Research on Biometric Identification Method of Nuclear Cold Source Disaster Based on Deep Learning

In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the disaster-causing creatures hairy shrimp and jellyfish were established. Then, in order to solve the problems of low recognition accuracy and unrecognizable small entities in disaster biometrics, Gamma correction algorithm was used to optimize the image of the data set, improve the image quality and reduce the noise interference. Transposed convolution is introduced into the convolution layer to increase the recognition accuracy of small targets. The experimental results show that the recognition rate of this algorithm is 6.75%, 7.5%, 9.8% and 9.03% higher than that of ResNet-50, MobileNetv1, GoogleNet and VGG16, respectively. The actual test results show that the accuracy of this algorithm is obviously better than other algorithms, and the recognition efficiency is higher, which basically meets the preset requirements of this paper.

Comparison of survival outcomes between transthoracic and transabdominal surgical approaches in patients with Siewert-Ⅱ/Ⅲesophagogastric junction adenocarcinoma:a single-institution retrospective cohort study

Objective： To compare the survival outcomes of transabdominal （TA） and transthoracic （TT） surgical approaches in patients with Siewert-II/III esophagogastric junction adenocarcinoma. Methods： This retrospective study was conducted in patients with Siewert-II/III esophagogastric junction adenocarcinoma who underwent either TT or TA operations in the West China Hospital between January 2006 and December 2009. Results： A total of 308 patients （109 in the TT and 199 in the TA groups） were included in this study with a follow-up rate of 87.3%. The median （P25, P75） number of harvested perigastric lymph nodes was 8 （5, 10） in the TT group and 23 （16, 34） in the TA group （P〈0.001）, and the number of positive perigastric lymph nodes was 2 （0, 5） in the TT group and 3 （1, 8） in the TA group （P〈0.004）. The 5-year overall survival （OS） rate was 36% in the TT group and 51% in the TA group （P=0.005）. Subgroup analysis by Siewert classification showed that 5-year OS rates for patients with Siewert II tumors were 38% and 48% in TT and TA groups, respectively （P=0.134）, whereas the 5-year OS rate for patients with Siewert III tumors was significantly lower in the TT group than that in the TA group （33% vs. 53%; P=0.010）. Multivariate analysis indicated that N2 and N3 stages, RI/R2 resection and a TT surgical approach were prognostic factors for poor OS. Conclusions： Improved perigastric lymph node dissection may be the main reason for better survival outcomes observed with a TA gastrectomy approach than with TT gastrectomy for Siewert III tumor patients.

SA-Model:Multi-Feature Fusion Poetic Sentiment Analysis Based on a Hybrid Word Vector Model

Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,etc.However,the existing research on sentiment analysis is relatively small.It does not effectively solve the problems such as the weak feature extraction ability of poetry text,which leads to the low performance of the model on sentiment analysis for Chinese classical poetry.In this research,we offer the SA-Model,a poetic sentiment analysis model.SA-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)and Enhanced representation through knowledge integration(ERNIE)to enrich text vector information;Secondly,it incorporates numerous encoders to remove text features at multiple levels,thereby increasing text feature information,improving text semantics accuracy,and enhancing the model’s learning and generalization capabilities;finally,multi-feature fusion poetry sentiment analysis model is constructed.The feasibility and accuracy of the model are validated through the ancient poetry sentiment corpus.Compared with other baseline models,the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.

A Visual Analysis and Prediction System for Infectious Diseases Based on Improved SIR Model

To effectively track the impact of population migration between regions on the spread of infectious diseases, this paper proposes a visualized analysis and prediction system of infectious diseases based on the improved SIR model. The research contents including: using the multi graph link interaction mode, visualizing the space-time distribution and development trend of infectious diseases;The LightGBM model is used to track the changes of infection rate and recovery rate, and the Mi/Mo SIR model is constructed according to the initial data of different populations;Mi/Mo SIR model is used to predict infectious diseases in combination with visual panel, providing users with tools to analyze and explain the space-time characteristics and potential laws of infectious diseases. The study found that the closure of cities and the restriction of personnel mobility were necessary and effective, and the system provided an important basis for the prediction and early warning of infectious diseases.

Underwater Inhomogeneous Light Field Based on Improved Convolutional Neural Net Fish Image Recognition

In this paper, artificial intelligence image recognition technology is used to improve the recognition rate of individual domestic fish and reduce the recognition time, aiming at the problem that it is difficult to easily observe the species and growth of domestic fish in the underwater non-uniform light field environment. First, starting from the image data collected by polarizing imaging technology, this paper uses subpixel convolution reconstruction to enhance the image, uses image translation and fill technology to build the family fish database, builds the Adam-Dropout-CNN (A-D-CNN) network model, and its convolution kernel size is 3 × 3. The maximum pooling was used for downsampling, and the discarding operation was added after the full connection layer to avoid the phenomenon of network overfitting. The adaptive motion estimation algorithm was used to solve the gradient sparse problem. The experiment shows that the recognition rate of A-D-CNN is 96.97% when the model is trained under the domestic fish image database, which solves the problem of low recognition rate and slow recognition speed of domestic fish in non-uniform light field.

Unraveling a genetic roadmap for improved taste in the domesticated apple

Although taste is an important aspect of fruit quality, an understanding of its genetic control remains elusive in apple and other fruit crops. In this study, we conducted genomic sequence analysis of 497 Malus accessions and revealed erosion of genetic diversity caused by apple breeding and possible independent domestication events of dessert and cider apples. Signatures of selection for fruit acidity and size, but not for fruit sugar content, were detected during the processes of both domestication and improvement. Furthermore, we found that single mutations in major genes affecting fruit taste, including Ma1, MdTDT, and MdSOT2, dramatically decrease malate, citrate, and sorbitol accumulation, respectively, and correspond to important domestication events. Interestingly, Ma1 was identified to have pleiotropic effects on both organic acid content and sugar:acid ratio, suggesting that it plays a vital role in determining fruit taste. Fruit taste is unlikely to have been negatively affected by linkage drag associated with selection for larger fruit that resulted from the pyramiding of multiple genes with minor effects on fruit size. Collectively, our study provides new insights into the genetic basis of fruit quality and its evolutionary roadmap during apple domestication, pinpointing several candidate genes for genetic manipulation of fruit taste in apple.

Comparison of long-term quality of life between Billroth-I and Roux-en-Y anastomosis after distal gastrectomy for gastric cancer: a randomized controlled trial

Background:The results of studies comparing Billroth-I(B-I)with Roux-en-Y(R-Y)reconstruction on the quality of life(QoL)are still inconsistent.The aim of this trial was to compare the long-term QoL of B-I with R-Y anastomosis after curative distal gastrectomy for gastric cancer.Methods:A total of 140 patients undergoing curative distal gastrectomy with D2 lymphadenectomy in West China Hospital,Sichuan University from May 2011 to May 2014 were randomly assigned to the B-I group(N=70)and R-Y group(N=70).The follow-up time points were 1,3,6,9,12,24,36,48,and 60 months after the operation.The final follow-up time was May 2019.The clinicopathological features,operative safety,postoperative recovery,long-term survival as well as QoL were compared,among which QoL score was the primary outcome.An intention-to-treat analysis was applied.Results:The baseline characteristics were comparable between the two groups.There were no statistically significant differences in terms of postoperative morbidity and mortality rates,and postoperative recovery between the two groups.Less estimated blood loss and shorter surgical duration were found in the B-I group.There were no statistically significant differences in 5-year overall survival(79%[55/70]of the B-I group vs.80%[56/70]of the R-Y group,P=0.966)and recurrence-free survival rates(79%[55/70]of the B-I group vs.78%[55/70]of the R-Y group,P=0.979)between the two groups.The scores of the global health status of the R-Y group were higher than those of the B-I group with statistically significant differences(postoperative 1 year:85.4±13.1 vs.88.8±16.1,P=0.033;postoperative 3 year:87.3±15.2 vs.92.8±11.3,P=0.028;postoperative 5 year:90.9±13.7 vs.96.4±5.6,P=0.010),and the reflux(postoperative 3 year:8.8±12.9 vs.2.8±5.3,P=0.001;postoperative 5 year:5.1±9.8 vs.1.8±4.7,P=0.033)and epigastric pain(postoperative 1 year:11.8±12.7 vs.6.1±8.8,P=0.008;postoperative 3 year:9.4±10.6 vs.4.6±7.9,P=0.006;postoperative 5 year:6.0±8.9 vs.2.7±4.6,P=0.022)were milder in the R-Y group than those of the B-I group at the postoperative 1,3,and 5-year time points.Conclusions:Compared with B-I group,R-Y reconstruction was associated with better long-term QoL by reducing reflux and epigastric pain,without changing survival outcomes.Trial Registration:ChiCTR.org.cn,ChiCTR-TRC-10001434.

Micro/nanomotor: A promising drug delivery system for cancer therapy

Micro/nanomotors(MNMs)are small-scale devices that can effectively convert various forms of energy into mechanical motion.Their controllable motility and good permeability have attracted the interest of researchers as promising drug carriers in cancer therapy.Compared with traditional formulations,micro/nanomotor drug delivery systems can greatly improve therapeutic efficiency and reduce the side effects of antitumor drugs.This review mainly discusses the advantages of micro/nanomotor drug delivery systems and the applications of MNMs propelled by exogenous,endogenous,and biohybrid power in cancer therapy.Finally,the main challenges of the applications of micro/nanomotor drug delivery systems,as well as future development trends and opportunities are discussed.