Previous behavioral studies have shown that native speakers of a non-tone language have difficulty discriminating among tone contrasts and are sensitive to different F0 dimensions than speakers of a tone language. The aim of the present ERP study was to investigate the effect of language background and training on the non-attentive processing of lexical tones. EEG was recorded from 12 adult native speakers of Mandarin Chinese, 12 native speakers of American English, and 11 Thai speakers while they were watching a movie and were presented with multiple tokens of low-falling, mid-level and high-rising Thai lexical tones. High-rising or low-falling tokens were presented as deviants among mid-level standard tokens, and vice versa. EEG data and data from a behavioral discrimination task were collected before and after a two-day perceptual categorization training task.Tone languages such as Thai and Mandarin Chinese use differences in fundamental frequency  in all language groups. Before, but not after training, the English speakers showed a larger MMN compared to the Chinese, even though English speakers performed worst in the behavioral tasks. The MMN was followed by a late negativity, which became smaller with improved discrimination. The High-rising deviants versus standards elicited a late negativity, which was left-lateralized only in the English and Chinese groups.0 contour differences (high-rising versus mid-level tones). In addition, native speakers of a non-tone language (English) were initially more sensitive to F0 onset differences , which was suppressed as a result of training. This result converges with results from previous behavioral studies and supports the view that attentive as well as non-attentive processing of F0 contrasts is affected by language background, but is malleable even in adult learners.Results showed that native speakers of English, Chinese and Thai recruited largely similar mechanisms when non-attentively processing Thai lexical tones. However, native Thai speakers differed from the Chinese and English speakers with respect to the processing of late F Tone languages, such as Thai and Mandarin Chinese, use differences in either average F0 or F0 contours (or slopes) over strings of otherwise identical phonemes to distinguish between different words in the lexicon from one another. For instance, the Thai syllable [kha:] means something completely different when pronounced with a tone that is low-falling , low-falling and then rising ("leg"), high-falling , high-rising ("to do business in") or mid-level ("to be lodged in"). In non-tone languages such as English, on the other hand, pitch variation is not used to differentiate word meaning. However, even though F0 is not used to distinguish meaning between words in English, it can make one syllable more perceptually prominent or more salient than neighboring syllables in multi-syllabic words. For example, the first syllable of the word 'cookie' is stressed, and perceptually more salient than the second syllable. The F0 or pitch (as well as intensity or loudness and vowel duration) of the stressed syllable is typically higher than its neighboring unstressed syllable. In addition, lexical stress can also be used to distinguish a compound word 'a hotdog' from a noun phrase 'a hot dog'. Variation in the linguistic functions of F0 may account for perceptual difficulty typically experienced among adult native speakers of a non-tone language when consciously perceiving and distinguishing among lexical tones differing in pitch level or pitch contours. The aim of the present ERP study was to investigate whether the processing of lexical tones is affected by the listener's native language (tone or non-tone) even when the participants are not paying conscious attention to the stimuli, and whether such non-attentive perception can be altered by laboratory training, even in adults.Variation in voice pitch, an auditory impression of the rate of vocal fold vibration  poorly discriminate among lexical tones as compared with native speakers of a tone language (e.g. Mandarin Chinese), even when the latter are unfamiliar with the tones being tested -5. This  contour -8. Inter contour ,4,9,10.  contour ,12. In a contour . This suThe discrimination or identification tasks used in the behavioral and fMRI studies on lexical tone perception involve conscious comparison or categorization. Performance in these experiments may therefore have been affected by factors such as working memory load or attention. In the present study we therefore studied the non-attentive discrimination of lexical tones and the effect of language background and training by using Event-Related brain Potentials (ERPs). ERPs can be recorded while the participant is presented with auditory stimuli, but engaged in an unrelated task such as watching a movie. The mismatch negativity (MMN) is a frontal negative ERP component occurring about 100–300 ms after stimulus onset. It is elicited by infrequent stimuli that deviate from frequently presented (standard) stimuli in pitch, duration, voice onset time, or other acoustic or phonetic properties . Since tOnly a few studies have employed the MMN to investigate the processing of lexical tones. Chandrasekaran et al.  investig0 onset values, the steep F0 slope right after the F0 onset, as well as with respect to a later, more gradually developing F0 slope. Given that speakers of a non-tone language (English) have been shown to be sensitive to F0 onset and offset differences, whereas native speakers of a tone language are more sensitive to the later F0 contour, we expected the native English speakers to initially show a larger MMN than the native Chinese and Thai speakers. The Chinese and Thai speakers, on the other hand, were expected to show a more pronounced later negative effect, which may be related to the later contour differences .The low-falling deviants  showed a MMN at the F3 and F4 electrodes. ERPs for the F3 electrode are displayed in Figure An ANOVA on the MMN amplitude (deviant minus standard) at the F3 and F4 electrodes showed an interaction of TEST TIME by HEMISPHERE : Before training, the MMN was numerically larger at the left hemisphere electrode ; after training, the MMN was numerically larger at the right electrode, with the difference almost reaching significance . Training also weakly affected the differences in the MMN between the groups : The groups weakly differed from each other before training , with the MMN being larger for the English compared to the Chinese , but not compared to the Thai [p = 0.12]. After training, the groups did not differ [ps > 0.68] (see Appendix).earlier the MMN peak and the smaller (i.e. less negative) the MMN amplitude was after compared to before training .The difference in MMN latency and amplitude after versus before training correlated weakly with the degree of learning: the greater the improvement in the behavioral discrimination task (d' scores post minus pre-training), the smaller the late negativity in the 350–500 ms interval post- versus pre-training .A late negativity was seen for the deviant versus standard in the 350–500 ms window   at F3 and F4 versus a hypothetical zero showed no significant differences in any of the groups before training [ps >0.18]. After training, the MMN was most robust in the native Thai speakers . ANOVAs showed no effects of the experimental manipulations on the MMN (amplitude or latency).Between 350 and 500 ms, a negativity was elicited by the high-rising deviants versus standards, particularly over the left hemisphere , see Figure ps>0.18].The negativity for the high-rising deviants persisted in the 500–700 ms interval , 12 native speakers of Mandarin Chinese (People's Republic of China) (6 men), and 11 native speakers of Thai (5 men) were recruited from the University of Florida community. Informed consent was obtained from each participant according to the procedures of the University of Florida Institutional Review Board. All participants were healthy young adults, aged 19–35, right handed as assessed by the Edinburgh handedness inventory , and witha:] produced by a female native speaker of Thai and digitized at 22050 Hz sampling rate with a 16-bit amplitude resolution. Using the Praat speech analysis software, the original mid-level tone was shortened from 610 ms to 450 ms. The pitch contour of this mid-level tone was then manually changed to approximate the pitch contours of the natural tokens of the Thai low-falling and high-rising tones. The entire F0 contour of each of the three resulting stimuli was then shifted down -15 Hz and -30 Hz to simulate three different talkers, thus yielding three tokens for each of the three tone types, see Figure Nine stimuli were synthesized on the basis of one naturally generated instance of the Thai mid-level tone syllable . Numerically, the English group showed a larger frontal negativity than the Thai and Chinese before, but not after training. This effect is the same as the weak LANGUAGE BY TESTTIME interaction found for the F3 and F4 electrodes discussed in the main text.Sound file of the first low-falling token.Click here for fileSound file of the second low-falling token.Click here for fileSound file of the third low-falling token.Click here for fileSound file of the first mid-level token.Click here for fileSound file of the second mid-level token.Click here for fileSound file of the third mid-level token.Click here for fileSound file of the first high-rising token.Click here for fileSound file of the second high-rising token.Click here for fileSound file of the third high-rising token.Click here for fileSpectrogram of first low-falling token.Click here for fileSpectrogram of second low-falling token.Click here for fileSpectrogram of third low-falling token.Click here for fileSpectrogram of first mid-level token.Click here for fileSpectrogram of second mid-level token.Click here for fileSpectrogram of third mid-level token.Click here for fileSpectrogram of first high-rising token.Click here for fileSpectrogram of second high-rising token.Click here for fileSpectrogram of third high-rising token.Click here for file


Geminivirus. Three elements are important in rolling-circle replication: i) a gene encoding initiator protein, ii) a double strand origin, and iii) a single strand origin. However, differences exist between viruses and plasmids and between viruses. Porcine circovirus replication probably involves a "melting pot" rather than "cruciform" rolling-circle mechanism.Porcine circoviruses are circular single-stranded DNA viruses that infect swine and wild boars. Two species of porcine circoviruses exist. Porcine circovirus type 1 is non pathogenic contrary to porcine circovirus type 2 which is associated with the disease known as Post-weaning Multisystemic Wasting Syndrome. Porcine circovirus DNA has been shown to replicate by a rolling circle mechanism. Other studies have revealed similar mechanisms of rolling-circle replication in plasmids and single-stranded viruses such as Circovirus genus. Based on various studies, the factors affecting replication are defined and the mechanisms involved in the different phases of replication are described or proposed.This review provides a summary of current knowledge of replication in porcine circoviruses as models of the  Circovirus genus in the Circoviridae family are animal viruses, most of which affect birds although type 1 and type 2 porcine circoviruses (PCV-1 and PCV-2) affect swine and wild boars. All circoviruses known at present, except PCV-1, are associated with immuno-suppressive or immuno-depressive diseases. They have small (around 2 kb), closed-circular, single-stranded (ss) DNA, ambisense genomes. The intergenic region contains the origin of replication with a stem loop structure which includes an octanucleotide sequence flanked by palindromes, and is bordered by two open reading frames, ORF1 and ORF2  strategy [Circovirus genus.Based on common features which include the initiator Rep protein and stem-loop structure, it has been proposed that PCVs, like strategy ,4. This strategy . Studiesstrategy . This reSynthesis of the leading strand and the lagging strand is uncoupled and rolling-circle replication is both unidirectional and asymmetric. After the infection of permissive cells, synthesis of the lagging strand, involving a still-unknown mechanism in PCVs, starts from the viral positive single-stranded DNA. This produces the supercoiled double-stranded replicating form (RF) of the genome figure . ThereafPCV replication studies have generally been performed on the PCV-1 model. Nevertheless, PCV-1 and PCV-2 exhibit 79.5% and 82% sequence homologies for the replication origin and Rep gene, respectively . Therefoin vivo proteins binding sites [The replication origin of the leading strand is a stem-loop structure. An AxTAxTAC sequence is located on the loop and the stem is a 10 nucleotides palindrome in which the 2 Cs at positions 3 and 10 of the right stem play a role in binding the REP complex to the DNA  , in the 2+ ions [The role of motif I in the Rep and Rep' proteins of PCV-2 i.e., FTLN located on the β5 sheet , is unkn2+ ions  and a he2+ ions , have be2+ ions . This re2+ ions . The C-t2+ ions .The first step in RCR is synthesis of the lagging strand which results in passage from single-strand DNA to the replicating double-strand DNA. As yet, neither the PCV genomic DNA sequence nor the proteins involved in this synthesis have been determined. However, the replication mechanisms described for RCR plasmids, Nanoviruses and Geminiviruses might provide some indication of the PCV mechanism.In RCR plasmids, synthesis is initiated and terminated at a single-stranded origin. This origin is a non-coding region that can generate an imperfect stem-loop and is usually located away from the double-stranded origin involved in leading strand synthesis. The single-stranded origin contains a promoter recognized by a host RNA polymerase that synthesizes an RNA primer essential to DNA synthesis by cellular DNA polymerase I . Two mecThe origin of replication of the lagging strand in PCV has not yet been identified. However the Geminivirus and Nanovirus models might help to explain PCV replication. It is possible that the origin of synthesis of the negative strand of the PCV genome is located in the intergenic region, which may or may not contain the origin of replication. A DNA molecule might thus either be encapsidated like Mastrevirus or produced after infection like Begomovirus.Initiation of the leading strand first requires binding of the REP complex to the replicating form of the PCV genome, followed by nicking of the DNA by this complex.The replication origin contains the elements essential to REP complex binding. The Rep protein binds to hexamers H1 and H2 and to the right strand of the stem-loop whereas the Rep' protein binds only to hexamers H1 and H2 . Rep pro2+, but not of ATP or of the stem-loop structure [The replication origin is destabilized by the REP complex bound to the DNA. The REP complex nicks then the AxTAxTAC sequence of positive single stranded DNA (+) between nucleotides T and A figure . The portructure ,28. In Ptructure . ReplicaUnlike the cruciform rolling-circle model of the Geminiviruses, PCVs exhibit "melting pot" organization . This isA termination model of rolling circle replication based on pT181 plasmid studies was described in the nineties by Novick's team . This meThe termination mechanism of PCV leading strand synthesis has not yet been studied. However the involvement of tyrosine in PCV replication suggests that the mechanism might be similar to that of the pT181 plasmid.This mini-review summarizes the various mechanisms and elements necessary for rolling circle replication of porcine circovirus. Although not all these mechanisms have been clarified, the explanatory indications are based on sequence homology between porcine circovirus and other models such as Geminivirus or pT181 plasmids. Supplementary studies on porcine circovirus should validate or not the hypotheses associated with the various models.The authors declare that they have no competing interests.FF was the main author of this review. DD, BG and AJ read and completed the review. All authors read and approved the final manuscript.


Phosphorylation of proteins plays a crucial role in the regulation and activation of metabolic and signaling pathways and constitutes an important target for pharmaceutical intervention. Central to the phosphorylation process is the recognition of specific target sites by protein kinases followed by the covalent attachment of phosphate groups to the amino acids serine, threonine, or tyrosine. The experimental identification as well as computational prediction of phosphorylation sites (P-sites) has proved to be a challenging problem. Computational methods have focused primarily on extracting predictive features from the local, one-dimensional sequence information surrounding phosphorylation sites.We characterized the spatial context of phosphorylation sites and assessed its usability for improved phosphorylation site predictions. We identified 750 non-redundant, experimentally verified sites with three-dimensional (3D) structural information available in the protein data bank (PDB) and grouped them according to their respective kinase family. We studied the spatial distribution of amino acids around phosphorserines, phosphothreonines, and phosphotyrosines to extract signature 3D-profiles. Characteristic spatial distributions of amino acid residue types around phosphorylation sites were indeed discernable, especially when kinase-family-specific target sites were analyzed. To test the added value of using spatial information for the computational prediction of phosphorylation sites, Support Vector Machines were applied using both sequence as well as structural information. When compared to sequence-only based prediction methods, a small but consistent performance improvement was obtained when the prediction was informed by 3D-context information..While local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites. A web-based service (Phos3D) implementing the developed structure-based P-site prediction method has been made available at  Protein phosphorylation is a ubiquitously occurring post-translational modification influencing many molecular processes in all complex cells. The recognition of phosphorylation sites by specific kinases and the subsequent phosphorylation generally leads to an alteration of the structure, function, or protein binding properties of the target protein, which has evolved as a mechanism to respond to environmental changes via phosphorylation-triggered complex signaling networks and cascades and is playing a crucial role in the regulation of enzymes or transporters in metabolic processes -4.in silico. Most established computational prediction methods rely solely on the local sequence surrounding the target amino acid residue. The developed prediction methods range from simple amino acid sequence pattern recognition methods to Markov Models, Neuronal Networks, and advanced machine learning methods such as Support Vector Machines  to  to 28] tThese enrichments are well captured by the sequence logos and are also reflected in the RCP-plots for the spatial environment considering sequence-local residues. In addition to the detected enrichments (red-colored segments), the RCP-plots also highlight significant depletions of amino acid types , the counts for both amino acids are not increased relative to the reference distribution. The PKC motifs exhibit an additional enrichment of serine in the sequence-local neighborhood, accompanied by a pronounced depletion of the amino acid residues histidine, glutamate, and tryptophan. The PKA motifs were observed to be depleted of the amino acid cysteine. For both families, PKA and PKC, a depletion of the hydrophobic amino acids alanine and leucine in the non-sequence-local neighborhood and an additional depletion of isoleucine in PKA motifs was detected Figure .Proline residues flanking the phosphorylated serines are the hallmark sequence feature of targets associated with CMGC kinase group which includes the CDK, CKII kinase families Figure  as well In the sequence-local environment of the MAPK, no enrichments of amino acids besides proline were detectable. Instead, depletions of eight amino acid types, glutamine, asparagine, phenylalanine, isoleucine, valine as well as glycine, serine, and threonine were detected. In the non-sequence-local environment of target serines, serine, and histidine residues were observed to be overrepresented.The active sites of CKII kinases are characterized by positively charged surfaces . This poThe PTK group comprises tyrosine phosphorylating kinases and as such were not included in the introduced phylogenetic tree of serine targeting kinases. The sequence-local spatial context of SRC-kinase family (PTK I) – for which sufficient data for analysis was available – is enriched in aspartate, proline, leucine, alanine, and tryptophan in the non-sequence-local spatial context. Depletions of several amino acids were also detectable, most consistently cysteine Figure .In summary, all kinase-family specific RCP-plots reveal specific spatial profiles and more information contents than was detectable when sites were investigated across all kinase families Figure . The proWe now turn to investigating whether incorporating 3D structural information can be used to improve the sensitivity and specificity of phosphorylation site predictions in proteins.For the general, kinase-family unspecific prediction of phosphorylated serine, threonine, and tyrosine sites, the SVM-predictors based on local sequence information alone that have been developed as part of this study were observed to perform at comparable or even slightly better performance levels compared to NetPhos and DisPhos, and consistently better compared to KinasePhos as judged by the area under the receiver operating characteristic (AUC) from 10-fold cross-validation test Table .Similarly, for the kinase-family specific predictions, the AUC-based performance of NetPhos and our SVM-based method was comparable or even in favor of our SVM Table  giving uWhile the magnitude of performance gain when including 3D-profile information was relatively small compared to the estimated standard error, for all target sites and across all kinase-families and target residue types, a consistent increase in performance was obtained suggesting that including three-dimensional structural information does indeed improve the sensitivity and specificity of phosphorylation site prediction.Similar conclusions can be drawn from comparing prediction accuracies as well as sensitivities and specificities associated with the predictions rather than AUCs Table  alone. UIn this work, we focused on the characterization and prediction of phosphorylation sites. Serine is the most frequent target amino acid residue type for phosphorylation followed by threonine and tyrosine. We pursued two major themes: the analysis phosphorylation in a kinase family specific fashion, and to investigate whether phosphorylation sites are characterized by specific three-dimensional (3D) structural motifs or epitopes constituted by amino acid residues that are not necessarily close in sequence, thereby providing additional information that can help in predicting phosphorylation sites for proteins with known structure or with available structural models. We used the simple radial distance to define structural motifs. Ideally, angular information would be included as well. However, much larger datasets of determined structures would be necessary to derive reliable statistical data for more refined approaches. Even by applying only this simple model, we observed that 3D-structural context information is indeed discernable, even though the most information contents appears to reside primarily in the local sequence, as judged by the sequence-local kinase unspecific RCP-plots and the modest increased performance when adding spatial-information to sequence-only based predictors. The most pronounced improvement of prediction of phosphorylation sites by augmenting sequence-only prediction by spatial information was obtained for targets of serine kinases. However, also for the prediction of threonine and tyrosine targets, a performance gain was obtained when adding 3D information.As several experimental techniques have been established to detect proteins that specifically bind to phosphorylated sites based on immobilized peptides (pull-down assays and peptide chips -36); i.eIt has been reported that phosphorylation is preferentially occurring in unstructured; i.e., flexible regions of proteins . These cGnad and co-workers, the authors of the PHOSIDA database, evaluated the preferences of secondary structure, accessibility of phosphorylated residues and the conservation rate of phosphorylation sites . The preA major problem in any effort to develop a computational predictor arises from the difficulty to define a reliable true-negative set; i.e., sites that are truly unphosphorylated. As phosphorylation is condition-dependent, experimental screens may well be incomplete as it is impossible to explore all environmental conditions under which phosphorylation events may occur. Even sites that are buried and inaccessible for phosphorylating kinases in one protein state may become exposed upon conformational changes and become phosphorylated ,39. ThusAn estimated two to five percent of eukaryotic genomes codes for kinase genes grouped into different kinase families ,30 and 3The high diversity of targets of particular kinases and the number of possible phosphorylated proteins accompanied with the pleiotropicity of kinases appear to contradict a specific regulatory role of phosphorylation. However, the specificity for the actual target site may not be the only source of kinase specificity and sensitivity of the regulatory system. In fact, it was shown that subcellular compartmentation accompanied with recognition of secondary target sites relatively distant to the catalytic domains is crucial for further selectivity and specificity. While 3D motifs near the actual target site for phosphorylation have been at the center of our investigations, for the kinase family CDK, in particular the kinase CDK2 , it has .The reliable prediction of phosphorylation sites and the identification of associated kinase enzymes are important steps that will ultimately lead to a deeper understanding of complex signaling events in cellular systems. Applying a simple radial distance model for the characterization of the 3D-structural context of phosphorylation sites, it is possible to extract kinase specific signature 3D-profiles. While local one-dimensional amino acid sequence information was observed to harbor most of the discriminatory power, spatial context information was identified as relevant for the recognition of kinases and their cognate target sites and can be used for an improved prediction of phosphorylation sites. A web-based service (Phos3D) implementing the developed structure-based P-site prediction method has been made available at The dataset of phosphorylation sites was obtained from the Phospho.ELM database . The amiWe removed the phos-Set motifs from the sequences of the respective protein chains with known protein structure. From the remaining sequence fragments, we extracted all non-overlapping Ser/Thr/Tyr site motifs. The resulting sets of sites served as the true-negative set. While our approach cannot guaranty that these extracted sites are truly unphosphorylated, we expect this dataset to be at least depleted in true phosphorylation sites.When kinase-family-specific phosphorylation events are analyzed, the true-positive counts are heavily outnumbered by true-negative sites posing the risk of dominating influences of the negative set rather than the positive set. In particular, the false-negatives; i.e., sites that we grouped as unphosphorylated that may, however, become phosphorylated under different conditions may then obscure any discernible signal. To alleviate this problem, while at the same time keeping a sufficient number of examples for training purposes, we reduced the negative set for kinase specific predictions by randomly eliminating sites from the non-phos-Set until the negative sets were no more than twice as large as the positive sets.Sequence motifs associated with putatively phosphorylated serines are partly annotated with their respective phosphorylating kinase and can be grouped into kinase families and groups according to the classification scheme proposed by Hanks and Hunter augment by the AURORA and ATM kinase group . ConsideSecondary structural assignments, relative side chain accessibilities, crystallographic B-Factors were obtained from the PDBFINDER II database -50.E, as proposed by Levitt (Equation 1) [SE) > 1 with odds-ratios inverted in cases where the propensity ratio was below 1; i.e., observed less than expected by chance.Propensity ratios (odds-ratios) defined by the normalized counts of a particular amino acid type around sites in the phos-Set relative to their counts observed around sites in the non-phos-Set representative set within radial distances ranging from 2 to 10 Å from the central Ser/Thr/Tyr were calculated according to Equation 1. The chosen distance range covers both direct contacts as well as through-space interactions such as electrostatic interactions. Beyond 10 Å, we did not find any significant enrichment or depletion signals. We used two different distance measures. Amino acid residues were considered to lie within a given radial cutoff distance if i) the distance between the putatively activated oxygen (β-hydrogen) in case of a central serine and threonine, or γ-carbon in case of tyrosine and any atom of that residue was shorter than the given cutoff distance, or, if ii) the distance between the interaction centers of residues as proposed by Park et al.  fell witation 1) . Odds-raAAk/s is the count for amino acids, where k designates a particular amino acid residue type and s is the count summed over all amino acid residue types; r is the considered radial distance to the central serine/threonine/tyrosine, f is the relative frequency of amino acid in a particular set, and g the relative frequency of the amino acid k in the reference non-phos set. Associated standard errors SE were estimated according to [Calculation of amino acid propensity ratios for the estimation of average depletion or enrichment given a particular motif set. #rding to .To predict phosphorylation sites from sequence and to evaluate the effect of using structural information on prediction performance, we applied Support Vector Machines (SVM), first using sequence information alone and, subsequently, enriched by the spatial information. We used the "kernlab" R-package developed by Alexandros and co-workers  applyingThe 10-fold cross-validation was based on training of the predictor on 9 out of 10 parts of the randomly ordered data set and subsequent classification of the remaining part. The test is repeated for all 10 possible partitions of the dataset. The classification results are then used for measuring the performance of the predictor. The developed classifiers based on Support Vector Machines included general, kinase-family unspecific serine, threonine, and tyrosine predictors; i.e., the parameters were trained across all proteins irrespective of annotated kinase family, as well as predictors specific for the serine-centric PKA, PKB, MAPK, and CKII kinase family as well as tyrosine-centric SRC kinase family, for which at least 10 annotated targets or more were contained in the dataset. The minimal number of targets allowed a 10-fold cross-validation with the lowest actual number being 12 kinase-annotated targets (MAPK). For threonine target sites, the respective kinase-family annotation information yielded only data sets of insufficient size for statistical analyses. The area under the ROC-curve (AUC) from 10-fold cross-validation was compared among different prediction approaches and programs to judge, whether the addition of spatial information can improve the prediction performance. Perfect prediction results would yield an AUC of 1, while guessing the outcome would, on average, yield AUCs of 0.5.DFV × N data matrix, where DFV is the number of components of the Feature Vector, and N is the number of example peptide sequences in the training set, and the components of the FV were replaced by the resulting principle components with non-zero Eigenvalues explaining the entire variance in the dataset. Note: as there are fewer examples (N peptide sequences in the training set) than dimensions, the dimensionality  can be at most N-1. The PCA was performed independently for the serine, threonine, and tyrosine motifs. The total variance contained all independent datasets was essentially completely covered by 228 principal components. This low number of PCs (compared to 530 properties) results from the high correlation of amino acid indices. Although computed by different approaches, various indices designed to capture different properties also show similar tendencies, e.g. hydrophobicity and polarity. Moreover, apparently different properties of amino acids, like hydrophobicity and solvent accessibility are based on similar attributes of amino acids and, therefore, also correlate. In addition, the preference for a particular secondary structure and structural flexibility correlate to a high degree. In this case, however, the correlation is negative and the respective loadings on the PC have different signs. By evaluation of the loading matrix from the PCAs, we observed that the PCs are mostly influenced by the hydrophobicity and flexibility values, which appear most often in combination. The PCs differed with regard to the particular position of the property, rather than to the loadings of different indexes. For serine sites, PC1 was most influenced by the sum of the hydrophobicity and flexibility values over the entire sequence motif, PC2 by these properties at positions -6/+2, and PC3 at positions +1/+4 and PC5 -3/+3. PC4 included the variance of amino acid propensities to rigid structures at position -1 and the sum of these propensity values. The PCs of the PCA of threonine sites were based on the variance of the sums of the hydrophobicity and accessibility index values for PC1 and sum of the amino acid propensities to rigid secondary structures for PC2. Variances of hydrophobicity and flexibility at position +4 were loaded in PC3, +5 in PC4 and +6 in PC4. For tyrosine sites we found the sum of the hydrophobicity and flexibility indices in PC1, at position +3 in PC2, the sum and position +1 of preferences to rigid secondary structures in PC3, -1/+1 hydrophobicity in PC4 and hydrophobicity, accessibility, and polarity at position -6 in PC5. For a comprehensive analysis, we provide the complete data matrix of variable (amino acid index) loadings as part of the newly developed Phos3D prediction server .The feature-vector (FV) used for the Support Vector Machines consisted of chemical-physical amino acid properties for the sequence-information-only approach and an additional spatial information component for the spatial prediction approach. For the amino acid property components of the FV, we utilized values from the collection of 530 commonly used indices provided by the AAindex database  includinThe spatial information component consisted of the normalized distribution ratios according to (Eq. 2). The ratios of amino acid residues within the local sequence, outside the local sequence, and irrespective of the position in the protein sequence were used for distances in a range of 2 to 10 Å between the putatively activated oxygen (β-hydrogen) in case of a central serine and threonine, or γ-carbon in case of tyrosine and the closest atom of all other amino acid residues, or between the interaction centers proposed by Park and coworkers .oddsSVM.Radial distance odds-ratios for particular amino acid residue types as explained in Eq. 1 with an additional rescaling rendering resulting values suitable for use in kernlab package by ensuring that values lie between zero and one and with 0.5 indicating a balanced count  designated as We compared the AUC from the 10-fold cross-validation results obtained by using NetPhos, NetPhosK, DisPhos, KinasePhos2.0. NetPhos and NetPhosK are both part of the NetPhos-3-1b package. While NetPhos was designed to generally predict serine, threonine, and tyrosine phosphorylation events, NetPhosK includes kinase-specific predictors. DisPhos is based on SVM, utilizing the binary representation of the motif sequence, the relative frequencies of amino acids in that sequence as well as outputs from predictors for structural disorder and secondary structure, comprisFor comparison with DisPhos, we submitted 60 randomly selected protein sequences covering at least 50 positive and 100 negative motifs for serine, threonine, and tyrosine sites to the DisPhos 1.3 server . AlthougAs an alternative measure of performance, we also computed the accuracy defined as the proportion of correct predictions (true positive or true negative predictions) among the predictions made as well as the respective sensitivity, defined as the proportion of correctly classified positive sites and specificity, defined as the proportion of (Eq. 3):where tp are true positive, tn-true negative, fp-false positive, and fn-false negative predictions.The accuracy measure also allowed our prediction approach to be compared directly to other available prediction programs, especially KinasePhos 2.0. For computing accuracies, a decision threshold for the assignment of a site to a particular group must be set. The positive assignment threshold for our predictors was set to zero. Negative decision values were judged as predicted to be non-phosphorylated and positive decision values to be phosphorylated. For the other predictors, this value was set to 0.5 as they reflect probabilities. For kinase-specific predictions, sequences from training set were submitted to the KinasePho2.0 server. For assessing the performance associated with a particular kinase family, only the results corresponding to the particular family were evaluated as relevant predictions. As the prediction reports usually estimates the accuracies based on equal sizes of the positive and negative set, the negative sets were adjusted by random removal of the respective prediction results to reflect this ratio. This adjustment was performed 1,000 times with different random removals and the mean accuracy as well as the standard deviation was determined..The protein structure-based phosphorylation site prediction method developed as part of this study has been implemented as a freely accessible web-based service, Phos3D, and is available at AAindex: collection of 530 commonly used indices of amino acid properties; AUC: area under the ROC; PDB: protein data bank; PKA, PKB, PKG, PKC, RSK, CDK, CDC, CKII, ATM: kinase families; CMGC, AGC, CAMK: kinase groups; SVM: support vector machine; CV: cross-validation; FV: feature vector; ROC: receiver operating characteristic; RCP-Plot: Radial Cumulative Propensity Plot.PD, WW, JS, and DW conceived the study. PD and DW designed the analyses, interpreted the results, and wrote the manuscript. PD implemented all computational methods and carried out all computational analyses. CS developed and implemented the Phos3D server. JS helped revising the manuscript. All authors read and approved the final version of the manuscript.Additional Figures. File contains a histogram of pairwise sequence comparisons of the protein sequences used in this study and ROC curves for Phos3D and other commonly used phosphorylation site prediction programs applied to serine, threonine, and tyrosine sites as well as selected kinase-specific sites.Click here for fileTraining set for Phos3D. File contains the positive and negative set from this study. Motifs as well as the associated PDB entries and annotated kinase family annotations are provided.Click here for file


Illumina Sentrix-6 Whole-Genome Expression BeadChips are relatively new microarray platforms which have been used in many microarray studies in the past few years. These Chips have a unique design in which each Chip contains six microarrays and each microarray consists of two separate physical strips, posing special challenges for precise between-array normalization of expression values.None of the normalization strategies proposed so far for this microarray platform allow for the possibility of systematic variation between the two strips comprising each array. That this variation can be substantial is illustrated by a data example. We demonstrate that normalizing at the strip-level rather than at the array-level can effectively remove this between-strip variation, improve the precision of gene expression measurements and discover more differentially expressed genes. The gain is substantial, yielding a 20% increase in statistical information and doubling the number of genes detected at a 5% false discovery rate. Functional analysis reveals that the extra genes found tend to have interesting biological meanings, dramatically strengthening the biological conclusions from the experiment. Strip-level normalization still outperforms array-level normalization when non-expressed probes are filtered out.Plots are proposed which demonstrate how the need for strip-level normalization relates to inconsistent intensity range variation between the strips. Strip-level normalization is recommended for the preprocessing of Illumina Sentrix-6 BeadChips whenever the intensity range is seen to be inconsistent between the strips. R code is provided to implement the recommended plots and normalization algorithms. Illumina Whole-Genome Expression BeadChips have been widely adopted for high-throughput gene expression analysis in the past few years. Most popular and comprehensive of these are the HumanWG-6 and MouseWG-6 (Sentrix-6) BeadChips. Each Sentrix-6 BeadChip allows the interrogation of six RNA samples in parallel and produces data that can be treated as coming from six independent microarrays. Physically, each Sentrix-6 BeadChip consists of twelve equally-spaced strips of beads Figure . Each paNormalization of microarray data is an essential analysis step which removes unwanted technical variation and ensures that expression values from different arrays are comparable. Normalization needs to adjust not only for technical variation between RNA samples but also for systematic measurement errors associated with the hybridizing and scanning steps. In the case of Illumina BeadChips, normalization needs to adjust for technical variation within each BeadChip, including spatial variation across the slide.All normalization strategies proposed so far for Illumina BeadChips carry out normalization at the array level, meaning that data from the two strips comprising each array are combined before normalization is conducted. The simplest and most popular strategy is to log-transform the probe-wise expression summaries for each array which are then quantile normalized between arrays -5. This This study illustrates by way of a data example that between-strip technical variation can be substantial. We demonstrate that normalizing at the strip level rather than at the array level can effectively remove this between-strip variation, improve the precision of gene expression measurements and discover more differentially expressed (DE) genes. Many of the extra genes found are functionally closely related to the knockout genes in the experiment.κB1 pathway which mediates the immune response to pathogen invasion. The experiment studied the response to LPS stimulation in macrophages from wild-type, Nfkb1 knockout and Tpl2 knockout mice. For each of the three genotypes, RNA samples were taken prior to LPS stimulation and 1 hour and 3 hours after LPS stimulation, making a total of nine experimental conditions (three genotypes by three times). Two biological replicates were conducted of the entire experiment, making a total of 18 RNA samples hybridized to three BeadChips. Illumina BeadStudio software was used to output a summary probe profile file containing a raw intensity value for each probe on each array of the experiment. The arrays contain 46,657 probes, so the raw data is a 46, 657 × 18 matrix of intensities.As an illustrative case study, we consider data from an experiment involving 18 RNA samples hybridized to Illumina Mouse-6 Version 1.1 BeadChips. The data were generated as part of a study designed to dissect molecular components of the NF-Figure Figure Figure -/- 180 mins. The plot shows that first-strip probes are systematically lower on the first replicate whereas the second-strip probes are systematically higher. This systematic bias has been introduced by the normalization. The bias is substantial, with the strip-1 and strip-2 bands in Figure The same problem can be seen even more vividly by comparing replicate arrays. An MA-plot compares two arrays by plotting probe-wise log-ratios between the arrays against average log-intensities. The MA-plot in Figure A solution to the above problem is to normalize the first and second strips separately. In this approach, quantile normalization is applied twice, once to the 18 first strips and once to the 18 second strips. This forces all first strips to have the same overall intensity distribution, and similarly for the second strips, but no relationship between the first and second strips is assumed. Figure M = 0 line, i.e., the expression log-ratios are now randomly scattered about zero for both first and second strips. This shows that the bias related to the physical position of the strips within the arrays has been removed.The success of strip-level normalization can be seen by comparing replicates, which now show high concordance. Figure 2-expression values between replicate arrays for each probe. Each of the nine experimental conditions has a pair of replicate arrays, so each pooled standard deviation has nine degrees of freedom. When array-level normalization was used, the median of the probe-wise standard deviations was 0.155. This implies that replicate expression values have a typical variation up or down by about 11% on the unlogged scale (because 20.155 = 1.11). When strip-level normalization was used, the median standard deviation decreased more than 10% to 0.139. This corresponds to a 20% increase (0.155/0.139 squared) in statistical information, equivalent to the gain which would be achieved by increasing the number of microarrays used in the experiment by 20%. Since normalization can reduce only the technical component of the residual variability, not the biological component, we can conclude that the technical standard deviation corresponding to microarray precision has been reduced by substantially more than 10%.The most basic aim of normalization is to increase the consistency between replicate arrays. To examine this, we computed the standard deviation of normalized logt-statistics [t-test by moderating the standard deviations across probes. Not surprisingly, the increased precision arising from strip-level normalization translates directly into greater numbers of DE probes. Table Next we identified probes which are differentially expressed between wild-type and the two knockout lines. Significance analysis was conducted using empirical Bayes moderated atistics ,7, a pop2 fold change on the vertical axis vs average log-intensity on the horizontal axis. DE genes are distributed more or less evenly across the whole intensity range. As expected, genes with the very largest fold changes were detected by both normalization methods, but the genes newly discovered by strip-level normalization were also amongst the larger fold-changes. For Tpl2-/-, the second, third and fourth ranked genes in terms of fold-change were found only by strip-level normalization. In fact, most of the genes up-regulated by the Tpl2 knockout are detected only after strip-level normalization. The few genes found only by array-level normalization tend to have small fold changes.Figure In the previous section, we have demonstrated that strip-level normalization gives a global and substantial improvement in precision for the microarray data. Naturally, the improved precision increases the number of statistically significant genes. Figures Our functional analysis consists of two parts. Firstly we used the functional annotation tool DAVID  to look Consider first the Tpl2 knockout. At a false discovery rate (FDR) cutoff of 0.05, no DAVID terms at all were found to be significantly enriched in the list of DE genes found by array-level normalization. After strip-level normalization, on the other hand, fifteen highly relevant terms were found to be significantly enriched (Table Now consider the Nfkb1 knockout Table . In thisP = 4e-6). The enrichment is virtually the same if we restrict to genes found only by strip-level normalization and not by array-level normalization (P = 0.0068). On the other hand, no interacting genes were found by array-level normalization only.Further evidence of the biological relevance of genes found by strip-level normalization can be seen from a gene interaction analysis using Ingenuity Pathway Analysis (IPA) tool. Only 1.7% of genes on the microarrays are known to interact with Nfkb1. Yet 7% of genes found by strip-level normalization are known to interact with Nfkb1, and almost all of these are in fact directly interacting genes Table . This enSix directly interacting genes were found by strip-level normalization only, and these are shown in an IPA display Figure . Three aA literature search confirms that these six genes are direct targets of or associates with Nfkb1. Il6 is required for B- and T-cell growth and differentiation, neuronal and macrophage differentiation . InhibitThis is far from a comprehensive study of the biological results of this experiment. Such a study will be published elsewhere, including RT-PCR confirmation of differential expression for selected genes and other follow-up assays. The bioinformatics analysis included here is however more than enough to demonstrate that the extra differential expression detected by strip-level normalization is biologically relevant and not merely a random sample of unrelated probes. Moreover the extra differential expression is sufficient to materially change the conclusions obtained.As demonstrated above, inconsistencies in the relativities between the two strips caused array-level normalization to perform poorly. Now we want to investigate if it is possible to alleviate these inconsistencies by filtering out non-expressed probes. Probe filtering has been reported to be able to increase the power of detecting DE genes .Probes with detection p-value greater than 0.1 on all arrays were deemed as non-expressed probes and filtered out. The intensity of a probe with detection p-value less than 0.1 will be greater than 90 percent of all negative controls . In totaBy filtering out non-expressed probes, the number of DE probes increased for both normalization strategies. However, strip-level normalization still discovered about twice as many DE probes as compared to array-level normalization irrespective of whether probe filtering was performed before or after the normalization step Tables  and 6. FIt has been shown that strip-to-strip technical variation can have a material effect on expression values from Illumina BeadChips. If the usual strategy of array-level normalization is used, technical variation on one strip can artificially perturb the normalized intensity values on the other strip for the array, introducing bias, disagreement between replicates, and less precise assessment of differential expression. On the other hand, strip-level normalization successfully removes technical artifacts within as well as between arrays, solving all of the above problems and resulting in more powerful statistical analysis of the expression values.The evidence we present consists of three major parts. Firstly we demonstrate that strip-level normalization gives a substantial improvement in precision for the microarray data. This improvement in precision is not restricted to individual genes, but is a global result affecting all the probes on the arrays. Secondly, we demonstrate by way of some carefully chosen plots, how this improvement arises by improving the alignment of the intensity values from the individual strips. The problem can be seen to arise from strips which have an unusually large or small intensity range (a long or short box in the boxplot) relative to their partner strip within the same array. We show that this phenomenon leads to mis-aligned bands for individual strips in MA-plots for replicate arrays. In this way, we have demonstrated the mechanism by which strip-level normalization leads to improved precision and removal of bias. Thirdly, we demonstrate by functional analysis that the extra genes discovered after strip-level normalization are not random but do indeed tend to be biologically related to the experimental conditions. Although this third component of our evidence is convincing, we view the first two components as the most compelling, because they demonstrate global results which affect all the probes on the arrays, not just those genes which happened to be significant in this experiment.illumina package for R available from http://bioinf.wehi.edu.au/illumina/.Detailed R code which should enable readers to repeat our analysis is provided in the Additional file Our Figure In our examples, we used what is probably the simplest and most popular normalization strategy for Illumina data. Our results are essentially the same using alternative preprocessing methods including model-based background correction ,16, variOur data example used Version 1.1 MouseWG-6 BeadChips. Although Illumina stopped shipping this version of the BeadChip in 2008, data from this platform continues to be highly current. At our institution, Version 1.1 MouseWG-6 BeadChips continued to be hybridized until late in 2008. It is likely that the majority of experiments worldwide, which used human or mouse Version 1 WG-6 BeadChips, have not yet been published and so are still subject to primary analysis. The majority of Illumina BeadChip data which is publicly available through databases such as GEO  is VersiThe currently shipped versions of Illumina BeadChips (HumanWG-6 Version 3 or MouseWG-6 Version 2) no longer imbed separate sets of probes in the first and second strips of each array. Rather, the entire library of probes (bead types) is now randomly distributed over each strip. This means that the marked difference in intensity distribution between first and second strips on the Version 1 BeadChips is no longer apparent. However, the physical layout of strips on the chip is unchanged. Therefore the possibility of intra-array variation and the consequent need for strip-level normalization remains.The probe-level expression summaries output by the Illumina BeadStudio software for HumanWG-6 Version 3 or MouseWG-6 Version 2 BeadChips do not provide any means to separate first and second strips. Expression values are already averaged over the two strips for each array. Instead, strip-level information can be obtained from the Bioconductor software package beadarray, which provides access to bead-level data summaries for Illumina BeadChips . This prIt is worth noting that the standard normalization method applied to data from Illumina's Infinium BeadChips for genotyping also occurs at the sub-array level. Similar to the WG-6 expression arrays, each Infinium array is made up of a number of strips . Beads manufactured together in the same pool are located on a particular strip on each array and are normalized separately using a between channel/allele affine transformation . The neeκB1 knockout line (Nfkb1-/-) [-/-) [Mice were obtained from three inbred lines, C57BL/6 (wild-type), an NF-fkb1-/-)  and a Tp-) [-/-) . Bone ma-) [-/-) . BMDMs whttp://www.r-project.org. Probe summary profiles output by Illumina BeadStudio Version 3.0.14 software were read into R using the lumiR function of the lumi Bioconductor software package [All analysis was undertaken using the R programming environment  package .8.The position of each probe on first or second strip was determined by probe ID number. For MouseWG-6 Version 1 BeadChips, all probes on the second strip have probe ID numbers greater than 102 transformed and quantile normalized [Raw summary expression data was logrmalized . In arraProbe annotation, including gene symbols and Entrez Gene IDs, were obtained from the most recent Illumina bead manifest file for the MouseWG-6 BeadChips .t-statistics from the Bioconductor software package limma [Differential expression analysis was conducted by fitting a linear model to the 18 microarrays and comparing the knockout genotypes with wild-type using empirical Bayes moderated ge limma ,7. The pThe National Institute of Health's DAVID tool  uses a variant of Fisher's Exact test to test for enrichment of Gene Ontology terms or KEGG pathways in a list of genes . DAVID rIngenuity Pathway Analysis software  was usedGKS and WS conceived the study. WS and MR performed the analysis and WS drafted the manuscript. SG and AB designed the biological experiment. AB performed the sample preparation. GKS finalized the manuscript. All authors read and approved the final manuscript.Supplemental document. This file contains descriptions to R functions and analysis scripts used in this study and also the supplemental figures and tables for probe filtering.Click here for fileAnalysis scripts. This file contains R scripts used for performing the analysis in this study. Instructions on how to run these scripts can be found in Additional file Click here for fileR functions. This file includes the code of R functions which implement strip-level data retrieval, strip-level plotting and strip-level normalization. Instructions on how to use these functions can be found in Additional file Click here for file


Tyrosine hydroxylase (TH) is the rate-limiting enzyme in dopamine biosynthesis. Because the TH Val81Met polymorphism is located in the amino-terminal regulatory domain of the tetrameric enzyme, it is a candidate marker for susceptibility to dopamine-related traits. We investigated the hypothesis that TH Val81Met polymorphism can influence susceptibility to tardive dyskinesia (TD) in schizophrenia.TH Val81Met polymorphism was analyzed by PCR-based methods in 83 schizophrenic patients with TD and 126 schizophrenic patients without TD, matched for antipsychotic drug exposure and other relevant variables.There was no significant association of the genotype and allele frequencies determined by the TH Val81Met polymorphism between TD and non-TD patients. In addition, there was no significant difference in terms of total Abnormal Involuntary Movement Scale scores among the three genotype groups.Within the limitations imposed by the size of the clinical sample, these findings suggest that the Val81Met polymorphism of the TH gene does not contribute significantly to the risk for TD. Tardive dyaskinesia (TD) is one of the most serious adverse effects of neuroleptics. The typical symptoms of TD are involuntary movements of the orofacial musculature, trunk and extremities. Given that only a small proportion of those treated with neuroleptics develops TD, individual vulnerabilities such as genetic predisposition are important. Genetic vulnerability to the development of TD has been suggested based on the results of studies in both animalsTyrosine hydroxylase (TH) is the rate-limiting enzyme in dopamine biosynthesis.11Recently, alpha methyl paratyrosine (AMPT), an inhibitor of TH, has been reported to be clinically efficacious in the treatment of antipsychotic-induced TD.12Thus, functional polymorphism of TH can be regarded as one of the candidate markers for susceptibility to neuroleptic-induced TD. We investigated a possible association between Val81Met polymorphism in exon 3 of the TH gene and TD in a genetically homogenous Korean population.All subjects were examined by trained psychiatrists using the Korean version of the Structured Clinical Interview for DSM-IV, leading to a diagnosis based on DSM-IV criteria.Blood samples (5-10 mL) were collected into ethylenediaminetetraacetic acid (EDTA), and genomic DNA was isolated using Accuprep Genomic DNA Extraction Kit  according to standard procedures. Analysis of genomic DNA was carried out using Polymerase Chain Reaction (PCR) and PCR-based Restriction fragment length polymorphism (PCR-RFLP). PCR amplification was performed with the forward primer 5'-ATC CCC TGC CTC TGT GTG CCA T-3'and the reverse primer 5'-TCA GGA ACT CAG CCC ACA CAG C-3', giving a 404 base pair (bp) product. The PCR product was digested by the restriction enzyme Nia III for approximately 3 hours at 37℃, then the reaction mixture was analyzed by 2% agarose gel electrophoresis to ensure correct amplification of the DNA fragment.2 test for goodness of fit. The χ2-test was used to compare categorical data and differences for continuous variables were evaluated by using the Student's t-test or analysis of covariance (ANCOVA). The cutoff p value was set at 0.05.The presence of Hardy-Weinberg equilibrium was tested with the χ2=0.46, p=0.50). There were no differences in age , sex distribution , the duration of illness , and current antipsychotic dosage in chlorpromazine units  between TD and non-TD groups. However, the duration of antipsychotic treatment of the non-TD group was significantly longer than that of the TD group . For genetic analysis, there was no significant difference of the TH genotype and allele frequencies between TD and non-TD patients  after chronic haloperidol administration and suggested an elevated TH protein expression by long-term haloperidol treatment. Recently, AMPT, an inhibitor of TH has been reported to be clinically efficacious for treating neuroleptic-induced TD.12Some studies indicate that conventional neuroleptics may exert a selective neuronal toxicity in the nigrostriatal dopaminergic neurons, and this neurotoxity may underlie extrapyramidal side effects of these drugs.Albanese et al.Allele frequencies in our sample were similar to those in other Asian samples.8Polymorphisms generating Val81 and Met81 were compared to the distributions of genotype and allele between the schizophrenic patients with TD and without TD. These comparisons did not support an association between the polymorphism and neuroleptic-induced TD. These results indicate that Val81Met functional polymorphism of TH is not associated with neuroleptic-induced TD in Korean schizophrenic patients.There are several limitations to generalization of the results of this study. First, although anticholinergics and benzodiazepines have been reported to be beneficial in some cases of TD, in this study we could not control for the prescription of those drugs. Secondly, we cannot exclude presence of population stratification bias. However, we do not think that stratification bias should be considered seriously in our sample because the Korean population is characterized by a genetic homogeneity.Taking these limitations into account, further investigations involving additional genes and markers and larger samples are warranted to fully understand the genetic pathophysiology of TD.


To evaluate the accuracy of using kilovoltage x-ray cone-beam computed tomography (kV-CBCT) imaging for in vivo dose calculations.A Region-of-Interest (ROI) CT number mapping method was developed to generate the cone-beam CT number vs. relative electron density calibration curve for 3D dose calculations. The stability of the results was validated for three consecutive months. The method was evaluated on three brain tumors and three head-and-neck tumor cases. For each patient, kV-CBCT images were acquired on the first treatment day and two-week intervals on the Elekta XVI system. The delivered dose distributions were calculated by applying the patients' treatment plans to the kV-CBCT images. The resulting dose distributions and dose volume histograms (DVHs) of the tumor and critical structures were compared to the original treatment plan.The kV-CBCT electron density calibration was stable within 1.5% over a three-month period. The DVH and dose distribution comparison based on the planning CT and the initial kV-CBCT showed good agreements for majority of cases. The doses calculated from the planning CT and kV-CBCT were compared on planes perpendicular to the beam axes and passing through the isocenter. Using γ analysis with a criterion of 2 mm/2% and a threshold of 10%, more than 99.5% of the points on the iso-planes exhibited γ <1. For one patient, kV-CBCT images detected 5.8% dose variation in the right parotid due to tumor shrinkage and patient weight loss.ROI mapping method is an effective method for the creation of kV-CBCT electron density calibration curves for head-and-neck and brain tumor patients. Dose variations as monitored using kV-CBCT imaging suggest that some patients can benefit from adaptive treatment plan re-optimization. Patients with head-and-neck and definitive brain tumor are routinely treated with intensity-modulated radiotherapy (IMRT) to enable delivery of highly conformal dose distribution to the tumor while sparing surrounding critical structures. Precise target localization is important for such treatments -3. IdealCone-beam computed tomography (CBCT) systems mounted on the linear accelerator has become available for image-guided radiotherapy (IGRT). Currently, there are two types of commercially available CBCT imaging systems: (1) the kV-CBCT system, which includes the Varian On-Board-Imaging (OBI)  and the Elekta XVI Synergy system ; and (2) the Siemens MVision system  -7. In ouThe purpose of this study is to assess the feasibility of using a mapping method to calibrate the kV-CBCT images for dose calculation in head-and-neck and definitive brain tumor radiation treatments. By monitoring the dose that patient receives from each fraction, physicians will be able to track the dose distribution during the course of radiation therapy and modify the treatment plan as needed based on the actual dose delivered.The kV CBCT images were acquired on a linear accelerator equipped with an integrated kV X-ray volumetric imaging system . For imaging the head-and-neck and brain tumor patients, we used the following parameters: 100 kVp, S20 collimator and F0 filter, total 65 mAs and a high-resolution reconstruction (512 × 512). A total of about 650 projections were acquired in a full rotation. The CBCT images were reconstructed with slice thickness of 2.5 mm and then transferred to the treatment planning system  for image registration and dose calculations.A phantom, Catphan-600 module CTP503  was used to evaluate the stability and uniformity of the CBCT numbers. The phantom has seven embedded rods made of different materials: air, PMP, LDPE, polystyrene, acrylic, Delrin and Teflon. Their electron densities relative to water range from 0.00 to 2.16. The CBCT image of the phantom was acquired every month for three consecutive months, and the CBCT numbers were obtained from the TPS and the relative electron densities were recorded accordingly. We also evaluated the maximal fluctuation in CBCT numbers on the image uniformity module part of the phantom.For the dose calculation in a treatment planning system, the relative electron density or physical density of each voxel of the CT images is required for inhomogeneity corrections . In thisWe used a Region-of-interest (ROI) CT number mapping method similar to Richter's report to generate the CT number to physical density conversion curve for the dose calculation . This prThree head-and-neck cases and three brain patients with different tumor sites treated on Elekta Synergy were selected for retrospective evaluation of the accuracy of CBCT-based dose calculations. The head-and-neck cases included two natural killer/T-cell (NK/T) lymphoma cases and one nasopharyngeal carcinoma (NPC) case. For all patients, conventional CT was acquired with slice thickness of 5 mm and the target and critical structures were delineated by the attending physicians. IMRT plans were designed according to the physician's prescriptions with beams of 6 MV. The beam angles were 0, 50, 110, 250 and 310 degree for NK/T cases, and 0, 45, 90, 120, 160, 200, 240, 280 and 320 degree for NPC cases. For brain cases, gantry angles were 0, 60, 230, 300 and 45 degree with a 90-degree couch kick. Two sets of CBCT images were acquired, one on the first treatment day and the other on two weeks later. The patients were initially set up to the skin markers then followed with a CBCT scanning. The CBCT images were acquired according to the appropriate protocol and then reconstructed with slice thickness of 2.5 mm. All images were transferred to treatment planning system for analysis. For each case, the CBCT images were first registered to the conventional kV-CT images using an automatic registration method based on normalized mutual information algorithm, and then manual adjustments were performed to achieve the optimal match. A second set of CBCT images were acquired two weeks later and registered to the reference planning CT with the same method. The contours were mapped from CT to CBCT images with slight changes if them were beyond the skin.For dose calculation, all the parameters  in the initial treatment plan were applied to the kV-CBCT images, and then the dose distribution was recalculated based on the new calibration curve. The dose calculation was performed in the Pinnacle treatment planning system using the collapsed cone superposition convolution algorithm with an isotropic 2 mm dose grid resolution. The contours delineated on the conventional CT were also mapped onto the kV-CBCT image data sets. Finally, the initial dose distribution matrix calculated on the planning CT was imported in the treatment planning system and displayed on the kV-CBCT dataset using scripts developed in-house. Dose volume histogram and the dose to tumor and normal structures were compared on the two image data sets. The differences in the dose distributions of the two plans were analyzed using γ analysis along planes through the isocenter perpendicular to each beam axis using commercial software .Because most patients complete their treatment courses within five weeks, we consider the three-month length of the stability test to be adequate. The maximal difference in CBCT numbers was 21, with a maximum standard error of less than 1.5%. The stability of kV-CBCT number and electron density indicates acceptable overall performance of the kV-CBCT system. The kV-CBCT images of the uniformity section of the phantom shows the maximal fluctuation of the CBCT numbers is ± 35 Hounsfield unit (HU), which translates to a fluctuation of approximately 1% in electron density values.A total of 13 different ROIs were used in generating the conversion curve, which include air, skin, muscle, brain stem, spinal cord, parotid gland, outer bone, inner bone, tooth and other structures. Table Only minimal changes and deformations were observed in the anatomical structures on the patients' first CBCT images as compared with the reference planning CT. The DVHs of one NK/T lymphoma case (patient1), one NPC case patient2) and one brain tumor case (patient3) are shown in Figure  and one For all clinical cases, the dose comparison was performed at a plane through the isocenter for each individual beam. Good agreement was found between the conventional CT and the first kV-CBCT based dose calculations. Using the γ analysis with a criterion of 2 mm and 2% and a threshold of 10%, more than 99.5% of the points at the iso-plane have the γ value less than 1.0. Table Table Five out of six patients didn't show significant anatomy changes and setup variations between the first CBCT images and the second CBCT images. But for one NK/T patient (patient5), a slight anatomical change in the patient's skin contour and air cavity was found in the second CBCT images compared to the conventional CT images, as shown in Figure On-board CBCT volumetric imaging can improve the accuracy of radiation therapy in two aspects, namely target localization and delivered dose verification ,17,18. BThe relative electron density can directly affect the dose calculation accuracy when inhomogeneity correction is involved. Unlike conventional kV CT or MV CBCT, kV-CBCT has a larger scatter radiation component and the image quality suffers from the beam hardening effect -23. It hIn this study, we used the ROI mapping method to generate calibration curve for kV CBCT image-based dose calculation . ObviousThe calibration curve for kV-CBCT is different from that for conventional CT. The conventional CT number is zero for the air outside the patient skin in the planning system; however, the CBCT number in such situation is much greater than zero. The mean CBCT value in the air around the skin is 1379 for the selected case, similar to other report . The steA good agreement of the calculated doses to the tumor and normal structures was found between the conventional CT and the first kV-CBCT images because there were virtually no anatomical changes between these images. The maximal dose deviation was found in the eye mainly due to the residual registration error and contour deviations, as the slice thickness was 5 mm for the conventional CT and 2.5 mm for CBCT images. The structures near the skin showed larger differences. The DTA and γ index analysis results also showed the good agreement between kV-CBCT based and conventional CT based dose calculation. Richter et al. used the same method and reported the dose difference between the planning CT and CBCT was 1.36% ± 1.96% in head patients with three-dimensional conformal plans. Our data showed the difference was within 1% of the target, which was consistent with their result. Our results demonstrated that the mapping method for CBCT correction is accurate both for three-dimensional conformal plans and IMRT plans in head and brain cases.Furthermore, we generated the density conversion table based on one patient and applied the same table to the other patients. There were only a small discrepancy between the doses calculated by using kV CBCT and conventional CT in all 6 cases with different tumor locations. This result suggests that, for head-and-neck and brain patients, variations in the scatter effect in imaging different tumor sites is relatively small from patient to patient, and therefore it is reasonable to use the same electron density conversion curve for kV CBCT based dose calculation. Compared to the patient group based conversion table in report of Richter et al. or CT-based HU mapping method in Mathilda et al., this specific case mapping method is less complex to develop and implement, but it is limited to the preset scanning parameters.Overall, our study showed good accuracy in CBCT based dose calculation. However, it is not recommended to replace the conventional planning CT by kV CBCT for the purpose of treatment planning as the inferior image quality of kV CBCT may affect the accuracy of target and normal structures delineation.The kV CBCT can also be used to evaluate the dose to tumor or the normal structures. In this study, one NK/T patient had slight changes in anatomy after two weeks' treatment, dose variations were found in the spinal cord and the right parotid gland. These results suggested that even within a relatively short period such as 2 weeks, dose verification based on CBCT or CT will be necessary for certain patients to account for dosimetric effects due to patient anatomical changes.Anatomic changes for head-and-neck patients, including nodal mass shrinkage and patient weight loss during the course of radiation therapy, can occur ,29. For Technically, the 26 cm field of view for the S20 collimator may limit the use of kV CBCT for dose calculation of patients with beams going through their shoulders. However for most head-and-neck patients, the FOV is sufficiently large to evaluate the dose to PTV, brain stem, spinal cord, eyes and parotid glands. For those patients who receive thoracic or pelvic treatment, S20 is not large enough to encompass all the structures and skin. Dose verification for other sites is part of our future research.ROI mapping method is a feasible method to overcome the effects of scatter for generating the kV CBCT relative electron density calibration curve for head-and-neck cancer and brain tumor patients. Dose variations as monitored using kV CBCT imaging were observed in a relatively short period of two weeks, which suggests potential benefits of adaptive treatment plan re-optimization for certain head-and-neck and brain tumor patients.The authors declare that they have no competing interests.Each author has participated sufficiently in the work to take public responsibility for appropriate portions of the content. JY, ZZ designed the study. WH, JW performed the study and analysis. XM provided the patients' images. The manuscript was written by WH, all other authors helped and finally approved the final manuscript.


Highly potent and selective inhibitors of neuronal nitric oxide synthase (nNOS) possessing a 2-aminopyridine group were recently designed and synthesized in our laboratory and were shown to have significant in vivo efficacy. In this work, analogs of our lead compound possessing 2- and 4-aminothiazole rings in place of the aminopyridine were synthesized. The less basic aminothiazole rings will be less protonated at physiological pH than the aminopyridine ring, and so the molecule will carry a lower net charge. This could lead to an increased ability to cross the blood-brain barrier thereby increasing the in vivo potency of these compounds. The 2-aminothiazole-based compound was less potent than the 2-aminopyridine-based analogue. 4-Aminothiazoles were unstable in water, undergoing tautomerization and hydrolysis to give inactive thiazolones. Neuronal nitric oxide synthase (nNOS) is the constitutive isoform of nitric oxide synthase (NOS) found in the CNS. It is believed to play a role in many neurological diseases, including Parkinson’s disease , AlzheimDesigning therapeutically useful nNOS-selective inhibitors is a difficult task as the substrate for all three isoforms is L-arginine, and so they have similar active sites. In recent years, we have developed several potent and highly selective inhibitors of nNOS that have solved this problem by exploiting subtle differences among the isoforms . In addi1 , presumably via hydrogen bonding and electrostatic interactions  = 9 μM), suggesting that the aminopyridine ring is critical for high affinity binding. It has been shown that the addition of electron-withdrawing groups to a 2-aminopyridine significantly decreases NOS affinity [Ka such that the ring nitrogen is insufficiently protonated to interact with the glutamate. We had hoped that the high acidity of the active site would protonate the aminothiazole so that it would interact well with the glutamate residue. This may not be the case. An alternative explanation for the loss of potency is that the 2-aminothiazole ring is much smaller than 2-amino-4-methylpyridine. The methyl group at the 4-position of the pyridine ring contributes a significant amount to binding, resulting in a 4-fold increase in potency. There is no functionality in the 2-aminothiazoles to provide a similar hydrophobic interaction. If the 4-aminothiazoles had been stable, the alkyl group in the 5-position could have contributed to binding to restore some of the lost potency.Compound rat nNOS , bovine rat nNOS , and murrat nNOS  using a rat nNOS , giving affinity . This is3 was synthesized via a condensation reaction between thiourea and the appropriate α-bromoketone. However, the inhibitor was less potent than the aminopyridine lead. Nonetheless, the synthetic methodology is useful for the construction of this ring system. 4-Aminothiazole-based inhibitors with various alkyl groups at the thiazole 5-position could be synthesized, but proved to be unstable in aqueous medium. This is a valuable insight for others contemplating this ring system for biological studies. Unfortunately, the use of an aminothiazole in place of the aminopyridine moiety is not a beneficial modification in the case of our nNOS inhibitors. Alternative methods to reduce the overall charge on the molecule are under investigation.2-Aminothiazole-based nNOS inhibitor 1H NMR, 13C NMR, ESMS), details of Autodock analysis, details of in vitro enzyme assay, and HPLC chromatograms for determining purity of compound 3.Supporting Information features full experimental details for all synthetic steps, characterization of intermediates (File 1Experimental and analytical data


It has long been proposed that much of the information encoding how a protein folds is contained locally in the peptide chain. Here we present a large-scale simulation study designed to examine the extent to which conformations of peptide fragments in water predict native conformations in proteins. We perform replica exchange molecular dynamics (REMD) simulations of 872 8-mer, 12-mer, and 16-mer peptide fragments from 13 proteins using the AMBER 96 force field and the OBC implicit solvent model. To analyze the simulations, we compute various contact-based metrics, such as contact probability, and then apply Bayesian classifier methods to infer which metastable contacts are likely to be native vs. non-native. We find that a simple measure, the observed contact probability, is largely more predictive of a peptide's native structure in the protein than combinations of metrics or multi-body components. Our best classification model is a logistic regression model that can achieve up to 63% correct classifications for 8-mers, 71% for 12-mers, and 76% for 16-mers. We validate these results on fragments of a protein outside our training set. We conclude that local structure provides information to solve some but not all of the conformational search problem. These results help improve our understanding of folding mechanisms, and have implications for improving physics-based conformational sampling and structure prediction using all-atom molecular simulations. Proteins must fold to unique native structures in order to perform their functions. To do this, proteins must solve a complicated conformational search problem, the details of which remain difficult to study experimentally. Predicting folding pathways and the mechanisms by which proteins fold is thus central to understanding how proteins work. One longstanding question is the extent to which proteins solve the search problem locally, by folding into sub-structures that are dictated primarily by local sequence. Here, we address this question by conducting a large-scale molecular dynamics simulation study of protein fragments in water. The simulation data was then used to optimize a statistical model that predicted native and non-native contacts. The performance of the resulting model suggests that local structuring provides some but not all of the information to solve the folding problem, and that molecular dynamics simulation of fragments can be useful for protein structure prediction and design. Indeed, the success of fragment insertion methods for The question we raise here is not about the success rates of secondary structure predictions. Secondary structure prediction methods such as PSIPRED use knowledge bases of known native structures and can achieve prediction success rates near 80%  for a total of 100 ns . In thesOur data provides an opportunity to draw inferences about what physical properties of intrachain contacts are predictive of whether a peptide conformation is native or not. To do this, we train probabilistic classifier models on several contact metrics, and interrogate the results. For each set of simulated fragments , we explored two kinds of per-contact classification models: a naive Bayes model and a logistic regression model see . To findWhich classification model best predicts native or non-native contacts from short fragment simulations? In all cases, the logistic regression model gave better classifications than the corresponding naive Bayes model, thus we present only the results from the logistic regression models. Also in all cases, contacts defined by a 7Å distance cutoff performed significantly worse than an 8Å cutoff, thus we only present results from the latter case. The best logistic regression coefficients for 8-mers, 12-mers, and 16-mers are shown in What metrics are the best predictors of whether a simulated fragment has formed native contacts? We examined several metrics see , each cabability .This is interesting because it might be expected that including multi-body terms would be more predictive than just the pairwise contact formation probability, since protein stability is likely to involve non-additivities that could only be captured in complex terms. Instead, we find that simple pairwise terms are the most predictive, with the multi-body terms producing small negative regression coefficients. The negative coefficients can be interpreted as providing a slight correction to the over-counting due to correlation between pairwise contact probability terms.We also tested whether we could obtain better classification models by training on local contacts  alone. We found that, overall, the classification success for the local-only or nonlocal-only data was comparable, but never as high as the classification success using the combined data see .Now, given the parameters obtained from the logistic-regression models described above, we can compute the probability that a given simulated peptide conformation has native contacts. per se. To test this possibility for our selected models, we built a null distribution of contact metrics to test the random-case performance of our models , a 70-residue CASP6 target with an mophilus . REMD siThese models make per-contact predictions. But, we are interested in predictions for whole peptide conformations. To turn our contact-based scores into conformation-based scores, we compute a score, We computed conformation scores for all the cluster conformations extracted from 8-mer, 12-mer, and 16-mer 1whz fragment simulations. For 8-mers and 12-mers, we observe a correlation  between a high value of ture see . For 16-ture see . Two of ture see , indicatWe have performed computer simulations of short peptides—8-mers, 12-mers and 16-mers—using the AMBER 96 force field and the OBC implicit solvation model. Our aim was to see whether the metastable structures of these fragments bear any resemblance to the conformations those fragments adopt in the native states of the proteins in which they appear. We find that the peptide contact probabilities in a logistic regression model lead to a 76% success rate in 16-mers in correctly classifying contacts as either native or nonnative. Across the chain lengths studied, the false negative rates (native contacts classified as non-native) of our best logistic regression models range from about 30–45%. The false positive rates (non-native contacts classified as native) vary from about 20–40%. These results show these predicted peptide conformations in water are significantly more native-like than would be expected from random conformers. Previously, Bystrof and Garde also showed a 75% success rate at predicting native helicity across 64 8-mer fragments simulated using AMBER ff94 and explicit TIP3P water These results may have useful application in physics-based methods, like ZAM per se generates random coil-like behavior, some simple additional logic about tertiary cooperativity and hydrogen bonding can predict native-like protein topologies and structures While our fragment simulations show that some peptide fragments sample native-like states, the sampling still produces many false positives and false negatives. This is consistent with the information-theoretic studies of Crooks and Brenner Our dataset of peptides was 8-mer, 12-mer, and 16-mer fragments of 8 CASP7 target sequences and 5 other protein sequences with known structures taken from the PDB see . The 8-mWe used the AMBER ff96 force field We simulated the fragments using the ZAM (Zipping and Assembly Method) protocol described in Classification models were trained on five different contact-based metrics, calculated on a per-contact basis from the simulation data: 1) contact probability (CPROB), 2) a distance profile score (DPROF), 3) a mutual stability score (MSTAB), 4) a mutual cooperativity score (MCOOP) and 5) mesoentropy score (MESO) . These mContact probability is calculated as the fraction of sampled states that have inter-residue distances less than 8Å . The distance profile score (DPROF) was developed to obtain more information about the interaction of two residues as a function of distance, by extracting the potential of mean force These metrics are designed to characterize, for any given contact, the average extent of cooperative (two contact pairs) interactions with the given contact which may indicate (thermodynamic) folding cooperativity.pairwise stability as pairwise cooperativity is a measure of how interdependent the distributions of The mutual stability and cooperativity scores can best be described by considering pairwise distributions of contact probabilities  0 and 1 . For a gThe mesoentropy score is related to the backbone entropy. It measures the distribution of backbone dihedral mesostates, defined by Ho and Dill prior knowledge of the probability of observing a native or non-native contact, given no other information about that contact. Given the various metrics above, of the peptide conformations observed from the simulations in solution, we now ask if there is a way to combine those metrics to make the best possible predictions of what the peptide's structure is in the native state of the protein. For each contact observed in our database of simulated fragments, we have a set of The ‘naive Bayesian’ approach would be to assume that, for any contact, our set of calculated metrics Using Equations 1 and 2, and taking the logarithm of the ratio of Since Substituting logistic regression method A potential improvement to the ‘naive Bayes’ model is the Solving for In practice, these coefficients (and their error estimates) are found with a maximum-likelihood optimization using Newton-Raphson gradient minimization. The optimization is equivalent to least-squared linear regression in the nonlinear ‘logit’ variables Note the similarity of the logistic regression model (Equation 6) to the naive Bayesian approach (Equation 4), with We built both naive Bayes and logistic regression models for 8-mer, 12-mer, and 16-mer fragments separately. For the naive Bayes models, this involved empirically computing histograms in 5−1) = 31 combinations of the five contact metrics . In addition, for each of the models, we tested three different inter-residue distance definitions  as the maximal value of To assess which model was the best, we used a statistical hypothesis testing scheme to find a model that most successfully classifies native contacts as well as non-native contacts. Consider a test where we use the statistic For the naive Bayes models built for each fragment length, the model that yielded the highest model quality (Q) when applied to testing data was chosen as the best model. For the logistic regression models, the 25 rounds of testing and training produced a series of models across which For each simulation, the probability of a contact being native can be estimated by Equation 7. However, in the case where there are multiple simulations of the same contact (in overlapping fragment simulations), we can use all of the simulation data to estimate this probability. Assuming that each of A null distribution in C-alpha RMSD values for 8-mers, 12-mers and 16-mers was calculated by taking 10000 random pairwise samples of 8-mer, 12-mer and 16-mer fragments from a set of 3465 protein structures taken from the SCOP database Because there are correlations between contact metrics due to chain connectivity, considerable care was taken to construct null distributions for contact metrics that preserved these correlations. We did this by constructing the null distribution on a fragment-by-fragment basis. For each fragment, the values of the contact metrics were retained, while the assignment of native and non-native contacts was randomized according to a per-fragment bootstrapping procedure. For each fragment, a random contact map was drawn (with replacement) from the full data set. This reassignment procedure, across the entire set of fragments, was repeated 1000 times to construct a distribution of random-case realizations.Text S1Supplemental Data and Results(8.84 MB PDF)Click here for additional data file.


The effect of surfactants on the biodegradation of mono-aromatic hydrocarbons such as benzene,chlorobenzene and 1,2-dichlorobenzene by an Escherichia coli JMI09(MI) recombinant strain, carrying agene cluster containing the genes for benzene dioxygenase, cis-benzene dihydrodiol dehydrogenase, andcatechol 2,3-dioxygenase from Pseudomonas putida ML2, has been investigated.We observed that the efficiency of the benzene dioxygenase catalyzed conversions to cis-dihydrodiolsdepends on the balance among real substrate specificity, bioavailability, and toxicity effects of highlyconcentrated aromatic hydrocarbons. The utilization of non ionic surfactants makes it possible to partlyovercome the limiting step of biodegradation processes for scarcely water soluble hydrocarbons hindered bytheir limited bioavailability.Furthermore the cis-benzene dihydrodiol dehydrogenase and the extradiol catechol 2,3-dioxygenase,which in the presently analyzed biodegradative pathway should further degrade the pollutants, are known, thefirst to be selectively specific for the -dihydrodiol derivative which is not produced by the benzenedioxygenase, the second, to be dead-end inhibited by the corresponding chlorinated catechois. In the presentexample this results in the accumulation of the corresponding chlorinated cis-dihydrodiols which can beuseful for asymmetric synthesis.On the other hand the practical utilization of the system for bioremediation purposes requires the efficient conversion of the chlorinated catechols by specific intradiol ring-cleaving dioxygenases, the crystal structuresof some of these last enzymes are currently under analysis in our laboratory to understand the structuralfunctionalcorrelations. Preliminary data show overall structures similar to the catechol 1,2-dioxygenase fromAcinetobacter sp. ADP1 thus suggesting that the substrate specificity differences are mainly related to subtledifferences in the catalytic site.


A 9-year-old Chinese boy presented with an anterior crossbite, no treatment was performed at that time because the incisors have open root apices. The crossbite self-corrected after one year. This case demonstrated that an anterior crossbite may self-correct without treatment. An anterior crossbite is the description of the upper anterior teeth having one or more occlusions at the lingual side of the lower anterior teeth. According to Lin JJ, the prevalence of anterior crossbite was 13.83% in a Taiwanese sample of 7090 elementary and junior high school students. Aged 9 to 15 years old [The patient was 9-year-old Chinese boy, was brought to our hospital by his mother to seek treatment for his “wrongly positioned front tooth”. His medical and dental histories were non-contributory. On extra-oral examination, his face was symmetrical with a slightly convex lateral profile. Intra-orally the tooth 11 was tipped disto-palatally and was in crossbite with the tooth 42 . FunctioThe case was followed up to permanent dentition. A palatal arch type space maintainer was placed to minimise space loss in the upper arch. The crossbite further self-corrected to normal overbite and overjet. The gingival condition of 42 improved . Apices An anterior crossbite may cause mandibular displacement which leads to various dental problems. Early correction of the anterior crossbite may facilitate the eruption of canines and premolars into Class I , elimina