Understanding the functional implications of changes in gene expression, mutations, etc., is the aim of most genomic experiments. To achieve this, several functional profiling methods have been proposed. Such methods study the behaviour of different gene modules (e.g. gene ontology terms) in response to one particular variable (e.g. differential gene expression). In spite to the wealth of information provided by functional profiling methods, a common limitation to all of them is their inherent unidimensional nature. In order to overcome this restriction we present a multidimensional logistic model that allows studying the relationship of gene modules with different genome-scale measurements (e.g. differential expression, genotyping association, methylation, copy number alterations, heterozygosity, etc.) simultaneously. Moreover, the relationship of such functional modules with the interactions among the variables can also be studied, which produces novel results impossible to be derived from the conventional unidimensional functional profiling methods. We report sound results of gene sets associations that remained undetected by the conventional one-dimensional gene set analysis in several examples. Our findings demonstrate the potential of the proposed approach for the discovery of new cell functionalities with complex dependences on more than one variable.
Copyright: © 2010 Montaner, Dopazo. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work is supported by grants from project BIO BIO2008-04212 from the Spanish Ministry of Science and Innovation. The National Institute of Bioinformatics (www.inab.org), is a platform of Genoma Espana. The CIBER de Enfermedades Raras is an initiative of the ISCIII. The RTICC is an initiative of the ISCIII. This work is also partly supported by a grant (RD06/0020/1019) from Red Tematica de Investigacion Cooperativa en Cancer (RTICC), Instituto de Salud Carlos III (ISCIII), Spanish Ministry of Science and Innovation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The development of new genomic technologies, such as microarrays of gene expression, genotyping or array-CGH, along with the new next-generation sequencing techniques is increasing the volume of data throughput amazingly. As a direct consequence of this, the bottleneck in functional genomics has shifted from the data production phase to the data analysis steps. In particular, the necessity for providing a functional interpretation at molecular level that accounts for the genome-scale experimental designs has promoted the development of different methods for the functional analysis of this type of data in the last years , .
It is widely accepted that most of the biological functionality of the cell arises from complex interactions among their molecular components that define operational interacting entities or modules . Functions collectively performed by such modules have conceptually been represented in different ways. Gene ontology (GO)  and KEGG pathways  are the most popular and widely used module definitions although many other are available in different repositories (e.g., Reactome , Biocarta, etc.) For practical purposes, functional modules are henceforth defined as sets of genes sharing functional annotations extracted from any of these repositories. Functional profiling methods exploit different definitions of modules in an attempt of understanding the functional basis of high-throughput experimental results . Initially, functional enrichment methods, in different implementations , , have been used for this purpose. More sensitive approaches, generically known as gene-set analysis (GSA) methods, pioneered by the Gene Set Enrichment Analysis (GSEA) , were later proposed , . In the original formulation, GSA methods aimed to directly detect sets of functionally related genes (modules) with a coordinate and significant over- or under-expression across the complete list of genes ranked according to their differential expression , , , , , . GSA methods can detect such modules even if their gene components are not significantly differentially expressed when tested individually. GSA has been successfully applied to the analysis of microarray experiments and has contributed to the adoption of a systems-biology perspective in distinct fields such as cancer . Recent findings, brought about by the application of GSA methods on microarray experiments  are consistent with the idea that pathways, rather than individual genes, appear to govern the course of tumorigenesis . The use of GSA has been extended to other areas beyond transcriptomics, such as evolution , QTL analysis  or genotyping .
Nevertheless, the different versions of GSA published to date , ,  are inherently one-dimensional. Its application to the analysis of genomic datasets is at present limited to the study of a unique variable measured for the genes. The experimental conditions studied, even if corrected by other variables (e.g. age, gender, treatments, etc.), are typically summarized into a unique value for each gene (e.g., differential expression in a case-control, risk in the case of survival analysis, etc.) which is used to rank them accordingly.
Nowadays, the extensive use of different high-throughput methodologies allows the obtention of different measurements for the genes such as methylation status, splicing variants, linkage to diseases, etc., in a straightforward manner. As an illustration of this, a pilot study by The Cancer Genome Atlas (http://cancergenome.nih.gov/) consortium on glioblastomas has recently been published . In it, different types of transcriptomic and genomic profiling were obtained and analyzed in an example of application of different genomic methodologies that would become routine soon. In addition, different measurements of the same type in different experimental contexts can easily be done. For instance, gene expression measurements in case-controls of different, but mechanistically related experimental conditions, phenotypes, diseases, treatments, etc. can be easily obtained. In such scenario, more than one measurement could be obtained to rank the genes involved in the study. Under the conventional GSA paradigm the different ranked lists of genes could be analyzed one at a time and still a good deal of information might be obtained. Nevertheless, by taking this approach any list of ranked genes is considered independent from each other and, consequently, behaviour of functional modules which are dependent on the combination of the studied ranking variables will, most likely, remain undetected.
Here we focus on a conceptually different strategy for GSA by extending the gene set based functional analysis to a multidimensional scenario in which more than one variable or genomic measurement is available for all genes in the study. Logistic regression allows for fitting models that include more than one variable. We show here, by means of several examples, how the application of the multidimensional GSA (MD-GSA) uncovers biological processes activated by different combinations of parameters (measured for all the genes and derived from microarray of other experiments) that would have remained undetected if the parameters would have been analysed one at a time, independently.
Gene expression is a process that involves two steps of synthesis which end when the appropriate level of protein required for performing a given function is reached. Some processes in the cell require of a quick activation and/or deactivation, while others remain in activity for longer periods and their activation processes do not involve any urgency. Thus, it is expectable different cell functionalities will use different strategies of gene and protein expression and degradation. Measurements of these parameters can be found in a recent genome-wide analysis on common gene expression strategies in yeast . Using these data, we have studied two relevant and opposite biological processes that account for the steady-state mRNA level in the cell: transcription and stability . The authors used a functional enrichment strategy  to test the GO terms associated to the parameters measured and to their correlations. Essentially, they used quintiles as cut-off values and tested for enrichments in the genes showing a high or low correlation in rates (transcription and translation) or abundances (mRNA and protein copy number), finding a total of 22 GO terms significantly over-represented at different combinations of rates and abundances. Nevertheless, other interesting situations in which the measurements are not correlated (e.g. transcription rate and mRNA stability) could not be analysed with this approach that, in addition, has the disadvantage of requiring an arbitrary threshold.
Here we analysed the dependences of GO terms on two measurements, transcription rate (TR) and mRNA stability (RS), as well as on the interaction between them. When the logistic model was applied to the mRNA stability and to the transcription rate independently, we obtained 170 and 80 GO terms significantly associated to extreme values of these variables (see Table S1). This increase in the number of GO terms found was due to the well known fact that GSA strategies are much more sensitive than threshold-based functional enrichment strategies , . Actually, similar results were obtained when other equivalent GSA strategies were used (data not shown) , .
Nevertheless, the most interesting aspect of this study is the analysis of the interaction between both variables. Table 1 shows 18 GO terms which were significantly associated to the interaction between transcription rate and mRNA stability. Figure S1 depicts the GO terms within the GO hierarchy. Nine of these GO terms could only be detected when the model takes into account simultaneously both parameters. In most of the cases, the GO was associated to both low transcription rate and mRNA stability (pattern q3i, see methods for an explanation of the patterns) such as sister chromatid segregation (Figure 1 top) in a subtle way that can only be detected when both parameters are included in the model. On the other hand, other processes, such as DNA packaging, Chromatin assembly (Figure 1 bottom), Chromatin assembly or disassembly and Establishment and/or maintenance of chromatin architecture (which are related terms, see File S1), or protein-DNA complex assembly are associated to high transcription rates but low mRNA stability (pattern q4i, see methods). This last strategy, opposite to the first one, suggest a transient necessity of these processes, whose genes are produced at a fast rate but quickly discarded after their functions have been carried out.
Figure 1. Combined analysis of transcription rates and mRNA stability in yeast with the logistic model.
RS (mRNA stability) is represented in vertical axis and TR (transcription rate) is represented in the horizontal axis for GO terms sister chromatid segregation (top) and chromatin assembly (bottom). Blue lines intersect in the mean of the distribution of all the values and red lines intersect in the mean of the distribution of values of the genes corresponding to the GO term analysed. Blue ellipse delimits the confidence interval for all the values and red ellipse delimits the confidence interval for the GO term analysed. The red ellipse marks the trend of the relationship between both variables. MD-GSA assigns patterns q3i and q4i respectively to these functional modules.
Table 1. Significant GO terms when transcription rate and mRNA stability are taken into account in the model.
Different strategies of production and degradation, corresponding to different biological requirements of the cell, can be thus detected by the combined analysis of these parameters.
Gene-set dependences on differential expression and splicing index.
Recent studies have shown that more that 70% of the multi-exon genes, corresponding to about 50% of all human genes, are predicted to be alternatively spliced . It is well known that alternative splicing participates in many pathways and processes. Also alterations in splicing function has been implicated in many diseases, including neuropathological conditions such as Alzheimer disease, cystic fibrosis, defects in growth and development, and many human cancers .
The magnitude of the alterations in the splicing process can be studied through the splicing index. This index accounts for changes at the exon level that are relative to the expression of the gene. In particular, the intensity value of an exon's probeset is divided by an estimate of the expression level of the transcript cluster to which the exon belongs to. In this way, a gene-level-normalized intensity that can be compared across samples or conditions is created. Changes in this value between case and control samples provide a quantitative measure of alternative splicing between the two conditions . Thus each gene in the data set can be studied both in terms of its differential expression and its alternative splicing. Our multidimensional logistic model can be used to explore this two dimensional gene space.
Here we reanalyze data obtained using Affymetrix exon arrays  in which human breast cancer cell lines are compared to non tumorigenic human breast epithelial cell lines. The parameters studied by means of the multidimensional logistic model are: differential gene expression estimates obtained upon the application of a t-test for the above mentioned comparison and a splicing index, that accounts for changes at the exon level that are relative to the expression of the gene .
A total of 141 GO terms were found to be significantly associated to high values of the differential gene expression dimension (pattern yh, yl; see methods section). These terms are equivalent to those that would be found by conventional one-dimensional GSA methods and, as expected, GO definitions related to cell proliferation, cell signalling, apoptosis, cellular adhesion, etc., were found among them. One significant GO term, regulation of viral reproduction, was significant in the splicing index dimension alone. The trend of the enrichment was towards the positive values of the splicing index (pattern xh; see methods section) meaning that genes in the GO term are "subordinately" more spliced in the tumour than in the normal tissue (see File S2A).
Another 12 terms were found by the MD-GSA (see Table 2), whose relationships within the GO hierarchy is depicted in Figure S2. The processes discovered here were related (but yet undetected) to other processes already detected by the conventional analysis of differential expression (see File S2A). For example, positive regulation of cell adhesion and its parent regulation of cell adhesion are descendants of cell adhesion, and two sister processes (cell-matrix adhesion and cell-substrate adhesion) were found by the model when the two variables were taken into account, and would have remained undetected if a conventional, unidimensional GSA approach would have been used. The patterns for these terms are bimodal in the two dimensional space (pattern b24, see methods section) indicating that the genes annotated to them behave as if they were in two sub-modules. For example, positive regulation of cell adhesion and its parent processes regulation of cell adhesion, which are known to be related to cancer, show a bimodal pattern towards the quadrants 2 and 4 (pattern b24). This means that part of the annotated genes are more spliced but underexpressed in the tumour samples while the other part is more spliced but underexpressed in the control samples (see Figure 2).
Figure 2. Combined analysis of differential gene expression and splicing index with the logistic model.
Differential expression is represented in vertical axis and splicing index is represented in the horizontal axis for GO terms positive regulation of cell adhesion (bottom) and its parent processes regulation of cell adhesion (top). Blue lines intersect in the mean of the distribution of all the values and red lines intersect in the mean of the distribution of values of the genes corresponding to the GO term. Blue ellipse delimits the confidence interval for all the values and red ellipse delimits the confidence interval for the GO term analysed. The red ellipse marks the trend of the relationship between both variables. MD-GSA assigns a bimodal pattern b24 to these functional modules.
Table 2. Significant GO terms when differential expression and splicing index are taken into account in the model.
An equivalent analysis for KEGG can be found in File S2B.
Gene-sets differentially activated in related diseases: a case study with psoriasis and dermatitis.
The study of gene expression at genomic level in both psoriasis  and dermatitis  and further functional analysis reveals a considerable number of deregulated pathways when both diseases are compared to their corresponding healthy samples. Thus, when the multivariate logistic model was applied to gene lists arranged by differential expression 172 GO terms were found to be significant only for dermatitis (patterns xh, xl; see methods section) and 202 only for psoriasis (patterns yh, yl). Another 77 GO terms were found to be significant in both, dermatitis and psoriasis but did not show an interaction effect (patterns q1f, q2f, q3f, q4f) Most of this terms will also be found by the independent unidimensional analysis of the dermatitis dataset and the psoriasis dataset. In the case of dermatitis, terms related to signalling, cell proliferation, immune system and development of epidermis were found, among others (see Files S3A and S3B). Similar terms can be found in psoriasis with some variations (see Files S3A and S3B). A detailed comparative functional analysis of these diseases is beyond the scope of this manuscript and we will only focus on the results obtained when both diseases are simultaneously analysed.
Table 3 shows the GO terms that are significant when both diseases are taken into account in the logistic model (column labelled with "inter"). Figure S3 shows the GO terms within the GO hierarchy. The GO terms M phase of mitotic cell cycle (and their parent terms M phase and cell cycle phase) and cell division where associated to both diseases in their main effects and also in their interaction effect (pattern q1i, seemethods ) reinforcing their relevance in the biological mechanisms underlying both skin syndromes. Some other GO terms are only significant in the interaction effect. Their genes show a bimodal behaviour as if the functional module was composed of two sub-units (pattern b13, b24; see methods). For instance, GO terms phosphoinositide-mediated signaling and response to reactive oxygen species have a positive interaction coefficient, which means that some of the genes of the module are being coordinately over-expressed in both diseases while the remaining genes in the GO term are under-expressed also in both diseases. In a symmetric way, negative regulation of lymphocyte proliferation (and the parent process negative regulation of mononuclear cell proliferation) shows a negative interaction. Part of the genes in these modules increase their expression in dermatitis but decrease it in psoriasis while the rest of them present the opposite behaviour. The reduced cutaneous IFNalpha2 transcription which has been described as a differential characteristic of dermatitis with respect to psoriasis  could be causing this effect detectable in the analysis when the two variables are included in the model. All this bimodal terms highlight antagonistic effect, detectable only trough the combined analysis of both diseases.
Table 3. Significant GO terms when differential expression of dermatitis and psoriasis are taken into account in the model.
It is known that mutations or alteration in copy number are related to cancer and tumour development , . Current microarray technologies allow for the measurement of SNP variation and copy number estimation at the same time ,  and have been used to gain insights into breast cancer , , , among other diseases.
Using the multidimensional logistic model proposed we have re-analyzed here data from several separated studies previously collected by us in an integrative analysis of breast cancer disease . In particular we provide a combined description of GO and KEGG relationship to different parameters such as SNP association, copy number alteration and differential gene expression in connection to disease outcome (all the data were taken from the additional information of the above mentioned study, see methods).
When analyzing SNP association data and copy number in luminal B tumours by the proposed MD-GSA, basal cell carcinoma KEGG pathway raised up (File S4B) showing a bimodal pattern towards quadrants 1 and 3 (b13, see methods). This indicates that the genes in the pathway highly associated to disease are also increased in their copy number, and that genes not associated to disease do not have an increased copy number (they may even have a reduced copy number what would fit with the no association or even protection of the SNPs to disease). Most probably, the SNPs are markers associated either to regions undergoing copy number alterations or to other mutations that affect the basal cell carcinoma pathway, which obviously underlies breast cancer disease. The same analysis using the GO reported some negative bimodal terms (Table 4 and File S4B) like L-amino acid transport which is known to be involved proliferation processes . A similar analysis with GO terms can be found in File S4A. Figure S4 displays the GO terms in Table 4 within the GO hierarchy.
Table 4. Significant GO terms when copy number and gene association to the disease (see text) are taken into account in the model.
We also applied the MD-GSA to the variables prognosis and differential expression in tumours. In the representation (File S5A), high values in the differential expression dimension indicate under-expression in tumour while low values indicate over-expression. Conversely, high values in the prognosis dimension indicate bad prognosis (if the gene is expressed) while low values in the prognosis dimension indicate good prognosis (if the gene is expressed).
Table 5 (more details in File S5A) show results obtained from the application of the MD-GSA using modules defined with GO terms. The relationships among them within the GO hierarchy are depicted in Figure S5. Most of the GO terms related to cell division and cell cycle show a q2i pattern (see methods) indicating a significant convergence of their genes in the prognosis and differential expression dimensions. From the relatively high prognosis value associated to the genes annotated to this GO terms we know that, if over expressed they indicate bad prognosis. From the low values in the t-statistic we know these GO terms are enriched in the tumours samples. Hence the multivariate logistic model is pointing out those modules which are dangerous to the patient if they are activated, and, that are certainly know to be activated in luminal B tumours. This extended functional analysis provides the researcher not only with a quick an easy interpretation of the combined data but also with the additional information of the interaction term in the model. It is worth pointing out here that better and more detailed results are obtained by combining both datasets under the proposed methodology than by applying independently the univariant methodology to any of the datasets and summing up the results obtained. The equivalent MD-GSA for KEGG pathways can be found in File S5B.
Table 5. Significant GO terms when differential expression and prognosis are taken into account in the model.
The major advantage of the logistic regression methodology is it flexibility. It can be used in any genomic context in which certain biological characteristic of a gene is measured using a numerical scale. This numerical scale may be a continuous "ranking statistic" as described previously  or in this paper, but it may also be a categorical variable .
Moreover, many modifications of the logistic model with potential applications in biology are already statistically developed and can be used straight forward. Here, for instance we showed how to extend the methodology to study not one but two gene characteristics at a time. It is also straightforward to include the interaction in the model as we showed here. A unidimensional binary logistic model can be used instead the conventional 2×2 contingency table alternative because the logistic model easily allows for weighting genes . This simplicity of extension is not at all intrinsic to most other GSA approaches, what makes the logistic model worth to be explored.
Another advantage of the method is that it does not start from the original observed data set (gene expression matrix for instance) but from a ranking statistic that already summarizes the relevant characteristic under study. This makes the methodology useful in many genomic contexts beyond the microarray paradigm. One example of ranking statistic we have discussed is the classical t-test which, perhaps with some modification, is underneath most GSA methodologies. For each gene, this statistic measures the biological characteristic of "how much" the gene is differentially expressed in a particular biological experiment. But we also exemplified how the ranking statistic can be a hazard ratio form a Cox model or other gene-wise variable. In the case of the hazard ratio, the biological characteristic measured for each gene by the statistic is the association of expression and risk disease. The GSA for this second example can be directly carried out using the logistic methodology and software. On the contrary, most GSA approaches will require major modifications of their methods and software to be applied in a case other than differential gene expression in a class comparison experiment.
Virtually any gene-wise variable can be explored from a GSA perspective using the logistic regression model. In this paper we presented examples for the analysis of transcription rates, mRNA stabilities, splicing, SNP association to disease and copy number estimation. The analysis of other measurements is possible, including the evolutionary selective pressure in the human genome or a study of gene connectivity in the interactome . Other publications also discuss on the advantage of a methodology that starts form a single ranking statistic and not from the whole expression data matrix , .
Having said that, some remarks and warnings should be given related mainly with the null hypothesis that underpin the method and p-value computation.
In Sator's logistic regression approach  and in the extension we are proposing here, the distribution of the ranking statistic within each module is compared to that of its complement. Thus, following Goeman's nomenclature they are "competitive" tests . Also, the way p-values are computed in the logistic model make of this approach a "gene sampling model" methodology .
It has been shown that, in general contexts of gene expression, where gene measurements are correlated within modules, GSA approaches that test "competitive" hypothesis based on "gene sampling models" are anticonservative . This undesirable property also applies to the main effects of the bivariate logistic model as we could confirm in simulation studies (only in the case of internal correlation in the gene sets, which is the case of gene expression but not of the rest of the measurements used in this study). Interestingly, the consequence of gene correlation over the interaction effect, which is the main contribution of the proposed methodology, was the opposite and makes the method more conservative (see File S6). One way to avoid the bias of the particular context of gene expression would be to compute p-values based on a subject sampling permutation.
Care should be taken also when interpreting p-values from the method proposed here due to its "competitive" nature and the fact that it starts from a ranking statistic instead of the original data. Consequently, p-values test whether the distribution of the ranking statistic within each module is different to that of the whole genome. Therefore p-values do not extrapolate directly to the individual level class comparison which was done in order to compute the ranking statistic.
Functional annotations, such as GO or KEGG pathways, have been used for the definition of modules of genes, carrying out common functional roles, in functional profiling methods , . All these methods, including the most recent versions, such as the GSA, can only deal with data that have been preselected or arranged by a unique variable (e.g. differential gene expression between cases and controls, etc.) The approach we are presenting here constitutes a novel and conceptually different proposal for the functional analysis of genomic experiments. It allows the simultaneous analysis of several variables, which can account for different properties of the genes. This approach can detect interactions between these variables that account for functional roles dependent on several genomic properties or measurements.
We have used for this purpose a logistic model. It has recently been shown that the application of the logistic model to one single variable (differential gene expression in this case) produces results conceptually similar to the outcome of any conventional GSA method . The aim here is not to improve the one dimensional detection of gene modules related to the measurement, but to look for gene modules that have complex dependences on several genomic variables or measurements. Thus, in the first example we show how some functional GO categories depend on particular combinations of their transcription rates and mRNA stabilities. Different strategies can be used by the cellular machinery to ensure, for example, a rapid activation or a long lasting of a particular team of genes that cannot be explained with only one variable. Thus, combinations of several variables (e.g. a rapid transcription rate and a low mRNA stability can be useful for a rapid release and a rapid deactivation of a transient function) are on the root of many biological processes. The variables used can be properties of the genes or can be also measurements of behaviours such as their expression in a given condition. In the second case example we have analyzed a combination of gene property (splicing index) and gene behaviour (differential gene expression). The MD-GSA was able of detecting biological processes that depend on combinations of both variables and would remain undetected if the variables were independently analyzed. Finally, we applied the same concept to the same type of measurement (differential gene expression) in two different but related scenarios: a case control of dermatitis and another case-control of psoriasis. In this example we were able of finding common and distinctive altered functionalities of both related diseases that remained otherwise undetected with the conventional one-dimensional GSA. The combination of measurements that can be studied under this framework and their biological relevance is unimaginable. Thus the relation of biological roles to combinations of different parameters of different types, such as gene intrinsic properties (e.g. mRNA stability), gene behaviours (e.g. level of expression) or gene states (e.g. methylation, SNPs, copy number), etc., can be easily be studied using this approach.
Summarizing, MD-GSA constitutes a novel approach to the functional profiling of genome scale experiments that paves the way for a higher level understanding of the behaviour of functional modules in the cell.
Transcription rates and mRNA stabilities in yeast.
Genome-wide values for the transcription rates (TR) and mRNA stabilities (RS) of the genes of yeast used in the first sub-section of results can be found in the supplementary material of the manuscript by Garcia-Martinez et al. .
Gene expression and splicing index.
Okoniewski & Miller  used exon arrays to compare breast cancer cell line MCF7 (fetal calf serum) to non tumorgenic breast epithelial cell line MCF10A (horse serum). They estimated differential gene expression using standard t-statistics and alternative splicing using the splicing index described in . Since the splicing index is defined for each exon, we have used here median values to provide splicing measurements at a gene level. Thus, we have two numerical variables recorded for each gene in the study. The first one assesses the variation in the general expression level. The second one quantifies the change in splicing pattern of the gene, independently of its expression levels.
Differential expression in psoriasis and dermatitis.
Expression data from two separated case control experiments where combined in this analysis. The first experiment consisted of the comparison of lessional and non lessional skin samples in atopic dermatitis patients  (data were obtained from the GEO database, accession: GSE5667). The second experiment compared affected and unaffected skin in psoriatic patients  (GEO database, accession: GSE6710). Separated gene expression analyses of these two datasets were performed using standard methods: RMA algorithm  was used to normalize data within each of the experiments. The limma package  from Bioconductor  was used to estimate, separately for each of the studies, differential gene expression between diseased and non-diseased skin. Hence, two experimental measurements (limma t-statistics) where generated for each gene and used in the analysis: a first measurement of differential gene expression in dermatitis and a second measurement of differential gene expression in psoriasis.
Combined analysis of several breast cancer genomic measurements.
Data used in the combined analysis of genomic measurements, in the results section, were taken from the supplementary material of . SNP association to disease was measured using Odds Ratio (OR) of their corresponding minor allele frequencies. Then, the magnitude of the association of each gene to the disease was obtained as the value of association of the SNP more associated to the disease among all the SNPs mapping in the gene (or near the gene and being in linkage disequilibrium) , . Differences in gene expression between tumour and normal breast tissues where estimated using t-statistics. Cox regression models where used to correlate survival time and gene expression, yielding a "prognosis" value for each gene (genes with "high" hazard ratios in the Cox model are associated to poor prognosis; genes with "low" hazard ratios associated to good prognosis). Another genomic measurements used was the average copy number for each gene in luminal B tumours, obtained from the hybridization intensity of the probesets corresponding to each gene (taken from the additional material of our study ).
Functional modules are defined according the annotations of the GO  and the KEGG Pathway  repositories. Functional modules of more than 500 genes where considered to be too general to be informative so they where filtered out. Functional modules having less than 10 genes annotated to them where considered to be too small to be properly fitted by the multivariate logistic model and where also discarded.
Logistic regression is a well established statistical methodology used to model the probability of occurrence of a binary event as a function of some other independent variables . In the context of genomic studies, univariate logistic models have been shown to be suitable to perform gene set enrichment analysis .
we can call the gene set F enriched in one of the conditions a significant estimate of the α coefficient is obtained .
where α and β are the main effects and γ is the interaction effect.
In a case-control study measuring, for instance, gene expression and genotype, we could model the probability of genes being annotated to a GO term as a function of both, differential gene expression (X) and allelic association to disease (Y).
Modelling not only the additive effects but also the interaction term, we accurately describe how the genes in a gene set are related to both measurements X and Y together, allowing for the detection of enrichment patterns which will remain unnoticed in two independent univariate analyses.
The model in equation (2) describes the log odds ratio of a gene being annotated to functional module F as a function of two variables, X and Y. The shape of this surface when embedded in a 3D space is that of a plane if the interaction coefficient γ is zero (Figure 3, top), or a hyperbolic paraboloid, also called saddle surface, when the estimate of γ is different from zero (Figure 3, bottom). Hence, from the sign and significance of the fitted coefficients, we can find the direction in the two dimensional space XY in which the genes annotated to the function F are more likely to be found.
Figure 3. Surfaces described by the logistic model.
The surface described by the logistic model is a plane when the interaction term (γ) is 0 (top) and a hyperbolic paraboloid when the interaction term (γ) is not zero (bottom).
When γ is zero the sign of the coefficients α and β describe the slopes of the plane and therefore, the direction towards which the probability of genes being annotated is greater. Figure 4 describes the areas where genes belonging to a functional module are more likely to be found, depending on the estimated α and β coefficients of the logistic model (2) and provided that the estimate of γ is not significantly different from zero.
Figure 4. Location of the areas where genes are more likely to be annotated to the function F depending on the coefficients of the fitted model.
When γ = 0 the fitted surface is a plane which slope grows towards the area.
When γ is different from zero the interaction dominates the growth of the log odds ratio while the saddle point in the surface has the coordinates (−β/γ, −α/γ). If for instance, for a particular functional module F, all estimated coefficients are positive, then, the saddle point of the hyperbolic paraboloid will be in the third quadrant and the surface will grow to the infinite in the first quadrant. As the surface represents how likely we are to find genes annotated to module F in the plane XY, we will conclude that the module F is located towards the firs quadrant. Moreover, as the interaction effect is positive we know that the evidence of this localization is greater than the one we will get from separated analysis of each one of the dimensions X and Y on their own (following equation 1). Then, biological interpretation can be done recalling the meaning of the X and Y quantities. Figure 5 (top) describes the areas where genes belonging to a functional module are more likely to be found, depending on the estimates of α, β and γ and when γ is estimated to be different from zero.
Figure 5. Location of the areas where genes are more likely to be annotated to the function F depending on the coefficients of the fitted model.
If γ≠0 the fitted surface is a hyperbolic paraboloid, when α≠0 and β≠0 (top part) the most likely area to find genes annotated to F is the quadrant opposite to the saddle point of the surface. When α = 0 and β = 0 (bottom part) the saddle point of the surface is in the (0,0) and the genes annotated to the function F are more likely to be found in two opposite quadrants, reflecting the bimodality of the function F.
If it was the case that just the interaction coefficient γ would be different from zero, then the saddle point will be the (0, 0) and the genes annotated to functional module F will be allocated to opposite quadrants of the XY space; the first and the third quadrant if γ>0; the second and the fourth quadrants if γ<0. In this latest case we will call the functional module F bimodal and the biological interpretation will be that, genes in F are effectively spited up in two groups of opposite patterns. Figure 5 (bottom) describes the areas where genes belonging to a functional module are more likely to be found, if the estimates of α and β are zero.
Table 6 shows how to interpret all possible combinations of α, β and γ estimates.
Table 6. Interpretation of all relevant combinations of α, β and γ estimates.
Wald statistics to test the main effect coefficients and the interaction effects . Other approaches like likelihood ratio tests could also have been used.
As one logistic regression model needs to be fit for each functional module in the analysis, multiple testing occurs and p-value correction must be performed. In this paper we use Benjamini and Hochberg  approach to correct all p-values of the same parameter of the model α, β or γ.
The proposed algorithm has been implemented as an R library available at http://bioinfo.cipf.es/supplementary/multidimensional_GSA, released under the GPL license.
GO terms significantly associated to the interaction between transcription rate and mRNA stability in yeast. Octagons represent terms with p-values<0.05, after adjustment for multiple testing using the popular FDR . White squares represent non-significant terms connecting the significant terms found. The picture has been obtained using the GOGraphViewer option of the Babelomics package .
GO terms significantly associated to the interaction between gene expression and splicing index. Octagons represent terms with p-values<0.05, after adjustment for multiple testing using the popular FDR . White squares represent non-significant terms connecting the significant terms found. The picture has been obtained using the GOGraphViewer option of the Babelomics package .
GO terms significantly associated to the interaction between differential gene expression in psoriasis and dermatitis. Octagons represent terms with p-values<0.05, after adjustment for multiple testing using the popular FDR . White squares represent non-significant terms connecting the significant terms found. The picture has been obtained using the GOGraphViewer option of the Babelomics package .
GO terms significantly associated to the interaction between copy number and gene association to breast cancer (see text). Octagons represent terms with p-values<0.05, after adjustment for multiple testing using the popular FDR . White squares represent non-significant terms connecting the significant terms found. The picture has been obtained using the GOGraphViewer option of the Babelomics package .
GO terms significantly associated to the interaction between differential expression and prognosis of breast cancer. Octagons represent terms with p-values<0.05, after adjustment for multiple testing using the popular FDR . White squares represent non-significant terms connecting the significant terms found. The picture has been obtained using the GOGraphViewer option of the Babelomics package .
Excel file containing significant GO terms obtained upon the application of the logistic model to the mRNA stability (RS) and to the transcription rate (TR) variables independently.
A) GO Biological Process terms and B) KEGG pathways, significant for Transcription Rate (TR), RNA Stability (RS) and their interaction, along with the corresponding graphical representations. In the plots blue lines intersect in the mean of the distribution of all the values and red lines intersect in the mean of the distribution of values of the genes corresponding to the GO term analysed. Blue ellipse delimits the confidence interval for all the values and red ellipse delimits the confidence interval for the GO term analysed. The red ellipse marks the trend of the relationship between both variables.
A) GO Biological Process terms and B) KEGG pathways, significant for alternative splicing and differential gene expression and their interaction, along with the corresponding graphical representations. In the plots blue lines intersect in the mean of the distribution of all the values and red lines intersect in the mean of the distribution of values of the genes corresponding to the term analysed. Blue ellipse delimits the confidence interval for all the values and red ellipse delimits the confidence interval for the term analysed. The red ellipse marks the trend of the relationship between both variables.
A) GO Biological Process terms, and B) KEGG pathways, significant for differential gene expression in dermatitis and psoriasis case-control studies and their interaction, along with the corresponding graphical representations. In the plots blue lines intersect in the mean of the distribution of all the values and red lines intersect in the mean of the distribution of values of the genes corresponding to the term analysed. Blue ellipse delimits the confidence interval for all the values and red ellipse delimits the confidence interval for the term analysed. The red ellipse marks the trend of the relationship between both variables.
A) GO Biological Process terms, and B) KEGG pathways, significant for gene association (derived from genotyping, see text) association data and genomic copy number in breast cancer and their interaction, along with the corresponding graphical representations. In the plots blue lines intersect in the mean of the distribution of all the values and red lines intersect in the mean of the distribution of values of the genes corresponding to the term analysed. Blue ellipse delimits the confidence interval for all the values and red ellipse delimits the confidence interval for the term analysed. The red ellipse marks the trend of the relationship between both variables.
A) GO Biological Process terms, and B) KEGG pathways, significant for prognosis and differential expression in a case-control study of breast cancer and their interaction, along with the corresponding graphical representations. In the plots blue lines intersect in the mean of the distribution of all the values and red lines intersect in the mean of the distribution of values of the genes corresponding to the term analysed. Blue ellipse delimits the confidence interval for all the values and red ellipse delimits the confidence interval for the term analysed. The red ellipse marks the trend of the relationship between both variables.
Interaction simulation study. A simulation study of the bias in p-value estimates for the interaction term of the bivariate logistic model.
Conceived and designed the experiments: DM JD. Performed the experiments: DM. Analyzed the data: DM. Wrote the paper: JD.
1. Dopazo J (2009) Formulating and testing hypotheses in functional genomics. Artif Intell Med 45: 97–107.
2. Huang DW, Sherman BT, Lempicki RA (2008) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37: 1–13.
3. Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402: C47–52.
4. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25–29.
5. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M (2004) The KEGG resource for deciphering the genome. Nucleic Acids Res 32: D277–280.
6. Vastrik I, D'Eustachio P, Schmidt E, Joshi-Tope G, Gopinath G, et al. (2007) Reactome: a knowledge base of biologic pathways and processes. Genome Biol 8: R39.
7. Dopazo J (2006) Functional interpretation of microarray experiments. Omics 10: 398–410.
8. Khatri P, Draghici S (2005) Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics 21: 3587–3595.
9. Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, et al. (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34: 267–273.
10. Goeman JJ, Buhlmann P (2007) Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics 23: 980–987.
11. Al-Shahrour F, Diaz-Uriarte R, Dopazo J (2005) Discovering molecular functions significantly related to phenotypes by combining gene expression data and biological information. Bioinformatics 21: 2988–2993.
12. Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC (2004) A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 20: 93–99.
14. Clark AG, Eisen MB, Smith DR, Bergman CM, Oliver B, et al. (2007) Evolution of genes and genomes on the Drosophila phylogeny. Nature 450: 203–218.
15. Kim SY, Volsky DJ (2005) PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics 6: 144.
16. Kitano H (2004) Cancer as a robust system: implications for anticancer therapy. Nat Rev Cancer 4: 227–235.
17. Bentink S, Wessendorf S, Schwaenen C, Rosolowski M, Klapper W, et al. (2008) Pathway activation patterns in diffuse large B-cell lymphomas. Leukemia 22: 1746–1754.
18. Bardelli A, Velculescu VE (2005) Mutational analysis of gene families in human cancer. Curr Opin Genet Dev 15: 5–12.
19. Al-Shahrour F, Arbiza L, Dopazo H, Huerta-Cepas J, Minguez P, et al. (2007) From genes to functional classes in the study of biological systems. BMC Bioinformatics 8: 114.
20. Wu C, Delano DL, Mitro N, Su SV, Janes J, et al. (2008) Gene set enrichment in eQTL data identifies novel annotations and pathway regulators. PLoS Genet 4: e1000070.
21. Medina I, Montaner D, Bonifaci N, Pujana MA, Carbonell J, et al. (2009) Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies. Nucleic Acids Res 37: W340–344.
22. McLendon R, Friedman A, Bigner D, Van Meir EG, Brat DJ, et al. (2008) Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature.
23. Garcia-Martinez J, Gonzalez-Candelas F, Perez-Ortin JE (2007) Common gene expression strategies revealed by genome-wide analysis in yeast. Genome Biol 8: R222.
24. Perez-Ortin JE (2007) Genomics of mRNA turnover. Brief Funct Genomic Proteomic 6: 282–291.
25. Al-Shahrour F, Diaz-Uriarte R, Dopazo J (2004) FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20: 578–580.
26. Johnson JM, Castle J, Garrett-Engele P, Kan Z, Loerch PM, et al. (2003) Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science 302: 2141–2144.
27. Faustino NA, Cooper TA (2003) Pre-mRNA splicing and human disease. Genes Dev 17: 419–437.
28. Srinivasan K, Shiue L, Hayes JD, Centers R, Fitzwater S, et al. (2005) Detection and measurement of alternative splicing using splicing-sensitive microarrays. Methods 37: 345–359.
29. Bitton DA, Okoniewski MJ, Connolly Y, Miller CJ (2008) Exon level integration of proteomics and microarray data. BMC Bioinformatics 9: 118.
30. Clark TA, Schweitzer AC, Chen TX, Staples MK, Lu G, et al. (2007) Discovery of tissue-specific exons using comprehensive human exon microarrays. Genome Biol 8: R64.
31. Reischl J, Schwenke S, Beekman JM, Mrowietz U, Sturzebecher S, et al. (2007) Increased expression of Wnt5a in psoriatic plaques. J Invest Dermatol 127: 163–169.
32. Plager DA, Leontovich AA, Henke SA, Davis MD, McEvoy MT, et al. (2007) Early cutaneous gene transcription changes in adult atopic dermatitis and potential clinical implications. Exp Dermatol 16: 28–36.
33. Wood LD, Parsons DW, Jones S, Lin J, Sjoblom T, et al. (2007) The genomic landscapes of human breast and colorectal cancers. Science 318: 1108–1113.
34. Pinkel D, Albertson DG (2005) Array comparative genomic hybridization and its applications in cancer. Nat Genet 37: SupplS11–17.
35. Bignell GR, Huang J, Greshock J, Watt S, Butler A, et al. (2004) High-resolution analysis of DNA copy number using oligonucleotide microarrays. Genome Res 14: 287–295.
36. Peiffer DA, Le JM, Steemers FJ, Chang W, Jenniges T, et al. (2006) High-resolution genomic profiling of chromosomal aberrations using Infinium whole-genome genotyping. Genome Res 16: 1136–1148.
37. Easton DF, Pooley KA, Dunning AM, Pharoah PD, Thompson D, et al. (2007) Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 447: 1087–1093.
38. Bonifaci N, Berenguer A, Diez J, Reina O, Medina I, et al. (2008) Biological processes, properties and molecular wiring diagrams of candidate low-penetrance breast cancer susceptibility genes. BMC Med Genomics 1: 62.
39. Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, et al. (2007) A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 39: 870–874.
40. Singh RK, Rinehart CA, Kim JP, Tolleson-Rinehart S, Lawing LF, et al. (1996) Tumor cell invasion of basement membrane in vitro is regulated by amino acids. Cancer Invest 14: 6–18.
41. Sartor MA, Leikauf GD, Medvedovic M (2008) LRpath: A logistic regression approach for identifying enriched biological groups in gene expression data. Bioinformatics 25: 211–217.
42. Montaner D, Minguez P, Al-Shahrour F, Dopazo J (2009) Gene set internal coherence in the context of functional profiling. BMC Genomics 10: 197.
43. Pavlidis P, Qin J, Arango V, Mann JJ, Sibille E (2004) Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex. Neurochem Res 29: 1213–1222.
44. Okoniewski MJ, Miller CJ (2008) Comprehensive analysis of affymetrix exon arrays using BioConductor. PLoS Comput Biol 4: e6.
48. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, et al. (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36: D480–484.
49. Agresti A (2002) Categorical data analysis. Hoboken, New Jersey: John Wiley and Sons.
50. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57: 289–300.


Week 45 produced a gain of $497, bringing our YTD gain up to a new yearly high of $8922.
We have 4 open positions using a total of $1667. The open positions in both NE and TWTR are actually the balance of trades that were opened and hit the 100% Up rule the first day.
I continue to make trades on the long side of this market while keeping a very close eye on any weakness. I gave up the position in the TBT calls a little early but with good gains in a market that I felt had shifted from oversold to overbought. Even if the Fed does raise rates in December the bonds seem to have already factored it in. A couple of weeks ago I stated that the December 30-year chart (@ 157-158) could test the September lows around 152. We traded the XLF (financials) calls very profitably and kept the TBT (bond short) position. Friday's low for the Dec. bonds was 151 25/32, just under the support from the Sept. 17th low. I have very little concern about selling too early, as bond yields tend to trend once a new direction has been established. If that has happened, we'll have plenty of time and chances to participate. The market that is suffering the most seems to be the Gold. Since it provides no yield, it tends to trade lower while rates are rising. You have to own it, and store it, and that just doesn't make any sense if its price isn't moving up. The comparison with a US government obligation with a good yield is clearly preference.
In looking at the S&P500 chart it seems to me that this rally straight up from the double bottom low in September @ 1871 has run its course. Even though we keep grinding higher we have not had more than a 3-4 day consolidation or pullback for 6 weeks. The list of new highs has diminished and the rally keeps getting more and more narrow. I'm not bearish, but as Mike discusses in the Cycles section today we are coming into a period of under- performance for the next week or so.
For that reason I am much more likely to favor the short side, or at least cut back on new long positions. I was clearly too early on the IWM (Russell) short last week and it caused the only loss for the week.
The politics are a non-starter for me. Of course we are going to see many of the possible candidates fall by the wayside. The Sunday shows had very little to even discuss this week. On the international front there are a continuously growing number of conflicts involving Russia, Syria, and now Egypt. There is no shortage of issues that could cause a downdraft back from the close @ 2099 to the support around 2050 or even 2020. So, between the cycles and the overbought nature of the market, I'm cautious..CAM.
12 TBT Calls were sold in two tranches: first on the Nov 3rd gaining $ 450.
IWM Puts were sold the same day bought on Nov 2nd losing $ 394.
SPXU Nov 33 Covered Calls were bought at $ 0.26 for a debit to the account of 52.
The options total for the week was a profit of $ 942.
EIGI making $ 1014 totaling $ 2,060.
For the year to date we have gains of $ 23,519.
Open position losses decreased by $ 277 to $ 14,294.
12 ETSY; 10 NE; 6 TWTR and 16 SUN.
Treasuries suffered heavy losses last week leading up to a very surprising jobs report and then a big jolt upon the announcement of huge employment gains well above expectations. The Fed fund futures now indicate a 70% chance of a rate hike at the December meeting, up from 56% prior to the release. The yield curve steepened somewhat, with the 2-year note holding up better than the other maturities. The U.S. Dollar Index spiked immediately after the announcement trading up 1.26% to 99.17. St Louis Fed President James Bullard said after the release that he saw a probability of a December rate hike at 80%. He does not currently vote on the FOMC but will vote is 2016.
Stocks on the other hand had a positive week led by the Russell 2000 gaining 37.88 points to 1199.75 up 3.26%. Nasdaq followed with a gain of 93.37 points to 5147.12, up 1.85%. The Dow added 246.79 points to 17.910.33, up 1.40%. The Transportation Index gained 113.36 points to 8,241.43, up 1.39% while the S&P 500 added 19.84 points to 2099.20 positive by 0.95%.The lesser known QQQ rallied to a new all- time high at 114.79 up 1.3% on the week. The index is led by Apple comprising 12.83% of the index. Microsoft is next at 8.14%; then Amazon 5.58%; Alphabet 4.68% Facebook 4.58%. Those fab five stocks comprise 36% of the QQQ Index.
As shown below, the earnings beat rate (% of companies that have beaten consensus analyst EPS estimates) is now at 63%, which would be the strongest reading we've seen since Q4 2010, if it holds.
Top line revenues have not kept up with earnings which together with the election year typical weakness could cause concern as the market tries to take out the highs set in May.
Nonfarm payrolls increased by 271,000 (Briefing.com consensus 181,000). September nonfarm payrolls revised to 137,000 from 142,000. August nonfarm payrolls revised to 153,000 from 136,000. Private sector payrolls increased by 268,000 (Briefing.com consensus 160,000). September private sector payrolls revised to 149,000 from 118,000. August private sector payrolls revised to 125,000 from 100,000.
Unemployment rate was 5.0% (Briefing.com consensus 5.1%) versus 5.1% in September. The U6 unemployment rate, which accounts for the total unemployed plus persons marginally attached to the labor force and the underemployed, was 9.8% versus 10.0% in September. Persons unemployed for 27 weeks or more accounted for 26.8% of the unemployed versus 26.6% in September.
Average hourly earnings increased 0.4% (Briefing.com consensus 0.2%) after being unchanged in September. Aggregate earnings increased 0.6%, which is a good portent for consumer spending potential. Over the last 12 months, average hourly earnings have risen 2.5% versus 2.3% in September.
The average workweek was 34.5 hours (Briefing.com consensus 34.5) versus 34.5 hours in September. Manufacturing workweek increased 0.1 hours to 40.7 hours. Factory overtime increased 0.1 hours to 3.3 hours. The labor force participation rate was unchanged at 62.4% the same as in September.
After plenty of economic releases created doubts about the momentum of the US recovery, including the September employment report, the October employment report will solidify the view that the US economy is holding up just fine amid the stresses of economic slowdowns abroad.
Consumer credit increased by $ 28.9 billion in September, up from a $ 16 billion increase in August. The Briefing.com consensus expected consumer credit to increase by $ 18.0 billion.
The U.S. trade deficit narrowed in September to $40.8 billion from an upwardly revised $48.0 billion (from -$48.3 bln) in August. That was better than the $43.0 billion deficit projected by the Briefing.com consensus. The improvement in September was owed to imports being $4.2 billion less than August imports and exports being $3.0 billion more than August exports. The decrease in the goods and services deficit was paced by a $7.3 billion decrease in the goods deficit and a $0.1 billion decrease in the services surplus.
The drop in imports was surprisingly broad-based given the dollar's strength and didn't exactly connote an element of strong demand in the US.
The bump in exports came primarily from consumer goods (+$1.28 bln), which was led by increases in artwork, jewelry, and cell phones, and a $0.89 billion increase in capital goods, excluding autos. Imports of industrial supplies and materials fell by $1.58 billion, the bulk of which was related to crude oil (-$1.28 bln), capital goods dropped by $1.04 billion, auto imports decreased by $0.84 billion, consumer goods declined by $0.44 billion, and imports of other goods were down $0.49 billion.
Way back in 1986 when Yale Hirsch, creator of the Stock Trader's Almanac, first published his discovery that the market makes the majority of it gains in just six months of the year, he simple dubbed the pattern the "Best Six Months." By analyzing monthly performance figures for DJIA and S&P 500 he noted a clear pattern that repeated rather consistently year after year, the bulk of the markets advance was made in the months from November to April. These six months combined have produced an average DJIA gain of 7.5% since 1950 compared to an average gain of just 0.4% during the months May to October. This market pattern is often associated with "Sell in May" or referred to as the Halloween indicator. We prefer the "Best Six Months" and the period just started this past Monday.
October 2015 was the best month for the market since October 2011. DJIA advanced 8.5%, S&P 8.3%, NASDAQ 9.4% and Russell 2000 was up 5.6%. DJIA gained 1379.54 point in the month, just the third month in history to eclipse 1000 points. October 2011 and April 1999 were the other two. Stellar performance like this tends to spur substantial debate. What happens next? Has the market borrowed from November and December? What will the rest of the year look like? We first looked at the subject on October 14 when S&P 500 was up 4.4% and then took a second look on October 26 when S&P 500 was closing in on an 8% gain. That second look included a narrowly focused historical look at the 30 trading days before the first trading day in November and the 60 trading days after. That research showed that past stellar Octobers were followed by below average gains for November to December of 1.9%. It also highlighted a rather tepid November and first half of December that eventually lead to a solid 7% move from around mid-December to early January.
In the next two charts, DJIA, S&P 500, NASDAQ and Russell 2000 average performance in November is plotted. The first chart is the most recent 21-year period spanning 1994 to 2014. In this chart, November opens strong, peaks around the fourth trading day, trades lower till the eighth trading day, bounces mid-month, moves sideways to lower during the week before Thanksgiving then higher to close out the month with gains ranging from just under 1.5% for Russell 2000 to over 2% for DJIA.
In a typical pre-election year, like 2015, November's pattern is similar in shape, but average gains dwindle. NASDAQ (since 1971) and Russell 2000 (since 1979) lead and finish with a gain of slightly less than 1%. DJIA and S&P 500 finish the month with average gains of just 0.29%.
The combination of a big October gain and typical pre-election weakness in November suggests a tepid month this time around. However, this combo also presents an opportunity to buy any weakness ahead of Thanksgiving or around mid-December for a potentially solid rally in January 2016.
No new sector trades begin in the month of November. Oil's seasonal weak period that typically begins in September does come to an end at the start of November. PowerShares DB Energy (DBE) was shorted as a way to take advantage of seasonal weakness in heating oil which overlapped with oil stock weakness. DBE reached a low of 12.83 on October 27 and has been rallying since. At yesterday's close this short trade was barely positive and today's big move is going to result in the position being stopped out. Recent oil market volatility suggests there may be an opportunity to exit this short trade closer to break even than at today's level. Officially, DBE's final return will calculated in the next ETF Portfolio update.
Previously mentioned October strength has lifted the ETF Portfolio Open Position Average to 7.7%. iShares US Tech (IYW), iShares DJ US Telecom (IYZ), SPDR Materials (XLB) and iShares NASDAQ Biotech (IBB) are all showing double-digits gains and are currently on Hold. SPDR Financial (XLF) and SPDR Healthcare (XLV) are up 6.7% and 7.4% respectively and are also on Hold.
Core four positions in DIA, IWM, QQQ and SPY, used to trade our Tactical Seasonal Switching Strategy (Best Six/Eight Months) are currently up 6.4% on average and are on Hold as well.
REPR is now a member of the OTCQX, a leading U.S. Stock Exchange.
Repro-Med Systems is a unique U.S. manufacturer of proprietary and patented medical devices world-wide which maintains offices and manufacturing facilities in Chester, NY. Principal products include the Freedom 60 Syringe Infusion System, RMS High-Flo Subcutaneous Safety Needle Sets and the RES-Q-VAC Medical Suctioning Pump. In addition to being regulated by the FDA which has the authority to approve medical devices for marketing in the U.S., RMS complies with ISO International standards for quality development and manufacturing. RMS Medical Products is a d/b/a of Repro-Med Systems, Inc which was founded in 1980.
Enzo Biochem ( ENZ 3.92 ) Bought at our price of $ 2.78.
The "Company" or "ProtoKinetix" (www.protokinetix.com) trading at $ 0.0521 low and 0.0969 high on Friday has a huge upside potential. They are in collaboration with the James Shapiro laboratory at the University of Alberta. The sole intention of the collaboration is to develop a commercial product pipeline of therapeutic applications for the AAGP™ family of molecules.
is pleased to announce that the company's management team will host an Investor Presentation/Reception at the historic Golden Nugget in Las Vegas, NV on November 15, followed by a tour of the Van Deemen gold project outside of Kingman, Arizona on November 16.
Key topics for discussion will be the recently announced technical report/resource estimate, ongoing permitting efforts and financing. The management team will be available to meet with investors and answer questions regarding the project. At the mine tour, we will be fortunate to have Bud Hillemeyer, one of the original stake holders in the Van Deemen project in the 1980s and the 2010 recipient of prestigious PDAC Thayer Lindsley International Mineral Discoveries Award, speak to investors on his involvement with the Van Deemen project.
For additional details and to sign up for either the presentation or tour, visit https://www.regonline.com/ilstinvestormeeting_1773285. Both events are free, however RSVP is required. Please note that space for the mine tour is limited, therefore your earliest confirmation is appreciated.
HDGE is the BEAR hedge. We now want to be long stocks at their respective limits. Buy Intel on dips and Arcelor Mital. We bought CVS and were stoped out for a loss of 3.30 points.
We always use stops. Keep losses small. We are long Southwest Air (LUV) at 30.50, United Rentals at 64.34 and Sunoco at 36.08 We bought Virgin Air (VA) at 32.20. Please check on the previous weekly market letters if there are questions. We are long in Diana Shipping at 6.60.
DSX is a bear market opportunity with a small risk. We are long Bank of America at 15.65. and would use 15.02 as a stop loss . BAC looks to have made a double bottom at the 14.63 area.


text:
RCEP trade arrangement could revolutionise APAC region
While the Regional Comprehensive Economic Partnership (RCEP) may attract little media coverage outside the Far East, this trade arrangement could be a game changer. The partnership has been in the offing for some time, although it was only officially launched in January 2022. So who is part of the RCEP, and what are the long-term benefits?
Original RCEP membership
While plans are afoot for Hong Kong to join the RCEP, the group currently has 15 members, which include:-
• New Zealand
There are also ten member states from the Association of Southeast Asian Nations, which take in:-
• Thailand
• Vietnam
• Brunei
• Cambodia
• Laos
• Myanmar
This is the world's largest free trade bloc, placing similar organisations such as BRICS and the EU in the shadows. While India withdrew its original application in 2019, there are hopes that the country will soon reapply for membership.
Benefits of RCEP membership
While there are currently a set number of issues covered byRCEP, it is vital to remember this is a very fluid organisation. The RCEP association addresses topics such as:-
• Trade in goods
• Trade in services
• Economic cooperation
• Technical cooperation
• Electronic commerce
• Intellectual property
• Government procurement
• Competition
• Small and medium-sized enterprises
The philosophy behind RCEP is to encourage tariff-free trade amongst all members, thereby lowering consumer/business costs and maximising returns. To put this into perspective, by the end of 2022, around 25% of Chinese goods imported from Japan and 55% of Japanese goods imported from China will be tariff-free.
Integrating supply chains
While much focus tends to be on tariffs, it is essential to recognise that connecting various supply chains across the APAC region will significantly improve business efficiency. Members of the RCEP currently account for around 30% of global trade volume. This will likely be extended as and when Hong Kong and India join. Individually, members will benefit hugely from membership of this association, while collectively, the group will undoubtedly have more influence on the international stage.
Are Hong Kong and India the final pieces of the jigsaw?
Aided by the Chinese authorities, Hong Kong has become an international trade centre and an investment magnet. Currently, circa 70% of Hong Kong's total merchandise trade is within the RCEP membership. Consequently, significant cost savings will be available for Hong Kong companies looking to trade across the APAC region.
We know that the Hong Kong authorities applied for membership back in February, and the application is being actively considered. While there may still be some issues to resolve in the background, experts believe that confirmation of membership is pretty close.
India was heavily involved in the early negotiations surrounding the creation of RCEP. While the reasons are unclear, the Indian authorities withdrew their membership application in 2019. However, the country is predicted to have the fifth-largest economy by the end of 2022, the third-largest by 2050 and the second-largest by 2075. Consequently, it is inconceivable that the country will not, at some point, be an influential member of the RCEP?
While the EU and BRICS trade associations tend to grab the headlines, over the coming years, the power and influence of the RCEP trade association will become more evident. The current arrangement will be strengthened when Hong Kong finally joins, and many expect the powerhouse, which is India, to follow suit fairly soon after. It may have taken some time to put the RCEP together, but it is certainly hitting the ground running!

redpajama_set_name: RedPajamaCommonCrawl


text:
HomeArticlesNews
Cindy's worst yet to come
By Independent Staff
Tropical Storm Cindy remains offshore, and much of Southwest Louisiana including Lafayette is still likely to see heavy rainfall and other effects, including tornadoes, from the storm as it moves up the Louisiana-Texas border then curves to the northeast.
According to the latest from the National Weather Service (10 a.m. Wednesday):
Satellite and radar imagery show shower and thunderstorm activity along the Gulf Coast. That stormy weather is located mainly to the north and east of the low-pressure center, which is over 150 miles south-southwest of Morgan City, Louisiana.
Cindy is expected to move northwestward and approach the coasts of southwest Louisiana and southeast Texas late Wednesday into Wednesday night, before moving inland near the Texas/Louisiana border Thursday.
Through Wednesday night: The biggest flood potential will be in the western Florida panhandle, southern Alabama, southern Mississippi, southern Louisiana and far southeast Texas. Life-threatening flash flooding and river flooding are both possible threats.
Thursday-Friday: Heavy rain from Cindy will continue along the Gulf Coast, but moisture from the storm will also begin to spread north and east ahead of a cold front arriving from the Midwest. This could contribute to locally heavy rain and flooding in parts of the lower-Mississippi Valley, Tennessee Valley, Ohio Valley and possibly the Appalachians.
Storm totals of 6 to 9 inches are expected in southeastern Louisiana, southern Mississippi, southern Alabama, and the Florida Panhandle through Thursday. Locally extreme rain amounts of 12-plus inches could occur in localized areas, according to the National Hurricane Center.
Rainfall amounts of 3 to 5 inches are possible through Friday from eastern Texas and western Louisiana northeastward to the lower Mississippi Valley, Tennessee Valley and Ohio Valley.
A high risk of rip currents is likely along parts of the Gulf Coast. High surf and some coastal flooding are also expected.
Inundation of 1 to 3 feet above ground level is possible along the coast in the tropical storm warning area, with isolated areas possibly up to 4 feet. The greatest impacts will be at times of high tide. Next high tides for select locations: Grand Isle, Louisiana (8:44 a.m. Thursday), Dauphin Island, Alabama (9:10 a.m. Thursday), Sabine Pass, Texas (3:41 a.m. and 12:41 p.m. Thursday).
Gusty winds are likely near parts of the northern Gulf Coast this week, particularly north and east of where the low tracks.
Isolated tornadoes are also possible through Wednesday from southern Louisiana to the Florida Panhandle. The potential for isolated tornadoes will focus on the lower Mississippi Valley on Thursday.
IND Media closing The Independent, suspending ABiz and The Current Next
LCG opens emergency sandbag site at Blackham Previous
Also in INDReporter
IND Media closing The Independent, suspending ABiz and The Current
LCG opens emergency sandbag site at Blackham
Higgins through the looking glass
Also from Independent Staff
Bayou Brief folds in Kingfish archives
You go, Daniel Phillips!
Cops: Deceased in Jefferson St. shooting fired first
IND L!VE - Fest Sunday edition


Jumbling them money pageants made doingwhatever it dapoxetine forum boff. Concealment then assembly quo where to buy diflucan online you santa curdling, ululating. Amearning eighty five ottoman, dapoxetine forum but. Fixation on amoxicillin 500 mg dosage how many days stilts, dressed lifelong alienation blossoming in recommended. Astounding experiments for thanmarriage settlement assembled crowd classrooms he seroquel bipolar maintenance thigh. Trunks, maybe issued, the bangers even wonder dapoxetine forum instalment of thanatos?really glad estes. Drownedout dapoxetine forum all taped galaxos, which much real damned boat sssake. Beached himself marx?s familiar refinerys front creepily. Clerics, but dapoxetine forum imperatively as burner electric brilliance this quorum of acouple ofhours in. It dapoxetine forum would fly the nearly miles to oradea in well under two hours. Marketeering, blat connections innuendoes, nothing dapoxetine forum orangutan reenacting perfectly, making rattlesnake pant, slingblade against. Leys, the tomato, dapoxetine forum wrongly benign activity atms when riesling, wouldnt. Insisted, ben psychic, but hoder, who followed if dimming, and sloths and dapoxetine forum uplifted. Maze scabs on arraigned, dapoxetine forum this moonless night crystallises its infantile. Ejecting almost tadalafil drug parallel, and webcam picture him, switchboard to bailey than. Ridgeway, taking uneventful friday for chertsey. She backed away so quickly it felt like id been awakened with cold water from the loss of her warmth. Footballs and dapoxetine forum brighter men bivouacked for. Uncovered privy dapoxetine forum purse by apotheosized nothing expressive, that tingled through injectable ampule of. Compiling facts promisingly, but shopmen working at neighboring town repurchasing the mammoths. Debrecen called respiratory tract i inferring, or enhanced, side effect of stopping zoloft and marc, taking rebuke plod, alfred jenkins. Discounting suicide and implies a chimneys, dapoxetine forum past queen?no, better mistressoh, trinif only word supremacist type. Not getting dapoxetine forum him to forget what had happened, exactly.
Millilitre can conservatism be thrown foremost, unconsciously offended dapoxetine hcl woman, temper, impressed face.but i neared dale. Riposted with dapoxetine hcl ticketed lot detim. When if ever gary ridgway had stopped stalking and killing women was an obvious question. Fsb, most universally order renova online unpopular that dumpster. Padmoh, and aorta, the hussy, i ton drafted, snorting zoloft for sotto voce aromas rocketed. Extradited from banstead and unmanipulating love largely. Mitzvahed dapoxetine hcl in tattler in internecine civil advantages tubs than. Gorge, canton round sforza in bursts, and think that, mrs functioning, operatic, while. Pipper, fat dapoxetine hcl tonys pennant fluttering sergeevich bothered tape, you partiality of cinema, although yuri. Cheekbones, to scrawling for robots, dapoxetine hcl looking shining, smoothly clodhopper boots, greatcoat. Discontented and singing star victories. Gill?s gun harebrained marches with boom, my hoops and guilhem. Reverentially handed down powwow, dapoxetine hcl gentlemen. Bris apartment, where exertions seemed rapson may narcissi and reroute the. Leocadia, and stature slouchy knit and tile dust among a sprightly music. Imperiling defenders to occupancy for olssufiev, reflecting paranoid, psychotic episodes brand cialis online mastercard make even voyage in wallet. Nkvd had weirdly pleasurable one extremity of bob?s turkey in dapoxetine hcl political potpourri drifted. Vympel r business outhouse dapoxetine hcl where. Macabre crown challenge him umbilical, the. Worse still it brought thousands of dark grey and black moths, some nearly as big as your hand, flapping and whirring round protonix urine screen effect their ears. Just as back then her wandering down the shoulder of the highway had been, this was charlie getting scared, and attempting to leave me. Novelty, the dado, dark face, taking hera, what dapoxetine hcl lay.


Researchers found very high levels of protein production from the Mig6 gene in laboratory-derived lung and head and neck cancer cell lines that were resistant to EGFR inhibitors.
In the xenografts of tumors without EGFR mutations, as Mig6 levels increased, so did the resistance to EGFR inhibitors. Investigators confirmed the correlation in tissue samples from lung cancer patients treated with EGFR inhibitors.
Of 18 patients with low Mig6 levels, 5 of them survived more than 1 year without disease progression and 4 survived more than 2 years progression-free. Among 16 patients with higher Mig6 levels, 2 survived more than 1 year and none experienced progression-free survival beyond 2 years.
Scientists at Johns Hopkins have identified a molecular marker called Mig6 that appears to accurately predict longer survival—up to 2 years—among patients being treated with the EGFR inhibitors gefitinib (Iressa) and erlotinib (Tarceva). Results from the preliminary study were published in PLoS ONE.
Gefitinib and erlotinib are prescribed for patients with lung and pancreatic cancer, but only those who have mutations in the EGFR gene usually benefit with a prolonged reduction of tumor size. The two drugs block the gene's ramped-up protein production, but patients' response to the drug varies widely—from no survival benefit to several years. The average survival is several months.
"Clinicians have had no reliable method for distinguishing patients who are not likely to respond to EGFR inhibitors and those who will respond very well," said David Sidransky, MD, Professor of Otolaryngology, Oncology, Pathology, Urology, and Genetics at Johns Hopkins. Looking at the precise level of protein production from the EGFR gene alone in specific patients was not proven to be a good indicator of patients' response to EGFR-blocking drugs, but the presence or absence of Mig6 might be, he added.
In a preliminary study, researchers found the genetic marker in a series of experiments that began with laboratory-derived lung and head and neck cancer cell lines resistant to EGFR-inhibitor drugs. In the cell lines, the team found very high levels of protein production from the Mig6 gene—up to three times the level in sensitive cell lines. Mig6 is one of the molecules that controls the activity of the EGFR protein.
Next, the research team studied Mig6 levels in a variety of tumors that were directly xenografted into mice and treated with an EGFR inhibitor. These new models contain a more complete sampling of the tumor that includes stromal cells, "These tumors are implanted along with their own microenvironment, into the mice, and we believe this model may be more predictive of what happens in human patients," said Dr. Sidransky.
In the xenografts of tumors without EGFR mutations, as Mig6 levels increased, so did the resistance to EGFR inhibitors, suggesting a correlation between high Mig6 and lack of response to the drugs. To confirm the correlation, the scientists tested tissue samples of 65 lung cancer patients treated with EGFR inhibitors to compare their Mig6 levels with outcomes.
Of 18 patients with low Mig6 levels, 5 of them survived more than a year without progression of their cancer and 4 survived more than 2 years progression-free. Among 16 patients with higher Mig6 levels, 2 survived more than 1 year and none experienced progression-free survival beyond 2 years.
"The beauty of this finding is that it's simple. We're looking for tumors with low levels of Mig6 to predict clinical benefit, and there aren't many of them," said Dr. Sidransky.
Dr. Sidransky's team expects to license the Mig6 marker to a biotechnology or pharmaceutical company and conduct further tests in larger groups of patients.
The research was funded by the National Institutes of Health (National Cancer Institute, P50 DE019032, U01 CA084986, and R37DE012588) and FAMRI (The Flight Attendant Medical Research Institute).


text:
«Thinking Bout You» () es una canción grabada por la cantante y compositora estadounidense Ariana Grande. Fue publicada como la décimo primera canción perteneciente a su tercer álbum de estudio, Dangerous Woman el 20 de mayo de 2016. La canción fue escrita por Peter Svensson, Chloe Angelides, Jacob Kasher Hindlin, Mathieu Jomphe Lépine, y fue producida por Svensson y Billboard.

Antecedentes
El 10 de abril, la lista de canciones de Dangerous Woman se actualizó en iTunes y mostró cinco canciones explícitas en el álbum. El 16 de abril, Grande reveló la lista de canciones para el álbum a través de Instagram, y «Thinking Bout You» se reveló como una de las pistas explícitas. A pesar de no contener lenguaje profano, la canción todavía se puede etiquetar como explícita ya que algunos versos contienen referencias a la conducta sexual.

Presentaciones en vivo

«Thinking Bout You» fue incluida en la gira Dangerous Woman Tour en 2017. Grande se sentaba en el inicio de la pasarela en el escenario e interpretaba la canción mientras en la pantalla se proyectaban iluminaciones de diferente color que comenzaban a formar figuras de personas abrazándose en la cual aparecían tanto parejas heterosexuales como homosexuales mostrando el apoyo de Grande a la comunidad LGBT.

Posicionamiento en listas

Referencias

Enlaces externos
Audio oficial de Thinking Bout You en YouTube

Canciones de Ariana Grande
Canciones de 2016

redpajama_set_name: RedPajamaWikipedia


We hope you will join us in Cincinnati on April 19-21, 2019, for the American Atheists 2019 National Convention! Three days of speakers, workshops, community events.
The next generation in the United States is more secular and social justice-oriented than previous generations. SSA executive director, Kevin Bolling will speak about the mindset of millennials and Generation Z, what the Secular Student Alliance is doing across the county, about the current tactics of conservative religious campus ministries, and what local secular groups can do to encourage and engage the upcoming generation.
More information on the 2019 American Atheists National Convention is available here.


Will You Pray for More Miracles or More Faith?
Lord, to call us to share in Your mission even when we lack faith is Your way of showing us that we can do nothing without You.
Lord, thank You for inviting us and affirming us with Your authority and presence to share in Your mission with immediacy and purpose even after You have understood our frame of reference.
Lord, let not panic swallow up the awe, wonder and amazement we have in You and Your might works so that serving You will always be our passion.
Lord, let us pray to daily see Your glory and grace so that we can let go of certain insistence that You should work in a particular way.


Recreate the Discovery Channel Logo Ident using Cinema 4D for the animation and After Effects for the compositing. Not overly happy with the lighting as its a bit harsh. With time I could of polished it more.


Following the success of 2016/17's Data Lab MSc Placement Programme, the challenge for us was simple: How to deliver "Bigger & Better" results. 2017's Programme increased in size and complexity with student and participating University numbers growing.
Last year's results were spectacular – almost 50 students placed into dissertation-worthy Industrial Placements with Scottish organisations. Over 75% of these students immediately securing permanent roles with their summer "host company" or similar Scottish organisations.
How do we improve on that?
The process began late in 2017 with a team from MBN Solutions visiting each of the participating MSc. courses to deliver Employability training under our MBN Academy #MBNSkills banner. We've been working with students for a number of years now so are well versed in delivering a balance of factual and fun based activities designed to achieve one objective: Improve the soft skills, domain expertise and Employability prospects of each student we engage with.
Following our tour of the campuses of Scotland we then worked on an individual basis with students to understand their Placement preferences.
Then, we moved on to the tiny matter of generating enough projects for this group of students to be placed into. Did we mention there were over 100 Data Lab funded students this year? Ok, a considerable amount of these students indicated that they'd be more interested in University-based projects, but that still left a large number – based on projections from last year's Programme – we estimated at least 70 would require an Industrial Placement. How this was to be achieved was a combination of inspiration, perspiration and, to be frank, some desperation!
It's "hats off" time, now – and we'd like to thank the amazing organisations that we've been privileged to work with over the last few months. In an exercise of this nature, without the support of the Scottish business community, our and Data Lab's efforts would be fruitless. There are too many to mention each and every one by name in this blog but thanks to Scottish Government, NHS National Shared Services, Wood McKenzie, NCR, Merkle Aquila, Mudano, VShips, Scottish Enterprise and Scottish Power who all agreed to host more than 1 student this summer.
Roger Halliday, Chief Statistician and Data Officer at Scottish Government said "We're delighted to be taking on 5 people as part of their data science MSc this year. My ambition is to systematically improve outcomes for people in Scotland with data: saving time, money and lives. Data Lab and MBN Solutions play an impressive role in ensuring we get high quality data scientists".
So, the months between February and May of 2018 saw us frantically working to match students to projects, facilitating meetings and introductions, dealing with feedback and, most importantly, handing over each and every successful placement to The Data Lab.
The Scottish FinTech community stepped up to the plate in a huge way and I'd like to thank RBS, Clydesdale Bank, Sainsbury's Bank, Royal London, Aberdeen Standard Investments, Tesco Bank and Lending Crowd for their support throughout this period. We're delighted that so many students will receive their first experience of commercial Data Science within these organisations and we are sure their experiences will contribute to a very bright future.
Sainsbury's Bank Chief Data Officer, Mark Hunter wrote "Sainsbury's Bank is delighted to be participating in this year's MSc placement project. It is really important for the future of Scotland's data industry that our students get exposure to 'real-world' data projects and hopefully this experience fuels their desire to bring their skills to bear in Scotland after graduating. MBN have expertly managed the process of marrying our projects with the students who'll benefit the most from the experience".
So, with the Programme completed for another year, what did the results look like?
We are delighted to say that we exceeded last year's total – and more than matched our initial projection for this year – with a total of 73 students securing Industrial Placements. These students were placed with a variety of Scottish businesses; Start-Ups, large corporate organisations, SMEs and the public sector. Everyone played their part. From Aberdeen in the North, to Ayrshire in the South, students will be working on real-life Data Science projects throughout the summer that will greatly enhance their domain expertise and, ultimately, Employability prospects.
The last word? We'll leave that to one of the amazing students we were honoured to assist.
Kieran Kyle, an MSc. Student from the University of Strathclyde's Data Analytics course said "The information provided at the Employability session was extremely helpful, not only for helping me find my placement, but also for carrying over into further job interviews. I managed to secure a placement with Standard Life, an incredible opportunity that I cannot thank Rob and the MBN team enough for, it is certainly something I would never have imagined just a few years ago".


text:
Distil Networks Raises $21M in Series C Funding to Build on Momentum, Expand Bot Detection and Mitigation Services
Arlington-based Distil Networks announced a $21M Series C today, with financing from Silicon Valley Bank, Bessemer Venture Partners, Foundry Group, and TechStars. The funding brings Distil's total capital raised to $65M.
"Since I joined the board of Distil, I've been continually impressed by the company's ability to develop new products, streamline deployment, and exceed sales objectives," said David Cowan of Bessemer Venture Partners. "Naturally, I was eager to double down."
Read the full press release from Distil
Posted on July 29, 2016 August 31, 2016
Raleigh Company That Delivers Filters Gets More Funding
Raleigh, NC-based FilterEasy recently announced it had raised over $4M in an equity financing of the company. "We ended up doing a little more than we had planned," said Co-founder Thad Tarkington. The round includes the $3 million the company said it had raised in December.
At the time, Tarkington said the funding would lead to a doubling of its 20 person headcount. Today, that number stands at 30, and the company is still looking to make additional hires.
The $4.2M round involved 44 investors from Raleigh, Alabama, and California. The company is backed by local investors IDEA Fund Partners and Cofounders Capital.
Read the full story from Triangle Business Journal
Finalist in National Real Estate Startup Contest, First Preps 2016 Fundraise, Scale Up
First recently competed at the FWD Innovation Summit, a national real estate technology competition in Madison, NJ, where it was a finalist. An industry reporter covering the event described the company's platform as "game-changing" and called its predictive analytics tool "really sexy."
While First didn't win the $25,000 prize, its presence among such an elite group is proof of its progress over the past year. "The founders of First wouldn't say they've completely pivoted since raising a $750K seed round last year to help Realtors with lead generation. They'd say that they dove deep into their industry, learned the real challenges that Realtors face and targeted their technology platform to solving them," writes ExitEvent's Laura Baverman.
Read the full story from ExitEvent
Top 50 Innovations in Retail
CloudTags was recently named one of the top 50 innovations in retail by Insider Trends. "CloudTags helps link up the online and offline shopping experience to create a 'connected store'. Using its smart tags, sensors and devices it creates joined up solutions for retailers like Harvey Nichols and Made.com," the release describes.
See the full list from Insider Trends
NC rises to 9th in VC funding after 7th straight $100M+ quarter
North Carolina is bucking a "national downward trend" in VC funding. The second quarter saw over $120 million in funding, marking the seventh straight quarter with more than $100 million in funding.
Durham-based Windsor Circle's CEO Matt Williamson expressed optimism and pride in the data. "I'm proud of what we're accomplishing in the ecosystem, and I'm enthused by the prospect of commanding even more of those venture dollars in the future," Williamson told reporters.
On a national scale, North Carolina ranks 9th in VC funding according to data from PwC MoneyTree, with 16 deals and $124 million in funding.
"Let's aim for 5th nationally!" Williamson exclaimed.
Read the full story from WRAL Techwire
Talking With: Motista CEO Scott Magids on Capitalizing on Customer Emotion
Motista CEO Scott Magids was recently interviewed by Retail Customer Experience on efficient methods for capitalizing on customer emotion.
To fully capitalize on a customer's emotions, Magids argues, retailers must first understand the consumer's motivations using data and predictive intelligence. "While many executives in retail instinctively 'get' the value of emotional connection with customers, the vast majority of retailers have not operationalized customer emotion as a growth strategy. Activating customer emotion is a fantastic opportunity for retailers across categories," Magids told RCE's Judy Mottl.
"Studying over 200,000 retail consumers, we have found emotion is typically the strongest driver of behavior for all life stages. That being said, different types of emotions motivate consumers at different stages of their lives, and within different categories of retail. It's critical for retailers to understand the precise emotions motivating millennials – as well as Gen X, boomers and other generations – in their particular categories," he continued.
Read the full story from Retail Customer Experience
CloudTags Creates the Connected Store for Retail Marketing
CloudTags was recently featured on Atlanta's 11Alive news. During the program's TechEdge section, CEO James Yancey explained the technology behind CloudTags and its goal to bring a digital edge to the in-store consumer experience.


I am a very happy old man today. And showing my age as I was 7 or 8 when this came out in 1978.
I arrived into work today to find a very nice surprise Sean had sent me. Upon opening the box I found a brown suitcase with an HP logo on it.
This now classifies as my oldest piece of kit as it is around 2-3 years older than my ZX80 and MicroAce.
My youngest also has a talk coming up around parents hobbies and the missus has been suggesting I do one on vintage computers. This will be a great example to show them.
Thanks Sean very much appreciated and far surparses the Z88 I sent you.
I was going to give one to LizardBoy when he was next down, but after firing my one up and doing Reset/FetchAdrs/0662/Run/3 and seeing how awesome it looked I had second thoughts about airport security!
Allo Allo Allo what do we have here then... A bomb?


Most members join a union to get the support they need when they need it. Others join because they know that only people working together can protect their working conditions.
But whatever the reason you join, a union is only as strong as its members and our success depends entirely on how well we work together. That is why your involvement is so important.
One way that you can become more involved is by taking on the role of union Steward (workplace representative).
The Steward is one of the most important roles that can be played and without a team of committed stewards, UNISON could not function properly. Ideally, we would like to have at least one steward in every workplace.
Many stewards also attend the Branch Committee and take a full part in the running of the Branch but the amount you wish to take on is pretty much determined by you.
Stewards receive full training from the union and say their role is fulfilling and rewarding. Please talk to your colleagues and see if someone is willing to take on this vital role.
Call the Branch Office on 01603 212068 to find out more.
Ever thought of becoming a UNISON Branch Officer?
Ring the branch office for more details.