Optimization of a Reaction System of Sequence Related Amplified Polymorphism and Segregation of Polymorphic Loci in an F_2 Population of Rice

An effective PCR protocol for detecting the sequence related amplified polymorphism （SRAP） in rice was developed. One hundred and ten pairs of SRAP primers were used for segregation analysis in an F2 population derived from a cross between Shennong 606 and Lijiangxintuanheigu. Among the 110 primer pairs, 35 pairs generated 143 polymorphic bands with an average of 4.09 polymorphic bands per primer pair, and 24 pairs （16.78%） showed the genetic distortion （P〈0.05）. Of the 24 primer pairs, 12 pairs deviated toward the male parent Shennong 606 and 11 pairs toward the female parent Lijiangxintuanheigu, only one toward heterozygote. It was found that the segregation distortion might be caused by the joint gametic and zygotic effects.

DETECTION OF HUMAN PAPILLOMAVIRUS TYPES 16, 18 DNA RELATED SEQUENCES IN BRONCHOGENIC CARCINOMA BY POLYMERASE CHAIN REACTION

In studying the relationship between human papillomavirus (HPV) and bronchogenic carcinoma, 'high-risk' HPV 16, 18 DNA sequences were detected in samples from 50 lung cancer patients, 18 patients with benign pulmonary diseases and 4 fetal lung tissues by polymerase chain reaction (PCR) and dot-blot hybridization with biotin-labelled probes. The results showed that HPV 16, 18 DNA related sequences were found in 32% of lung cancer specimens, with 10 cases of HPV 16, 5 cases of HPV 18 and 1 case of both types. 48.15% (13 / 27) of squamous cell carcinomas were shown to be positive for HPV 16, 18 DNA. In addition, two adenocarcinomas and one small cell carcinoma were positive for HPV 16 DNA. No specimens from benign diseases tissues and fetal lung tissues showed positive results. These results suggest that primary bronchogenic carcinoma is related to HPV infection.

Real-time risk prediction of chemical processes based on attention-based Bi-LSTM

Refined risk prediction must be achieved to guarantee the safe and steady operation of chemical production processes.However,there is high nonlinearity and association coupling among massive,complicated multisource process data,resulting in a low accuracy of existing prediction technology.For that reason,a real-time risk prediction method for chemical processes based on the attention-based bidirectional long short-term memory(Attention-based Bi-LSTM)is proposed in this study.First,multisource process data,such as temperature,pressure,flow rate,and liquid level,are preprocessed for denoising.Data correlation is analyzed in time windows by setting time windows and moving step lengths to explore correlations,thus establishing a complex network model oriented to the chemical production process.Second,network structure entropy is introduced to reduce the dimensions of the multisource process data.Moreover,a 1D relative risk sequence is acquired by maxemin deviation standardization to judge whether the chemical process is in a steady state.Finally,an Attention-based Bi-LSTM algorithm is established by integrating the attention mechanism and the Bi-LSTM network to fit and train 1D relative risk sequences.In that way,the proposed algorithm achieves real-time prediction and intelligent perception of risk states during chemical production.A case study based on the Tennessee Eastman process(TEP)is conducted.The validity and reasonability of the proposed method are verified by analyzing distribution laws of relative risks under normal and fault conditions.Also,the proposed algorithm importantly improves the prediction accuracy of chemical process risks relative to that of existing prediction technologies.

A Method on the Item Investment Risk Interval Decision-making of Processing Ranking Style

In this paper, on the bases of the defect of riskful type and indefinite type decisions, the concept of the type of item investment probability scheduling decision is given, and a linear programming model and its solution are made out. The feasibility of probability scheduling type item investment plan is studied by applying the quality of interval arithmetic.

ON THE RELATIVE GENERALIZED HAMMING WEIGHTS OF A 4-DIMENSIONAL LINEAR CODE AND A SUBCODE WITH DIMENSION ONE

Finite projective geometry method is effectively used to study the relative generalized Hamming weights of 4-dimensional linear codes, which are divided into 9 classes in order to get much more information about the relative generalized Hamming weights, and part of the relative generalized Hamming weights of a 4-dimensional linear code with a 1-dimensional subcode are determined.