Passent M. El-Kafrawy

Screening for novel central nervous system biomarkers in veterans with Gulf War Illness

Gulf War illness (GWI) is primarily diagnosed by symptom report; objective biomarkers are needed that distinguish those with GWI. Prior chemical exposures during deployment have been associated in epidemiologic studies with altered central nervous system functioning in veterans with GWI. Previous studies from our group have demonstrated the presence of autoantibodies to essential neuronal and glial proteins in patients with brain injury and autoantibodies have been identified as candidate objective markers that may distinguish GWI. Here, we screened the serum of 20 veterans with GWI and 10 non-veteran symptomatic (low back pain) controls for the presence of such autoantibodies using Western blot analysis against the following proteins: neurofilament triplet proteins (NFP), tubulin, microtubule associated tau proteins (Tau), microtubule associated protein-2 (MAP-2), myelin basic protein (MBP), myelin associated glycoprotein (MAG), glial fibrillary acidic protein (GFAP), calcium-calmodulin kinase II (CaMKII) and glial S-100B protein. Serum reactivity was measured as arbitrary chemiluminescence units. As a group, veterans with GWI had statistically significantly higher levels of autoantibody reactivity in all proteins examined except S-100B. Fold increase of the cases relative to controls in descending order were: CaMKII 9.27, GFAP 6.60, Tau 4.83, Tubulin 4.41, MAG 3.60, MBP 2.50, NFP 2.45, MAP-2 2.30, S-100B 1.03. These results confirm the continuing presence of neuronal injury/gliosis in these veterans and are in agreement with the recent reports indicating that 25years after the war, the health of veterans with GWI is not improving and may be getting worse. Such serum autoantibodies may prove useful as biomarkers of GWI, upon validation of the findings using larger cohorts.

A Panel of Autoantibodies Against Neural Proteins as Peripheral Biomarker for Pesticide-Induced Neurotoxicity

In the present study, we screened the sera of subjects chronically exposed to mixtures of pesticides (composed mainly of organophosphorus compounds (OPs) and others) and developed neurological symptoms for the presence of autoantibodies against cytoskeletal neural proteins. OPs have a well-characterized clinical profile resulting from acute cholinergic crisis. However, some of these compounds cause neuronal degeneration and demyelination known as organophosphorus compound-induced delayed neurotoxicity (OPIDN) and/or organophosphorus compound-induced chronic neurotoxicity (OPICN). Studies from our group have demonstrated the presence of autoantibodies to essential neuronal and glial proteins against cytoskeletal neural proteins in patients with chemical-induced brain injury. In this study, we screened the serum of 50 pesticide-exposed subjects and 25 non-exposed controls, using Western blot analysis against the following proteins: neurofilament triplet proteins (NFPs), tubulin, microtubule-associated tau proteins (Tau), microtubule-associated protein-2 (MAP-2), myelin basic protein (MBP), myelin-associated glycoprotein (MAG), glial fibrillary acidic protein (GFAP), calcium-calmodulin kinase II (CaMKII), glial S100-B protein, and alpha-synuclein (SNCA). Serum reactivity was measured as arbitrary chemiluminescence units. As a group, exposed subjects had significantly higher levels of autoantibody reactivity in all cases examined. The folds of increase in of autoantibodies against neural proteins of the subjects compared to healthy humans in descending order were as follows: MBP, 7.67, MAG 5.89, CaMKII 5.50, GFAP 5.1, TAU 4.96, MAP2 4.83, SNCA 4.55, NFP 4.55, S-100B 2.43, and tubulin 1.78. This study has demonstrated the presence of serum autoantibodies to central nervous system-specific proteins in a group of farmers chronically exposed to pesticides who developed neurological signs and symptoms of neural injury. These autoantibodies can be used as future diagnostic/therapeutic target for OP-induced neurotoxicity.

Clustering Web Documents based on Efficient Multi-Tire Hashing Algorithm for Mining Frequent Termsets

Document Clustering is one of the main themes in text mining. It refers to the process of grouping documents with similar contents or topics into clusters to improve both availability and reliability of text mining applications. Some of the recent algorithms address the problem of high dimensionality of the text by using frequent termsets for clustering. Although the drawbacks of the Apriori algorithm, it still the basic algorithm for mining frequent termsets. This paper presents an approach for Clustering Web Documents based on Hashing algorithm for mining Frequent Termsets (CWDHFT). It introduces an efficient Multi-Tire Hashing algorithm for mining Frequent Termsets (MTHFT) instead of Apriori algorithm. The algorithm uses new methodology for generating frequent termsets by building the multi-tire hash table during the scanning process of documents only one time. To avoid hash collision, Multi Tire technique is utilized in this proposed hashing algorithm. Based on the generated frequent termset the documents are partitioned and the clustering occurs by grouping the partitions through the descriptive keywords. By using MTHFT algorithm, the scanning cost and computational cost is improved moreover the performance is considerably increased and increase up the clustering process. The CWDHFT approach improved accuracy, scalability and efficiency when compared with existing clustering algorithms like Bisecting K-means and FIHC.

Investigate the Performance of Document Clustering Approach Based on Association Rules Mining

The challenges of the standard clustering methods and the weaknesses of Apriori algorithm in frequent termset clustering formulate the goal of our research. Based on Association Rules mining, an efficient approach for Web Document Clustering (ARWDC) has been devised. An efficient Multi-Tire Hashing Frequent Termsets algorithm (MTHFT) has been used to improve the efficiency of mining association rules by targeting improvement in mining of frequent termset. Then, the documents are initially partitioned based on association rules. Since a document usually contains more than one frequent termset, the same document may appear in multiple initial partitions, i.e., initial partitions are overlapping. After making partitions disjoint, the documents are grouped within the partition using descriptive keywords, the resultant clusters are obtained effectively. In this paper, we have presented an extensive analysis of the ARWDC approach for different sizes of Reuters datasets. Furthermore the performance of our approach is evaluated with the help of evaluation measures such as, Precision, Recall and F-measure compared to the existing clustering algorithms like Bisecting K-means and FIHC. The experimental results show that the efficiency, scalability and accuracy of the ARWDC approach has been improved significantly for Reuters datasets. The internet has become the largest data repository, facing the problem of information overload. The existence of an abundance of information, in combination with the dynamic and heterogeneous nature of the Web, makes information retrieval a tedious process for the average user. Search engines, Meta-Search engines and Web Directories have been developed in order to help the users quickly and easily satisfy their information need. The Search engine performs exact matching between the query terms and the keywords that characterize each web page and presents the results to the user. These results are long lists of URLs, which are very hard to search. Furthermore, users without domain expertise are not familiar with the appropriate terminology thus not submitting the right query terms, leading to the retrieval of more irrelevant pages. This has led to the need for the development of new techniques to assist users effectively navigate, trace and organize the available web documents, with the ultimate goal of finding those best matching their needs. Document Clustering is one of the techniques that can play an important role towards the achievement of this objective. Document clustering has become an increasingly important task in analyzing huge numbers of documents distributed among various sites. Furthermore organizing them into different groups called as clusters, where the documents in each cluster share some common properties according to defined similarity measure. The fast and high-quality document clustering algorithms play an important role in helping users to effectively navigate, summarize, and organize the information. Document clustering has been studied intensively because of its wide applicability in areas such as Web Mining, Search Engines, Information Retrieval, and Topological Analysis. Document Clustering is different than document classification. In document classification, the classes (and their properties) are known a priori, and documents are assigned to these classes; whereas, in document clustering, the number, properties, or membership (composition) of classes is not known in advance. Thus, classification is an example of supervised machine learning and clustering that of

Integrated Ontology for Agricultural Domain

provide a shared and common understanding of a domain that can be communicated between people and across application systems. An ontology for a certain domain can be created from scratch or by merging existing ontologies in the same domain. Establishing ontology from scratch is hard and expensive. Multiple ontologies of different systems for the same domain may be dissimilar, thus, various parties with different ontologies do not fully understand each other in spite of these ontologies are for the same domain. To solve this problem, it is necessary to integrate these ontologies. Integrated ontology, should be consistent and has no redundancy. This work presents a semi-automated system for building an integrated ontology by matching and merging existing ontologies. The proposed system has been applied on the agricultural domain for Faba Bean crop to get a dynamic integrated ontology, it can be applied also on all crops whatever field crops or horticulture crops. Source ontologies in the proposed system have been implemented in XML language. CommonKADS Methodology has been used in building the target ontology. CommonKADS Methodology deals with the following kinds of entities: Concepts, properties, and values. The proposed system proposed a technique to solve the matching and merging problems by using a multi-matching technique to find the correspondences between entities in the source ontologies and merging technique which deals with concepts, properties, values and hierarchical classifications. The outcome of the proposed system is an integrated ontology in hierarchical classification of the concepts .

Tubulin and Tau: Possible targets for diagnosis of Parkinson’s and Alzheimer’s diseases

Neurodegenerative diseases including Alzheimer’s disease (AD) and Parkinson’s disease (PD) are characterized by progressive neuronal loss and pathological accumulation of some proteins. Developing new biomarkers for both diseases is highly important for the early diagnosis and possible development of neuro-protective strategies. Serum antibodies (AIAs) against neuronal proteins are potential biomarkers for AD and PD that may be formed in response to their release into systemic circulation after brain damage. In the present study, two AIAs (tubulin and tau) were measured in sera of patients of PD and AD, compared to healthy controls. Results showed that both antibodies were elevated in patients with PD and AD compared to match controls. Curiously, the profile of elevation of antibodies was different in both diseases. In PD cases, tubulin and tau AIAs levels were similar. On the other hand, AD patients showed more elevation of tau AIAs compared to tubulin. Our current results suggested that AIAs panel could be able to identify cases with neuro-degeneration when compared with healthy subjects. More interestingly, it is possible to differentiate between PD and AD cases through identifying specific AIAs profile for each neurodegenerative states.

Using Class Decomposition for Building GA with Fuzzy Rule-Based Classifiers

A classification problem is fully partitioned into several small problems each of which is responsible for solving a fraction of the original problem. In this paper, a new approach using class-based partitioning is proposed to improve the performance of genetic-based classifiers. Rules are defined with fuzzy genes to represent variable length rules. We experimentally evaluate our approach on four different data sets and demonstrate that our algorithm can improve classification rate compared to normal Rule-based classification GAs [1] with non-partitioned techniques.

Graphical Deadlock Avoidance

Deadlock avoidance in sequential resource allocation and concurrency control is a well-defined problem. The problem is extensively studied and investigated in order to define a safe, i.e. deadlock free, scheduling mechanism to a set of finite- resources. Originally a simple graphical solution has been given theoretically, however, practically the problem has been studied by defining deadlock avoidance policies (DAP) proved to be NP- hard through different formal frameworks using petri nets and finite state automata. In this paper a new technique is proposed as an extension to the original graphical solution.

Semantic Question Answering System Using Dbpedia

Due to the rapid increase of data generated on the web, there is a need for efficient techniques to access required data. Question Answering (QA) is a multi-disciplinary field of information retrieval and natural language processing, which aims at answering users’ query written closer to human language. Users can thus submit their requests as they think it and conceptually closer to their intended outcomes. The upcoming trend in query languages, and programing languages in general, towards more human-like language for increased user-friendliness subject to enhanced efficiency with usage of English-like words. In this paper, an architecture of factoid question answering system is presented using Dbpedia ontology. The discussed architecture is tested and results are compared to those of other systems.

Forming System Requirements for Software Development Using Semantic Technology

Requirements Engineering (RE) is one of the most important phases in the software development process, more than fifty percent of the projects failed due to lack of RE. Therefore, most of the developers in order to achieve high software quality they need to satisfy user’s requirement without errors (i.e. specific, clear, precise, …etc.). In this regard, this paper presents system requirement formulation from user’s stories based on previous similar verified requirements with semantic analysis. After semantic verification, the English written requirements are verified by a Case Based Reasoning Engine to be formulated as a standard requirements form. The generated requirements should support the decisions and resolutions of problems arising from new requirements.