Flexible Strain Sensors Based on an Interlayer Synergistic Effect of Nanomaterials for Continuous and Noninvasive Blood Pressure Monitoring.

The continuous, noninvasive monitoring of human blood pressure (BP) through the accurate detection of pulse waves has extremely stringent requirements on the sensitivity and stability of flexible strain sensors. In this study, a new ultrasensitive flexible strain sensor based on the interlayer synergistic effect was fabricated through drop-casting and drying silver nanowires and graphene films on polydimethylsiloxane substrates and was further successfully applied for continuous monitoring of BP. This strain sensor exhibited ultrahigh sensitivity with a maximum gauge factor of 34357.2 (∼700% sensitivity enhancement over other major sensors), satisfactory response time (∼85 ms), wide strange range (12%), and excellent stability. An interlayer fracture mechanism was proposed to elucidate the working principle of the strain sensor. The real-time BP values can be obtained by analyzing the relationship between the BP and the pulse transit time. To verify our strain sensor for real-time BP monitoring, our strain sensor was compared with a conventional electrocardiogram-photoplethysmograph method and a commercial cuff-based device and showed similar measurement results to BP values from both methods, with only minor differences of 0.693, 0.073, and 0.566 mmHg in the systolic BP, diastolic BP, and mean arterial pressure, respectively. Furthermore, the reliability of the strain sensors was validated by testing 20 human subjects for more than 50 min. This ultrasensitive strain sensor provides a new pathway for continuous and noninvasive BP monitoring.

Bubbles-Induced Porous Structure-Based Flexible Piezoresistive Sensors for Speech Recognition.

Flexible piezoresistive sensors with a porous structure that are used in the field of speech recognition are seldom characterized by both high sensitivity and ease of preparation. In this study, a piezoresistive sensor with a porous structure that is both highly sensitive and can be prepared by using a simple method is proposed for speech recognition. The preparation process utilizes the interaction of bubbles generated by ethanol evaporation and active agents with polydimethylsiloxane to produce a porous flexible substrate. This preparation process requires neither templates nor harsh experimental conditions such as a low temperature and a low pressure. Furthermore, the prepared piezoresistive sensor has excellent properties, such as a high sensitivity (27.6 kPa-1), a satisfactory response time (800 µs), and a good stability (10,000 cycles). When used for speech recognition, more than 1500 vocalizations and silent speech signals obtained from subjects saying numbers from "0" to "9" were collected by the sensor for training a convolutional neural network model. The average accuracy of the recognition reached 94.8%. The simple preparation process and the excellent performance of the prepared flexible piezoresistive sensor endow it with a wide application prospect in the field of speech recognition.

Characteristics of Lactic Acid Bacteria as Potential Probiotic Starters and Their Effects on the Quality of Fermented Sausages.

The aim of this study was to explore the potential of commercial lactic acid bacteria (LAB) as probiotic starters in fermented sausages. We initially investigated the growth activity, acid production capability, and tolerance to fermentation conditions of Lactobacillus sakei, Lactiplantibacillus plantarum, and Pediococcus pentosaceus. All three LAB strains proved viable as starters for fermented sausages. Subsequently, we explored their potential as probiotics based on their antibacterial and antioxidant capabilities. L. plantarum exhibited stronger inhibition against Escherichia coli and Staphylococcus aureus. All three strains displayed antioxidant abilities, with cell-free supernatants showing a higher antioxidant activity compared to intact cells and cell-free extracts. Moreover, the activities of superoxide dismutase, glutathione peroxidase, and catalase were stronger in the cell-free supernatant, cell-free extract, and intact cell, respectively. Finally, we individually and collectively inoculated these three LAB strains into sausages to investigate their impact on quality during the fermentation process. External starters significantly reduced pH, thiobarbituric acid reactive substances, and sodium nitrite levels. The improvements in color and texture had positive effects, with the L. plantarum inoculation achieving higher sensory scores. Overall, all three LAB strains show promise as probiotic fermentation starters in sausage production.

The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data.

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.

Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers.

Gastrointestinal (GI) cancer accounts for one in four cancer cases and one in three cancer-related deaths globally. A deeper understanding of cancer development mechanisms can be applied to cancer medicine. Comprehensive sequencing applications have revealed the genomic landscapes of the common types of human cancer, and proteomics technology has identified protein targets and signalling pathways related to cancer growth and progression. This study aimed to explore the functional proteomic profiles of four major types of GI tract cancer based on The Cancer Proteome Atlas (TCPA). We provided an overview of functional proteomic heterogeneity by performing several approaches, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), t-stochastic neighbour embedding (t-SNE) analysis, and hierarchical clustering analysis in oesophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ) tumours, to gain a system-wide understanding of the four types of GI cancer. The feature selection approach, mutual information feature selection (MIFS) method, was conducted to screen candidate protein signature subsets to better distinguish different cancer types. The potential clinical implications of candidate proteins in terms of tumour progression and prognosis were also evaluated based on TCPA and The Cancer Genome Atlas (TCGA) databases. The results suggested that functional proteomic profiling can identify different patterns among the four types of GI cancers and provide candidate proteins for clinical diagnosis and prognosis evaluation. We also highlighted the application of feature selection approaches in high-dimensional biological data analysis. Overall, this study could improve the understanding of the complexity of cancer phenotypes and genotypes and thus be applied to cancer medicine.

Using feature selection and Bayesian network identify cancer subtypes based on proteomic data.

The Cancer Proteome Atlas (TCPA) project collects reverse-phase protein arrays (RPPA)-based proteome datasets from nearly 8000 samples across 32 cancer types. This study aims to investigate the pan-cancer proteome signature and identify cancer subtypes of glioma, kidney cancer, and lung cancer based on TCPA data. We first visualized the tumor clustering models using t-distributed stochastic neighbour embedding (t-SNE) and bi-clustering heatmap. Then, three feature selection methods (pyHSICLasso, XGBoost, and Random Forest) were performed to select protein features for classifying cancer subtypes in training dataset, and the LibSVM algorithm was empolyed to test classification accuracy in the validation dataset. Clustering analysis revealed that different kinds of tumors have relatively distinct proteomic profiling based on tissue or origin. We identified 20, 10, and 20 protein features with the highest accuracies in classifying subtypes of glioma, kidney cancer, and lung cancer, respectively. The predictive abilities of the selected proteins were confirmed by receiving operating characteristic (ROC) analysis. Finally, the Bayesian network was utilized to explore the protein biomarkers that have direct causal relationships with cancer subtypes. Overall, we highlight the theoretical and technical applications of machine learning based feature selection approaches in the analysis of high-throughput biological data, particularly for cancer biomarker research. SIGNIFICANCE: Functional proteomics is a powerful approach for characterizing cell signaling pathways and understanding their phenotypic effects on cancer development. The TCPA database provides a platform to explore and analyze TCGA pan-cancer RPPA-based protein expression. With the advent of the RPPA technology, the availability of high-throughput data in TCPA platform has made it possible to use machine learning methods to identify protein biomarkers and further differentiate subtypes of cancer based on proteomic data. In this study, we highlight the role of feature selection and Bayesian network in discovery protein biomarker for classifying cancer subtypes based on functional proteomic data. The application of machine learning methods in the analysis of high-throughput biological data, particularly for cancer biomarker researches, which have potential clinical values in developing individualized treatment strategies.

Effect of compound dietary fiber of soybean hulls on the gel properties of myofibrillar protein and its mechanism in recombinant meat products.

Response surface methodology was used to determine the optimum ratio of rice husk dietary fiber, soybean hull dietary fiber, and inulin as 1.40, 1.42, and 3.24%. The effects of compound and single dietary fiber on water holding capacity, gel strength, secondary structure, rheological properties, chemical action force, and microstructure of myofibrillar proteins (MP) gel were investigated. The application of composite dietary fiber significantly (P < 0.05) improved the gel strength, water holding capacity and storage modulus (G') of MP gel. Fourier transform infrared spectrum analysis shows that the addition of compound dietary fiber can make the gel structure more stable. The effect of dietary fiber complex on the chemical action of MP gel was further studied, and it was found that hydrophobic interaction and disulfide bond could promote the formation of compound gel. By comparing the microstructure of the MP gel with and without dietary fiber, the results showed that the MP gel with compound dietary fiber had smaller pores and stronger structure. Therefore, the rice hull dietary fiber, the soybean hull dietary fiber and the inulin are compounded and added into the low-fat recombinant meat product in a proper proportion, so that the quality characteristics and the nutritional value of the low-fat recombinant meat product can be effectively improved, the rice hull dietary fiber has the potential of being used as a fat substitute, and a theoretical basis is provided for the development of the functional meat product.

Purification, characterization and inactivation kinetics of polyphenol oxidase extracted from Cistanche deserticola.

MAIN CONCLUSION: PPO was purified from Cistanche deserticola, and its enzymatic characteristics were clarified. It was found that microwave treatment was an efficient way to inactivate PPO. Polyphenol oxidase (PPO) from Cistanche deserticola was obtained and purified through an acetone precipitation and anion exchange column, the enzymatic characteristics and inactivation kinetics of PPO were studied. The specific activity of PPO was 73135.15 ± 6625.7 U/mg after purification, the purification multiple was 48.91 ± 4.43 times, and the recovery was 30.96 ± 0.27%. The molecular weight of the PPO component is about 66 kDa by SDS-PAGE analysis. The optimum substrate of PPO was catechol (Vmax = 0.048 U/mL, Km = 21.70 mM) and the optimum temperature and pH were 30 °C and 7, respectively. When the temperature is above 50 °C, pH < 3 or pH > 10, the enzyme activity can be significantly inhibited. The first-order kinetic fitting shows that microwave inactivation has lesser k values, larger D values and shorter t1/2. It was found that microwave treatment is considered as an efficient and feasible way to inactive PPO by comparing the Z values and Ea values of the two thermal treatments.

Highly stretchable and sensitive strain sensors based on modified PDMS and hybrid particles of AgNWs/graphene.

High-performance strain sensors have received extensive attention due to their wide range of applications in pulsebeat detection, speech recognition, motion detection, and blood pressure monitoring. However, it is difficult to simultaneously attain high sensitivity and excellent stretchability. In this work, a strain sensor based on modified polydimethylsiloxane (PDMS) and conductive hybrid particles of silver nanowires (AgNWs)/graphene was successfully fabricated. A facile solvothermal polymerization process was used to change the structure of cross-linking networks and to obtain the PDMS elastomer with excellent stretchability. The application of the modified PDMS endows the strain sensor with a large strain range (∼20%), which is 100% higher than that of the strain sensor with unmodified PDMS. The AgNWs/graphene hybrid particles were prepared by a simple coprecipitation, reduction, and drying method. AgNWs serve as bridges between graphene sheets, endowing the strain sensor with a large gauge factor (GF = 400). The stability of the strain sensor was also verified. Besides, the strain sensor was successfully used in fields such as finger bending and speech recognition. Considering its high sensitivity, excellent stretchability, and high working stability, the sensor has great potential in health monitoring and motion detection.

IL-17D-induced inhibition of DDX5 expression in keratinocytes amplifies IL-36R-mediated skin inflammation.

Aberrant RNA splicing in keratinocytes drives inflammatory skin disorders. In the present study, we found that the RNA helicase DDX5 was downregulated in keratinocytes from the inflammatory skin lesions in patients with atopic dermatitis and psoriasis, and that mice with keratinocyte-specific deletion of Ddx5 (Ddx5∆KC) were more susceptible to cutaneous inflammation. Inhibition of DDX5 expression in keratinocytes was induced by the cytokine interleukin (IL)-17D through activation of the CD93-p38 MAPK-AKT-SMAD2/3 signaling pathway and led to pre-messenger RNA splicing events that favored the production of membrane-bound, intact IL-36 receptor (IL-36R) at the expense of soluble IL-36R (sIL-36R) and to the selective amplification of IL-36R-mediated inflammatory responses and cutaneous inflammation. Restoration of sIL-36R in Ddx5∆KC mice with experimental atopic dermatitis or psoriasis suppressed skin inflammation and alleviated the disease phenotypes. These findings indicate that IL-17D modulation of DDX5 expression controls inflammation in keratinocytes during inflammatory skin diseases.

Identification of gene signatures for COAD using feature selection and Bayesian network approaches.

The combination of TCGA and GTEx databases will provide more comprehensive information for characterizing the human genome in health and disease, especially for underlying the cancer genetic alterations. Here we analyzed the gene expression profile of COAD in both tumor samples from TCGA and normal colon tissues from GTEx. Using the SNR-PPFS feature selection algorithms, we discovered a 38 gene signatures that performed well in distinguishing COAD tumors from normal samples. Bayesian network of the 38 genes revealed that DEGs with similar expression patterns or functions interacted more closely. We identified 14 up-DEGs that were significantly correlated with tumor stages. Cox regression analysis demonstrated that tumor stage, STMN4 and FAM135B dysregulation were independent prognostic factors for COAD survival outcomes. Overall, this study indicates that using feature selection approaches to select key gene signatures from high-dimensional datasets can be an effective way for studying cancer genomic characteristics.

A hybrid feature selection algorithm and its application in bioinformatics.

Feature selection is an independent technology for high-dimensional datasets that has been widely applied in a variety of fields. With the vast expansion of information, such as bioinformatics data, there has been an urgent need to investigate more effective and accurate methods involving feature selection in recent decades. Here, we proposed the hybrid MMPSO method, by combining the feature ranking method and the heuristic search method, to obtain an optimal subset that can be used for higher classification accuracy. In this study, ten datasets obtained from the UCI Machine Learning Repository were analyzed to demonstrate the superiority of our method. The MMPSO algorithm outperformed other algorithms in terms of classification accuracy while utilizing the same number of features. Then we applied the method to a biological dataset containing gene expression information about liver hepatocellular carcinoma (LIHC) samples obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). On the basis of the MMPSO algorithm, we identified a 18-gene signature that performed well in distinguishing normal samples from tumours. Nine of the 18 differentially expressed genes were significantly up-regulated in LIHC tumour samples, and the area under curves (AUC) of the combination seven genes (ADRA2B, ERAP2, NPC1L1, PLVAP, POMC, PYROXD2, TRIM29) in classifying tumours with normal samples was greater than 0.99. Six genes (ADRA2B, PYROXD2, CACHD1, FKBP1B, PRKD1 and RPL7AP6) were significantly correlated with survival time. The MMPSO algorithm can be used to effectively extract features from a high-dimensional dataset, which will provide new clues for identifying biomarkers or therapeutic targets from biological data and more perspectives in tumor research.

Gut Microbiome Signature Are Correlated With Bone Mineral Density Alterations in the Chinese Elders.

Objective: Osteoporosis (OP), clinically featured with a low bone mineral density (BMD) and high risk of bone fracture, has become a major risk factor of disability and death in the elders, especially in postmenopausal women. The gut microbiome (GM) is thought to be implicated in bone metabolism. Herein, we clarified the composition signature and gene functional profile of GM in older people with normal and low BMD. Design and Methods: A total of 455 participants underwent the BMD measurement and biochemical detection. GM analysis was further performed on 113 cases of postmenopausal women and men aged over 50, including both 16S rRNA and metagenomic sequencing. Results: Generally, the BMD value was significantly lower in the older age groups, especially in the postmenopausal women. Consistently, we observed obvious vitamin D deficiency or insufficiency in females (compared to the male, P < 0.0001). The results from 16S rRNA sequencing revealed higher numbers of OTUs and diversity indexes in females than in males. The abundance in composition of Firmicutes and Clostridiales were correlated with the BMD values in females. LEfSe analysis discovered several enriched bacteria taxons in OP and normal control (NC) subgroups. A positive correlation between the number of genes and BMD values was observed in females based on metagenomic sequencing analysis. Furthermore, we identified the connecting modules among the GM composition - gene functional signature - BMD value/T score in both females and males. Conclusions: This study provides evidences upon which to understand the mechanisms of the effects of GM on bone health, consequently revealing the physiology status and potential diagnostic/therapeutic targets based on GM for OP and postmenopausal osteoporosis (PMOP). Besides, the status of vitamin D deficiency or insufficiency need to be concerned and improved in the Chinese people.

Proteomic Changes in Sarcoplasmic and Myofibrillar Proteins Associated with Color Stability of Ovine Muscle during Post-Mortem Storage.

The objective of this study was to investigate the proteomic characteristics for the sarcoplasmic and myofibrillar proteomes of M. longissimus lumborum (LL) and M. psoasmajor (PM) from Small-tailed Han Sheep. During post-mortem storage periods (1, 3, and 5 days), proteome analysis was applied to elucidate sarcoplasmic and myofibrillar protein changes in skeletal muscles with different color stability. Proteomic results revealed that the identified differentially abundant proteins were glycolytic enzymes, energy metabolism enzymes, chaperone proteins, and structural proteins. Through Pearson's correlation analysis, a few of those identified proteins (Pyruvate kinase, Adenylate kinase isoenzyme 1, Creatine kinase M-type, and Carbonic anhydrase 3) were closely correlated to representative meat color parameters. Besides, bioinformatics analysis of differentially abundant proteins revealed that the proteins mainly participated in glycolysis and energy metabolism pathways. Some of these proteins may have the potential probability to be predictors of meat discoloration during post-mortem storage. Within the insight of proteomics, these results accumulated some basic theoretical understanding of the molecular mechanisms of meat discoloration.

The Intratumor Microbiota Signatures Associate With Subtype, Tumor Stage, and Survival Status of Esophageal Carcinoma.

Altered human microbiome characteristic has been linked with esophageal carcinoma (ESCA), analysis of microbial profiling directly derived from ESCA tumor tissue is beneficial for studying the microbial functions in tumorigenesis and development of ESCA. In this study, we identified the intratumor microbiome signature and investigated the correlation between microbes and clinical characteristics of patients with ESCA, on the basis of data and information obtained from The Cancer Microbiome Atlas (TCMA) and The Cancer Genome Atlas (TCGA) databases. A total of 82 samples were analyzed for microbial composition at various taxonomic levels, including 40 tumor samples of esophageal squamous cell carcinoma (ESCC), 20 tumor samples of esophageal adenocarcinoma (EAD), and 22 adjacent normal samples. The results showed that the relative abundance of several microbes changed in tumors compared to their paired normal tissues, such as Firmicutes increased significantly while Proteobacteria decreased in tumor samples. We also identified a microbial signature composed of ten microbes that may help in the classification of ESCC and EAD, the two subtypes of ESCA. Correlation analysis demonstrated that compositions of microbes Fusobacteria/Fusobacteriia/Fusobacteriales, Lactobacillales/Lactobacillaceae/Lactobacillus, Clostridia/Clostridiales, Proteobacteria, and Negativicutes were correlated with the clinical characteristics of ESCA patients. In summary, this study supports the feasibility of detecting intratumor microbial composition derived from tumor sequencing data, and it provides novel insights into the roles of microbiota in tumors. Ultimately, as the second genome of human body, microbiome signature analysis may help to add more information to the blueprint of human biology.

An Improved Approach towards Multi-Agent Pursuit-Evasion Game Decision-Making Using Deep Reinforcement Learning.

A pursuit-evasion game is a classical maneuver confrontation problem in the multi-agent systems (MASs) domain. An online decision technique based on deep reinforcement learning (DRL) was developed in this paper to address the problem of environment sensing and decision-making in pursuit-evasion games. A control-oriented framework developed from the DRL-based multi-agent deep deterministic policy gradient (MADDPG) algorithm was built to implement multi-agent cooperative decision-making to overcome the limitation of the tedious state variables required for the traditionally complicated modeling process. To address the effects of errors between a model and a real scenario, this paper introduces adversarial disturbances. It also proposes a novel adversarial attack trick and adversarial learning MADDPG (A2-MADDPG) algorithm. By introducing an adversarial attack trick for the agents themselves, uncertainties of the real world are modeled, thereby optimizing robust training. During the training process, adversarial learning was incorporated into our algorithm to preprocess the actions of multiple agents, which enabled them to properly respond to uncertain dynamic changes in MASs. Experimental results verified that the proposed approach provides superior performance and effectiveness for pursuers and evaders, and both can learn the corresponding confrontational strategy during training.

Global Analysis of Microbiota Signatures in Four Major Types of Gastrointestinal Cancer.

The gut microbiota has been previously linked with tumorigenesis and gastrointestinal cancer progression; however, intra-tumor microbiota analysis has just emerged and deserves increasing attention. Based on the public databases of The Cancer Microbiome Atlas (TCMA) and The Cancer Genome Atlas (TCGA), this study identified the tissue/organ microbial signatures generated from 443 biosamples of four major gastrointestinal cancer types, including esophageal carcinoma (ESCA), which further includes esophageal adenocarcinoma (EAD) and esophageal squamous cell carcinoma (ESCC), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ). According to partial least squares discrimination analysis (PLS-DA), the profile differences in microbial communities between the tumor and normal samples were not particularly noticeable across the four cancer cohorts, whereas paired comparison analyses revealed several specific differences in bacteria between tumor and normal samples in the EAD, STAD, and COAD samples. The taxa classified from the phylum to genus level revealed a trend of distinguishable microbial profiles between upper and lower gastrointestinal tumors. The Bacteroidetes/Firmicutes ratio in lower gastrointestinal tract tumors was nearly three times that in upper gastrointestinal tract tumors. We also determined the relative tissue/organ-prevalent microbes for each of the four cohorts at the order and genus levels. Microbe Alistipes, Blautia, Pasteurellales, and Porphyromonas compositions were correlated with the clinical characteristics of patients with gastrointestinal cancer, particularly colorectal cancer. Taken together, our findings indicate that microbial profiles shift across different gastrointestinal cancer types and that microbial colonization is highly site-specific. Composition of specific microbes can be indicative of cancer stage or disease progression. Overall, this study indicates that the microbial community and abundance in human tissues can be determined using publicly available data, and provides a new perspective for intra-tissue/organ microbiota research.

Improved Local Search with Momentum for Bayesian Networks Structure Learning.

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

Graphene and its Derivatives-Based Optical Sensors.

Being the first successfully prepared two-dimensional material, graphene has attracted extensive attention from researchers due to its excellent properties and extremely wide range of applications. In particular, graphene and its derivatives have displayed several ideal properties, including broadband light absorption, ability to quench fluorescence, excellent biocompatibility, and strong polarization-dependent effects, thus emerging as one of the most popular platforms for optical sensors. Graphene and its derivatives-based optical sensors have numerous advantages, such as high sensitivity, low-cost, fast response time, and small dimensions. In this review, recent developments in graphene and its derivatives-based optical sensors are summarized, covering aspects related to fluorescence, graphene-based substrates for surface-enhanced Raman scattering (SERS), optical fiber biological sensors, and other kinds of graphene-based optical sensors. Various sensing applications, such as single-cell detection, cancer diagnosis, protein, and DNA sensing, are introduced and discussed systematically. Finally, a summary and roadmap of current and future trends are presented in order to provide a prospect for the development of graphene and its derivatives-based optical sensors.

A Novel BN Learning Algorithm Based on Block Learning Strategy.

Learning accurate Bayesian Network (BN) structures of high-dimensional and sparse data is difficult because of high computation complexity. To learn the accurate structure for high-dimensional and sparse data faster, this paper adopts a divide and conquer strategy and proposes a block learning algorithm with a mutual information based K-means algorithm (BLMKM algorithm). This method utilizes an improved K-means algorithm to block the nodes in BN and a maximum minimum parents and children (MMPC) algorithm to obtain the whole skeleton of BN and find possible graph structures based on separated blocks. Then, a pruned dynamic programming algorithm is performed sequentially for all possible graph structures to get possible BNs and find the best BN by scoring function. Experiments show that for high-dimensional and sparse data, the BLMKM algorithm can achieve the same accuracy in a reasonable time compared with non-blocking classical learning algorithms. Compared to the existing block learning algorithms, the BLMKM algorithm has a time advantage on the basis of ensuring accuracy. The analysis of the real radar effect mechanism dataset proves that BLMKM algorithm can quickly establish a global and accurate causality model to find the cause of interference, predict the detecting result, and guide the parameters optimization. BLMKM algorithm is efficient for BN learning and has practical application value.