High-throughput prioritization of cancer-causing mutations (drivers) is a key challenge of cancer genome projects, due to the number of somatic variants detected in tumors. One important step in this task is to assess the functional impact of tumor somatic mutations. A number of computational methods have been employed for that purpose, although most were originally developed to distinguish disease-related nonsynonymous single nucleotide variants (nsSNVs) from polymorphisms. Our new method, transformed Functional Impact score for Cancer (transFIC), improves the assessment of the functional impact of tumor nsSNVs by taking into account the baseline tolerance of genes to functional variants.
With the advent of high-throughput sequencing, our ability to identify single nucleotide variants (SNVs) in the genome or exome of individuals has far exceeded our capacity to experimentally validate their impact on disease phenotypes. Therefore, computational methods that predict the impact of non-synonymous SNVs (nsSNVs) on protein function have become very important and of wide interest. Bioinformatics methods have been developed and tested over the past decade that distinguish disease-related nsSNVs from neutral polymorphisms [1–11]. A different, although related, problem is assessing the relevance of nonsynonymous somatic variants in cancer emergence. In principle, functional somatic mutations can only be causative of cancer if they affect cancer driver genes, which upon mutation confer a distinct selective advantage or a newly acquired capability to the cell [12, 13].
The need for computational methods to predict the functional impact of cancer-causing somatic variants contrasts with the low number of methods that have been designed or tested specifically for this purpose [4, 14]. One likely explanation is the absence of curated sets of true driver and passenger cancer mutations. Many recently published cancer resequencing projects use methods like SIFT [15, 16], and PolyPhen2 (PPH2) [17–19] to predict the functional impact of cancer somatic mutations, although these methods were not developed or tested for this purpose and the quality of their performance in this context is not clear.
Existing methods provide a predictive functional impact score (FIS) for each mutation . The FIS calculated for nsSNVs relies mainly on the conservation of single residues across multiple sequence alignments. In other words, these methods employ evolutionary information to assess the likely impact of an amino acid change on the structure or function of the altered protein. Nevertheless, the ultimate effect of this amino acid change on the functioning of a cell depends on other factors as well, such as the particular role played by the altered protein in the cellular machinery. The criticality of that role will determine the protein's tolerance to amino acid changes. Our view is that a score purporting to assess the likelihood of individual mutations to provide a somatic cell with an acquired advantage - and possibly give origin to a tumoral clone - must take this feature into consideration.
The present study has two interrelated goals: first, to determine the tolerance of different proteins to functional variants, and second, using this information to develop a method that improves the capacity of existing bioinformatics tools to assess the likelihood that a specific somatic mutation is a cancer driver. We have called it transFIC (transformed Functional Impact Scores in Cancer) and we distribute it as a PERL script that users can download for local use. We also provide a web server  that can be queried using an internet browser or programmatically to obtain the transFIC of somatic cancer nsSNVs.
We downloaded all SNVs (approximately 30 million) detected by the 1000 Genomes Project  within the genomic sequences of 1,197 individuals (May 2011 release). We then used the Ensembl Variant Effect Predictor [22, 23] (VEP v.62) to detect nsSNVs and to retrieve their SIFT [1, 24] and PPH2  FISs. We retrieved the corresponding MutationAssessor (MA) FISs through the MA webAPI service (release 1.0) . At the end of this process we obtained 168,803 distinct SNVs, of which 155,453 were successfully scored by at least one method and 110,397 were scored by all three methods.
The distribution of MutationAssessor functional impact scores of nonsynonymous single nucleotide variants differs significantly in proteins belonging to different functional groups. (a) Candlestick representation of the distributions of MutationAssessor (MA) scores of germline single nucleotide variants (SNVs) in genes in all Gene Ontology Molecular Function (GOMF) categories, ordered from higher to lower mean. (b,c) Thirty least-tolerant and 30 most-tolerant GOMF groups of nsSNVs ordered by their mean MA scores. Groups in the lower end of the tolerance scale (less tolerant) correspond to essential GOMF categories, involved in signal transduction, transcription, and translation. On the other hand, the most tolerant molecular functions correspond mainly to metabolic-related activities.
We then built one FIS distribution for each human protein-coding gene following this simple pipeline (see the section 'Using baseline tolerance to functional variants to transform original scores' in the Results and discussion for an example).
We obtained all the functional terms assigned to the gene under analysis by each of the four functional annotation systems. If an annotation system contained no annotation for a particular gene, the pipeline was stopped at this stage and the mean and standard deviation of FISs of the germline nsSNVs tolerated by the gene were taken from the values corresponding to the distribution of the entire dataset of nsSNVs.
From the list retrieved for the gene of interest in step 1, we culled the SNVs that occur in genes annotated to the most specific functional term (that is, the term containing the fewest genes).
If we culled fewer than 20 SNVs, step 2 was repeated including the SNVs occurring in the genes annotated to the second functional term in ascending order of gene content, then SNVs of genes in the third category with the fewest genes, reiterating the process until at least 20 nsSNVs scored by the three methods were pooled. The aim was to obtain for each gene a pool of nsSNVs large enough to compute the FIS distribution, but as small as possible to permit a better grasp of the gene's baseline tolerance of mutations related to its specific function.
The SIFT and PPH2 FISs underwent a logit transformation to approximate them to a normal distribution.
The mean and standard deviation of the FISs provided by each method were calculated within the pooled set.
This process yielded four output files, each containing the mean and standard deviation of the three FIS distributions (one for each method) assigned to each human protein-coding gene that completed this pipeline. These values can be easily used to transform the scores of somatic mutations as described in the equation in the Results and discussion section. The PERL script simply reads the SIFT, PPH2 and MA FISs that assess the functional impact of the somatic mutation under analysis, searches the distributions that have been assigned to the gene where that somatic mutation occurs and automatically transforms the original FIS.
From version 57b of the Catalog of Somatic Mutations in Cancer (COSMIC), we downloaded all somatic nSNVs. We then counted the number of samples containing each mutation. We obtained SIFT and PPH2 FISs from the Ensembl VEP v.62 and MA FISs by querying the MA webAPI, as described above.
We assembled the whole genome (WG) dataset by downloading the coordinates of somatic mutations from the International Cancer Genome Consortium (ICGC) Data Coordination Center  or from the data provided with the software implementing the MEMo algorithm  (Table 1) of 12 cancer exome (or selected genes) sequencing projects. (The MEMo algorithm is designed to find highly interconnected mutually exclusive cancer driver genes.) Mutations in hg18 coordinates were transformed to hg19 using the Liftover program obtained from the UCSC genome browser . The SIFT, PPH2 and MA FISs were then obtained as described above.
Only mutations successfully scored by at least one method were included. Original sources: breast(JHU) [40, 41], breast(WTSI) , ovary(The Cancer Gene Atlas) , CLL(MICINN) [44, 45], colorectal(JHU) [40, 46], pediatricbrain(DKFZ) [47, 48], glioblastoma(TCGA) , glioblastoma(JHU) , lung(TSP) , pancreatic(JHU) ;, pancreatic(OICR) and pancreatic(QCMG) are unpublished lists of mutations downloaded through the ICGC data coordination centre . DKFZ, German Cancer Res Center; ICGC, Data Coordination Center ; MEMo, datasets of mutations packed with the software implementing the MEMo algorithm ; MICINN, Spanish Ministry of Science and Innovation; OICR, Ontario Institute for Cancer Research; QCMG, Queensland Centre for Medical Genomics.
We obtained a list of driver cancer genes from the Cancer Gene Census (CGC) . Somatic mutations from COSMIC and from the WG dataset that appeared in any of the genes in the CGC constituted the positive subsets of two proxy datasets. The negative subsets were composed of COSMIC or WG somatic mutations occurring in other genes and are not recurrent in the corresponding dataset (Table 2).
HumVar is a dataset of disease-related SNVs and neutral polymorphisms . WG (whole genome) is a dataset of somatic mutations pooled from different tumor exome-sequencing projects (Table 1).
In summary, recurrent COSMIC or WG mutations, manually curated driver mutations within COSMIC or mutations within COSMIC or WG occurring in CGC genes formed the positive subsets of the nine proxy datasets employed (Table 2), whereas the negative subsets were formed by non-recurrent COSMIC or WG mutations, COSMIC mutations outside the manually curated drivers list, non-recurrent COSMIC or WG mutations in non-CGC genes, or known polymorphisms.
Finally, we downloaded and ran the CHASM program [5, 14, 32]. Following the recommendation by the developers in their wiki pages, we used the ovarian classifier to classify mutations within these two datasets. Next we computed the transFIC (GOMF) of CHASM using the procedure described above. To evaluate the performance of the original and transFIC score we used WG2+/1 and WGCGC/nonCGC datasets, and a modification of them in which we removed the mutations that appear within the training set of CHASM (WG2+/1* and WGCGC/nonCGC*). Manually curated driver mutations used to train CHASM were identified within COSMIC to serve as the positive subset in two of the proxy datasets (COSMICD/O and COSMICD/Pol).
We hypothesized that we could use the pool of nsSNVs that occur naturally in human populations to assess gene tolerance to perturbing nsSNVs. Since in principle all nsSNVs that interfere with the natural development of a human organism or with its ability to produce offspring have been eliminated from this pool by negative selection, the range of nsSNVs that remain in a gene would reflect the 'baseline tolerance' of the cell or organism to perturbations to the function of that gene. We propose that this baseline tolerance can complement the evaluation of violations of evolutionary constraints imposed on individual amino acid residues by protein structure and function. Therefore, we propose to use it as a means to transform the FISs of nsSNVs provided by bioinformatics tools.
One way to visualize the score transformation that we propose is that mutations with the same FIS should affect the cell differently if they occur in genes encoding essential proteins rather than in genes with numerous backup and redundancy mechanisms - for instance, those with a higher degree of paralogy. Our assumption is that genes within the former class will mainly possess germline SNVs with relatively low FISs, while those within the latter will accumulate more functional SNVs. To accomplish this transformation we devised two interrelated objectives: first, to measure whether this baseline tolerance to nsSNVs actually differs for distinct genes, and second, to evaluate whether a differential baseline tolerance to SNVs could be used to improve the scoring of functional somatic mutations in cancer. To carry out the study, we selected the nsSNV FISs provided by SIFT [1, 24], PPH2  and MA  because they can be readily obtained for high-throughput analysis of large datasets of mutations, a critical feature for somatic mutation analysis in the context of cancer genome resequencing projects.
Although cancer-related genes are better conserved than average human genes [33, 34], which has aided in the discovery of new cancer genes , to our best knowledge this is the first attempt to evaluate whether baseline tolerance to germline SNVs can improve the FIS of somatic mutations.
To detect differences in baseline tolerance across genes, we first needed a pool of nsSNVs that occur naturally across human populations. We decided to use the catalog of SNVs detected by the 1000 Genomes Project  because of its unbiased nature. However, the number of nsSNVs deposited in this catalog does not allow computation of each individual gene's baseline tolerance, because the catalog still lacks the necessary coverage. Therefore, we clustered the genes according to functional criteria (as described in Materials and methods) and then computed the baseline tolerance of these groups of functionally related genes. This approach must be seen only as an imperfect effort to compensate for the low resolution of our current catalogs of SNVs, which prevents gene-by-gene calculation of baseline tolerance to SNVs. Nevertheless, as the genomes of more individuals are sequenced and the catalog of human germline nsSNVs progresses toward completion, eventually this assessment will become possible.
The four systems of functional annotation we used to partition the dataset of SNVs and form these pools of functionally related genes were (as introduced in Materials and methods) the GOBP and GOMF categories, the CP annotations and Doms. Let us illustrate this process with the GOMF terms represented in Figure 1. Each of these terms contains a group of functionally related human protein-coding genes. The nsSNVs that occur in these genes are pooled together to build the distribution of the three FIS values (one for each bioinformatics tool assayed) in each category. Then, the distribution of, for instance, MA scores for the nsSNVs that occur in the genes of each GOMF group may be represented as a candlestick centered at the mean of the distribution, and whose whiskers extend outward in proportion to the standard error of the mean of the distribution. If the groups are ordered in ascension by their MA score means, we obtain the plot shown in Figure 1a. The group located at the extreme left of the graph (interleukin-3 receptor) possesses nsSNVs with lower MA scores, on average, than its counterpart at the extreme right of the graph (immunoglobulin receptor). Genes in GOMF groups at the extreme left of Figure 1a have lower tolerance to perturbing nsSNVs (they have, on average, lower mean MA scores) than those at the extreme right, which tend to bear more deleterious nsSNVs.
We have observed that this same segregation between genes with low baseline tolerance and genes with high baseline tolerance holds if the genes and the nsSNVs they bear are grouped following other functional classification systems (Additional files 1 to 3). For example, canonical pathways (Additional file 1) that group genes related to biological processes such as cell cycle, central signal transduction pathways, or DNA damage repair are located at the lower end of the MA score spectrum, which means that only germline SNVs with relatively low functional impact are tolerated in these genes. On the other hand, most metabolic pathways appear to tolerate germline SNVs with higher functional impact, as they are primarily located at the upper end of the MA score spectrum. This finding may be related with the fact that many known inherited metabolic disorders are known to be recessive  (see below).
The distributions of MA scores of nsSNVs across all GOBP and Dom groups, which follow this same general structure, are presented in Additional files 2 and 3. A comparison of the MA baseline tolerance of genes - the mean MA score of SNVs - assigned according to the GOBP and the GOMF pooling (Additional file 4) shows some weak correlation between the two. Even weaker correlations are observed when the other classification schemes are compared to the baseline tolerance according to GOBP. These differences in baseline tolerance measurements are probably the reason why dissimilar classification schemes perform differently when separating the proxy datasets (see below).
In an effort to understand the reasons for these observed differences in baseline tolerance between diverse groups of genes and especially whether they could be the product of artifacts in the data, we analyzed their correlation with several variables. We found that differences in baseline tolerance between groups of proteins cannot be explained by differences in the height of multiple sequence alignments used to produce the MA scores. Baseline tolerance also does not correlate with nsSNVs or allelic frequency. However, genes in the least tolerant groups are significantly more conserved, on average, than genes of the most tolerant groups. This is in agreement with our hypothesis that genes with low baseline tolerance are more critical to the cell - and therefore tend to evolve at a slower rate - than those exhibiting high baseline tolerance to nsSNVs. On the other hand, we found that dominant disease genes are significantly overrepresented among least tolerant genes and recessive disease genes are overrepresented among the most tolerant genes. In addition, known cancer genes are overrepresented in the least tolerant groups with respect to most tolerant groups. However, tumor suppressor genes and oncogenes are not significantly enriched for amongst lowly tolerant or highly tolerant GOMF groups (Additional file 5).
We wanted to transform the FISs of SNVs provided by SIFT, PPH2 and MA by taking into account these differences in tolerance to functional mutations in the germline. We are using the generic term 'functional impact score' - originally employed by the MA team  - to refer to the scores provided by these various methods. The rationale behind the transformation is that if two mutations with the same FIS affect genes with different germline tolerance to functional SNVs, the impact of the mutation on the least tolerant gene is expected to be greater than its impact on the most tolerant one. If GOMF results are taken as reference (Figure 1), a mutation on a gene with one of the functions shown in Figure 1b is expected to have a higher impact than another mutation affecting a protein with a function shown in Figure 1c.
As explained above, another way to present this transformation is to think of it as adjusting the FIS of the mutation to compensate for the importance of the gene to cell operation. Genes with essential cellular functions would appear on the lower end of the functional impact score scale, while genes whose malfunction can be compensated for by diverse mechanisms or does not lead to very deleterious phenotypes are located at the upper end of the FIS scale.
Figure 2 presents the flowchart used to transform the original FIS. Let us illustrate this process with one specific PIK3CA mutation detected in breast invasive carcinoma by the The Cancer Gene Atlas. This particular mutation involves the change of the glutamic acid residue at position 545 of the protein to an alanine residue. The MA FIS for this mutation is 1.775, which makes it a low impact mutation.
Outline of the method to transform the scores. (a) Functional impact scores (FISs) of all germline single nucleotide variants (SNVs) from the 1000 Genomes Project are computed. (b) SNVs are partitioned into subsets according to the category of the genes that harbor them (for example, Gene Ontology Molecular Function). (c) FISs of a given cancer somatic mutation are computed and transformed using the distribution of the scores of SNVs in the same category as the protein where the mutation under analysis occurs. We give these transformed scores the generic name transFIC (transformed Functional Impact scores in Cancer).
where transfic represents the transformed FIS, os the original score, and dm and dstd are the mean and the standard deviation of the distribution of FISs of nsSNVs computed as explained above. In our illustrative example, this implies that the E545A mutation in PIK3CA will see its MA FIS score of 1.775 converted to a transFIC MA of 2.82, which being above 2 will be labeled by the transFIC webserver as a highly affecting mutation (see below). In the spirit of our interpretation of the transFIC presented in the Hypothesis section, we may propose that, in this case, the transFIC compensates for the effect of a relatively mild mutation - one that occurs at a site of the gene does not possess strong evolutionary constraints. The resulting transFIC would thus more accurately present the impact of this relatively mild malfunction of a key signaling protein - whose essentiality is reflected in its low baseline tolerance - on cell operation.
To compare the capability of the transformed FIS to that of the original FIS to identify mutations involved in cancer, we needed a set of somatic mutations involved in cancer development (positive set) and a set of passenger somatic variants (negative set); however, no gold-standard dataset exists. Previously used datasets are based on the recurrence of mutations found in the COSMIC database [3, 37] or manually curated sets of cancer driver mutations [5, 14]. However, each of these datasets has its own biases; in particular, they are enriched for mutations in well-known genes that have been widely studied in cancer. Instead of employing only one data source, we decided to use several proxy datasets with nsSNVs gathered from different sources, under the assumption that each will have its own biases and errors.
We devised these proxy datasets so that the positive subset of mutations is enriched in likely driver mutations - either because they have been manually curated from previous reports, because they occur in known cancer genes, or because they appear recurrently in the dataset - and is complemented by a negative subset of mutations enriched in passenger mutations. Known driver mutations are the result of years of cancer genetic and genomics research and are, in most cases, experimentally verified . Mutations that occur in cancer genes have an increased likelihood of being drivers because they are prone to affect likely driver genes. On the other hand, mutations that recur in several different tumors also have an increased likelihood of being drivers, because their increased frequency makes it more likely that they have been positively selected and less likely to have appeared by chance in tumors. As stated above, both recurrent cancer mutations and mutations in cancer genes have been employed elsewhere as datasets enriched in driver mutations.
By using several datasets derived from different sources we can assess if the transFIC works systematically better than the original FIS. In other words, we assume that each dataset has an unknown percentage of misclassified mutations. For this reason, instead of focusing on the net performance of each method in a particular dataset we look for the systematic improvement of the transformed FIS.
where tp, tn, fp and fn are the number of true positive, true negative, false positive and false negative cases detected by the FIS (or transformed FIS) in question. Because all datasets are relatively biased towards an excess of negative cases, the MCC is a better estimator of performance than accuracy .
We found that the transformed FIS outperforms the original FIS on all nine proxy validation sets. In the case of MA, this is true for transformed FISs computed from the GOMF partition for all proxy datasets tested (Figure 3; Additional file 6), while the gain is more modest or nonexistent when other partitions are used. In the case of PPH2 and SIFT the transformed FISs systematically outperform their original counterparts in all partitions and all proxy datasets tested, with up to 12-fold improvement in some cases.
Transformed Functional Impact for Cancer (transFIC) systematically outperforms original scores in the task of differentiating cancer driver mutations from neutral variants. (a) Performance of GOMF transFIC is compared to the three original functional impact scores (FISs) classifying the nine proxy datasets, using as cutoff the value of FIS (or transFIC) that maximizes the Mathews correlation coefficient (MCC) in each case. (b) Performance of GOMF transFIC is compared to the original score of CHASM (q-value cutoff <0.05) in two proxy datasets after removal of mutations within the training set of CHASM.
Since PPH2 was trained using HumVar polymorphisms, we did an additional validation of this method using polymorphisms not present in the HumVar dataset (Additional file 6). The MCC of the original score of PPH2 diminished in these datasets but interestingly transFIC score performed comparably.
It is important to note that the transformation of the FIS affects the SNVs in all proxy datasets equally, depending solely on the functional annotation of the gene where the SNV is located. In other words, a somatic mutation (from COSMIC, for instance) and a common polymorphism (from HumVar) will see their FISs diminished in exactly the same quantity if they occur in genes within the same functional category. The resulting transFIC computed for the polymorphism in this hypothetical example will be probably lower than the transFIC of the cancer mutation, but only because the original FIS of the polymorphism was closer to the baseline tolerance of its gene.
Next we decided to test the transFIC approach with CHASM [5, 14, 32], a method trained to distinguish manually curated driver mutations from randomly generated mutations. Thus, in this case, the distribution of CHASM scores across GOMF groups computed from 1000 Genomes Project nsSNVs reflects the tolerance of different functional groups of genes to driver-like mutations in the human population. We used WG2+/1 and WGCGC/nonCGC datasets to test CHASM and the transFIC of CHASM. Note that since CHASM was trained with manually curated driver mutations from COSMIC we could not use COSMIC datasets to assess CHASM; moreover, the datasets used were modified to eliminate the mutations that appear within the training set of CHASM. The result of this assessment shows that the transFIC of CHASM outperforms significantly the original score in the two datasets tested (Figure 3b; Additional file 7). Thus, the transFIC approach is also useful to transform scores that prioritize driver-like nsSNVs.
One final remark that must be made about the transFIC approach is that it is not in principle specific to cancer somatic mutations. Although our basic interest, as stated in the Hypothesis section was to improve the FIS provided by known tools that rank cancer mutations according to their likelihood of being drivers, in principle the basic reasoning on baseline tolerance may be applied also to disease mutations as well. This is why we tested the performance of the transformed FIS of SIFT, PPH2 and MA on the classification of HumVar, a dataset of disease-related/neutral nsSNVs . We found no improvement whatsoever with respect to their original counterparts (Additional file 8). To understand the reason for this result, we checked the distribution of disease-related genes (those annotated in OMIM ) across the GOMF groups ordered by baseline tolerance, as in Figure 1. We found that unlike cancer genes (discussed above), disease-related genes are more or less evenly distributed across all categories with different baseline tolerance. As a result, the original scores provided by the tools for SNVs within these genes are transformed in either direction, with no clear resulting trend. (In the case of cancer genes, the scores of their SNVs generally tend to become amplified upon transformation, because they usually appear in lowly tolerant classes.) Nevertheless, we also observed that groups with low tolerance tend to be enriched in dominant disease genes, while the opposite occurs with recessive disease genes. Therefore, we hypothesize that, upon transformation, the FISs of nsSNVs in dominant disease genes increase, but those in recessive disease genes decrease, making them similar to neutral variants.
The approach we have described to transform well-established FIS calculations to take into account the differences in baseline tolerance to nsSNVs between protein families can be easily implemented. It is important to highlight that although we have used SIFT, PPH2, MA and CHASM to present and test our approach, in principle this transformation can be applied to any other FIS.
The best overall performance in the classification of the nine proxy datasets was achieved by the transformed FIS based on the GOMF. Therefore, we decided to follow that classification system for implementation of our transFIC, as well as for the web server.
Note that the inferior limit of 20 SNVs to compute the baseline tolerance (described in the 'Detecting differences in baseline tolerance across genes' section) applies not to a single gene but rather to the SNVs pooled from genes within the same functional group(s). With this limit, we were able to successfully transform the FISs of nsSNVs in 15,651 genes using the GOBP classification scheme, 17,229 genes using GOMF, 11,642 using Doms and 6,830 using CPs. For nsSNVs in the remaining genes - which are either not classified within a given system, or do not belong to groups that account for at least 20 SNVs - we compute a transFIC using the mean and standard deviation of all the SNVs in the 1000 Genomes Project.
To facilitate the interpretation of transFIC SIFT, PPH2 and MA results, we have devised three categories (low, medium and high impact) into which somatic mutations can be classified based on their transformed FIS. For each transFIC, complementary cumulative distributions of non-recurrent, recurrent and highly recurrent COSMIC mutations were taken into account in defining the categories, an idea that we adapted from the MA tool .
The boundaries of these categories were defined as follows: low impact upper boundary (SIFT -1, PPH2 -1, MA -1), drawn at the transFIC score above which lays approximately 95% of the distribution of highly recurrent COSMIC mutations (in other words, this category contains at most approximately 5% of highly recurrent COSMIC mutations); high impact lower boundary (SIFT 2, PPH2 1.5, MA 2), a transFIC cutoff establishing a category with at most approximately 25% of the distribution of nonrecurrent COSMIC mutations; and medium impact, the remaining mutations with transFIC scores between these two limits. The concept of this categorization, as well as the categories themselves for the three transFIC presented here, are illustrated in Figure 4a-c. The specificity and sensitivity attained by the transFIC of the three tools at separating highly recurrent from non-recurrent COSMIC mutations and recurrent from non-recurrent COSMIC mutations at each of these cutoffs are presented in Additional file 9.
Complementary cumulative distribution of the three transFIC of subsets of nonsynonymous single nucleotide variants from COSMIC. (a-c) Complementary cumulative distribution of transFIC SIFT (a), transFIC PPH2 (b) and transFIC MA (c) of nonrecurrent (blue), recurrent (orange) and highly recurrent (red) COSMIC mutations.
The results shown in Figure 4 (as well as those in Figure 3) reveal that the MA transFIC exhibits the best performance amongst the three transformed scores in the transFIC website to distinguish between highly recurrent and non-recurrent COSMIC mutations. Nevertheless, we believe that it is important for the researcher to appraise the three transFIC scores of their mutations to make an informed decision regarding the likely functional impact of their somatic mutations. Also, it is important to bear in mind that the researcher may replicate the approach described in this paper to transform any other score of functional impact of SNVs to produce their own transFIC.
We observed large differences in the FIS distribution of nsSNVs from different protein groups, which indicates that genes with distinct functions possess a different baseline tolerance to deleterious mutations. We exploited these differences of baseline tolerance to transform the FISs of cancer somatic mutations provided by three well-known bioinformatics tools. The transformed FIS systematically outperforms the original FIS on nine proxy validation sets, each composed of a positive set of mutations enriched in driver nsSNVs and a negative set of mutations enriched in passenger nsSNVs (or polymorphisms).
Therefore, we recommend the use of a transformed FIS to assess the functional impact of cancer mutations. We have implemented the method to compute the transformed FIS of these three tools, which we call transFIC (transformed Functional Impact Scores in Cancer). We distribute it as a PERL script that users can download and use locally. We have also set up a web server that can be queried to obtain the transFIC of somatic cancer nsSNVs.
We are grateful to Xavier Rafael-Palou for contributing in the development of the transFIC web server. We acknowledge funding from the Spanish Ministry of Science and Technology (grant number SAF2009-06954) and the Spanish National Institute of Bioinformatics (INB). We are thankful to Elaine M Lilly, PhD, for assistance in language editing.
AGP carried out the study and contributed to drafting and editing the manuscript. NLB supervised the study and contributed to drafting and editing the manuscript. JDP configured and set up the transFIC webserver. All authors read and approved the final manuscript.


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Q: Scripts generated by SQL Server 2012 can not run again? I have a problem when generating scripts from a database. It's quite simple, but i don't know how to fix it.
The Stored Procedure objects is created before the Table objects. Here is all scripts:
 USE [ComicSellerManagement]
GO
/****** Object:  StoredProcedure [dbo].[sp_AddingComicGenre]    Script Date: 3/3/2016 3:39:38 PM ******/
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create proc [dbo].[sp_AddingComicGenre]
@name nvarchar(50),
@info nvarchar(max)
as begin
    insert into COMIC_GENRE values (@name, @info)
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create proc [dbo].[sp_DeletingComicGenre]
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/****** Object:  StoredProcedure [dbo].[sp_LoadingComicGenre]    Script Date: 3/3/2016 3:39:38 PM ******/
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create proc [dbo].[sp_LoadingComicGenre]
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create proc [dbo].[sp_UpdatingComicGenre]
@id int,
@name nvarchar(50),
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    update COMIC_GENRE
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/****** Object:  Table [dbo].[COMIC]    Script Date: 3/3/2016 3:39:38 PM ******/
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CREATE TABLE [dbo].[COMIC](
    [ID] [int] IDENTITY(1,1) NOT NULL,
    [Name] [nvarchar](50) NULL,
    [Genre] [int] NULL,
    [PublishYear] [int] NULL,
    [Publisher] [int] NULL,
    [Actor] [nvarchar](50) NULL,
    [Price] [money] NULL,
 CONSTRAINT [PK_COMIC] PRIMARY KEY CLUSTERED
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CREATE TABLE [dbo].[COMIC_GENRE](
    [ID] [int] IDENTITY(1,1) NOT NULL,
    [GenreName] [nvarchar](50) NULL,
    [Info] [nvarchar](255) NULL,
 CONSTRAINT [PK_COMIC_GENRE] PRIMARY KEY CLUSTERED
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/****** Object:  Table [dbo].[COMIC_PUBLISHER]    Script Date: 3/3/2016 3:39:38 PM ******/
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GO
CREATE TABLE [dbo].[COMIC_PUBLISHER](
    [ID] [int] IDENTITY(1,1) NOT NULL,
    [PublisherName] [nvarchar](50) NULL,
    [Adress] [nvarchar](max) NULL,
    [PhoneContact] [char](11) NULL,
    [Discount] [float] NULL,
 CONSTRAINT [PK_COMIC_PUBLISHER] PRIMARY KEY CLUSTERED
(
    [ID] ASC
)WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]
) ON [PRIMARY] TEXTIMAGE_ON [PRIMARY]

GO
SET ANSI_PADDING OFF
GO
ALTER TABLE [dbo].[COMIC_PUBLISHER] ADD  CONSTRAINT [DF_COMIC_PUBLISHER_Discount]  DEFAULT ((0)) FOR [Discount]
GO
ALTER TABLE [dbo].[COMIC]  WITH CHECK ADD  CONSTRAINT [FK_COMIC_COMIC_GENRE] FOREIGN KEY([Genre])
REFERENCES [dbo].[COMIC_GENRE] ([ID])
GO
ALTER TABLE [dbo].[COMIC] CHECK CONSTRAINT [FK_COMIC_COMIC_GENRE]
GO
ALTER TABLE [dbo].[COMIC]  WITH CHECK ADD  CONSTRAINT [FK_COMIC_COMIC_PUBLISHER] FOREIGN KEY([Publisher])
REFERENCES [dbo].[COMIC_PUBLISHER] ([ID])
GO
ALTER TABLE [dbo].[COMIC] CHECK CONSTRAINT [FK_COMIC_COMIC_PUBLISHER]
GO

I can not run this sql file to recover my database in any computer. Any suggestions for me to fix this?

A: *

*Try to change this option when generate your script -

*Another solution try to use dbForge Schema Comparison

*Use backup's

redpajama_set_name: RedPajamaStackExchange


A Port Hedland business called the Aboriginal Building and General Maintenance Organisation run by brothers Colin and Harvey Beck. Pundulmurra College. The Goldfields Aboriginal Art Gallery, has a large collection of artefacts and paintings by the Wangkatha people (includes an interview with curator Don Green). Arthur "Sharkey" Ryan talks about his boxing career. Video clip of Scrap Metal's song "Nimunburr"


Welcome to the student, faculty and staff login section of the Events Management website.
Now that you're logged in, you'll be able to view additional tools and resources available only to members of the Harvard community, as well as direct contact information for venues and offices across campus.
Events Management provides useful tools, information and resources to help create successful events. From simply offering ideas and guidance, to comprehensive event planning and management, our office is here to support the diverse needs of those coordinating events at Harvard.


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text:
January 2017 — Vol. 81, No. 7
Fabiano and Martínez in the last act of HGO's Faust
© Lynn Lane
ON OCTOBER 28, Houston Grand Opera reveled in the spectacle of Gounod's Faust, in a revival of its longstanding Francesca Zambello production, directed this time around by Garnett Bruce. The result was a near-great performance enlivened by an array of visual extravagances—imposing stage sets by artist Earl Staley, such as Marguerite's thickly verdant and luxuriously flowering garden or the grimly cavernous cathedral over whose altar looms a menacing life-size crucifix; well-placed lighting effects by Ken Billington to signal Méphistophélès's supernatural interventions, realized for this revival by Michael James Clark; copious dry ice to capture the nebulous, mysterious moment of Marguerite's ascent into heaven; and a teeming, chaotic fairground scene that combined dancing, acrobatics, juggling, a mountebank morality play and a festively milling crowd.
In their 1859 retelling of the Faust legend, via Goethe, Charles Gounod and his librettists, Jules Barbier and Michel Carré, placed Marguerite at the heart of the story; as that beleaguered heroine, HGO favorite Ana María Martínez, an outstanding soprano and powerful stage presence, brought home the opera's moral lesson and emotional force. Martínez's voice alone—with its chocolate-rich vibrancy in the low and middle range and its thrilling brilliance at the top—is worth the price of admission, but she also has the expressive range as a singing actress to portray Marguerite's complex transformation from youthful innocent in the throes of first love to outcast sinner consumed by tragedy and madness, who then achieves the miracle of redemption.
In his role debut as Méphistophélès, Luca Pisaroni bore the weight of the dark, supernatural tone of the opera: by turns suave, swaggering, wittily comic and threatening, Pisaroni's solid bass-baritone had a reserve of dominating power that offered a glimpse of his character's superhuman abilities. The title role is a less complex character than Marguerite and Méphistophélès, but in tenor Michael Fabiano's performance—an HGO debut—Faust's emotional life was richly realized, with an expressive range from tender poignancy to soaring passion and then searing grief.
Stepping in on short notice, Joshua Hopkins played the dignified if too-judgmental Valentin. Hopkins began Act I with some pitch-obscuring unsteadiness to his singing, but he then settled in, bringing the wondrously sustaining resonance of his baritone to the young soldier's Act III death scene. HGO Studio artist Megan Mikailovna Samarin brought her warm mezzo-soprano to the trouser role of Siébel; Margaret Lattimore delivered a satisfyingly no-nonsense, bustling, comically amorous Marthe Schwerlein. The assortment of musical strengths in this performance was completed by the HGO chorus, prepared by Richard Bado to vary dramatically between hushed reverence and transcendent power, and the HGO orchestra, which often sounded bigger, richer and more sweeping than its numbers under the expressive direction of Antonino Fogliani.
In spite of the musical and visual grandeur of this HGO Faust, the production needed stronger choreography to realize the potential of the opera's design, especially given all of the ballet music that Gounod included. There was dancing, but it amounted to casual shuffling. Likewise, the build-up to the return of Valentin and his soldiers, substantial and detailed in Gounod's score, came off as haphazard and anticlimactic. These details stand out because this production—with its luxurious costumes and set design and numerous, multitalented supernumeraries—otherwise did so much to capture the spirit of Faust. —Gregory Barnett

redpajama_set_name: RedPajamaCommonCrawl


text:
Le génotype est une partie donnée de l'information génétique (composition génétique) d'un individu. Le génotype d'un individu est donc la composition allélique de tous les gènes de ce dernier. La définition de génotype sert également lorsque l'on considère la composition allélique d'un individu pour un nombre restreint de gènes d'intérêt. Par exemple, s'il existe deux formes du gène X : l'allèle Xa et l'allèle Xb, alors le génotype d'un individu pour le gène X peut être soit homozygote (Xa/Xa ou Xb/Xb), soit hétérozygote (Xa/Xb). L'être humain possède deux copies de chaque gène, donc potentiellement, 2 allèles différents au maximum (sauf en cas d'aneuploïdie, par exemple pour les cas de trisomie, où on trouve trois chromosomes, donc trois copies pour un même gène, comme dans le cas de la trisomie 21).

Définition

Le génotype est l'information portée par le génome d'un organisme, contenu dans chaque cellule sous forme d'acide désoxyribonucléique (ADN). Porté par les chromosomes, il est localisé à l'intérieur du noyau chez les eucaryotes et dans le cytoplasme chez les procaryotes. Dans la molécule d'ADN, c'est la séquence des nucléotides qui constitue l'information génétique.

Chez l'être humain, on estime que le nombre de gènes est compris entre  et . Il existe pour une majorité de ces gènes plusieurs allèles dans la population. S'il existe trois génotypes différents possibles pour un gène existant sous deux formes alléliques, il est facile d'imaginer le nombre faramineux de génotypes possibles pour un individu. C'est pour cela que l'on dit que nous sommes tous différents : la probabilité que deux individus aient, au hasard, hérité des mêmes allèles pour tous leurs gènes est extrêmement faible voire impossible. Seul les vrais jumeaux font exception à cette règle : étant issus de la même cellule œuf (zygote), ils sont donc exactement de même composition allélique.

Relations génotype/phénotype

Au début du , William Bateson invente les termes Génotype et Phénotype. Il proposera le terme « génétique » en 1905 pour désigner la science de l'hérédité et de la variation.

Le génotype détermine les caractères d'un individu, constituant le phénotype, et se transmet de génération en génération.

Le phénotype est l'ensemble des caractères observables d'un individu, à toutes les échelles : macroscopique (la couleur des yeux), cellulaire (la concentration sanguine en hématies) et moléculaire (l'activité d'une enzyme). L'expression du génotype produit (en partie) le phénotype. Autrement dit, la composition allélique de chaque individu pour chaque gène va produire un phénotype particulier à chaque fois. Par exemple, la couleur de la peau peut varier suivant la composition allélique des individus pour les gènes impliqués dans la voie de biosynthèse de la mélanine (échelle moléculaire), le pigment qui colore la peau (échelle macroscopique). Les personnes de couleur noire possèdent des allèles codant des enzymes très actives pour la production de la mélanine, contrairement aux personnes de couleur blanche. Le génotype n'est pourtant pas seul responsable du phénotype : l'environnement y tient également une part non négligeable. Cette observation triviale peut être illustrée ainsi : à génotype égal (vrais jumeaux) si un enfant passe ses journées au soleil, en plein été par exemple, il bronzera (produira plus de mélanine), alors que son jumeau, qui préfère jouer à l'ombre, restera blanc. Le phénotype (couleur de la peau) sera donc différent à génotype égal.

La relation génotype-phénotype, malgré son apparente simplicité et l'extraordinaire essor qui en a résulté pour la science génétique, demeure un problème central de la biologie contemporaine. En effet, connaître le phénotype total (c'est-à-dire l'ensemble des phénotypes ou « phénome ») d'un individu demanderait de l'observer avec tous les outils d'analyse possibles et dans toutes les circonstances et environnements externes et internes possibles. Outre cette difficulté pratique de définir exhaustivement la relation génotype-phénotype, il en est une autre plus fondamentale : les  (environ) gènes humains ne suffisent certainement pas aux innombrables caractères phénotypiques d'un individu humain, c'est pourquoi le génotype produit seulement en partie le phénotype. L'autre partie peut résider en dehors des « gènes » classiques, par exemple dans des régions intergéniques exprimant des ARN (petits ARN non codants (ARNnc)) ou, alternativement, ne pas être génétique (non inscrite dans l'ADN) mais épigénétique (par exemple dépendante de l'état de la chromatine).

Waddington qui mit le terme épigénétique à l'honneur disait de manière visionnaire dès 1939 : « the appearance of a particular organ [is] the product of the genotype and the epigenotype, reacting with the external environment » (en français : « l'apparition d'un organe particulier [est] le produit du génotype et de l'épigénotype, réagissant avec l'environnement extérieur »)

Génotype et hérédité

Chaque individu produit dans ses gonades des gamètes mâles ou femelles qui, en fusionnant, produisent un zygote/une cellule œuf, voué à donner un nouvel individu. La production de chaque gamète passe par deux divisions cellulaires successives originales, regroupées sous le nom de méiose. Lors de cette méiose, chaque parent réduit la quantité d'information génétique qu'il va donner à chaque gamète: on passe d'une cellule diploïde (deux copies de chaque gène, donc potentiellement deux allèles différents) à des cellules haploïdes (une seule copie de chaque gène, donc un seul allèle). Un gamète ne contient donc qu'une copie de chaque gène de l'individu parent. Un parent transmet donc tous ses gènes, mais pas toutes ses allèles (seulement un sur deux dans le cas d'un gène hétérozygote). Lors de la formation de la cellule œuf par fusion du gamète mâle et du gamète femelle, on restaure une cellule diploïde, avec deux copies de chaque gènes, mais à la composition allélique remaniée, qui n'est ni celle du père, ni celle de la mère, mais un mélange des deux. Par exemple, si pour le gène X le père était homozygote Xa/Xa, et la mère homozygote Xb/Xb, alors l'enfant sera hétérozygote Xa/Xb, car chaque parent aura donné une version (un allèle) du gène X. Le génotype d'un enfant est donc le résultat du mélange d'une partie du génotype du père et de la mère.

Génotype et mutations

L'existence de plusieurs allèles d'un même gène est le fruit de mutations. Un gène code un produit (ARN ou protéine) qui a une fonction dans l'organisme. La séquence de ce gène n'est pas contrainte pour toutes les bases qui la composent, aussi certaines mutations dites "silencieuses" peuvent survenir sans provoquer d'altérations du fonctionnement du produit de ce gène. L'allèle ainsi créé (il est bien différent de l'allèle duquel il dérive, c'est donc un nouvel allèle) se propage dans la population par un mécanisme nommé dérive génétique. Parfois cette mutation rend le produit du gène plus efficace, ce qui confère un avantage sélectif à l'organisme qui porte cet allèle. S'il est avantagé par rapport aux autres membres de la population, il aura tendance à survivre plus longtemps, et donc à transmettre plus ses gènes : il y a donc sélection positive de cet allèle, et il se propage dans la population plus rapidement que par dérive génétique. Et en dernier lieu, cette mutation peut être délétère pour le fonctionnement du produit du gène, et est généralement contre-sélectionnée si son effet s'exprime, et nuit à l'organisme. En revanche, si cette mutation inactive l'allèle, le second allèle fonctionnel peut suffire à conserver un phénotype normal. Cette mutation est appelée récessive, car elle n'a pas d'incidence sur le phénotype à l'état hétérozygote. Elle peut donc se propager par dérive génétique. Cependant, quand deux individus porteurs de cet allèle délétère se reproduisent, ils peuvent tous les deux donner l'allèle muté (1 chance sur 4), ce qui provoquera un phénotype malade chez l'enfant.

Génotype et maladies

Souvent, pour la caractérisation d'une maladie, on recherche quelle part y prend l'environnement, et la génétique. Certaines maladies sont uniquement liées à l'environnement (varicelle), et d'autres entièrement au génotype (maladie de Huntington). D'autres maladies sont dépendantes des deux composantes à des degrés divers (récemment il a été découvert une cause génétique à l'autisme). Il existe plusieurs méthodes pour déterminer si une maladie est d'origine génétique : on étudie des pedigrees, pour essayer de mettre en évidence une agrégation familiale de la maladie, signe de la transmission de l'allèle morbide au sein de la famille. Si la fréquence d'apparition de la maladie est plus élevé dans la famille que dans la population générale, ceci traduit une cause génétique. Lorsqu'on soupçonne fortement la maladie d'avoir une origine génétique, on essaye de localiser le gène responsable (étude de liaison avec des marqueurs génétiques : microsatellites ou SNP). Une fois le gène isolé et caractérisé, on peut étudier l'allèle délétère. La détection du génotype d'un individu, lors d'un diagnostic prénatal par exemple, permet d'estimer la probabilité qu'un individu développe une maladie, et de mettre en place précocement un traitement approprié.

Botanique

Chez les plantes, qui ne disposent pas de système immunitaire à base d'anticorps comme les humains, plusieurs génotypes différents peuvent « cohabiter » sans problème pour former un même individu. C'est pourquoi on peut greffer des plantes sans souci, alors qu'une lutte médicamenteuse continue est impérative pour assurer le maintien d'une greffe humaine et empêcher le rejet.

Ainsi, chez les plantes, il est courant que des mutations de bourgeons ou des infections virales modifient une partie du patrimoine génétique. Ce phénomène a été et est toujours à l'origine de sélection de cultivars très intéressants : poire Williams rouge par exemple ou espèces panachées (souvent d'origine virale).

Le génotype est écrit en un langage universel

Lors de la transgenèse, l'organisme receveur est capable d'exploiter l'information génétique détenue par le transgène (gène transféré). En effet, cet organisme va exprimer le caractère contrôlé par ce transgène : l'information génétique est inscrite dans l'ADN de ce transgène selon un mode universel.

Notes et références

Articles connexes

 Gène
 Génétique
 Génotypage
 Génome
 Génomique
 Séquence (acide nucléique)
 Dominance (génétique)

Concept de génétique
Information génétique

redpajama_set_name: RedPajamaWikipedia


I first read about this novella on a blog and pretty much immediately bought it. And then once started, I couldn't put it down. Set a supernatural book in New Orleans, and I am a happy reader, and this one is steeped in all things New Orleans.
Thyme is a beautiful young woman that works in her grandmother's ice cream shop. She has a serious boyfriend and a good life. But someone is wanting her to sell the ice cream shop because it sits on prime real estate. Leon is a very strange man, and used to getting what he wants. When he realizes that there is something different about Thyme, it doesn't bode well for her, to say the least.
Thyme is the kind of character that was almost a total innocent. The people in her life love her mostly, and she is just a good person. She adores her boyfriend, Shane, and he adores her right back. She completely embraces her life in New Orleans. Shane is rich, and a medical student. He has a mother that doesn't approve of Thyme due to the side of town she lives in, and her bi-racial heritage. I loved that he didn't care what his mother thought.
The prologue of this novella, is all about Billy a superstar country/pop singer on tour with his band. He has his own supernatural ability. When the prologue was over and the novella actually began, it jumped to Thyme's story, and left Billy behind.
The Thyme part of the story starts pretty normally until Leon comes into the picture. You realize that he is very evil, and actually gradually realize that there is something supernatural going on. Of course since it is in New Orleans, that's pretty much a given.
I ended up really loving this story until the end, when I read the massive cliffhanger. That's probably the fastest I have ever bought a follow up book. I had to know what was happening. I highly recommend this novella, and as of my writing this, it is free, so moving on to the next book doesn't hurt the pocketbook quite as bad. Keep in mind, there is quite a bit of violence, horrific at times, so it is an adult read for that reason.
I also love New Orleans, but your warning about horrific violence, makes me hesitate. I will await your review of the first full lenght book.
You're welcome! I can't wait to hear how fast you order part 2 🙂 And New Orleans in the summer? I did that in July once and I have never felt heat and humidity like that in my life.I hope you have a great time though!
I know! I'm not looking forward to the heat and humidity, but I am looking forward to the book conventions. I have Author Reader Con in New Orleans July 29-August 2nd and Speculative Fiction Con in Orlando September 25-27th. Holy heat and humidity, Batman!
That's what I thought too and I was super glad I bought it!
At first I wasn't sure whether I wanted to read this book or not, but I've read some good reviews about it and for free I can't resist giving this series a try. Great review!
If you love paranormal romance with a side of mythology you might really like this one. And thanks!
For some reason the gothic mystic setting of New Orleans is almost impossible for me to resist. I really think I would like this as much as you did Lorna, wonderful review!
Thank you! I think you will enjoy it too 🙂 And you are right NO is just the best place to write a supernatural story about.
I haven't read many supernatural books set in New Orleans, only 1 Night Huntress book and I really enjoyed it! Sounds like this one did the setting justice. Great review Lorna!
Gosh I still need to read Part 2! I should get off the internet & start reading! Love this though. The ending was ugh! Strike through the heart!


text:
Q: Prepend scalar and arrays to another array in C I am working on a set of C functions that will allow for dynamically growing arrays of the user definition.  I have been able, with the help of some on this site to develop functions that act as an array initialize,and another function that can append a scalar to the array or append an array to the array.  I am using a struct titled Array which acts as a container for the array array, the active length of the array len, the total allocated size of the array size, and the memory per indice elem.
The first function initialize_array allocates the memory and initializes the array, and the function init_array is a wrapper for initialize_array that pre-formats some information and returns an Array data type.  Finally I have a function append_array that can take a scalar or an array and can append the array within the Array struct using a memcp method that is cast to a char to assist in pointer arithmetic. I am trying to recreate the append_array function with a prepend_array function that will add the scalar or array passed in *items in the very first indices and push the other indices to the left.  I am a bit lost on how to do this, any suggestions would be helpful.
array.h
#ifndef ARRAY_H
#define ARRAY_H

#include <stdlib.h>
#include <stdio.h>
#include <string.h>
typedef struct
{
    void *array; // Container for array
    size_t len;  // Active length of array
    size_t size; // Size of allocated memory
    int elem;    // Memory consumption per element
} Array;

void initiate_array(Array *array, size_t num_indices);
Array init_array(int size, size_t num_indices);
int append_array(Array *array, void *items, size_t count);
int prepend_array(Array *array, void *items, size_t count);

#endif /* ARRAY_H */

array.c
#include "array.h"

void initiate_array(Array *array, size_t num_indices) {
    void *pointer;

    pointer = malloc(num_indices * array->elem);

    if (pointer == NULL) {
        printf("Unable to allocate memory, exiting.\n");
        free(pointer);
        exit(0);
    }
    else {
        array->array = pointer;
        array->len = 0;
        array->size = num_indices;
    }
}

Array init_array(int size, size_t num_indices) {
    Array array;
    array.elem = size;
    initiate_array(&array, num_indices);
    return array;
}

int append_array(Array *array, void *items, size_t count) {
    if (array->len + count > array->size) {
        size_t size = (array->len + count) * 2;
        void *pointer = realloc(array->array, size * array->elem);
        if (pointer == NULL) {
            return 0;
        }
        array->array = pointer;
        array->size = size;
    }
    memcpy((char *)array->array + array->len * array->elem, items, count * array->elem);
    array->len += count;
    return 1;
}

int prepend_array(Array *array, void *items, size_t count) {
    if (array->len + count > array->size) {
        size_t size = (array->len + count) * 2;
        void *pointer = realloc(array->array, size * array->elem);
        if (pointer == NULL) {
            return 0;
        }
        array->array = pointer;
        array->size = size;
    }
    // Not sure how to handle this with memcp like was used for append_array
    array->len += count;
    return 1;
}

main.c
#include <stdio.h>
#include <stdlib.h>
#include "array.h"

int main(int argc, char** argv)
{
    int i, j;
    float ii = 0.0;
    size_t indices = 20;
    Array float_test = init_array(sizeof(float), indices);
    Array int_test = init_array(sizeof(int), indices);
    // Populate both arrays
    for (i = 0; i < 30; i++) {
        ii += 1.1;
        append_array(&int_test, &i, 1);
        append_array(&float_test, &ii, 1);
    }

    int a[3] = {10, 9, 8};
    prepend_array(&int_test, a, 3);
    // Print int array values
    for (i = 0; i < int_test.len; i++)
    {
        printf("Value: %d Size:%zu \n",((int *) int_test.array)[i], int_test.len);
    }
    return (EXIT_SUCCESS);
}

A: To prepend elements to the array, after allocating additional memory, you'll first need to move the existing elements.
When the destination and source memory has the possibility of overlapping, we use memmove to safely move our data around1.
After moving our existing elements, we then copy the new elements into place.
Visualized:
A is [11, 22, 33, 44, 55]
V is [99, 88, 77]

resize(A):      [11, 22, 33, 44, 55, __, __, __]
move(A + 3, A): [__, __, __, 11, 22, 33, 44, 55]
copy(A, V):     [99, 88, 77, 11, 22, 33, 44, 55]

Based on your code, this would look something like:
memmove(
    ((char *) array->array) + count * array->elem,
    array->array,
    array->len * array->elem);

memcpy(array->array, items, count * array->elem);

1. The use of memcpy to copy to and from overlapping memory is Undefined Behavior, and a source of many bugs.


text:
/*
 * findbinding.c
 *
 *    Here is a simple approach to find the location of the binding
 *    in an open book that is photographed.  It relies on the typical
 *    condition that the background pixels near the binding are
 *    darker than those on the rest of the page, and further, that
 *    the lightest pixels in each column parallel to the binding
 *    exhibit a large variance by column near the binding.  This is
 *    because the pixels at the binding are typically even darker
 *    than the pixels near the binding.
 *
 *    Accurate results are obtained in this example at the very low
 *    resolution of 45 ppi.  Better results can be expected at higher
 *    resolution.
 */

#include "allheaders.h"

int main(int    argc,
         char **argv)
{
l_int32      w, h, ystart, yend, y, ymax, ymid, i, window, sum1, sum2, rankx;
l_uint32     uval;
l_float32    ave, rankval, maxvar, variance, norm, conf, angle, radangle;
NUMA        *na1;
PIX         *pix1, *pix2, *pix3, *pix4, *pix5, *pix6, *pix7;
PIXA        *pixa;
static char  mainName[] = "findbinding";

    if (argc != 1)
        return ERROR_INT(" Syntax:  findbinding", mainName, 1);

    lept_mkdir("lept/binding");
    pixa = pixaCreate(0);

    pix1 = pixRead("binding-example.45.jpg");
    pix2 = pixConvertTo8(pix1, 0);

        /* Find the skew angle */
    pix3 = pixConvertTo1(pix2, 150);
    pixFindSkewSweepAndSearch(pix3, &angle, &conf, 2, 2, 7.0, 1.0, 0.01);
    fprintf(stderr, "angle = %f, conf = %f\n", angle, conf);

        /* Deskew, bringing in black pixels at the edges */
    if (L_ABS(angle) < 0.1 || conf < 1.5) {
        pix4 = pixClone(pix2);
    } else {
        radangle = 3.1416 * angle / 180.0;
        pix4 = pixRotate(pix2, radangle, L_ROTATE_AREA_MAP,
                         L_BRING_IN_BLACK, 0, 0);
    }

        /* Rotate 90 degrees to make binding horizontal */
    pix5 = pixRotateOrth(pix4, 1);

        /* Sort pixels in each row by their gray value.
         * Dark pixels on the left, light ones on the right. */
    pix6 = pixRankRowTransform(pix5);
    pixDisplay(pix5, 0, 0);
    pixDisplay(pix6, 550, 0);
    pixaAddPix(pixa, pix4, L_COPY);
    pixaAddPix(pixa, pix5, L_COPY);
    pixaAddPix(pixa, pix6, L_COPY);

        /* Make an a priori estimate of the y-interval within which the
         * binding will be found.  The search will be done in this interval. */
    pixGetDimensions(pix6, &w, &h, NULL);
    ystart = 0.25 * h;
    yend = 0.75 * h;

        /* Choose a very light rank value; close to white, which
         * corresponds to a column in pix6 near the right side. */
    rankval = 0.98;
    rankx = (l_int32)(w * rankval);

        /* Investigate variance in a small window (vertical, size = 5)
         * of the pixels in that column.  These are the %rankval
         * pixels in each raster of pix6.  Find the y-location of
         * maximum variance. */
    window = 5;
    norm = 1.0 / window;
    maxvar = 0.0;
    na1 = numaCreate(0);
    numaSetParameters(na1, ystart, 1);
    for (y = ystart; y <= yend; y++) {
        sum1 = sum2 = 0;
        for (i = 0; i < window; i++) {
            pixGetPixel(pix6, rankx, y + i, &uval);
            sum1 += uval;
            sum2 += uval * uval;
        }
        ave = norm * sum1;
        variance = norm * sum2 - ave * ave;
        numaAddNumber(na1, variance);
        ymid = y + window / 2;
        if (variance > maxvar) {
            maxvar = variance;
            ymax = ymid;
        }
    }

        /* Plot the windowed variance as a function of the y-value
         * of the window location */
    fprintf(stderr, "maxvar = %f, ymax = %d\n", maxvar, ymax);
    gplotSimple1(na1, GPLOT_PNG, "/tmp/lept/binding/root", NULL);
    pix7 = pixRead("/tmp/lept/binding/root.png");
    pixDisplay(pix7, 0, 800);
    pixaAddPix(pixa, pix7, L_COPY);

        /* Superimpose the variance plot over the image.
         * The variance peak is at the binding. */
    pixRenderPlotFromNumaGen(&pix5, na1, L_VERTICAL_LINE, 3, w - 120, 100, 1,
                                                           0x0000ff00);
    pixDisplay(pix5, 1050, 0);
    pixaAddPix(pixa, pix5, L_COPY);

        /* Bundle the results up in a pdf */
    fprintf(stderr, "Writing pdf output file: /tmp/lept/binding/binding.pdf\n");
    pixaConvertToPdf(pixa, 45, 1.0, 0, 0, "Binding locator",
                     "/tmp/lept/binding/binding.pdf");

    pixDestroy(&pix1);
    pixDestroy(&pix2);
    pixDestroy(&pix3);
    pixDestroy(&pix4);
    pixDestroy(&pix5);
    pixDestroy(&pix6);
    pixDestroy(&pix7);
    pixaDestroy(&pixa);
    numaDestroy(&na1);
    return 0;
}

redpajama_set_name: RedPajamaGithub


Filmmakers entering films for competition must complete and send in the entry form and fee (if applicable). A screening committee will evaluate and select films to be presented at the 2019 MIFF. During the Festival, a jury of industry professionals will select category winners, with the exception of the Audience and People's Choice Awards, which will be decided by the voting audience.
Good News! Extended Submission Deadline to February 7, 2019.
All films must be in English or have English subtitles. Non-subtitled or dubbed foreign language films will not be considered.
All clearances must be obtained for exhibition.
Only the Producer, Director, Screenwriter or someone closely connected to the film may complete the application.
Schools may submit films on behalf of student filmmakers in the Student Category.
Only complete entries will be accepted. This includes the non-refundable entry fee, (paid to the order of Desert Love Community Development), any DVDs, digital files, or online screeners.
Feature-length films (narrative or documentary) must be 40 minutes in length or longer.
Short films (narrative, documentary, experimental, and student films) must be 39 minutes in length or less.
Big Mans films (narrative, documentary, experimental, Student Film) must be filmed in Texas, and the subject matter must somehow be about Texas. Must be 39 minutes in length or less.
Student films must have been completed after January 1, 2018 and proof of enrollment is required. Student films may not exceed 30 minutes.
Although MIFF is a competitive festival, some films may be included in the program in a non-competition section.
Unfortunately, MIFF is unable to provide comments about films not accepted to the competition due to time and resource constraints. DVDs and online screeners will NOT be returned.
Filmmakers whose films are accepted for competition will be notified by email by approximately February 15, 2019.
For entries selected to the competition, an exhibition print or video of the film in proper format (DCP, Bluray, MP4, MOV, or H.264) must be received by MIFF no later than February 22, 2019. Failure to comply with technical specifications may result in the removal of the film from competition.
The individual or corporation submitting the film hereby warrants that it is authorized to commit the film for screening and understands and accepts these requirements and regulations. The individual or corporation submitting this film additionally grants the Mansfield International Film Festival the right to one or more public screenings of the entry during the dates of April 6-10, 2019.


text:
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As a police officer, Sergeant Mary Ramirez spends her days serving Palo Alto College and the Alamo Colleges District. Although she isn't always on duty, she is always looking to make a difference in the lives of those around her.
Read her story
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In spring 2020, Palo Alto College will launch its first-ever Viticulture and Enology program to give students the opportunity to join the wine industry in Texas.
Column: ¡Celebración! nets nearly $180,000 in support of students
This "¡Celebración! nets nearly $180,000 in support of students" column by Dr. Robert Garza was originally published in his biweekly column for the San Antonio Express-News' Southside Reporter and mysanantonio.com.
"Rising Star" invited to apply for 2021 Aspen Prize
After being named a "Rising Star" by the Aspen Institute College Excellence Program, Palo Alto College has already been named to the list of 150 community colleges eligible to compete for the 2021 Aspen Prize.
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Palo Alto College officially opened its new Community Garden to help combat food insecurity among students and the surrounding community.
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Visit our entire Events Calendar
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Welcome back, Palominos! Join us for Palomino Round-up for food, prizes, and lots of fun.
Palo Alto College Farmers Market
Monday, January 27, 9 a.m. –2 p.m.
Fresh affordable produce, artisan goods, kids activities corner, local vendors, & more!
Nursing Program : Transfer Advising Session
University of Texas Health - San Antonio will be conducting one-on-one, 15-minute advising sessions by appointment only for students who are interested in applying for their Nursing program.
Student Trustee Deadline
Saturday, February 1, All day
Eligible students are encouraged to apply to their home college. Each of the five colleges will then submit a nominee to the Board of Trustees.
Club Rush
Come learn about more than 30 student clubs and organizations ready to help you get involved on campus! Plus, enjoy free food, music, and giveaways.
Horticulture Career Expo
Come learn about opportunities in garden cultivation and management
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Tuesday, February 11, 12 p.m. - 2 p.m.
Don't miss out - meet Palo Alto College president, Dr. Robert Garza, while enjoying free pizza and drinks! While supplies last.
District 4 Senior Sweetheart Dance
Friday, February 14, 10 a.m. - 2 p.m.
Celebrate love and laughter at the District 4 Senior Sweetheart Dance, a social gathering for community seniors ages 60 years and older, hosted by Councilman Rey Saldaña and Palo Alto College.
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text:
Home > Health Sciences > JHDRP > Vol. 7 (2014) > Iss. 1
What do Parents in Mississippi Really Think about Sex Education in School?: Results of a State-Level Survey
Colleen McKee, Mississippi State UniversityFollow
Kathleen Ragsdale, Mississippi State UniversityFollow
Linda H. Southward, Mississippi State UniversityFollow
Comprehensive sex education; Abstinence-plus education; Abstinence-only education
Public Health | Public Health Education and Promotion | Social and Behavioral Sciences
Purpose: Despite broad public support for comprehensive sex education, its implementation remains controversial in the United States, especially in states such as Mississippi that have been identified as politically conservative. This study examined parental opinions regarding the implementation of age-appropriate sex-related education (SRE) (i.e., abstinence-plus education) in Mississippi public schools.
Methods: Data were used from the first state-level survey of a randomized sample of parents (N = 3,600) of public school students in Mississippi. The sample was relatively equally distributed between non-Hispanic whites (52.8%) and African Americans (48.2%). Bivariate and multivariate analyses were conducted to determine parental support for a number of components associated with comprehensive sex education (i.e., condom use demonstration).
Results: More than 90% of parents endorsed implementing age-appropriate SRE in Mississippi public schools, discussing the transmission and prevention of HIV/STIs during SRE, and discussing how to get tested for HIV/STIs during SRE. More than 80% endorsed discussing where to obtain birth control during SRE and more than 70% endorsed demonstrating correct condom use during SRE. Results varied somewhat across race/ethnicity and gender, such that African American parents who were female were most supportive.
Conclusions: Although Mississippi has been identified as a politically conservative state, our results indicate that an overwhelming majority of surveyed parents endorsed age-appropriate SRE. Results may not be fully generalizable to parents across the nation, yet they are consistent with similar surveys conducted among parents in Minnesota, North Carolina, and Texas to assess attitudes towards school-based sex education.
Statement of disclosure There are no potential conflicts of interest, real or perceived, for the authors of this manuscript. Our sponsors had no role in the collection, analysis, and interpretation of data; the writing of the report; or the decision to submit the manuscript for publication.
McKee, Colleen; Ragsdale, Kathleen; and Southward, Linda H. (2014) "What do Parents in Mississippi Really Think about Sex Education in School?: Results of a State-Level Survey," Journal of Health Disparities Research and Practice: Vol. 7: Iss. 1, Article 9.
Public Health Education and Promotion Commons, Social and Behavioral Sciences Commons