The multitude of motif detection algorithms developed to date have largely focused on the detection of patterns in primary sequence. Since sequence-dependent DNA structure and flexibility may also play a role in protein-DNA interactions, the simultaneous exploration of sequence- and structure-based hypotheses about the composition of binding sites and the ordering of features in a regulatory region should be considered as well. The consideration of structural features requires the development of new detection tools that can deal with data types other than primary sequence.) is a machine learning tool for the detection of conserved features in DNA. The software suite contains programs to extract different regions of genomic DNA from flat files and convert these sequences to indices that reflect sequence and structural composition or the presence of specific protein binding sites. The machine learning component allows the classification of different types of sequences based on subsamples of these indices, and can identify the best combinations of indices and machine learning architecture for sequence discrimination. Another key feature of GANN is the replicated splitting of data into training and test sets, and the implementation of negative controls. In validation experiments, GANN successfully merged important sequence and structural features to yield good predictive models for synthetic and real regulatory regions.GANN (available at GANN is a flexible tool that can search through large sets of sequence and structural feature combinations to identify those that best characterize a set of sequences. In bacteria, activator and repressor proteins bind to operator sequences that are typically found near the promoter, and promoter specificity is typically conferred through the sigma subunit of RNA polymerase, which binds the promoter directly [The minimal requirement for transcriptional activation is recruitment of an RNA polymerase complex to a promoter sequence of DNA upstream of an open reading frame (ORF). Most genes are also potentially under the control of DNA-binding regulatory proteins or directly . Eukaryodirectly . Regulatdirectly .linearly separable [The identity and spacing of these protein binding sites are key contributors to the responsiveness of a gene to changing cellular conditions, and sets of genes or operons that are expressed under similar conditions often have similar sets of regulatory elements in their 5' upstream regions. Recent programs such as ClusterBuster  and Promeparable , and woueparable ,8.additivity in DNA-protein interactions when position-specific scoring matrices (PSSMs) are used to model binding sites. In fact, binding affinity has been shown in some cases to depend on interactions between sites [Another issue in the detection and modelling of regulatory regions is the assumption of en sites -11, whicen sites ), but thcis-regulatory module detection, with the stochastic GA approach shown to yield similar outcomes to an exact search method in substantially less time. ClusterScan [The genetic algorithm (GA) is a powerful tool for combinatorial problems of model optimization and feature selection when the 'model space' is complex and has many local optima. Genetic algorithms carry out a number of simultaneous searches in model space, with one or more recombination operators to periodically combine the results of two or more searches, permitting large scale 'jumps' out of locally optimal regions. GAs have been applied to several tasks in computational biology, including sequence alignment (SAGA: ) and physterScan  is anothsterScan .Escherichia coli integration host factor (IHF) to its target sequence [Another important question in modelling regulatory regions involves the representation of binding sites. While position-specific scoring matrices (PSSMs) are a popular and effective way of representing conserved sites , other ssequence , the corsequence  and the sequence . DNA strsequence ). While sequence , and thesequence -27, careWe have developed GANN, a software suite that uses machine learning methods to identify combinations of the features listed above that best distinguish between a positive set  and a negative set. GANN implements all of the binding site representations described above, allowing examination of models of different complexity as warranted by the type of binding site modelled and the amount of training data that is available.GANN contains a set of programs for sequence extraction, retrieval and grouping of requested patterns, neural network analysis of these patterns and collection of results. Each program in the suite can accept either the output of the previous program, or an appropriate set of data generated from an external method. The components of GANN and the flow of data through the system are shown in Figure The first program (GetSeq) reads in either a GenBank file of annotated genome sequence, or raw sequence and a list of open reading frames (ORFs) of interest generated with the NeuroGadgets Inc. Bioinformatics Web Service . GetSeq The Indices program takes as input a set of positive and negative set sequences (such as those generated by GetSeq), and can compute various properties of the sequences. The input sequences can be subdivided into overlapping windows of any size prior to the calculation of index values. The following indices can be calculated:k-mer within a window, then dividing by the length of that window. The program can determine the frequency of all k-mers of a specific length, or can assess any user-specified set of k-mers, which may include IUPAC notation to represent degenerate nucleotides.- Oligonucleotide frequencies are computed by counting the number of instances of a given - User-specified PSSMs can be counted for each sequence window. The user provides a set of scores for each type of nucleotide at each position within a PSSM, and a threshold score. The program will then count and record the number of sequence instances within each window that yield a PSSM score greater than the specified threshold.k-mer of a given length. The average score for a given sequence window is then computed by adding the scores for each overlapping k-mer within the sequence, and dividing by the total number of k-mers considered. Any numeric encoding of a complete set of k-mers can be specified: features sampled from publications such as [- Structure and flexibility rules are implemented via a text file, by assigning floating-point values to each  such as -31 are aAfter the extraction of indices, the Combine program merges the different index files into a single large file that is used as the input for the machine-learning software. Combine also allows the computation of Z-scores, thus representing each index value in terms of the number of standard deviations from the mean, and can identify peak values for a given index across a set of windows. Combine randomly subdivides the positive and negative index sets into training and test sets, and can also generate a negative control by randomly reassigning some positive and negative set members to the opposite category, yielding a disruption of patterns that were previously consistent within a single set. This type of control sets a 'baseline' for classification accuracy that can be compared to real experimental results.The core of GANN is the neural network classification system. The indices generated from 'Indices' and 'Combine' are presented as input to an artificial neural network, which is trained with either backpropagation or a genetic algorithm to maximize the discrimination between the positive and negative sets. Since the number of indices associated with each sequence is potentially very large, the Outer Genetic Algorithm (OGA) presents random subsets from the pool of indices to a series of neural networks. The unit of selection for the OGA is a 'Chromosome' that contains a predetermined number of indices sampled from the larger pool, and a set of parameters that define the architecture and connectivity of the ANN. The constitution of a population of OGA Chromosomes is determined randomly in the first generation, with random sampling of indices from the pool and ANN parameters sampled randomly from within a set of ranges specified by the user. Each OGA Chromosome is used to construct an ANN, which is then trained to yield optimal predictive accuracy on the training set defined by the Combine program above. At the end of training, performance on the test set is evaluated, and the fitness of the OGA Chromosome is equal to its predictive accuracy on the test set samples. The predictive accuracy is defined as follows:Pos|| and ||Neg|| the size of the positive and negative test sets, respectively. This formula assigns equal weight to the positive and negative sets regardless of their size, so the ANN cannot achieve an artificially high score by predicting every case as a member of the larger (training or test) set.Where TP and TN are the number of correctly classified positive and negative test set examples, and ||The OGA Chromosomes with highest fitness are then permitted to 'recombine', yielding new subsets of indices that are trained in the same manner, while less successful indices are gradually lost from the population. The classification potential of indices can be evaluated by examining the scores of neural networks that include these indices in their input set, and through a 'population genetics' approach that traces the frequency of indices through several rounds of OGA recombination and selection.training set. IGA Chromosomes with relatively high fitness are then subjected to stochastic recombination and mutation of parameters to yield a new population of Chromosomes that are used in the next round of training. While gradient-descent training methods for ANNs (such as backpropagation) can easily get trapped in local optima of the solution space, the recombination option of genetic algorithms permits a search to 'jump' through the solution space and escape local optima. The optimisation of network architecture and connection weights is similar to the 'structure evolution' method of [Training of the neural networks is performed using either backpropagation of errors  or with ethod of , which hethod of ,35, but There are many variables within GANN whose values can be specified by the user. While most default settings will be adequate in most situations, parameters such as the number of feature combinations generated by the OGA and the number of features in each combination should be chosen carefully. The 'DefineVars' program provides a set of menus that allow the user to set these parameters and write them to a configuration file that is input to GANN.In addition to reporting the scores of trained artificial neural networks, GANN will save information about the topology, connection weights and constituent indices of each neural network instance that achieves a generalization score above a specified minimum threshold. If GANN is invoked with any of these saved neural networks as input in addition to a table of indices, then it will use the input neural network to classify the new table. This process can yield functional predictions for sequences whose true classification is unknown.Escherichia coli K12 genome, are presented to illustrate the performance of GANN. Both of these experiments included a set of 250 nucleotide sequences, each 100 nucleotides (nt) in length, which were extracted from between convergently transcribed genes in the E. coli genome using the GetSeq program. These sequences were chosen because they are intergenic like upstream regulatory regions and not subject to the evolutionary constraints of protein-coding sequences, but are not expected to contain functional transcriptional regulatory features since they are exclusively 'downstream' of one gene in each direction. In the first experiment, we created an artificial positive set by inserting conserved sequences into a subset of the 250 sequences, with the remainder constituting the negative set. The entire set of 250 sequences was used as the negative set in the second experiment, while the positive set consisted of 212 upstream regulatory regions containing experimentally validated binding sites for the σ70 protein of E. coli.Two sets of detection experiments, both based on DNA sequences extracted from the Several run parameters were consistent across both experiments. Each OGA Chromosome contained a total of 8 indices, to allow the simultaneous representation of several sequence and structure properties. The population size (= number of OGA Chromosomes) was determined by multiplying the total number of indices by 10, then dividing by the number of indices (8) per OGA Chromosome. This formula ensured that indices would be represented 10 times each on average in the initial randomly generated population, and 99.9% of all indices should occur at least 3 times in the population according to the Poisson distribution. Thirty rounds of OGA Chromosome evaluation and selection were performed in each run. While GANN can evolve ANN architecture and learning parameters as well as combinations of indices, we chose reasonable ANN parameters (available at the GANN website) and fixed them for the entire run.k-mers) may yield multiple alternative solutions that are equally good. A final indicator of index performance is the composition of OGA Chromosomes that yield high predictive accuracy: if a set of features is important for characterization of a set of sequences, then each of those features should be represented by at least one index in the best OGA Chromosomes.The performance of different sets of indices was expressed in terms of the predictive accuracy (= score on the test set of sequences) described above. Differences in predictive accuracy are expected across replicates, because random partitioning of sequences into training and test sets is likely to yield variation in the frequency of some features that do not define the whole set. However, average predictive accuracy can be estimated by taking the mean across replicates. Indices that are retained in every replicate of an experimental run are more likely to reflect true characteristics of the sequences under consideration, though the redundancy of many indices  described above. Each member of the positive set was modified by adding a nucleotide decamer with high conformational mobility (CM). Five thousand unique decamers were generated randomly, and each of these was assigned a CM score based on the dinucleotide table in % describ, and eacThe construction of synthetic conserved regions is summarized in Figure Once the positive and negative set sequences were obtained and assembled, the Indices program was used to extract several different types of information from them, with varying window sizes depending on the features being examined. The overlap between adjacent windows was chosen to be 50% of the window length, so if a window of size 10 covered sites 1–10 in a sequence, the next window would cover sites 6–15.th, 95th, 90th, and 80th percentiles. The number of sequence matches above each PSSM threshold was computed for windows of size 20 and 40.- PSSMs for Lrp and CRP binding sites were constructed from the set of binding sites in RegulonDB. Frequency matrices for each site were constructed by dividing the number of occurrences of each residue at each site by the total number of sites . These frequencies were then divided by the 'background' frequency of each corresponding nucleotide in the set of intergenic sequences. Since the background frequency of each nucleotide was within the range 0.250 ± 0.005, background frequencies of 0.25 were assigned to each nucleotide. The PSSM was then obtained by taking the natural logarithm of each value in the corrected frequency matrix. Threshold values for PSSM predictions were determined by scoring 50 000 random sequences of the appropriate length against the PSSM, and identifying the scores that corresponded to the 99- The conformational mobility of sequence windows of size 10 and 20 was computed according to the dinucleotide values in .k-mers of size 1 (mononucleotides) and size 2 (dinucleotides) were computed for windows of size 10 and 20.- Counts of all k-mer counts, 64 describing the counts of PSSM 'hits' at different thresholds, and 36 describing the conformational mobility of different windows of sequence. These indices were then combined in four different ways to yield separate tests of different subsets. The k-mer frequencies were included in every set as a 'background' measure of predictive power with no explicit hypothesis. Set 1.1 included only the k-mer frequencies, while set 1.2 added the indices of conformational mobility, set 1.3 included the PSSM scores, and set 1.4 included all three types of index. We initially performed runs where index values were not standardized, but found that indices with values that were not close to zero, particularly the CM indices which ranged between 40 and 70, did not perform well and were consistently eliminated from the population of OGA Chromosomes. In response to this, we standardized all indices for the experiments described below and in Experiment 2.The computations described above yielded a total of 450 indices: 360 describing Generation of Sequence and Structure Indices above.Set 1.4, with the full set of 450 indices, was used to define the number of OGA Chromosomes. The formula at the beginning of this section yielded a recommendation of 562.5 OGA Chromosomes per generation, which was rounded up to 600 and applied to all four sets. 'Combine' was used to randomly subdivide the positive and negative set sequences into training and test sets with a ratio of 2:1. This random reassignment was repeated five times, and five corresponding negative control sets were generated as described in k-mer counts, yielded a mean generalization score of 0.880, a substantial improvement over set 1.1 with no overlap in the range of maximum scores between the two sets. Set 1.3, which considered k-mer counts and PSSM scores at several thresholds, yielded a mean best generalization score of 0.883, which was substantially better than set 1.1 and indistinguishable from set 1.2. Finally, set 1.4 yielded a small improvement over sets 1.2 and 1.3 in generalization score, with a mean of 0.900. These results suggest that the inclusion of PSSMs and flexibility indices yielded a substantial increase in predictive accuracy over the background of k-mer counts, with the combination of the two possibly producing a further slight increase.The mean of the best generalization scores achieved in each replicate over 30 rounds of OGA evaluation and selection is shown in Figure Figure th percentile of the CRP matrix between positions 60 and 99, another measuring the same quantity for Lrp sites between positions 20 and 59, an index describing the conformational mobility between sites 80 and 90, and two indices representing the count of GC dinucleotides and C mononucleotides between positions 29 and 39. The first three indices are easy to understand, as they correspond directly to features (binding sites and a high CM region) that were deliberately inserted into the positive sequence set. However, the third index is less clear until the Lrp sites from RegulonDB are examined in detail: these sites are G+C poor in general, and of the 72 × 11 = 792 dinucleotides contained in the full set of Lrp binding sites, only 20 of these are GC steps. If all 16 possible dinucleotides were present with equal frequencies, then the pair would be present 49 or 50 times, so GC is strongly underrepresented in this data set. Thus, it appears that the GC content in this region is included in many OGA Chromosomes because it is another indicator of the presence or absence of the Lrp binding site in this region.If sets of indices that yield the best predictive accuracy are preferentially selected for recombination by the OGA, then good indices should increase in frequency with successive rounds of testing and recombination. Only five indices were present in the final population of all five replicated experimental runs of set 1.4: one measuring the count of sites scoring in the 9970 subunit of RNA polymerase in E. coli. The 212 promoter-containing sequences in the positive set were extracted from a larger set defined in [The second experiment tested the ability of GANN to distinguish between the 250 unmodified intergenic sequences described earlier, and a set of sequences containing binding sites that are recognized by the 'housekeeping' σfined in . Where m70 were constructed from 250 promoter sequences in the data set, again with background frequencies of 25% for each nucleotide. As with CRP and Lrp above, the 99th, 95th, 90th and 80th percentile scores were generated for the -10 and -35 boxes from a set of random sequences, though only 1000 random sequences were generated for each case. Since the two halves of the σ70 consensus sequence are only six bases in length, indices based on PSSM matches for a window size of 10 nt as well as 20 nt and 40 nt were calculated. Indices of conformational mobility and k-mer frequency were calculated as in Experiment 1 above. A total of 618 standardized indices were generated in this experiment: 360 describing k-mer counts, 222 describing the counts of matches to the -10 and -35 PSSMs at different thresholds, and 36 describing the conformational mobility of different windows of sequence. Five sets of experiments were performed, with five experimental and five negative control runs in each. All sets (2.1 to 2.5) included the k-mer count indices, and these were the only indices considered in set 2.1. Sets 2.2 and 2.3 also included the -35 and -10 PSSMs respectively, while set 2.4 included both. All 618 indices were considered by set 2.5.Indices were constructed in a similar manner as in Experiment 1. Separate PSSMs for the -10 and -35 boxes recognized by σRuns were performed as in Experiment 1 above, with the exception of the OGA Chromosome population size. With 618 indices in total, the formula described at the beginning of this section yielded a recommendation of 772.5 OGA Chromosomes per generation, which was rounded up to 800 and applied to all five sets.k-mer counts alone, and that the inclusion of the σ70 PSSMs did not yield a more-precise model of the promoter sequence.The mean of the best generalization scores achieved in each replicate over 30 rounds of OGA evaluation and selection is shown in Figure th percentile) were favoured in Experiment 1, the PSSMs most commonly retained in the σ70 experiments favoured lower percentile thresholds, with a roughly even distribution of indices representing the 95th, 90th and 80th percentiles. This effect may be due to greater degeneracy of σ70 sites relative to the CRP and Lrp sites modelled earlier. Finally, four out of five replicate experimental runs of set 2.5 retained a CM index that covered positions 30–50 in the sequence. While no single index was retained in all five replicate runs of set 2.5, the OGA Chromosomes with the highest generalization scores all contained at least one instance of a PSSM for each of the two halves of the σ70 consensus and a flexibility feature. Thus, more-selective indices such as those based on PSSMs were included in the majority of OGA Chromosomes when they were present in the experimental set, even if the gain in predictive accuracy was marginal.While the predictive accuracy did not change across multiple sets of experiments, the type of indices that were selected by the OGA varied from set to set. The most successful index overall described the frequency of the dinucleotide 'TA' between positions 65 and 74, which corresponded to the position of the Pribnow box in the positive set sequences. This index was the only one present in all five replicates of sets 2.1, 2.3 and 2.4 after 30 rounds of OGA training, and was one of only three such indices for set 2.2. The other two indices that were retained by all five replicates of set 2.2 described the frequency of TA between positions 60 and 69, and the frequency of CA between positions 55 and 64. No single index was retained in all five replicate runs of set 2.5. In all sets that included PSSM representations, the appropriate PSSM component(s) were always retained in four out of five replicated runs. While PSSMs with a very high score threshold (9970 promoter set is similar to the sensitivity of 86% and specificity of 85% reported by [We have explored several features of GANN, most notably the ability to build classification rules for positive set members that form natural subsets, and the capacity to search through large sets of DNA sequence and structural indices to find combinations that yield optimal predictive accuracy. Our generalization accuracy of ~84% on the σorted by , though orted by , GANN coIn Experiment 1, we found that indices with mean values that are not close to zero should be standardized. A disadvantage of this approach is that standardization of a column of values is entirely sample dependent, since different finite samples from the same population of values will typically have different means and standard deviations, which may limit the accuracy of an ANN trained on one sample that is used to classify other subsamples from the same population. If standardization is to be applied, then there should be sufficient values in each index to yield a stable estimate of the population mean, as indicated with a low standard error. This is of particular concern since biological sequence samples often do not represent a random sample of all possible sequences, leading to biased estimates of sample mean and standard deviation.The two primary goals of GANN are to allow multiple alternative representations of DNA features, and to permit the discovery of important combinations of these features through the hybrid genetic algorithm / neural network approach. A long-term goal is to use GANN to identify important combinations of motifs predicted from programs such as PatSer , which w- Project name: GANN- Project home page: - Operating systems: Win32, UNIX- Programming language: C++, Perl- Other requirements: none- License: GNU GPL- Any restrictions to use by non-academics: noneCM – Conformational mobilityGANN – Genetic Algorithm Neural NetworksOGA – Outer Genetic AlgorithmPSSM – Position-specific scoring matrixRGB was responsible for software design and implementation, and manuscript writing. RLC contributed to the design, and to the implementation of the genetic algorithm classes in C++.


Schistosoma mansoni and malaria infections are often endemic in the same communities in sub-Saharan Africa, and both have pathological effects on the liver and the spleen. Hepatosplenomegaly associated with S. mansoni is exacerbated in children with relatively high exposure to malaria. Treatment with praziquantel reduces the degree of hepatosplenomegaly, but the condition does not completely resolve in some cases. The present analysis focused on the possibility that exposure to malaria infection may have limited the resolution of hepatosplenomegaly in a cohort of Kenyan schoolchildren.S. mansoni infection, a river, was molluscicided regularly over the following three years to limit S. mansoni re-infection, whereas malaria exposure was uninterrupted. Hepatic and splenic enlargement was assessed annually outside the malaria transmission season.Ninety-six children aged 6–16, from one community in Makueni district, Kenya, were treated with praziquantel. At baseline, all children had hepatomegaly and most had splenomegaly. The source of S. mansoni exposure but high exposure to malaria, and maximal in an area with relatively low exposure to both infections.Children living in an area of relatively high exposure to both infections presented with the largest spleens before treatment and at each follow-up. Spleens of firm consistency were associated with proximity to the river. The regression of hepatomegaly was also affected by location, being minimal in an area with relatively low The outcome of treating cases of hepatosplenomegaly with praziquantel in this cohort of Kenyan children depended strongly on their level of exposure to malaria infection. Furthermore, a residual burden of hepatosplenic morbidity was observed, which was possibly attributable to the level of exposure to malaria. The results suggest that exposure to malaria infection may be a significant factor affecting the outcome of praziquantel treatment to reduce the level of hepatosplenic morbidity. Schistosoma mansoni and malaria infections are endemic. Severely enlarged and hard organs may be an indicator of increased portal pressure, as well as pointing to an increased risk of portal hypertension and its sequelae, including oesophageal varices and haematemesis [S. mansoni infection, and on a global scale up to 8.5 million individuals may be affected [S. mansoni and malaria infections are endemic [P. falciparum schizont antigen (Pfs Ag) were higher in Kenyan children with S. mansoni infection and hepatosplenomegaly compared with children with infection but no hepatosplenomegaly [Hepatosplenomegaly is a widespread but neglected condition affecting many communities in sub-Saharan Africa where both atemesis . Historiaffected , with thaffected . Enlargeaffected ,5. One paffected . However endemic ,8, and i endemic ,10. Recenomegaly , furtherS. mansoni infection and seasonal malaria infection [S. mansoni egg count and the degree of both hepatomegaly and splenomegaly [S. mansoni infections [Given that malaria and schistosome infections are often endemic in the same communities, and are both targets of large-scale but independent, intervention programmes, it is important to understand how continued exposure to one parasite may affect the outcome of intervention against the other. The current longitudinal study evaluated praziquantel treatment on the regression of hepatosplenomegaly amongst children living in an area with endemic nfection ,13. Befonomegaly . Howeverfections . The preMbeetwani is situated in the district of Makueni, half way between Nairobi and Mombasa and approximately 10 km east of the main highway. The ethnic background of the population is predominantly Akamba. During the dry seasons, the river Kambu is the main source of water. Running from west to east, the river lies to the north of the community . Area-wide application and focal spraying using backpacks was undertaken twice yearly to coincide with periods when snail populations were reduced due to environmental conditions. Snail sampling activities at 9 sites on a monthly basis were undertaken to confirm that snail numbers were kept at very low levels .Regular mollusciciding of the river Kambu was undertaken with Bayluscide ®, Shin Poon Pharmaceuticals, Seoul, Republic of Korea), at 40 mg/kg body weight. The children were again treated in 2001 and 2002 under the same regimen. No other source of treatment was available locally during this period. Tablets were administered together with a piece of bread and a soft drink in order to minimise gastrointestinal side effects. The study was approved by the Kenya Medical Research Institute Ethical Review Committee.The objectives of the study were carefully explained to the local community including the parents and teachers before the start of any activities. Informed assent was obtained from the parents or guardians of the children. After the clinical and ultrasound examination in October 1999, all children were treated with a single dose of praziquantel .Baseline data for each parameter were compared with 3 year follow up data amongst 71 children who attended the first and last surveys to estimate the number of children within each sector who presented with a lower degree of organomegaly at the end of the study. Repeated-measures ANOVA of each organ enlargement parameter was also undertaken for the 67 children who attended all surveys, with age, sex and sector of residence as fixed factors. Since ANOVA does not indicate the direction of any change, a S. mansoni egg counts and IgG3 responses within each sector are given elsewhere [S. mansoni only, and sector D was an area of low exposure to both infections.Figure lsewhere . BrieflySignificant variation in the prevalence of hard spleens over time was observed within each sector Table , attribuThe prevalence of firm livers in all sectors varied over time, except in sector B Table . In all post-hoc Wilcoxon Rank analysis . Figure MSL liver enlargement varied significantly over time , with the variation being affected by sector . There was no consistent decline in MSL hepatomegaly over time within any one sector Figure . AlthougThere was no evidence of periportal fibrosis in any of the children at any time point.S. mansoni and malaria varies over small areas [S. mansoni and malaria, but also for estimating the outcome of interventions.Many clinical surveys of sub-Saharan communities have reported that organomegaly of the spleen or liver is a very common condition, but attribution to a specific aetiology has always been problematic. Distinguishing the contribution of malaria from that of schistosome infections is particularly complex since they are often endemic in the same communities. However, it is well established that exposure to both ll areas -21, and S. mansoni and S. haematobium [S. mansoni egg counts at the western end of the study area. Such clustering is likely related to variation in the amount of surface water along the river, and hence variation in transmission potential due to the strong relationship between snail abundance and water level observed during long term studies of transmission in this area [The generation of these results was facilitated by several important features of the study design. The intermittent mollusciciding of the river Kambu was particularly important, since it reduced the potential for morbidity to rebound as a result of re-infection, as has been seen elsewhere in studies of both matobium . The timhis area . The cluhis area .Although we observed several significant changes following praziquantel treatment of the cohort, it is important to note that this was a retrospective analysis, which carries a few limitations. The observations made in this study were based on a small, case-only cohort, and therefore do not represent the outcome of an intervention programme involving mass treatment of a population. As this was a retrospective analysis, no control was possible concerning treatment for malaria during the follow-up period. The population had access to antimalarial drugs at local shops, but so far as we are aware there was no systematic intervention against malaria infection during the follow-up period. The results therefore encompass the effects of background treatment for, as well as exposure to, malaria infections.Reliability of the clinical measurements is an important factor, especially given the size of the cohort. We have assessed this procedure elsewhere and it has been found to be satisfactory (unpublished observations). The involvement of three or four clinicians at each examination also reduced the degree of imprecision in the organomegaly measurements, and previous analysis has demonstrated that interpretable changes in the measurements occurred within this cohort .S. mansoni re-infection, depended strongly on where members of the cohort were resident. Previously, we demonstrated that children within the same cohort that had relatively high-level exposure to both S. mansoni and malaria presented with significantly larger spleens before treatment than children highly exposed to either parasite alone [The major result from this analysis is that the outcome of treatment with praziquantel, combined with very limited te alone . This cote alone . Here, wS. mansoni infection [S. mansoni infection was the trigger for the improvement [Hardness of enlarged spleens at baseline was most commonly observed amongst children living close to the river, irrespective of their level of pre-treatment intensity of nfection . In the rovement . One posS. mansoni [S. mansoni infection and limiting re-infection, we may have abrogated any synergistic effects of co-infection on the spleen, and thereby uncovered the residual burden of chronic exposure to malaria. Importantly, the rate of regression of MAL splenomegaly was not affected significantly by location, which suggests that the effects of praziquantel treatment on splenomegaly attributable to S. mansoni infection are not dependent on the level of exposure to either malaria or S. mansoni.Three years after treatment with praziquantel, and with very limited re-infection by  mansoni , the preS. mansoni infection was a critical factor for improvement. However, upon analysing hepatomegaly data across sectors, it emerged that children from the sector with relatively high anti-Pfs IgG3 responses, but relatively low S. mansoni egg counts, had the lowest rate of regression. A possible explanation of this observation is that an aetiological agent other than S. mansoni was responsible for the observed hepatomegaly. A likely candidate is malaria infection. Enlargement of the liver in acute malaria infection is temporary and recedes rapidly after treatment [The effects of praziquantel treatment on hepatomegaly in this cohort were more subtle. There was gradual decline in the prevalence of hepatomegaly after praziquantel treatment  indicatireatment ; howeverreatment ,26. Our S. mansoni nor malaria infection. Alternatively, because they were relatively lightly infected with S. mansoni, and experienced relatively low exposure to malaria infection, it is possible that they were more likely to regress in terms of hepatomegaly within the follow-up period.Children from the sector with relatively low exposure to both infections experienced a significant decrease in liver enlargement along the mid sternal line when baseline data were compared with data from the third year follow-up. It is possible that hepatomegaly in these children was attributable to neither S. mansoni and malaria infections. Specifically, although the degree of hepatomegaly or splenomegaly may be correlated with S. mansoni infection, there is likely to be further exacerbation of the condition if a child is concurrently exposed to malaria. We have now observed that the apparent benefits of treating a case of hepatosplenomegaly with praziquantel may be reduced if a child lives in an area of relatively high exposure. We have therefore confirmed the long-standing, but untested, hypothesis that co-infections of S. mansoni and malaria may obscure the clinical evaluation associated with infection by either species. Our results also introduce the necessity of considering the level of exposure to malaria when evaluating the clinical outcome of praziquantel treatment for S. mansoni infection.In conclusion, our observations lend further support to the hypothesis that severity of hepatosplenomegaly in Kenyan school-aged children is related to their degree of exposure to both The author(s) declare that they have no competing interests.MB conceived of, and conducted, the analysis, and drafted the manuscript. BJV and AEB performed clinical examinations and participated in the design of the study. CA and AO conducted clinical examinations. CHK, GK, JM, JHO and EM participated in the planning and execution of field activities. DWD participated in the design of the study and in fieldwork.The pre-publication history for this paper can be accessed here:


Despite heavy recent emphasis on blood pressure (BP) control, many patients fail to meet widely accepted goals. While access and adherence to therapy certainly play a role, another potential explanation is poor quality of essential care processes (QC). Yet little is known about the relationship between QC and BP control.We assessed QC in 12 U.S. communities by reviewing the medical records of a randomly selected group of patients for the two years preceding our study. We included patients with either a diagnosis of hypertension or two visits with BPs of ≥140/90 in their medical records. We used 28 process indicators based on explicit evidence to assess QC. The indicators covered a broad spectrum of care and were developed through a modified Delphi method. We considered patients who received all indicated care to have optimal QC. We defined control of hypertension as BP < 140/90 in the most recent reading.Of 1,953 hypertensive patients, only 57% received optimal care and 42% had controlled hypertension. Patients who had received optimal care were more likely to have their BP under control at the end of the study . Patients were more likely to receive optimal care if they were over age 50 , had diabetes , coronary artery disease , or hyperlipidemia , and did not smoke .Higher QC for hypertensive patients is associated with better BP control. Younger patients without cardiac risk factors are at greatest risk for poor care. Quality measurement systems like the one presented in this study can guide future quality improvement efforts. Hypertension affects approximately 58 million Americans . LowerinOne potential explanation for uncontrolled hypertension is suboptimal quality of care. Although the U.S. Joint National Committee on Prevention, Detection, and Treatment of High Blood Pressure (JNC-VII)  has codiIf deficits in process quality are indeed related to BP control, then which patients are failing to receive optimal care? The literature suggests that ethnic minorities and older patients are less likely to have controlled hypertension , but we We hypothesized that overall process quality for hypertensive care is associated with better BP control. We developed indicators of hypertensive care and determined whether patients had received the indicated care by reviewing medical records for a national sample of patients receiving care in a variety of settings. We also investigated whether patients with other cardiac risk factors received better hypertensive care and had better BP control.Our examination of hypertensive care was part of a larger study called the Community Quality Index Study (CQI), a cross-sectional study that assessed effectiveness of care for 32 different clinical conditions by examining medical records for patients in 12 randomly selected communities with populations greater than 200,000  . The metWe developed process indicators for hypertensive quality of care by reviewing the scientific literature and clinical practice guidelines  pertainiAfter a two-week training course, twenty nurse abstractors used a computer-based abstraction tool with branching logic to abstract from the medical records we collected the following data for the two-year study period: a diagnosis of hypertension; a diagnosis of co-morbid disease, including CAD, diabetes, and hyperlipidemia; BP readings; laboratory results, including serum creatinine, cholesterol, trigylcerides, sodium, potassium, and urinalyses; prescriptions for anti-hypertensive agents; and counseling for lifestyle modification. The Kappa statistic for inter-rater reliability for indicator eligibility was .75 and, given agreement on eligibility, the kappa statistic for the indicated care was .91.86% of patients with a visit in the last 13 months of the study period had a blood pressure measured. Patients were included in the study if their provider noted a diagnosis of hypertension in their medical record or if they had BP readings greater than 140/90 on two occasions at least two weeks apart. However, patients were only classified as having a new diagnosis of hypertension if this was specifically noted in their medical records. Per JNC-VI, we defined Stage 1 hypertension as BP 140/90–159/99 and Stage 2 hypertension as BP > 160/100; we determined stage by averaging a patient's BP over the study period, regardless of notation of diagnosis or treatment regimen. We defined uncontrolled hypertension as a last systolic BP ≥ 140 or a last diastolic BP ≥ 90.We determined both individual and overall quality scores for each patient. We calculated individual scores by determining a patient's eligibility for an indicator and whether s/he received the indicated care. We calculated overall quality scores by adding the number of instances in which a patient was eligible for and received the indicated care and dividing this number by the total number of instances for which s/he was eligible for the indicated care; this score was expressed as a percentage. We defined optimal quality as patients having received all of the indicated care for which they were eligible. We adjusted the scores for non-response using multivariate models that weighted respondents to be representative of the population from which they were drawn. We used the bootstrap method to directly estimate standard errors for all of the individual indicator scores . StatistOf the 6,712 adult patients for whom we analyzed medical records1,953 (29%) had a diagnosis of hypertension noted in their medical record or repeated BP readings indicative of hypertension. Of these, 241 (12%) were newly diagnosed during the study period; 1,102 (57%) had hypertension noted in the medical record, but the diagnosis was not new; 610 (31%) had repeated BPs ≥ 140 systolic and/or 90 diastolic evidencing hypertension, but no diagnosis in the chart. The sample included 1,070 women (55%), 1,326 patients over age 50 (68%), and 918 (47%) patients with CAD, hyperlipidemia, or diabetes noted in their medical records. The average last systolic BP was 139.2 and the average last diastolic BP was 82.0. Of the 1,368 patients (70%) who were receiving pharmacological treatment for hypertension, 25% were prescribed beta-blockers, 37% diuretics, 30% calcium channel blockers, and 35% angiotensin converting enzyme inhibitors or angiotensin II receptor antagonists. Patients with a diagnosis of hypertension noted in their records were more likely to have received pharmacotherapy during the study period than those with elevated BP readings but no notation of a diagnosis .On average, patients were eligible for 3.8 indicators (range: 1–21 and visited their providers 13 times during the study period. Table The last BP reading for 42% (825) of patients indicated good control (<140/90). Table A logistic regression (not shown) revealed that the relationship between higher quality scores and BP control persisted when controlling for number of physician visits, baseline blood pressure control, gender, age, smoking status, and presence of CAD, diabetes, or hyperlipidemia. Other than quality, only baseline BP control was significantly associated with BP control at the end of the study. The relationship between quality and BP control also persisted when we restricted the study sample to those with at least two BP readings 12 months apart . Likewise, there were no changes in direction or significance when we controlled for BP at 160/100 or used the continuous measures of last systolic or diastolic BP reading as the outcome.Figure Our data suggest that quality of care for hypertensive patients falls short of the ideal. Overall, patients received about 72% of recommended essential care processes, and 77% of patients with persistently elevated blood pressure had some change in therapy noted in the medical record over the course of two years. Though still concerning, these rates are higher than in some previous studies in more limited populations ,19. SomePatients with additional risk factors for cardiovascular events were more likely to receive recommended care, suggesting that providers may be targeting patients at highest risk for hypertensive complications. Alternatively, these patients may have had better BP control because they were already engaged in treatment for other conditions. Smoking was the exception to this pattern. Smokers received lower quality of care even though they are at higher risk for cardiac complications from hypertension. This is particularly surprising because cigarette smoking is the only measured risk factor that directly and immediately affects BP, though its effect on long-term hypertension is unclear . FurtherAs in previous studies of hypertensives who have visited their physician, just under half of hypertensive patients had controlled BP. However, patients who received optimal care  were more likely to have controlled BP at the end of the study, supporting the hypothesis that a broad range of hypertensive process quality is associated with better intermediate outcomes. The study was not powered to analyze which specific subcomponents of hypertensive care were most associated with BP control. However, because controlled hypertension is correlated with fewer myocardial infarctions and cerebrovascular accidents, it also seems likely that better overall care processes would eventually be associated with fewer complications from hypertensive care, although we could not directly test for this.This process-outcome relationship between hypertensive care and BP control was observed in almost all subgroups, but differences in quality were statistically significant primarily in patients without other cardiac risk factors . UnfortuIn addition to poor process quality, there are other possible explanations for the high rates of uncontrolled BP observed in this study. Even among patients with at least one medical visit in the last two years, impaired access to care might be a factor, and this problem was likely worse in the 863 patients with no visits during the study period who were not included in the analysis . Among tOur study is the first to examine the relationship between the quality of hypertensive care and outcomes among a national population; however, some study limitations exist beyond those inherent in retrospective medical record reviews. First, in constructing the indicators, our expert panel was instructed to rate indicators highly only if documentation of the process in the medical record was common and required for good quality care. Nonetheless, it is possible that documentation differences rather than true quality differences explain some of the observed variations in process quality or outcomes. A previous study found that differences in process scores were 10% lower for medical record abstraction compared to standardized patients with audiotapes of encounters ; thus, oQuality assessment in hypertension is only useful if it is linked with efforts to improve care. There is some evidence that quality improvement (QI) programs can lead to BP control among hypertensive patients. Godley has recently showed that a QI program in a group-model managed care organization increased BP control from 37% to 49% . Our resWe found that the average hypertensive patient in the United States did not receive one in four essential care processes, and those with sub-optimal care had worse BP control. Our data indicate that improvements in processes of care may lead to better outcomes. While it appears that providers appropriately focus attention on patients with additional cardiovascular risk factors, they are under-treating other hypertensive patients who (as previous research would suggest) are nonetheless at increased risk for the same adverse outcomes. Future research should test if the application of routine measurement of hypertensive process quality in provider groups or plans leads to improved processes and BP control.Drs. Asch, McGlynn, Adams, and Kerr declare they have no competing interests. Ms. Hiatt, Hicks, and DeCristofaro declare they have no competing interests. Drs. Chen and LaPuerta are employees and own shares of Bristol-Myers Squibb Company.SA and BM served as principal investigators of this study and were involved in all aspects, as were EK and LH. PL and RC helped arrange partial funding and were involved in the analysis and writing of the manuscript. JA led the analysis, AD and JH were involved in supervising the data collection and participated in the analysis.The pre-publication history for this paper can be accessed here:


Defining blocks forming the global protein structure on the basis of local structural regularity is a very fruitful idea, extensively used in description, and prediction of structure from only sequence information. Over many years the secondary structure elements were used as available building blocks with great success. Specially prepared sets of possible structural motifs can be used to describe similarity between very distant, non-homologous proteins. The reason for utilizing the structural information in the description of proteins is straightforward. Structural comparison is able to detect approximately twice as many distant relationships as sequence comparison at the same error rate..Here we provide a new fragment library for Local Structure Segment (LSS) prediction called FRAGlib which is integrated with a previously described segment alignment algorithm SEA. A joined FRAGlib/SEA server provides easy access to both algorithms, allowing a one stop alignment service using a novel approach to protein sequence alignment based on a network matching approach. The FRAGlib used as secondary structure prediction achieves only 73% accuracy in Q3 measure, but when combined with the SEA alignment, it achieves a significant improvement in pairwise sequence alignment quality, as compared to previous SEA implementation and other public alignment algorithms. The FRAGlib algorithm takes ~2 min. to search over FRAGlib database for a typical query protein with 500 residues. The SEA service align two typical proteins within circa ~5 min. All supplementary materials  are available for download on-line at The joined FRAGlib/SEA server will be a valuable tool both for molecular biologists working on protein sequence analysis and for bioinformaticians developing computational methods of structure prediction and alignment of proteins. Such segments, identified from structure, are used extensively in description and analysis of protein structures ,2. Sever I-sites . Those m I-sites . Predict I-sites . The undPrediction of local structure segments can be approached in two different ways. A first possibility, used in most nearest neighbor secondary structure algorithms, is to use a representative set of proteins with known structure as source of structure segments, but without any restrictions on a number or type of segments. In this approach, we don't make any assumptions about the compositions and distributions of segments in the library and this approach can be compared to unsupervised learning approach. In a second approach, used for instance in the I-site method, only segments from a specifically constructed fragment library are used in prediction, thus this approach is similar to supervised learning. Interestingly, some limited tests suggest that the former approach leads to lower prediction accuracy . The samab initio folding predictions and anecdotal evidence suggested that it may not be ideally suited for alignment purposes. In this note we describe a combined FRAGlib/SEA server and first benchmarking results of this method.Following this observation, we have developed the FRAGlib – a fragment library specifically designed to complement a segment alignment SEA. SEA alignment algorithm was developed previously in our group  and origFRAGlib is based on the idea of developing a uniform coverage of all known types of local structural regularity with the distribution based on that observed in natural proteins. The collection of segments is constructed using representative set of proteins from the ASTRAL database ,12. For HGEeBdbLlxc}, each representing a certain backbone dihedral (phi and psi) region [Local structure is described by the SLSR  codes consisting of 11 symbols {) region ,13. Prot) region ,15, deriIn a next step, FRAGlib segment library is used to assign local structure segments for a new protein (query) based only on sequence information using a variant of the FFAS profile-profile alignment algorithm . A profiAs the result of this procedure, each position in the query protein can be assigned to all of the possible LSSs in the database, each with a specific score , where C1 and C2 are the average numbers of segments that cover a position in each protein (the segment coverage). Detailed description of the SEA mathematical algorithm together with benchmarks results obtained using the I-site server calculated PLSSs network can be found elsewhere [A full description of the SEA algorithm is available in the previous manuscript , so onlylsewhere .The integrated FRAGlib and SEA server is available at . The FRAWe use here as a benchmark the database of 409 family-level similar pairs . Each prTo evaluate the improvement we use two measures of alignment quality: the classical root mean square deviation (RMSD) and the shift score . The shiF in the Table) alignment with that of the structural alignment prepared with the CE algorithm [I), SEA algorithm used with the actual (not predicted) local structure segments (SEAT), local single predicted structures (SEAloc) and few other publicly available alignment tools. All the results other than the FRAGlib/SEA alignments, as well as alignment quality evaluation, were adopted from the original SEA manuscript [F significantly improves the alignment quality as compared to all other methods, including SEAI (SEA using I-site prediction), bringing it close to  the SEA algorithm using the actual structure segments.'Table lgorithm  and the nuscript . The resF) integrated prediction service better incorporate diversities of local structure predictions over known methods. It produces also more accurate alignments in comparison to SEAI (based on the I-site library), or the SEA with single predicted structures (SEAloc). Comparing those sequence pairwise alignments we can observe that predicted local structure information seems to improve the alignment qualities. Alignments from SEA using FRAGlib method of describing diversities of local structure prediction have the same quality as alignments using true local structures derived from their known 3D structures SEAT.The benchmarks show that SEA with FRAGlib (SEAdarman@bioinfo.pl or Adam Godzik adam@burnham.org for information on obtaining the local copy of a software.An integrated SEA/FRAGlib server is available at . Both coDP designed, implemented, and evaluated the FRAGlib program. The benchmark dataset and programme for aligning two short sequence profiles were provided by LJ. The integration of FRAGlib predictions within SEA network alignment software together with benchmark evaluation of the SEA method was done by YY. AG was responsible for the overall project coordination. All authors have read and approved the final manuscript.


Escherichia coli. To date, these methods have only been used to screen for proteins with better folding properties but have never been used to select from a large library of mutants. In this paper we apply one of these methods to select mutations that increase the soluble expression of two antibody fragments in the cytoplasm of E. coli.Antibody fragments are molecules widely used for diagnosis and therapy. A large amount of protein is frequently required for such applications. New approaches using folding reporter enzymes have recently been proposed to increase soluble expression of foreign proteins in E. coli cytoplasm. After four rounds of mutagenesis and selection from large library repertoires (>107 clones), clones exhibiting high levels of β-galactosidase activity were isolated. These clones expressed a higher amount of soluble fusion protein than the wild type in the cytoplasm, particularly in a strain deficient in the cytoplasmic Lon protease. The increase in the soluble expression level of the unfused scFv was, however, much less pronounced, and the unfused proteins proved to be more aggregation prone than the wild type. In addition, the soluble expression levels were not correlated with the β-galactosidase activity present in the cells.We used the β-galactosidase α-complementation system to monitor and evolve two antibody fragments for high expression levels in This is the first report of a selection for soluble protein expression using a fusion reporter method. Contrary to anticipated results, high enzymatic activity did not correlate with the soluble protein expression level. This was presumably due to free α-peptide released from the protein fusion by the host proteases. This means that the α-complementation assay does not sense the fusion expression level, as hypothesized, but rather the amount of free released α-peptide. Thus, the system does not select, in our case, for higher soluble protein expression level but rather for higher protease susceptibility of the fusion protein. E. coli is a widely used organism for the production of proteins, including antibody fragments such as Fab or single chain variable domains (scFv). Active scFv can be obtained by targeting the protein to E. coli periplasm, where the two disulfide bonds needed for protein folding and stability  supE44 Δ(hsdM-mcrB)5 thi Δ(lac-proAB). CAG626F' is E. coli K-12, [F' lacIqΔ(lacZ)M15 zzf::mini-Tn10(KanR)proA+B+] lacZ(am) pho(am) lon trp(am) tyrT [supC(ts)] rpsL mal(am) Click here for fileAminoacid sequences of 225.28S and mutants scFv. The position of the six CDRs are indicated as H1, H2 and H3 for the VH and L1, L2 and L3 for the VL. Proteins are numbered according to Kabat [57]Click here for fileDNA sequences of HuLys11 and mutants scFv. The position of the six CDRs are indicated as H1, H2 and H3 for the VH and L1, L2 and L3 for the VL. The scFv HuLys11 translated sequence is shown above the DNA. Proteins are numbered according to Kabat[57]Click here for fileAminoacid sequences of HuLys11 and mutants scFv. The position of the six CDRs are indicated as H1, H2 and H3 for the VH and L1, L2 and L3 for the VL. Proteins are numbered according to Kabat[57]. The secondary structure of the protein, as indicated in the header of the PDB file 1BVK, is summarized above the sequence .Click here for file


Drosophila melanogaster egg into a multicellular embryo with 14 segments. Previously, von Dassow et al. reported that a mathematical model of the genetic interactions that defined the polarity of segments (the segment polarity network) was robust (A complex hierarchy of genetic interactions converts a single-celled s robust . As quan The striped segmentation pattern of the Drosophila embryo is remarkably insensitive to variation (robust). Ingolia uses computational methods to show that this robustness results from specific positive feedback loops Drosophila melanogaster embryo has been extensively studied. The segment polarity pattern emerges from a sequence of developmental events that each refine the pattern produced by the previous event. During the early cell cycles of the embryo, cell division is suppressed and maternal morphogens induce a transcriptional cascade of genes (the gap and pair-rule genes). These in turn create a prepattern of local expression of the segment polarity genes, genes that encode a collection of signaling molecules and transcription factors whose expression specifies the location and polarity of parasegment boundaries in the embryo. After cellularization, interactions amongst the segment polarity genes maintain narrow boundaries between parasegments as the embryo grows through cell division , the second stripe expresses engrained (en), and the third stripe, which is two cells wide, expresses neither. The prepattern is produced by the transient expression of gap and pair-rule genes, but maintaining and elaborating this pattern depends on the activity of wg and en and of genes that interact with them (en-expressing stripe must start to express hedgehog (hh), as shown in hh expression, but the target pattern as defined by von Dassow et al. requires it to be expressed in the en stripe. Because EN protein induces hh expression, simply maintaining the initial pattern of wg and en expression suffices to produce the desired final pattern  cells that initially express wg must continue to do so, (2) cells that initially express en must continue to do so, and (3) cells that express neither wg nor en must not turn on either gene.I began by asking if the von Dassow et al. model could be decomposed into the properties of individual cells. The simplest hypothesis is that parameters that allow individual cells to maintain their initial state of wg-expressing cells, as WG protein is modeled as controlling en expression, but not vice versa (data not shown). I used the equations of von Dassow et al. to model the dynamic behavior of an individual cell, starting from the standard prepattern .I began by studying the properties of epattern . I testewg stripe maintain en expression while cells on the anterior side of the stripe do not begin expressing en despite experiencing the same level of WG signaling as their neighbors on the other side of the stripe  For high rocesses A. The prs  Transport of WG from a neighbor with high levels of wg expression must be greater than KEWG→en, the amount of extracellular WG signal needed for half-maximal induction of en expression. Extracellular WG signaling must be sufficient to activate en in the absence of Cubitus Interruptus (CI) repression. The parameter KEWG→en indicates the amount of extracellular WG signal needed for half-maximal en activation. The WG signal produced by cells in the high-wg-expression steady state must be strong enough to activate en and thus greater than KEWG→en. Working parameter sets satisfied this constraint ((3) Extracellular WG from two neighbors with high levels of nstraint D, while enCN┤, the amount of CN needed for half-maximal repression of en expression. Repressive CI must also be sufficient to block en expression in cells that are near the WG stripe, but which lack en expression. In the absence of en expression, levels of CN are governed by transcriptional regulation of Patched (PTC). The equations in the model give a single steady-state level of CN. This must be greater than KenCN┤, the amount of CN needed for half-maximal repression of en. As shown in en is expressed and represses ci, and one in which ci is expressed and maintains en repression.(4) The steady-state level of the CI amino-terminal fragment (CN) must be greater than Kwg and en positive feedback loops  that are expressed in the final segment polarity pattern. Because the initial conditions are substantially different from the stable segment polarity pattern, there are initially large, rapid changes in the concentrations of the components that can drive the collection of cells towards a different final pattern. These early dynamics are complicated, and I could not determine simple rules that predicted which of the parameter sets that satisfied our bistability criteria would generate the segment polarity pattern starting from the initial conditions used by von Dassow et al.While 8.2% of all random parameter sets are consistent with the above restrictions, only 0.56% actually form the segment polarity pattern see  whereas en expression. Increasing the initial concentrations of CI and PTC is biologically reasonable, as ci and ptc are both expressed before en induction  is the second factor involved in a mutual inhibition loop with en. I therefore removed the repression of en by CN and introduced mutual inhibition of slp and en, with slp mediating the positive effect of EN on hh expression  the amount of intercellular WG in a cell with high wg expression must be enough to activate wg expression; (2) the amount of intracellular WG in a cell with low wg expression, but receiving strong HH signaling, must not be high enough to activate wg expression; (3) the amount of EN in a cell with low slp expression and high WG signaling from neighbors must be enough to repress slp expression; and (4) the amount of EN in a cell with high slp expression, but high WG signaling from neighbors, must not be sufficient to repress slp. Nearly all working parameter sets obeyed these rules, as I found for the von Dassow et al. model  the amount of intracellular WG in a cell with high wg expression, but receiving no HH signaling, must not be sufficient to maintain wg expression  = (1 −kte) · E˜i,j as the amount of WG protein on neighboring cells for the isolated cell simulations.The parameters wg expression, I simulated a single cell with no HH signaling from its neighbors. I calculated the amount of WG protein expected to be present on neighbors by an iterative process. Starting with on,0w = 1 and off,0w = 0, I computed the steady-state extracellular WG protein E˜i,j presented by the neighbors of the cell expressing wg and used these in simulating a cell with initial w = 1. Similarly, I computed the amount of WG protein on neighbors of a cell next to the stripe of wg expression and used this in simulating a cell with initial w = 0. The final values of w in those two cells were used as won,i+1 and woff,i+1 to compute the levels of extracellular WG protein for the next iteration. This process quickly converged, and I took the resulting w values as onw and offw. I verified that onw was above 0.1, the expression level threshold used in scoring pattern formation, and that offw was below 0.1.To verify bistability of onw and offw, to simulate a cell next to a stripe of wg expression. I used initial en mRNA and protein levels of 1 or 0 and ensured that, at the end of the simulation period, the former cell had en expression levels over the threshold but the latter did not. Finally, I verified that a cell with high initial wg mRNA but low initial en mRNA, receiving signals as if it were in the stripe of wg expression, still had low en expression at the end of the simulation.I then used the same levels of extracellular WG protein, computed from x, isThese equations make repeated use of a particular equation form representing saturable and cooperative action of a protein, for instance as a transcriptional activator. In general, the amount of activation, Φ, as a function of the concentration of activator, K indicates the concentration of activator needed for half-maximal activation; it is essentially an affinity of the activator for its target. The parameter ν controls the degree of cooperativity in activator function, with large values of ν giving stronger cooperativity. The function produces sigmoidal curves which asymptotically approach 1 when x is large relative to K. In the model, there is a different Φ for each instance of transcriptional regulation controlled by an affinity parameter K and a cooperativity parameter ν for that interaction. For instance, the activation of en by extracellular WG is controlled by KEWG→en, which indicates the amount of extracellular WG needed for half-maximal activation, and by νEWG→en, which determines how cooperative the activation is.Here, ptc and ci subnetwork in the absence of en expression. The concentrations of ptc and ci mRNAs are p and c, and the concentrations of PTC protein, activating CI protein, and repressive CN are P, actC, and repC. The equations governing this system, entirely contained within a single cell, areI designed parameter rules for bistability by analyzing different subnetworks in the model and solving for steady states consistent with bistability from positive feedback. I solved for the stationary state of the ciH and ptcH are the half-lives of ci and ptc mRNAs, and similarly the parameters PtcH, CiH, and CNH are the half-lives for the protein species. The level of Bicoid, a constitutive activator of ci expression, is indicated by the parameter B. Finally, C0 is an affinity parameter for the cleavage of CI by PTC. To find the stationary state, I solve for the simultaneous zero of all five equations. Two variables, c and P, can be trivially eliminated. The remaining three equations in three variables always yielded a unique stationary state. The level of CN at this state, C˜rep, was compared to KenCN┤, the amount needed for half-maximal repression of en expression (parameter rule 4).The affinity and cooperativity parameters for each Φ have been suppressed for clarity. The parameters wg expression. The strength of this activation was indicated by β, a single term encompassing activation by CI and repression by CN.The levels of CI and CN were also used to compute their influence on The only parameters in this expression are the affinity and cooperativity parameters for each Φ.wg mRNA in the ith cell, iw, are governed by β, which indicates the influence of CI and CN on wg expression, and by iI, the amount of intracellular WG in the cell.Levels of wgH, the half-life of wg mRNA, there are scalars αCI→wg and αWG→wg, which determine the relative strengths of CI/CN and WG influences on wg expression. When iI>KWG→wg, then Φ(iI) will be large and wg expression high. I computed steady-state intracellular and extracellular WG protein levels as a function of wg expression as described above for the isolated cell rules.In addition to affinity and cooperativity parameters for each Φ, and wg-expressing cell remain high enough to maintain wg expression. I computed successive approximations to steady-state levels of wg mRNA and protein. I found I˜w=1=iI(iw=1) by setting w˜on=iw(iI=I˜w=1) by setting I˜on=iI(iw=w˜on)>KWG→wg, meaning that the level of intracellular WG is sufficient to maintain wg expression (parameter rule 1). I found no cases in which this much faster test gave different results than actually solving the self-consistent equations for wg must not be activated by WG from a neighboring cell. I used w˜on to compute the amount of intracellular WG in a cell next to the wg stripe but not itself expressing wg, I˜nbr=Ii+1, and found w˜off=wi+1(Ii+1=I˜nbr) and I˜off=Ii+1(wi+1=w˜off). I then verified that I˜nbr+I˜off<KWG→wg, meaning that the sum of intracellular WG transported into a wg “off” neighbor and the intracellular WG produced by the wg “off” neighbor is not enough to activate wg expression (parameter rule 2). Finally, I find levels of extracellular WG signaling E˜on,j and E˜off,j in the same manner as I˜on and I˜off, respectively. These are used to ensure that the level of extracellular WG signal received by a cell in the en stripe is ΣE˜>KEWG→en (parameter rule 3).Bistability requires that intracellular WG levels in a c˜, C˜rep, C˜act, p˜, and P˜, which were used for the initial conditions in the stripe of wg expression and in the stripe expressing neither wg nor en. Initial conditions for components of the CI and PTC subnetwork in the stripe of en expression were kept at 0.The modified initial conditions were generated by solving for the steady state of the CI and PTC subnetwork as described above. This yielded steady-state values I˜i and E˜i,j values were computed as described above under the assumption of a single column of cells with maximal wg expression and three columns with no wg expression. This latter change had a very modest impact on the fraction of parameter sets which formed the segment polarity pattern, and I did not pursue it further.The modified initial conditions also used steady-state levels of intracellular and extracellular WG protein. The steady-state x), I employed a related functional form Ψ that represents the effects of an activator and a repressor that compete with equal affinity for a common binding site.The equations governing the modified model were similar in form to those in the original model. In addition to using the functional form Φ·(1−Φ(S)). I used this to compute S˜off using the steady-state equation s˜=S˜=(1−Φ(N)). Finally, I used S˜off and E˜w=1 to find N˜on in the en steady-state equation. I compared N˜>KEN┤slp to ensure that steady-state levels of EN were sufficient to repress slp expression.I computed N˜S=1 using the steady-state en equation and used this to find S˜on using the steady-state slp equation. The S˜on was then used to find N˜off, and I required that N˜off<KEN┤slp. This ensured that repressed levels of en expression were not sufficient to repress slp expression.Similarly, I found wg expression was dependent on HH signaling, I first found I˜on as described before. I also computed C˜act and C˜rep using c=1 but H¯Nbr=0, representing a loss of HH signaling. I then used I˜on and the new C˜act and C˜rep to find w˜H=0 and I˜H=0 with the steady state wg equation. I then found w˜on→off and I˜on→off using the steady state wg equation, the new H=0 values for C˜act and C˜rep, and I˜H=0. Finally, I verified that I˜on→off<KWG→wg, which ensure that wg autoactivation is not sufficient to maintain its expression after HH signaling is removed.To test that en expression was dependent on WG signaling, I started with N˜on and S˜off as described above. I found E˜off in the same way in which I found I˜off and used E¯Nbr,off=6E˜off. I used this new level of WG signaling to find N˜on→off with the steady state en equation, and then used this value to find S˜off→on with the steady-state slp equation. To verify parameter rule 6, I checked that S˜off→on>KSLP┤en, ensuring that the unrepressed level of slp expression can block en expression.To check whether Protocol S1Additional background and explanation of bistability in gene expression.(109 KB PDF).Click here for additional data file.http://flybase.bio.indiana.edu/) accession numbers for the genes discussed in this paper are ci (FBgn0004859), en (FBgn0000577), hh (FBgn0004644), ptc (FBgn0003892), slp (FBgn0003430 and FBgn0004567), and wg (FBgn0004009).The FlyBase (


A neuropsychiatrist reviews "The Midnight Disease: The drive to write, writer's block and the creative brain" The Last Judgment on the wall of the Sistine Chapel. What genetic influences shaped their brains to create—and to create these very specific wondrous things? How did their environments promote or impede them? Would Michelangelo have been great without the patronage of the Medicis or the competitive edge induced by Leonardo? Great art and great science are indeed often forged in the smithy of pain—with the fire fueled by self-doubt, obsessive preoccupation, sorrow, depression, competition, or economic needs.Creative human beings are the torch-bearers of civilization. How does their creativity arise? What causes some minds/brains to achieve awe-inspiring artistic or scientific achievements? We cannot help but be fascinated by the fact that Shakespeare—a merchant's son with “small Latin and less Greek”—could emerge from the “nowhere” of rural Stratford to create the richest literary treasure in the English language. We wonder how Michelangelo—a stonecutter's son who also came from a rural nowhere—found within himself the vision to see the shape of David in a block of discarded marble or the apolcalyptic fresco of The Midnight Disease: The Drive to Write, Writer's Block, and the Creative Brain by Alice Weaver Flaherty unites two intrinsically fascinating domains of knowledge—the workings of the brain and the nature of creativity. Its author, a neurologist who has also become a writer by virtue of having published her first nonacademic book, draws on her knowledge of neuroscience, her medical career as a clinician, and her experiences as a patient. Early in the book, she describes her own hospitalization for manic-depressive illness, a disclosure that implicitly places her in the pantheon of other artists who have suffered from serious mental illness and provides her with lustre-by-association. The result of all these juxtapositions is, however, a somewhat disconcerting blend of pop-science and pop-confessional genres. The author frequently talks to us in the first person, but one is not quite sure which person  is actually speaking. In other words, this book has a jarring lack of a strong single voice, despite a knack for often finding a fine turn-of-phrase or a clever word choice.Given that the book topic is promising and that the author can often write very well, it is dismaying that this book is not better than it is. It is written for the intelligent lay public, many of whom avidly collect and read “brain books” to expand their minds. Most painful is the fact that this book is filled with factual errors, glib and misleading generalizations, and careless misstatements. Perhaps most shocking and most erroneous, we are told (by a neurologist!) that “The tips of the temporal lobe can be lopped off without much changing a person's behavior.” HM, the most famous patient to receive bilateral temporal lobectomy, remains frozen in a past linked to a never-changing present because he lost the capacity to retain new memories. Temporal lobe syndromes are discussed more accurately later in the book, but that is a weak excuse for this early error.We are also told that “manic depression is a genetically transmitted syndrome” , that “a very high proportion of manic depressives become writers” , and that “electrophysiology, because it is dangerous, is rarely performed” . There are many more such careless misstatements. The intelligent lay reader deserves better than this.The book raises and addresses a variety of interesting questions that have intrigued many thoughtful people for more than two millennia. What is the nature of creativity? What is the difference between skill and creativity? What is the relation between mental illness and creativity? Is creativity inhibited when mental illnesses are treated? What is the relation between mind and brain? The book also addresses some unique and interesting twists on these questions. Its focus is the domain of writing, drawing from the author's own experience of a compulsion to write, or hypergraphia, following a pyschic break. What is the relationship between hypergraphia and the brain? Between writer's block and the brain? Are these problems always pathological, or do they sometimes enhance creativity? Does that college student who can't finish a term paper have a “disease”? Can “mind-expanding” drugs that affect the brain enhance creativity?The Midnight Disease raises many important questions, but fails to address them completely and accurately. There is much more to learn, and much more to say, about the nature of creativity, its origins in the mind/brain and in the human genome, and its boundaries with health and disease.In short,


Unlike salmonellosis with well-known routes of transmission, the epidemiology of campylobacteriosis is still largely unclear. Known risk factors such as ingestion of contaminated food and water, direct contact with infected animals and outdoor swimming could at most only explain half the recorded cases.Campylobacter; 2) the ability of flies to function as mechanical vectors; 3) a ubiquitous presence of the bacteria in the environment; 4) a seasonality of the disease with summer peaks in temperate regions and a more evenly distribution over the year in the tropics; 5) an age pattern for campylobacteriosis in western travellers to the tropics suggesting other routes of transmission than food or water; and finally 6) very few family clusters.We put forward the hypothesis that flies play a more important role in the transmission of the bacteria, than has previously been recognized. Factors supporting this hypothesis are: 1) the low infective dose of Campylobacter infection, we believe it would be unwise to rule out flies as important mechanical vectors also of this disease.All the evidence in favour of the fly hypothesis is circumstantial and there may be alternative explanations to each of the findings supporting the hypothesis. However, in the absence of alternative explanations that could give better clues to the evasive epidemiology of  Campylobacter infection is a zoonotic disease, observed in most parts of the world. The disease is caused by Campylobacter jejuni, or less commonly Campylobacter coli. It is estimated to cause 5–14% of diarrhoea, worldwide [Campylobacter infection has emerged to be the most important bacterial cause of gastrointestinal infection. Animals  are the major reservoir for the bacteria. Campylobacter does not easily grow in food, but the critical infective dose is low [Campylobacter infections, that may coincide with the summer peaks in the temperate regions include direct animal contact, eating barbecued meals, swimming in lakes, and drinking untreated water from streams and other natural sources [e is low . Unlike e is low . Known re is low ,4-6. Dire is low ,5,7,8. I sources . However sources . InsteadMusca domestica) and other muscid flies thrive in excreta and other filth. They could act as mechanical vectors, by carrying bacteria on the hairs and surface of their bodies or on the glandular hairs on their feet, but they could also act as biological vectors by passage through the alimentary tract, where pathogens have opportunity to multiply [E. coli infection [Campylobacter from animals to human food [Campylobacter infection has been largely neglected [The common houseflies , and non typhoidal Salmonella species (105-1010 bacteria). Although less tolerant to desiccation than some other food-borne pathogens [Campylobacter can survive on dry surfaces for at least seven days [The infective dose of bacteria , which iby flies ,12,15, aathogens , Campyloven days , thus enCampylobacter could easily be transmitted from the environment to flies [Campylobacter could also be transmitted from flies to chickens [Campylobacter could be isolated from 4 of 49 (8%) of flies caught outside a broiler house in Denmark. Furthermore, Wright showed that Campylobacter could be isolated from five of 210 (2.4%) living flies, isolated from three different locations [Campylobacter from animals to human food is small. On the contrary, giving the numerous contacts between flies and human food, we find it highly likely that if one out of every 40 flies carries Campylobacter the health hazard would be significant.Studies have shown that to flies ,24, and chickens . In a reocations . From thShigella is a strict human pathogen, while the major source of Campylobacter is the faeces of both humans and animals such as chickens, cattle and pigs, which are often kept in close proximity of humans. Stanley and Jones have previously shown the importance of cattle and sheep farms as reservoirs of Campylobacter [Campylobacter is also common in the droppings from wild birds [Campylobacter spp have been isolated from sewage contaminated water [Campylobacter than Shigella in the close vicinity of humans. Since flies have been shown to be an important mechanical vector of shigellosis, it would be surprising if they could not also be so for campylobacteriosis. Direct transmission from the soil could probably account for some of the cases in children, but less likely for adult cases.lobacter . Campylold birds ,28, and ed water , contamied water  and aquaed water , and in ed water . There aCampylobacter. Some of the recorded association between barbeque and campylobacteriosis could very well be due to contamination of the food by flies, rather than undercooked meat or cross-contaminations, as has previously been postulated. A recent study from the UK has shown a close temporal association between the incidence of campylobacteriosis and fly density [The distinct seasonality in the temperate regions ,5,7,8,33 density . Althoug density ,14. Ther density . Lack ofCampylobacter infection is largely confined to children below the age of two years, and the decreasing incidence thereafter has been attributed to a lasting immunity [Campylobacter was ingestion of contaminated food, one would expect the infection to be relatively evenly distributed among the largely non-immune westerners coming to high prevalence countries. Again we turned to the risk estimates for campylobacteriosis in returning Swedish travellers. While, the incidence pattern in travellers returning from temperate countries closely mimicked the age pattern of indigenous Swedish cases, we noted that among travellers returning from tropical areas of Africa and Asia, the youngest children had twice as high risk as young adults, and more than four times the risk compared to older children [Small children are less able to protect themselves from flies than older children and adults, and are more likely to have their hands on fly-soiled surfaces. In the tropics, the immunity . On the immunity ,36. Thenchildren . This agIf intake of chicken and undercooked meat (or cross-contamination from these food items) was a major route of transmission, clusters of cases within the same family should be common. Instead a striking feature of indigenous campylobacteriosis in Sweden is that the cases (except for in a few larger outbreaks) are solitary. A survey of notification data in one Swedish county over several years showed that it was exceptionally rare that cases shared the same address . InformaCampylobacter infection by fly control, as has previously been done for shigellosis and diarrhoea [Campylobacter sources, e.g. cattle farms, sewage treatment or fowls, should be included in forthcoming case-control studies on campylobacteriosis. This has been a neglected line of questioning so far. More data on the carriage of Campylobacter by flies in different settings where people could be exposed are also needed. An alternative, and more innovative, approach would be to combine information on the likely place/s of infection with data on environmental sources, in analytic studies using geographical information systems (GIS) tools.The fly hypothesis needs to be backed by further experimental and epidemiological studies. The best evidence would be if controlled intervention studies could show an effect on the incidence of iarrhoea -15. SuchCampylobacter infection, we believe it would be unwise to rule out flies as important mechanical vectors also of this disease.All the evidence in favour of the fly hypothesis is circumstantial and there may be alternative explanations to each of the findings supporting the hypothesis. However, in the absence of alternative explanations that could give better clues to the evasive epidemiology of Campylobacter; 2) the ability of flies to function as vectors; 3) a ubiquitous presence of the bacteria in the environment; 4) a seasonality of the disease with summer peaks in temperate regions and a more evenly distribution over the year in the tropics; 5) an age pattern for campylobacteriosis in western travellers to the tropics suggesting other routes of transmission than food or water; and finally 6) very few family clusters. The hypothesis should be further tested with experimental and epidemiological studiesWe put forward the hypothesis that flies play a more important role in the transmission of the bacteria, than has previously been recognized. Factors supporting this hypothesis are: 1) the low infective dose of The author(s) declare that they have no competing interests.Bengt Normann raised the original idea and has studied the (lack of) family clustering. Yvonne Andersson contributed with in depth knowledge of campylobacteriosis. Karl Ekdahl did the literature search, looked into the seasonality and age distribution of the disease in domestic cases and returning travellers, and prepared the first draft of the manuscript. All authors have participated in revising the draft manuscript.The pre-publication history for this paper can be accessed here: