Combining the Brownian ratchet models and the power stroke models,the authors proposed a new mathematical scheme to quantitatively describe the dynamics of molecular motors.That was,a set of functions,periodic either in time or in space,was introduced to modeling the power strokes.Analytic solutions for the probability current are achieved and the relationships of the current with load and asymmetry were seen about.The results were consistent with experimental data.There was no need for the interaction potential between the molecular motor and its track was asymmetric in this model,which makes it more robust than the Brownian ratchet models.

A new algorithm of image layer-presentation was proposed. The key concept of the algorithm was in that the image grayscale function f(x,y), which was comparatively irregular, was approximated by a series of high-regular grayscale functions gn(x,y). The algorithm had the feature of fast convergence and so it was a good approximator. Due to its high-regularity, gn might be stored in a quite small memory space. Thus, the algorithm gives a new way for data processing and image reconstruction.

Recognition of lung cancer cells is very important to the clinical diagnosis of lung cancer. In this paper we present a novel method to extract the structure characteristics of lung cancer cells and automatically recognize their types. Firstly soft mathematical morphology methods are used to enhance the grayscale image, to improve the definition of images, and to eliminate most of disturbance, noise and information of subordinate images, so the contour of target lung cancer cell and biological shape characteristic parameters can be extracted accurately. Then the minimum distance classifier is introduced to realize the automatic recognition of different types of lung cancer cells. A software system named "CANCER.LUNG" is established to demonstrate the efficiency of this method. The clinical experiments show that this method can accurately and objectively recognize the type of lung cancer cells, which can significantly improve the pathology research on the pathological changes of lung cancer and clinical assistant diagnoses.

This paper introduces a three-dimensional (3D) reconstruction algorithm of the brain stem nuclei based on fast centroid auto-registration. The research is based on methods and theories of computer stereo vision, and by image information processing three-point pattern local search, registration and auto-tracing for the centroids of the brain stem nuclei were accomplished. We adopt two-peak threshold, edge detection and grayscale image enhancement to extract contours of the nuclei's structures. The experimental results obtain the spatial structure information and 3D image of the brain stem nuclei, show spatial relationship between 14 pairs of nuclei, and quantitate morphological parameters of each type of nuclei's 3D structure. This work is significant to neuroanatomy research and clinic applications. Furthermore, a software system named BRAIN.HUK is established.

Biomolecular motors are tiny engines that transport materials at the microscopic level within biological cells. In recent years, Elston and Peskin et al have investigated the effect of the elastic properties of the tether that connects the motor to its cargo at the speed of the motor. In this paper we extend their work and present a tether in the form of symmetric linear potential. Our results show that when the driving mechanism is an imperfect Brownian ratchet, the average speed decreases as the stiffness of the tether increases in the limit of large motor diffusion coefficient, which is similar to the results of Elston and Peskin. However, a threshold for the stiffness of the tether connecting the motor to its cargo is found in our model. Only when the tether is stiffer than the threshold can the motor and its cargo function co-operatively, otherwise, the motor and its cargo depart from each other. This result is more realistic than that of the spring model of Elston and Peskin.

生物医学图像在成像时不可避免地受到噪声影响,因此噪声去除是生物医学图像处理的一项重要研究课题.将小波神经网络引入图像去噪领域中,通过多种技术优化网络学习过程,最终建立一种图像去噪新算法.实验结果表明,该算法在去除噪声上优于传统的中值滤波等方法,并具有较强的鲁棒性;同时能够最大限度地保护图像的细节信息,具有很好的保真度.

In this paper the recognition of Small Cell Carcinoma (SCC) is studied. For each type we select 128 samples for training, and randomly measure 200 cells in each sample. We introduce multi-scale morphology based on centroid coordinates to extract the boundaries of nuclei and obtain feature images of nuclei. The features of lung cancer cells are described by morphological and colorimetrical parameters, which is valuable to recognize SCC. Then the architecture of self-organizing feature mapping (SOFM) neural network is studied for recognition of SCC. The weights of the network are adjusted by self-organizing competition, and finally inputted patterns are classified. This algorithm has the advantage of parallelism and fast-convergence, and may simplify the analysis of SCC. Clinical experiment results show that the correctness ratio of this system may reach 95.3% while recognizing lung cancer cell types. Our work is significant to the pathological researches of lung cancer, assistant clinic diagnosis, and assessment of therapeutic effects. Meanwhile a software system named as SCC. LUNG is established for automatic analysis.

Ultrastructure of nucleolar DNA is most important to elucidate transcription site of rRNA. Many judgments were made in this respect but all of them were based on direct experimental observations. A new technology for the quantitative analysis of the location of nucleolar DNA in situ from EM data of the ultrastructure of the nucleolus in allium sativum cells and from the modified NAMA-Ur DNA specific staining, method was developed. For this purpose, a new methodology of multi-scale morphological gradient operator and automatic analysis software named "HEREN. CELL" were created and adopted. By using this method, superfine contour of the nucleolar ultrastruture was obtained and showed the location and location of nucleolar DNA in situ. From the results, a "petaline envelope" model was proposed, which indicated that nucleolar DNA fibrils radiate from center to periphery of the nucleolus. The study will contribute to the development of the EM image processing technology and the study of nucleolar ultrastructure of the nucleolus.

Successful segmentation of chromosomes is important for their analysis. Isolation of chromosomes is the major topic of the segmentation in previous studies, but it is at low resolution. While the model-based neural network (MBNN), in this paper, is adopted to segment the G bands of Triticum monococcum chromosomes, which is a high-resolution approach. Combined with the MBNN as a core technique, various auxiliary techniques have been used to find out the optimal method. A series of experimental results indicate that the optimal method for segmentation of G bands, here termed as MBNN-3P, is that using the MBNN as a core technique simultaneously aided by all of the three auxiliary techniques. They are presegmenting by the threshold T=173, preassigning a class number and providing teacher’s information. Therefore, it is feasible to apply the MBNN-3P to segment the G band images of T. monococcum chromosomes. This study might be of great significance to improve the accuracy and speed of automated analysis of plant chromosomes.

Introducing the theory of fuzzy set, mathematical morphology and computerized mask fast scanning, we developed the TOOTH.SCA software and method to analyze the effect of fluoride (NaF) on ore content of human tooth enamel automatically and quantitatively. And we obtained some characteristic parameters, such as the depth, the type and the demineralized content of every scathing layer of dental caries. The smallest scale of mask scanning is 0.1 microm x 0.1 microm and the time required to analyze a sample is only 12 s. The applied software and method we built play an important role to the research on the mechanism of pathological changes of teeth and preventing dental caries.

This paper established the extracting method and algorithm for liposome images on the basis of analyzing the form feature of liposome, and its corresponding automatic analysis software. Some algorithms, such as the logical algorithms of mathematical morphology, the grayscale-gradient co-occurrence matrix-based method and the image enhancement method of grayscale image, were used to restrain the noise effectively and increase the definition of image. The result on bipolysaccharide liposome indicates that the method increases the measuring speed and precision.

Wavelet neural network is a kind of neural networks, which can closely combine wavelet theory with neural network theory, and avoid the blindness of framework designs for BP neural networks and the problem of nonlinear optimizations, such as local optimization. So it can greatly simplify the training of neural networks. It has good abilities of function learning and dissemination with a vast range of prospects for application. The theories of wavelet transformation and multiresolution analysis are expounded, and then the mathematical model for the wavelet neural networks and its applications are introduced.

Some computer applications for cell characterization in medicine and biology, such as analysis of surface structure of cell wall-deficient EVC (El Tor Vibrio of Cholera), operate with cell samples taken from very small areas of interest. In order to perform texture characterization in such an application, only a few texture operators can be employed: the operators should be insensitive to noise and image distortion and be reliable in order to estimate texture quality from images. Therefore, we introduce wavelet theory and mathematical morphology to analyse the cellular surface micro-area image obtained by SEM (Scanning Electron Microscope). In order to describe the quality of surface structure of cell wall-deficient EVC, we propose a fully automatic computerized method. The image analysis process is carried out in two steps. In the first, we decompose the given image by dyadic wavelet transform and form an image approximation with higher resolution, by doing so, we perform edge detection of given images efficiently. In the second, we introduce many operations of mathematical morphology to obtain morphological quantitative parameters of surface structure of cell wall-deficient EVC. The obtained results prove that the method can eliminate noise, detect the edge and extract the feature parameters validly. In this work, we have built automatic analytic software named "EVC.CELL".

AIM Automatic measuring and quantitative analysis of form feature parameter of the liposome and emulsion image. METHODS On the basis of analyzing the form feature of liposome, some algorithms, such as the logical algorithms of mathematical morphologic, the grayscale-gradient co-occurrence matrix-based method and the image enhancement method of grayscale image, were used to construct the parameter of extracting method and software. RESULTS The noise is restrained effectively and the definition of image increases, implementing the automatic measuring of particle parameters. CONCLUSION The analytical result of bipolysaccharide liposome and some emulsions indicate that the method can definitely increase the speed and precision of the measurement.

The metabolism in plasma of apo(a) and apoB100, the major protein components of lipoprotein(a) [Lp(a)], and the mechanism by which estrogen lowers Lp(a) concentration are both not well understood. Estrogen or placebo were administered to 12 postmenopausal women in a double-blind cross-over design; and after each treatment, apo(a) and apoB100 in Lp(a) were endogenously labeled by iv trideuterated leucine. After estrogen treatment, mean Lp(a) concentration decreased during estrogen, from 25 mg/dL, by 20% (P < 0.01); and the mean production rate of apo(a) decreased, from 0.31 nmol/kg·day, by 34% (P = 0.046). In contrast, the mean fractional catabolic rates of apo(a) were similar, 0.36 vs. 0.31/day (P = 0.23). In 6 women, the kinetics of apo(a) and apoB100, the two major proteins of Lp(a), were studied during estrogen and placebo periods. During both periods, the rate of appearance of tracer was similar in Lp(a)-apo(a) and Lp(a)-apoB100, as were the resulting metabolic rates and the changes during estrogen treatment. In conclusion, the findings are more compatible with intracellular synthesis of Lp(a) from nascent apo(a) and apoB100 than extracellular assembly from plasma low-density lipoproteins. Reduced flux into plasma of Lp(a), an atherogenic lipoprotein, could contribute to the lower cardiovascular disease rates in women receiving estrogen replacement therapy.

A strategy of corner-point extraction for non-rigid body contour was presented in this paper. After studying deeply the forms of the contour of non-rigid biomedical objects such as the contour of micro-vessels, an algorithm of corner-point extraction of curves expressed by chain code was provided, i.e. the so-called twice determining corner-point set with a low threshold and two window sizes. Firstly, calculated curvature of all points at a curve with two different window sizes according to our deriving formulations, and then choose all the candidates of corner points. Secondly, determined the candidates with maximum curvature for every cluster according to cluster analysis, and then deleted unsuitable cluster corner points under the condition of the neighbor corner-point distance constrain. The results showed that the strategy was useful and helpful for micro-vessel contour extraction. It was meaningful for corner-point extraction of a soft body's contour.