The ?-radioactive seed brachytherapy source has been widely employed in the implantation therapy for the prostatic carcinoma and the ophthalmic lesions.In this study the dosimetric parameters for characterization of a low-energy interstitial brachytherapy source 125I were calculated according to dose calculation formalism recommended by AAPM TG-43U1.For data processing,a 0.28 cm active length was used for the geometry function.The dosimetry parameter air-Kerma strength,dose rate constant,radial dose function and anisotropy function were estimated by means of the EGS5 Monte Carlo code.The results obtained from this study are in good agreement with the corresponding values recommended by TG-43U1 and with the data reported by Dolan,et al.

Objective To improve the knowledge of X-ray findings and clinic-pathology of primary non-Hodgkin's lymphoma of bone(PNHLB).Methods X-ray findings and clinical-pathological manifestatioms of PNHLB proved by operation and pathology in 5 cases were reported and review of literature.Results The X-ray findings included infiltrative,osteolytic and cystic destruction of bone, among them,4 cases were B-lymphocytes,while 1 case was T- lymphocytes.Conclusion PNHLB is rare,its clinical symptoms are not in accord with X-ray findings,the finial diagnosis depends on pathology.

0.05). CONCLUSION The PCNA labeling index may reflect the proliferating condition of NIP, but does not have relationship with NIP recurrence. And the role of p27 in the development of NIP needs more investigation.

The independent component analysis (ICA) is a new technique in statistical signal processing, which decomposes mixed signals into statistical independent components. The reported applications in biomedical and radar signal have demonstrated its good prospect in various blind signal separation. In this paper, the progress of ICA in such as its principle, algorithm and application and advance direction of ICA in future is reviewed. The aim is to promote the research in theory and application in the future.
Algorithms ; Brain ; physiology ; Humans ; Likelihood Functions ; Magnetic Resonance Imaging ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted

How to effectively remove the magnetic resonance imaging (MRI) artifacts in the electroencephalography (EEG) recordings, when EEG and functional magnetic resonance imaging (FMRI) are simultaneous recorded, is a challenge for integration of EEG and FMRI. According to the temporal-spatial difference between MRI artifacts and EEG, a new method based on sparse component decomposition in the mixed over-complete dictionary is proposed in this paper to remove MR artifacts. A mixed over-complete dictionary (MOD) of waveletes and discrete cosine which can exhibit the temporal-spatial discrepancy between MRI artificats and EEG is constructed first, and then the signals are separated by learning in this MOD with matching pursuit (MP) algorithm. The method is applied to the MRI artifacts corrupted EEG recordings and the decomposition result shows its validation.
Algorithms ; Artifacts ; Electroencephalography ; Evoked Potentials ; Magnetic Resonance Imaging ; Phantoms, Imaging ; Principal Component Analysis ; Signal Processing, Computer-Assisted

Independent component analysis (ICA) is a new technique in statistical signal processing to extract independent components from multidimensional measurements of mixed signals. In this paper, for the processing of functional magnetic resonance imaging(fMRI) data, two signals of near voxels are used as the mixed signals and are separated by ICA. The correlation coefficients between the reference signal and the separated signals are calculated and those voxels whose correlation coefficients are greater than a threshold are considered to be the activated voxels by the stimulation, and so the functional localization of the stimulation is completed. The validity of the method was primarily proved by trial of real brain functional magnetic resonance imaging data.
Algorithms ; Brain ; pathology ; physiology ; Humans ; Magnetic Resonance Imaging ; statistics & numerical data ; Photic Stimulation ; Principal Component Analysis

OBJECTIVE: To investigate the significance of expressions of p53 and p21 in nasal NK/T-cell lymphoma (NKTL) and their relationship with cell proliferation and apoptosis. METHOD: Sixty-two cases of NKTL were examined for p53, p21 and Ki67 proteins by means of tissue microarray technique, TUNEL and immunohistochemistry. The proliferation index (PI) was determined by expression of Ki67 proteins. RESULT: The positive expression rates of p53 and p21 proteins in NKTL were 79.03% and 58.06% respectively. The positive expression rates of p53 in Ann Arbor stage I, II, III and IV NKTL were 69.57%, 75%, 86.67% and 100% respectively, while those of p21 were 47.83%, 56.25%, 60% and 87.50%. With the progression of tumor, the positive expression rates of p53 and p21 proteins gradually increased. And there were significant differences between them (P<0.05). The positive expression rates of p53 in NKTL with large, medium and small size tumor cells were 92.86%, 78.95% and 53.33% respectively, while those of p21 were 67.86%, 57.89% and 40.00%. With the expanding of tumor cells, the positive expression rates of p53 and p21 proteins gradually increased. And there were significant differences between them (P<0.05). The expression of p53 was positively correlated with the expression of p21 (P<0.05). The intensity of p53 and p21 expression, the Ann Arbor stage and the size of tumor cell all were positively correlated with PI (Spearman correlation analysis, P<0.05), while no correlation with AI (P>0.05). CONCLUSION The expressions of p53, p21 and Ki67 proteins are closely related with the pathogenesis and progression of NKTL. Combined detection of p53, p21 and Ki67 is a good marker to judge the biological behavior of NKTL, such as the proliferation and the invasiveness of the tumor.
Adolescent ; Adult ; Aged ; Apoptosis ; Cell Proliferation ; Cyclin-Dependent Kinase Inhibitor p21 ; metabolism ; Female ; Humans ; Ki-67 Antigen ; metabolism ; Lymphoma, Extranodal NK-T-Cell ; pathology ; Male ; Middle Aged ; Tumor Suppressor Protein p53 ; metabolism ; Young Adult

Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.
Algorithms ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Evoked Potentials, Visual ; Photic Stimulation