Exploration of the normal human bronchoalveolar lavage fluid proteome

We obtained insight into normal lung function by proteome analysis of bronchoalveolar lavage fluid (BALF) from six normal human subjects using a "Lyse-N-Go' shotgun proteomic protocol. Intra-sample variation was calculated using three different label-free methods, (i) protein sequence coverage; (ii) peptide spectral counts and (iii) peptide single-ion current areas (PICA), which generates protein expression data by summation of the area under the curve for a given peptide single-ion current trace and then adding values for all peptides from that same parent protein. PICA gave the least intra-subject variability and was used to calculate differences in protein expression between the six subjects. We observed an average threefold inter-sample variability, which affects analysis of changes in protein expression that occur in different diseases. We detected 167 unique proteins with >100 proteins detected in each of the six individual BAL samples, 42 of which were common to all six subjects. Gene ontology analysis demonstrated enrichment of several biological processes in the lung, reflecting its expected role in gas exchange and host defense as an immune organ. The same biological processes were enriched compared to either plasma or total genome proteome, suggesting an active enrichment of plasma proteins in the lung rather than passive capillary leak.     

Proteomics and the lung: Analysis of bronchoalveolar lavage fluid

Our knowledge of the complex bronchoalveolar lavage fluid (BALF) proteome has increased significantly over the last decade; but still, there remain many aspects of the BALF proteome that need characterization. Current proteomic methodologies resolve proteins within limited dynamic ranges: thereby, being limited in their ability to examine important areas of the BALF proteome, such as low molecular weight, low abundance proteins. To ensure proper coverage of these proteins in the BALF proteome, a refined 2-DE standard operation protocol is presented, highlighting important issues in sample collection, sample preparation, and 2-D DIGE analysis. It is hoped that this will help advance the field of BALF proteomics, BALFomics, which has lagged behind similar biofluids such as plasma and serum.     

Shotgun MS proteomic analysis of bronchoalveolar lavage fluid in normal subjects

We provide a review of proteomic techniques used to characterize the bronchoalveolar lavage fluid (BALF) proteome of normal healthy subjects. Bronchoalveolar lavage (BAL) is the most common technique for sampling the components of the alveolar space. The proteomic techniques used to study normal BALF include protein separation by 2DE, whereby proteins were identified by comparison to a reference gel as well as high pressure liquid chromatography (HPLC)-MS/MS, also known as shotgun proteomics. We summarize recent progress using shotgun MS technologies to define the normal BALF proteome. Surprisingly, we find that despite advances in shotgun proteomic technologies over the course of the last 10 years, which have resulted in greater numbers of proteins being identified, the functional landscape of normal BALF proteome was similarly described by all methods examined.     

Learning fuzzy partitions in FIR methodology

The main goal of this research is the development of a hybrid genetic fuzzy system (GFS), composed by the fuzzy inductive reasoning (FIR) methodology and a genetic algorithm (GA) that is responsible of learning the fuzzy partitions needed in the recode process of FIR. A partition includes the number of fuzzy sets (classes) per variable and the membership function of each class. The resulting GFS is applied to two real problems, i.e. the estimation of the maintenance cost of medium voltage lines in Spanish towns and the prediction of ozone levels in Austria. The results obtained in each application are compared with some of the most popular classical statistical modeling methods, neural networks and other hybrid evolutionary data analysis techniques.     

Compression and decompression of images with discrete fuzzy transforms

In foregoing papers we have used the compression/decompression method of images based on the concept of discrete fuzzy transform (and its inverse) of a function f defined on a real interval with respect to the fuzzy sets A₁,…,A_n forming a fuzzy partition of such interval. Here we make a detailed experimental comparison with the similar method based on the fuzzy transforms F^↑ and F^↓ of f defined via a continuous triangular norm and its corresponding residuum, respectively. We consider some images of sizes 256 × 256 (pixels) extracted from the well-known database Corel Galery (Arizona Directory). By using the same compression rate in both methods, we have that the PSNR (Peak Signal to Noise Ratio) obtained with the discrete fuzzy transform (and its inverse) of f is more higher than the PSNR determined with the operators F^↑ and F^↓ defined via the usual Lukasiewicz, product and minimum triangular norms. Moreover, we compare our results with the classical JPEG method for values of compression rate approximately equal to those used in the previous methods.     

A risk assessment methodology using intuitionistic fuzzy set in FMEA

Most current risk assessment methods use the risk priority number (RPN) value to evaluate the risk of failure. However, conventional RPN methodology has been criticised as having five main shortcomings as follows: (1) the assumption that the RPN elements are equally weighted leads to over simplification; (2) the RPN scale itself has some non-intuitive statistical properties; (3) the RPN elements have many duplicate numbers; (4) the RPN is derived from only three factors mainly in terms of safety; and (5) the conventional RPN method has not considered indirect relations between components. To address the above issues, an efficient and comprehensive algorithm to evaluate the risk of failure is needed. This article proposes an innovative approach, which integrates the intuitionistic fuzzy set (IFS) and the decision-making trial and evaluation laboratory (DEMATEL) approach on risk assessment. The proposed approach resolves some of the shortcomings of the conventional RPN method. A case study, which assesses the risk of 0.15?µm DRAM etching process, is used to demonstrate the effectiveness of the proposed approach. Finally, the result of the proposed method is compared with the listing approaches of risk assessment methods.     

Segmentation of remotely sensed images with fuzzy thresholding,and quantitative evaluation

Effectiveness of various fuzzy thresholding techniques (based on entropy of fuzzy sets, fuzzy geometrical properties, and fuzzy correlation) is demonstrated on remotely sensed (IRS and SPOT) images. A new quantitative index for image segmentation using the concept of homogeneity within regions is defined. Results are compared with those of probabilistic thresholding, and fuzzy c-means and hard c-means clustering algorithms, both in terms of index value (quantitatively) and structural details (qualitatively). Fuzzy set theoretic algorithms are seen to be superior to their respective non-fuzzy counterparts. Among all the techniques, fuzzy correlation, followed by fuzzy entropy, performed better for extracting the structures. Fuzzy geometry based thresholding algorithms produced a single stable threshold for a wide range of membership variation.     

基于空间约束的模糊C均值聚类肝脏CT图像分割

传统模糊C均值（FCM）聚类算法应用于肝脏CT图像分割时仅考虑像素本身特征,无法解决灰度不均匀造成的影响以及肝脏边界模糊造成的边界泄露的问题。为解决上述问题,提出一种结合空间约束的模糊C均值（SFCM）聚类分割算法。首先,使用二维高斯分布函数构建卷积核,利用该卷积核对源图像进行空间信息提取得到特征矩阵;然后,引入空间约束惩罚项,更新并优化目标函数得到新的迭代方程;最后,通过多次迭代,完成对肝脏CT图像的分割。实验结果表明,SFCM算法分割具有灰度不均匀和边界粘连的肝脏CT图像时得到的肝脏轮廓形状更加规则,准确率达到92.8%,比FCM和直觉模糊C均值（IFCM）算法的分割准确率分别提升了2.3和4.3个百分点,过分割率分别降低了4.9和5.3个百分点。     

Segmentation of lip region in color images by fuzzy clustering

Extraction of the lip region is essential to lip reading, which is a field of image processing that is used to obtain meaningful information by the analysis of lip movement from human face images. Many conventional methods for extraction of the lip region have been proposed. One is to identify the lip position by using geometric face structure. Another discriminates lip and skin regions by using color information only. The former is more complex than the latter; however, it can analyze black and white images as well as color images. The latter is simpler than the former; however, it is difficult to discriminate lip and skin regions because of the high similarity between these two regions, and it is less accurate than the former. Conventional methods usually analyze color coordinate systems to extract lip regions rather than analyzing the coordinate system itself. In this paper, the best color coordinate system for lip extraction was selected by the analysis of discriminability. Segmentation of the lip region with this coordinate system and a new feature vector are proposed.     

A fuzzy clustering methodology for linguistic opinions in group decision making

The objective of this paper is to automate a decision-aid tool, which provides homogeneous clusters from a set of heterogeneous opinions for a particular criterion to evaluate an item under group decision scheme. In such situation, decision-making by a group of experts becomes more realistic and consistent while they provide more or less homogeneous responses. But in real practice, the homogeneity among the opinions for a specific criterion to evaluate an item is not maintained due to the diversity among the experts’ several cognitive factors as well as biasness. As a result, the group's overall effectiveness is suffered and making a true decision becomes difficult as well as sometimes confusing. In order to avoid the heterogeneity among the opinions (fuzzy numbers), we propose here a fuzzy clustering methodology based on a fuzzy distance measure. Also a ranking index is introduced on the basis of Ordered Weighted Average (OWA) operator. Finally, a fuzzy multi-criteria decision-making problem on a flight simulator software development project is considered here to employ the proposed technique. The results are discussed and compared.     

A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques

In this research work, a novel framework for the construction of augmented Fuzzy Cognitive Maps based on Fuzzy Rule-Extraction methods for decisions in medical informatics is investigated. Specifically, the issue of designing augmented Fuzzy Cognitive Maps combining knowledge from experts and knowledge from data in the form of fuzzy rules generated from rule-based knowledge discovery methods is explored. Fuzzy cognitive maps are knowledge-based techniques which combine elements of fuzzy logic and neural networks and work as artificial cognitive networks. The knowledge extraction methods used in this study extract the available knowledge from data in the form of fuzzy rules and insert them into the FCM, contributing to the development of a dynamic decision support system. The fuzzy rules, which derived by these extraction algorithms (such as fuzzy decision trees, association rule-based methods and neuro-fuzzy methods) are implemented to restructure the FCM model, producing new weights into the FCM model, that initially structured by experts. Concluding, our scope is to present a new methodology through a framework for decision making tasks using the soft computing technique of FCMs based on knowledge extraction methods. A well known medical decision making problem pertaining to the problem of radiotherapy treatment planning selection is presented to illustrate the application of the proposed framework and its functioning.     

Interactive segmentation of the cerebral lobes with fuzzy inference in 3T MR images.

Measurement of volume and surface area of the frontal, parietal, temporal and occipital lobes from magnetic resonance (MR) images shows promise as a method for use in diagnosis of dementia. This article presents a novel computer-aided system for automatically segmenting the cerebral lobes from 3T human brain MR images. Until now, the anatomical definition of cerebral lobes on the cerebral cortex is somewhat vague for use in automatic delineation of boundary lines, and there is no definition of cerebral lobes in the interior of the cerebrum. Therefore, we have developed a new method for defining cerebral lobes on the cerebral cortex and in the interior of the cerebrum. The proposed method determines the boundaries between the lobes by deforming initial surfaces. The initial surfaces are automatically determined based on user-given landmarks. They are smoothed and deformed so that the deforming boundaries run along the hourglass portion of the three-dimensional shape of the cerebrum with fuzzy rule-based active contour and surface models. The cerebrum is divided into the cerebral lobes according to the boundaries determined using this method. The reproducibility of our system with a given subject was assessed by examining the variability of volume and surface area in three healthy subjects, with measurements performed by three beginners and one expert user. The experimental results show that our system segments the cerebral lobes with high reproducibility.     

A nonlinear-programming methodology for multi-attribute decision-making problem with interval-valued intuitionistic fuzzy soft sets information

Interval-valued intuitionistic fuzzy (IVIF) soft set is one of the useful extensions of the fuzzy soft set which efficiently deals with the uncertain data for the decision-making processes. In this paper, an attempt has been made to present a nonlinear-programming (NP) model based on the technique for order preference by similarity to ideal solution (TOPSIS), to solve multi-attribute decision-making problems. In this approach, both ratings of alternatives on attributes and weights of attributes are represented by IVIF sets. Based on the available information, NP models are constructed on the basis of the concepts of the relative-closeness coefficient and the weighted distance. Some NP models are further deduced to calculate relative-closeness of sets of alternatives which can be used to generate the ranking order of the alternatives. A real example is taken to demonstrate the applicability and validity of the proposed methodology.     

A multiobjective fuzzy clustering method for change detection in SAR images

On account of the presence of speckle noise, the trade-off between removing noise and preserving detail is crucial for the change detection task in Synthetic Aperture Radar (SAR) images. In this paper, we put forward a multiobjective fuzzy clustering method for change detection in SAR images. The change detection problem is modeled as a multiobjective optimization problem, and two conflicting objective functions are constructed from the perspective of preserving detail and removing noise, respectively. We optimize the two constructed objective functions simultaneously by using a multiobjective fuzzy clustering method, which updates the membership values according to the weights of the two objectives to find the optimal trade-off. The proposed method obtains a set of solutions with different trade-off relationships between the two objectives, and users can choose one or more appropriate solutions according to requirements for diverse problems. Experiments conducted on real SAR images demonstrate the superiority of the proposed method.     

Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology

Energy planning is a complex issue which takes technical, economic, environmental and social attributes into account. Selection of the best energy technology requires the consideration of conflicting quantitative and qualitative evaluation criteria. When decision-makers’ judgments are under uncertainty, it is relatively difficult for them to provide exact numerical values. The fuzzy set theory is a strong tool which can deal with the uncertainty in case of subjective, incomplete, and vague information. It is easier for an energy planning expert to make an evaluation by using linguistic terms. In this paper, a modified fuzzy TOPSIS methodology is proposed for the selection of the best energy technology alternative. TOPSIS is a multicriteria decision making (MCDM) technique which determines the best alternative by calculating the distances from the positive and negative ideal solutions according to the evaluation scores of the experts. In the proposed methodology, the weights of the selection criteria are determined by fuzzy pairwise comparison matrices. The methodology is applied to an energy planning decision-making problem.     

Low light enhancement algorithm for color images using intuitionistic fuzzy sets with histogram equalization

Multimedia Tools and Applications - In this work, a new fuzzy logic-based algorithm is proposed for the enhancement of low light color images. A generalization of a fuzzy set known as an...     

模糊Bayes 理论在遥感影像变化检测中的应用

针对传统基于Bayes 决策规则的遥感影像变化检测方法中参数估计的不足以及分类过程中的硬划分问题,采用动态更新变化和未变化两类像元模糊子集的方法,实现对两类像元模糊子集中参数的动态更新,利用估计参数获得各子集的后验概率函数,再将后验概率函数转化为模糊子集的模糊隶属函数,从而获得各子集的指标函数,根据指标函数对影像中未分类的像元值进行判断,实现遥感影像的变化区域提取。实验结果表明：与现有的基于Bayes 决策规则的遥感影像变化检测方法及ERDAS 软件生成结果相比,提出的方法具有更好的变化检测精度。