云南眼镜蛇毒因子用于预致敏大鼠同种心脏移植的实验研究

目的观察纯化的云南眼镜蛇毒因子(Y-CVF)对预致敏大鼠同种心脏移植急性体液排斥反应的作用。方法BN大鼠到Lewis大鼠连续3次皮肤移植预致敏后行颈部异位心脏移植。将15对大鼠用随机数字法分成2组,实验组(n=8)于心脏移植前24h静脉给予Y-CVF80μg/kg;对照组(n=7)不用Y-CVF。观察移植心的生存时间,移植心停跳后病理学检查排斥类型,免疫组织化学染色观察移植心IgG和补体C3的沉积。结果Lewis大鼠预致敏后抗BN大鼠抗体滴度由0升高至1∶1028~1∶2056。对照组移植心存活时间为12·71h±13·94h,实验组移植心存活时间为99·50h±38·72h,与对照组比较差异有统计学意义(t=5·599,P<0·01)。病理检查结果证实,实验组均未发生急性体液排斥,仅见以大量单核淋巴细胞浸润为特征的急性细胞排斥反应。对照组则见以小血管内血栓形成为特征的急性体液排斥反应。免疫组织化学IgG染色实验组和对照组均为阳性,C3染色对照组为阳性,而实验组为阴性。结论使用Y-CVF可克服预致敏大鼠同种心脏移植急性体液排斥反应的发生。

猪到猕猴异种心脏移植超急性排斥反应的免疫学及病理学变化

　观察猪到猕猴异种心脏移植超急性排斥反应时的免疫学及病理学变化。方法　 采用猪到猕猴腹腔内异位心脏移植模型,检测发生超急性排斥反应者的血液中补体、天然抗体及T 淋巴细胞亚群的变化,并对移植心脏进行免疫组化(测定C3 、C4 、C5b29 、IgG及IgM 的沉积) 及病理学 分析。结果　发生超急性排斥反应时,血清补体C3 、C4 的含量、总补体活性及抗猪内皮细胞天然抗 体均有一定程度的下降;CD4 + / CD8 + T 淋巴细胞的比率也有所下降;移植心脏中均有补体C3 、C4 、 C5b29 的沉积, IgG及IgM 也均有沉积,但IgG和IgM 沉积强度的差异无统计学意义;病理学改变主 要为心肌间质弥漫性出血、水肿,毛细血管内普遍淤血。结论　补体通过经典途径激活参与猪到猕猴 异种心脏移植超急性排斥反应;超急性排斥反应时受者血中天然抗体水平明显下降;CD4 + T 淋巴细 胞可能参与异种移植超急性排斥反应过程并有所消耗;发生超急性排斥反应的移植物突出病理表现 为间质出血。

A Cancer Recognition Method Based on DNA Microarray

The accurate cancer classification is of great importance in clinical treatment. Recently, the DNA microarray technology provides a promising approach to the diagnosis and prognosis of cancer types. However, it has no perfect method for the multiclass classification problem. The difficulty lies in the fact that the data are of high dimensionality with small sample size. This paper proposed an automatic classification method of multiclass cancers based on Biomimetic pattern recognition (BPR). To the public GCM data set, the average correct classification rate reaches 80% under the condition that the correct rejection rate is 81%.

The application of DBF neural networks for object recognition

On the basis of DBF nets proposed by Wang Shoujue, the model and properties of DBF neural network were discussed in this paper. When applied in pattern recognition, the algorithm and implement on hardware were presented respectively. We did experiments on recognition of omnidirectionally oriented rigid objects on the same level, using direction basis function neural networks, which acts by the method of covering the high dimensional geometrical distribution of the sample set in the feature space. Many animal and vehicle models (even with rather similar shapes) were recognized omnidirectionally thousands of times. For total 8800 tests, the correct recognition rate is 98.75%, the error rate and the rejection rate are 0.5% and 1.25% respectively. (C) 2003 Elsevier Inc. All rights reserved.

Iris recognition algorithm based on point covering of high-dimensional space and neural network

In this paper, we constructed a Iris recognition algorithm based on point covering of high-dimensional space and Multi-weighted neuron of point covering of high-dimensional space, and proposed a new method for iris recognition based on point covering theory of high-dimensional space. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the rejection rate is 98.9%, the correct cognition rate and the error rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the rejection rate of test samples excluded in the training samples class is very high. It proves the proposed method for iris recognition is effective.

Application of multi-weighted neuron for iris recognition

In this paper, from the cognition science point of view, we constructed a neuron of multi-weighted neural network, and proposed a new method for iris recognition based on multi-weighted neuron. In this method, irises are trained as "cognition" one class by one class, and it doesn't influence the original recognition knowledge for samples of the new added class. The results of experiments show the correct rejection rate is 98.9%, the correct cognition rate and the error recognition rate are 95.71% and 3.5% respectively. The experimental results demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high. It proves the proposed method for iris recognition is effective.

Manifold covering theory in biomimetic pattern recognition

We describe a new model which is based on the concept of cognizing theory. The method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional space. We definite manifold covering in biomimetic pattern recognition, and study its property. Furthermore, we propose this manifold covering algorithm based on Biomimetic Pattern Recognition. At last, the experimental results for face recognition demonstrates that the correct rejection rate of the test samples excluded in the classes of training samples is very high and effective.

Optimization of mechanically induced long-period fibre grating transmission spectrum

A new double-layer grating template is designed to reduce the out-of-band loss as much as 1.8dB when the loss of LP03 reaches 10.2 dB. Meanwhile, we propose a method to remove the sidelobes in the transmission spectra by the adjustment of the thickness of pressure plates. The plate-thickness-induced shift of resonant wavelength and the attenuation of loss peak intensity when removing sidelobes can be modified by the fibre distance and contact point on the pressure plates.

Cognitive models in biomimetic pattern cognition

We describe a new model which is based on the concept of cognizing theory. The method identifies subsets of the data which are embedded in arbitrary oriented lower dimensional space. We definite k-mean covering, and study its property. Covering subsets of points are repeatedly sampled to construct trial geometry space of various dimensions. The sampling corresponding to the feature space having the best cognition ability between a mode near zero and the rest is selected and the data points are partitioned on the basis of the best cognition ability. The repeated sampling then continues recursively on each block of the data. We propose this algorithm based on cognition models. The experimental results for face recognition demonstrate that the correct rejection rate of the test samples excluded in the classes of training samples is very high and effective.

Object-recognition with oblique observation directions based on biomimetic pattern recognition

In this paper, we propose a new scheme for omnidirectional object-recognition in free space. The proposed scheme divides above problem into several onmidirectional object-recognition with different depression angles. An onmidirectional object-recognition system with oblique observation directions based on a new recognition theory-Biomimetic Pattern Recognition (BPR) is discussed in detail. Based on it, we can get the size of training samples in the onmidirectional object-recognition system in free space. Omnidirection ally cognitive tests were done on various kinds of animal models of rather similar shapes. For the total 8400 tests, the correct recognition rate is 99.89%. The rejection rate is 0.11% and on the condition of zero error rates. Experimental results are presented to show that the proposed approach outperforms three types of SVMs with either a three degree polynomial kernel or a radial basis function kernel.

Circuit design and simulation of a CMOS-based preamplifier for brain neural signals

A novel CMOS-based preamplifier for amplifying brain neural signal obtained by scalp electrodes in brain-computer interface (BCI) is presented in this paper. By means of constructing effective equivalent input circuit structure of the preamplifier, two capacitors of 5 pF are included to realize the DC suppression compared to conventional preamplifiers. Then this preamplifier is designed and simulated using the standard 0.6 mu m MOS process technology model parameters with a supply voltage of 5 volts. With differential input structures adopted, simulation results of the preamplifier show that the input impedance amounts to more than 2 Gohm with brain neural signal frequency of 0.5 Hz-100 Hz. The equivalent input noise voltage is 18 nV/Hz(1/2). The common mode rejection ratio (CMRR) of 112 dB and the open-loop differential gain of 90 dB are achieved.

A New Discrimination Method of Maize Seed Varieties Based on Near-Infrared Spectroscopy

A new discrimination method for the maize seed varieties based on the near-infrared spectroscopy was proposed. The reflectance spectra of maize seeds were obtained by a FT-NIR spectrometer (12 000-4 000 cm(-1)). The original spectra data were preprocessed by first derivative method. Then the principal component analysis (PCA) was used to compress the spectra data. The principal components with the cumulate reliabilities more than 80% were used to build the discrimination models. The model was established by Psi-3 neuron based on biomimetic pattern recognition (BPR). Especially, the parameter of the covering index was proposed to assist to discriminating the variety of a seed sample. The authors tested the discrimination capability of the model through four groups of experiments. There were 10, 18, 26 and 34 varieties training the discrimination models in these experiments, respectively. Additionally, another seven maize varieties and nine wheat varieties were used to test the capability of the models to reject the varieties not participating in training the models. Each group of the experiment was repeated three times by selecting different training samples at random. The correct classification rates of the models in the four-group experiments were above 91. 8%. The correct rejection rates for the varieties not participating in training the models all attained above 95%. Furthermore, the performance of the discrimination models did not change obviously when using the different training samples. The results showed that this discrimination method can not only effectively recognize the maize seed varieties, but also reject the varieties not participating in training the model. It may be practical in the discrimination of maize seed varieties.

Architecture research and hardware implementation on simplified neural computing system for face identification

This paper describes a special-purpose neural computing system for face identification. The system architecture and hardware implementation are introduced in detail. An algorithm based on biomimetic pattern recognition has been embedded. For the total 1200 tests for face identification, the false rejection rate is 3.7% and the false acceptance rate is 0.7%.

Biomimetic (topological) pattern recognition - A new model of pattern recognition theory and its application

A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

Polyamide thin film composite membranes prepared from isomeric biphenyl tetraacyl chloride and m-phenylenediamine

Three novel of isomeric tetra-functional biphenyl acid chloride: 3,3',5,5'-biphenyl tetraacyl chloride (mm-BTEC), 2,2',4,4'-biphenyl tetraacyl chloride (om-BTEC), and 2,2',5,5'-biphenyl tetraacyl chloride (op-BTEC) were synthesized, and used as new monomers for the preparation of the thin film composite (TFC) reverse osmosis (RO) membranes through interfacial polymerization with m-phenylenediamine (MPDA). The results of membrane performance test showed that membranes prepared from om-BTEC and op-BTEC had higher flux at the expanse of rejection compared with membranes prepared from mm-BTEC.