On-chip silicon photonic micro-ring processor lights up optical image encryption.

Optical image encryption has long been an important concept in the fields of photonic network processing and communication. Here, we propose a convolution-like operation-based optical image encryption algorithm exploiting a silicon photonic multiplexing architecture to achieve content security. Particularly, the encryption process is completed in a 3 × 3 cross-shaped photonic micro-ring resonator (MRR) array on chip. For the first time, to the best of our knowledge, this algorithm encodes information in an integrated intensity modulation, effectively reducing the encoding difficulty. Moreover, the high reliability and scalability of optical encryption are ensured using both linear and nonlinear operations on photonic chips according to characteristics of MRRs. As the encryption and decryption experiments show, the image restoration accuracy of our optical encryption algorithm exceeds 99% under real system noise at the pixel level, indicating its noise-robust property. Meanwhile, the peak signal-to-noise ratios of the restored and encrypted images are >60 and <15 dB, respectively, revealing both the high accuracy of the restored image and the small correlation between the encrypted and original images. This work adds to the rapidly expanding field of optical image encryption on photonic chips.

Light-triggered AND logic tetrapeptide dynamic covalent assembly.

We demonstrate light-triggered dynamic covalent assembly of a linear short tetrapeptide containing two terminal cysteine residues in an AND logic manner. A photobase generator is introduced to accomplish light-mediated pH regulation to increase the reduction potential of thiols in the tetrapeptide, which activates its oxidative polymerization through disulfide bonds. Interestingly, it is elucidated that under light irradiation, mere co-existence of photobase generator and the oxidizing agent permits the polymerization performance of this tetrapeptide. Hence, a light-triggered AND logic dynamic covalent assembly of a tetrapeptide is achieved. Further, upon redox response, the reversible aggregation and disaggregation can be transformed for numerous times due to the dynamic covalent feature of disulfide bond. As a comparison, no assembly occurs for a short peptide containing one terminal cysteine residue under the same stimuli condition. This work offers a new approach to remotely control programmable molecular assembly of short linear peptides based on dynamic covalent bond, holding great potential in wide bioapplications.

The Simulation of Ester Lubricants and Their Application in Weak Gel Drilling Fluids.

To enhance the performance and reduce the amount of ester-based lubricants used in weak gel drilling fluids, a shear dynamics simulation under extreme pressure conditions was employed to refine the formulation of the base oil and pressure additives. The simulation results were validated using fatty acid methyl, ethyl, and butyl esters. Fatty acid methyl ester demonstrated the lowest temperature increase and the highest load-bearing capacity post-shear. The four-ball friction test revealed that methyl oleate had a coefficient of friction of 0.0018, approximately a third of that for butyl oleate, confirming the simulation's accuracy. By using methyl oleate as the base oil and oleamide as the pressure-resistant component, the optimal shear stress was achieved with a 10% addition of oleamide. A lubricant composed of 90% methyl oleate and 10% oleamide was tested and showed a coefficient of friction of 0.03 when 0.5% was added to bentonite slurry, indicating a strong lubricating film. Adding 1% of this lubricant to a low gel drilling fluid system did not affect its rheological properties, and the gel structure remained stable after seven days of aging. Field tests at the Fu86-3 well in the Jiangsu Oilfield of Sinopec confirmed that adding 1% of the ester-based lubricant to the drilling fluid significantly improved drilling efficiency, reduced drag by an average of 33%, and increased the drilling rate to 22.12 m/h. This innovation effectively prevents drilling complications and successfully achieves the objectives of enhancing efficiency.

Scientific objectives and payload configuration of the Chang'E-7 mission.

As the cornerstone mission of the fourth phase of the Chinese Lunar Exploration Program, Chang'E-7 (CE-7) was officially approved, and implementation started in 2022, including a main probe and a communication relay satellite. The main probe, consisting of an orbiter, a lander, a rover and a mini-flying probe, is scheduled to be launched in 2026. The lander will land on Shackleton crater's illuminated rim near the lunar south pole, along with the rover and mini-flying probe. The relay satellite (named Queqiao-2) will be launched in February 2024 as an independent mission to support relay communication during scientific exploration undertaken by Chang'E-4, the upcoming Chang'E-6 in 2024 and subsequent lunar missions. The CE-7 mission is mainly aimed at scientific and resource exploration of the lunar south pole. We present CE-7's scientific objectives, the scientific payloads configuration and the main functions for each scientific payload with its key technical specifications.

The association between metabolic dysfunction-associated steatotic liver disease, cardiovascular and cerebrovascular diseases and the thickness of carotid plaque.

BACKGROUND: The relationship between metabolic dysfunction-associated steatotic liver disease (MASLD) and atherosclerosis has been controversial, which has become a hit of recent research. The study aimed to explore the association between MASLD, cardiovascular and cerebrovascular diseases (CCVD), and the thickness of carotid plaque which was assessed by ultrasound. METHODS: From September 2018 to June 2019, 3543 patients were enrolled. We asked participants to complete questionnaires to obtain information. All patients underwent liver ultrasound and bilateral carotid ultrasound to obtain carotid intima-media thickness (IMT) and maximum carotid plaque thickness (CPT). Hepatic steatosis was quantified during examination according to Hamaguchi's ultrasonographic score, from 0 to 6 points. A score < 2 was defined as without fatty liver, and a score ≥ 2 was defined as fatty liver. Information about blood lipids was collected based on the medical records. RESULTS: We found common risk factors for CCVD events, MASLD, and atherosclerosis. There was a significant correlation between MASLD and carotid plaque, but not with CPT. No association was found between MASLD and CCVD events. CPT and IMT were thicker in CCVD patients than in non-CCVD patients. No significant difference was found between IMT and CPT in MASLD patients and non-MASLD patients. CCVD was independently and consistently associated with higher IMT, and free fatty acid (FFA). CONCLUSIONS: According to our results, we recommend carotid ultrasound examination of the patients when FFA is increased, regardless of the presence of risk factors and MASLD. Due to the distribution of CPT of both CCVD and MASLD patients in the CPT 2-4 mm group, contrast-enhanced ultrasound is necessary to assess the vulnerability of the plaque when CPT ≥ 2 mm. Timely treatment of vulnerable plaques may reduce the incidence of future CCVD events.

The clinical value of carotid plaque score in patients with metabolic syndrome and cardiovascular diseases.

OBJECTIVE: To investigate the relationship between carotid plaque load score (CPS) and metabolic syndrome (MS) and cardiovascular diseases (CVD), in order to provide theoretical basis for the precaution and control of MS and CVD. METHODS: A total of 1962 patients were incorporated into the study and divided into MS group and non-MS group, CVD group and non-CVD group. CPS and CIMT were obtained by carotid artery ultrasound, and the data of each group were statistically analyzed. RESULTS: Age, BMI, basal metabolic rate, body fat rate, gender, and the incidence of central obesity, hypertension, diabetes and dyslipidemia are statistical different between MS group and non-MS group (P < 0.05). CIMT between CVD group and non-CVD group are statistical different (1.040 ± 0.239 VS 0.972 ± 0.297, P < 0.001). CPS was statistically significant between MS group and non-MS group(2.254 ± 2.728 VS 1.548 ± 2.219, P = 0.003) and between CVD group and non-CVD group (2.322 ± 2.760 VS 1.688 ± 2.347, P = 0.004). CONCLUSION: Patients in MS group and CVD group have higher carotid plaque burden than those in non-MS group and non-CVD group. The higher the CPS was, the higher the incidence of MS and CVD was, and the distribution of CPS in MS and CVD population was consistent.

Highly Efficient Active Learning With Tracklet-Aware Co-Cooperative Annotators for Person Re-Identification.

Supervised person re-identification (ReID) has attracted widespread attentions in the computer vision community due to its great potential in real-world applications. However, the demand of human annotation heavily limits the application as it is costly to annotate identical pedestrians appearing from different cameras. Thus, how to reduce the annotation cost while preserving the performance remains challenging and has been studied extensively. In this article, we propose a tracklet-aware co-cooperative annotators' framework to reduce the demand of human annotation. Specifically, we partition the training samples into different clusters and associate adjacent images in each cluster to produce the robust tracklet which decreases the annotation requirements significantly. Besides, to further reduce the cost, we introduce a powerful teacher model in our framework to implement the active learning strategy and select the most informative tracklets for human annotator, the teacher model itself, in our setting, also acts as an annotator to label the relatively certain tracklets. Thus, our final model could be well-trained with both confident pseudo-labels and human-given annotations. Extensive experiments on three popular person ReID datasets demonstrate that our approach could achieve competitive performance compared with state-of-the-art methods in both active learning and unsupervised learning (USL) settings.

Observation of the protein expression level via naked eye: Pt clusters catalyze non-color molecules into brown-colored molecules in cells.

α v ß 3 is overexpressed in various tumor cells and plays a key role in tumor genesis, invasion, and metastasis. Therefore, it is of great significance to precisely detect the α v ß 3 level in cells via a simple method. For this purpose, we have constructed a peptide-coated platinum (Pt) cluster. Due to its bright fluorescence, well-defined Pt atom numbers, and peroxidase-like catalytic activity, this cluster can be used to evaluate α v ß 3 levels in cells by fluorescence imaging, inductively coupled plasma mass spectrometry (ICP-MS), and catalytic amplification of visual dyes, respectively. In this report, the expression level of α v ß 3 in living cells is well-detected by the naked eye under an ordinary light microscope when the Pt cluster binds to αvß3 in cells and catalyzes non-color 3,3'-diaminobenzidine (DAB) into brown-colored molecules in situ. Moreover, SiHa, HeLa, and 16HBE cell lines with different α v ß 3 expression levels can be visually distinguished by the peroxidase-like Pt clusters. This research will provide a reliable method for the simple detection of α v ß 3 levels in cells.

Coherent optical neuron control based on reinforcement learning.

Optical neural networks take optical neurons as the cornerstone to achieve complex functions. The coherent optical neuron has become one of the mainstream implementations because it can effectively perform natural and even complex number calculations. However, its state variability and requirement for reliability and effectiveness render traditional control methods no longer applicable. In this Letter, deep reinforcement coherent optical neuron control (DRCON) is proposed, and its effectiveness is experimentally demonstrated. Compared with the standard stochastic gradient descent, the average convergence rate of DRCON is 33% faster, while the effective number of bits increases from less than 2 bits to 5.5 bits. DRCON is a promising first step for large-scale optical neural network control.

Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions.

A data-enhanced deep greedy optimization (DEDGO) algorithm is proposed to achieve the efficient and on-demand inverse design of multiple transition metal dichalcogenides (TMDC)-photonic cavity-integrated heterojunctions operating in the strong coupling regime. Precisely, five types of photonic cavities with different geometrical parameters are employed to alter the optical properties of monolayer TMDC, aiming at discovering new and intriguing physics associated with the strong coupling effect. Notably, the traditional rigorous coupled wave analysis (RCWA) approach is utilized to generate a relatively small training dataset for the DEDGO algorithm. Importantly, one remarkable feature of DEDGO is the integration the decision theory of reinforcement learning, which remedies the deficiencies of previous research that focused more on modeling over decision making, increasing the success rate of inverse prediction. Specifically, an iterative optimization strategy, namely, deep greedy optimization, is implemented to improve the performance. In addition, a data enhancement method is also employed in DEDGO to address the dependence on a large amount of training data. The accuracy and effectiveness of the DEDGO algorithm are confirmed to be much higher than those of the random forest algorithm and deep neural network, making possible the replacement of the time-consuming conventional scanning optimization method with the DEDGO algorithm. This research thoroughly describes the universality, interpretability, and excellent performance of the DEDGO algorithm in exploring the underlying physics of TMDC-cavity heterojunctions, laying the foundations for the on-demand inverse design of low-dimensional material-based nano-devices.

On-Cell Catalytic Detection of Epithelial-to-Mesenchymal Transition by a Clusterzyme Bioprobe.

We construct a peptide-conjugated metal cluster as an enzyme-like catalytic bioprobe to enhance quantitative analysis of a membrane protein biomarker and detect epithelial-to-mesenchymal transition of tumor cells. This bioprobe with atomically precise formula, termed clusterzyme, possesses selective recognition and intrinsic enzyme-like activity. These favorable features facilitate sensitive quantitative analysis of the membrane protein in situ through on-cell catalytic signal amplification. This clusterzyme-based analytical method exhibits excellent compatibility with a traditional enzyme-linked immunosorbent assay and improved detection sensitivity with accuracy and robustness. Further, the expression level of the membrane protein reflects the ability of migration and invasion of model tumor cells, revealing epithelial-to-mesenchymal transition process. This work offers a facile and sensitive approach to monitor tumor cell type evolution at the molecular level, demonstrating a potential application of early cancer diagnosis and therapy assessment.

Relation-Based Deep Attention Network with Hybrid Memory for One-Shot Person Re-Identification.

One-shot person Re-identification, which owns one labeled sample among numerous unlabeled data for each identity, is proposed to tackle the problem of the shortage of labeled data. Considering the scenarios without sufficient labeled data, it is very challenging to keep abreast of the performance of the supervised task in which sufficient labeled samples are available. In this paper, we propose a relation-based attention network with hybrid memory, which can make full use of the global information to pay attention to the identity features for model training with the relation-based attention network. Importantly, our specially designed network architecture effectively reduces the interference of environmental noise. Moreover, we propose a hybrid memory to train the one-shot data and unlabeled data in a unified framework, which notably contributes to the performance of person Re-identification. In particular, our designed one-shot feature update mode effectively alleviates the problem of overfitting, which is caused by the lack of supervised information during the training process. Compared with state-of-the-art unsupervised and one-shot algorithms for person Re-identification, our method achieves considerable improvements of 6.7%, 4.6%, and 11.5% on Market-1501, DukeMTMC-reID, and MSMT17 datasets, respectively, and becomes the new state-of-the-art method for one-shot person Re-identification.

Achieving efficient inverse design of low-dimensional heterostructures based on a vigorous scalable multi-task learning network.

A scalable multi-task learning (SMTL) model is proposed for the efficient inverse design of low-dimensional heterostructures and the prediction of their optical response. Specifically, several types of nanostructures, including single and periodic graphene-Si heterostructures consisting of n×n graphene squares (n=1∼9), 1D periodic graphene ribbons, 2D arrays of graphene squares, pure Si cubes and their periodic array counterparts, are investigated using both traditional finite element method and SMTL network, with the former providing training data (optical absorption) for the latter. There are two important algorithms implemented in SMTL model: one is the normalization mechanism that makes different parameters of different structures on the same scale, ensuring that SMTL network can deal with tasks with different dataset impartially and without bias; the other one is used to capture the impact of nanostructures' dimensions on their optical absorption and thus improve the generalization ability of SMTL. Utilizing SMTL model, we first study the absorption property of the multiple shaped nanostructures and look deeper into the impacts of n×n graphene squares and Si cuboid on the optical absorption of their heterostructures. Equally important, the multi-structure inverse design functionality of SMTL is confirmed in this context, which not only owns high accuracy, fast computational speed, and excellent generalizable ability, but also can be applied to contrive new structures with desired optical response. This work adds to the rapidly expanding field of inverse design in nanophotonics and establishes a multi-task learning framework for heterostructures and more complicated nanoparticles.

Downregulation of microRNA­221 facilitates H1N1 influenza A virus replication through suppression of type­IFN response by targeting the SOCS1/NF­κB pathway.

Accumulating data has indicated that host microRNAs (miRNAs/miRs) play essential roles in innate immune responses to viral infection; however, the roles and the underlying mechanisms of miRNAs in influenza A virus (IAV) replication remain unclear. The present study examined on the effects of miRNAs on hemagglutinin (H)1 neuraminidase (N)1 replication and antiviral innate immunity. Using a microarray assay, the expression profiles of miRNA molecules in IAV­infected A549 cells were analyzed. The results indicated that miR­221 was significantly downregulated in IAV­infected A549 cells. It was also observed that IAV infection decreased the expression levels of miR­221 in A549 cells in a dose­ and time­dependent manner. Functionally, upregulation of miR­221 repressed IAV replication, whereas knockdown of miR­221 had an opposite effect. Subsequently, it was demonstrated that miR­221 overexpression could enhance IAV­triggered IFN­α and IFN­ß production and IFN­stimulated gene expression levels, while miR­221­knockdown had the opposite effect. Target prediction and dual luciferase assays indicated that suppressor of cytokine signaling 1 (SOCS1) was a direct target of miR­221 in A549 cells. Furthermore, knockdown of SOCS1 efficiently abrogated the influences caused by miR­221 inhibition on IAV replication and the type­I IFN response. It was also found that the miR­221 positively regulated NF­κB activation in IAV­infected A549 cells. Taken together, these data suggested that miR­221­downregulation promotes IAV replication by suppressing type­I IFN response through targeting SOCS1/NF­κB pathway. These findings suggest that miR­221 may serve as a novel potential therapeutic target for IAV treatment.

Metal Cluster-Based Electrochemical Biosensing System for Detecting Epithelial-to-Mesenchymal Transition.

N-cadherin serves as an important oncobiomarker of epithelial-to-mesenchymal transition (EMT) progression, which identifies invasion and metastasis of malignant tumor cells. Although many efforts have been devoted to quantitative detection of N-cadherin, efforts to analyzing the protein of interest at intact cellular levels are scarce. Herein, a metal cluster-based electrochemical biosensing system is developed to determine the expressing levels of N-cadherin during the EMT process of tumor cells. To be specific, a peptide with a unique sequence and function is designed as a reductant and an anchor to synthesize metal clusters in a precise manner. Consequently, peptide-modified metal clusters possess N-cadherin-targeting, photoluminescence, and electrocatalytic properties. Especially, the redox-active metal clusters function as both an electron-transfer mediator and an electronic conductor for enhanced electrochemical sensing. These favorable features enable them as a rapid, sensitive, and reliable whole-cell biosensor, which integrates the fluorescence and electrochemical signals. This cytosensor can accurately quantify the expression levels of N-cadherin on at least 5000 tumor cells. Further, the current signals of model cancer cells gradually increase with EMT progression, indicating tumor cell-type evolution. Our study represents the advanced bioprobe and analytical methods for accurate quantitation of a biomarker to identify tumor progression.

An artificial metalloenzyme for catalytic cancer-specific DNA cleavage and operando imaging.

Metalloenzymes are promising anticancer candidates to overcome chemoresistance by involving unique mechanisms. To date, it is still a great challenge to obtain synthetic metalloenzymes with persistent catalytic performance for cancer-specific DNA cleavage and operando imaging. Here, an artificial metalloenzyme, copper cluster firmly anchored in bovine serum albumin conjugated with tumor-targeting peptide, is exquisitely constructed. It is capable of persistently transforming hydrogen peroxide in tumor microenvironment to hydroxyl radical and oxygen in a catalytic manner. The stable catalysis recycling stems from the electron transfer between copper cluster and substrate with well-matched energy levels. Notably, their high biocompatibility, tumor-specific recognition, and persistent catalytic performance ensure the substantial anticancer efficacy by triggering DNA damage. Meanwhile, by coupling with enzyme-like reactions, the operando therapy effect is expediently traced by chemiluminescence signal with high sensitivity and sustainability. It provides new insights into synthesizing biocompatible metalloenzymes on demand to visually monitor and efficiently combat specific cancers.

Joint Deep Model with Multi-Level Attention and Hybrid-Prediction for Recommendation.

The Recommender System (RS) has obtained a pivotal role in e-commerce. To improve the performance of RS, review text information has been extensively utilized. However, it is still a challenge for RS to extract the most informative feature from a tremendous amount of reviews. Another significant issue is the modeling of user-item interaction, which is rarely considered to capture high- and low-order interactions simultaneously. In this paper, we design a multi-level attention mechanism to learn the usefulness of reviews and the significance of words by Deep Neural Networks (DNN). In addition, we develop a hybrid prediction structure that integrates Factorization Machine (FM) and DNN to model low-order user-item interactions as in FM and capture the high-order interactions as in DNN. Based on these two designs, we build a Multi-level Attentional and Hybrid-prediction-based Recommender (MAHR) model for recommendation. Extensive experiments on Amazon and Yelp datasets showed that our approach provides more accurate recommendations than the state-of-the-art recommendation approaches. Furthermore, the verification experiments and explainability study, including the visualization of attention modules and the review-usefulness prediction test, also validated the reasonability of our multi-level attention mechanism and hybrid prediction.

Peptide-Templated Gold Clusters as Enzyme-Like Catalyst Boost Intracellular Oxidative Pressure and Induce Tumor-Specific Cell Apoptosis.

Anticancer metallodrugs that aim to physiological characters unique to tumor microenvironment are expected to combat drug tolerance and side-effects. Recently, owing to the fact that reactive oxygen species' is closely related to the development of tumors, people are committed to developing metallodrugs with the capacity of improving the level of reactive oxygen species level toinduce oxidative stress in cancer cells. Herein, we demonstrated that peptide templated gold clusters with atomic precision preferably catalyze the transformation of hydrogen peroxide into superoxide anion in oxidative pressure-type tumor cells. Firstly, we successfully constructed gold clusters by rationally designing peptide sequences which targets integrin ανß3 overexpressed on glioblastoma cells. The superoxide anion, radical derived from hydrogen peroxide and catalyzed by gold clusters, was confirmed in vitro under pseudo-physiological conditions. Then, kinetic parameters were evaluated to verify the catalytic properties of gold clusters. Furthermore, these peptide decorated clusters can serve as special enzyme-like catalyst to convert endogenous hydrogen peroxide into superoxide anion, elevated intracellular reactive oxygen species levels, lower mitochondrial membrane potential, damage biomacromolecules, and trigger tumor cell apoptosis consequently.

Photo-induced excitonic structure renormalization and broadband absorption in monolayer tungsten disulphide.

Atomically thin transition metal dichalcogenides (TMDCs) have emerged as a new class of two-dimensional (2D) material for novel optoelectronic applications. In particular, 2D TMDCs are viewed as intriguing and appealing materials to construct Q-switching and mode-locked modulators, due to their broadband saturable absorption even of photon energy below their excitonic energies. However, the dynamics and mechanism of saturable absorption inside TMDCs has yet to be investigated. In this paper, the relaxation dynamics of monolayer tungsten disulphide (WS2) was investigated considering different excitonic transitions. WS2 illustrates dramatic changes in optical responses when excited by intense laser pulses, which are characterized by the broadband photo-induced nonresonance absorption and the giant excitonic bands renormalization process. The experimental results show that strong photo-induced restructuring of excitonic bands has picosecond lifetime and full recovery of optical responses takes hundreds of picosecond. Additionally, our observations reveal that heavy renormalization and overlap of excitonic bands are induced by strong many-body Coulomb interactions. Moreover, the broadband absorption feature of WS2 opens up new applications in broadband saturable absorbers and ultrafast photonic devices.

Matching-range-constrained real-time loop closure detection with CNNs features.

The loop closure detection (LCD) is an essential part of visual simultaneous localization and mapping systems (SLAM). LCD is capable of identifying and compensating the accumulation drift of localization algorithms to produce an consistent map if the loops are checked correctly. Deep convolutional neural networks (CNNs) have outperformed state-of-the-art solutions that use traditional hand-crafted features in many computer vision and pattern recognition applications. After the great success of CNNs, there has been much interest in applying CNNs features to robotic fields such as visual LCD. Some researchers focus on using a pre-trained CNNs model as a method of generating an image representation appropriate for visual loop closure detection in SLAM. However, there are many fundamental differences and challenges involved in character between simple computer vision applications and robotic applications. Firstly, the adjacent images in the dataset of loop closure detection might have more resemblance than the images that form the loop closure. Secondly, real-time performance is one of the most critical demands for robots. In this paper, we focus on making use of the feature generated by CNNs layers to implement LCD in real environment. In order to address the above challenges, we explicitly provide a value to limit the matching range of images to solve the first problem; meanwhile we get better results than state-of-the-art methods and improve the real-time performance using an efficient feature compression method.