Cytochrome p450 1b1, a new keystone in gene-environment interactions related to human head and neck cancer?

Alcohol consumption and tobacco smoking are major causes of head and neck cancers, and regional differences point to the importance of research into gene-environment interactions. Much interest has been focused on polymorphisms of CYP1A1 and of GSTM1 and GSTT1, but a number of studies have not demonstrated significant effects. This has mostly been ascribed to small sample sizes. In general, the impact of polymorphisms of metabolic enzymes appears inconsistent, with some reports of weak-to-moderate associations, and with others of no elevation of risks. The classical cytochrome P450 isoenzyme considered for metabolic activation of polycyclic aromatic hydrocarbons (PAH) is CYP1A1. A new member of the CYP1 family, CYP1B1, was cloned in 1994, currently representing the only member of the CYP1B subfamily. A number of single nucleotide polymorphisms of the CYP1B1 gene have been reported. The amino acid substitutions Val432Leu (CYP1B1*3) and Asn453Ser (CYP1B1*4), located in the heme binding domain of CYP1B1, appear as likely candidates to be linked with biological effects. CYP1B1 activates a wide range of PAH, aromatic and heterocyclic amines. Very recently, the CYP1B1 codon 432 polymorphism (CYP1B1*3) has been identified as a susceptibility factor in smoking-related head-and-neck squamous cell cancer. The impact of this polymorphic variant of CYP1B1 on cancer risk was also reflected by an association with the frequency of somatic mutations of the p53 gene. Combined genotype analysis of CYP1B1 and the glutathione transferases GSTM1 or GSTT1 has pointed to interactive effects. This provides new molecular evidence that tobacco smoke-specific compounds relevant to head and neck carcinogenesis are metabolically activated through CYP1B1 and is consistent with a major pathogenetic relevance of PAH as ingredients of tobacco smoke.

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Evidence of associations between cytokine gene polymorphisms and quality of life in patients with cancer and their family caregivers

PURPOSE/OBJECTIVES: To identify latent classes of individuals with distinct quality-of-life (QOL) trajectories, to evaluate for differences in demographic characteristics between the latent classes, and to evaluate for variations in pro- and anti-inflammatory cytokine genes between the latent classes. DESIGN: Descriptive, longitudinal study. SETTING: Two radiation therapy departments located in a comprehensive cancer center and a community-based oncology program in northern California. SAMPLE: 168 outpatients with prostate, breast, brain, or lung cancer and 85 of their family caregivers (FCs). METHODS: Growth mixture modeling (GMM) was employed to identify latent classes of individuals based on QOL scores measured prior to, during, and for four months following completion of radiation therapy. Single nucleotide polymorphisms (SNPs) and haplotypes in 16 candidate cytokine genes were tested between the latent classes. Logistic regression was used to evaluate the relationships among genotypic and phenotypic characteristics and QOL GMM group membership. MAIN RESEARCH VARIABLES: QOL latent class membership and variations in cytokine genes. FINDINGS: Two latent QOL classes were found: higher and lower. Patients and FCs who were younger, identified with an ethnic minority group, had poorer functional status, or had children living at home were more likely to belong to the lower QOL class. After controlling for significant covariates, between-group differences were found in SNPs in interleukin 1 receptor 2 (IL1R2) and nuclear factor kappa beta 2 (NFKB2). For IL1R2, carrying one or two doses of the rare C allele was associated with decreased odds of belonging to the lower QOL class. For NFKB2, carriers with two doses of the rare G allele were more likely to belong to the lower QOL class. CONCLUSIONS: Unique genetic markers in cytokine genes may partially explain interindividual variability in QOL. IMPLICATIONS FOR NURSING: Determination of high-risk characteristics and unique genetic markers would allow for earlier identification of patients with cancer and FCs at higher risk for poorer QOL. Knowledge of these risk factors could assist in the development of more targeted clinical or supportive care interventions for those identified.

The ADAMTS1 protease gene is required for mammary tumor growth and metastasis

A disintegrin and metalloprotease with thrombospondin motifs protein 1 (ADAMTS1) is a protease commonly up-regulated in metastatic carcinoma. Its overexpression in cancer cells promotes experimental metastasis, but whether ADAMTS1 is essential for metastatic progression is unknown. To address this question, we investigated mammary cancer progression and spontaneous metastasis in the MMTV-PyMT mouse mammary tumor model in Adamts1 knockout mice. Adamts1−/−/PyMT mice displayed significantly reduced mammary tumor and lung metastatic tumor burden and increased survival, compared with their wild-type and heterozygous littermates. Histological examination revealed an increased proportion of tumors with ductal carcinoma in situ and a lower proportion of high-grade invasive tumors in Adamts1−/−/PyMT mice, compared with Adamts1+/+/PyMT mice. Increased apoptosis with unaltered proliferation and vascular density in the Adamts1−/−/PyMT tumors suggested that reduced cell survival accounts for the lower tumor burden in ADAMTS1-deficient mice. Furthermore, Adamts1−/− tumor stroma had significantly lesser amounts of proteolytically cleaved versican and increased numbers of CD45+ leukocytes. Characterization of immune cell gene expression indicated that cytotoxic cell activation was increased in Adamts1−/− tumors, compared with Adamts1+/+ tumors. This finding is supported by significantly elevated IL-12+ cell numbers in Adamts1−/− tumors. Thus, in vivo ADAMTS1 may promote mammary tumor growth and progression to metastasis in the PyMT model and is a potential therapeutic target to prevent metastatic breast cancer.

Oestrogen-modulated gene expression in the human endometrium

To identify key regulatory mechanisms in the growth and development of the human endometrium, microarray analysis was performed on uncultured human endometrium collected during menstruation (M) and the late-proliferative (LATE-P)-phase of the menstrual cycle, as well as after 24 h incubation in the presence of oestradiol (17beta-E2). We demonstrate the expression of novel gene transcripts in the human endometrium. i.e. mucin-9, novel oestrogen-responsive gene transcripts, i.e. gelsolin and flotillin-1, and genes known to be expressed in human endometrium but not yet shown to be oestrogen responsive, i.e. connexin-37 and TFF1/pS2. Genes reported to be expressed during the implantation window and implicated in progesterone action, i.e. secretoglobin family 2A, member 2 (mammaglobin) and homeobox-containing proteins, were up-regulated in uncultured LATE-P-phase endometrium compared to M-phase endometrium. Some gene transcripts are regulated directly by 17beta-E2 alone, others are influenced by the in vivo environment as well. These observations emphasise that the regulation of endometrium maturation by oestrogen entails more then just stimulation of cell proliferation.

Estrogen and the endometrium: lessons learned from gene expression profiling in rodents and human

To date, research into the biological processes and molecular mechanisms associated with endometrial receptivity and embryo implantation has been a focus of attention, whereas the complex events that occur in the human endometrium during the menstrual and proliferative phase under the influence of estrogen have received little attention. The objective of this review is to provide an update of our current understanding of the actions of estrogen on both human and rodent endometrium, with special emphasis on the regulation of uterine growth and cell proliferation, and the value of global gene expression analysis, in increasing understanding of these processes.

Tumor-suppressor gene promoter hypermethylation in saliva of head and neck cancer patients

Head and neck squamous cell carcinoma (HNSCC) accounts for a bulk of the oral and laryngeal cancers, the majority (70%) of which are associated with smoking and excessive drinking, major known risk factors for the development of HNSCC. In contrast to reports that suggest an inverse relationship between smoking and global DNA CpG methylation, hypermethylation of promoters of a number of genes was detected in saliva collected from patients with HNSCC. Using a sensitive methylation-specific polymerase chain reaction (MSP) assay to determine specific methylation events in the promoters of RASSF1A, DAPK1, and p16 genes, we demonstrate that we can detect tumor presence with an overall accuracy of 81% in the DNA isolated from saliva of patients with HNSCC (n = 143) when compared with the DNA isolated from the saliva of healthy nonsmoker controls (n = 31). The specificity for this MSP panel was 87% and the sensitivity was 80%(with a Fisher exact test P < .0001). In addition, the test panel performed extremely well in the detection of the early stages of HNSCCs, with a sensitivity of 94% and a specificity of 87%, and a high. concordance value of 0.8, indicating an excellent overall agreement between the presence of HNSCC and a positive MSP panel result. In conclusion, we demonstrate that the promoter methylation of RASSF1A, DAPK1, and p16 MSP panel is useful in detecting hypermethylation events in a noninvasive manner in patients with HNSCC.

DNA methylation at the novel CpG sites in the promoter of MED15/PCQAP gene as a biomarker for head and neck cancers

Head and neck cancers (HNCs) represent a significant and ever-growing burden to the modern society, mainly due to the lack of early diagnostic methods. A significant number of HNCs is often associated with drinking, smoking, chewing beetle nut, and human papilloma virus (HPV) infections. We have analyzed DNA methylation patterns in tumor and normal tissue samples collected from head and neck squamous cell carcinoma (HNSCC) patients who were smokers. We have identified novel methylation sites in the promoter of the mediator complex subunit 15 (MED15/PCQAP) gene (encoing a co-factor important for regulation of transcription initiation for promoters of many genes), hypermethylated specifically in tumor cells. Two clusters of CpG dinucleotides methylated in tumors, but not in normal tissue from the same patients, were identified. These CpG methylation events in saliva samples were further validated in a separate cohort of HNSCC patients (who developed cancer due to smoking or HPV infections) and healthy controls using methylation-specific PCR (MSP). We used saliva as a biological medium because of its non-invasive nature, close proximity to the tumors, easiness and it is an economically viable option for large-scale screening studies. The methylation levels for the two identified CpG clusters were significantly different between the saliva samples collected from healthy controls and HNSCC individuals (Welch's t-test returning P, 0.05 and Mann-Whitney test P, 0.01 for both). The developed MSP assays also provided a good discriminative ability with AUC values of 0.70 (P, 0.01) and 0.63 (P, 0.05). The identified novel CpG methylation sites may serve as potential non-invasive biomarkers for detecting HNSCC. © the authors.

Component modeling for SCADA network mapping

Supervisory Control and Data Acquisition systems (SCADA) are widely used to control critical infrastructure automatically. Capturing and analyzing packet-level traffic flowing through such a network is an essential requirement for problems such as legacy network mapping and fault detection. Within the framework of captured network traffic, we present a simple modeling technique, which supports the mapping of the SCADA network topology via traffic monitoring. By characterizing atomic network components in terms of their input-output topology and the relationship between their data traffic logs, we show that these modeling primitives have good compositional behaviour, which allows complex networks to be modeled. Finally, the predictions generated by our model are found to be in good agreement with experimentally obtained traffic.

Identification of pre-spike network in patients with mesial temporal lobe epilepsy

Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation.

A Memetic Algorithm for the location-based continuously operating reference stations placement problem in Network Real-Time Kinematic

Network Real-Time Kinematic (NRTK) is a technology that can provide centimeter-level accuracy positioning services in real time, and it is enabled by a network of Continuously Operating Reference Stations (CORS). The location-oriented CORS placement problem is an important problem in the design of a NRTK as it will directly affect not only the installation and operational cost of the NRTK, but also the quality of positioning services provided by the NRTK. This paper presents a Memetic Algorithm (MA) for the location-oriented CORS placement problem, which hybridizes the powerful explorative search capacity of a genetic algorithm and the efficient and effective exploitative search capacity of a local optimization. Experimental results have shown that the MA has better performance than existing approaches. In this paper we also conduct an empirical study about the scalability of the MA, effectiveness of the hybridization technique and selection of crossover operator in the MA.

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.

Association hierarchy mining and its application for network traffic characterisation

This thesis presents an association rule mining approach, association hierarchy mining (AHM). Different to the traditional two-step bottom-up rule mining, AHM adopts one-step top-down rule mining strategy to improve the efficiency and effectiveness of mining association rules from datasets. The thesis also presents a novel approach to evaluate the quality of knowledge discovered by AHM, which focuses on evaluating information difference between the discovered knowledge and the original datasets. Experiments performed on the real application, characterizing network traffic behaviour, have shown that AHM achieves encouraging performance.

Co-movements in commodity prices : a note based on network analysis

This article analyses co-movements in a wide group of commodity prices during the time period 1992–2010. Our methodological approach is based on the correlation matrix and the networks inside. Through this approach we are able to summarize global interaction and interdependence, capturing the existing heterogeneity in the degrees of synchronization between commodity prices. Our results produce two main findings: (a) we do not observe a persistent increase in the degree of co-movement of the commodity prices in our time sample, however from mid-2008 to the end of 2009 co-movements almost doubled when compared with the average correlation; (b) we observe three groups of commodities which have exhibited similar price dynamics (metals, oil and grains, and oilseeds) and which have increased their degree of co-movement during the sampled period.

Identifying influential users of micro-blogging services : a dynamic action-based network approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our actionbaseduser influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered.