The accurate determination of transcription factor binding affinities is an important problem in biology and key to understanding the gene regulation process. Position weight matrices are commonly used to represent the binding properties of transcription factor binding sites but suffer from low information content and a large number of false matches in the genome. We describe a novel algorithm for the refinement of position weight matrices representing transcription factor binding sites based on experimental data, including ChIP-chip analyses. We present an iterative weight matrix optimization method that is more accurate in distinguishing true transcription factor binding sites from a negative control set. The initial position weight matrix comes from JASPAR, TRANSFAC or other sources. The main new features are the discriminative nature of the method and matrix width and length optimization.The algorithm was applied to the increasing collection of known transcription factor binding sites obtained from ChIP-chip experiments. The results show that our algorithm significantly improves the sensitivity and specificity of matrix models for identifying transcription factor binding sites.de novo motif discovery. Generating more accurate position weight matrices will ultimately contribute to a better understanding of eukaryotic transcriptional regulation, and could potentially offer a better alternative to ab initio motif discovery.When the transcription factor is known, it is more appropriate to use a discriminative approach such as the one presented here to derive its transcription factor-DNA binding properties starting with a matrix, as opposed to performing  Gene expression is controlled by the interaction of transcription factors (TFs) and their DNA binding sites. Promoters often contain multiple binding sites for different TFs that work in tandem to control the regulation (activation or inhibition) of gene expression. TFs bind to short  degenerate DNA sequence patterns or motifs, which makes them relatively difficult to find. Understanding the regulation of gene expression in higher eukaryotes is still a major challenge and the current algorithms are not always able to clearly distinguish real transcription factor binding sites (TFBS) from random look-alike sequences .Drosophila melanogaster  false positive rate interval. The performance increase was small here since the initial matrix had strong information content.The overall ROC performance of the DISPARE matrix (AUC of 0.98) and the width-optimized matrix (AUC of 0.99) were similar to the ones obtained using JASPAR (AUC of 0.98), BayesMD (AUC of 0.98) and MEME (AUC of 0.98), and outperformed the PREGO and WEEDER matrices which only captured the left half-site. These two mm9 assembly as background sequences.Nanog is one of the key TFs that regulates self-renewal and pluripotency in embryonic stem cells. The initial matrix (M01123) comes from TRANSFAC. The motif was identified by NestedMICA  in the NAfter running the discriminative refinement algorithm on the ChIP-PET dataset for 2 iterations, the algorithm converged and we obtained the DISPARE matrix shown in Figure k-mer based methods: PREGO (AUC of 0.69) and WEEDER (AUC of 0.67), and probabilistic methods: BayesMD (AUC of 0.55) and MEME (AUC of 0.67). Statistical approaches such as MEME, BayesMD and NestedMICA  tend to produce noisier PWMs than k-mer constrained approaches, weakening the ROC performance. In this case, the width-optimized matrix (AUC of 0.58) did not improve the performance of the DISPARE matrix. The width optimization algorithm tends to center the matrix around the strongest signal in a motif. However, the Nanog motif consists of two parts, with a weak left side. The proposed new width missed this left side of the motif and was therefore suboptimal compared to the DISPARE matrix. The results of this test demonstrate that the DISPARE algorithm can significantly improve PWM with low information content and represents a good alternative to classical de novo motif discovery methods.We then compared the ROC performances of the DISPARE matrix and the width-optimized matrix with the ones of the initial TRANSFAC matrix, and matrices obtained with BayesMD, MEME, PREGO and WEEDER Figure . We ran nnnTGACC-3', where n is any nucleotide. There are two ER subtypes, ERα and ERβ that belong to the nuclear receptor family. ERα whose gene name is ESR1 is over-expressed in >70% of breast cancer cases and is likely to be involved in tumorigenesis, although the mechanism underlying this involvement is still unclear [α binding sites ChIP-PET sequences [hg18 assembly as background sequences. Running the discriminative optimization algorithm with the JASPAR ESR1 matrix (MA0112) as input  and clearly showed the canonical ERE consensus with a gap of 3 random nucleotides between the two half-sites.The estrogen receptor (ER) is a ligand-dependent TF that binds to conserved estrogen response elements (EREs), 5'-GGTCA unclear . We usedequences  as positt Figure , we obtat Figure . The widWe then compared the ROC performances of the DISPARE matrix and the width-optimized matrix with the ones of the initial JASPAR matrix and matrices obtained with BayesMD, MEME, PREGO and WEEDER Figure . MEME waComputational characterization of the binding specificity of transcription factors (TFs) is a challenge to the analysis of transcriptional regulation. PWMs are widely used to identify TFBS although they result in a large number of false positives. The general concept of TFBS prediction is to find a set of over-represented sites in a set of sequences of interest compared to a TFBS depleted background set. Many strategies have been proposed, falling into two large categories. Probabilistic approaches are the most widely used methodologies to solve motif discovery problems. The general concept consists of performing a local multiple alignment on the input sequences to build a PWM. Programs such as the Gibbs Sampler , NestedMet al. (1999) [et al. (2005) [A number of studies have used computational methods to improve PWMs. Bucher (1990)  proposed. (1999)  improved. (2005)  proposedIn this paper, we have investigated the refinement of existing matrix models and have focused on the optimization of their width and information content. DISPARE requires a set of positive sequences, a set of background sequences and an initial PWM as input. Our iterative algorithm used a variable threshold for each iteration, so that DISPARE selected subsequences in the original set that scored above the current threshold to build a new matrix. The algorithm stopped after a fixed number of iterations or when the dissimilarity between two consecutive PWMs was smaller than a certain threshold. In the present study, we considered only the maximum scoring occurrence per sequence. In addition, we proposed the optional width and phase optimization step to search a better width for the DISPARE matrix. It returns the best candidate with optimal length and phase. Although changing the phase of the DISPARE matrix can slightly affect the AUC, the width optimization step can also improve the 'biological value' of the matrix as described in the p53 study. In Figure The DISPARE algorithm was evaluated on artificial and real datasets in order to measure its performance in both controlled and practical conditions. The simulations on distorted synthetic data demonstrated that the algorithm was able to recover and enhance the matrix of a known TFBS if the matrix was strongly distorted. The test on distorted matrices showed that our algorithm improves the information content of weak motifs, even though the information content is not directly optimized. We used the original and distorted matrices as input in order to evaluate the robustness against noise.Since known matrices often differ from models that are derived from real datasets, it would have been interesting to repeat the simulation using the original matrix as input to derive the models from the two distorted datasets. Instead, we performed this test on real data. Using ChIP-PET data, we demonstrated that our algorithm optimized the initial matrices, thereby improving the sensitivity and specificity of the initial matrix. Width optimization step can significantly improve the performance of the initial and DISPARE matrices, especially in cases of symmetrical motifs that do not contain strong information on the flanking columns. The choice of an optimal window is not trivial and several solutions can exist. Therefore, the width optimization algorithm can be used in a semi-automated way, by selecting which window to return.k-mer based approaches on real data, achieving performances that were comparable to DISPARE. K-mer based approaches were penalized by their motif length limitations since the motifs in the ChIP-PET datasets consisted of 12 or more nucleotides.Furthermore, the comparison with four motif discovery tools BayesMD, MEME, PREGO and WEEDER showed, in most cases, that our refined matrices performed better than those derived from these tools. Probabilistic methods were, in general, better than de novo motif discovery methods. In addition, combining motif discovery with matrix refinement into an integrated analysis framework in order to enhance the signal of computationally discovered matrices can significantly improve the results, making matrix annotation and identification easier.Therefore, when the TF is known, the use of a discriminative matrix refinement approach, starting with a known matrix, seems to be more appropriate and offers a good alternative to presently available de novo motif discovery programs. In this version, we only kept one occurrence of a motif, corresponding to the maximum score per sequence and the algorithm returned the most centered and informative refined matrix only. However, because the same transcription factor binding site (TFBS) often occurs in many instances, it would be interesting to compare the final matrices, obtained with several scores per sequence, with those obtained with the maximum score per sequence. In the future, the program can be extended to support multiple motif occurrences and allow the optimization of several windows.In this study, we presented DISPARE, an algorithm to refine a motif that best discriminates between a positive set of sequences and a background one. Refined matrices are centered, their widths are optimized and they show an improvement in their information content. The algorithm was applied to several sets of synthetic data, distorted data and three sets of ChIP-PET data. The results showed that the DISPARE algorithm improves the sensitivity and specificity of matrix models for TFBSs. This can be used as a good alternative to classical motif scanning methods or combined with α binding sites ChIP-PET sequences [http://genome.ucsc.edu[We used three sets of ChIP-PET data: 542 human p53 ChIP-PET sequences , 376 mouequences . For the.ucsc.edu.S+, supposed to contain binding sites of the transcription factor F, and S-, supposed to contain no binding sites of F. S+ is used as the positive sequences and S- as the background sequences. The output is a newly generated PWM. The main steps of the algorithm are scoring all the sequences of positive and negative sets, determining a cutoff, creating a new PWM and, if the end criterion is not satisfied, returning to the scoring step. Finally, either an optimized PWM, referred to as the DISPARE matrix, is obtained, or the initial PWM is retained. The flowchart of the algorithm is described in Additional file This program refines a PWM to discriminate maximally between a positive and a negative set of sequences. As input, the algorithm uses two sets of sequences and a threshold. There are two sets of sequences: score_seq) consists of scoring all the sequences (S+ and S-) with the current PWM. Only the maximum score per sequence is used. For the first iteration, we chose to start with a threshold T = 0, the sequences S+, the sequences S- and the initial PWM. For each iteration, the sequences S+ and the sequences S- are scanned with the PWM using the current threshold. If a hit of a score is higher than the threshold T in the sequences S+, the score is termed as a true positive. Similarly, if a hit of a score higher than T is found in S-, it is referred as a false positive.The first step of the algorithm (get_percentile) involves determining the cutoff that best separates S+ from S-. In other words, we want to find a threshold that both maximizes the number of matching sequences in S+ and minimizes this number in S-.The second step % of the scanned sequences do not match the matrix. As we aim to minimize the number of matches in the negative set, the percentile value in S- is chosen as the threshold. The threshold value is chosen by the user and can vary. However, we found that p = 30 was the value that gave the best compromise when separating S+ from S- in our datasets. The value of p was determined empirically by looking at the number of matches in both sets at different thresholds, while varying values of p. Then all the matches in S+ above the cutoff were picked to estimate a new PWM.For each iteration, a measure of an over-representation between compute_newpwm) is to make a new matrix with all the matches in S+ that scored higher than the threshold T. The newly generated matrix has the same width as the initial one. The positional matrix which contains the number of occurrences of a nucleotide at each position is computed. Each column represents a position in the motif and each row corresponds to the number of occurrences of a nucleotide in the motif. From the count matrix, a log-odd matrix is calculated using a uniform background frequency. A pseudo-count value (= 0.1) is added to the count matrix. The log-odd matrix is normalized so that the columns add up to 1 and the log-odd scores are calculated.The third step of the algorithm (compare_mat) is to compare the newly generated position frequency matrix (PFM) to the previous one. The Kullback-Leibler Divergence (KLD) [The fourth step (ce (KLD)  or relatwhere P represents the initial PFM (or the previously generated PFM) and Q represents the newly generated PFM (or the next PFM to be generated).epsilon, (that is when the optimized matrix does not change significantly after a given iteration), where epsilon is a fixed threshold, e.g 0.001.KLD is always non-negative but in general it is not symmetric. The matrix which provides the smallest value of divergence is the matrix that is most similar to the reference matrix. The KLD is calculated between two consecutive PFMs. The iteration stops before 10 iterations or when KLD ≤ S+ and S- are scanned using the newly generated PWM. Steps 2 to 4 are then repeated with the new PWM.After the computation of the new PWM, the next iteration is performed as follows. All the sequences from Determining the correct width of a PWM is an important problem in TFBS characterization. Although programs such as MEME , GLAM2  or NesteThe length of a PWM can be adjusted by either adding or removing one or several flanking columns of the matrix. Finding the optimal shorter matrix is intended to compact the matrix to its most informative part by removing non-informative flanking columns. Thus, the part of the matrix that contributes the most to the total information content, i.e core motif can be identified. However, in real studies, it is often more interesting to look at the nucleotides surrounding actual TFBSs and therefore extend the length of a PWM. The DNA that surrounds TFBSs defines the context in which a site is implanted and may also contribute to the binding. Therefore, picking up these extra nucleotides surrounding TFBSs can improve the performance of a PWM and reduce the number of false positives.Another important problem in width optimization is the phase shift. Two PWMs with the same length can perform very differently depending on their phase. The goal is therefore to determine the phase shift that achieves the best performance for a given PWM.In motif discovery studies, PWMs for known TFBSs, or computationally predicted TFBSs, often have a non-optimal frame or window, i.e wrong positioning of the window with regards to the actual biological binding site and non optimal length. Non-optimal framing can be caused by either the addition of extra flanking columns in database matrix models, e.g. two columns in JASPAR, or using experimental or computational TFBS characterization methods that returned a wrong shift of the optimal binding site. In order to handle these sub-optimal matrices, DISPARE provides a width optimization feature that determines the best width and phase shift, if any, after the discriminative optimization step.The matrix width optimization procedure takes a DISPARE matrix, the optimized matrix by our algorithm, as input and returns a width-optimized matrix, if any, and improved information content. The algorithm consists of two steps: i) width and phase optimization and ii) matrix enhancement according to the new width.n columns from the original matrix. For example, increasing the length of the initial matrix by two can be done by adding two columns on the left or two columns on the right or one on each side of the matrix. The nucleotide counts that are needed for matrix enlargements are taken from the sequences flanking the sites in the original matrix.In the width and phase optimization step, the algorithm first explores all the possible ways of adjusting the length of the DISPARE matrix. Matrices are generated in a combinatorial manner by exploring all possibilities of adding or removing The new lengths are defined within an interval, for example, ± 6 nucleotides around the length of the input. For the CHIP-PET data, we chose to explore the ways of increasing the width of the DISPARE optimized matrix by 0 to 6 columns, as we wanted to know whether there was additional information about the binding sites outside the original width of the input matrix.After generating all the matrix length combinations, the algorithm computes all possible phase shifts of the newly generated matrices by shifting the matrix from 0 to 3 nucleotides from the original phase. These two systematic matrix computation steps provide the candidates from which the best phase and shift are chosen.The selection of the optimal width is done by ranking, based on the information content and centering of the matrices. For each matrix, the average information per column is evaluated. The highest average information value per column corresponds to the most compact matrix. The candidate matrices are first ranked based on this value. In the second step, the algorithm evaluates the centering of each matrix by calculating the distance between the barycenter, i.e position around which the information is centered, and the position of the middle column. The best ranking matrix corresponds to the best centered. Finally, the selected width is the one that achieves the best cumulated rank, ensuring that the optimal matrix is the most informative and well-centered.After the width optimization step, the optimized matrix undergoes a refinement process that gradually maximizes the information according to the newly selected width. The algorithm follows the same column-wise procedure as described in . In the To evaluate the performance of different classifiers, it is routine to use Receiver Operating Characteristic (ROC) graphs ,35. In aAccuracy is measured by the area under curve (AUC): an area of 1 represents the best classifier  and an area of 0 the worst possible classifier (no true positives are found). The more the curve is situated towards the top left, the better the classifier is.de novo motif discovery programs: BayesMD [k-mer based, use of background sequences, etc), data preprocessing  was not allowed.We assessed the performance of the DISPARE matrix refinement algorithm by comparing our findings with those of four  BayesMD , MEME [2 BayesMD , PREGO [ BayesMD  and WEED BayesMD . Motif pde novo programs were evaluated on test and background sequences for the synthetic, p53, Nanog and ESR1 datasets. We used the ROCR package [The effectiveness of the initial matrix, the DISPARE, the width-optimized matrix and the matrices predicted by the four  package  to compuwm and 2 nucleotides for matrix wma) was applied to allow sites to occur within a certain interval around the exact position of the implanted sites. For the ChIP-PET data, we considered the motif that had the highest score for each sequence as a "true positive" and any other scores as "false positives". We also assumed that the background sequences contained no motifs.For the synthetic data, we were able to evaluate the real fitness of the different matrices as we knew the positions of the motifs in each sequence. Our planted hits are defined as "true positives" (TP) while any other hits are considered as "false positives" (FP). A tolerance of 30%  proposed in the paper, did the data analysis and drafted the manuscript. M.H.T implemented the width matrix optimization, produced the data on width matrix optimization and helped with the draft. O.E conceived and supervised the research plan. All authors read and approved the final manuscript.Figure S1. Flowchart of the DISPARE algorithm.Click here for fileFigure S2. ROC curves for synthetic data: matrix wma. 5 datasets.Click here for fileFigure S3. ROC curves for synthetic data: matrix wm. 5 datasets.Click here for fileSupplementary Material. Supplementary material.Click here for file


Growing interest in the application of natural language processing methods to biomedical text has led to an increasing number of corpora and methods targeting protein-protein interaction (PPI) extraction. However, there is no general consensus regarding PPI annotation and consequently resources are largely incompatible and methods are difficult to evaluate.We present the first comparative evaluation of the diverse PPI corpora, performing quantitative evaluation using two separate information extraction methods as well as detailed statistical and qualitative analyses of their properties. For the evaluation, we unify the corpus PPI annotations to a shared level of information, consisting of undirected, untyped binary interactions of non-static types with no identification of the words specifying the interaction, no negations, and no interaction certainty.We find that the F-score performance of a state-of-the-art PPI extraction method varies on average 19 percentage units and in some cases over 30 percentage units between the different evaluated corpora. The differences stemming from the choice of corpus can thus be substantially larger than differences between the performance of PPI extraction methods, which suggests definite limits on the ability to compare methods evaluated on different resources. We analyse a number of potential sources for these differences and identify factors explaining approximately half of the variance. We further suggest ways in which the difficulty of the PPI extraction tasks codified by different corpora can be determined to advance comparability. Our analysis also identifies points of agreement and disagreement in PPI corpus annotation that are rarely explicitly stated by the authors of the corpora..Our comparative analysis uncovers key similarities and differences between the diverse PPI corpora, thus taking an important step towards standardization. In the course of this study we have created a major practical contribution in converting the corpora into a shared format. The conversion software is freely available at  Natural language processing (NLP) methods have recently been widely applied to improve access to the enormous and rapidly growing amount of information that is available mainly in the biomedical research literature. Protein-protein interactions (PPI) are the most widely studied information extraction (IE) target in the BioNLP field, with the key subproblem of protein name recognition being the most commonly addressed task.Recent shared tasks and studies of biomedical named entities have increasingly clarified the concept of a protein name, brought about a rough consensus on how to annotate them, and established both the best-performing entity name recognition methods and their performance . By cLacking explicit, widely accepted definitions of PPI and their annotation, the best way to approach these topics is through the corpora produced by the groups studying PPI extraction. By analyzing corpora it is possible to identify points of agreement and disagreement regarding the definition of a protein-protein interaction and how they should be annotated, thus taking a step toward reaching a consensus on this topic. A comparative evaluation of PPI extraction methods on the various corpora could also complement broad single-corpus evaluation efforts such as BioCreative  and helpIn the following, we describe and present analysis results of the five selected corpora: AIMed, BioInfer, HPRD50, IEPA, and LLL. See the Materials and Methods section for further details regarding the corpora, their selection, and the evaluation methodology.Table All five corpora contain annotation identifying entities such as proteins and genes in the text, but only LLL and BioInfer contain information regarding the types of the entities, and for LLL, this is limited to distinguishing between genes and proteins. More importantly for use in PPI extraction system development, only the AIMed and BioInfer corpora aim to include exhaustive annotation of entities of the types relevant to the corpus, while other corpora have based entity annotation on lists of entity names or named entity recognizer output.The differences in interaction annotation are even greater than those in entity annotation: in particular, only the BioInfer and IEPA corpora contain information identifying the words stating an interaction, all but HPRD50 specify the direction of interactions, BioInfer alone contains complex or negative interactions, and only HPRD50 annotates different interaction certainties. Finally, BioInfer is the only corpus to contain annotation for static entity relations such as protein family membership. These properties dictate the greatest common factor for the purpose of unifying the corpora and evaluating them with a shared methodology: undirected, untyped interactions of non-static types with no text binding of words specifying the interaction, no complex structure, no negations, and no interaction certainty. We created custom software for each corpus, filtering and transforming its data into such a unifying subset . All results and discussion below concern these transformed versions of the corpora. Unfortunate as it may be that other information is discarded when unifying the corpora, this level of annotation is also a reflection of the state of the art in biomedical IE methods, and doesn't presently limit the applicability of the resulting dataset.We next present evaluation results for the unified corpora using two PPI extraction methods as benchmarks: a simple co-occurrence based method and RelEx , a full The performance of the two methods on the various corpora is given in Table the choice of corpus has a larger effect on the result than the choice between a naive PPI extraction method and an advanced one. This has the immediate and important implication that results for methods evaluated on different corpora should in general not be directly compared, and suggests limits on how meaningful such comparisons are when the comparability of the corpora has not been established. This result can also be seen as calling into question whether F-score is a meaningful performance measure for PPI extraction, but we expect that its position as a de facto standard is unlikely to be shaken.For RelEx, we find that F-score performance is better for all corpora than for co-occurrence. We note that the performance of the two methods correlates closely  equals the precision of the co-occurrence method evaluated above. Further, as recall for this method can be approximated to be 1 (ignoring rare self-interactions), p is precision and r recall. I/EP correlates closely with RelEx performance, although due in part to the conservativeness of the significance test we apply, this correlation is only weakly significant (p ≈ 0.08). We propose that I/EP may serve as a rough, easily computable baseline for determining the comparative difficulty of PPI extraction tasks across corpora. As more proteins are annotated, we would not expect I to grow more than linearly, while EP grows quadratically.More significantly, we observe that the average number of annotated entities per sentence varies almost twofold from just above two to more than four, translating to even greater differences in the average number of entity pairs: here we find e.g. an over fivefold difference between IEPA and BioInfer. The average number of annotated interactions per sentence also varies considerably, from just half on AIMed to more than two on LLL. These last two statistics are connected to co-occurrence based PPI extraction performance through a simple relationship: the average number of interactions divided by the average number of entity pairs per sentence .Causal-Change subtree of the BioInfer ontology, with the BioInfer corpus having the most exceptions to this rule. The interactions in the Causal-Change subtree correspond to events occurring as part of biochemical processes in living cells as opposed to static, non-event relations such as family membership and structural similarity. This supports our understanding that the corpora do not contain notable annotation of static relations between entities, with the exception of BioInfer, from which most static relations were filtered. The most common specific type were interactions related to a Change in the Physical properties of entities, with the IEPA corpus a notable outlier in containing no interactions of this type. We observe that the corpus type distributions, while surprisingly different, appear to mostly follow the definitions of “interaction” in the corpora, with e.g. IEPA interactions focusing on Change in Amount and Dynamics (see the Corpora section).Figure To identify similarities in the type distributions of the corpora, we evaluated the correlation of the interaction type frequencies, using the non-cumulative counts , the coIn conclusion, the qualitative analyses uncovered several similarities and differences between the corpora with respect to many of the analysed characteristics, identifying some of the often unstated rules applied in PPI annotation. Nevertheless, although we studied several potentially relevant factors, the analysis did not identify a clear reason for the remaining over 10% average PPI extraction performance difference between corpora. These results suggest that the remaining differences may, in fact, be largely due to idiosyncrasies of the corpora instead of general properties. As an example of such a feature, we note that approximately 5% of interactions in the AIMed corpus are self-interactions (a single protein interacting with itself), which the methods applied here do not attempt to extract — and essentially no self-interactions occur in the other corpora. Further regularities might also be uncovered by the use of additional benchmark methods, and we expect analysis applying machine-learning approaches to be a particularly fruitful avenue of further research in comparative PPI corpus analysis. I/EP. The use of standard datasets, metrics, and evaluation protocols is naturally also important. We further recommend evaluation on several corpus resources: with the five corpora considered in this study now available in a common format, we believe that future PPI extraction system evaluations would be enhanced by reporting performance for all of these corpora, thus obtaining broader insight into system performance.The results of our evaluation underline the importance of establishing the relative difficulty of the extraction tasks represented by corpora. Based on these results, we strongly recommend that corpus creators and PPI extraction method evaluations measure this difficulty by reporting performance for an established baseline method or, minimally, simple corpus statistics such asIn corpus annotation, we would stress in particular the value of annotation manuals or similar documentation and the use of standoff annotation and XML formats in making corpora accessible to users. Cohen et al.  discuss We have presented the first comparative evaluation of PPI corpora, studying the AIMed, BioInfer, HPRD50, IEPA, and LLL corpora. Our evaluation showed great divergence in PPI extraction performance between corpora, finding an average F-score difference of 19% for a recently proposed extraction method. We studied the properties of the corpora to identify factors causing these differences, and established that approximately half of the performance difference between corpora stems from the annotation given to non-interacting entities. We also performed a detailed qualitative analysis of the corpora, uncovering notable differences in the distribution of interaction types and identifying often-unstated points of agreement and disagreement in annotation regarding negation, speculative statements, the explicitness of annotated statements, and the directness of the corpus interactions..As a major practical contribution of this study, we have defined a conversion for each corpus into a shared annotation scheme, allowing the diverse PPI corpora to be merged into a large, multi-domain corpus. The conversion programs are freely available at IEPA corpus [For the survey, we selected freely available PPI corpora with specifically identified named entities and manually annotated interactions. We further required that negative examples of PPI be either explicitly marked or that they can be validly generated under the closed-world assumption. These criteria are approximately minimal for evaluation using the general type of extraction methods applied in this study. We selected all corpora fulfilling these requirements that we were aware of, including those that were included at the time of selection in the public resource collecting information regarding BioNLP corpora maintained by Cohen et al. ,10. The A corpus , and theA corpus . In the AIMed is a corpus created for PPI extraction method comparison [mparison ,14. The mparison  as contaBioInfer was created as a corpus for training and testing PPI extraction programs [programs ,16. The HPRD50 corpus was created as a test set for the RelEx system [The x system ,17. It wIEPA corpus [a direct or indirect influence of one on the quantity or activity of the other” [The A corpus ,18 was ce other” .LLL corpus was created as the shared dataset for the Learning Language in Logic 2005 (LLL05) challenge [The hallenge ,19. The The unified format to which the corpora were transformed follows the standoff annotation principle, where the original sentence text is preserved and the entities are identified through character offsets. The corpora in the unified format are stored in XML files with a very simple structure.The transformation of the corpora into the unified format is a tedious process requiring significant efforts. This is due to the often highly idiosyncratic native formats of the corpora requiring the development of relatively complex transformation programs and, in several cases, manual intervention to resolve ambiguity present in the native format. This work was in many cases very similar to the refactoring efforts described in detail in .Causal-Change subtree of the BioInfer relationship type ontology.The conversion for the BioInfer corpus was particularly challenging due to its more complex interactions and broader scope than either the other corpora or the extraction methods. To convert BioInfer, we removed interactions annotated as negative, flattened nested entities, transformed complex interactions into binary according to a set of custom-developed rules so that only pairs of entities take part in interactions, and finally narrowed the set of interactions to the We provide the transformation programs for all of the corpora under an open-source license, enabling other researchers to access the five PPI corpora in this study in a unified format .We use the precision, recall and F-score metrics, which are nearly universally applied in PPI extraction system evaluation. However, these metrics can be measured in different ways, potentially leading to considerably different results. We measured extraction accuracy according to the following rules:No directionality: REL is considered equivalent to REL.No interaction type: it is not necessary to identify the type of an interaction.Entity identity: REL does not match REL unless P2 = P′2, even if P2 and P′2 are different occurrences of the same protein name.We applied a simple co-occurrence method and a full parsing-based method, described in the following.P1,P1)), we do not extract such “co-occurrences” as this would have a detrimental effect on overall extraction performance. Despite their simplicity, co-occurrence methods have been successfully applied to many information extraction tasks in the biomedical domain  and proThe RelEx method of Fundel et al. is a full parsing-based PPI extraction method with state-of-the-art performance . Due to We next give a brief overview of our implementation and application of RelEx. Similarly to the setup described by Fundel et al., we apply the MedPost tagger  and the Out of Ontology.In this section we briefly define the methods and definitions used to perform the qualitative corpus analysis. We measured the distribution of the types of the annotated interactions by mapping them into the BioInfer relationship ontology. We used this ontology because of its broad coverage of protein-protein interaction types  and becaDirectness indicates whether the interaction involves physical contact of the proteins. This definition follows that in [Explicitness reflects the way in which the interaction is stated. An explicitly stated interaction is one that can be understood without knowledge of biology-specific terms and inference supported by external biological knowledge. Certainty describes whether an interaction is stated in a definite manner or speculatively, with qualifiers such as may. We apply a binary distinction between speculative and definite statements, following the guidelines in [Polarity refers to whether the statement asserts the existence or the non-existence of an interaction. We annotate negated statements following the annotation guidelines in [ that in . Explicilines in . Polaritlines in . While tp value is computed using the standard Best and Gipps algorithm as implemented in the R statistical package [p = 0.05 as the cut-off value. We note that these statistics are very conservative due to the fact that they do not make any assumptions regarding the distribution of the results and do not consider their magnitude.Throughout the paper, correlation refers to Kendall's correlation coefficient and the associated test with null hypothesis being that the correlation coefficient equals zero. The  package ,27. We uThe authors declare that they have no competing interests.FG, JB, JH and SP created the conversions of the corpora to the unified format. AA implemented the PPI extraction methods, and all authors participated in the analyses. SP was the main author of the manuscript with contributions from all other authors, all of whom read and approved the final version.


To the Editor: Transfusion-transmitted malaria, although extremely uncommon in most countries not endemic for malaria, may have fatal consequences if undetected  would have to be processed to achieve this level. In addition, one would have to assume that parasites are equally distributed in the peripheral blood at the time of donation.The PCR, like any method based on direct detection of the parasite, does have a shortcoming: the amount of specimen processed determines the limit of detection. Even if the described PCR method detected a single parasite in the 5-mL blood sample used by the authors Little is known about the frequency of very low parasitemias. No large-scale epidemiologic studies have been conducted in which large amounts of blood  were collected. The authors On the other hand, one might argue that screening the whole blood supply for malaria by PCR may detect the rare blood donation with undetected malaria, with higher parasitemias. However, the generally accepted deferral criteria for blood donors at risk seem highly efficient. In the United States, only 14 cases of transfusion-transmitted malaria were reported from 1990 through 1999 Several novel diagnostic methods have been developed recently


A standardized and cost-effective molecular identification system is now an urgent need for Fungi owing to their wide involvement in human life quality. In particular the potential use of mitochondrial DNA species markers has been taken in account. Unfortunately, a serious difficulty in the PCR and bioinformatic surveys is due to the presence of mobile introns in almost all the fungal mitochondrial genes. The aim of this work is to verify the incidence of this phenomenon in Ascomycota, testing, at the same time, a new bioinformatic tool for extracting and managing sequence databases annotations, in order to identify the mitochondrial gene regions where introns are missing so as to propose them as species markers.The general trend towards a large occurrence of introns in the mitochondrial genome of Fungi has been confirmed in Ascomycota by an extensive bioinformatic analysis, performed on all the entries concerning 11 mitochondrial protein coding genes and 2 mitochondrial rRNA  specifying genes, belonging to this phylum, available in public nucleotide sequence databases. A new query approach has been developed to retrieve effectively introns information included in these entries.After comparing the new query-based approach with a blast-based procedure, with the aim of designing a faithful Ascomycota mitochondrial intron map, the first method appeared clearly the most accurate. Within this map, despite the large pervasiveness of introns, it is possible to distinguish specific regions comprised in several genes, including the full NADH dehydrogenase subunit 6 (ND6) gene, which could be considered as barcode candidates for Ascomycota due to their paucity of introns and to their length, above 400 bp, comparable to the lower end size of the length range of barcodes successfully used in animals.The development of the new query system described here would answer the pressing requirement to improve drastically the bioinformatics support to the DNA Barcode Initiative. The large scale investigation of Ascomycota mitochondrial introns performed through this tool, allowing to exclude the introns-rich sequences from the barcode candidates exploration, could be the first step towards a mitochondrial barcoding strategy for these organisms, similar to the standard approach employed in metazoans. Among the living organisms with the largest effect on human society health and development, Fungi are very widespread. Indeed, man has learned to employ them to produce and transform food, agricultural and industrial resources, drugs and cosmetics or, in certain cases, to fear them as toxic contaminants. In particular, despite the huge progress in feed technology, the contamination of food due to some species, frequently belonging to Fungi, present in natural environment or accidentally introduced during incorrect fabrication or storage procedures, is however possible. In this scenario, the achievement of an effective, rapid and cheap monitoring system of contaminant species to preserve the food quality and foresee possible risks is strongly required. A deeper knowledge of the classification of fungal species and the possibility to discriminate them in an efficient way could strongly support this urgent task. At the same time this is a very challenging mission: Fungi include a broad range of taxa presenting a great variety of morphologies, ecologies and life strategies . Indeed,).At present the possibility to apply DNA barcoding to the identification of fungal species has recently been suggested -5. MoreoObviously the idea of using a DNA marker to classify taxonomical relationships is not that recent, especially within the Fungi, but, until now, scarce attention has been paid to standardization of the marker to be used. On the contrary, the use of a standard marker is, perhaps, the main innovation of the ambitious DNA Barcode Initiative -8, whoseCurrently, the molecular identification of species in Fungi is based primarily on nuclear DNA markers, such as "nuclear large ribosomal subunit" (LSU rDNA) , "nucleaThe distribution and size of introns in 11 of the 13 protein coding genes and 2 rRNA specfying genes, belonging to Ascomycota mitochondrial DNA, available in public databases, has been assessed using two bioinformatic methodologies: a Blast-based approach and a Query-based approach. ATP synthase F0 subunit 8 (ATP8) and NADH dehydrogenase subunit 4L (NDH4L) genes were discarded from the analysis because we considered them too small compared to the standard size of barcode markers. Indeed in literature the barcodes have generally a size larger than 400 bp whereas the coding region maximum size is 340 bp and 150 bp for NDH4L gene and ATP8 gene, respectively. Designing conserved primers for such small regions would probably produce small amplified regions sizes (probably less than 250 bp for NDH4L gene and less than 50 bp for ATP8 gene) which, even considering some exceptions , could hAspergillus niger, Ustilago maydis, Neurospora crassa, and Rhizopus oryzae. In the case of the protein coding genes, the probes used were the amino acidic sequences and the blast procedure was performed using the tblastn algorithm. In the case of the rRNA genes, the probes were nucleotide sequences and the blast protocol used was blastn.The information retrieval system SRS , availabFor each gene, the Blast results obtained using the gene probes belonging to all the four species were compared and, in all the cases, it was possible to verify that the results set of at least one search were inclusive of all the sequences found in the others. Thus it was not necessary to integrate the result sets but, simply, to select the most complete one for each gene  federated database (LIBI DB) . AlthougQuery A shown in Figure Organism, with gene belonging to the mitochondrial genome, that contain one term as {"CDS" -coding sequence-, "tRNA" -transfer RNA-, "rRNA"} in the field feature key, and one of {"gene", "product", "note", "translation"} in the field qualifier name.In our implementation, GenBank federation is achieved through a web-services wrapper interfacing the web-services exposed at NCBI . Fig. 1 Due to both the database "noise" reported before , and complex evaluations to be performed to extract pieces of information relevant for our analysis, a custom algorithm has been designed and implemented in order to evaluate automatically retrieved data. The main steps of the algorithm are depicted in Figure Query A against the federated database. The retrieved result set is managed by the algorithm in order to obtain refined data organized in a tabular way; this is a suitable format for the statistical analyses conducted later. To each tuple of the result set obtained by Query A the four steps . This check is performed evaluating if the qualifier value field contains a pattern included in a predefined list of intron gene qualifier . If the tuple matches a pattern, it is discarded (step 1'A); otherwise the tuple will be handled by step (1'a): this ensures that it is related to a target gene. Step (1'a) evaluates the qualifier value field in order to identify the gene name. This piece of information is extracted from the field qualifier value corresponding to a qualifier name with value "gene" . In order to avoid gene name synonymy, a suitable dictionary has been used to normalize the gene name the tuple corresponds to . Step (1'b) gives also the possibility to extract the protein sequence from the qualifier value field (when available) if requested by the analysis.Step (1) analyzes the feature location field. Step (2') infers the correct strand on which the feature lays by checking on the feature location for the word "complement". Then, step (2") infers the gene completeness by checking on the presence of special characters  in the feature location field. Step (2"') evaluates the presence of introns inside the gene and extracts information about them by searching for the word "join" in the feature location. If this word is not present, we mark the gene as lacking introns and the algorithm proceeds with the step (2"'A) in order to calculate the start and end position of the gene; if the word "join" is present, the gene contains introns, so the following three steps are performed: step (2"'a) extracts from the feature location field the start and end positions for each intron; finally, the start position of the first exon [step (2"'b)] and the end position of the last exon [step (2"'c)] are extracted.The whole step (2) is devoted to analyze the feature key field in order to extract the DNA sequence of the genes specifying rRNA or encoding proteins. So the feature key field is checked for the words "rRNA" or "CDS"; if the gene refers to rRNA or CDS a new query, named Query B in Figure Query B is compiled using the SQL user-defined function COMPL_REVERSE we appositely built to complement and reverse the DNA sequence <sequence_without_introns> depending on the annotated strand. In Query B the DNA sequence <sequence_without_introns> is reconstructed by means of some of the pieces of information we gathered in the previous steps, while the value of <accession> is that related to the tuple under evaluation.Step (3) is responsible for the analysis of the Finally, step (4) summarizes all extracted information in a file where the data are formatted as comma separated values (CSV).Query A (12499) the number of tuples, such as genes, relevant for our study is 11037. The algorithm had been able to extract automatically and correctly all the information contained in the entries' features for about the whole set of the tuples except for 17 of them, for which human examination was requested. However these numbers allow us to state the algorithm accuracy to 99.8%. The 17 records were found in a cross control procedure between assignment proposed by gene name and that suggested by gene description.The performance of such an approach to query GenBank in order to extract relevant information for the analysis of introns in mitochondrial genes in Ascomycota can be effectively evaluated by taking into account some of the quantities reported in Table Finally of the 7234 Genbank entries obtained using the described procedure, 5802 have been selected since they concerned the 13 target genes (11 of the 13 protein coding genes and 2 rRNA coding genes), belonging to Ascomycota mitochondrial DNA, used in this work. See additional file The developed query system at present is not publicly available, however we are evaluating a solution to make it available as a service that researchers could use through the internet.The results of the Blast-based approach, shown in Figure Looking at the distribution of intron sizes . Indeed, as shown in Figure ) or Entrez , although giving the possibility to carry out complex research in which several criteria are combined and the false positives minimized, appear rather ineffective when it is necessary to retrieve selected information contained in particular fields of the entries. This problem is particularly evident when we wish to extract the data included in the so-called Feature Tables which, in a typical entry of a nucleic acids database, contain a detailed description of the various functional and structural portions of the sequence. The new query system, implemented in the LIBI platform, has been very useful for a rigorous extraction of only introns start and end positions data, present in the lines of the Feature Tables and it has allowed focusing of the query only on the mitochondrial entries of the Ascomycota annotated in Genbank.The study described here has allowed an evaluation of the impact of the mitochondrial introns in the development of a mitochondrial barcode system for the molecular discrimination of Ascomycota species. At the same time, an accurate methodological test, in which two alternative bioinformatics approaches to reveal intron size and position has been performed. The Query-based approach, developed as part of a database federation system, including public (such as Genbank) and private resources, has allowed to characterize precisely the intron position and size, performing a data retrieval directed only to obtain specific information usually present in the entries of a biological database, but hardly selectable through the classical query systems. Indeed, the commonly used databases query systems such as SRS  free from introns which could be considered good barcode candidates but, in this case, the results of Blast approach strongly contradict this observation requiring further investigations to understand if the intrinsic frame shift mistakes in the rRNA nucleotide alignment performed by Blast lead to false interruptions of the CDS, as strongly suggested by the dimensions of the majority of introns (<100 bp).The comparison of the two approaches used in the analysis presented above show a higher efficiency of the Query-based system, giving that it aims directly to the information annotated in the database and avoids a series of alignment and retrieval stages based on the similarity calculation which inevitably produce false positives and negatives in the final results. In conclusion we were not able to claim that the Blast approach can be a watchdog of the query approach, as illustrated by the case of the huge number of introns found in the large and small ribosomal RNA genes and the problem of excessive intron length in the protein coding genes shown in Fig. The idea of using a mitochondrial gene as a barcode marker in Fungi, just as it adopted in animals, derives from several simple considerations. First of all, the general favorable features of mitochondrial DNA, suitable for the role of a taxonomic marker, have to be considered. Among them, above all, there are the high copy number, the possibility of an easier and cheaper recovering and the paucity of repetitive DNA which makes the assessment of the homology, both at the level of loci and at the level of a single site, quite simple. Indeed comparative studies on mitochondrial coding genes are not plagued by doubts on the orthology of a particular locus compared between different samples as it could occur for nuclear genes, although the accidental amplification of nuclear mitochondrial (NUMTs) pseudogenes could lead to problems -29. In aIn conclusion, it seems difficult to identify a "universal barcode" able to resolve species in all Life, even if encouraging results come from the numerous studies in animals, where the CO1 gene seems successful in many taxonomic groups ,40. In FThe Barcode of Life Initiative represents an innovative resource for the world biodiversity knowledge at the level of its basic units: the species. This original and emergent system is opening the way to new and inspiring applications both in basic research and practical issues due basically to its high degree of standardization. The huge amount of data which have to be produced and analysed along this massive project unquestionably requires a strong bioinformatics support and the improvement of experimental and practical methodologies working effectively on a large scale. A contribution in this sense could be given by the development of a new query system able to extract and manage specific information, hardly selectable with the classical retrieval systems, from Genebank. In particular, this system has been applied to generate a very accurate and detailed map of Ascomycota mitochondrial genome introns in order to recognize one or more regions, free or poor of introns, which could be easily used as barcode markers in this phylum. Indeed, the non-coding sequences interrupting quite all the Fungi mitochondrial genes could represent one of the most serious difficulties in amplifying and analysing a taxonomic molecular marker. The encouraging results obtained testing this new bioinformatic query tool suggest that it could represent a preliminary step towards a mitochondrial barcoding strategy for Fungi, similar to the standard approach routinely employed in metazoa, allowing to choose as barcode candidates those mitochondrial genome regions not perturbed by the "intron problem".The authors declare that they have no competing interests.MS participated in the design and interpretation of data of the Blast-based study, in the conceiving and development of the Query-based analysis protocol and in analysis and interpretation of its data and drafted the manuscript. SV designed and developed the Blast-based study, participate in the conceiving of the Query-based protocol and to the analysis and interpretation of its data, and help to draft the manuscript. GP participated in design and development of the Query-based approach and helped to draft the manuscript. GS participated in design and development of the Query-based approach and helped to draft the manuscript. CS (Claudio Scazzocchio) participated in the conceiving of the study and revised critically the manuscript. CS (Cecilia Saccone) participated in the conceiving and coordination of the study and revised critically the manuscript.List of the EMBL entries obtained through the Blast-based search. This file contains the final non-redundant list of the EMBL accession numbers concerning the 4385 sequences obtained from the comparison of the results sets produced by Blast procedures in which the sequences of each protein or rRNA coded by the mitochondrial genome of four species, such as Aspergillus niger, Ustilago maydis, Neurospora crassa, and Rhizopus oryzae are used as probes. The alignments between the EMBL sequences and the probes, during the Blast runs, allowed to picture the introns map of the 13 Ascomycota mitochondrial genes included in this work.Click here for fileList of the Genbank entries retrieved through the Query-based search. This file contains the final non-redundant list of the Genbank accession numbers related to the 5802 entries obtained and further analysed through the new Query-system described in this work, with the aim to read the introns positions and length directly from the Feature Tables.Click here for fileIntron map of Ascomycota mitochondrial genes as retrieved in the query-based analysis. The table summarize the presence and the position of introns in the totality of mitochondrial Ascomycota records present in GenBank. Each row describe a record without introns or with an intron and for each one the following information, were applicable, were add: "AC": GeneBank accession number; "Gene.Name": standardized locus name; "Intron.Start": intron starting position relative to the start of the CDS in the record; "Intron.End": intron ending position relative to the start of the CDS in the records; "Abs.Intron.Start": intron starting position relative to the start of the whole record; "Abs.Intron.End": intron ending position relative to the start of the whole record; "Insertion.point.in.Ref": intron insertion position mapped on the protein of the reference organism; "L.intron": length of the intron. Each row is identified with a name composed by the accession number, the standardized locus name and an increasing number to distinguish more than one intron present in the same CDS.Click here for file


A novel method of detecting mechanical movement of the heart, Mechanocardiography (MCG), with no connection to the subject's body is presented. This measurement is based on radar technology and it has been proven through this research work that the acquired signal is highly correlated to the phonocardiograph signal and acceleration-based ballistocardiograph signal (BCG) recorded directly from the sternum. The heart rate and respiration rate have been extracted from the acquired signal as two possible physiological monitoring applications of the radar-based MCG device. The ballistocardiograph (BCG) is a vital signal in the 1–20 Hz frequency range whichis caused by the movements of the heart and blood. The BCG can be recorded from the surface of human body by noninvasive means. In the early 1930s, Isaac Starr recognized that the BCG signals closelyreflect the strength of myocardial contraction and classified them into fourgroups to distinguish normal and abnormal heart performances . The ideIn addition to a number of clinical studiesthat have been performed with the BCG, specialized BCG instruments, includingbeds and chairs, have been developed by different research groups –7. Due tBased on these observations, we areimproving the BCG signal acquisition system that reflects the mechanicalfunctioning of the heart. In our first study , we usedThe current research, reportedin this paper, is aiming to go one step further by applying contactlessmeasurement of the mechanical movement of the heart, using a radar-based MCGdevice operating in the range of 2.45 GHz . The extThis paper is organized as follows: in the second section of thepaper our signal acquisition methodology is explained and then the comparisonof the acquired MCG signal to our previous BCG recordings is presented. Insection three, the electronic circuit board design developed for theradar-based MCG device is explained. In section four the results of breathing rateand heart rate measurements using the new technology are presented. Thecomparison results of the phonocardiograph and the acquired signal are alsopresented clearly showing the correspondence of our MCG signal to cardiacevents.The block diagram in While monitoring the heart from the outside of the body contactlessly from the sternal location,the signal passes through only a few layers of different tissues between the sternumand the heart which can be seen in RF signal with the carrier frequency of2.45 GHz was transmitted toward the subject's chest, and the reflected signalwas band-passed filtered between 0.5 Hz to 25 Hz. The filtered signal wasdifferentiated and then band-pass filtered again between 4 Hz to 20 Hz. Thecomparison of the processed MCG signal to the BCG signal recordedsimultaneously from the sternum can be seen in It can be observed that there is a closecorrelation between the signal acquired from the radar-based MCG device and thesignal acquired from the accelerometer-based BCG device attached to thesternum. The systolic and diastolic phases of cardiac cycle are shown toidentify the correlations of these mechanical signals to the heart functioning.Phonocardiograph signal reflects the heartsounds that can be hared using stethoscope. Heart sound S1 corresponds tosystolic phase of the heart cycle, and S2 corresponds to the diastolic phase ofit. For the comparison purpose, the MCG signal was acquired synchronously withphonocardiograph signal and it was observed that S1 and S2 sounds of thephonocardiograph signal corresponded to the similar complexes on the MCGsignal. The clear correlation of this new signal to the heart cycle's eventscan be observed in As stated before, in ourexperiments heart's MCG signal was measured by a MCG device operating in therange of 2.45 GHz. At this electromagnetic frequency, the surface of the body ishighly reflective to incident electromagnetic field. Biological tissue is verylossy at this frequency, and there is minimal penetration of radiatedelectromagnetic energy into the body. Therefore, a return signal from aradiated electromagnetic field incident on the body will primarily containinformation associated with movement events.The effects of radio-frequency fields onhuman health have been monitored and studied by the scientists around the worldfor about 60 years now. The 2.45 GHz frequency is considered to be safe forhuman health. This is the same frequency range as emitted from a cell phone. The speTheprincipal design of the radar-based MCG device is shown in Theoutput signal from the receiver is filtered and amplified and is sent to themicroprocessor for further processing. Thecutoff frequencies for the band-pass filter shown in the schematic are 1 Hz and100 Hz, and the gain of the amplifier is around 800. After filtering andamplifying, the MCG signal is sent to the A/D unit and then to the ATMEL CPUfor further processing. The CPU is connected to a thin-film transistor (TFT)display via its serial peripheral interface (SPI) port. The MCG device operateswith two AA batteries (2.45 V).Consideringthat the MCG device has its own CPU and monitor, it can be used stand alone toacquire and process the MCG signal. There is also another option of sending thedata to a personal computer for more advanced processing of the data usingMatlab. To have this option on our device, the digitized MCG signal istransformed to packets and sent through UART to the USB and finally to the hostpersonal computer for possible further processing.The data from subjectswere acquired at Burnaby General Hospital  and in all the measurements presented in this paper the distance from the sensorto the chest was set to 10 cm. The processed MCG signal has been superimposed onthe phonocardiograph signal, and it has been noticed that S1 and S2 sounds ofthe phonocardiograph signal corresponded to the similar complexes on the radarMCG signal. The correlation of the MCG signal to the heart cycle's events isobserved, and the results of this superimposition can be seen in For the heart rate measurement our experimental setup includedthe acquisition of the MCG signal and two leads of ECG as a reference. Forrespiration rate measurement, the setup included the acquisition of MCG signaltogether with the respiration signal as the reference. Eight subjects took partin the respiration measurement tests, and six of these subjects took part inthe heart rate measurement tests too. Breathing rate measurement experimentswere 60 seconds long while heart rate measurement experiments were 15 secondslong.Fordetection of breathing rate, the radar signal was low pass filtered under 0.4 Hz,and the peaks were counted and compared to the results acquired from a straingauge transducer that measures the changes in thoracic circumference, using abelt which is fastened to the subject's thorax. As can be seen in As an example,for subject six we had 14 cycles of respiration from which 13 of them wherecorrectly identified. Thus, for this subject, the 92 percent accuracy forrespiration rate detection was achieved. For the same subject and out of 19heart beats, 17 of them were correctly identified. Thus, for heart beatdetection for this subject, the accuracy of 89.4 percents was achieved. Usingour device, the data can be seen real time and can be processed in near realtime. The reason for quick processing time is that the algorithm includesfiltering of the data and then peak searching to find the corresponding peaksto the inspiration and heart beat events.The sources ofinterference for contactless heart motion signal acquisition can be categorizedinto two main categories. First, foreign electromagnetic radiation that maydisrupt the desired signal on its way back to the antenna, and second, motioninterference of nearby subjects. As discussed previously, the radiationfrequency of the contactless MCG device is centered about 2.4 GHz. Thisfrequency falls within the Industrial Scientific and Medical (ISM) bands. The ISM bands are recognized internationally,where it has been traditionally reserved for industrial, scientific, andmedical purposes excluding communications .The 2.4 GHz ISM band in many urbansettings is in fact quite saturated with devices. Examples include the verycommon wireless local area network (WLAN) devices, Bluetooth devices,microwaves ovens, and household cordless phones. These devices can exhibitrelatively high signal broadcast power levels during full power transmission. Inthe North American/US market, the safe power level is governed by the FCC .As a re Motioninterference is another possible source of signal disruption when operating thedevice in a high-density, busy setting. The antenna patch on the contactlessMCG device is a printed patch antenna and at ranges greater than what isspecified operational for this application , itsside-lobes begin to pickup motion in its path. Also the main-lobe of theantenna begins to exhibit relatively wide angle. In attempts to acquire usefulheart motion signals at longer ranges, more than 10 cm, the motion interferencefrom the subject's general body movement or nearby movements that are notsubject-related will both disrupt the heart motion signal acquisition greatly.In efforts to evaluate the reliability of this device considering the two majorsources of interference, in addition to the measurements at Burnaby General Hospital, as explained previously, measurements on 200-plus participants during the WiredNextFest convention were obtained . The envTheheart motion measurements were compared simultaneously with the correspondingheart beat of each individual participant as assessed via wrist pulse byresearch staff members. It was observed that when the subject held still, andwithout other people within 50 cm of the device radius, approximately only one outof ten subjects during 30 second recording session would exhibit grosslydelayed or out-of-sync heart rates between the wrist pulse and device reading.Althoughit is unknown how many wireless broadcasting devices, using the 2.4 GHz band,were present during the convention, nor the associated power levels, and hencethe random interference power density in the foot-ball sized convention hall,the quasi field-test of the device provided assurance that even in the busiesteveryday environments, filled with urban electronics and random motion, whenthe subject recordings are controlled properly, the reliability of the heartrate detection is seemingly good.Furthertesting on the effects of both electromagnetic and motion interference is necessary to fully assess the performanceof the proposed contactless MCG device. Though, it suffices to say that theobservations through the combined evaluations and field-trials give high-hopeson the performance and for the eventual perfection of this device.In this research work, the MCGsignal was extracted from the radar-based MCG device and by using two otherheart related signals, BCG and phonocardiograph, it has been proven that theextracted MCG signal corresponds to the mechanical functioning of the heart.Two physiological signals, heart rate and respiration rate, were measured using theMCG signal and a noticeable accuracy of 91.35% for respiration rate, and 92.9%for heart rate were achieved. It should be noticed that these results areacquired from the first generation of our device, and the accuracy of thedetection can be improved by focused targeting of the MCG device on the sternumand further hardware improvements. Another important factor, which is an asset ofour device, is the fact that all the electronics and the radar sensor can befabricated for less than $200 USD which makes our device economical for generalheart monitoring purposes.Consideringthe short amount of processing and the low cost of the contactless measurementof heart rate and breathing rate as we presented, our device can have numerousapplications in the health care system such as, prevention of sudden infantdeath syndrome (SIDS), sleep apnea, and other areas in which contactlessmonitoring is desired.In this researchwork, we have proposed a new methodology and a new device based on radartechnology. We proved the correspondence of our acquired MCG signal to sternalBCG and phonocardiograph signals. Further improvement of the current device canprovide us with more accurate MCG signal corresponding to mechanicalperformance of the heart and ultimately development of an additionalcontactless heart diagnostic tool.


The Screening Inventory of Psychosocial Problems (SIPP) is a short, validated self-reported questionnaire to identify psychosocial problems in Dutch cancer patients. The one-page 24-item questionnaire assesses physical complaints, psychological complaints and social and sexual problems. Very little is known about the effects of using the SIPP in consultation settings. Our study aims are to test the hypotheses that using the SIPP (a) may contribute to adequate referral to relevant psychosocial caregivers, (b) should facilitate communication between radiotherapists and cancer patients about psychosocial distress and (c) may prevent underdiagnosis of early symptoms reflecting psychosocial problems. This paper presents the design of a cluster randomised controlled trial (CRCT) evaluating the effectiveness of using the SIPP in cancer patients treated with radiotherapy.A CRCT is developed using a Solomon four-group design (two intervention and two control groups) to evaluate the effects of using the SIPP. Radiotherapists, instead of cancer patients, are randomly allocated to the experimental or control groups. Within these groups, all included cancer patients are randomised into two subgroups: with and without pre-measurement. Self-reported assessments are conducted at four times: a pre-test at baseline before the first consultation and a post-test directly following the first consultation, and three and 12 months after baseline measurement. The primary outcome measures are the number and types of referrals of cancer patients with psychosocial problems to relevant  caregivers. The secondary outcome measures are patients' satisfaction with the radiotherapist-patient communication, psychosocial distress and quality of life. Furthermore, a process evaluation will be carried out. Data of the effect-evaluation will be analysed according to the intention-to-treat principle and data regarding the types of referrals to health care providers and patient satisfaction about the with radiotherapists will be analysed by means of descriptive techniques. The process evaluation data will also be analysed by means of descriptive techniques.Using the SIPP may prevent underdiagnosis of early symptoms reflecting psychosocial problems, should facilitate communication between physicians and patients about psychosocial distress and may contribute to adequate referral to relevant  caregivers.NCT00859768 Cancer is a leading cause of death worldwide, accountable for 7.9 million deaths  in 2007 . The preNevertheless, psychosocial distress in cancer patients is often underdiagnosed by medical staff in oncology settings ,12. RecoValidated screening instruments are useful tools to recognise psychosocial distress in cancer patients . BesidesAlthough the SIPP is being used in several hospitals and health care facilities in the Netherlands, there is still little known about the effects of using this questionnaire in consultation settings . TherefoPrimary aims are to study the effect of the SIPP on the number and types of referrals of cancer patients with psychosocial problems to psychosocial caregivers. Secondary aims are to study the effects of the SIPP on: 1) patients' satisfaction with the radiotherapist-patient communication during first consultation, 2) psychological distress in both the short- and long-term, and 3) quality of life in both the short- and long-term. Additionally, a process evaluation will be performed. The aim of the process evaluation is to gain insight into factors potentially influencing the effectiveness of the SIPP and factors facilitating future implementation of the SIPP in oncology care settings, if the SIPP proves to be effective.The design of this study is a CRCT because it is less prone to contamination bias -26. In aThe SIPP is a short, valid and reliable 24-item self-reported questionnaire that systematically identifies psychosocial problems in Dutch cancer patients. The psychometric properties of the SIPP were studied in an as yet unpublished study . This questionnaire was originally developed in the Netherlands by Pruyn and colleagues in 1997  , and was• physical complaints ,• psychological complaints ,• social and financial problems , and• sexual problems ).Items are rated on a three-point scale of 0 (no) to 2 (yes). Higher scores indicate poorer functioning.Before the start of the study, the radiotherapists in the experimental condition are trained in using and interpreting the SIPP. According to the intervention procedure, the patient receives the SIPP at two different time points during their RT period: just before the first consultation with the radiotherapist (one to three weeks before starting RT) and before the last consultation with the radiotherapist at the end of the RT period (five to nine weeks after completing the first SIPP). At both times, the completed SIPP is handed to the radiotherapist at the start of the consultation. The radiotherapist checks the scores of the SIPP to get an overview of potential psychosocial problems and the patient's needs for psychosocial care. Psychosocial problems are discussed with the patient during the consultation. Referral to a psychosocial caregiver at the Institute Verbeeten in Tilburg occurs only with the permission of the patient.Patients in the control group receive care as usual. No recent guidelines for the systematic assessment of psychosocial problems in cancer patients exist at the Institute Verbeeten. In the control group, radiotherapists may refer patients to psychosocial caregivers  at the Institute Verbeeten. However, this occurs according to the radiotherapist's judgment about the presence or absence of psychosocial problems in patients. Referrals to psychosocial caregivers at the Institute Verbeeten will be registered in both the patient's records and by the psychosocial caregivers.Recruitment of cancer patients takes place at the Institute Verbeeten, a radiation oncology department in Tilburg, the Netherlands. Only cancer patients receiving RT are eligible for this study.The following inclusion criteria are used: 1) patients with the most common cancer types such as lung, prostate, bladder, rectum, breast, cervix, endometrial, skin carcinoma and Non-Hodgkin; 2) patients 18 years of age or older and 3) patients with no metastases. Exclusion criteria are: 1) patients receiving palliative treatment, ≤ 10 fractions of RT; 2) patients unable to read and speak Dutch and 3) patients unable to complete questionnaires. Patients who meet the inclusion criteria are sent information about the study. Those willing to participate in the study are asked to sign a consent form and are only allowed to participate after completing and returning it.To reduce contamination bias, radiotherapists, instead of patients, are randomly allocated to the experimental or control groups. First, the radiotherapists are stratified according to the general percentages of incoming patients they referred to a  caregiver in the period 2006–2007. Within their stratum, they are either randomly assigned to the experimental or to the control condition  caregivers. Sample size calculation shows that 92 patients per condition are required to compare the number of referred patients to  caregivers between conditions with a 80% power and a significance level of 0.05 (one-sided) . This caThe consequence of adopting a CRCT is that the outcome for each patient can no longer be assumed as independent . Such a It is to be noted that stratification by baseline value of the outcome variable decreases the sample size required . In our Table The primary outcome measures are the number and types of referrals of patients with psychosocial problems to psychosocial workers at the Institute Verbeeten and/or to external health care providers . The number and types of referrals to  caregivers are measured at three months (T3) and 12 months (T4) after baseline assessment with a self-developed questionnaire by the patient and from registration records of the psychosocial caregivers at the Institute Verbeeten. This questionnaire comprises eight items on whether the patient is referred to a psychosocial caregiver and the types of problems the patient has experienced . The psychosocial caregivers at the Institute Verbeeten collect data from all referred patients, including types of problems, number of sessions needed and further referral to an external  caregiver.The secondary outcomes are classified in three dimensions: 1) patients' satisfaction with the radiotherapist-patient communication during the first consultation; 2) extent of psychological symptoms at T3 and T4; and 3) patients' quality of life at T3 and T4.Patients' satisfaction with the radiotherapist-patient communication is measured directly after the first consultation with the radiotherapist (T2) by the patient completing a self-developed questionnaire comprising five items. The first four items measure whether physical complaints, psychological complaints and social and sexual problems were discussed with the radiotherapist (item range 1 (yes) to 3 (no) plus "not applicable"). The fifth item measures the patient's general opinion (item range 1 (very bad) to 6 (very good)) about the communication with the radiotherapist during the first consultation.The extent of psychological symptoms is measured with the Hospital Anxiety and Depression Scale (HADS) [e (HADS)  and the e (HADS) . PatientThe HADS is a valid, reliable and useful instrument  that is The GHQ-12 is a well-validated instrument  that hasQuality of life is measured using the European Organisation for Research and Treatment of Cancer, Quality of life Questionnaire (EORTC QLQ-C30) [QLQ-C30) . The EORQLQ-C30) -50. It i• five functional subscales: physical (five items), role (two items), emotional (four items), cognitive (two items) and social functioning (two items);• a subscale about global health status and quality of life during the past week (two items);• three symptom scales: nausea/vomiting (two items), fatigue (three items) and pain (two items); and• six single items measuring appetite loss, insomnia, constipation, diarrhoea, dyspnoea and financial difficulties due to illness or treatment.The items on the five functional scales and three symptom scales have four-point response choices of 1 , 2 (a little), 3 (quite a bit) and 4 (very much). Categories 3 and 4 are regarded as indicators of clinically-significant symptom levels. Both items of the subscale about global health status and quality of life use a seven-point visual analogue scale (VAS) ranging from 1 (very poor) to 7 (excellent) . PatientSocio-demographic and medical variables are gathered to provide insight into the characteristics of the sample and to interpret the outcomes of the study.Socio-demographic variables age, gender, marital status and educational level are assessed directly after the first consultation with the radiotherapist (T2).Medical variables before RT include cancer site, adjuvant chemo-treatment, TNM-classification and Karnofsky Performance Index (KPI). The TNM-classification is a systematic way of describing the size, location and spread of a tumour. Once established, it must remain unchanged in the patient's record because the definitive TNM-classification is determined just before either initiation of treatment or making the decision not to treat [to treat . The KPIto treat . MedicalThe aim of the process evaluation is to gain insight into factors potentially influencing the effectiveness of using the SIPP and factors facilitating future implementation of the SIPP. Process evaluation data are collected using self-developed questionnaires completed by patients of the intervention groups directly after their first consultation with the radiotherapist and by radiotherapists directly after the first consultation and twice during the total recruitment period of patients. Table • nine items on the opinion of radiotherapists about the usefulness of the SIPP in general, e.g. "Did you find discussing the SIPP with the patient useful?" (item range 0 (not useful) to 10 (very useful));• four items on the opinion of radiotherapists about the usefulness of the SIPP after each consultation with a specific patient, e.g. "How much extra time (in minutes) does discussing the results of the SIPP take?";• opinion of the patients about the usefulness of the SIPP after the first consultation, e.g. "Did you find discussing the SIPP with the radiotherapist useful?" (item range 0 (not useful) to 10 (very useful); and• two open questions for suggestions for improvement of the intervention or other remarks.Descriptive techniques will be used to describe patients' background characteristics. To detect potential differences between the experimental and control groups at the start of the study, the baseline levels of psychological distress and quality of life will be compared. Furthermore, to identify potential differences between the two experimental groups and the two control groups (both with and without pre-measurement) at T3 the extent of psychological distress and quality of life will be compared. If these latter differences emerge, the four groups need to be handled separately in the analyses, i.e. the experimental and control groups with pre-measurement and the experimental and control groups without pre-measurement will be compared separately. If not, the two experimental groups and the two control groups will be combined in one group.Data of the effect evaluation will be analysed according to the intention-to-treat principle. Univariate, multivariate and descriptive techniques are applied to estimate the effect of the intervention by comparing the experimental with the control groups with regard to the primary and secondary outcomes at the follow-up measurements and by comparing the pre-test (T1) with post-tests (T3 and T4). Potential confounding factors and baseline differences will be checked and included in the model if necessary. Since dependency between outcome variables from the same cluster may exist, as well as between repeated measurements within patients, multilevel modelling will be carried out. Furthermore, multilevel modelling minimizes the loss of data through dropout by including all available data from participants in the analyses. Dropout will be described.The process evaluation data  will be analysed by means of descriptive techniques.The Medical Ethics Committee of the Twee Steden Hospital in Tilburg, the Netherlands, granted approval for conducting this study.The protocol is registered in the ClinicalTrials.gov register number NCT00859768.This study will provide insight into the actual systematic effects of using the SIPP in consultation settings. It is important to know the possible effects of using the SIPP on aspects such as communication between physicians and patients, early recognition and treatment of psychosocial problems, the extent and severity of experienced psychological problems among cancer patients and the quality of life of patients over a long period. Until now, there have been no RCTs on the effectiveness of the SIPP on aspects such as quality of life among cancer patients and the extent of psychological distress that cancer patients experience over a long period after the initial radiation or other treatment of the cancer. Until now, there have also been no reports on the differences in number of patients receiving psychosocial help because of using the SIPP.The design of this study is a CRCT. Cluster randomisation is applied because randomisation at the patient level may jeopardise the validity of the study since patients of the experimental and control conditions may visit the same radiotherapist for consultation. Furthermore, radiotherapists may alter their communication style, attitude or may pay more attention to patients' psychosocial distress because of using the SIPP. The Solomon four-group design is chosen in order to check for potential pre-measurement effects on intervention outcomes ,53.In this study, we decided to include patients with the most common cancer types and with a reasonably good prognosis because patients were asked to complete questionnaires 12 months after baseline measurement.One limitation of this study is that the results can not be generalised to all Dutch cancer patients since our study population consisted of cancer patients that received RT. Further studies outside the radiotherapy setting would be required to generalise the results.Using the SIPP may prevent underdiagnosis of early symptoms reflecting psychosocial problems and may contribute to adequate referrals to psychosocial caregivers. Therefore, using the SIPP may lead to a reduction of psychological problems and a better quality of life among Dutch cancer patients in both the short- and long-term. If the SIPP proves to be effective, the results of this study may help motivate physicians to use the SIPP as a standard method for early detection of psychosocial problems in oncology departments in the Netherlands and abroad.Recruitment of eligible patients commenced in April 2008 and will end in July 2009, resulting in 520 eligible patients being included in the study. The follow-up period will continue until July 2010. Results will be published in relevant journals.The authors declare that they have no competing interests.LL and GK obtained funding together with FG, RH, DE and AB. LL, GK, FG, RH, and AB were involved in conception and design of the study. AB is the investigator and works under supervision of LL and GK. LL, GK and AB designed some questionnaires. RH and TA were advisors in the statistical analysis plan. AB and DE coordinated the entry and validation of the data. AB drafted the manuscript, with input from the other authors. All authors mentioned in the manuscript read and approved the final version.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/9/177/prepub