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Background: The occurrence of Vibrio spp. in raw fish is common, especially from temperate climates regions, in both natural and farm environments, and in all kinds of fish. Whereas, limited data have been reported on antibiotic resistance strains of Vibrio spp. Aim: The main objectives of this study were to determine the prevalence of Vibrio spp. including antibiotic resistance strains in fresh finfish samples as well as evaluation of their antibiogram. Material and methods: Samples of 122 fresh finfish were purchased from two main fish markets in Tripoli, Libya. Samples were examined for the presence of Vibrio spp. and its antibiogram. Results: A total of 68 Vibrios belonging to six different species were isolated, the most predominant species were V. fluvialis 45(66.2%), followed by V. alginolyticus 10(14.7%), V. cholera 9(13.2%), V. parahemolyticus 2(2.9%), V. vulnificus and V. hollisae 1(1.5%). All of the isolates showed different degrees of resistance toward tested antibiotics. The isolates showed high resistance to cloxacillin, amoxicillin, vancomycin, amoxicillin/clavulanic acid, oxytetracyline, cephalothin, while for streptomycin the resistance rate was the lowest. Conclusion: The results demonstrated a high incidence of Vibrio spp. in raw finfish samples and this finding suggests that, fish sold in Tripoli fish markets could be a source of potential health hazard. This is because some of the isolated strains were resistant to the most popular antibiotics used in human and aquaculture treatments. Besides, study results indicated that, antibiotic resistance strains are common in raw finfish sold in both markets. Improvements in handling and processing are needed to minimize the prevalence of such pathogenic bacteria. Keywords: Vibrio spp., Finfish, Mullet, Sardine, Antibiotic Resistance.

Background: The occurrence of Vibrio spp. in raw fish is common, especially from temperate climates regions, in both natural and farm environments, and in all kind of fish. Whereas, limited data have been reported on antibiotic resistance strains of Vibrio spp. Aim: The main objectives of this study were to determine the prevalence of Vibrio spp. including antibiotic resistance strains in fresh finfish samples as well as evaluation of their antibiogram. Material and methods: Samples of 122 fresh finfish were purchased from two main fish markets in Tripoli, Libya. Samples were examined for the presence of Vibrio spp. and its antibiogram. Results: A total of 68 Vibrio’s belonging to six different species were isolated, the most predominant species were V. fluvialis 45(66.2%), followed by V. alginolyticus 10(14.7%), V. cholera 9(13.2%), V. parahemolyticus 2(2.9%), V. vulnificus and V. hollisae 1(1.5%). All of the isolates showed different degree of resistance toward tested antibiotics. The isolates showed high resistance to cloxacillin, amoxicillin, vancomycin, amoxicillin/clavulanic acid, oxytetracylin, cephalothin, while for streptomycin the resistance rate was the lowest. Conclusion: The results demonstrated a high incidence of Vibrio spp. in raw finfish samples and this finding suggests that, fish sold in Tripoli fish markets could be a source of potential health hazard. This is because some of isolated strains were resistant to most popular antibiotics used in human and aquaculture treatments. Besides, study results indicated that, antibiotic resistance strains are common in raw finfish sold in both markets. Improvements in handling and processing are needed to minimize the prevalence of such pathogenic bacteria. &nbsp

Cancer is a leading cause of death and majority of cancer patients are diagnosed in the late stages of cancer by using conventional methods. The gene expression microarray technology is applied to detect and diagnose most types of cancers in their early stages. Furthermore, it allows researchers to analyze thousands of genes simultaneously. To acquire knowledge from gene expression data, data mining methods are needed. Due to the rapid evolution of cancer detection and diagnosis techniques, a survey of modern techniques is desirable. This study reviews and provide a detailed description of these techniques. As a result, it helps to enhance existing techniques for cancer detection and diagnosis as well as guiding researchers to develop new approaches.

Introduction: Benign prostatic hyperplasia (BPH) is a common disease affecting men 50 years and older. Treatment options consist of observation, pharmacological treatment, minimally invasive surgery and traditional surgery. Alpha-blockers and 5-alpha-reductase inhibitors are the primary medications used to treat BPH. Transurethral resection of the prostate (TURP) is the gold standard of surgical management of BPH. Objective: To evaluate the effect of six weeks finasteride therapy before TURP on overall surgical outcomes and early postoperative quality of life (QoL). Patients and methods: Between June 2014 and August 2016, patients with BPH at our department were randomly assigned to one of two groups: group (A) receiving 5 mg of finasteride daily for six weeks and group (B) did not receiving finasteride before TURP. All patients were assessed using a modified validated Arabic version of the International Prostate Symptom Score (IPSS). Intra operative serum hemoglobin concentration and hemoglobin concentration in irrigating fluid were recorded. One month post-surgery, IPSS, storage, voiding subscores and QoL scores were measured. Results: Out of a total of 115 patients, 98 patients completed the study. Before surgery, there was no significant difference between the two groups in prostate size (Prostate size was 54.52 ± 7.3 g in group A and 50.19 ± 6.8 g in group B, p value = 0.72), IPSS (19.86 ± 4.68 in group A vs. 21.14 ± 4.33 in group B, p value = 0.17), maximum urinary flow rate and post-void residual urine test results. No significant difference between Qol score in both groups before surgery (p value = 0.96). Group A patients had significantly less intraoperative blood loss than group B patients (308.29 ± 48.1 ml vs. 431.11 ± 96.4 ml, p = 0.001). One month postoperatively, group A patients showed greater improvement in QoL than group B patients (p = 0.03). Conclusions: Finasteride therapy for 6 weeks before TURP reduced intraoperative blood loss and statistically improved quality of life in the early postoperative period. However, larger number of patients and longer duration of follow up is recommended to confirm its clinical significance. Keywords: Finasteride, TURP, Quality of life

The growing interest in the different social network platforms leads to huge number of interactions between different users all around the globe. These unlimited interactions provide a suitable environment for spammers to spread as the complexity of the social networks increases. Automatic detection of such malicious users inside this crowd of complex interactions is one of the most difficult research problems. Different approaches have been adopted to contend against malicious activities. Among the different promising approaches is the one relying on using graph analysis techniques. In this paper, we suggest two representation models for social interaction's graph-based datasets. The representation models are mainly developed based on analyzing interactions and relations between users. The first model is developed based on graph-based analysis, while the other one is developed based on sequential processing of user interactions. Based on the conducted experiments, we conclude that the two representation models show high spam detection accuracy. However, graph-based analysis models produce higher accuracy levels compared to those produced by interaction sequences processing models.

ملخص: هدفت هذه الدراسة إلى اكتشاف العلاقة بين بعض أنماط الذكاء ومكونات الجودة الشخصية لدى طلبة الجامعة في المملكة العربية السعودية، والفروق بين أفراد العينة على تلك المتغيرات وفق (التخصص الأكاديمي، النوع). واستخدم الباحث المنهج الوصفي على عينة مكونة من (1074) من طلبة الجامعة، أختيرو بالطريقة المتاحة (المتيسرة). ولتحقيق ذلك استخدم مقياسين من إعداده، هما: أنماط الذكاء، والجودة الشخصية.وتوصلت الدراسة لنتائج عدة، أهمها: إن نمط الذكاء الأكثر شيوعاً لدى طلبة الجامعة هو الذكاء الاجتماعي، ويليه الذكاء الشخصي وكلاهما في مستوى مرتفع، وارتفع متوسط الذكاء ككل عند الطلبة، ارتفاع مستويات الجودة الشخصية لدى طلبة الجامعة، ويعد محور العلاقات الإنسانية هو الأكثر شيوعاً بين الطلبة، ويليه الأداء الفائق في العمل، ثم سمات الشخصية الإيجابية، وجود ارتباط موجب مرتفع عند مستوى دلالة (0.01) بين أنماط الذكاء ومكونات الجودة الشخصية، وجود فروق ذات دلالة إحصائية عند مستوى دلالة (0.05) بين طلبة الجامعة على متغير الذكاء الإنفعالي بحسب التخصص، وجود فروق في مستويات الذكاء لدى طلبة الجامعة، وجود فروق في مكونات الجودة الشخصية لدى طلبة الجامعة بحسب النوع والتخصص. Some Types of Intelligence and Their Relationship to the Components of Personal Quality among University Students in The Kingdom of Saudi Arabia Abstract: This study aims at exploring the relationship between some types of intelligence and the components of personal quality among university students in the Kingdom of Saudi Arabia. The study also aims at exploring the differences among the study’s sample in light of the variables, academic specialization and gender. The researcher used the descriptive method on a sample that consisted of 1074 university students. The researcher followed the Available method to select the sample. Two scales were designed and used by the researcher. These scales measured types of intelligence and personal quality. The study found that the most common types of intelligence among university students are social intelligence, followed by intrapersonal intelligence. Both of these types scored high degree. The average of Intelligence was found to be high among students in general. Personal quality levels among university students was found to be high. The domain of personal relations was found to be the most prevalent among students, followed by high performance at work, and positive character traits. Moreover, the results revealed strong positive correlation at the level of 0. 01 between types of intelligence and personal quality components. There were statistically significant differences at the level of 0. 05 among university students in regard to emotional intelligence due to the variables of academic specialization. There were statistically significant differences in the levels of intelligence among university students and in personal quality components due to gender and academic specialization. Keywords: Intelligence Types, Verbal, Intrapersonal, Social, Emotional, Personal Quality.&nbsp

The static Young's modulus is one of the most important geomechanical parameters that are used in the evaluation of the wellbore stability during drilling operations. The static Young's modulus is also important during the design of the hydraulic and acid fracturing operations for conventional and unconventional reservoirs. Static Young's modulus also is important in the evaluation of the in-situ stresses profiles and it can be used to evaluate the reservoir pressures. The existing correlations that determine the static Young's modulus either depend on the dynamic Young's modulus or compressional shear velocity. No previous studies considered the different log parameters to estimate the static Young's modulus. Some of the previous correlations were developed for specific type of lithology and did not consider the lithology variation within the single well. In this paper and for the first time we developed correlations for the static Young's modulus from the log data based on clustering technique. More than 300 measured static Young's modulus values were correlated to the log parameters such as shear transit time, compressional transit time, and bulk density. Using R-project statistical software the clustering was performed for the measured data along with the corresponding log data. Six clusters were identified based on the shear transit time because it has the highest relative importance to the measured static Young's modulus. Six correlations were developed for the six clusters that can be used to determine the static Young's modulus based on the log data. The developed correlations were tested on three cases from the field given the measured core data for each case. The developed correlations based on clustering predicted the static Young's modulus perfectly when compared to the measured one for three different cases with different lithology sets. Other correlations did not match the measured static Young's modulus and bad match was obtained in several cases . Log parameters such as shear transit time, compressional transit time, and bulk density showed high relative importance to be included in the correlations that predict the static Young's modulus. Data clustering is a good method to apply to obtain better match between the estimated and measured static Young's modulus.

Plasma and gas particle dynamics in atmospheric pressure helium-filled small volume are investigated using a two-dimensional model. The model includes the conservation equations for the plasma and the neutral gas. In this paper, results are presented from simulation of the interaction between gas and charged species, which in turn causes heating and thrust generation for this microengine. Gas heating and neutral depletion initiations are observed, highlighting the close interaction between gas and charged species in governing the evolution of the small-space plasma inside a microthruster designed for microsatellites.

Natural and anthropogenic aerosols over the Kingdom of Saudi Arabia (KSA) play a major role in affecting the regional radiation budget. The long-term variability of these aerosols&rsquo, physical and optical parameters, including aerosol optical depth (AOD) and &Aring, ngstr&ouml, m exponent (&alpha, ), were measured at a location near central KSA using the Solar Village (SV) AERONET (Aerosol Robotic Network) station during the period December 1999&ndash, January 2013. The AERONET measurements show an overall increase in AOD on an annual basis. This upward trend is mainly attributed to a prolonged increase in the monthly/seasonal mean AOD during March&ndash, June and during August&ndash, September. In contrast, lower AOD values were observed during November&ndash, December. This can be attributed to a low frequency of dust outbreaks and higher precipitation rates. An overall, weak declining trend in &alpha, was observed, except during the summer. The spring and summer seasons experienced a pronounced increase in the number of coarse particles (~2 &micro, m) during April 2006&ndash, January 2013 as compared to December 1999&ndash, March 2006, suggesting an increase in natural aerosol loadings. Using the HYSPLIT model, it was found that the March 2009 dust storm contributed to the mixing of long-transported dust with anthropogenic local emissions near the SV. The results suggest that extensive industrial activity contributed to the increase of anthropogenic emissions over KSA during the period April 2006&ndash, January 2013.

Flow cytometry (FCM) is a very well-known method that is broadly used in clinical and research laboratories. Both clinical and research laboratories have been the target domains of FCM applications. The key research question in this particular field is “how to effectively automate FCM data analysis?”. To answer this question, this paper systematically reviews current advances in the automation of FCM data analysis. All recent techniques have been studied in a way readers can recognize current trends, challenges, limitations and future directions. For future research, we have identified three main venues. First, the identification of the number of clusters prior to starting cell population identification is still a challenging process. Second, automating the process of cluster labeling still requires more improvement to be fully automated. Last, benchmark datasets are essential in order for researchers to be able to comparatively evaluate different techniques of FCM data analysis under fixed conditions.We end up this paper with a discussion about how flow cytometry data analysis techniques and datasets are correlated with open source technology.

Protein–protein interaction networks are receiving increased attention due to their importance in understanding life at the cellular level. A major challenge in systems biology is to understand the modular structure of such biological networks. Although clustering techniques have been proposed for clustering protein–protein interaction networks, those techniques suffer from some drawbacks. The application of earlier clustering techniques to protein–protein interaction networks in order to predict protein complexes within the networks does not yield good results due to the small-world and power-law properties of these networks. In this paper, we construct a new clustering algorithm for predicting protein complexes through the use of genetic algorithms. We design an objective function for exclusive clustering and overlapping clustering. We assess the quality of our proposed clustering algorithm using two gold-standard data sets. Our algorithm can identify protein complexes that are significantly enriched in the gold-standard data sets. Furthermore, our method surpasses three competing methods: MCL, ClusterOne, and MCODE in terms of the quality of the predicted complexes. The source code and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/GACluster.zip .

Massive biological datasets are generated in different locations all over the world. Analysis of these datasets is required in order to extract knowledge that might be helpful for biologists, physicians and pharmacists. Recently, analysis of biological networks has received a lot of attention, as an understanding of the network can reveal information about life at the cellular level. Biological networks can be generated that examine the interaction between proteins or the relationship amongst different genes at the expression level. Identifying information from biological networks is recognized as a significant challenge, due to the inherent complexity of the structures. Computational techniques are used to analyze such complex networks with varying success. In this paper, we construct a new method for predicting phenotype-gene association in breast cancer using biological network analysis. Several network topological measures have been computed and fed as features into two classification models to investigate phenotype-gene association in breast cancer. More importantly, to overcome the problem of the skewed datasets, a synthetic minority oversampling technique (SMOTE) is adapted in order to transform an imbalanced dataset to a balanced one. We have applied our method on the gene co-expression network (GCN), protein–protein interaction network (PPI), and the integrated functional interaction network (FI), which combined the PPIs and gene co-expression, amongst others. We assess the quality of our proposed method using a slightly modified cross-validation. Our method can identify phenotype-gene association in breast cancer. Moreover, use of the integrated functional interaction network (FI) has the potential to reveal more information and hidden patterns than the other networks. The software and accompanying examples are freely available at http://faculty.kfupm.edu.sa/ics/eramadan/NetTop.zip .

We describe a novel algorithm for identifying the modular structure of a protein interaction network by computing overlapping clusters. The network is initially decomposed into a high degree network and a residual subnetwork, and clusters are computed separately in both networks, before highly interconnected clusters in both networks are merged. We propose the concept of bridge proteins, proteins that are connected to multiple clusters, and identify them from the clustering. We show that bridge proteins are more likely to be essential. The clustering algorithm is used to identify modules in a collection of proteomic networks from the yeast, human, and the worm. The new algorithm is efficient at detecting both overlapping clusters and bridge proteins, and performs better than earlier algorithms in various measures of clustering quality.

Breast cancer is the most common malignancy and a leading cause of cancer related deaths in women. In recent years, gene expression profiling has proved useful in delineating molecular subtypes of breast cancer and in the development of prognostic signatures. We are developing an analytical pipeline to characterize the transcriptional regulators of these subtypes and signatures. Our approach complements current bioinformatics approaches for transcription factor analysis with a vertex cover algorithm on hypergraphs. We utilize this approach to build a network of differentially expressed genes in a tumor subtype or based on a predefined signature and the candidate transcription factors regulating these genes. Maximum cardinality and minimum weight vertex covers in hypergraphs are used to choose a set of candidate transcription factors that (1) are provably within a small factor of the optimum cover, and (2) are the key regulators of disease pathogenesis. Our model can then be used to predict the most important transcription factors regulating the network. We then use this approach to find modules or combinations of transcription factors regulating different functional subsets of genes. We test our approach using data generated with cell lines in the context of estrogen receptor mediated transcription and demonstrate that we can recover previously known or expected regulators. Then, we apply the method to a primary breast cancer cohort partitioned into two groups with prognostic differences defined by high or low levels of an insulin-like growth factor gene expression signature. These results suggest the method has the potential to identify transcription factors regulating different molecular subtypes of breast cancer.

Understanding the interaction and crosstalk between pathways is important for understanding the function of both physiological and pathological biological systems. We have taken a computational approach to explore interactions among modules within biological networks by comparing and contrasting various topological measures which may be useful in the identification and prediction of critical connectivity points between modules. Node degree, betweenness, bridges, and articulation points may define connections among modules with distinct functions. Structural holes are another topological feature of networks which are important in identifying the role of nodes in the relationships among subclusters of graphs. Structural holes separate non-redundant sources of information, sources that are more additive than overlapping. We explore the performance of these among protein-protein interactions in yeast, then apply them to gene networks derived from a cohort of early stage breast cancer patients in whom different levels of IGF ligand have been associated with differing outcomes. We compare the different approaches to identifying and ranking genes based on these measures to reveal clues about cross-talk and feedback mechanisms and their role in mediating communication and coordination among modules.

Background We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process. Results We first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of these four proteins were assembled from available human protein interaction databases. Through an analysis of betweenness and closeness centrality measures, and numbers of interactions, we ranked proteins in the first neighborhood. When this rank list was compared to the list of physical Tax-binding proteins, DNA-PK was the highest ranked protein common to both lists. An overlapping clustering of the Tax-specific second-neighborhood protein network showed DNA-PK to be one of three bridge proteins that link multiple clusters in the DNA damage response network. Conclusion The interaction of Tax with DNA-PK represents an important biological paradigm as suggested via consensus findings in vivo and in silico. We present this methodology as an approach to discovery and as a means of validating components of a consensus Tax interactome.

We describe an approach to clustering the yeast protein-protein interaction network in order to identify functional modules, groups of proteins forming multi-protein complexes accomplishing various functions in the cell. We have developed a clustering method that accounts for the small-world nature of the network. The algorithm makes use of the concept of k-cores in a graph, and employs recursive spectral clustering to compute the functional modules. The computed clusters are annotated using their protein memberships into known multi-protein complexes in the yeast. We also dissect the protein interaction network into a global subnetwork of hub proteins (connected to several clusters), and a local network consisting of cluster proteins.

Summary form only given. We consider a hypergraph model for the protein complex network obtained from a large-scale experimental study to characterize the proteome of the yeast. Our model views the yeast proteome as a hypergraph, with the proteins corresponding to vertices and the complexes corresponding to hyperedges. Previous work has modeled the protein complex data as a protein-protein interaction graph or as a complex intersection graph; both models lose information and require more space. Our results show that the yeast protein complex hyper-graph is a small-world and power-law hypergraph. We design an algorithm for computing the k-core of a hypergraph, and use it to identify the core proteome, the maximum core of the protein complex hypergraph. We show that the core proteome of the yeast is enriched in essential and homologous proteins. We implement greedy approximation algorithms for variant minimum weight vertex covers of a hypergraph; these algorithms can be used to improve the reliability and efficiency of the experimental method that identifies the protein complex network.