We performed a genetic analysis of 280 families with congenital indirect inguinal hernia ascertained in Shandong province. The multifactorial threshold model and segregation analysis were applied to these families to investigate the mode of inheritance of congenital indirect inguinal hernia. Our results indicate that congenital indirect inguinal hernia is not compatible with a multifactorial threshold model, and the frequent vertical transmission and high segregation ratio suggest autosomal dominant inheritance with incomplete penetrance and sex influence. Through further pedigree analysis of the multiple case families with at least two closely related affected members, we noted preferential paternal transmission of the disease gene, which might suggest the role of genomic imprinting in the aetiology of this condition.(JT Med Genet 1994;31:187-192 discriminate between multifactorial and mendelian inheritance."-" These characteristics include correlations of the proband's sex to the recurrence risk of relatives, and the severity of the proband's defect to the recurrence risk of relatives. These correlations were evaluated in the first degree relatives of different types of probands.Classical segregation analysis was performed on 341 nuclear families. According to the parents' mating type, we divided nuclear families into three groups: (1) one parent affected (U x A matings), (2) both parents normal with positive family history, that is, one parent has a first or second degree relative with IIH (U x U(f) matings), and (3) both parents normal (U x U matings). We used the following distributions for the expectation of families of size s with only one affected sib (1) and with more than one affected sib (2).
This article analyzes the difficulties associated with the preservation and transmission of religious cultural resources and the difficulties encountered in the new development environment and background. It does so in light of the current state of religious, cultural resources. The protection, growth, and use of religious and cultural resources against the backdrop of the digital era are elaborated upon and critically analyzed in this article. Based on the foregoing discussion, this article conducts a thorough analysis of the development of a digital platform for religious and cultural resources and its big data analysis, and it also suggests an image feature extraction algorithm based on DL. This article develops a clustering CNN based on the network with PCA vector as convolution kernel, which clusters small images and computes principal component vectors according to categories, generating multiple groups of convolution kernels to extract more features so that the input image can select feature extractors adaptively. Simulation and comparative analysis are used in this article to confirm the algorithm’s effectiveness. Compared to the conventional NN algorithm, simulation results indicate that this algorithm is more accurate, with a maximum accuracy of about 95.14 percent. It has some reference value for the research that will be done in relation to the creation of the next digital platform for religious and cultural resources.
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