Biomedical subjects

Xiaodong Tian

Publications and source records attributed to Xiaodong Tian.

2 recordsLinked to original sources

PubMed2002

[Expression of PCNA and the count of AgNOR in squamous cell carcinoma of larynx and hypopharynx].

OBJECTIVE: 1. To investigate the expression of PCNA (Proliferating Cell Nuclear Antigen) and the count of AgNOR(Nucleolar Organized Region) in respectively from larynx and hypopharynx. 2. To analysis the correlation between them and the clinical stage, the histological grade, the relapse, the lymph node involving and the survival period. 3. To evaluate their value on clinical prognosis. METHOD: Immunohischemical staining was used to detect the expression of PCNA. AgNOR was detected by using Crocker's staining. The lesion tissue and normal tissue specimens were obtained from 61 patients with squamous cell carcinoma and 9 of patients with benign tumor respectively. RESULT: The expression of PCNA is correlate with the count of AgNOR. Both of them were increased in patients with advance clinical stage, low different tumor, tumor relapse, lymph nod involving or short survival period (P < 0.05). CONCLUSION: The positive expression rate of PCNA and the count of AgNOR may be used as prediction of tumor malignancy and prognosis in squamous cell carcinoma of larynx and hypopharynx.

Adult↗

PubMed1997

Performance Bounds for Single Layer Threshold Networks when Tracking a Drifting Adversary.

This paper finds upper bounds for the generalization error of three tracking algorithms when confronted with a worst case adversary. A system identification model is used where both the target and tracking network are single layer threshold networks, with the target weights changing slowly (the drift problem). Previous work considered random unbiased drifting adversaries. This paper focuses on the analysis of a worst case drifting adversary. For a small drift rate of gamma, we find that upper bounds for the optimal conservative tracker, the perceptron tracker, and the least mean square (LMS) tracker are respectively 2gamma/cos(gammapi),2gamman/, and gamma(2n + 2.5) where n is the number of inputs. Simulation results validate the analysis and also show that the bounds are tight when gamma is small for the perceptron and LMS tracker. The effects of additive noise, correlated inputs and non-Gaussian inputs are also discussed. Copyright 1997 Elsevier Science Ltd.

Journal Article↗