Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR.

Transmission lines are the basis of human production and activities. In order to ensure their safe operation, it is essential to regularly conduct transmission line inspections and identify tree risk in a timely manner. In this paper, a power line extraction and tree risk detection method is proposed. Firstly, the height difference and local dimension feature probability model are used to extract power line points, and then the Cloth Simulation Filter algorithm and neighborhood sharing method are creatively introduced to distinguish conductors and ground wires. Secondly, conductor reconstruction is realized by the approach of the linear-catenary model, and numerous non-risk points are excluded by constructing the tree risk point candidate area centered on the conductor's reconstruction curve. Finally, the grading strategy for the safety distance calculation is used to detect the tree risk points. The experimental results show that the precision, recall, and F-score of the conductors (ground wires) classification exceed 98.05% (97.98%), 99.00% (99.14%), and 98.58% (98.56%), respectively, which presents a high classification accuracy. The Root-Mean-Square Error, Maximum Error, and Minimum Error of the conductor's reconstruction are better than 3.67 cm, 7.13 cm, and 2.64 cm, respectively, and the Mean Absolute Error of the safety distance calculation is better than 6.47 cm, proving the effectiveness and rationality of the proposed tree risk points detection method.

Symbiotic relationship between Prevotella denticola and Streptococcus mutans enhances virulence of plaque biofilms.

OBJECTIVES: This study aimed to explore that whether interactions between Prevotella denticola and Streptococcus mutans could promote the establishment of hypervirulent biofilms on teeth surface and eventually influence the occurrence and development of caries. DESIGN: Based on single-species biofilms of either P. denticola or S. mutans, and dual-species biofilms of both bacteria, we compared the virulence properties associated with cariogenicity in vitro, including carbohydrate metabolism and acid productivity, synthesis of extracellular polysaccharides, biomass and architecture of biofilms, level of enamel demineralization and expression of virulence genes associated with carbohydrate metabolism and adhesion in S. mutans. RESULTS: The data demonstrated that, compared to single-species of above two taxa, dual-species produced lactate by metabolizing carbohydrates at a higher level during the observation period. Moreover, dual-species biofilms accrued more biomass and exhibited more dense microcolonies and abundant extracellular matrix. And it's noticeable that the level of enamel demineralization in dual-species biofilms was more augmented than that of single-species. In addition, the presence of P. denticola induced the expression of virulence genes gtfs and gbpB in S. mutans. CONCLUSIONS: Symbiotic relationship between P. denticola and S. mutans enhances caries-associated virulence of plaque biofilms, which might provide new strategies for effective prevention and treatment of caries.

Construction of a caries diagnosis model based on microbiome novelty score.

OBJECTIVES: This study aimed to analyze the bacteria in dental caries and establish an optimized dental-ca-ries diagnosis model based on 16S ribosomal RNA (rRNA) data of oral flora. METHODS: We searched the public databa-ses of microbiomes including NCBI, MG-RAST, EMBL-EBI, and QIITA and collected data involved in the relevant research on human oral microbiomes worldwide. The samples in the caries dataset (1 703) were compared with healthy ones (20 540) by using the microbial search engine (MSE) to obtain the microbiome novelty score (MNS) and construct a caries diagnosis model based on this index. Nonparametric multivariate ANOVA was used to analyze and compare the impact of different host factors on the oral flora MNS, and the model was optimized by controlling related factors. Finally, the effect of the model was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS: 1) The oral microbiota distribution obviously differed among people with various oral-health statuses, and the species richness and species diversity index decreased. 2) ROC curve was used to evaluate the caries data set, and the area under ROC curve was AUC=0.67. 3) Among the five hosts' factors including caries status, country, age, decayed missing filled tooth (DMFT) indices, and sampling site displayed the strongest effect on MNS of samples (P=0.001). 4) The AUC of the model was 0.87, 0.74, 0.74, and 0.75 in high caries, medium caries, low caries samples in Chinese children, and mixed dental plaque samples after controlling host factors, respectively. CONCLUSIONS: The model based on the analysis of 16S rRNA data of oral flora had good diagnostic efficiency.

Seasonal deformation monitoring of the northern Yellow River Delta based on InSAR technology.

Due to overexploitation of natural resources in the northern Yellow River Delta and the resulting land subsidence, interferometric synthetic aperture radar (InSAR) technology is utilized to analyze the deformation trend. Based on Advanced Land Observation Satellite PHASE Array Type L-band Synthetic Aperture Radar and Sentinel-1 data, this paper obtains the annual subsidence rate in two time periods (2007-2010 and 2017-2020) and verifies the correctness by comparing the ascending and descending orbit results. Subsequently, the 11 deformation interference pairs of the three-month interval are extracted to analyze the time series displacement of deformation areas. The results show that there are three large-scale subsidence areas with maximum annual subsidence of 250 mm, all of which are located in the oil or brine exploitation areas, and each deformation area displays a larger linear rate from January to May and then displays different nonlinear deformation from June to December.

Influence of Standardized Nursing Management of Hospital Based on Smart Electronic Medical Blockchain on Nursing Quality of Digestive Endoscopy Room.

With the increase of the incidence rate of digestive system diseases, digestive endoscopy has become an essential measure. The nursing quality of digestive endoscopy room will have a direct impact on the examination results and treatment effect, so improving the nursing quality of digestive endoscopy room has always been the focus of attention. In this paper, the smart electronic medical blockchain technology and hospital standardized nursing management mode are combined to develop the smart medical standardized nursing management mode. Firstly, 88 patients with digestive endoscopy in a hospital from October 2018 to October 2019 were selected as the experimental subjects: 44 cases in the control group and 44 cases in the experimental group. The control group implemented the traditional management mode of digestive endoscopy room, and the experimental group implemented the standardized nursing management mode of smart medicine. The blood pressure level before and after nursing and the probability of adverse reactions in the process of nursing were counted. After nursing, the patients were asked to fill in the nursing satisfaction questionnaire, evaluate the nursing quality of nursing staff, and make statistics and comparison on the occurrence of safety events in digestive endoscopy room. After nursing, the systolic pressure drop of the experimental group was 125.36 ± 7.27 mmhg, diastolic pressure drop was 73.24 ± 4.21 mmhg, and the incidence of adverse reactions was 11.36%. The satisfaction rate of the experimental group was 15%, 29.55%, and 22.73% higher than that of the control group. The average scores of nursing skills, operation level, and examination results of nursing staff in the experimental group were 94.49, 95.12, and 95.89, respectively; the qualified rate of disinfection of digestive endoscopy room in the experimental group was 100%, the degree of cooperation between doctors and nurses was 100%, the timely rate of emergency measures was 95.45%, and the incidence of accidents was 0%. This shows that, under the standardized nursing of smart medicine, the blood pressure of patients is more stable, the incidence of adverse reactions is lower, the nursing satisfaction is higher, and the nursing quality of nursing staff and the safety of digestive endoscopy room are also improved.

A Novel Change Detection Method Based on Statistical Distribution Characteristics Using Multi-Temporal PolSAR Data.

Unsupervised change detection approaches, which are relatively straightforward and easy to implement and interpret, and which require no human intervention, are widely used in change detection. Polarimetric synthetic aperture radar (PolSAR), which has an all-weather response capability with increased polarimetric information, is a key tool for change detection. However, for PolSAR data, inadequate evaluation of the difference image (DI) map makes the threshold-based algorithms incompatible with the true distribution model, which causes the change detection results to be ineffective and inaccurate. In this paper, to solve these problems, we focus on the generation of the DI map and the selection of the optimal threshold. An omnibus test statistic is used to generate the DI map from multi-temporal PolSAR images, and an improved Kittler and Illingworth algorithm based on either Weibull or gamma distribution is used to obtain the optimal threshold for generating the change detection map. Multi-temporal PolSAR data obtained by the Radarsat-2 sensor over Wuhan in China are used to verify the efficiency of the proposed method. The experimental results using our approach obtained the best performance in East Lake and Yanxi Lake regions with false alarm rates of 1.59% and 1.80%, total errors of 2.73% and 4.33%, overall accuracy of 97.27% and 95.67%, and Kappa coefficients of 0.6486 and 0.6275, respectively. Our results demonstrated that the proposed method is more suitable than the other compared methods for multi-temporal PolSAR data, and it can obtain both effective and accurate results.

Stabilization of anti-aromatic and strained five-membered rings with a transition metal.

Anti-aromatic compounds, as well as small cyclic alkynes or carbynes, are particularly challenging synthetic goals. The combination of their destabilizing features hinders attempts to prepare molecules such as pentalyne, an 8π-electron anti-aromatic bicycle with extremely high ring strain. Here we describe the facile synthesis of osmapentalyne derivatives that are thermally viable, despite containing the smallest angles observed so far at a carbyne carbon. The compounds are characterized using X-ray crystallography, and their computed energies and magnetic properties reveal aromatic character. Hence, the incorporation of the osmium centre not only reduces the ring strain of the parent pentalyne, but also converts its Hückel anti-aromaticity into Craig-type Möbius aromaticity in the metallapentalynes. The concept of aromaticity is thus extended to five-membered rings containing a metal-carbon triple bond. Moreover, these metal-aromatic compounds exhibit unusual optical effects such as near-infrared photoluminescence with particularly large Stokes shifts, long lifetimes and aggregation enhancement.