network takes the shortest time and has the highest accuracy. Furthermore, transfer learning is introduced into the model, and the optimal training parameters are selected training the network. Using the dataset from Southeast University, the TL-Pro-MobilenetV3 model is compared with four classical fault diagnosis models. The experimental results show the accuracy of the method proposed can reach 100% and the training time is the shortest in two working conditions, proving the proposed model has a good performance in generalization ability, recognition accuracy and training time.

The measurement matrix in compressive imaging controls the crucial feature information for high performance recognition. In this study, a deterministic orthogonal measurement matrix design method using the discrete cosine transform and a compressive feature selection scheme are proposed to implement high-end computational optics for imaging. The selection scheme systematically evaluates the recognition importance for the frequency features, combined with a scaling of the contribution of the various coefficients used to produce a base matrix for the new group of measuring patterns, which ensures the minimal recognition difference for each individual order of frequency filters and combining a relatively complex expression to quickly find the best quantization values. The model parameters are gradually adjusted and eventually converge to the best result through training with a large number of pre-determined samples from the dataset and backpropagating the feature selection loss along with the recognition loss, and the data processing capabilities can be enhanced because the measurement matrix is a priori information for the recognition phase. The systematic ability of the proposed technique was verified through simulations and experiments on two standard datasets: MNIST and CIFAR-10. The results show that the proposed method outperforms state-of-the-art methods in terms of both the model complexity and classification accuracy, which indicates that our study provides a new practical solution for compressive imaging recognition.

Dental fluorosis (DF) is a widely prevalent disease caused by excessive fluoride with limited awareness of its underlying pathogenesis. Here, a pilot population study was conducted to explore the pathogenesis of DF from the perspective of intestinal microbiome changes, and verified it in animal experiments combining intestinal microbiome and metabolomics. A total of 23 children were recruited in 2017 in China and divided into DF (n = 9) and control (n = 14) groups (DFG and CG, respectively). The SD rat model was established by drinking water containing sodium fluoride (NaF). Gut microbiome profiles of children and rats were analyzed by16S rDNA V3-V4 sequencing, and the intestinal metabolomics analysis of rats was performed by LC-MS methods. The 16 S rDNA sequencing revealed that the gut microbiome composition was significantly perturbed in children in DFG compared to that in CG. Acidobacteria and Thermi were specifically observed in DFG and CG, respectively. Besides, 15 fecal microbiotas were significantly altered at the genus level in DFG. Furthermore, only the expression of annotated genes for pentose and glucuronate interconversion pathway was significant lower in DFG than that in CG (P = 0.04). Notably, in NaF-treated rats, we also observed the changes of some key components of pentose and glucuronate interconversion pathway at the level of microorganisms and metabolites. Our findings suggested that the occurrence of DF is closely related to the alteration of intestinal microorganisms and metabolites annotated in the pentose and glucuronate interconversion pathway.

Abstract Background The intellectual loss induced by fluoride exposure has been extensively studied, but the association between fluoride exposure in different susceptibility windows and children’s intelligence is rarely reported. Hence, we conducted a cross-sectional study to explore the association between fluoride exposure in prenatal and childhood periods and intelligence quotient (IQ). Methods We recruited 633 local children aged 7–13 years old randomly from four primary schools in Kaifeng, China in 2017. The children were divided into four groups, of which included: control group (CG, n = 228), only prenatal excessive fluoride exposure group (PFG, n = 107), only childhood excessive fluoride exposure group (CFG, n = 157), both prenatal and childhood excessive fluoride exposure group (BFG, n = 141). The concentrations of urinary fluoride (UF) and urinary creatinine (UCr) were determined by fluoride ion-selective electrode assay and a creatinine assay kit (picric acid method), respectively. The concentration of UCr-adjusted urinary fluoride (CUF) was calculated. IQ score was assessed using the second revision of the Combined Raven’s Test-The Rural in China (CRT-RC2). Threshold and saturation effects analysis, multiple linear regression analysis and logistic regression analysis were conducted to analyze the association between fluoride exposure and IQ. Results The mean IQ score in PFG was respectively lower than those in CG, CFG and BFG (P

Integrated zoom and image stabilization system based on deformable mirrors (DMs) has advantages of miniaturization, rapid response and low energy consumption. Integrating the two capabilities on one DM-based imaging system poses considerable challenges. First, limited DM stroke will result in limited changeable aberration correction value for zoom and image stabilization. Second, the DM-based off-axis imaging system suffers a simple relative movement between optical axis and objects caused by carrier vibration may result in complicated optical system aberrations. To address these challenges, a reasonable assignment of the changeable aberration correction value for zoom and image stabilization is needed. Image stabilization and aberration correction in DM-based off-axis imaging system, should be considered when translation or rotation occurs. To troubleshoot the assignment issues of the correcting aberrations, we propose a graded optimization method which comprises two steps. To troubleshoot the image stabilization and aberration correction issues, we introduce vectorial ray tracing method and image point freezing principle to transform the aberration correction problem into an optimization problem of image coordinates. With the proposed method, we construct practical integrated freeform surfaces of DMs for a space camera with three mirrors. The sagittal heights of designed DM surface profiles are limited within their available stroke.

Deep learning has been proven to provide solutions for computational ghost imaging (CGI). However, in current CGI techniques, the quality of the reconstructed image is adversely affected by the position and intensity of the scattering medium. In this study, the feasibility of using a deep neural network (DNN) by using the hybrid simulated data to facilitate CGI through a scattering medium is demonstrated, particularly when the scattering medium is in front of an object. Under a specific order of the measurement matrix, the CGI measurement equation is introduced along with a disturbance factor of the scattering effect to generate simulation data, thereby representing imaging situations with the scattering medium at different positions. The selection of disturbance parameters is determined by the correlation between the simulation signal and the experimental signal. Then an end-to-end DNN is trained using experimentally obtained light intensity signals, and it shows that the quality of the reconstruction results is improved when there is a scattering medium in the emission path. The reported technique effectively solves the problem of CGI reconstruction under different scattering paths with a common DNN, and may have broad applications in fast adaptive CGI for new and uncertain scenarios.

Convolutional neural networks (CNN) with their deep feature extraction capability have recently been applied in numerous image fusion tasks. However, the image fusion of infrared and visible images leads to loss of fine details and degradation of contrast in the fused image. This deterioration in the image is associated with the conventional “averaging” rule for base layer fusion and relatively large feature extraction by CNN. To overcome these problems, an effective fusion framework based on visual saliency weight map (VSWM) combined with CNN is proposed. The proposed framework first employs VSWM method to improve the contrast of an image under consideration. Next, the fine details in the image are preserved by applying multi-resolution singular value decomposition (MSVD) before further processing by CNN. The promising experimental results show that the proposed method outperforms state-of-the-art methods by scoring the highest over different evaluation metrics such as Q0, multiscale structural similarity (MS_SSIM), and the sum of correlations of differences (SCD).

Finding and segmenting objects in noisy low-contrast in situ underwater plankton images is challenging because of the difficulty of separating potential plankton objects from the complex background and numerous and diverse other particles. In the present study, a dynamic downscaling model was developed to rapidly extract complete and clean regions of interest (ROIs) from images with highly variable content and quality. The original image was downscaled, and dynamic segmentation was performed in a scale pyramid space to ensure the integrity of weak targets based on local two-dimensional (2D) entropy parameters. Subsequently, a series of local thresholds and clustering gradients was examined iteratively for ROI selection. The performance of the local 2D entropy parameters relative to water turbidity (in a scattering medium and with high background noise) and image size was examined. To suppress the background and increase the sharpness of potential targets, a sharpness descriptor and gradient clustering were employed. The method was compared with the currently commonly used local threshold-based Sauvola segmentation using the same set of images. The results showed that the proposed method improves the ROI extraction accuracy and reduces oversegmentation for in situ underwater plankton images. It was concluded that the proposed method is a fast and robust segmentation technique and could facilitate the deployment of in situ plankton imaging systems for process-based research and routine plankton monitoring.

Antibiotics in breeding industry can enter the environment through multiple pathways, thus accelerating the emergence and spread of antibiotic resistance genes (ARGs), among which aerosol transmission is easily achieved and often overlooked. To elucidate the role of aerosols in this situation, the present study investigated the distribution characteristics of 107 ARG subtypes (targeting to eight different ARG types) and nine mobile genetic elements (MGEs) and bacterial community in animal (chicken cloaca), environment (aerosols) and human (nasopharynx) of a chicken farm (n = 42) in Henan Province. In total, 116 ARG subtypes and MGEs were identified in the poultry farm. The total bacterial concentration of aerosols inside the chicken house (3.117 × 104 CFU/m3) exceeded the corresponding limit. The microbial communities in the samples of cloaca swab (C) and the workers’ nasopharyngeal swab (N) were closer, while the abundance distribution of ARGs/ MGEs in cloacal swab (C) and aerosol (AI) in chicken house were much similar. There were certain consistency of the microbial community structure and the distribution of ARGs among the three groups of chicken cloaca, air aerosol, and workers’ nasopharynx. Our results highlighted that animal breeding does have a certain impact on the surrounding environment and human, and aerosols play an important role in this process.

This study examined the neuroprotective properties of resveratrol (Res) and its target sirtuin1 (SIRT1) against lead (Pb)-mediated toxicity and discovered that both resveratrol treatment and SIRT1 overexpression restored blocked autophagic flux as well as reduced β-amyloid (Aβ) contents. Four-week-old male C57BL/6 mice were employed to consumed 0.2% Pb(Ac)2 solution or deionized water for 3 months followed by 12 months of Res (50 mg/kg BW) or vehicle gavage. In in vitro study, SH-SY5Y cells were pretreated with the SIRT1 activator SRT1720 (2 μM) or the inhibitor EX527 (2 μM) for 2 h, then 25 μM of Pb(Ac)2 was added and incubated for 48 h. Western blotting, RT-qPCR, enzyme-linked immunosorbent assay (ELISA), and Lyso-Tracker Red Staining were next used to estimate the potential alterations of the autophagic pathway as well as BACE1-mediated amyloid processing in response to Pb exposure, respectively. Our data revealed that Res treatment or SIRT1 activation resisted the induction of autophagy by Pb exposure through inhibition of LC3 and Beclin-1 expression and promoted the degradation of Aβ and Tau phosphorylation. Besides, the SIRT1 activator (SRT1720) downregulated the expression of BACE1, the rate-limiting enzyme for Aβ production, by inhibiting the activation of nuclear factor-κB (NF-κB) in Pb-treated SH-SY5Y cells, which resulted in reduced Aβ production. Collectively, we verified the role of Res-SIRT1-autophagy as well as the SIRT1-NF-κB-BACE1 pathway in Pb-induced neuronal cell injury by in vivo or in vitro models. Our findings further elucidate the important role of SIRT1 and Res in counteracting Pb neurotoxicity, which may provide new interventions and targets for the subsequent treatment of neurodegenerative diseases.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s12889-020-09765-4.

Target tracking and imaging through scattering media are challenges that must be overcome for important applications in various fields. The fundamental problem is the randomly diffused light in scattering media that prevents the formation of diffraction-limited images. In recent years, speckle correlation has emerged as a powerful approach for restoring targets through scattering media and tracking targets with high scattering. However, this method fails to achieve high-accuracy target tracking in multidimensional motion. Furthermore, retrieving the deterministic phase of a target in a low-resolution speckle pattern is limited. Here, a method based on speckle-difference-combined bispectrum analysis (SDCBA) is presented to simultaneously track and image targets. The proposed SDCBA requires no high-resolution pattern, randomly assigned initial values, nor numerous iterations. The target is tracked simultaneously in multidimension by using speckle difference autocorrelation and restored with the deterministic phase via bispectrum analysis of the low-resolution speckle pattern. This work carries out simulations and experiments to demonstrate simultaneous multidimensional target tracking and imaging in low-resolution speckle pattern through scattering media via the SDCBA. This work will benefit various fields, including biomedical applications, materials science, and military security.

Despite the rapid increase in the number and applications of plankton imaging systems in marine science, processing large numbers of images remains a major challenge due to large variations in image content and quality in different marine environments. We constructed an automatic plankton image recognition and enumeration system using an enhanced Convolutional Neural Network (CNN) and examined the performance of different network structures on automatic plankton image classification. The procedure started with an adaptive thresholding approach to extract Region of Interest (ROIs) from in situ plankton images, followed by a procedure to suppress the background noise and enhance target features for each extracted ROI. The enhanced ROIs were classified into seven categories by a pre-trained classifier which was a combination of a CNN and a Support Vector Machine (SVM). The CNN was selected to improve feature description and the SVM was utilized to improve classification accuracy. A series of comparison experiments were then conducted to test the effectiveness of the pre-trained classifier including the combination of CNN and SVM versus CNN alone, and the performance of different CNN models. Compared to CNN model alone, the combination of CNN and SVM increased classification accuracy and recall rate by 7.13% and 6.41%, respectively. Among the selected CNN models, the ResNet50 performed the best with accuracy and recall at 94.52% and 94.13% respectively. The present study demonstrates that deep learning technique can improve plankton image recognition and that the results can provide useful information on the selection of different CNN models for plankton recognition. The proposed algorithm could be generally applied to images acquired from different imaging systems.

Currently, there are various calibration methods available to reduce the errors caused by the polarizing section of a dual-rotating-retarder polarimeter. Although these methods have high measurement accuracy, their robustness must be improved and the influence of the imaging section needs be discussed when they are applied in Mueller matrix microscopes. In this paper, a method of error source analysis and element calibration for the Mueller matrix microscope is proposed by using error transform coefficient matrices to account for the polarizing effect of the imaging section. Using Taylor expansion, an approximate linear relationship is established between the sources of errors and the Mueller matrix elements of the measured sample. From this relationship, error magnification coefficient matrices are calculated to determine the specific parameter errors in both the polarizing and imaging sections. Furthermore, elements in the fourth row or column of the error magnification coefficient matrix are especially important for the imaging section. The measurement and simulation results for an air sample and a quarter-wave plate sample as the standard samples, as well as a Daphnia organism sample with complex internal structure, are investigated and discussed. Furthermore, the comparison results reveal the effect of the imaging section on the birefringence characteristics of the Mueller matrix. With the proposed method, the maximum error can be reduced to be less than 0.01 for all the matrix elements and for the amplitude parameter of birefringence, even when the two system parameters a 2 and a 3 of the rotating mechanical part deviate from the default.

In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset.

The deformation measurement of a specular surface is of great importance during the quality inspection and installation of optical elements or wafers, especially those with large apertures. We propose a deflectometry method with speckle digital image correlation (DeSDIC) to realize the dynamic and high-accuracy measurement of the deformation on specular surfaces, with a simple system structure and robustness to noises and environmental vibrations. Random speckle pattern displayed on liquid crystal display is reflected by the original surface under test (SUT), and the distorted pattern is recorded by a camera. This originally distorted pattern is taken as the reference image, and the patterns captured afterwards are digitally correlated with the reference image to calculate the gradient change and deformation of the SUT. The theoretical relationships and an experimental one-step calibration scheme are proposed. Both static and dynamic deformations of a deformable mirror were experimentally measured to demonstrate the feasibility and accuracy of DeSDIC, which is comparable to phase-measuring deflectometry and interferometry.

Microcystin-leucine arginine (MC-LR) is a cyclic heptapeptide intracellular toxin released by cyanobacteria that exhibits strong reproductive toxicity. However, little is known about its biotoxicity to the female reproductive system. The present study investigates unexplored molecular pathways by which oxidative stress acts on MC-LR-induced endoplasmic reticulum stress (ERs) and autophagy. In the present study, immortalized murine ovarian granular cells (KK-1 cells) were exposed to 8.5, 17, and 34 μg/mL (IC50) of MC-LR with or without N-acetyl-l-cysteine (NAC, 10 mM) for 24 h, and C57BL/6 mice were treated with 12.5, 25.0, and 40.0 μg/kg⋅bw of MC-LR with or without NAC (200 mg/kg⋅bw) for 14 days. The results revealed that MC-LR could induce cells apoptosis and morphologic changes in ovarian tissues, induce oxidative stress by stimulating the generation of reactive oxygen species (ROS), destroying antioxidant capacity, and subsequently trigger ERs and autophagy by inducing the hyper-expression of ATG12, ATG5, ATG16, EIF2α (phosphorylated at S51), CHOP, XBP1, GRP78, Beclin1, and PERK (Thr980). Furthermore, NAC pretreatment partly inhibited MC-LR-induced ERs and autophagy via the PERK/ATG12 and XBP1/Beclin1 pathways. These results suggest that oxidative stress mediated MC-LR-induced ERs and autophagy in KK-1 cells and C57BL/6 mice ovaries. Therefore, oxidative stress plays an important role in female toxicity induced by MC-LR.

An image stabilization optical system using deformable freeform mirrors is proposed that enables the ray sets to couple dynamically in the object and image space. It aims to correct image blurring and degradation when there is relative movement between the imaging optical axis and the object. In this method, Fermat’s principle and matrix methods are used to describe the optical path of the entire optical system with a shift object plane and a fixed corresponding image plane in the carrier coordinate system. A constant optical path length is determined for each ray set, so the correspondence between the object and the shift free image point is used to calculate the solution to the points on the surface profile of the deformable mirrors (DMs). Off-axis three-mirror anastigmats are used to demonstrate the benefits of optical image stabilization with one- and two-deformable mirrors.

Pb is a potential risk factor for cognition, mainly mediated by enhanced oxidative stress. Resveratrol, a natural polyphenol with crucial anti-oxidative property, is recently implicated in preventing cognitive deficits in normal aging and neurodegenerative disorders. Its beneficial effects have been linked to sirtuin 1(SIRT1) activation. The aim of this work is to investigate the possible linkage between alterations in Pb-induced oxidative damage and cognitive impairment by prolonged treatment of resveratrol. Male C57BL/6 mice were given Pb(Ac)2 treatment or deionized H2O for 12 weeks, and subjected to resveratrol gavage at the dose of 50 mg/kgBw•d or vehicle after Pb exposure. Results from biochemical analysis and immunohistofluorescence showed that Pb induced oxidative DNA damage and decreased cortical antioxidant biomarker. As expected, these abnormalities were improved by resveratrol treatment. Morris water maze test, Western blotting, immunohistofluorescence staining and RT-qPCR indicated that resveratrol ameliorated spatial learning and memory deficits with alterations in hippocampal BDNF-TrkB signaling, promoted nuclear localization and phosphorylation of hippocampal SIRT1, partly increased protein levels of AMPK and PGC-1α involving in modulation of antioxidant response in Pb-exposed mice. Our results support the hypothesis that resveratrol could attenuate Pb-induced cognitive impairment which was associated with activating SIRT1 via modulation of oxidative stress. Additionally, resveratrol also repressed the Pb-induce amyloidogenic processing with resultant decline in cortical Aβ1−−40. Noteworthy, such effects were not mediated by resveratrol treatment alone. These findings emphasize the potential of SIRT1 activator as an efficacious dietary intervention to downgrade the Pb-induced neurotoxic lesion.

This paper introduces a compact optical information storage sensing system. Applications of this system include longitudinal surface plasmon resonance detection of gold nanorods with a single femtosecond laser in three-dimensional space as well as data storage. A diffractive optical element (DOE) is applied in the system to separate the recording-reading beam from the servo beam. This allows us to apply a single laser and one objective lens in a single optical path for the servo beam and the recording-reading beam. The optical system has a linear region of 8 λ, which is compatible with current DVD servo modules. The wavefront error of the optical system is below 0.03 λrms. The minimum grating period of the DOE is 13.4 µm, and the depth of the DOE is 1.2 µm, which makes fabrication of it possible. The DOE is also designed to conveniently control the layer-selection process, as there is a linear correlation between the displacement of the DOE and the layer-selection distance. The displacement of DOE is in the range of 0–6.045 mm when the thickness of the layer-selection is 0.3 mm. Experiments were performed and the results have been verified.

Microcystins (MCs), the secondary metabolites of blue-green algae, are a ubiquitous and major cyanotoxin contaminant. Besides the hepatopancreas/liver, the reproductive system is regarded as the most important target organ for MCs. Although reactive oxygen species (ROS) have been implicated in MCs-induced reproductive toxicity, the role of MCs in this pathway remains unclear. In the present study, Sertoli cells were employed to investigate apoptotic death involved in male reproductive toxicity of microcystin-LR (MC-LR). After exposure to various concentrations of MC-LR for 24 h, the growth of Sertoli cells was concentration-dependently decreased with an IC50 of approximately 32 μg/mL. Mitochondria-mediated apoptotic changes were observed in Sertoli cells exposed to 8, 16 and 32 μg/mL MC-LR including the increased expression of caspase pathway proteins, collapse of mitochondrial membrane potential (MMP) and generation of ROS. Pretreatment with a global caspase inhibitor was found to depress the activation of caspases, and eventually increased the survival rate of Sertoli cells, implying that the mitochondrial caspases pathway is involved in MC-LR-induced apoptosis. Furthermore, N-acetyl-l-cysteine (NAC) attenuated the MC-LR-induced intracellular ROS generation, MMP collapse and cytochrome c release, resulting in the inhibition of apoptosis. Taken together, the observed results suggested that MC-LR induced apoptotic death of Sertoli cells by the activation of mitochondrial caspases cascade, while its effects on the ROS-mediated signaling pathway may contribute towards the initiation of mitochondrial dysfunction.

Microcystin-leucine arginine (MC-LR), a cyclic heptapeptide produced by cyanobacteria, is a strong reproductive toxin. Studies performed in rat Sertoli cells and Chinese hamster ovary cells have demonstrated typical apoptosis after MC-LR exposure. However, little is known on how to protect against the reproductive toxicity induced by MC-LR. The present study aimed to explore the possible molecular mechanism underlying the anti-apoptosis and protective effects of resveratrol (RES) on the co-culture of Sertoli–germ cells and rat testes. The results demonstrated that MC-LR treatment inhibited the proliferation of Sertoli–germ cells and induced apoptosis. Furthermore, sirtuin 1 (SIRT1) and Bcl-2 were inhibited, while p53 and Ku70 acetylation, Bax expression, and cleaved caspase-3 were upregulated by MC-LR. However, RES pretreatment ameliorated MC-LR-induced apoptosis and SIRT1 inhibition, and downregulated the MC-LR-induced increase in p53 and Ku70 acetylation, Bax expression, and caspase-3 activation. In addition, RES reversed the MC-LR-mediated reduction in Ku70 binding to Bax. The present study indicated that the administration of RES could ameliorate MC-LR-induced Sertoli–germ cell apoptosis and protect against reproductive toxicity in rats by stimulating the SIRT1/p53 pathway, suppressing p53 and Ku70 acetylation and enhancing the binding of Ku70 to Bax.