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In daily life, two common algorithms are used for collecting medical disease data: data integration of medical institutions and questionnaires. However, these statistical methods require collecting data from the entire research area, which consumes a significant amount of manpower and material resources. Additionally, data integration is difficult and poses privacy protection challenges, resulting in a large number of missing data in the dataset. The presence of incomplete data significantly reduces the quality of the published data, hindering the timely analysis of data and the generation of reliable knowledge by epidemiologists, public health authorities, and researchers. Consequently, this affects the downstream tasks that rely on this data. To address the issue of discrete missing data in cardiac disease, this paper proposes the AGAN (Attribute Generative Adversarial Nets) architecture for missing data filling, based on generative adversarial networks. This algorithm takes advantage of the strong learning ability of generative adversarial networks. Given the ambiguous meaning of filling data in other network structures, the attribute matrix is designed to directly convert it into the corresponding data type, making the actual meaning of the filling data more evident. Furthermore, the distribution deviation between the generated data and the real data is integrated into the loss function of the generative adversarial networks, improving their training stability and ensuring consistency between the generated data and the real data distribution. This approach establishes the missing data filling mechanism based on the generative adversarial networks, which ensures the rationality of the data distribution while filling the missing data samples. The experimental results demonstrate that compared to other filling algorithms, the data matrix filled by the proposed algorithm in this paper has more evident practical significance, fewer errors, and higher accuracy in downstream classification prediction.

This research aimed to probe the expression characteristics of poly(A)-binding protein cytoplasmic 1 (PABPC1) and its role on the phenotype of ovarian cancer (OC) cells and to further investigate the possible underlying mechanism. The expression of PABPC1 was analyzed according to the data from gene expression omnibus, The Cancer Genome Atlas (TCGA) and Oncomine databases and the RNA sequencing data set from TCGA were downloaded for evaluating the prognostic values. We revealed that compared with the healthy samples, PABPC1 was upregulated in OC samples. High expression of PABPC1 had a connection with a shorter survival for patients with OC. Loss and gain of function assays revealed that silencing PABPC1 significantly suppressed the viability, invasion and migration of SK-OV-3 cells, while PABPC1 overexpression in A2780 cells showed the reverse outcomes. Moreover, Western blot demonstrated that silencing PABPC1 notably inactivated the epithelial–mesenchymal transition (EMT) process, while upregulation of PABPC1 promoted the mitigation of epithelial phenotype and the acquisition of mesenchymal phenotype. Taken together, PABPC1 was upregulated in OC cells and served as a carcinogene to promote the OC cell growth and invasion partly by modulating the EMT process, which implied that PABPC1 might be considered as a useful biomarker for OC therapeutics.

Abstract In order to solve the problem that the traditional DV-Hop location algorithm has a large error in locating unknown nodes, this paper proposes an improved DV-Hop algorithm based on dual communication radius. By adding a communication radius, the algorithm updates the minimum hop number obtained by the unknown node closer to the beacon node to a smaller hop value and keeps the minimum hop number information of unknown nodes farther away. This method reflects the gap between the actual distances in terms of hop number, which solves the problem of the large difference between the actual distances of the same hops to a certain extent. Therefore, it is more advantageous to estimate the more accurate average jump distance and can calculate the more precise distance between them and obtain the more precise coordinates of unknown nodes. MATLAB simulation results show that the improved algorithm based on dual communication radius DV-Hop can effectively reduce the location error and improve the location accuracy and stability compared with the traditional DV-Hop location algorithm.

This study is conducted to further review the rationality of the standard for rice irrigation quotas in Jianshui of southern central Yunnan.On the basis of the observation data of Jianshui Meteorological Station from 1958 to 2013 and the rice-season test results of irrigation test sites in Jianshui from 2016 to 2018,the crop and soil parameters in the CROPWAT model are calibrated,and the processes of water in the rice soaking stage and irrigation amount in the prime field stage are simulated.With frequency arrangement results of irrigation amount in the whole growth stage,the rice irrigation schedule is drafted by the typical-year method.The results indicate that under the irrigation probability (P) of P=50%,P=75%,and P=90%,the irrigation quota of Jianshui rice in the whole growth stage is 510.1,591.1,and 651.2 mm,respectively,including the quota for the rice soaking stage of 185.3,209.2,and 213.9 mm,as well as the quota for the prime field stage of 324.8,381.9,and 437.3 mm,respectively.The irrigation frequency in the prime field stage is 8,9,and 10 times,respectively,and the irrigation quota is between 20.3 to 46.7 mm.The standard for rice irrigation quotas in Jianshui of southern central Yunnan is between 240.0 to 255.0 mm in the rice soaking stage,and under the conditions of P=50%,P=75%,and P=90%,the rice irrigation quota in the prime field stage is 427.5~435.0,487.5~502.5,540.0~562.5 mm,respectively.The current standard for rice irrigation quotas is basically reasonable,but from the viewpoint of water conservation and protection,there is still some water-saving space for rice irrigation.

With the development of speech signal processing, universality, easy collection and personal speech signal uniqueness, many researchers are attracted to the field of speech verification. Most of the current speech verifications are based on long training data sets in order to achieve good results, and there are no good verification schemes in case of inadequate training data sets. This paper proposes a novel architecture for speech verification using a multilevel method, which extracts feature parameters through a multiple wavelet transform for mobile phone voice. The experiments show that the multilevel wavelet authentication architecture improves performance in speech verification. The recognition rate of the mobile phone system is more robust and superior to other methods.

Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. After decades of rapid development, artificial neural networks (ANN) have been successfully applied in many disciplines and have enabled many remarkable research achievements. However, no quantitative method has yet been found that can identify every parameter to optimize a neural network. The BP neural network is most frequently used but suffers from the following defects with respect to complex and multidimensional training data or setting of different parameters, i.e., overfitting, slow convergence speed, trapping in local optima and poor prediction effect, and these obstacles have greatly restricted its practical applications. Therefore, this paper proposes a method that uses ant colony optimization (ACO) to train the parameters and structure of the neural network, optimizes its weight and threshold to solve its defects, and applies the model in the optimization scheme of urban operation and management to verify its effect. The experimental simulation proves that the method in this paper is effective and that it makes certain improvements in the local and global search ability, speed, and accuracy of the neural network.

The visual design of the infographic is designed to compress complex information and present it to the audience through an intuitive and easy-to-understand expression, so that they can effectively absorb the content therein. With the continuous development of science and information visualization technology, the production methods and presentation forms of information charts have become more and more abundant, and the direction from two-dimensional information charts to multi-dimensional information charts and dynamic information charts has continuously evolved. This paper cuts in from the perspective of user experience, and proposes optimization suggestions for the current status of visual design of infographics.

The reaction of aromatic aldehyde and 1,3-cyclohexanedione in aqueous media has been catalyzed by p-dodecylbenezenesulfonic acid (DBSA) or sodium dodecyl sulfate (SDS) and yields two products: 9-aryl-1, 8-dioxooctahydroxanthene derivatives and 2,2´-arylmethylene bis(3-hydroxy- 2-cyclohexene-1-one). This method provides several advantages such as good yield, simple work-up procedure and environment friendly.

Recently, a new bacterial disease was detected on cucumber stalks. In order to study the pathogenesis of this disease, the pathogenic bacteria were isolated and identified on the basis of morphological and molecular characteristics, and further analyzed for pathogenicity and antagonistic evaluation. Pathogenicity analysis showed that HlJ-3 caused melting decay and cracking in cucumber stems, and the strain reisolated from re-infected cucumber stalks was morphologically identical to HlJ-3 colonies, which is consistent with the Koch's postulates. The pathogenic strain HlJ-3 was identified as having similar morphological characteristics to Bacillus subtilis. Meanwhile, its internal transcribed spacer sequence (ITS) and DNA gyrase A subunit (gyrA) were both more than 99% homologous and clustered on the same branch with B. subtilis. Therefore, combined with morphological and molecular biological features, strain HlJ-3 was identified as B. subtilis. In addition, B. subtilis, which has a wide range of hosts, was able to infest other common crop species, including potato, tomato, pepper, melon, and radish. Furthermore, antagonistic evaluation confirmed that strain HlJ-3 strongly inhibited the mycelial growth of Colletotrichum coccodes and Alternaria tenuissima in vitro, with antagonistic effects of 69.92% and 68.08%, respectively. In conclusion, our results showed that strain HlJ-3 is B. subtilis, which is pathogenic to cucumber in vivo and can infect plants of Solanaceae, Cucurbitaceae and Brassicaceae with a wide range of hosts. In addition, this strain has good biocontrol effects against C. coccodes and A. tenuissima in vitro. The findings of this research will help to prevent and control the occurrence of this pathogen and regulate its use as a biocontrol agent. [ABSTRACT FROM AUTHOR]