[An investigation on the transmission routes and early diagnosis of intrauterine infection induced by hepatitis B virus].

OBJECTIVES: To analyze the relationship between the fetus infection and HBV M, HBV DNA in amniotic fluid, umbilical cord blood, maternal blood and placenta, and to explore the mechanism of vertical transmission of HBV. METHODS: Immunonetric assay and nucleic acid amplification hybri-comb were used. Both HBV M and HBV DNA were detected in amniotic fluid, vein blood, umbilical cord blood for each of 65 HBV-positive women in their different gestational periods, while immunohistochemical analysis was carried out on the tissue of placenta, liver, lung or heart from each abortive fetus/dead infant in the case. RESULTS: For all of the 65 HBsAg-positive women in their different gestational periods, the detected positive rate of HBsAg was 21.50% in amniotic fluid, and 20.00% in umbilical blood. The positive rate of HBsAg, HBeAg, Anti-HBc and HBV DNA detected in blood, amniotic fluid and umbilical blood was 6.15%. The cases with positive HBsAg, Anti-HBe, Anti-HBc and negative HBV DNA were in a percentage of 13.85%. Immunohistochemical analysis on placentas after birth/abortion as well as the tissues of livers, lungs, hearts of the fetuses/dead infants in 4 cases of pregnant women with positive HBsAg, HBeAg, Anti-HBc or HBV DNA in blood, amniotic fluid or umbilical blood showed that HBsAg, HBcAg positive cells in the scope could be seen in every layer of the tissue of placenta, in the hepatic/pulmonary tissue, but not in the cardiac tissue. CONCLUSION: The infection in amniotic fluid or placenta relates to HBV infection in fetus; intrauterine HBV may result in infection in organs such as blood, liver, or lung of a fetus; infection in the amniotic fluid may be another key route of the intrauterine infection of fetus, and the detection on HBV M or HBV DNA in amniotic may be used as one of diagnostic proofs of HBV infection of fetus in its early stage.

Depth estimation of face images using the nonlinear least-squares model.

In this paper, we propose an efficient algorithm to reconstruct the 3D structure of a human face from one or more of its 2D images with different poses. In our algorithm, the nonlinear least-squares model is first employed to estimate the depth values of facial feature points and the pose of the 2D face image concerned by means of the similarity transform. Furthermore, different optimization schemes are presented with regard to the accuracy levels and the training time required. Our algorithm also embeds the symmetrical property of the human face into the optimization procedure, in order to alleviate the sensitivities arising from changes in pose. In addition, the regularization term, based on linear correlation, is added in the objective function to improve the estimation accuracy of the 3D structure. Further, a model-integration method is proposed to improve the depth-estimation accuracy when multiple nonfrontal-view face images are available. Experimental results on the 2D and 3D databases demonstrate the feasibility and efficiency of the proposed methods.

Face recognition with multi-resolution spectral feature images.

The one-sample-per-person problem has become an active research topic for face recognition in recent years because of its challenges and significance for real-world applications. However, achieving relatively higher recognition accuracy is still a difficult problem due to, usually, too few training samples being available and variations of illumination and expression. To alleviate the negative effects caused by these unfavorable factors, in this paper we propose a more accurate spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm for face recognition, with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images; this can greatly enlarge the training set. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Using the above strategies, the negative effects caused by those unfavorable factors can be alleviated efficiently in face recognition. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method.

A large scale test dataset to determine optimal retention index threshold based on three mass spectral similarity measures.

Retention index (RI) is useful for metabolite identification. However, when RI is integrated with mass spectral similarity for metabolite identification, many controversial RI threshold setup are reported in literatures. In this study, a large scale test dataset of 5844 compounds with both mass spectra and RI information were created from National Institute of Standards and Technology (NIST) repetitive mass spectra (MS) and RI library. Three MS similarity measures: NIST composite measure, the real part of Discrete Fourier Transform (DFT.R) and the detail of Discrete Wavelet Transform (DWT.D) were used to investigate the accuracy of compound identification using the test dataset. To imitate real identification experiments, NIST MS main library was employed as reference library and the test dataset was used as search data. Our study shows that the optimal RI thresholds are 22, 15, and 15 i.u. for the NIST composite, DFT.R and DWT.D measures, respectively, when the RI and mass spectral similarity are integrated for compound identification. Compared to the mass spectrum matching, using both RI and mass spectral matching can improve the identification accuracy by 1.7%, 3.5%, and 3.5% for the three mass spectral similarity measures, respectively. It is concluded that the improvement of RI matching for compound identification heavily depends on the method of MS spectral similarity measure and the accuracy of RI data.