Unveiling Patient Trust in Physicians Across China: Insights from a Nationwide Cross-Sectional Study.

Background: Trust is a major factor affecting patient-physician relationship and healthcare quality. However, there has been a lack of comprehensive study on the extent of and major factors affecting patient trust in healthcare providers of China, the world's largest developing country. The objective of this study is to elucidate the current status of outpatient trust in physicians in China and its influencing factors, providing empirical evidence to enhance doctor-patient trust and improve doctor-patient relationships. Methods: Between December 2017 and January 2018, 28,760 patients seeking care at the outpatient departments of 136 tertiary hospitals were interviewed, where they were asked to rate their trust in physicians. We applied a multilevel logistic regression model to explore the association between patients trust and characteristics of hospitals, physicians and patient characteristics. We conducted a series of sensitivity analysis to check the robustness of our findings. Results: Among 28,760 participants included in this study, 91.54% expressed trust in their physicians, while 7.52% showed moderate trust. Only 0.94% expressly distrusted or strongly distrusted their physicians. Outpatients of hospitals with convenient payment, volunteer guidance and enough seats available in the rest and waiting area showed higher levels of trust. Physicians who had a senior title, showed patience and protection of the patient's privacy were more likely to be trusted by their patients in the outpatient setting. Compared to their female counterparts, male outpatients showed a higher degree of trust. Conclusion: While this study highlights an overall high level of Trust in physicians (TIP) among patients in China's tertiary hospitals, it is found to vary with patient demographic factors as well as provider's attributes. Hospitals with a more keen sense of protecting patients privacy and better meeting patients' need for efficient and caring service provision process appeared to yield a higher level of trust.

Impact of typhoon events on microplastic distribution in offshore sediments in Leizhou Peninsula of the South China Sea.

Microplastic is an emerging pollutant and a technical fossil in Anthropocene sediments. Typhoon frequency and intensity have increased due to climate change, which has a major effect on the distribution patterns of microplastics. It is still unknown, though, how the topography of the peninsula affects the reconstruction of the distribution of microplastic in typhoons. Due to frequent typhoons, the Leizhou Peninsula (LZP) in the north part of the South China Sea is an ideal place to study the impact of topographic variations on microplastic distribution during typhoon events. This study investigated microplastics ranging in size from 50 µm to 5 mm in sediment. Microscopic inspection and µ-FTIR tests were used to identify microplastic characteristics from offshore surface sediments before and after typhoons. The average microplastic abundance in offshore sediments decreased from 18 ± 17 items/kg to 15 ± 15 items/kg after typhoons. Results show that typhoons only increase the microplastic abundance in topographically protected areas along the northeast coast of LZP, with no significant difference observed in other regions. The influence of typhoon on the morphological characteristics of microplastics in sediments is more pronounced and widespread, as evidenced by a shift in the predominant shape of microplastics from fibers to fragments and a decrease in size accompanied by an increased abundance within the 100 µm-1 mm fraction. The color of microplastics remained similar before and after typhoons, and the polymer composition of microplastics became more uniform. The alteration of microplastic morphology may be attributed to the enhancement of wave intensity induced by typhoons. This study enhances the comprehension of typhoon-induced impacts on pollutant redistribution, specifically microplastics, thereby providing essential empirical evidence and theoretical foundations for pollution regulation.

PySNV for complex intra-host variation detection.

MOTIVATION: Intra-host variants refer to genetic variations or mutations that occur within an individual host organism. These variants are typically studied in the context of viruses, bacteria, or other pathogens to understand the evolution of pathogens. Moreover, intra-host variants are also explored in the field of tumor biology and mitochondrial biology to characterize somatic mutations and inherited heteroplasmic mutations. Intra-host variants can involve long insertions, deletions, and combinations of different mutation types, which poses challenges in their identification. The performance of current methods in detecting of complex intra-host variants is unknown. RESULTS: First, we simulated a dataset comprising 10 samples with 1869 intra-host variants involving various mutation patterns and benchmarked current variant detection software. The results indicated that though current software can detect most variants with F1-scores between 0.76 and 0.97, their performance in detecting long indels and low frequency variants was limited. Thus, we developed a new software, PySNV, for the detection of complex intra-host variations. On the simulated dataset, PySNV successfully detected 1863 variant cases (F1-score: 0.99) and exhibited the highest Pearson correlation coefficient (PCC: 0.99) to the ground truth in predicting variant frequencies. The results demonstrated that PySNV delivered promising performance even for long indels and low frequency variants, while maintaining computational speed comparable to other methods. Finally, we tested its performance on SARS-CoV-2 replicate sequencing data and found that it reported 21% more variants compared to LoFreq, the best-performing benchmarked software, while showing higher consistency (62% over 54%) within replicates. The discrepancies mostly exist in low-depth regions and low frequency variants. AVAILABILITY AND IMPLEMENTATION: https://github.com/bnuLyndon/PySNV/.

Genome-scale cis-acting catabolite-responsive element editing confers Bacillus pumilus LG3145 plant-beneficial functions.

Rhizosphere dwelling microorganism such as Bacillus spp. are helpful for crop growth. However, these functions are adversely affected by long-term synthetic fertilizer application. We developed a modified CRISPR/Cas9 system using non-specific single-guide RNAs to disrupt the genome-wide cis-acting catabolite-responsive elements (cres) in a wild-type Bacillus pumilus strain, which conferred dual plant-benefit properties. Most of the mutations occurred around imperfectly matched cis-acting elements (cre-like sites) in genes that are mainly involved in carbon and secondary metabolism pathways. The comparative metabolomics and transcriptome results revealed that carbon is likely transferred to some pigments, such as riboflavin, carotenoid, and lycopene, or non-ribosomal peptides, such as siderophore, surfactin, myxochelin, and bacilysin, through the pentose phosphate and amino acid metabolism pathways. Collectively, these findings suggested that the mutation of global cre-like sequences in the genome might alter carbon flow, thereby allowing beneficial biological interactions between the rhizobacteria and plants.

Environment change recorded by lake sediment magnetism in the Songnen Plain, northeastern China.

Stratigraphic determination of the Anthropocene, the "Great Acceleration", requires more key globally synchronous stratigraphic markers which reflect the significant human impacts on Earth. Lacustrine sediment magnetic characteristics are of considerable importance in Anthropocene studies because they respond sensitively to environmental changes. There are many shallow lakes in the Songnen Plain (SNP) in northeast China, which are conducive to obtaining Anthropocene sedimentary records. This study explored magnetic materials in lacustrine sediment responses to environmental evolution impact by human activities on the SNP by measuring magnetic parameters in dated sediment cores from 5 shallow lakes in the SNP, northeast China. The results revealed that detrital magnetite and hematite dominated the magnetic minerals in lake sediments. The persistently low value of magnetic susceptibility might be caused by the low content of natural ferrimagnetic minerals in Quaternary fluvial deposits and humus-rich black soil in the catchment, and the loss of magnetic materials during the transport process. In Lake Longjiangpao (LJP), the magnetic concentrations significantly responded to regional precipitation, whereas in the other 4 lakes in the center of the plain, the parameters tended to reflect complex human activities. However, the isothermal remanent magnetization ratio (S-300), which is indicative of the ratio of hematite to magnetite, exhibited relatively consistent variations in the 5 studied lakes. After 1950, the "Great Acceleration", the increase of S-300 indicated a relative proportion of magnetite in sediments, and was positively correlated with the growth of human-activity proxies (Gross Domestic Product (GDP) and population). Thus, this proxy can be regarded as a useful indicator of the beginning of the Anthropocene in the studied region. This study provides new insights into the estimation of local human activities in history and possible evidence for the global definition of the Anthropocene.

Enhanced neutralization of SARS-CoV-2 variant BA.2.86 and XBB sub-lineages by a tetravalent COVID-19 vaccine booster.

Emerging SARS-CoV-2 sub-lineages like XBB.1.5, XBB.1.16, EG.5, HK.3 (FLip), and XBB.2.3 and the variant BA.2.86 have recently been identified. Understanding the efficacy of current vaccines on these emerging variants is critical. We evaluate the serum neutralization activities of participants who received COVID-19 inactivated vaccine (CoronaVac), those who received the recently approved tetravalent protein vaccine (SCTV01E), or those who had contracted a breakthrough infection with BA.5/BF.7/XBB virus. Neutralization profiles against a broad panel of 30 sub-lineages reveal that BQ.1.1, CH.1.1, and all the XBB sub-lineages exhibit heightened resistance to neutralization compared to previous variants. However, despite their extra mutations, BA.2.86 and the emerging XBB sub-lineages do not demonstrate significantly increased resistance to neutralization over XBB.1.5. Encouragingly, the SCTV01E booster consistently induces higher neutralizing titers against all these variants than breakthrough infection does. Cellular immunity assays also show that the SCTV01E booster elicits a higher frequency of virus-specific memory B cells. Our findings support the development of multivalent vaccines to combat future variants.

In vitro inhalation bioaccessibility and health risk assessment of difenoconazole in the atmosphere.

BACKGROUND: Assessment of the risk of pesticide inhalation in populations around farmland is necessary because inhalation is one of the ways in which pesticides can risk human health. This study aimed to identify the inhalation risk of difenoconazole on humans by using dose-response and exposure assessments. RESULTS: In the field simulation application, respiratory exposure in populations around farmland ranged from 71 to 430 ng/m3 . Using response surface methodology, the maximum bioaccessibility of difenoconazole in three simulated lung fluids was 35.33% in Gamble's solution (GS), 34.12% in artificial lysosomal fluid (ALF), and 42.06% in simulated interstitial lung fluid (SLF). Taking the proliferation activity of the A549 cell model as the endpoint, the benchmark dose limit and benchmark dose of difenoconazole on A549 cells were 16.36 and 5.60 mg/kg, respectively. The margin of exposure to difenoconazole in GS, ALF and SLF were, respectively, 8.66 × 105 to 5.28 × 106 , 8.97 × 105 to 5.47 × 106 and 7.28 × 105 to 4.44 × 106 . CONCLUSION: The risk assessment results indicate that under all circumstances, applying difenoconazole is safe for populations around farmland. However, a fan-shaped nozzle, suspension concentrate and greater inhalation height increase the risk of inhalation. © 2023 Society of Chemical Industry.

Population-level variation in gut bifidobacterial composition and association with geography, age, ethnicity, and staple food.

Bifidobacteria are key gut commensals that confer various health benefits and are commonly used as probiotics. However, little is known about the population-level variation in gut bifidobacterial composition and its affecting factors. Therefore, we analyzed Bifidobacterium species with amplicon sequencing of the groEL gene on fecal samples of 1674 healthy individuals, who belonged to eight ethnic groups and resided in 60 counties/cities of 28 provinces across China. We found that the composition of the bifidobacterial community was associated with geographical factors, demographic characteristics, staple food type, and urbanization. First, geography, which reflects a mixed effect of other variables, explained the largest variation in the bifidobacterial profile. Second, middle adolescence (age 14-17) and age 30 were two key change points in the bifidobacterial community development, and a bifidobacterial community resembling that of adults occurred in middle adolescence, which is much later than the maturation of the whole gut microbial community at approximately age 3. Third, each ethnicity showed a distinct bifidobacterial profile, and the remarkable amount of unknown Bifidobacterium species in the Tibetan gut suggested undiscovered biodiversity. Fourth, wheat as the main staple food promoted the flourish of B. adolescentis and B. longum. Fifth, alpha diversity of the bifidobacterial community decreased with urbanization. Collectively, our findings provide insight into the environmental and host factors that shape the human gut bifidobacterial community, which is fundamental for precision probiotics.

A fast and accurate method for SARS-CoV-2 genomic tracing.

To contain infectious diseases, it is crucial to determine the origin and transmission routes of the pathogen, as well as how the virus evolves. With the development of genome sequencing technology, genome epidemiology has emerged as a powerful approach for investigating the source and transmission of pathogens. In this study, we first presented the rationale for genomic tracing of SARS-CoV-2 and the challenges we currently face. Identifying the most genetically similar reference sequence to the query sequence is a critical step in genome tracing, typically achieved using either a phylogenetic tree or a sequence similarity search. However, these methods become inefficient or computationally prohibitive when dealing with tens of millions of sequences in the reference database, as we encountered during the COVID-19 pandemic. To address this challenge, we developed a novel genomic tracing algorithm capable of processing 6 million SARS-CoV-2 sequences in less than a minute. Instead of constructing a giant phylogenetic tree, we devised a weighted scoring system based on mutation characteristics to quantify sequences similarity. The developed method demonstrated superior performance compared to previous methods. Additionally, an online platform was developed to facilitate genomic tracing and visualization of the spatiotemporal distribution of sequences. The method will be a valuable addition to standard epidemiological investigations, enabling more efficient genomic tracing. Furthermore, the computational framework can be easily adapted to other pathogens, paving the way for routine genomic tracing of infectious diseases.

Immune evasion and ACE2 binding affinity contribute to SARS-CoV-2 evolution.

Mutations in the SARS-CoV-2 genome could confer resistance to pre-existing antibodies and/or increased transmissibility. The recently emerged Omicron subvariants exhibit a strong tendency for immune evasion, suggesting adaptive evolution. However, because previous studies have been limited to specific lineages or subsets of mutations, the overall evolutionary trajectory of SARS-CoV-2 and the underlying driving forces are still not fully understood. Here we analysed all open-access SARS-CoV-2 genomes (up to November 2022) and correlated the mutation incidence and fitness changes with the impacts of mutations on immune evasion and ACE2 binding affinity. Our results show that the Omicron lineage had an accelerated mutation rate in the RBD region, while the mutation incidence in other genomic regions did not change dramatically over time. Mutations in the RBD region exhibited a lineage-specific pattern and tended to become more aggregated over time, and the mutation incidence was positively correlated with the strength of antibody pressure. Additionally, mutation incidence was positively correlated with changes in ACE2 binding affinity, but with a lower correlation coefficient than with immune evasion. In contrast, the effect of mutations on fitness was more closely correlated with changes in ACE2 binding affinity than with immune evasion. Our findings suggest that immune evasion and ACE2 binding affinity play significant and diverse roles in the evolution of SARS-CoV-2.

Water-Stable Hybrid Lead-Free Perovskite for Negative Temperature Coefficient Thermistors.

Negative temperature coefficient (NTC) thermistors feature higher sensitivities and faster response speeds and thereby have particular applications in many fields. However, current NTC thermistors are mostly based on inorganic ceramic materials, which show obvious drawbacks in material synthesis, property modulation, and flexible film fabrication. Herein, we report, for the first time, the promising application of an inorganic-organic hybrid NTC thermistor. A new lead-free hybrid iodo bismuthate [1,1',1″-(benzene-1,3,5-triyl)tris(3-methyl-1H-imidazol-3-ium)]Bi2I9 [denoted as (Me3TMP)Bi2I9] was synthesized by a "double-free" strategy. (Me3TMP)Bi2I9 features a lead-free binuclear bismuth iodine anion charge compensated by a "classic hydrogen-bond-free" cation. (Me3TMP)Bi2I9 exhibits remarkable stability in water and UV light irradiation and shows the largest temperature sensitivity coefficient among all reported NTC materials. Theoretical calculation and detailed structural analysis disclose that the seriously distorted (BiI6) octahedra are responsible for the intriguing NTC effect for (Me3TMP)Bi2I9.

CRISPR-Cas13d effectively targets SARS-CoV-2 variants, including Delta and Omicron, and inhibits viral infection.

The recent pandemic of variants of concern (VOC) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlights the need for innovative anti-SARS-CoV-2 approaches in addition to vaccines and antiviral therapeutics. Here, we demonstrate that a CRISPR-Cas13-based strategy against SARS-CoV-2 can effectively degrade viral RNA. First, we conducted a cytological infection experiment, screened CRISPR-associated RNAs (crRNAs) targeting conserved regions of viruses, and used an in vitro system to validate functional crRNAs. Reprogrammed Cas13d effectors targeting NSP13, NSP14, and nucleocapsid transcripts achieved >99% silencing efficiency in human cells which are infected with coronavirus 2, including the emerging variants in the last 2 years, B.1, B.1.1.7 (Alpha), D614G B.1.351 (Beta), and B.1.617 (Delta). Furthermore, we conducted bioinformatics data analysis. We collected the sequence information of COVID-19 and its variants from China, and phylogenetic analysis revealed that these crRNA oligos could target almost 100% of the SARS-CoV family, including the emerging new variant, Omicron. The reprogrammed Cas13d exhibited high specificity, efficiency, and rapid deployment properties; therefore, it is promising for antiviral drug development. This system could possibly be used to protect against unexpected SARS-CoV-2 variants carrying multiple mutations.

Genomic Analysis of the Suspicious SARS-CoV-2 Sequences in the Public Sequencing Database.

SARS-CoV-2 has infected more than 600 million people. However, the origin of the virus is still unclear; knowing where the virus came from could help us prevent future zoonotic epidemics. Sequencing data, particularly metagenomic data, can profile the genomes of all species in the sample, including those not recognized at the time, thus allowing for the identification of the progenitor of SARS-CoV-2 in samples collected before the pandemic. We analyzed the data from 5,196 SARS-CoV-2-positive sequencing runs in the NCBI's SRA database with collection dates prior to 2020 or unknown. We found that the mutation patterns obtained from these suspicious SARS-CoV-2 reads did not match the genome characteristics of an unknown progenitor of the virus, suggesting that they may derive from circulating SARS-CoV-2 variants or other coronaviruses. Despite a negative result for tracking the progenitor of SARS-CoV-2, the methods developed in the study could assist in pinpointing the origin of various pathogens in the future. IMPORTANCE Sequences that are homologous to the SARS-CoV-2 genome were found in numerous sequencing runs that were not associated with the SARS-CoV-2 studies in the public database. It is unclear whether they are derived from the possible progenitor of SARS-CoV-2 or contamination of more recent SARS-CoV-2 variants circulated in the population due to the lack of information on the collection, library preparation, and sequencing processes. We have developed a computational framework to infer the evolutionary relationship between sequences based on the comparison of mutations, which enabled us to rule out the possibility that these suspicious sequences originate from unknown progenitors of SARS-CoV-2.

Hardness-and-Type Recognition of Different Objects Based on a Novel Porous Graphene Flexible Tactile Sensor Array.

Accurately recognizing the hardness and type of different objects by tactile sensors is of great significance in human-machine interaction. In this paper, a novel porous graphene flexible tactile sensor array with great performance is designed and fabricated, and it is mounted on a two-finger mechanical actuator. This is used to detect various tactile sequence features from different objects by slightly squeezing them by 2 mm. A Residual Network (ResNet) model, with excellent adaptivity and feature extraction ability, is constructed to realize the recognition of 4 hardness categories and 12 object types, based on the tactile time sequence signals collected by the novel sensor array; the average accuracies of hardness and type recognition are 100% and 99.7%, respectively. To further verify the classification ability of the ResNet model for the tactile feature information detected by the sensor array, the Multilayer Perceptron (MLP), LeNet, Multi-Channel Deep Convolutional Neural Network (MCDCNN), and ENCODER models are built based on the same dataset used for the ResNet model. The average recognition accuracies of the 4hardness categories, based on those four models, are 93.6%, 98.3%, 93.3%, and 98.1%. Meanwhile, the average recognition accuracies of the 12 object types, based on the four models, are 94.7%, 98.9%, 85.0%, and 96.4%. All of the results demonstrate that the novel porous graphene tactile sensor array has excellent perceptual performance and the ResNet model can very effectively and precisely complete the hardness and type recognition of objects for the flexible tactile sensor array.

Temporal dynamics of SARS-CoV-2 genome mutations that occurred in vivo on an aircraft.

We analyzed variations in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome during a flight-related cluster outbreak of coronavirus disease 2019 (COVID-19) in Shenzhen, China, to explore the characteristics of SARS-CoV-2 transmission and intra-host single nucleotide variations (iSNVs) in a confined space. Thirty-three patients with COVID-19 were sampled, and 14 were resampled 3-31 days later. All 47 nasopharyngeal swabs were deep-sequenced. iSNVs and similarities in the consensus genome sequence were analyzed. Three SARS-CoV-2 variants of concern, Delta (n = 31), Beta (n = 1), and C.1.2 (n = 1), were detected among the 33 patients. The viral genome sequences from 30 Delta-positive patients had similar SNVs; 14 of these patients provided two successive samples. Overall, the 47 sequenced genomes contained 164 iSNVs. Of the 14 paired (successive) samples, the second samples (T2) contained more iSNVs (median: 3; 95% confidence interval [95% CI]: 2.77-10.22) than did the first samples (T1; median: 2; 95% CI: 1.63-3.74; Wilcoxon test, P = 0.021). 38 iSNVs were detected in T1 samples, and only seven were also detectable in T2 samples. Notably, T2 samples from two of the 14 paired samples had additional mutations than the T1 samples. The iSNVs of the SARS-CoV-2 genome exhibited rapid dynamic changes during a flight-related cluster outbreak event. Intra-host diversity increased gradually with time, and new site mutations occurred in vivo without a population transmission bottleneck. Therefore, we could not determine the generational relationship from the mutation site changes alone.

Correlation analysis between the occurrence of epidemic in ancient China and solar activity.

As the globe has witnessed the pandemic, epidemic diseases exert a strong impact on human beings and ecosystems. Since the Sun is the primary energy source of the Earth, some scientific pioneers attempted to search for the discernible relation between solar activity and the incidence of epidemics. In this study, the periodic changes and trends of ancient Chinese epidemic data were analyzed in comparison with those of sunspot numbers, a solar activity proxy. The results show that the epidemic and solar activity changes are in good agreement to a certain extent, especially during the Gleissberg and the de Vries cycles. The wavelet coherence shows that the frequency of the epidemic data and sunspot numbers are highly associated. In addition, results from the ensemble empirical mode decomposition illustrate consistent variations in low-frequency decompositions. This study has important implications for further understanding of the potential impact of solar activity on Earth's biosphere, the underlying mechanism of which needs further exploration.

Toward efficient and high-fidelity metagenomic data from sub-nanogram DNA: evaluation of library preparation and decontamination methods.

BACKGROUND: Shotgun metagenomic sequencing has greatly expanded the understanding of microbial communities in various biological niches. However, it is still challenging to efficiently convert sub-nanogram DNA to high-quality metagenomic libraries and obtain high-fidelity data, hindering the exploration of niches with low microbial biomass. RESULTS: To cope with this challenge comprehensively, we evaluated the performance of various library preparation methods on 0.5 pg-5 ng synthetic microbial community DNA, characterized contaminants, and further applied different in silico decontamination methods. First, we discovered that whole genome amplification prior to library construction led to worse outcomes than preparing libraries directly. Among different non-WGA-based library preparation methods, we found the endonuclease-based method being generally good for different amounts of template and the tagmentation-based method showing specific advantages with 0.5 pg template, based on evaluation metrics including fidelity, proportion of designated reads, and reproducibility. The load of contaminating DNA introduced by library preparation varied from 0.01 to 15.59 pg for different kits and accounted for 0.05 to 45.97% of total reads. A considerable fraction of the contaminating reads were mapped to human commensal and pathogenic microbes, thus potentially leading to erroneous conclusions in human microbiome studies. Furthermore, the best performing in silico decontamination method in our evaluation, Decontam-either, was capable of recovering the real microbial community from libraries where contaminants accounted for less than 10% of total reads, but not from libraries with heavy and highly varied contaminants. CONCLUSIONS: This study demonstrates that high-quality metagenomic data can be obtained from samples with sub-nanogram microbial DNA by combining appropriate library preparation and in silico decontamination methods and provides a general reference for method selection for samples with varying microbial biomass.

Characteristics of replication and pathogenicity of SARS-CoV-2 Alpha and Delta isolates.

The continuously arising of SARS-CoV-2 variants has been posting a great threat to public health safety globally, from B.1.17 (Alpha), B.1.351 (Beta), P.1 (Gamma), B.1.617.2 (Delta) to B.1.1.529 (Omicron). The emerging or re-emerging of the SARS-CoV-2 variants of concern is calling for the constant monitoring of their epidemics, pathogenicity and immune escape. In this study, we aimed to characterize replication and pathogenicity of the Alpha and Delta variant strains isolated from patients infected in Laos. The amino acid mutations within the spike fragment of the isolates were determined via sequencing. The more efficient replication of the Alpha and Delta isolates was documented than the prototyped SARS-CoV-2 in Calu-3 and Caco-2 â€‹cells, while such features were not observed in Huh-7, Vero E6 and HPA-3 â€‹cells. We utilized both animal models of human ACE2 (hACE2) transgenic mice and hamsters to evaluate the pathogenesis of the isolates. The Alpha and Delta can replicate well in multiple organs and cause moderate to severe lung pathology in these animals. In conclusion, the spike protein of the isolated Alpha and Delta variant strains was characterized, and the replication and pathogenicity of the strains in the cells and animal models were also evaluated.

Contact Pattern Recognition of a Flexible Tactile Sensor Based on the CNN-LSTM Fusion Algorithm.

Recognizing different contact patterns imposed on tactile sensors plays a very important role in human-machine interaction. In this paper, a flexible tactile sensor with great dynamic response characteristics is designed and manufactured based on polyvinylidene fluoride (PVDF) material. Four contact patterns (stroking, patting, kneading, and scratching) are applied to the tactile sensor, and time sequence data of the four contact patterns are collected. After that, a fusion model based on the convolutional neural network (CNN) and the long-short term memory (LSTM) neural network named CNN-LSTM is constructed. It is used to classify and recognize the four contact patterns loaded on the tactile sensor, and the recognition accuracies of the four patterns are 99.60%, 99.67%, 99.07%, and 99.40%, respectively. At last, a CNN model and a random forest (RF) algorithm model are constructed to recognize the four contact patterns based on the same dataset as those for the CNN-LSTM model. The average accuracies of the four contact patterns based on the CNN-LSTM, the CNN, and the RF algorithm are 99.43%, 96.67%, and 91.39%, respectively. All of the experimental results indicate that the CNN-LSTM constructed in this paper has very efficient performance in recognizing and classifying the contact patterns for the flexible tactile sensor.