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G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligands interact and activate GPCRs, leading to signal transduction within cells. Most of these responses are mediated through the interaction of GPCRs with heterotrimeric GTP-binding proteins (G-proteins). Due to the information explosion in biological sequence databases, the development of software algorithms that could predict properties of GPCRs is important. Experimental data reported in the literature suggest that heterotrimeric G-proteins interact with parts of the activated receptor at the transmembrane helix-intracellular loop interface. Utilizing this information and membrane topology information, we have developed an intensive exploratory approach to generate a refined library of statistical models (Hidden Markov Models) that predict the coupling preference of GPCRs to heterotrimeric G-proteins. The method predicts the coupling preferences of GPCRs to Gs, Gi/o and Gq/11, but not G12/13 subfamilies.
Using a dataset of 282 GPCR sequences of known coupling preference to G-proteins and adopting a five-fold cross-validation procedure, the method yielded an 89.7% correct classification rate. In a validation set comprised of all receptor sequences that are species homologues to GPCRs with known coupling preferences, excluding the sequences used to train the models, our method yields a correct classification rate of 91.0%. Furthermore, promiscuous coupling properties were correctly predicted for 6 of the 24 GPCRs that are known to interact with more than one subfamily of G-proteins.
Our method demonstrates high correct classification rate. Unlike previously published methods performing the same task, it does not require any transmembrane topology prediction in a preceding step. A web-server for the prediction of GPCRs coupling specificity to G-proteins available for non-commercial users is located at http://bioinformatics.biol.uoa.gr/PRED-COUPLE webcite.
G-protein coupled receptors are important receivers of information input to eukaryotic cells. They share a common fold of seven transmembrane helices arranged as a seven α-helix bundle, as confirmed by analysis of the crystal structure of Rhodopsin [1] that has been extensively used as template for homology-based modeling of GPCRs [2-4]. A collection of messages of extreme diversity including photons and native agonists, such as ions, odorants and pheromones, amino acids, nucleotides, peptides, biogenic amines, prostaglandines and glycoprotein hormones [5] interact with different extracellular and/or transmembrane domains of GPCRs, in order to convey their messages to the interior of the cell [2,6]. Based primarily on shared sequence motifs, six distinct families of GPCRs are traditionally defined: A, B, C, D, E and the frizzled/smoothened family, as summarized in the GPCRDB classification scheme [7]. Various methods have been deployed for higher-level classification of GPCRs including profile Hidden Markov Models [8,9], support vector machines [10] and Position Specific Scoring Matrices [11].
The physiological response of the interaction between a GPCR and one of its ligands is judged by the subset of the inactive heterotrimeric (αβγ) G-proteins within the cell that interact with the activated receptor complex, although many receptors mediate their actions through G-protein independent signaling pathways [2]. Different agonists may stabilize complexes of GPCRs with G-proteins belonging to different subfamilies (Gs, Gi/o, Gq/11 or G12/13) resulting in the activation of different signaling pathways [12].
G-proteins are heterotrimeric complexes, named after their α subunits. On a basis of sequence identity, at least 16 discrete α subunits have been identified and classified into four subfamilies: Gs and Gi/o, which stimulate and inhibit respectively adenylate cyclase, Gq/11 which stimulate phospholipase C, and the less characterized G12/13 subfamily that activate the Na+/H+ exchanger pathway [13-17]. We should mention at this point, that in the gpDB classification [18], the term "families" has been reserved for this level of hierarchy of G proteins, however hereinafter we will use the term "subfamilies" instead.
Agonist binding to GPCRs leads to association of the heterotrimeric G-protein with the receptor, which triggers the exchange of the guanosine diphosphate (GDP) bound on the α-subunit of the G-protein with guanosine triphosphate (GTP). These events promote the dissociation of the α subunit of the G-protein from the receptor and the βγ complex. The dissociated subunits can activate or inhibit several effector proteins, such as adenylyl cyclase 1–9, PLCβ 1–4, tyrosine kinases, ion channels and molecules of the mitogen-activated protein kinase pathway, resulting in a variety of cellular functions that depend on the biological specificity of the dissociated subunits [17,19]. G-protein α subunits possess an intrinsic GTPase activity, which enables them to act as time switches: Hydrolysis of the bound GTP to GDP promotes the re-association of the α subunit with the βγ dimer and renders the G-protein in an inactive form.
Due to the lack of structural data for activated GPCR complexes, several complementary approaches have been used to decipher the molecular events leading to G-protein activation, and to identify the regions that determine the coupling specificity of a GPCR to a subset of the pool of intracellular G-proteins. These biochemical approaches, that were focused mainly on A GPCRs, include site-directed mutagenesis studies [20], chimeric receptor engineering [21,22], the use of synthetic peptides to mimic the GPCR regions that activate G-proteins [23] and antibodies to neutralize GPCR binding sites on the G-proteins [24,25]. These studies revealed the major role of GPCR intracellular loops, especially the second and third, and the C-terminal region, as the main determinants of GPCR coupling specificity. Furthermore, structural data from high resolution X-ray diffraction of the light-sensing GPCR rhodopsin, as well as complementary methods (Nuclear Magnetic Resonance Spectroscopy, Electron Spin Resonance Spectroscopy, protein engineering, amino acid fluorescent replacement) [26-28] have indicated that ligand binding induces large conformational changes. These conformational changes reveal GPCR regions buried within the membrane which could interact with the G-protein [5]. Through a combination of entropy variability plots and correlated mutation analysis, key residues for a variety of GPCR functions, including coupling to G-proteins, can be identified and a mechanism of GPCR activation has been proposed [29-31].
Due to their role as information receivers of eukaryotic cells, GPCRs are involved in many pathophysiological responses. They comprise attractive drug targets for a variety of diseases, including cancer [32], Alzheimer's syndrome [33] and AIDS [34]. Indeed, over 50% of all prescribed drugs target on GPCRs [35]. Furthermore, the information explosion in biological sequence databases has resulted in many GPCR entries of unknown ligand binding properties, known as orphan receptors. In order to screen these orphan receptors with libraries of potential ligands, researchers must be able to assay the GPCR-ligand interaction through a downstream event. Such events are transcription of a reporter gene or rise in second messenger concentration, which is dependent on the interaction of the GPCR under study with members of a specific G-protein subfamily. Thus, knowing or being able to predict, the coupling specificity of orphan GPCRs to G-protein subfamilies, is essential for choosing the appropriate cell lines for heterologous expression and any further in vitro and in vivo studies of potential drug targets [36]. Meanwhile, a dataset of GPCRs of known coupling specificity exists [37], large enough to guide an in silico database mining approach that could aid further in vivo GPCR research. Furthermore, in a work published recently, many GPCRs and their interactions with G-proteins have been summarized in the gpDB system [18].
As in every biological interaction, the specificity of GPCR coupling to specific G-proteins is determined by structural components located on contact regions of the molecules. Since the three-dimensional architecture of a protein is encoded in protein sequence, GPCR coupling specificity could be defined by sequence alone. However, GPCRs with low sequence similarity may couple to members of the same subfamily of G-proteins, while members of the same GPCR subfamilies often couple to members of distinct G-protein subfamilies [38]. In addition, GPCR coupling is not a one-by-one function since many GPCRs, known as promiscuous GPCRs, have been proven to couple to members of more than one G-protein subfamilies. Due to these limitations, GPCR coupling specificity prediction in one step using sequence comparison methods such as the BLAST [39] or CLUSTALW [40] algorithms is insufficient [36]. However a weak sequence signal can be detected among receptor subfamilies where G- protein selectivity was a recent evolutionary process, such as the biogenic amines receptors [41].
Previous computational methods of GPCR coupling specificity to G-protein subfamilies have been applied on a priori selected intracellular regions of GPCR sequences. A Naive Bayes model [42] yields a 72% correct classification rate, while a data-mining approach that combined pattern discovery with membrane topology prediction [43] has also been applied in an effort to model GPCR regions that determine coupling specificity. However, previous approaches are either context-dependent on the a priori knowledge that GPCR coupling specificity is governed by the entire intracellular regions sequence or limited by the non-probabilistic nature and limited descriptive power of patterns as regular expressions, that cannot implement weights to different sequence variation. The approach of this study is exploratory regarding the length and localization of the coupling determining regions among the intracellular regions sequences and recruits profile Hidden Markov Models (pHMMs) as highly discriminative models of biological sequences that have a formal probabilistic basis [44]. The results obtained by this method, presented below, justify the chosen approach.
Results and discussion
Our primary aim was to develop a wide-range predictive system that can be applied with the same discriminative power globally, for all three main GPCR coupling groups, being also able to model promiscuous receptor coupling. Our method proved to be self-consistent: Using a set of 282 GPCR sequences of experimentally identified coupling properties, according to the Trends in Pharmacological Sciences nomenclature supplement of receptors and ion channels (TiPS) [37], that were used to train the models and adopting a five-fold cross-validation procedure, the methods yielded a 89.7% correct classification rate. When tested in 479 sequences of GPCRs (retrieved also from the UniProt database [45]) that are homologous to the sequences used to train the models and whose coupling properties are also summarized in [37], at a subtype level, our method yields a 91.0% correct classification rate (Table 1). Finally, the method predicts correctly the coupling specificity of 25 out of 30 GPCRs derived from the gpDB database [18] that were not included in [37] (Table 2).
Table 1. Results of the cross-validation and independent set tests. A. Correct classification rate results obtained from the three main G-protein coupling groups, in a five-fold cross-validation procedure. The training set was randomly divided to five equally balanced sets. Afterwards, we trained a model using the sequences in the four sets whereas the last set was used for testing. This procedure was repeated five times. B. The library of refined profile Hidden Markov models (pHMMs) derived from the primary dataset of 282 GPCRs (see text) was tested against a validation set comprised of all GPCR sequences of subtypes with known coupling preference summarized in [37], excluding the sequences used to train the models (479 GPCRs in total). This independent test yielded 91% correct classification rate. Numbers in the diagonal of the charts represent true positive predictions. The total number of predictions for each group (row) is not equal to the total number of observations, since several GPCRs were not classified in any group.
Table 2. Prediction results for 30 different GPCR subtypes with known coupling properties extracted from the gpDB database [18] that are not included in the training set [37]. The annotation of GPCR coupling properties in gpDB is based on data from the scientific literature. According to the gpDB classification scheme, Ggust and Gt G-proteins belong to the Gi/o subfamily and Golf to the Gs subfamily. The GPCR sequences were extracted from gpDB and parsed into our prediction server http://bioinformatics.biol.uoa.gr/PRED-COUPLE webcite.
In order to assess the efficiency of the same method trained on a smaller and non-redundant dataset, the same procedure was applied to a dataset containing only the human GPCRs in the original training set. Alternative pHMMs were generated and integrated into a second predictive system that proved to be also self-consistent. On this human-only dataset, correct classification rate in a five-fold cross-validation, is 86% (data not shown). When these models were applied to the 479 sequences of the validation set, the correct classification rate was 88.9%, showing an insignificant decrease, as one would expect for a non-overfitted method. Additionally, when the model that was trained on human sequences, was applied to the remaining 178 non-human sequences derived from [37], yields also a high correct classification rate of 88.8%.
Due to insufficient experimental data, resulting in uncertainty about whether or not most receptors that are known to couple with a specific G-protein group can couple with G-proteins of another subfamily under different physiological conditions, we cannot estimate whether all of the promiscuous predictions are correct or not. For instance, a GPCR that is reported to couple only to G-proteins members of Gi/o subfamily, may proved that couples also to members of Gs subfamily. It is also well-known that the same GPCR may also couple to different G-protein subfamilies in different heterogenous expression systems. Promiscuous coupling was correctly predicted for 6 out of 24 GPCRs of known promiscuous coupling properties according to information in [37], as one can observe in Table 3. We did not attempt to train any pHMMs from sequences that have been proven to be promiscuous, in order to avoid unnecessary complexity and unequal distribution of the training set to the three major coupling groups of GPCRs.
Table 3. Promiscuous validation test. All 24 sequences of promiscuous coupled GPCR subtypes (as summarized in [37] and other sources [38]) were parsed in an hmmpfam query against our refined library of profile Hidden Markov models (pHMMs). The query sequences were not included in the training set. Promiscuous coupling was correctly predicted for 6 queries.
The main reason that no pHMMs have been constructed that indicate coupling to G12/13 proteins is the limited amount of data available for the coupling properties of this subfamily of G-proteins. For this reason, this feature is not provided by any of the already published methods that perform the same task. Furthermore, to the knowledge of the authors no promiscuous GPCRs are included in the training set (i.e. GPCRs that couple to members from multiple subfamilies of G-proteins), and no receptors that preferentially couple only to members of the G12/13 subfamily have been identified [2]. Therefore, constructing pHMMs that classify G12/13 coupled GPCRs with high discriminative power, at this moment, is practically impossible. Once larger datasets have been established in the future, promiscuous receptors could be included in the training set, allowing predictions for G12/13 coupled receptors.
Our exploratory approach resulted in the discovery of sub-regions within the intracellular GPCR domains that play a key role in determining GPCR coupling specificity to G-proteins. The contribution of these regions to the overall coupling scheme of GPCRs could arise through short-range protein-protein interactions with their structural counterparts in G-proteins, that is, through intermolecular stabilizing interactions that enable several regions of the GPCR molecule to interact with G-proteins. The conformation of the intracellular regions of GPCRs is regulated by intramolecular interactions between the intracellular segments [38]. Furthermore, each query against the refined library of pHMMs reveals regions of high identity to the profiles, if such exist in the target sequence. Residues in these identified intracellular regions could be targeted for site-directed mutagenesis approaches in order to elucidate the structural features of GPCR – G-protein coupling.
Our method can only predict the potential of interaction between a GPCR and a G-protein subfamily, since its only input is the GPCR sequence. Thus, common in vivo regulators of GPCR coupling specificity including mechanisms such as selective targeting of GPCRs to specific cell-membrane regions, post-translational modifications [46,47] or effects of accessory/scaffolding proteins interacting with GPCRs (reviewed in [2]) cannot be modeled by our prediction system. Also, GPCR homo- or hetero-dimerization, that appears to be a common feature of many GPCRs, necessary for G-protein activation [48-50] cannot be directly included in our prediction system.
pHMMs derived from this study have been trained to model sub-regions within GPCR intracellular domains rather than entire GPCR sequences. The a priori knowledge that a query sequence belongs to a GPCR would be valuable in enhancing the predictive power of the method. When the method is applied to the non-GPCR receptor and the globular protein non-redundant test sets, it produces false positives with a rate 19.2% and 6.4% respectively. However filtering the query sequences, by using 7-transmembrane domain pHMMs derived from the Pfam database Version 14.0 [51] in a preceding step, diminishes completely the above false positive results without affecting the overall sensitivity of the method. All six pHMMs for 7-transmembrane receptors contained in the Pfam database Version 14.0 have been integrated into our publicly available method. In conclusion, the method could effectively be used in combination with existing 7-transmembrane receptor predictive systems for genome-wide applications.
Compared to other previously published methods, performing the same task, our method does not only perform significantly better in terms of overall accuracy, but also employs additional superior features. Firstly, it does not rely on the identification of intracellular loops as does the Naive Bayes method in [42]. Our method was trained using the annotations for the transmembrane regions (which in most cases come from prediction methods) but in the testing phase no such information is required, thus it operates using as input solely the sequence. Compared to the pattern discovery method of [43], our method uses a more sophisticated scheme for whole-sequence scoring that has a formal probabilistic interpretation. We should note, however, that most of the patterns discovered by [43] were captured by our pHMMs (Figure 1), but in a more mathematically sound and exploitable manner. In addition, in [43] no overall measures of accuracy were reported in order to assess a fair comparison. Finally, our method is the only method reported until now, that is publicly available through a web-server. At the URL: http://bioinformatics.biol.uoa.gr/PRED-COUPLE webcite, the user may submit a sequence in Fasta format, and receive the prediction. The method is rather fast, producing a self-explanatory output, and, thus it may be used by both molecular biologists requesting information for a single GPCR, and by bioinformaticians performing large-scale computational analyses.
Figure 1. Comparison of the methods. Coupling determining patterns discovered by Moller et al. [43], show redundancy in their targets, since different patterns may apply to the same sequences in an overlapping manner. In addition, there are loop sequences of major GPCR subfamilies not characterized by any pattern, possibly due to low sequence identity. In comparison to patterns, profile Hidden Markov models (pHMMs) provide a whole sequence scoring scheme. Sequence information contained in multiple patterns can be integrated in a single pHMM derived from a low entropy region of a multiple sequence alignment. Thus, every query sequence can be given a score that has a formal probabilistic interpretation.
At the final stages of preparation of this manuscript, another method developed independently by Sreekumar and coworkers, was published [52], which uses also pHMMs. However, the method of Sreekumar and coworkers, does not treat the multiple intracellular loops of a given GPCR independently, but instead it concatenates them into a single sequence. These concatenated sequences are then used to build pHMMs with the HMMER package. Although, the method performs very well as reported by the authors (they claim a 99% correct classification rate in a cross validation test), there are some severe disadvantages arising from the aforementioned strategy: With this method, to test a newly found protein, one has to perform predictions on the GPCR regarding its transmembrane topology, extract the intracellular loops and concatenate them into a single sequence. This adds another source of error, originating from the prediction errors of the transmembrane topology prediction algorithm. Having in mind, that to date, even the best topology prediction algorithms, predict correctly the full topology of a protein with an accuracy of no more than 75% [53,54], this will further reduce the performance of the method. We should also note that regarding GPCRs, the most accurate predictors fail to even predict seven transmembrane segments for more than 15% of the presented examples [54]. Furthermore, the method does not control appropriately the level of false positives, since it was not tested on non-GPCR sequences. On the contrary, the method proposed in this work, although it uses essentially the same principles in extracting the loop regions, it treats them independently using the Qfast algorithm, and thus, in the prediction phase, no a-priori knowledge of loops and transmembrane topology is needed. In addition to that, the rate of false positive predictions is controlled, providing us a confidence about the validity of the results. Last, and perhaps more important, our method is the only one until now that is fully automated and publicly available via a web-server.
We applied here, a data-mining exploratory approach combined with the high discriminative power of profile Hidden Markov Models (pHMMs), to generate a system that predicts GPCR coupling specificity to the three main subfamilies of G-proteins (Gi/o, Gq/11 and Gs), based solely on the information included in the protein sequence. We report superior correct classification rate compared to other previously published methods, and we have created a web-server, running the application, freely available for academic users (Commercial users should contact Professor S. J. Hamodrakas to obtain a licence). At present, this is the only web-based server for prediction of GPCRs coupling to G-proteins. Expanding this information to characterize the coupling properties for thousands of orphan GPCRs in large-scale proteome annotation studies, our understanding of receptor signaling pathways might improve and new targets for drug research may be uncovered. Future studies, utilizing larger representative training sets of GPCRs with known coupling specificity to G-proteins, and more advanced algorithmic techniques are needed in order to increase the accuracy of the prediction method, as also as to handle more efficiently the promiscuity in preferential coupling of GPCRs to G-proteins. This way, we may also be capable of predicting the coupling of GPCRs to G-proteins members of the G12/13 subfamily, a feature neither addressed in this study, nor in previously published methods.
Our primary training dataset consists of 282 sequences of GPCRs of known coupling properties to G-proteins (120 Gi/o, 94 Gq/11 and 68 Gs) according to the Trends in Pharmacological Sciences 2000 Receptor and Ion Channel nomenclature supplemement [37]. All sequences in the training dataset were of GPCRs with non-promiscuous coupling according to [36] and were retrieved from the Uni Prot 1.10 database [45], excluding fragments. Based on their coupling preference, they were grouped into Gi/o, Gq/11 or Gs coupled receptors. The Uniprot Accession numbers of the proteins in the training set can be found in our web-page http://bioinformatics.biol.uoa.gr/PRED-COUPLE/training.txt webcite. Moreover, an alternative non-redundant dataset comprised only of the 104 human counterparts of GPCRs in the original dataset was used to train the method. This was done in order to investigate the effect of redundancy posed by homologous sequences. A validation set was also generated, including 479 GPCR species homologues of the receptor subtypes with known coupling specificity according to [37] (256 Gi/o, 102 Gq/11 and 121 Gs). Finally, the method was also validated on an independent set, composed of GPCRs, belonging to different subtypes with known coupling properties extracted from the gpDB database [18] that are not included in the training set [37].
As mentioned above, a sufficient amount of experimental data signifies the role of GPCR intracellular regions (the three intracellular loops and the carboxyl terminal region) and the membrane proximal intracellular extensions of transmembrane α-helices (approximately 1.5 turns) as the main regions of interaction between the G-proteins and the activated receptor complex [5]. Based on this experimentally derived information as well as membrane topology information derived from the UniProt annotation of each entry (in the "FT TRANSMEM" lines), we adapted our primary dataset, extracting sequence regions that corresponded to intracellular regions or transmembrane regions with intracellular proximity, spanning for approximately 7 residues within the cell membrane.
In order to investigate the performance of the method when applied to non-GPCR sequences, we used two alternative datasets. The first dataset includes a total of 1361 non-GPCR transmembrane receptors, whereas the second includes 1239 non-homologous globular proteins with structures known at atomic resolution [8].
Data mining: Generation and evaluation of HMMs
A multiple alignment was generated for each group of intracellular sequence regions derived from GPCRs with same coupling preference using the ClustalX package [55]. Pairwise alignment scoring parameters were set as: BLOSUM 30 substitution matrix, Gap-opening penalty of 10.00 and gap extension penalty of 0.10. Multiple alignment parameters were set as BLOSUM 30 series substitution matrix, gap-opening penalty of 10.00 and gap extension penalty of 0.20. Multiple sequence alignments were then scanned for low entropy regions of high scoring alignment rows. Thus, the training dataset was further diminished to low-entropy sequence regions with a sequence identity criterion. The resulting multiple alignment rows were then used to generate a library of HMMs in an explorative way: for a given multiple alignment low entropy block starting from every offset and with any window of seven or more alignment rows a HMM was constructed. Thus, a low entropy block of n alignment rows generates
potential alignments, where w is the window length. This analysis yielded a total of 6149 HMMs that were tested on the fly with the hmmsearch program against the training set, in order to compare the discriminative power among alternative HMMs. As an estimator of the discriminative power of HMMs we calculated the Coverage of the results, i.e. the percentage of positives scoring an e-value lower than the lowest e-value of the negatives. This critical e-value corresponds to the noise cutoff of Pfam entries [51]. In order to compare Coverage values between HMMs derived from different coupling groups, we calculated the p-value of Coverage as a random variable (probability of a model derived from alignment rows of random sequences scoring a Coverage greater or equal to a specific observed Coverage), as follows:
In a set of n sequences containing κ positives, the probability of choosing x positives before the first negative, without reset, equals to:
where f(x) is the probability function of the negative hypergeometric distribution. The cumulative probability function, i.e. the probability of choosing x or less positives before the first negative without reset is:
Thus, the probability of choosing × or more positives before the first negative (i.e. the p-value of the test) equals to 1 - F(x).
Based mainly on Coverage measurements and their p-values, we discovered HMMs from several sub-regions within the loops that show up to 12-fold increase of Coverage, in comparison to models derived from the entire loop sequences. Our exploratory approach resulted in 5 to 7 refined pHMMs for each one of the three groups of GPCRs in the dataset, as summarized in Table 4. The overall flow chart of the method, is presented in Figure 2.
Table 4. Fingerprint discovery results. Coverage values of profile Hidden Markov models (pHMMs) given after each alignment of entire loop sequence regions in comparison to maximized Coverage values of highly discriminative pHMMs derived from selected sub-alignments within low-entropy regions. pHMMs derived from sub-alignments showed up to 12-fold increase in their discriminative power, as measured by Coverage, in comparison to pHMMs that characterize the entire loop sequence alignments. Alternative alignment regions with the same discriminative power are separated by commas, double dots separate beginning and ending of sub-alignments used to generate HMMs. In the case of discovery of non-overlapping sequence fragments with high discriminative power (e.g. Gs loop1), separate pHMMs were generated and appended in the refined library of that group.
Figure 2. Flow chart of the method. GPCR entries in UniProt of known, non-promiscuous coupling specificity to G-proteins summarized in [37] were used to extract intracellular regions sequences, based on membrane topology information of the UniProt annotation. ClustalX was used to generate multiple sequence alignments of intracellular regions from which low-entropy blocks were selected based on ClustalX row scores. For every begin and end row, within low-entropy regions, sub-alignments were extracted and profile Hidden Markov models (pHMMs) were built. The discriminative power of each pHMM was assayed, after an hmmsearch run against the training set. The most discriminative HMMs for each intracellular region were selected for each one of the three main coupling groups and appended in the refined library. E-value thresholds were then set for each pHMM included in the refined library. The reverse course is followed during a query against the library of refined pHMMs.
Cutoff optimisation
Due to sequence similarity among intracellular regions of GPCRs of different coupling preferences, an hmmpfam query of the training set against the refined HMM library under a default e-value cutoff threshold, yielded artifact promiscuity in the predictions. HMMs derived from different intracellular regions show extensive variation in their discriminative power, as a consequence of different participation of different regions among the three groups of GPCRs in the overall coupling preference of the molecule. Our aim was not to exclude promiscuity from the predictive power of the method, so each HMM in the refined library was given a discrete cut off threshold resulting from ROC curve analysis after evaluation of the distribution of scores that corresponded to positive and negative targets of the training set. For each refined HMM of our library, we estimated and applied cutoff values that maximize the value:, where TP and FP are the percentages of positives and negatives respectively that score an e-value equal or less than a defined threshold at an hmmpfam query against the training set. This value is represented by the distance from the diagonal f(x) = x of a ROC curve, as presented in Figure 3. Once we had estimated the optimized cutoff thresholds for each HMM fingerprint, we combined the e-values of queries against HMMs that characterize the same GPCR coupling group using the QFAST algorithm [56]: , where p is the maximum product of n independent, uniform random variables and F(p) the combined p-value. In order to maximize the discriminative power of the combined predictions, a cutoff threshold was set for each one of the three e-value combinations, based mainly on the shape of the distribution of e-values scored by positives and negatives of the test set. A threshold was also set for the difference of combined e-values scored by the first and second match, expressed in logarithmic units, based on the combined e-values scored by HMMs characterizing different coupling groups in a hmmpfam search against a set of all 24 coupling-promiscuous GPCR sequences summarized in [37]. This estimation was based on the observation that the distributions of combined e-value differences, between the first two coupling predictions, from queries against promiscuous and non-promiscuous GPRCs are distinguishable when expressed in a logarithmic scale. Thus, alternative coupling groups are predicted as multiple combined e-value hits when querying the library against GPCR sequences of promiscuous coupling properties.
Figure 3. ROC curve analysis. ROC curve as an assay of discriminative power of a profile Hidden Markov models (pHMMs) that models a sub-region within the second intracellular loop of GPCRs that are known to couple with G-proteins of the Gs subfamily. The space under the ROC curve is a measure of the discriminative power of the model, while the distance of each point from the diagonal of the chart y = x is a measure of combined specificity and sensitivity of the model. The e-value that corresponds to the maximal distance from the diagonal spot is set as the threshold that discriminates positive from negative predictions in an hmmpfam run, regarding the selected pHMM. Similar charts were applied to optimize e-value cutoffs of all HMMs in the refined final library.
In order not to over-estimate the correct classification rate, a five-fold cross-validation procedure was adopted. Initially, the training set was randomly divided to five equally balanced sets. Afterwards, we trained a model, according to the above-mentioned procedure, using as training set the sequences in the four sub-sets, whereas the last sub-set was used for testing. This procedure was repeated five times, and the final results are the overall results obtained from the five sets.
Authors' contributions
NS performed the analysis, and implemented the algorithms and the web-interface. PB formulated the problem, collected the training and testing sets and designed the training scheme. PP implemented the Qfast algorithm and participated in the optimization procedure. SH coordinated and supervised the project suggesting innovative features. NS, PB and SH drafted the manuscript. All authors have read and accepted the manuscript.
PB was supported by a grant from the IRAKLEITOS fellowships program of the Greek Ministry of National Education, supporting basic research in the National and Kapodistrian University of Athens. We thank the University of Athens for financial support. The authors would like to thank the anonymous referee for his valuable criticism and comments on the manuscript.
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Wong SK: G protein selectivity is regulated by multiple intracellular regions of GPCRs.
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Moller S, Vilo J, Croning MD: Prediction of the coupling specificity of G protein coupled receptors to their G proteins.
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Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O'Donovan C, Redaschi N, Yeh LS: UniProt: the Universal Protein knowledgebase.
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Daaka Y, Luttrell LM, Lefkowitz RJ: Switching of the coupling of the beta2-adrenergic receptor to different G proteins by protein kinase A.
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Kroeger KM, Pfleger KD, Eidne KA: G-protein coupled receptor oligomerization in neuroendocrine pathways.
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Flex Floss is a key material used as rib, bodies on Chironomids, or wiggly legs and antennae. You can even string egg beads on it from salmon and steelhead. Use as a tough, stretchabe replacement for nylon floss.
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The U.S. Financial Crimes Enforcement Network has announced an agreement with the Mexican National Banking and Securities Commission to "share supervisory information" to fight terrorist financing and money laundering. "Our countries share more than a border," FinCEN Director Jennifer Shasky Calvery said. "We also share many of the same strategic concerns. And we are aligned in our efforts to stem illicit money flows between our two nations, as well as combat the ongoing threats posed by transnational organized crime." Read Shasky Calvery's statement.
View Full Article in:Wall Street Journal (tiered subscription model), The
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'One giant leap' toward a NASA Armstrong center?
Tuesday - 6/11/2013, 7:48pm EDT
AP Science Writer
LOS ANGELES (AP) -- Neil Armstrong's name is attached to a lunar crater, an asteroid, more than a dozen schools and a museum, but not a single NASA facility is christened in honor of the man whose "giant leap" made him the first to walk on the moon.
All that could soon change on the fringes of the Mojave Desert, where leaders at the space agency's top flight research center are mulling the consequences of a proposed name change at the place where Armstrong was a test pilot.
The push by some in Congress to strike the name of former NASA executive Hugh Dryden from the facility has brought with it some questions: Is it justified to substitute one accomplished figure for another? At a time of squeezed budgets, is it worth the cost? And, besides: How long before the next space hotshot upends the world's first moonwalker?
Managers at the Dryden Flight Research Center have no say in what they're called -- final approval rests with the U.S. House and Senate -- and so they have left the soul-searching to others.
"I'm happy with the name Dryden Flight Research Center, but I'll be equally happy with Armstrong," center Director David McBride said. "Both men were leaders in the field."
Though not a done deal, brainstorming is already underway: Welcome signs bearing the Dryden logo would have to be updated. Research aircraft would need their sides repainted. Letterhead and pamphlets would have to be recycled. And then there's the obligatory dedication ceremony.
Dryden officials have not calculated a total makeover cost but don't foresee extra funds, meaning they would have to work within their $65 million operating budget to pay for the changes.
It wouldn't be the first rebranding of a NASA facility.
In 1999, the Lewis Research Center in Ohio -- named for George Lewis, the first executive officer of NASA's predecessor agency -- was changed to the John H. Glenn Research Center, after the first American to orbit Earth and former senator. A daylong celebration was held, complete with an F-16 flyover and a parade filled with floats, marching bands and a cameo appearance by Glenn.
Any festivities marking a Dryden-to-Armstrong swap would likely be more muted to save money.
A name switch often occurs to raise a center's profile and is not unlike what happens at universities, which shuffle the nameplate on buildings and stadiums as memories fade and institutions try to cash in on a bigger celebrity or generous donor.
"Dryden had a tremendous influence on the original space program," said American University space policy professor Howard McCurdy. Still, he added: "With few exceptions, time diminishes everyone's legacy."
The Dryden moniker has existed since 1976. Before that the center, located on the grounds of Edwards Air Force Base about 90 miles north of Los Angeles, was not named for a specific person. It was here where the sound barrier was broken and where the now-retired space shuttle fleet once landed. Experimental jets routinely buzz the skies.
Between 1955 and 1962, Armstrong was a test pilot at the facility -- then called the High-Speed Flight Station. He logged 2,400 hours of flight there, including on the X-15 rocketplane that opened the way for manned spaceflight.
Less of a household name, Dryden was a child prodigy who enrolled in college at age 14. An aerospace engineer, he served as director of the National Advisory Committee for Aeronautics, the predecessor to NASA, and later as the space agency's first deputy administrator. He died in 1965; four years later, Armstrong stepped on the moon.
After the House in late February voted unanimously for a Neil A. Armstrong Flight Research Center, Dryden officials started a checklist of signs that would need replacing on buildings, highway exits and aircraft. This is the second attempt at a name change by Republican Rep. Kevin McCarthy, whose district includes Dryden. The measure is awaiting consideration in the Senate, which failed to act last year.
Some in Congress have questioned the idea, given the fiscal climate.
"I doubt in this era of declining funding for NASA that either Neil Armstrong or Hugh Dryden would want a single precious dollar to be spent on a cosmetic facility name change when that money could be spent instead on fulfilling NASA's mission to reach for the stars," Rep. Donna Edwards, D-Maryland, said during the bill's debate. Edwards nonetheless voted for bill.
The Glenn Center spent about $260,000 on a new website, fresh signs and updated printed materials. To cut down on costs, employees were encouraged to use up the old letterhead when communicating with one another.


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Yup, Tim From 'Tim And Eric' Can Make A Weirder Herman Cain Ad Than Herman Cain Can
Wednesday, October 26 by Penn Collins
This is where I would put in my interpretation of the video you're about to see, but it's a 'Tim and Eric' video, so that's impossible.
Infographic: The Netflix Exodus In Perspective
Wednesday, October 26 by Noah Griffith
What do 800,000 customers really look like?
Your Very Own Bean Avatar!
Wednesday, October 26 by Screen Junkies
Our 9 Favorite Scenes Involving People Killing Defenseless Animals
Wednesday, October 26 by Penn Collins
To help get you through the rest of the week, here's a list (with video) of people killing animals.
Eddie Murphy To Try Something New With A Family-Oriented 'Beverly Hills Cop' TV Show
Wednesday, October 26 by Penn Collins
I could see "Axel F" being redone by Cut Copy or Chromeo.
My 10 Favorite 'Beavis and Butt-Head' Episodes
Wednesday, October 26 by Wookie Johnson
I found the perfect way to get zero work done today.
'Like Crazy' Actress Keeley Hazell
Wednesday, October 26 by Screen Junkies
A model turned actress? Who has heard of such a thing?
Steven Spielberg Didn't Like 'Indiana Jones 4′ Either
Wednesday, October 26 by Wookie Johnson
Once again, the blame is on George Lucas.
45 Fan-Made Tributes To The 'Star Wars' AT-AT Walker
Wednesday, October 26 by Jame Gumb
The AT-AT might be highly impracticable and extremely vulnerable, but the important thing is that it looks totally bad ass.
31 Creepy Animated GIFs From 'The Shining'
Wednesday, October 26 by Wookie Johnson
Some of these cannot be unseen.
Summit Talks With McG About Non-Board Game Film 'Puzzle Palace'
Wednesday, October 26 by Wookie Johnson
Surprisingly, it's not based on a board game.
Links Away: Muahaha!
Tuesday, October 25 by Screen Junkies
Tim Tebows Newest Song (TotalProSports)
Hot Babes Named Tiffani (GorillaMask)
The Greatest Facial Hair in MMA History (CagePotato)
The Wahlberger Reality Show (FilmDrunk)
25 Badass Hoodies (Smosh)
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RIP Leonardo (Unreality)
3 Hotties On The Beach (DoubleViking)
The Greatest Speech Ever Made (SocialHype)
David Slade Is a Coward (Slashfilm)
The Cats Paw Review (31Nights)
The Hottest Actresses Who Posed For Playboy (MadeMan)
Karissa Shannon Hotness (Ehowa)
Meet The Walking Dead Composer (USAToday)
9 Ways The World Might End (MadeMan)
Grace Potter Hotness (Maxim)
Tim Allen Still Sucks (WarmingGlow)
GTA 5! (GameFront)
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Tower Heist
Tuesday, October 25 by Screen Junkies
Director: Brett Ratner Cast: Eddie Murphy, Ben Stiller, Casey Affleck Synopsis: When a group of hard working guys find out they've fallen victim to a wealthy business man's Ponzi scheme, they conspire to…
8 Movies-Within-Movies That Should Be Made
Tuesday, October 25 by Penn Collins
Instead of the Battleship film, why didn't someone sack up and make 'Nation's Pride'?
Jay Leno's Joke Kills Two Motorcyclists
Tuesday, October 25 by Penn Collins
This is worse than when the Dancing Ito's went haywire and took out that orphanage.


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Endukante is a love story that will enthrall audiences and stars Ram as Arjun,Tamannaah as Lalitha, Radhika Apteas Sumathi, Suman as Prakash, Sayaji Shinde as Jaganathan, Suvalakshmias Jothi, Raghu Babu as Gunasekar,Suman Setty As Kumar & Anu Hasanas Ramya. Take your loved one along for a film that is more romantic than anything you could imagine. Book your tickets right now at Kyazoonga.com.
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WILLIS, Texas The owner of a Willis dog refuge appeared in court on Friday after hundreds of injured animals were found on the property.
Earlier this week, The Humane Society of the United States and local authorities seized 298 dogs from the Spindletop Pit Bull Refuge, where they also discovered a mass grave with the bodies of 38 other dogs.
In court in Montgomery County, Leah Purcell agreed to sign the dogs over to the county and never operate a sanctuary there again.
The condition of the animals, most of them pitbulls, has prompted outrage from around the country.
The animals were living in buildings with little to no ventilation, said HSUS State Director Katie Jarl. The cages were so small most of them couldn t stand up or turn around. They were living in their own waste, and we see from vet exams that many of them have scalded paws from standing in their own urine.
Purcell waived her right to appeal. The case will be presented to the grand jury for possible criminal charges.
Individuals with proper documentation should be able to start claiming dogs within the next few days. For more information contact the Montgomery County Precinct 3 Constable s Office.


text:
How to improve quickly.
Decide to.
make an agreement with yourself to work on serve and return, first strike.
Find good practice partners and treat them well.
make an agreement to get the reps you need to improve: consistency weapons sequences serving returning attacking net on short balls second serve speed and rpms
Lose weight if you need to.
Start split stepping.
Unit turn/coil early, before any ball bounces.
Use the non dominant hand/arm to point to the side fence after unit turning. It will solve a lot of problems, such as: coiling more, earlier preparation, more kinetic pathway.
Play down to get confidence.
Use practice to add and match play to subtract.
Uncoil faster.
Defend your contact point.
Master lull-jam-finish modes.
Learn to recognize quickly weaknesses of opponents.
Learn to protect your own.
Video tape your self to check on form. Buy a gohero pro camera at about $279. HIgh quality fps and easily edited.
Shadow swing.
Use a flex bar to increase endurance and speed/coiling and to prevent tennis elbow injuries.
Practice to improve.
Play for it, instead of the victory only.
Feel no fear.
Feel the ball, and realize in pressure, it's all about feel without fear.
Change your string and frame to match your style.
Realize that in match play, it will always come down to: who holds more, wins more second serve points, and who breaks more, and wins more second serve points.
Decide to become faster on unit turns and preparation/coil/uncoils.
Let low balls come closer and attack high balls farther out.
When returning, gauge your contact point off the sidelines in front.
One lunge step only.
Decide where to hit returns ahead of serve.
Decide where to serve ahead of time.
Use all types of spin and pace against opponents to see what they don't like.
Why would you let low balls come in closer? Move in and take it out in front of you before it gets too low.
lots of good info. now how to do that quickly pls..
And net it. Your arm will force you to be on top of the ball on a low ball too far out front. YOu have to be able to decide where you are going to return to, and be able to hit either dtl or cc off both sides no matter what serve is coming in.
What the hell is this, a poem for the sport of tennis?
The key is finding good partners to work with who are willing to cooperate.
The key is knowing what to work on.
The key is knowing what tension you like, what frame, what string.
The key is being able to decide something during a match and then making it come true.
It's like blowing out a birthday cake candle. Simple, mindless, unconscious, only a breath. Just imagine beautiful mindless smile you will have on your face when you win. Use visualization and creativity.
That's what this list is.
A thoughtless beauty.
I never liked it or did well with it in my game though. I like to be more instinctive
with it.
Ps: The more you do it , the more options you can create and the faster you decide and visualize (even just a few milliseconds before the shot). I am now in the phase neutralize the server when it's big, attack on the opposite corner on server where I have more time. Try to practice, you'll it pays off :).
try to force a ball to a spot if that is not where it needs to go. When I predetermine,
it tends to narrow my options, which may be good for some, but not in my best
interest. When the ball comes, I see things and it is clear for me where the ball
should go.
I don't agree in principle, so I wouldn't want to go in that direction. Maybe it helps
someone if they are not seeing the right options in real time?
get a serve like raonic. that'll take u up a few levels stat. like from a 3.0 to a 5.0.
If you happen to be 6'5" AND A GENETIC FREAK. He used a stock kblade strung with m2pro at 43/44 or 45/47 at IW.
Editted to the aphorisms I didn't fully understand? How does one use the sidelines to guage contact point of serve?
What the heck is a lull-jam-finish mode?
"decide where to hit returns ahead of serve"...
Definitely something any good player should do.
Sure, you cannot possibly hit every return to the area you want.
BUT, you will now have a preconcieved target, with some room for error in either direction.
Some of you would be to day...hit up the middle. Well, that's basic 3.0-3.5 level returns, isn't it?
Why do you try to return serves? Most people, to neutralized the server's advantage. So hit to his weaker side, or the side you can handle his shots from.
Jam energy: It's energy that jams your opponent's timing. Heavy top spin, heavy pace, heavy slice, great drop shots, great kick serves, great flat shots that skid, any shot that changes the height, or depth, or pace radcially after the ball bounces, is jam energy. Even no pace slow balls are jammers. It's a transition energy, that is higher risk and faster in nature. It is felt inside your body as a higher speed, higher risk application of spin and speed/depth/height change. Even drop shots have to be disguised quickly. This energy changes the speed of the ball radically or the direction or the height just after the ball bounces, and it's this "radical change" which jams internal opponent rhythm. It's as if you are jamming a spike into his body and causing his energy to jam. Psyches are also used to jam.
Finish energy: The riskiest type of energy. It's low net clearance, high speed or high touch. It's simply higher risk, put the ball away. Some of those bursts are finish based: they are clean winners. This applies to drop shots as well as flat or angled winners. This is lower net clearance, higher risk, higher stick speed shot. There are psychological components of each of these energies as well as the physical incoming shot.
Mastering the energies requires the ability to master both psych and body energy.
DEFEND THE BODY CLOCK INTERNAL SPEED Blazing fast Cheetah feet, and drunken monkey upper torso.
There are two internal body clock/engines running us at all times, an upper body engine and a lower body engine which are fueled by our energy types at all times. Our mental unit turn tells us to kill a shot, or push a shot, or jam a shot, or lull a shot, and the feet are on board if moving quickly in a martial arts, choppy way, and the upper body is on board if moving fluidly in a whip snapping relaxed way.
When the feet slow down, your clock/engine jams up. When the engine running your torso slows down and there is no fluid coil and no load to your shots.... If the incoming shot upsets your timing, it has succeeded. Most of the time when we make errors it's due to a bad coil, or failure to maintain contact point. Both of these are caused by energy flow into our bodies.
The body clock is jammed when one or both of the bodies' engines slows down . They have to be running at the same speed, a fast one, no matter what incoming shot! You have to defend your body clock speed just as you defend your contact point, regardless of incoming shots.
Keep the engine speeds high and defend the contact point in front of you. Most errors are either coil errors, or contact point errors. Both are more commonly made in a transition from one mode to the next mode, lull to jam to finish. Most transition errors are made due to slow reactions and jammed engines. We are trained to hit the ball the same distance in front of us, no matter what the incoming shot. That causes a huge amount of mistakes. The correct contact point is not straight wall up/down in front of you. It varies according to the incoming shot's height. It is shaped like an arced curve. This arc is determined by your own length of arm. Higher incoming shots have to be struck more out front, and lower shots are allowed into the body more before striking your own outgoing shot, due to the length of your own arm. If you place your arm straight out, eye level high, and let it drop, that is the arc you are defending on contact, both at net and on ground. If you use the same contact point on a low ball that you use on a medium high ball, you will net it. If you use the same contact point on a high ball that you use on a medium ball, you will hit it late and go out long. Our best contact point is when our arm is barred out front so the wrist can be locked at contact for maximum consistency, and that point is determined by the incoming shot's height. Another facet of contact point is the spot in the string bed. It has to be in the right place in the bed. If too low, or too high, the strings are too short, their frequency level of rebound is too high, and there is not enough control. Some like to strike the ball high up, such as 3rd cross down from top. Some like it in the middle, 10th cross down. Most are inbetween.
Edberg came into net almost sitting down in his split. That gave him quicker lateral movement. Murray will go 6"-9" up in the air on his split. So did Chang and Hewitt. Players with the biggest or widest most extreme splits, often have the best defense/better foot work/quicker feet, ie, Chang, Hewitt, Sanchez vicario, Nadal, Murray, etc.. The split step affects the unit turn so profoundly. Ever notice how wide Djokovic stands while returning serve? It's a full shoulder width and a half. So do all top returners, wider than anyone else. The advanced split step will point one foot to the side fence (the same side the ball is heading towards) and plant the other foot pointing towards the net, perpendicular to each other. This advanced split step causes a faster unit turn, because the turned foot pivots the upper body when that pivoted foot lands. When the pointed foot lands, it forces a faster unit turn, a faster decision on which shot you are going to hit. Turning sideways faster is a strategy is all about removing time from the unit turn, deciding ahead of time which shots to hit, so you don't have to waste time thinking about it during the point, and to at the same time to force your opponent to spend more time reacting and thinking! Your mental unit turn has to be just as practiced and polished as the physical, and your decision on which energy type to use has to be automatic: lull-jam-finish. That is the decision of which energy to use, lull, jam, or finish, and how deep and hard and high the shot will be.
Typical combinations are: two lull shots, two jam shots, and two finish shots.
The physical unit turn is the only thing all top pros have in common. It's the mental unit turn and their mastery over the three energy types that separates the very top from the next tier down.
When returning, spot a sideline or the lines in the service box. Use those to time your contact point, ie, if going cc in ad, use doubles alley line to be just behind your contact pt. for a cc bh return.
One lunge step refers to the one step you should take during returns if forced to reach.
I plan my serve returns as well. I faced a lot of serve and volley players in my day though. I don't find it odd to do this. I have broken serves by returning the ball to the same spot against certain players.
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text: Meta Battle Subway PokeBase - Pokemon Q&A Login Register All Activity Questions Unanswered Tags Users Ask a Question Rules Chat In Pokemon platnium I some how encountd a pidove what happened? 0 votes I caught it but later turned into a starly .it was 12 o clock so I didn't hallucinate. platinum asked Jul 29, 2013 by Awesomesauce Please log in or register to add a comment. Please log in or register to answer this question. 3 Answers 0 votes Best answer Another possibility is that you are using an emulator. Emulators usually have peculiar glitches that would usually not take place in the game cartridge versions such as de-evolving and having a turtle dance around your screen as if it were mad. Catching a Pokemon that isn't in a specific generation can also be one of them. If you are using a game cartridge then it just might be that it was a minor bug or glitch that repaird itself once you caught it. It could have also been that you got used to seeing a Pidove that when you saw a similar bird, Starly you then taught it was a Pidove until after the capture. This can happen when you see or look at things quite often. I had a moment were I would keep seeing the Giant huntsman spider anytime I saw a spider, it is practically just a mind twist. It could also be a side effect when you were playing for a long time without a notable rest. Then it is very possible that without noticing you were semi-asleep and that you were day-dreaming that you encountered a wild pidove instead of a wild starly answered Jul 30, 2013 by Aeternis selected Jul 30, 2013 by Awesomesauce Please log in or register to add a comment. 0 votes Well in Platinum Pidove didn't exist, so it might actually be a possibility that you hallucinated. I think that is really most likely. Or, you could have been dreaming or something. I don't know if this is possible, but it could be the result of hacking and trading between B/W(2) or something. Hope I helped! :) answered Jul 29, 2013 by JarJar~ Please log in or register to add a comment. 0 votes Considering Pidove is not exist until Gen 5. The possible way to tell, is either glitch in your Platinum, or you are daydreaming/hallucinating. Well, I know this not much, hope it's helping. answered Jul 30, 2013 by KiraRia57 Please log in or register to add a comment. Related questions How do you remove a HM in Platnium? Can anyone explain how to go to the the Wireless Room and get traded pokemon from Platnium to Soulsilver? What is a good team to form in platnium that you can get almost right from the beginning? How do you catch Cresselia or just find her? (Platnium)? I threw a poke ball and it caught it in mid air without shaking? what happened? I was playing my game and this happened-is it a glitch?! What it would happened if you beat every battle frontier facility 100 times ? What happened to my Luxray? Useful Meta Posts How to change your username How to add a user avatar Why was my post hidden? The points system Question etiquette Flags vs Downvotes Anime FAQ


text:
Open My Heart to Worship
By Mark Hayes
Open My Heart to Worship (11 of the Most Popular Praise and Worship Songs Masterfully Arranged for Solo Piano). Arranged by Mark Hayes. For piano. Piano Collection; Piano Supplemental; Worship Resources. Alfred's Sacred Performer Collections. Christian, Worship and Gospel. SMP Level 7 (Late Intermediate). Collection. Chord names and lyrics. 60 pages. Published by Alfred Music (AP.25654).
ISBN 0739041258. With chord names and lyrics. Christian, Worship and Gospel. 9x12 inches.
This title has selections that range in difficulty from SMP Level 6-7.
About SMP Level 7 (Late Intermediate)
4 to 5-note chords in both hands and scales in octaves in both hands.


text:
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OPM sets reinvestigations at every 5 years for 'public trust' feds
Wednesday - 11/9/2011, 3:57pm EST
Starting Dec. 9, agencies will have to reinvestigate employees in "public trust" positions every five years.
The Office of Personnel Management issued the final rule today to implement a 2009 Executive Order. President Bush called for employees doing policy-making, having major program responsibility and those in public safety and health, law enforcement duties and fiduciary responsibilities to be reinvestigated at least once every five years.
The rule stated public trust positions are those that are "designated at a moderate or high risk level, based on the position's potential for adverse impact on the efficiency or integrity of the service."
OPM took nearly two years to come up with a final rule. It issued an initial proposed rule in December 2009, but received comments from interested parties that they were confused. OPM re-released a proposed rule in November 2010. This time it received comments from 17 unions, organizations and people.
"[T] he investigative product for reinvestigations of employees occupying nonsensitive public trust positions will be the National Agency Check with Local Agency Check and Credit Check (NACLC) or Periodic Reinvestigation (PRI) depending on the level of public trust," the rule stated. "As proposed, reinvestigations must occur frequently enough to ensure that continued employment of persons in public trust positions remains appropriate."
OPM said the cost to reinvestigate employees every five years could be prohibitive. To lessen the burden on agencies, OPM chose to depend on the NACLC and PRI, which are low-cost services.
One minor change from the proposed to the final rule is the need to reinvestigate an employee who has left federal service for less than two years. Previously, the agency would need to do a new examination.
Additionally, OPM plans on issue another final rule in early 2012 covering reinvestigation requirements for employees with national security positions and access to classified information. It issued a proposed rule in December 2010.
OPM reissues proposal to update suitability checks
OPM proposes reinvestigation rule for certain employees
OPM proposes to change definition of national security jobs


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Tablet. Hotels for Global Nomads
Travel Better.
Rosewood Castiglion Del Bosco
Castiglion del Bosco, Montalcino, Italy
Check availability as rates may vary for dates. Best rate guaranteed.
This place is a big deal. There aren't a lot of rooms, but in every other way it's a big deal none the less. Basically, the intimate hotel portion of Rosewood Castiglion del Bosco is just the tip of the bucolic iceberg — in total, more than 4,500 acres of intact, 800-year-old agricultural estate property provide a spectacular backdrop. Wine is actively produced here and much of the property is comprised of preserved lands for grape growing or forests. The rest has been sensitively formed into a community of sandstone villas with the original estate buildings, Il Borgo, at its heart. The idea is for guests to immerse themselves in the landscape, functions and pace of Tuscany, wander the massive property as if it's the year 1552 and the estate belongs only to them. But in terms of Tuscan hotel luxury, it's also very much in the here and now, with two restaurants, an organic kitchen garden, a cooking school, a fine spa and plentiful sport and fitness facilities.
Suites at Il Borgo are large, ranging from 580 to nearly 2,000 square feet. However, many feature giant terraces that are sometimes larger than the suite itself, stretching the experience luxuriously into the rolling landscape. The décor is traditional with the rustic, Tuscan color palette one expects, un-fussy wooden or wrought iron furnishings, and thick draperies to blow in the breeze. But to reiterate, as intoxicatingly comfortable as the suites may be, the experience is the estate in its ancient totality, to breathe and live slowly in the countryside that makes Tuscany Tuscany.
And should the 4,500 acres of Tuscan countryside within the bounds of the estate prove insufficient, the villages and vineyards of the Val d'Orcia lie just beyond, while Siena is less than an hour's drive away. There are a range of excursions to choose from, though there's no need to stress over which one to do; any choice you make will be a good one.
Please note: Rosewood Castiglion Del Bosco is open seasonally late March through the end of November.
How to get there:
Rosewood Castiglion del Bosco is about an hour and a half from Florence, 2 hours from Pisa and 2.5 hours from Rome. The estate also has an on-site helipad. A helicopter flight from Siena or the Grosseto private airport takes approximately 45 minutes. Please contact email@example.com for assistance with transportation.
- 23 Rooms
- Style: Contemporary Classic
- Atmosphere: Quiet
- 10 Guest Reviews
- Rosewood Castiglion Del Bosco
- Castiglion del Bosco
- Montalcino, Italy
Hotel amenities and services
- 100% non-smoking hotel
- 24 hour front desk
- 24 hour gym
- Adjoining rooms
- Babysitting (on request)
- Bar
- Bicycle rentals nearby
- Central air conditioning
- Free parking
- Free wi-fi
- Garden
- Golf nearby
- Library
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- Multi-lingual staff
- Outdoor Swimming Pool (seasonal)
- Pet Friendly
- Restaurant
- Room service
- Sauna
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- Spa treatments (on request)
- Steam rooms
- Wake-up service
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- Wood burning fireplace
Other Siena hotels
10 Guest Reviews
All reviews are from Tablet guests with verified stays.
- Michael
- Stayed July 2014
- Rating: Excellent
What I liked:
Fabulous location, stunning scenery, amazing facilities.
What the hotel could do better:
Nothing, had a wonderful stay!
- Tiago
- Stayed October 2013
- Rating: Excellent
What I liked:
It's a magical place with the most kind staff I have ever known.
The rooms are absolutely incredible.
Perfect place for a beautiful honeymoon.
What the hotel could do better:
I can't think about anything they could do better.
- Carola
- Stayed September 2013
- Rating: Excellent
What I liked:
This hotel is immaculately looked after with great attention to detail combined with excellent service. The rooms are very comfortable with luxurious bathrooms and everyday there is a new treat of complimentary mini bar items (non alcoholic) including small home made cookies, roasted almonds etc. The garden and the pool area is neatly looked after and the service is excellent. And wherever you look, there is a breath taking view of the beautiful tuscan landscape. We travelled with small kids and planned to stay for 2 nights but extended our stay to 4 nights since it was such a great place.
What the hotel could do better:
The only area of improvement is the cuisine, the food is good but not great (which it usually is Italy).


text:
Amsterdam Canals Candlelight Cruise Including Wine and Cheese
We've selected the best candid trip photos submitted by our users to help you decide if the Amsterdam Canals Candlelight Cruise Including Wine and Cheese is right for you. Make the most of your next trip to Amsterdam with Viator's exclusive Photo Galleries.
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100% Meinu
February 2010
Just a really good time
la dégustation de vin et fromages
May 2015
Présentation sur la table lorsque nous pénétrons sur le bateau. Nous avons hâte...
July 2014
Amazing night on the Canal
August 2013
Romantic Candle-lit Cruise on the Amsterdam Canal.
June 2013
Grachtenfahrt Mai 2013
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Candle Light Cruise Amsterdam May 2013
No barco à noite em Amsterdam
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Cruzeiro à luz de velas
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Brightly lid canals.
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text: Ice Fishing Lake Minocqua Posted on March 3, 2014 by My Outdoor Website Wrong Page ? Cant find it? It's here somewhere! Please search again if need be. Weekend of January 6, 2012. Nice northerns and walleyes on Lake Minocqua. To view this fishing spot on a map, please visit View m. Its not the clearest pictu... View Our Outdoor Source>>>>>>>>>>>>> .


text: Tap.me raises $3.2M for in-game mobile ad network March 12, 2012 5:51 PM Dean Takahashi 0 Enterprise companies tackle mobile marketing automation slightly differently—and that's why they're on top. Register today for this free VB Insight webinar with AEG's VP of Social and Marketing on May 28th. Tap.me, a mobile ad network focused on monetizing games, has raised $3.2 million in funding. Chicago's Hyde Park Venture Partners, an early-stage venture fund, led the investment. Other investors include I2A Fund, Western Technology Investment, Great Oaks Venture Capital, Firestarter Fund, and Hyde Park Angels. Tap.me targets brand advertisers who want to get noticed by gamers without interrupting gameplay sessions. It creates contextual and authentic branded "integration points" within web and mobile games. It does that by getting developers to identify something in a game that can be sponsored, like an ability to jump higher. Tap.me then finds advertisers to sponsor that capability. Jeffrey Lapin, former chief executive of Atari, has joined Tap.me's board. Tap.me's platform is expected to hit 25 million gamers by June. Matt Spiegel (pictured), chief executive of Tap.me, said the round will help give the company more momentum. Tap.me said that Edmonton, Canada-based Fluik Entertainment has agreed to integrate Tap.me's platform into Fluik's games, including the popular Office Jerk mobile game. Fluik's games have been downloaded more than 20 million times. "Fluik is thrilled to partner with Tap.Me, whose platform will help us monetize gameplay in a way that enhances the experience for the gamer, which is of paramount importance to us," said Victor Rubba, co-founder, Fluik. "This is the future of meaningful game and brand integration." Additions to Tap.me's board include Sam Yagan, co-founder of OKCupid and Excelerate Labs, and Stuart Larkins, managing director of I2A. Tap.me was originally founded in 2010 as a game company, but it changed its focus to in-game ads. [Photo credit: Tap.me]


text:
Euphoria Blossom
Euphoria Blossom
by Calvin Klein
WFor Women
Item #
Other Brands by Calvin Klein
- Calvin Klein Beauty
- Calvin Klein Man
- Calvin Klein Sheer Beauty
- Calvin Klein Sheer Beauty Essence
- Ck Be
- Ck Free
- Ck In2u
- Ck One
- Ck One Red Edition
- Ck One Shock
- Ck One Summer
- Contradiction
- Crave
- Dark Obsession
- Downtown Calvin Klein
- Encounter Calvin Klein
- Encounter Fresh Calvin Klein
- Escape
- Eternity
- Eternity Aqua
- Eternity Moment
- Eternity Night
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- Euphoria Gold
- Euphoria Men
- Euphoria Men Intense
- Obsession
- Obsession Night
- Reveal Calvin Klein
- Secret Obsession
- Truth
- Design House: Calvin Klein
- Fragrance Notes: Pomegranate Orchid, Green Note Pink Peony, Lotus Blossom, White Amber, Woods, Frosted Musk
- Year Introduced: 2006
- Recommended Use: casual
No reviews written for this product
I like the perfume very much. Euphoria Blossom.
Written by Anonymous on April 07, 2015Read Review details ►
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Been wearing Euphoria Blossom for about a month now. This light fragrance is what I was looking for...absolutely wonderful! Extremely happy paying half price for a tester. Talk about an AWESOME buy!!! Totally satisfied. Thank you FragranceNet!
Written by najo on October 05, 2014Read Review details ►
This is a nice flowery scent but I prefer the stronger scent of the original Euphoria. This one is too light for me.
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I just love Euphoria Blossom, it is just gorgeous.
Written by Anonymous on December 19, 2012Read Review details ►


text:
2012-06-19 14:38:00
Power 96.5
How often do mothers of high profile athletes' kids have to resort to food stamps? We saw it happen to Basketball Wives' Tami Roman, now it's happening to Chris Bosh's ex, Allison Matthis.
Matthis is the mother of the Miami Heat's 3-year-old daughter, Trinity. According to Matthis, her recent job loss as a secretary at a construction company has caused her Florida home, where she and Trinity live, to be put up for foreclosure and she was forced to apply for food stamps. But how does the ex of an NBA star, who is currently paying child support, turn to food stamps to feed their child? Jane Carey, Matthis' lawyer, told the press:
"Believe it or not, Bosh pays her a mere $2,600-a-month in child support! My client just lost her job and applied for food stamps for her and Mr. Bosh's daughter. She's about to be foreclosed on, he won't help her and all he wants to do is go to London with his daughter. Are (you) kidding?"
Carey was referring to Bosh's request to take Trinity to Long with him for the Summer Olympics. His request was denied by the judge.
Bosh and Matthis will obviously be heading back to the courthouse to battle it out over his current child support wages.
According to Florida state guidelines, a person of Bosh's status, who's salary is around $18 million, would be expected to pay around $30,000 a month on child support.
Someone will pay, but who? Bosh? Or taxpayers?
1. Omarion - Post To Be f/ Chris Brown& Jhene Aiko
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4. Trey Songz - Slow Motion
5. Ciara - I Bet
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Cecilia Metra
Board of Governors (2013-2015)
Cecilia Metra is the 2015 Secretary and the first vice chair of the IEEE Computer Society Test Technology Technical Council. She has served as the associate editor in chief of the IEEE Transactions on Computers, editor in chief of the IEEE CS online publication Computing Now, and Vice President for Technical and Conference Activities. She is on the editorial boards of several professional journals, and has been involved in numerous IEEE-sponsored symposia and workshops as general/program chair or co-chair (14 times) and as technical program committee member (74 times).
Metra is a professor of electronics at the University of Bologna, Italy, where she has worked since 1991 and from which she received a PhD in electronic engineering and computer science. In 2002, she was also a visiting faculty consultant for Intel in the US. Her research activities have been sponsored by state and industrial agencies, as part of various research and educational consortia at the national and international levels. Her involvement with industry was recognized by a joint patent on commercial products.
An IEEE Senior Member and a Golden Core Member of the IEEE Computer Society, Metra has also received two Meritorious Service Awards and two Certificates of Appreciation from the Society.


text:
SANTA ANA The puzzle begins with a Facebook page. It shows a grinning imp of a man with a dusting of white hair on his head and a few days of white stubble on his chin.
"Cheerios," the site says by way of introduction. "Santa Ana's icon."
It has more than 1,400 fans.
Who is Cheerios? Most of those fans will tell you he's the old guy who hangs around high school football games, shining a flashlight at passing cars to remind the drivers to turn on their headlights.
They might also tell you a story, about the night a car with its headlights out hit and killed his wife – or was it his son? They'll tell you that Cheerios has been out there ever since, watching out for the young people, keeping them safe – and they love him for that.
But who is Cheerios, really? That's the puzzle. And it begins with his name.
Keith Winn has to think back many years to slide that piece of the puzzle into place. He's 44 years old now, and he remembers playing outside one afternoon when a man from the neighborhood came around selling boxes of cereal.
He remembers the kids shouting: Hey! Whaddya got? And the answer:
"Cheerios! Cheerios!"
In time, everyone came to know the man as Cheerios. They'd call to him after football games, and he'd answer with a wave of his flashlight. If there was a party in town, the high school kids made sure he knew about it. Girls sometimes planted wet kisses on his cheeks.
He was something special to the generations of kids who came to know him – a simple man of pure intention who spent his life watching out for others. "He was like a celebrity," said Robert Sanchez, 33, who created the Facebook page to pay tribute to a man he had been waving at since he was a boy. "We treasured this guy."
Most people never knew too much about the man with the flashlights, just that old story about a dark car hitting someone he loved. But there was one other important detail that many of his fans knew, one more piece to the puzzle. His real name.
Kenneth Pierre.


text:
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The scientific committee has completed the review process and all contributors are informed by email on the status of their two page abstract proposal. Owing to the large number of submissions of high quality, we are unable to include all proposals in the final program. In particular, we restrict the number of accepted abstracts to one for each participant. We thank everybody for their interest in this symposium and the response to our call for papers.
Review process completedJune 15th, 2010
Extended DeadlineApril 29th, 2010
The deadline for the two page abstract submission has been extended to May 8, 2010 at the latest.
Abstract submission is openMarch 9th, 2010
I'm happy to you inform you that the abstract template and submission page are now available.
We are looking forward to your contribution.
Registration is openFebruary 1st, 2010
As from today the website is up and the registration for the Symposium is open.