Grid topologies for the self-organizing map

The original Self-Organizing Feature Map (SOFM) has been extended in many ways to suit different goals and application domains. However, the topologies of the map lattice that we can found in literature are nearly always square or, more rarely, hexagonal. In this paper we study alternative grid topologies, which are derived from the geometrical theory of tessellations. Experimental results are presented for unsupervised clustering, color image segmentation and classification tasks, which show that the differences among the topologies are statistically significant in most cases, and that the optimal topology depends on the problem at hand. A theoretical interpretation of these results is also developed.     

集束化护理提高小耳畸形患者再造耳清洁度的效果观察

目的 探索集束化护理提高小耳畸形清洁度的临床效果.方法 2019年1月成立品管圈小组,通过对再造耳清洁度的现况调查,分析影响清洁度的因素,设定目标、拟定对策并实施.结果 开展品管圈活动后,再造耳的清洁度得分由53分上升到81分,目标达成率为105％,进步率为53％.小组成员从解决问题的能力、积极性、品管圈手法掌握程度、...     

ICP: A novel approach to predict prognosis of prostate cancer with inner-class clustering of gene expression data

Prostate cancer has heterogeneous characteristics. For that reason, even if tumors appear histologically similar to each other, there are many cases in which they are actually different, based on their gene expression levels. A single tumor may have multiple expression levels with both high-risk cancer genes and low-risk cancer genes. We can produce more useful models for stratifying prostate cancers into high-risk cancer and low-risk cancer categories by considering the range in each class through inner-class clustering. In this paper, we attempt to classify cancers into high-risk (aggressive) prostate cancer and low-risk (non-aggressive) prostate cancer using ICP (Inner-class Clustering and Prediction). Our model classified more efficiently than the models of the algorithms used for comparison. After discovering a number of genes linked to prostate cancer from the gene pairs used in our classification, we discovered that the proposed method can be used to find new unknown genes and gene pairs which distinguish between high-risk cancer and low-risk cancer.     

A divide-and-combine method for large scale nonparallel support vector machines

Nonparallel Support Vector Machine (NPSVM) which is more flexible and has better generalization than typical SVM is widely used for classification. Although some methods and toolboxes like SMO and libsvm for NPSVM are used, NPSVM is hard to scale up when facing millions of samples. In this paper, we propose a divide-and-combine method for large scale nonparallel support vector machine (DCNPSVM). In the division step, DCNPSVM divide samples into smaller sub-samples aiming at solving smaller subproblems independently. We theoretically and experimentally prove that the objective function value, solutions, and support vectors solved by DCNPSVM are close to the objective function value, solutions, and support vectors of the whole NPSVM problem. In the combination step, the sub-solutions combined as initial iteration points are used to solve the whole problem by global coordinate descent which converges quickly. In order to balance the accuracy and efficiency, we adopt a multi-level structure which outperforms state-of-the-art methods. Moreover, our DCNPSVM can tackle unbalance problems efficiently by tuning the parameters. Experimental results on lots of large data sets show the effectiveness of our method in memory usage, classification accuracy and time consuming.     

一起不明原因急性肾小球肾炎流行调查

[目的]探讨急性肾小球肾炎的流行特征,分析病例在学校聚集发生的高危因素,为制定针对性防制措施提供依据。[方法]在医疗机构和学校搜寻病例,在发生聚集性肾炎病例的3所学校采用统一的调查表病例对照研究。对部分病例学生进行血清学检测和细菌培养。[结果]2005年7～11月,在2个相邻县共发病89例,死亡2例,病死率为2.2%,3所村小学发炎病例罹患率为7.2%,男性49例(55%),女性40例(45%),年龄从3～17岁,平均(10±3.3)岁。病例抗“O”检测阳性率为63.0%(17/27)、血清补体下降率100%(6/6),咽拭子培养β溶血性链球菌阳性率为66.7%(4/6)。各年级发病、是否住校和男女生发病差异无统计学意义。疾病发生与浮肿前上呼吸道感染明显相关(χ2=15.7,P﹤0.01),学生用手直接擦课桌是可能的危险因素(OR=2.3,95%CI:1.1～4.9)。本次急性肾小球肾炎流行范围较广,有明显的学校、年龄和家庭聚集性。[结论]该次急性肾小球肾炎的发病与咽部β溶血性链球菌感染有关。及时对上呼吸道感染学生的早期治疗,养成良好的卫生习惯,是预防链球菌感染后肾炎疫情的关键。     

Unsupervised Measurement of Brain Tumor Volume on MR Images

We examined unsupervised methods of segmentation of MR images of the brain for measuring tumor volume in response to treatment. Two clustering methods were used: fuzzy c-means and a nonfuzzy clustering algorithm. Results were compared with volume segmentations by two supervised methods, k-nearest neighbors and region growing, and all results were compared with manual labelings. Results of individual segmentations are presented as well as comparisons on the application of the different methods with 10 data sets of patients with brain tumors. Unsupervised segmentation is preferred for measuring tumor volumes in response to treatment, as it eliminates operator dependency and may be adequate for delineation of the target volume in radiation therapy. Some obstacles need to be overcome, in particular regard-big the detection of anatomically relevant tissue classes. This study shows that these improvements are possible.     

Movement discrimination by single cells in the human pallidum characterised by hidden Markov models

In patients undergoing pallidotomy for Parkinson's disease, we recorded extracellularly from single neurons in the two internal segments (GPii, GPie) and the external segment (GPe) of the globus pallidus (GP) in response to active (cued) movements of the contralateral wrist, elbow or ankle. The patterns of cell activity occurring both before and after movement onset were analysed using hidden Markov models (HMMs) and clustered by movement type using the generative topographical mapping algorithm. Cluster separation was quantified in order to measure a cell's ability to discriminate between movements. Statistical analysis of variance indicated a significant regional gradient (GPii > GPie > GPe) of movement discrimination, while cells in all regions differentiated better between movements of different joints (wrist, elbow or ankle) than between flexion and extension of the same joint. We found that GP cells generally showed distinguishable firing patterns corresponding to more than one type of movement per cell, in support of the hypothesis that cells in these regions of the basal ganglia are not involved in preparation or execution of a single type of movement but participate in many different movements, analogous to the hidden units of a neural network. Our results also indicate that cell activity both preceding a movement and during its execution may be modelled by HMMs with only a small number of states.