Volatile organic compounds (VOCs) are small molecules that exhibit high vapor pressure under ambient conditions and have low boiling points. Although VOCs contribute only a small proportion of the total metabolites produced by living organisms, they play an important role in chemical ecology specifically in the biological interactions between organisms and ecosystems. VOCs are also important in the health care field as they are presently used as a biomarker to detect various human diseases. Information on VOCs is scattered in the literature until now; however, there is still no available database describing VOCs and their biological activities. To attain this purpose, we have developed KNApSAcK Metabolite Ecology Database, which contains the information on the relationships between VOCs and their emitting organisms. The KNApSAcK Metabolite Ecology is also linked with the KNApSAcK Core and KNApSAcK Metabolite Activity Database to provide further information on the metabolites and their biological activities. The VOC database can be accessed online. Recently big data has become an important topic that has significant roles to play in versatile disciplines of scientific research. Big data biology is a data-intensive science which has emerged because of the rapidly increasing volume of molecular biological data in omics fields such as genomics, transcriptomics, proteomics, and metabolomics –3. With Metabolomics is the scientific study of quantification of profiles and analysis of chemical processes involving metabolites in a comprehensive fashion. In general, metabolites can be divided into two groups: primary and secondary metabolites. Primary metabolites are directly involved in the normal growth, development, and reproduction. On the other hand, secondary metabolites are not directly involved in these processes but usually have important ecological function, such as inter- or intraspecies communication, antifungal and antimicrobial activities, and also defense against pests and pathogens. Large portions of these defense compounds are volatile organic compounds (VOCs) that are involved in different ways of defense: direct defense and indirect defense. VOCs constitute only a small proportion of the total number of secondary metabolites produced by living organisms; however because of their important roles in chemical ecology specifically in the biological interactions between organisms and ecosystems, revealing and analyzing the roles of these VOCs is essential for understanding the interdependence of organisms . Mycobacterium tuberculosis bacteria can be used as a noninvasive and rapid diagnostic marker for detection of Tuberculosis (TB) diseases [VOCs originate from major pathways of secondary metabolisms of many living organisms, including human, animals, microorganisms, and plants. In plant kingdom, VOCs are responsible for internal and external communication between plants and herbivores, pathogens, pollinators, and parasitoids such as defense and attractant . Microbidiseases . Human adiseases . Therefodiseases . Breath diseases , colorecdiseases , pulmonadiseases , and lundiseases .Advancements in analytical methods such as gas chromatography-mass spectrometry (GCMS), proton transfer reaction mass spectrometry (PTR-MS), and selected ion flow tube mass spectrometry (SIFT-MS) have provided an opportunity to identify the volatile metabolites of living organisms in research laboratories. These analytical approaches generate a large amount of data and require specialized mathematical, statistical, and bioinformatics tools to analyze such data. Despite the advances in sampling and detection by these analytical methods, only few databases have been developed to handle these large and complex datasets. There are few volatile organic compound databases which can be accessed freely; however their applicability is often limited by several elements. Most of these databases only focus on volatiles which are emitted by certain living organisms and have limited applications. For example, the Superscent database  only prohttp://kanaya.naist.jp/MetaboliteEcology/top.jsp. Apart from the database development, we also analyzed the VOC data using hierarchical clustering and network clustering based on DPClus [In the present study, we have developed a VOC database of microorganisms, fungi, and plants as well as human being, which comprises the relation between emitting species, the volatiles, and their biological activities. We have deposited the VOC data into KNApSAcK Metabolite Ecology Database and this database is currently available at n DPClus , 25. In http://www.ncbi.nlm.nih.gov/pubmed) and Google Scholar (http://scholar.google.co.jp/). The PubMed search provided 60 articles based on the keywords “volatile organic compounds” and “metabolites.” The information on VOCs, emitting species, target species, and their biological activities was extracted and deposited into KNApSAcK Metabolite Ecology Database. The KNApSAcK Metabolite Ecology is also linked to the KNApSAcK Core and KNApSAcK Metabolite Activity Database to provide further information on the metabolites and their biological activities. Data were divided into two types: (1) microorganisms species-VOC binary relations; (2) emitting species-VOC-target species triplet relations.The data were collected by an extensive literature search up to January 2015 on PubMed  based on similarity or distance measures. This technique is important for knowledge discovery and has been applied in many applications such as machine learning, pattern recognition, image analysis, and bioinformatics –28. In tM and Mik = 1 if the species i is related to the kth VOC or otherwise Mik = 0. Hierarchical methods require a distance matrix and hence we determined the Euclidean distances between species. Euclidean distance d between species i and species j can be calculated as follows:n is the number of VOCs and there are 1088 VOCs in our data. Based on Euclidean distance, we perform Ward's hierarchical clustering analysis using R, an open source programming language.We used hierarchical agglomerative clustering method, which starts out by putting each observation into its own separate cluster. It then examines all the distances between all the observations and pairs together the two closest ones to form a new cluster. The process continues until all the observations are included in a single cluster. The result of clustering is usually represented by a dendrogram. In our case, we used a species versus VOC matrix. Let this matrix be called G =  that consists of a finite set of nodes N and a finite set of edges E. Two important parameters are used in this algorithm, which are density dk and cluster property cpnk. Density dk of any cluster k is the ratio of the number of edges present in the cluster (|E|) and the maximum possible number of edges in the cluster (|E|max). The cluster property of node n with respect to cluster k is represented byNk is the number of nodes in cluster k. Enk is the total number of edges between the node n and each of the nodes of cluster k. In this study, we apply the DPClus algorithm to identify certain groups of microorganism species based on VOC similarity. A network is constructed where a node represents a microorganism species and an edge indicates high VOC similarity between the corresponding species pair. We selected 5% of the organism pairs based on lower Euclidean distance between them. We used the nonoverlapping mode with the following DPClus settings: Cluster property cpnk was set to 0.5, density value dk was set to 0.6, and minimum cluster size was set to 2.DPClus is a graph clustering software , which hWe also performed a classification of VOCs based on their chemical structure similarity. In order to determine the similarity between two chemical compounds, we used Tanimoto coefficient as similarity measure. The Tanimoto coefficient is defined as , which iAB is the number of features (or on-bits in binary fingerprint) common in both compounds, while A and B are the number of features that are related to individual compounds, respectively. The Tanimoto coefficient has a range from 0 to 1 with higher values indicating greater similarity than lower ones. Additionally, a Tanimoto coefficient value larger than 0.85 indicates that the compared compounds may have similar biological activity [https://pubchem.ncbi.nlm.nih.gov) and then were imported into ChemmineR package in one batch file. The atom pair descriptors are calculated during the SDF import and stored in a searchable descriptor database as a list object.The variable activity . For theactivity , 32. 2D p values of the clusters based on hypergeometric distribution usingBased on Tanimoto similarity measure between chemical structures, heatmap clustering was performed for classifying the VOCs. We also determined the N is the total number of VOCs, C is the size of a cluster, and V and K, respectively, are the number of VOCs of a certain category in the whole data and in the cluster. The hypergeometric distribution is used to calculate the statistical significance of having drawn specific K successes  from the whole population. The test is often used to identify which subpopulations are over- or underrepresented in a sample. The calculated p value implies the probability of getting K or more VOCs of a particular category in a cluster when the cluster is formed by random selection. Lower p value indicates that the statistical significance is high.Here Our purpose is to relate a structure group to a biological activity if and only if the structure group is overrepresented by VOCs associated with that biological activity.http://kanaya.naist.jp/MetaboliteEcology/top.jsp.  Solanum lycopersicum. In the future, we will find more information on these VOCs and assign the KNApSAcK compound ID to these metabolites. On the other hand, information related to the VOCs that have KNApSAcK compound ID can be obtained by clicking the C_ID as in  alpha-pinene (C_ID C00000805) has few biological activity categories such as antimicrobial, antioxidant, biomarker, defense, plant growth enhancement, anticholinesterase, antifungal, dermatitic, irritant, psychotomimetic, and toxic.At present, we have accumulated 1088 VOCs emitted by 517 microorganisms species and 341 VOCs emitted by other biological species including plants, animals, and human. These VOC data have been deposited into KNApSAcK Metabolite Ecology Database, which allows users to search information on VOCs using the KNApSAcK compound ID and metabolite name. This KNApSAcK Metabolite Ecology Database is also linked to the KNApSAcK Core and KNApSAcK Metabolite Activity Database to provide further information on the volatile metabolites and their biological activities. The VOC database can be accessed online at M, and the frequency of species, N. The pattern roughly follows power law [x). Highest 50 types of VOCs are emitted by an individual species and there are 14 such species in our present data (Point y). From this statistical analysis, we can say that most microorganism species emit a few VOCs, which can act as their odor fingerprint. The information of emitting species and compounds has been converted into a 517 × 1088 binary matrix (“1” indicates presence while “0” indicates absence). The binary matrix then was used to calculate the Euclidean distance between species. From the Euclidean distance, hierarchical clustering of species was performed. http://dx.doi.org/10.1155/2015/139254) shows the species name with their corresponding clusters and the pathogenicity of the microorganism species. Interestingly, 77 species from 517 species are known as pathogenic bacteria and are classified into six clusters, which are clusters 6, 27, 35, 40, 47, and 48. Out of these six clusters, three clusters, that is, clusters 35, 40, and 47, contain 100% pathogenic bacterial species such as P. aeruginosa, K. pneumoniae, and E. coli. The other three clusters contain both pathogenic and nonpathogenic species. For example, cluster 6 consists of 11 (7.2%) pathogenic bacterial species while cluster 27 comprises only one (7.7%) pathogen species. Cluster 48 contains 4 (16%) pathogenic bacterial species. Out of all 50 clusters, the rest of 44 clusters contain nonpathogenic species. These results imply that VOCs emitted by some pathogenic bacteria are different from those emitted by nonpathogenic bacteria. These results show consistency between VOC and pathogenicity based classification of microorganisms.Initially, the accumulated VOC data were divided into two types: (1) microorganisms species-VOC binary relations; (2) emitting species-VOC-target species triplet relations. This section focuses on the clustering analysis result of the first type of data, which is the relationship between microorganism species and their emitting VOCs. Until now, we have accumulated 1088 compounds produced by 517 microorganisms. ower law .  FigureIn order to extract different and more information, we constructed a network by inserting edges between species for which the Euclidean distance is less than a threshold. The threshold was decided to include the lowest 5% distances as edges in the network. We then determined the high-density clusters in that network by applying the graph clustering algorithm DPClus. Supplementary Figure  1 shows the overall network, which displays all the generated clusters in such a way that intracluster edges are green and intercluster edges are red.  Klebsiella pneumoniae, Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa are very highly connected to other pathogenic clusters, for example, clusters 2 and 7. Overall, DPClus generated 50 clusters where 20 clusters are connected nodes to each other while the remaining 30 clusters are independent nodes. Only cluster 1 contains both pathogenic and nonpathogenic microorganisms. Clusters 2, 7, 14, 21, 26, and 40 consist of only pathogenic bacteria while the other clusters are consisting of only nonpathogenic bacteria. These results imply that pathogenicity of microorganisms can be linked to characteristic combinations of identical VOCs emitted by them. Some of the pathogenic members of cluster 1 such asThe results of network clustering and hierarchical clustering are similar in the sense that both results indicated that VOC based classification of microorganisms is consistent with their classification based on pathogenicity. However, clustering by DPClus further revealed existence and nonexistence of relations between different pathogenic and nonpathogenic groups of microorganisms.https://pubchem.ncbi.nlm.nih.gov) and then were imported into ChemmineR package in one batch file. We calculated the chemical structure similarity using Tanimoto coefficient. Then, we converted the Tanimoto similarity matrix into distance matrix by subtracting each of the similarity values from 1. Based on distance matrix, we performed heatmap clustering and the result is shown in The first type of data focused on microorganism species only but the second type of data includes VOCs emitted by other biological species such as plants, animals, and humans. The second data type that we have accumulated until now is 1044 species-species interactions via 341 VOCs associated with 11 groups of biological activities. The biological activities of VOCs are classified into two types: (i) chemical ecology related activities, in which most VOCs are involved in interaction between species for survival of organisms such as defense and antimicrobial, and (ii) human health care related activities, in which many VOCs are widely used as disease biomarker and odor. From our accumulated data, 57.3% of the activities belong to chemical ecology such as antifungal, antimicrobial, attractant, defense, plant growth enhancement, root growth inhibition, and repellent activities and 42.7% are human health related activities such as disease biomarker, odor, anticholinesterase, and antioxidant as shown in p values based on hypergeometric distribution which are also shown in From the heatmap plot, we tentatively outlined 11 clusters of VOCs. The count of VOCs belonging to each activity group in each cluster is shown in p values corresponding to anticholinesterase, antimicrobial, and antioxidant are 4.849 × 10−4, 9.696 × 10−4, and 4.849 × 10−4, respectively. Some of the VOCs that are classified into cluster 2 are monoterpenoids and sesquiterpenoids such as beta-linalool, terpinen-4-ol, p-menth-1-en-8-ol, drimenol, and nerolidol. 17 VOCs are alcohol, aldehyde, ketone, epoxide, and ester of terpenoids. The other VOCs are alcohol, aldehyde, carboxylic acid, ester, and ketone of straight-chain alkenes.From this result, we can see that there are 55 VOCs belonging to cluster 1 and mainly involved with anticholinesterase, antimicrobial, antioxidant, and defense activities, for example, beta-caryophyllene, isocaryophyllene, and caryophyllene. All compounds in cluster 1 are terpenoids, of which 15 VOCs are monoterpenoids (10 carbon units) and 40 VOCs are sesquiterpenoids (15 carbon units). There are 33 VOCs in cluster 2 and the p value (1.835 × 10−3) for biomarker activity of cluster 3. All compounds are alkanes; most of them are emitted in human breath such as octane, isobutane, 2-methylpentane, methylcyclohexane, hexane, and cyclohexane.For cluster 3, there are 41 compounds and the main biological activity involved is biomarker for various diseases such as colorectal cancer and asthma. We obtained small p value for this activity is 2.561 × 10−2. For cluster 5, there are 21 VOCs which are aldehyde, ester, carboxylic acid, and ketone of C8–C18 alkanes. Cluster 5 is significantly related with multiple biological activities, which are anticholinesterase, antimicrobial, antioxidant, biomarker, and repellent activities.There are 18 compounds in cluster 4 and all of them are alkenes such as beta-farnesene, alpha-caryophyllene, ocimene, and beta-ocimene. These compounds are mainly associated with chemical ecology activity, which is antifungal and the p value for plant growth enhancement activity (3.531 × 10−3), root growth inhibition activity (4.111 × 10−2), and odor activity (2.29 × 10−5) for cluster 6. An example of VOCs involved in plant growth enhancement activity is 2,3-butanediol and there are many reports that this compound released by soil microorganisms had improved plant growth and increased pathogen resistance [There are 25 VOCs in clusters 6 and 21, of them are alcohol and ether of C3–C8 alkanes. We also obtained small sistance , 35. Forp value = 3.829 × 10−2) and biomarker for various diseases . Aldehydes belong to cluster 7 such that acetaldehyde, propanal, hexanal, 2-methyl-butanal, pentanal, heptanal, and 3-methyl-butanal are mostly used as biomarker for various diseases including cancers and irritable bowel syndrome. In cluster 8, there are 15 VOCs belonging to this cluster, which consist of epoxide, ethers, esters, and alcohols. In cluster 9, there are 42 VOCs and the main biological activity is attractant . All VOCs belonging to cluster 9 are aromatic compounds, in which 24 VOCs are aromatic alcohols, carboxylic acids, esters, ketones, and ethers. 16 VOCs are aromatic compounds consisting of C and H atoms. One VOC consists of C, H, and Br atoms. One VOC is an alkane ester. Also, there are 14 VOCs in cluster 10 which are aromatic compounds. 12 VOCs are heteroaromatic compounds that consist of one or more sulfur, nitrogen, or oxygen atoms. On the other hand, 30 VOCs belonging to cluster 11 are of diverse types of C0–C6 small molecules with low molecular weight, ranging from hydrogen cyanide (27.02534) to tetrachloroethyene (165.8334). The main biological activity for cluster 10 and cluster 11 is biomarker for various diseases. The p values for biomarker activity for cluster 10 and cluster 11 are 1.036 × 10−2 and 1.963 × 10−3, respectively. The major VOCs involved in this activity are isoxazole, 2,3-dimethyl-pyrazine, and 2-methyl-pyrazine which are mostly produced in human urine and can be used as biomarker for autism spectrum disorders.There are 47 VOCs in clusters 7 and 45, of them are ester, carboxylic acid, ketone, and aldehyde of noncyclic C2–C9 alkanes. Cluster 7 is significantly related with multiple biological activities, which are attractant , the three example clusters of microorganism species that classify the microorganism species according to their pathogenicity  and chemical structures of VOCs belonging to all clusters .


Background: Previous studies have reported that children score better in languagetasks using sung rather than spoken stimuli. We examined word detection ease insung and spoken sentences that were equated for phoneme duration and pitchvariations in children aged 7 to 12 years with typical language development(TLD) as well as in children with specific language impairment (SLI ), andhypothesized that the facilitation effect would vary with language abilities.Method: In Experiment 1, 69 children with TLD (7–10 years old) detected words insentences that were spoken, sung on pitches extracted from speech, and sung onoriginal scores. In Experiment 2, we added a natural speech rate condition andtested 68 children with TLD (7–12 years old). In Experiment 3, 16 children withSLI and 16 age-matched children with TLD were tested in all four conditions.Results: In both TLD groups, older children scored better than the younger ones.The matched TLD group scored higher than the SLI group who scored at the levelof the younger children with TLD . None of the experiments showed a facilitationeffect of sung over spoken stimuli. Conclusions: Word detection abilitiesimproved with age in both TLD and SLI groups. Our findings are compatible withthe hypothesis of delayed language abilities in children with SLI , and arediscussed in light of the role of durational prosodic cues in wordsdetection. Language and music perception abilities develop in early infancy. A number of studieshave suggested that these two cognitive functions are subserved by common cerebralstructures  and, thudevelopmentaldysphasia  presentdeficits in syllable perception. SLI, also known as ia e.g., ,is a heia e.g., . On compia e.g.,  found thMage = 6 years). The stimulus set consisted ofpairs of spoken sentences. The first sentence provided the linguistic context whilethe second contained the monosyllabic target word, whose location in the sentencevaried among the 3rd, 7th, and 10th syllable position. The children had to detectthe target that was presented in the picture and auditory stimulus as quickly aspossible. The results revealed a significant position effect for mean reaction times(RTs) in both groups, who detected targets in the 7th and 10th syllable positionmore quickly than those in the 3rd. The authors suggested that both groups ofchildren were influenced by the meaning of the sentence when detecting themonosyllabic target word, because they were faster after having heard more words ofthe sentences. The effect of linguistic context on word detection was furtherinvestigated in older children. Montgomery (Mage = 9 years) and two groups of children withTLD: one age-matched group and one group matched for receptive syntax level(Mage = 7 years). The procedure was similar to theone used in previous studies. The results showed an age effect on word detection inthe children with TLD, whereby the older TLD group  was faster than the younger TLDgroup . Moreover, the children in the younger TLD group, although matched forreceptive syntax level, were faster in word detection than the children with SLI.The results also showed a significant position effect in all three groups, withshorter RTs found for monosyllabic word targets located in the middle of thesentence than at the beginning. This finding confirmed that the two age groups ofchildren with TLD, and the group of children with SLI, used the meaning of thesentence to aid detection of the monosyllabic target words.In previous work, Montgomery, Scudder, and Moore  tested tntgomery  tested wThe above studies have shown that children with SLI may have impaired abilities inauditory detection for verbal units. As the present study also aims to investigatethe ways in which the perceptual abilities in language and music could possiblyinteract, an important source of information comes from studies that have comparedauditory processing of spoken and sung stimuli.In adults, Schön et al.  haverepdi/ or/du/. The participants were required to decide whether thetarget was sung on a syllable containing the phoneme /i/ or/u/. The musical context was manipulated so that the targetchord acted as either a tonic chord, or a subdominant chord. Schellenberg et al.hypothesized that if the participants were influenced by the musical context, theirdetection of the target vowel /i/ or /u/ should befaster in syllables corresponding to the tonic chord because, although both chordsare congruent in that context, the tonic is more expected than the subdominant chordin Western music. Results confirmed this hypothesis by showing that phonemedetection was faster in tonic chords than in subdominant chords. These findingsindicated that school-aged children showed implicit knowledge of syntactical rulescharacterizing the Western musical system, and that phoneme detection was influencedby harmonic context.Much of the evidence above has shown that the detection of monosyllabic target wordswas faster when targets were located at the end than at the beginning of sentences.The authors of these studies have suggested that the children were influenced bylinguistic context—that is, processing of the meaning of the sentence helpedthem to detect the monosyllabic target word. Another manipulation of context forsung stimuli involves modifying the harmonic structure of sung stimuli according tothe standards of Western music. This is exactly what was done in a series ofexperiments conducted with French-speaking adults  andFrenIn Western music, sung syllables are longer than spoken syllables . The pitTo summarize, the ability to segment speech into syllables emerges early in infancy,particularly in the French language . MonosylThis experiment aimed to assess the effect of pitch variations on RTs in a worddetection task in children aged 7 to 10 years. Three types of sentences werecreated: 1) sentences from nursery rhymes sung to the tune of the original melodies(Sung condition), 2) sentences sung using the pitch variations extracted from anormal speech rate (Prosody condition), and 3) sentences spoken using the durationof the syllables in the Sung condition (Slow speech condition).According to the literature, abilities in word detection improve with languagedevelopment, and, thus, word detection should get quicker as children get older.Children should also be quicker when the words are sung (Sung and Prosodyconditions) than when they are spoken (Slow Speech condition). Following Montgomery,if lingn = 15, Mage = 7.5 years),8 years , 9 years , and 10 years . All participants werenative French-speaking children attending regular school, and none of themhad a documented history of language problems or neurological disordersaccording to a questionnaire completed by their parents.Sixty-nine children (39 girls and 30 boys) aged 7 to 10 years were recruitedin three different schools in Reims and in Lille (France) to participate inthis study. They were subdivided into four age groups: 7 years(Sentences. Twenty-four sentences containing 12 to 20syllables were extracted from a repertoire of songs for children (AppendixA). Very popular songs , songs containing lyrics with word repetition, slang words, incorrectsyntax, or dialectal words were excluded. The 24 selected sentences wereeach produced by a professional singer in the three conditions, resulting ina total of 72 sentences. In all three conditions, the singer was instructedto pronounce one syllable per pulsation.In the Sung condition, each sentence was sung using the original score. Inthe Slow speech condition, each sentence was produced slowly, respecting thenumber of syllables produced in the Sung condition (one syllable per noteand per pulsation). In the Prosody condition, we first identified the pitchof each syllable in the Slow speech condition using the Melodyne software and adjuSD = 1,553 ms).As mentioned earlier, because we were interested in testing the independenteffect of pitch variations we edited the acoustic signal to equalize theduration of syllables across the three conditions. In order to do that, wefirst measured the duration of each phoneme of the 24 sentences in the SungCondition. Using Praat Software , we editTarget words. Twenty-four monosyllabic words , were selected astargets. One word was used in each sentence. Half of the targets occurred inthe first half of the sentence and half in the second part of the sentence,which from now on are referred to as Beginning position, and End position,respectively. To facilitate the task and minimize the number of trials inwhich children missed the target, the first three syllables and sentencefinal syllables were never selected as targets. The mean duration of the 24target words was 476 ms (SD = 86 ms) and ranged from 357 msto 662 ms.Fundamental frequency (F0) of syllables in the threeconditions. The analysis of pitch variations within eachcondition revealed that the mean fundamental frequency (F0) of syllables inthe Sung condition was higher (MF0 = 202 Hz)than in the Slow speech condition (MF0 = 136Hz), t(46) = 8.8, p < .001, and theProsody condition (MF0 = 151 Hz),t(46) = 4.8, p < .001, the last twoconditions not being different, t(46) = 1.8,ns. Within each condition, each F0 value of eachsyllable was converted into semitones in order to assess the difference insemitones between successive syllables. The mean semitone difference betweensuccessive syllables was smaller in the Sung condition  compared to the Slowspeech condition (MD = 4 semitones), t(46)= 5.7, p < .001, and Prosody condition(MD = 3.9 semitones), t(46) = 5.6,p < .001, and as expected, the mean differencebetween the last two conditions was not significant, t(46)= 0.3, ns. To verify that the pitch saliency of the targetwords was comparable across conditions, we calculated the mean differencebetween the pitch of the target word and the pitch of the precedingsyllable. This mean difference in the Sung condition (MD =2.7 semitones) did not differ significantly from the mean of the Slow speech(MD = 3.4 semitones), t(46) = 1,ns, or Prosody condition (MD = 3.5 semitones),t(46) = 1.1, ns. The mean duration ofthe target words, minimum and maximum F0 in the sentences, mean F0 of thesentences, and mean F0 of the target words in the three conditions arereported in Each participant wearing headphones (Sennheiser HD 265) was seated in a quietroom in front of a computer (Dell-Latitude 531). To engage children, theword detection task was presented as a video game. Each child was instructedthat s/he had to help an animated character understand what its new friendwas telling it. The target words were spoken (not sung) by a male speaker,different from the speaker who recorded the sentence stimuli. One thousandfive hundred milliseconds after hearing the word, a sentence was presented.The child was instructed to press the space bar as quickly as possible onhearing the target word. The sentence was stopped as soon as the childpressed the space bar. If the child did not respond, the whole sentence waspresented. The next trial started immediately thereafter. RTs were recordedusing E-Prime 2 (Psychology Software Tools).Twenty-four sentences were separated into three sets of 8 sentences. Eachchild was tested on 48 trials, which were divided into three blocks of 16sentences. Each child heard one sentence in two of the three conditions inorder to limit fatigue and learning effects (see Appendix C and D for thecomposition of the blocks). Sentences and conditions within a block werepresented in a random order. The 16 blocks were separated by a pause of avariable duration (1 to 3 min) determined individually by the participant,resulting in a total testing duration of around 15 min.In order to determine whether a response was valid or not, we had to verifythat the child did not press the space bar before the target word occurredin the sentence. Responses were categorized as valid if, and only if, the RTmeasured at the beginning of the target was not shorter than 150 ms, and notlonger than 1,500 ms. The lower limit of 150 ms was used because itcorresponds to the shortest RT recorded in a detection task for simpleauditory stimuli . The uppF = 11.84, p < .001.Post-hoc comparisons with Holm-Bonferroni corrections showed that the 8-yearolds (Mlog(RT) = 6.24) responded significantlyfaster than the 7-year olds (Mlog(RT) = 6.39),and the 9-year olds (Mlog(RT) = 6.11) respondedsignificantly faster than the 8-year olds, but that the 10-year olds(Mlog(RT) = 6.11) did not respond fasterthan the 9-year olds. The ANOVA also revealed a significant effect ofCondition, F = 3.89, p < .03,with shorter RTs in the Slow speech condition(Mlog(RT) = 6.14) than in both the Prosody(Mlog(RT) = 6.20) and the Sung condition(Mlog(RT) = 6.20). Post-hoc comparisonsrevealed that the Slow speech condition differed significantly from both theProsody and Sung conditions. The ANOVA revealed no significant interactionsbetween Age and Condition factors, F = 0.54,ns. A significant effect of Position was found,F = 31.02, p < .001, withshorter RTs when the target was located at the end(Mlog(RT) = 6.13) than when it was locatedat the beginning (Mlog(RT) = 6.23), and this wasirrespective of the Condition. The Position by Condition interaction was notsignificant, F = 0.70, ns, neitherwas the Age by Condition interaction, F = 0.54,ns, and finally, the triple Age by Target Position byCondition interaction was not significant, F = 1.75,ns. Mean RTs in the three conditions as a function ofAge are reported in The analysis of RTs was run only on those participants and sentences forwhich the valid response rate was higher than 70%. The application of thiscriterion led to the exclusion of all the data from 17 participants and allthe data from five sentences. Mean RT and standard deviations were thencalculated on kept data. RTs deviating from the mean by two or more standarddeviations were removed from the data set. A logarithmic transformation (ln)was applied to the RTs to normalize the data. An analysis of variance(ANOVA) with repeated measures was run using the logarithm of RT values asthe dependent variable. The ANOVA had two between-subjects factors: Age,with four levels , and Target Position,with two levels , and one within-subject factor Condition,with three levels . The results, illustrated inThe first result of this experiment was that word detection in sung or slowlyspoken sentences was difficult for 7- to 10-year olds. The percentage of validresponses was much lower than expected, leading us to exclude a large part ofthe responses (49.5%). The percentage of correct responses in the presentexperiment was also lower than in comparable studies by Montgomery  in EngliNotwithstanding the high rate of invalid responses, the task seemed easier forolder children, since RTs showed a gradual decrease between 7 and 9 years. Thesefindings are in agreement with those reported in similar experiments conductedwith English-speaking children  and confdi/ and/du/) whereas we used 22 different word targets. If melodicexpectations apply only to the last position of a musical sequence, this couldexplain why the detection of words was not facilitated in the Sungcondition.Another surprising finding was the absence of a difference between the Prosodyand the Sung condition. The melody in the Prosody condition did not respect therules of Western musical harmony and thus, following the results of Schellenberget al.  in nativThe linguistic context effect was confirmed in that shorter RTs were found in theEnd than in the Beginning position, irrespective of the condition. This findingis congruent with that of previous work  and suggTo summarize, the analysis of RTs on valid responses indicated that older (9- and10-years old) children detected target words faster than the younger children(7- and 8-years olds). It also showed that word detection was faster in thespoken condition  than in the sung conditions, irrespective of the age of thechildren. We can speculate that the Slow speech condition was easier because allchildren have been more exposed to spoken than to sung speech. However, becausethe duration of the phonemes and syllables was equalized across the threeconditions, although less artificial than in the other two conditions, thespeech in the Slow speech condition remained unnatural, preventing us fromdrawing firm conclusions about exposure. A second problem with the paradigm inExperiment 1 was that the criterion for the validity of the responses was basedon speed, not on accuracy. Word detections were considered accurate as long asthe children’s responses fell within the time window in which the targetwords occurred. However, this constraint can raise problems in testing childrenwith learning disabilities. Experiment 2 aimed to correct these two flaws.The second experiment had five main objectives: 1) to assess accuracy in worddetection rather than speed as in Experiment 1; 2) to compare accuracy in worddetection in Sung, Prosody, and Slow speech conditions, as well as in a Naturalspeech condition—that is, a condition with a faster speech rate; 3) toreplicate the linguistic context effect; 4) to reveal a melodic context effect byincreasing the number of valid responses; 5) to validate the use of the paradigm toassess word detection in children with SLI aged between 7 and 12 years.From the results of Experiment 1, we hypothesized that performance would increasewith age. With respect to the difference between the Slow speech and Natural speechconditions, two hypotheses can be posited: 1) If word detection relies onintelligibility, which is better in slow speech than in natural speech e.g., , childreMage = 9.3 years, SD = 1.6years) participated in this study. For further comparison of results inExperiment 3, the children were subdivided into two groups: the Young group(n = 37) comprising children aged 7 to 9 years,and the Old group (n = 31) comprising children aged 10 to12 years . They were recruited in four different schools in Reims and in Lille(France). The selection criteria were the same as in Experiment 1.Sixty-eight children (35 girls and 33 boys) aged 7 to 12 years, which was significantly shorter thanthe sentences in the three other conditions, t(46) = 5.6,p < .001. The syllable duration variability (meanstandard deviation of syllables) in the Natural speech condition was alsosignificantly greater than in the other three conditions , t(46) = 4.2, p < .001.Target and foil syllables were identical to those used in Experiment 1.The stimuli set was identical to the one used in Experiment 1. Twenty-foursentences extracted from children’s songs were used in the same threeconditions: Slow speech, Sung, and Prosody, to which we added a fourthcondition, the Natural speech condition. In this Natural speech condition,sentences were uttered by the professional singer using his natural way ofspeaking. Sentences in this fourth condition were not acoustically modifiedand, thus, included the prosodic variations found in normal speech (AppendixF). The durations of the sentences in the Natural speech condition rangedfrom 3,910 ms to 9,976 ms, with a mean duration of 5,681 ms. The next trial did notbegin until the participant responded. Responses were recorded with E-prime2 software (Psychology Software Tools). Each testing session began withthree practice trials.In order to shorten the duration of the task and to avoid learning effects,each child was tested in the four conditions but in two sessions separatedby one week. Words that were present in sentences in one condition were notpresent in the other one. Sentences were divided into three blocks eachfollowed by a few minutes pause, the duration of which was determined by theparticipant. The approximate duration of testing was 15 min per session.SD = 9.6%). Five of the children hada percentage of CRs lower than 70%. After excluding them, the mean percentage ofCRs was 90% (SD = 6.4). In the subsequent analyses of variance,the percentage of Hits minus False Alarms (FAs), an unbiased accuracy score, wasused as the dependent variable.In order to allow the comparison with results of Experiment 1, and to keep onlythe children who performed above chance level, we calculated individualpercentages of correct responses (CR). Across the four conditions, the meanpercentage of CRs was 88%  = 10.7, p < .002,whereby the 10-12 year group obtained higher scores (M = 85.1)than the 7-9 year group (M = 75.3). There was no effect ofcondition, F = 1.05, ns. An effect ofPosition, illustrated in F = 29.5, p < .001), thisscore being higher when the target was located at the beginning(M = 83.4) than at the end (M = 76.8).Finally, none of the interactions were significant, Age × Condition,F = 0.2, ns; Age × Position:F = 1.8, ns; Condition ×Position, F = 0.6, ns; Age ×Condition × Position, F = 2.2,ns.An ANOVA with one between-subjects factor, Age, with two levels , and two within-subject factors, Condition, with four levels , and Target Position, with two levels was run on the percentage of Hits minus FAs. The results,illustrated in The percentage of CRs in this second experiment was higher (90%) than thatobtained in the first experiment (49.5%) and comparable to that reported inprevious studies by Montgomery and collaborators  who repoAs predicted, the percentage of Hits minus FAs increased with age, whichconfirmed a progressive improvement in word detection accuracy. The abilities inword detection, thus, appeared to improve until pre-adolescence. Contrary to ourpredictions and to results from Experiment 1, no effect of condition was found.Accuracy in word detection was, thus, not influenced by the duration of thesyllables since accuracy in the Natural speech condition did not differ fromthat in the three other conditions, all of which included syllables with longerdurations.Contrary to both the findings in Experiment 1, and the predictions based on thelinguistic context effect, we found a position effect in the opposite direction:Children were more accurate for word targets in the Beginning than in the Endpositions. This apparent discrepancy may stem from procedural differencesbetween the two tasks. In Experiment 1, the children were requested to press abutton as quickly as possible when they detected the word. In Experiment 2, thechildren had to decide whether or not the target word was present in thesentence by pressing the Yes or the No button, without any time constraint. Bothrequirements and expectations, thus, differed in the two tasks with respect tothe position of the target within the sentence. In Experiment 1, expectationswere certainly higher at the end than at the beginning of the sentence becausechildren were told that each sentence contained a target, and that their taskwas to detect it as quickly as possible. Thus, it is possible that a higherlevel of expectation led to shorter RTs. In Experiment 2, the detection oftarget words located at the end of sentences may have required more sustainedattention to keep the target in short-term memory compared to the detection oftargets located at the beginning of sentences. Thus, the task may have been lessdemanding when the target was located at the beginning rather than at the end ofsentences.To summarize, analysis of word detection accuracy indicated that older childrenaged 10 to 12 years detected more words than younger children aged 7 to 9 years,whether the sentences were spoken  or sung . Both young and older children detected words better at the beginningthan at the end of sentences.An important objective of Experiment 2 was to modify the paradigm so as toincrease the percentage of valid responses. We have achieved this goal in thatthe overall percentage of CRs was significantly higher in Experiment 2 (90%)than in Experiment 1 (49.5%). The findings in Experiment 2 showed that childrenfrom 7 to 12 years were able to perform the task, and that the modified paradigmis suitable for testing in children with language deficits.Montgomery  reportedA significant age effect was found in both Experiments 1 and 2, revealing that olderchildren detected words more quickly and accurately than younger children. Abilitiesin word detection, thus, appear to improve with language development. Assuming thatthe development of language abilities in children with SLI is delayed , we alsoMage = 10.1 years) with Specific LanguageImpairment (SLI group) in schools with special programs for children withlanguage disorders in Reims and Charleville Mézičres (France). Asin Experiment 2, the 16 children were divided into two groups: the Younggroup, composed of the 9 children aged 7 to 9 years, andthe Old group, composed of the 7 children aged 10 to 12 years. Allchildren had been enrolled in speech therapy training for 2 to 10 years(Myear = 5.8). They suffered from deficitsaffecting either expressive or receptive language, or both, with differentlevels of severity , were paired, as much aswas possible, to children with SLI for age, sex, and scores in non-verbalintelligence tests (see description of the tests in Appendix E). Similarlyto the SLI group, the TLD group was divided into two groups: the Young groupcomposed of the 8 children aged 7 to 9 years, andthe Old group, composed of the 8 children aged 10 to 12 years. None ofthe children in the TLD group had a history of language disorder, asreported by their parents.Sixteen healthy children with TLD, aged 7 to 12 years  = 0.01, ns, ornon-verbal intelligence, t(30) = 0.4, ns.No children in either group had received musical training, and had noreported auditory, physiological, or neurological problems. All participantsreceived a short hearing screen using an audiometer. Sounds were presentedto the left and the right ear at a range of frequencies , and all participants were sensitive to sounds withinthe 20 dB HL range.As reported in For the word detection task, the stimuli and procedure were similar toExperiment 2.A repeated measures ANOVA was run using the percentage of Hits minus that of FAsas the dependent variable. The ANOVA had two between-subjects factors, Groupwith two levels , and Age with two levels , and two within-subject factors, Conditions with four levels , and Target Position with two levels.F = 6.1,p < .02 (M = 72.1)than younger children in the SLI group (M = 39.1), whereas thiswas not the case for children in the TLD groups . Furthermore, post-hoc comparisons withHolm-Bonferroni corrections did not reveal a significant difference between HitsFA percentages for older children in the SLI group and younger children in theTLD group. The analysis showed an effect of Group, F =38.1, p < .001), whereby children in the SLI group (M% HitsFAs = 56.6) were impaired compared to children in the TLD group(M% Hits FAs = 83.8). There was also an effectof Age, F = 22.5, p < .001, wherebythe 7-9 year group (M% Hits FAs = 58.9) obtainedlower scores than the 10-12 year group (M% Hits FAs= 80.6). As illustrated in F = 22.9,p < .001, whereby the percentage of Hits FAs was higherwhen the target was located at the Beginning position (M%Hits FAs = 75.3) than at the End position (M%Hits FAs = 64.2). However, the effect of Condition was notsignificant, F = 0.1, p > .05, norwere any of the other interactions , Group× Condition, F = 1.3; Age × Condition,F = 0.8; Group × Position,F = 0.7; Age × Position, F= 2.3; Condition × Position, F = 0.3; Group ×Age × Condition, F = 2.5; Group × Age ×Position, F = 0.9; Group × Condition ×Position, F = 0.3; Age × Condition × Position,F = 0.8; Group × Age × Condition ×Position, F = 1.1.The results revealed a Group by Age interaction, p < .02 .Post-hor(13) = 0.51, p = .03; Natural speech,r(13) = 0.52, p = .02); Prosody,r(13) = 0.47, p = .04; Sung conditions,r(13) = 0.80, p < .001.In the SLI group, scores in word detection in the four conditions  were positively and significantlycorrelated with scores in metaphonological tasks: Slow speech,As predicted, children in the SLI group scored lower than children with TLD,which confirms findings of previous studies in English-speaking children . The ANOThe position effect found in Experiment 2 was replicated in Experiment 3, in bothchildren with TLD and children with SLI, without an interaction with age.Consistent with our interpretation of Experiment 2’s results, this robusteffect is considered to reflect attentional abilities.To summarize, the two subgroups of children with SLI showed impairments in worddetection compared to children with TLD, although groups were matched for sex,chronological age, and non-verbal IQ scores. There was no effect of condition,however. Even the group of young children with SLI, who obtained the lowestscores, did not benefit from words with a longer duration (Slow speechcondition) or words that were sung (Sung and Prosody conditions) compared withwords spoken at a natural rate of speech . Results ofboth Experiments 2 and 3 revealed that word detection abilities seem to show aprogressive development and are not yet at ceiling levels at 12 years of age.Findings from Experiment 3 indicate that the development of these abilities inchildren with SLI is delayed, and these abilities continue to develop with aneven steeper slope.This study aimed to assess the development of word detection abilities in childrenaged 7 to 12 years with TLD and SLI. Based on the facilitation effect documented inthe literature, we hypothesized that word detection would be easier in sung than inspoken sentences. In order to single out one acoustic difference between spoken andsung sentences, we created conditions in which the sung and the spoken sentenceswere equated for syllable durations and pitch variations. The sole differencebetween the two sentence types (Sung and Slow speech conditions) was that pitchvariations in the sung sentences followed the harmony rules of Western music,whereas those in the spoken sentences did not.We failed to find any advantage of sung over spoken sentences, either in the group ofchildren with TLD, or the group of children with SLI. Moreover, no facilitationeffect was found when we compared the conditions located at the two extremities ofthe continuum from spoken to sung sentences—that is, sentences spoken at anatural rate of speech (characterized by short syllable durations and large pitchvariations) to sung sentences . These findings contrast with the documented advantage of sungover spoken speech in verbal learning in children  and in aAnother interesting consideration concerns the influence of rhythm. Our primary focuswas to determine the effect of pitch and melody on word detection, and, as such, wespecifically isolated and manipulated this pitch dimension. While this method iscertainly not without merit and it is important to understand the relativecontributions of different musical dimensions, our results may be explained by thefact that syllables were produced in an isochronous manner across Sung, Prosody, andSlow speech conditions. In this case, it seems likely that syllables in the samesentence (over these three conditions) had more similar syllable durations, and may,thus, have contributed to the results. The fact that syllable duration was indeedsignificantly more variable in the Natural speech condition compared to the otherthree conditions supports the idea that variability of syllable durations maycontribute to speech processing. Rhythm influences pitch processing in both adults and infaWe conclude that children with SLI were impaired in comparison to children with TLD.In particular, we note that their word detection skills were significantlycorrelated with their performance on tests of metaphonological awareness. Overall,we found no effect of the isolated musical dimension—pitchvariations—on language processing, but future studies may investigate theeffect of other musical dimensions. Further, if music and language do share commonneural substrates and common auditory working memory , one wou


Background. Childbirth medicalization has reduced the parturient's opportunity to labour and deliver in a spontaneous position, constricting her to assume the recumbent one. The aim of the study was to compare recumbent and alternative positions in terms of labour process, type of delivery, neonatal wellbeing, and intrapartum fetal head rotation. Methods. We conducted an observational cohort study on women at pregnancy term. Primiparous women with physiological pregnancies and single cephalic fetuses were eligible for the study. We considered data about maternal-general characteristics, labour process, type of delivery, and neonatal wellbeing at birth. Patients were divided into two groups: Group-A if they spent more than 50% of labour in a recumbent position and Group-B when in alternative ones. Results. 225 women were recruited (69 in Group-A and 156 in Group-B). We found significant differences between the groups in terms of labour length, Numeric Rating Scale score and analgesia request rate, type of delivery, need of episiotomy, and fetal occiput rotation. No differences were found in terms of neonatal outcomes. Conclusion. Alternative maternal positioning may positively influence labour process reducing maternal pain, operative vaginal delivery, caesarean section, and episiotomy rate. Women should be encouraged to move and deliver in the most comfortable position. For a long time, positions during labour could be freely changed or modified according to parturient desires.Unfortunately, in developed countries the hospital admission of labouring women leads obstetrical practice to restrain spontaneous and instinctive attitude and to focus strictly on intrapartum fetal wellbeing and maternal comorbidities , 2.This way, the parturient receives fewer opportunities to labour and deliver in a preferred position, assuming the recumbent one as standard because of its easier monitoring of fetal wellbeing, administration of intravenous therapy, loco-regional anaesthesia, and performance of medical procedures, perineal support, and birth assistance , 3.The effects of different maternal positions during labour on maternal-fetal and neonatal outcomes are rarely in agreement and available evidences in this field are often controversial and fragmentary , 4, 5.The vertical positions may benefit from “gravity effect” potentially able to reduce aortocaval compression, to make uterine contractions effective and to favour a better fetus alignment in the birth canal and to increase pelvic outlet diameters, reducing intrapartum maternal and neonatal complications –9.Anyway counterparts evidences reported an increased haemorrhagic risk associated with upright positions , 10–12 dCertainly, recumbent position makes it easier to palpate the mother's abdomen in order to monitor contractions, to perform vaginal examinations and invasive manoeuvres, to check the fetal head position, and to assess the fetal heart rate.Conversely, because of increased risk of maternal abdominal blood vessels compression, less effectiveness of uterine contractions, less perineal muscle relaxation, high rate of analgesia request, and long labour length, recumbent position seems associated with more operative deliveries, severe pain , 3, 16, Last but not least, since intrapartum complications are frequently reported when fetal occiput is in posterior position (OP), some authors investigated if maternal labouring position may have a role in facilitating spontaneous rotation to occiput anterior position (OA) without conclusive evidences –21.The aim of our study was to compare patients spending in a recumbent position more than 50% of labour to those assuming a preferred alternative position  in terms of intrapartum, maternal/fetal, and neonatal outcomes. The second aim of the study was to establish if differences exist among two groups in terms of fetal head rotation rate from OP to OA.We conducted an observational cohort study on pregnant women admitted to the delivery room of University of Padua, Woman and Child Health Department, in the interval time between January 2013 and December 2013.All the enrolled patients have been properly informed about the aim of the study and they consented to the usage of their data according to the Italian law (675/96).We considered primiparous women with uncomplicated pregnancies and single fetuses in cephalic presentation before or at the onset of labour eligible for the study.According to the defined criteria, labour onset was defined by regular uterine contractions and cervical dilatation of at least 2 cm; the second labour stage was defined when a full dilatation of the cervix is attained .2, assessment of occiput fetal position at the labour onset and confirmed by ultrasound [We considered inclusion criteria to be as follows: age more than 18 years, BMI between 18 and 30 Kg/mtrasound , and infGeneral data of parturients were collected in an electronic service at recovery. Data about intrapartum care were collected through Friedman labour partogram and midwifery records. One of the authors, supported by the midwifery (V.B.), collected data daily and reported them in an excel database.For all women, data about maternal general characteristics , labour process  score detected during labour and before analgesia administration when required, and analgesia request rate), mode of delivery , need of episiotomy, and rate of perineal tears in cases of vaginal deliveries), and neonatal wellbeing at birth  were recorded.Patients were included in Group-A when they spent more than 50% of their labour in recumbent position  and in Group-B when they preferred an alternative position .All eligible patients received exclusively a midwifery intrapartum care, except for urgent CS or operative vaginal deliveries cases. All eligible patients assumed a spontaneous position without any medical or midwifery prescription. Regarding the analgesia, all women received epidural analgesia, when required, without the use of opioid.Recumbent position: the pregnant is lying on her back, above the bed at an angle up to 45 degrees, or on her side preferring that one on which the fetal back and the occiput are located. A pillow between the legs (extended or flexed) was allowed.Upright position: the woman is in an upright position standing by herself or against to a support .Squatting position: the patient crouches during contraction and then recuperates during relaxation.Sitting position: the pregnant is sitting on a bed, on a chair, or on a ball.Position “on all fours”: the pregnant is kneeling and bent forward in order to support her weight with arms.The primary endpoint of the study was to collect possible differences between two groups in terms of labour process, type of delivery, and neonatal wellbeing.Maternal positions were considered as follows.The secondary endpoint was to establish if differences exist among two groups (Group-A versus Group-B) in terms of fetal head rotation rate from OP to OA and into Group-B which is the best alternative position.t-test, and categorical variables have been tested with the χ2 test or Fisher's exact test when appropriate. Statistical significance was defined as P < 0.05.Statistical analysis was performed using SPSS  software for Windows version 18. We performed the Kolmogorov-Smirnov to test normality of distribution. Continuous data have been tested with the In the considered interval time, 225 women were eligible for the study. Among them, 69 patients joined Group-A and 156 joined Group-B. In detail, Group-B patients assumed the upright position in 46.1% of the cases, the sitting position in 21.1% of the cases, the “on all fours” position in 16.2% of the cases, and a balloon-squatting position in 16.6% of the cases.2 (ranging 18–30 Kg/m2), and mean gestational age was 39.2 ± 1.2 weeks (ranging 37–41 weeks). Group-A and Group-B were homogenous for general maternal characteristics (Mean age was 31.13 ± 6.1 years (ranging 18–44 years), mean BMI was 23.59 ± 3.5 Kg/meristics .Data about labour process, intrapartum pain and analgesia request, type of delivery, and neonatal outcome are detailed, reported in P < 0.001) . The two groups significantly differed for the analgesia request rate, respectively, with 34.8% versus 9.6% rate (P < 0.0001)  .Regarding the mode of delivery, 47.8% of Group-A patients delivered by vaginal route, 26.1% required operative vaginal delivery, and 26.1% underwent CS.P < 0.001)  .P < 0.05)  .P < 0.001), while 1st-2nd degree vaginal tears occurred, respectively, in 5.9% versus 49% of the cases (P < 0.001); no differences between two groups in terms of neonatal outcomes were reported.Episiotomy was performed in 100% of Group-A patients who delivered by vaginal route compared to the 32.7% of Group-B (P: n.s.).Concerning the distribution of fetal occiput position at the labour onset, the OP rate resulted in being comparable in two groups with 40.6% in Group-A and 36.5% in Group-B (Considering OP cases (28 cases in Group-A and 57 cases in Group-B) a strong significant difference was found in terms of delivery outcome.P < 0.0001).CS was necessary in 27 patients: 46.4% in Group-A compared to the 12.3% in Group-B (P < 0.001).Significant differences in terms of OP persistence at delivery were also found in those delivering vaginally: in Group-A patients, OP persisted till birth in 39.6% of the cases while in Group-B only in 28% of the cases  and interventions  are required and spontaneous movements and position change are not feasible .Nevertheless, several studies reported that when a spontaneous analgesic and comfortable position is allowed, labouring women may benefit from a shorter labour length, avoidance of augmentation, and lower pain, reaching childbirth with strong motivation , 16, 33.If on one hand intrapartum epidural analgesia is considered a safe and effective procedure , 35, on Many authors postulated that intrapartum persistent OP position  represents a risk factor for poor maternal and neonatal outcomes , 23, 36.When a persistent OP occurs, different adverse obstetrical events are reported such as prolonged first and second labour stages, increased epidural analgesia request, higher risk of postpartum haemorrhage, increased CS, operative vaginal deliveries, and 3rd- and 4th-degree perineal tears rate , 23, 36.Frequently these intrapartum conditions resulted in low Apgar score, neonatal trauma, acidemic cord blood gas concentrations, admission to neonatal intensive care unit, and newborn encephalopathy , 23, 36.Different obstetrical manoeuvres have been proposed to facilitate the fetal head rotation from OP to OA , but none resulted in being more effective than maternal vertical position during labour , 20.In a cohort of 225 parturients we found that vertical positions appeared helpful in foetal head rotation during labour, reducing the rate of operative vaginal deliveries and CS. Unfortunately, our sample did not allow us to discriminate which vertical position has to be preferred. Stremler et al. suggested that hands-and-knees positioning was more effective than others in reducing persistent back pain and favouring rotation of OP to OA but their study sample did not reach statistical power .The upright position takes advantage of the gravity, increased size of the pelvic diameter, thanks to the nutation movement and to the coccyx retropulsion, the decline of the extreme cephalic, less painful and more effective contractions, pain relief for reduced pressure on the sacrum, increased confidence in the second labour stage, and lower perineum stretch. The sitting position takes advantage on gravity, on use of lumbar massage, and on an increased pelvic diameter with better fetal alignment to the pelvis, but it may increase the pressure on the sacrum with a major risk of perineal trauma. The “on all fours” position reduces the effect of gravity, the peak and duration of the contractions, and the pain due to a lower fetal pressure on the pelvis; it allows practicing the lumbar massage and favors the fetal internal rotation. This is the most recommended position to correct and prevent fetal malposition, to reduce cervical edema and the sacral pressure of the presenting part, and to increase the pelvic anteroposterior diameter in the expulsive phase. The squatting position allows using gravity, increasing the pelvic diameters and the counternutation for the fetal head descent, and strengthening the feeling of thrust and relaxation of perineal muscles , 38.Our encouraging results about vertical positions need to be further confirmed by large cohort studies and do not solve the existing debate.Unfortunately, nowadays the “gravity effect” able to increase the maternal perineum compliance to fetal progression should be considered a theoretical assumption. Considering available literature and in absence of clear and strong evidences, it seems reasonable not to impose on women a labouring posture different from the spontaneous one .Although Golara et al.  suggesteOur data on a series of uncomplicated pregnancies allows us to suggest that, in absence of prelabour or intralabour complications, the alternative vertical positions may positively influence labour process reducing maternal pain and operative vaginal delivery, CS, and episiotomy rate.Our study is actually the first one assessing the role of maternal labouring vertical position in occiput rotation from OP to OA demonstrating its real benefit on labour process and delivery.Although further studies in this field are mandatory and most theoretical speculations need to be clarified, in absence of prepartum/intrapartum maternal-fetal complications, all women should be encouraged to move and to deliver in the most comfortable position, preferring a vertical position when OP is diagnosed.


Undernutrition continues to pose challenges to Uganda's children, but there is limited knowledge on its association with physical and intellectual development.In this cross-sectional study, we assessed the nutritional status and milestone development of 6- to 8-month-old children and associated factors in two districts of southwestern Uganda.z-scores for weight-for-age (WAZ), length-for-age (LAZ), weight-for-length (WLZ), head circumference (HCZ), and mid-upper arm circumference. Chi-square tests, correlation coefficients, and linear regression analyses were used to relate background variables, nutritional status indicators, and infant development.Five hundred and twelve households with mother–infant (6–8 months) pairs were randomly sampled. Data about background variables ) were collected using questionnaires. Bayley Scales of Infant and Toddler Development (BSID III) and Ages and Stages questionnaires (ASQ) were used to collect data on child development. Anthropometric measures were used to determine p<0.05) with WAZ, LAZ, and WLZ. Regression analysis showed that gender, sanitation, CDDS, and likelihood to be below the poverty line were predictors (p<0.05) of undernutrition. BSID III indicated development delay of 1.3% in cognitive and language, and 1.6% in motor development. The ASQ indicated delayed development of 24, 9.1, 25.2, 12.2, and 15.1% in communication, fine motor, gross motor, problem solving, and personal social ability, respectively. All nutritional status indicators except HCZ were positively and significantly associated with development domains. WAZ was the main predictor for all development domains.The prevalence of underweight, stunting, and wasting was 12.1, 24.6, and 4.7%, respectively. Household head education, gender, sanitation, household size, maternal age and education, birth order, poverty likelihood, and CDDS were associated (Undernutrition among infants living in impoverished rural Uganda was associated with household sanitation, poverty, and low dietary diversity. Development domains were positively and significantly associated with nutritional status. Nutritional interventions might add value to improvement of child growth and development. Undernutrition continues to pose challenges to Uganda's children . InadequStunting in Uganda has been linked with low socio-economic status and associated factors such as poor health, sanitation and lack of appropriate knowledge on health and nutrition, gender disparities, low or no education of mothers, and suboptimal infant feeding practices , 14. PooStunting in early life (0–2 years) has been linked to impaired cognitive, language, and motor development –17. Howez-scores (LAZ)  .The southwestern part of Uganda, including the Kabale and Kisoro districts, has been known to be nutritionally insecure , and theThe study was conducted in Kabale and Kisoro because of the high levels of stunting. Town centres were excluded to minimize differences in socio-economic status and feeding practices. Inhabitants were predominantly small-scale farmers cultivating small plots of land. Both districts were densely populated with populations of 534,160  and 287,The cross-sectional study was conducted between October 2013 and February 2014. The sampling unit was a household with a lead caregiver as the respondent and an infant aged 6–8 months. Households were excluded if the child had 1) congenital malformation(s), 2) a physical handicap that would influence growth or preclude anthropometric measurements, or influence nutrient intake, or 3) been diagnosed with mental or brain illness as evidenced by the child's mother or a health worker.Sample size calculations were based on a prevalence of 35% for stunting among 6- to 8-month-old children in southwestern Uganda . Power wBasing on the 2002 housing and population census, an average of six households per 150 in every village was assumed to have an infant between 6 and 8 months old. Furthermore, the included households were stratified according to the districts. Proportionate simple random sampling was used to select sub-counties to participate in the study . Using the list of all villages, nine villages were randomly selected in each of the participating sub-counties. Finally, all consenting households with children aged 6–8 months within a participating village were recruited. If a household had more than one eligible child, the youngest was selected to participate, and in the case of twins, we randomly selected one of them. All tools and questionnaires were pretested in the area and adjusted before use.Data were collected during home visits using a semi-structured questionnaire with both open- and close-ended questions. The questionnaire was based on previously validated questionnaires , with miSanitation was evaluated by observation and recording the presence or absence of stagnant water in the compound, human faeces around the house/veranda, animal droppings, and litter in the compound. If any of these were present, the score would be 0; if none was found, the score would be 1. The presence or absence and the condition of the plate stand (drying rack), bath shelter, and latrine were also obtained by observation. These were scored as good=3, fair=2, poor=1, and none=0. The scores were then combined to make the composite variable (household sanitation) with scores ranging from 0 to 12.z-scores , weight-for-length z-scores (WLZ), mid-upper arm circumference z-scores (MUACZ), and head circumference z-scores (HCZ) by use of the software Anthro package (version 3.2.2), a nutritional assessment tool using the WHO standards , and mid-upper arm circumference (MUAC), which were measured using standard procedures and calibrations recommended by WHO , 32. Anttandards , 34. Undtandards , 35, 36.The socio-economic status of the households was obtained by the ‘Simple Poverty Scorecard for Uganda’ , which hInfant feeding practices were determined by asking mothers about the first food/drink that was given to the infant, the number of times the infant was fed, the person that normally fed the infant, and the combination of foods normally given. The child dietary diversity scores (CDDS), adapted from the Household Diversity Score tool , were usThe Household Food Insecurity Access Scale (HFIAS), without any modifications, was used to determine household food insecurity including all nine generic questions that require a recall about the worry of food availability and accessibility in the previous 4 weeks , 45. If Infant development was determined by the BSID III  on threeThe infant development was also assessed using the Ages and Stages questionnaires (ASQ) , 49–51. r=0.71–0.87) for both scales.The administration of the tests was done by personnel fluent in English and local language to enhance adequate communication with the children and the caregivers. Administration instructions of time, reversal, and discontinuation rules were strictly followed. Feedback on administration styles and data records were reviewed at the end of each assessment day. Social-emotional and adaptive behaviour subtests of the BSID III were not used due to time constraint and some of these aspects were covered by the ASQ. Inter-rater results showed good correlation (p<0.05) association in bivariate analyses were included in linear regression models for the different nutritional status indicators to determine the predictors of the dependent variables (Supplementary Table 1). Linear regression was used to examine the relationship between nutritional status and composite scores of cognitive, language, and motor development in children taking into account other factors. The statistical analyses were performed using SPSS version 22 . All putative determinants from bivariate analysis were simultaneously included in the various models. In order to identify the most significant determinants, a linear regression analysis was performed using the enter procedures, retaining only the strongest determinants. The variables that did not explain additional variance were not included in the final regression models; all necessary assumptions were met. Variables that showed a significant  and approved by the Uganda National Council for Science and Technology. It was also approved by the Norwegian Regional Committee for Medical and Health Research Ethics (no. 2013/1833). All respondents gave informed consent by reading and signing a consent form to participate in the study and allowing publication of the findings. The consent form was designed and translated into local language for the participants.All 536 households that were identified consented to participate. Then, 24 households were excluded, mainly due to incorrect reporting of children's ages, leaving a total of 512 households. The mean (± SD) number of years a mother had spent in school was 4.9±3.4, and most of the mothers were peasant farmers. Mean age of mothers was 26.6±6.5 years, and the mean age at first birth was 19.6±2.8 years. The mean number of children per mother was 3.4±2.3, and 3.4% of the mothers had more than eight biological children . The proDuring the last 2 weeks prior to the survey, 65.0% of the children had suffered various illnesses, and 32.4% were still sick at the time of survey. The most common illnesses were cough and flu (56.9%), diarrhoea (26.6%), and fever . Other illnesses reported were skin rash, sore throat, nose bleeding, and eye infections (data not shown). There were no significant differences in gender morbidity (Supplementary Table 1).Most of the index children (97%) were still breastfeeding, and many of them (96%) had started complementary feeding. About 70% of the mothers reported having given only breast milk to their babies up to 6 months, although some of them continued beyond that point, while others had not started complementary feeding at the time of survey . MoreoveA majority of the children were first introduced to porridge alone. The combination of foods commonly given to children was beans and a carbohydrate source , or a carbohydrate source alone, as reported by approximately one-third of the respondents in each case. The mean (± SD) CDDS was 3.1±1.7 (range 0–8). Few children (4.5%) were fed foods of animal origin .The means (SD) of WAZ, LAZ, and WLZ were −0.67 (1.1), −1.14 (1.2), and 0.13 (1.2), respectively. The prevalence of underweight, stunting, and wasting in all children were 12.1, 24.6, and 4.7%, respectively . UndernuThe mean (± SD) composite scores for cognitive, language, and motor development based on the BSID III were 102.6±13.7; 101.8±14.8, and 104.4±15.2, respectively. There were no severely cognitively impaired children, but the language and motor scores indicated that one (0.2%) and four (1.1%) of the children had severe impairment in the two domains, respectively. Results from the ASQ indicated delayed development in all five domains of communication, fine motor, gross motor, problem solving, and personal social ability . There wp<0.05; Supplementary Table 1). Household heads with 0–7 years of school were likely to have more stunted children compared to households whose head had more years of school (p<0.01); similarly children with low CDDS were more likely to be stunted than those with higher CDDS (p<0.05).The results of the bivariate analysis using the chi-square statistics showed that bigger households and those in extreme likelihood of being below the poverty line were likely to have more underweight children (p>0.05).Household head education was positively and significantly associated with all the nutritional status indicators, whereas maternal education was significantly associated with only WAZ and WLZ (Supplementary Table 2). There was a negative and significant association between household size and WLZ as well as MUACZ. Child birth order was negatively and significantly associated with WAZ and WLZ. The CDDS were positively and significantly associated with WAZ and LAZ. Poverty likelihood scores were negatively and significantly associated with WAZ, WLZ, HCZ, and MUACZ. Morbidity did not show any significant association with any of the indicators of nutritional status (R2=0.068) .The linear multivariate regression analyses indicated that the sex of a child was a main predictor of WAZ, followed by sanitation (2=0.068) . Moreovep<0.05) associated with language, cognitive, and motor domains (BSID III). However, the correlations between WLZ, HCZ, and cognitive domain were not significant. In the same way, using the ASQ, most of all the nutritional status indicators were positively (p<0.05) associated with the development domains, except for LAZ with fine motor and HCZ with personal social abilities, where the associations were not significant.Supplementary Table 3 shows results of nutritional status indicators’ bivariate correlations with the BSID III and ASQ indicators. Most of the nutritional status indicators were positively (R2=0.104) (R2=0.068), whereas the motor domain was predicted by the sex of a child (boy), WAZ, LAZ, and likelihood of being below the poverty line (R2=0.086).Multiple regression (using BSID III) showed that the cognitive domain was predicted by WAZ, the sex of a child (boy), and household head education (2=0.104) . FurtherNutritional status indicators showed growth faltering as in many other developing countries . Boys weWe noted that complementary feeding practices were poor. Some mothers (16.8%) had not started complementary feeding, and the majority (68%) of children had their first complementary food as diluted porridge nothing added. At the time of study, the proportion of children who were being fed foods containing primarily carbohydrate sources and beans was 32.6%; for carbohydrate sources alone, it was 32.6%. The consumption of animal protein foods was limited (4.5%), as also shown in another study , and a gThe significant association between the nutritional status of children and household characteristics related to sanitation, poverty, size, and maternal and household head education children are consistent with UNICEF's conceptual framework of the underlying causes of under nutrition , 14. FurMaternal age at the first child and number of children did not show significant associations with child nutritional status. It is, however, worth noting that some of the mothers were very young (11–16 years) at their first child and that 18.9% were younger than 18 years old and 3.4% had more than eight children. The negative and significant association between household size with WLZ and MUACZ probably indicates inadequate food supply for the larger households and possibly the lack of time to prepare food for infants. Collectively, these factors may have an effect on the health of the mothers, both physically and/or psychologically, that could interfere with child care, including feeding , 58.Cognitive and other development domains correlated positively and significantly with almost all the nutritional status indicators. This is in line with the fact that adequate nutrition is paramount to the development of the brain directly for structural and functional development, and indirectly for children's behaviour and experience , 23. GooThe findings of this study indicate that the infants’ HCs were adequate and only 2.7% of the infants had HCZ <−2 SD. Studies have shown that HC <−2 SD may be an indicator of severe undernutrition in the first year of life that may eventually affect brain development  and henc2 in linear regression models suggest that several variables, though not statistically significant, each had a small impact on our outcomes. It is also possible that other variables, not included in the regression model or study, had significant impact on the outcomes.The two scales (BSID III and ASQ) showed differences in detecting developmental delay evaluated as proportions of low scores. ASQ is a caregiver report that captures a wide range of adaptive behaviours  that BSIThis study has several strengths. It was the first of its kind to use both the BSID III scales and ASQ on children of 6–8 months in the Ugandan setting, and to explore the possibility of correlating these findings to various indicators of nutritional status. We were also able to recruit a large number of children. The main limitation of this study was the cross-sectional design, precluding any conclusion about possible cause and effect. Moreover, neither BSID III nor ASQ has been validated for use in the Ugandan setting. Some of the BSID III tool kit materials were novel to the children and could have intimidated them. Also it was difficult to fit good models that could explain the outcomes in our study population, probably indicating a complex network of variables affecting the outcomes. These findings indicate a need for multi-intervention programmes.Undernutrition and growth faltering had already set in as early as 6–8 months among children living in impoverished rural Uganda. Poor household sanitation, poverty, and low dietary diversity, among other factors, were associated with undernutrition in complex interrelationships. Development domains  were positively and significantly predicted by the nutritional status of the children confirming previous studies. There is a need for multi-intervention programmes addressing dietary diversity, with food hygiene, infant feeding, and care practices to improve infant and child growth as well as development.Click here for additional data file.


Chronic temporomandibular disorder (TMD) is known to have strong correlations with psychological factors and to display gender disparity. However, while chronic TMD is known to affect quality of life, large-scale studies investigating the influence on quality of life by gender are scarce.th Korean National Health and Nutrition Examination Survey (2007–2009). We adjusted for covariates  in regression analysis for complex sampling design to calculate regression coefficients and 95% CIs for gender difference in the association between chronic TMD and quality of life. We also evaluated which covariates of somatic health, mental health, health behavior, and sociodemographic factors weakened the relationship between TMD and EQ-5D.This cross-sectional study assessed the data of 17,198 participants aged ≥19 years who completed chronic TMD and EuroQol-5 Dimension sections in the 4Prevalence of chronic TMD was 1.6% , and chronic TMD persisted to negatively impact quality of life even after adjusting for confounding variables. Low sociodemographic factors and health behavior had a negative effect on quality of life. Somatic health and mental health were most affected by chronic TMD. As for quality of life, women were affected to a greater extent than men by TMD. Women were more affected by osteoarthritis and general mental health , and men by employment.These results imply that chronic diseases and psychological factors are important in chronic TMD, and that there may be physiological and pathological gender differences in TMD. Temporomandibular disorder (TMD) is a general term that encompasses a heterogeneous group of musculoskeletal and psychophysiological pain conditions involving the temporomandibular joint (TMJ) and adjacent structures . PrevalePain is the most characteristic feature in most TMD cases, and the main reason patients seek treatment . TMD is Murray et al. reported the health-related quality of life (HRQoL) in patients referred to a craniofacial pain clinic due to TMD and facial pain measured with the Oral Health Impact Profile . Of respThe main aims of this study were to (a) use a cross-sectional design to examine whether the prevalence of chronic TMD has a negative association with quality of life; and (b) identify possible mediators in the relationship between chronic TMD and EQ-5D scores.th Korean National Health and Nutritional Examination Survey (KNHANES) (2007–2009) conducted by the Korean Ministry of Health and Welfare. The survey consisted of three sections  and used a rolling sampling design with a complex, stratified, multistage, probability-cluster survey in a nationally representative sample of South Koreans. More information is available in ‘‘The 4th (2007–2009) KNHANES Sample Design” and the 1st-3rd Sample Design reports. Data is made available upon email request on the KNHANES website [th KNHANES . After exclusion of data of 5,226 participants aged <19 years, 1,095 participants missing information on chronic TMD prevalence, and 113 participants missing EuroQol (EQ) values, data of 17,198 participants was analyzed. Missing values in data of the 17,198 respondents were excluded in corresponding adjustments for confounding variables.This study used data obtained from the 4 website . Out of Chronic TMD was defined as self-reported experience of temporomandibular pain persisting 3 months or longer during the past year. Ever-TMD was defined as ever-experience of TMD at time of KNHANES survey.EuroQol-5 Dimension (EQ-5D) is a HRQoL assessment tool widely used in the health and medical care sector. Outcomes range from ‘health worse than death’, represented with -1, to ‘perfect health’, represented with 1 . EQ-5D cIf a grade 2, M2, SC2, UA2, PD2, and AD2 were assigned values of 1, and 0 if not. If a grade 3, M3, SC3, UA3, PD3, and AD3 were assigned values of 1, and 0 if not. If any of the 5 EQ-5D items was a grade 3, N3 was assigned a value of 1, and 0 if not. If all 5 EQ-5D items was grade 1, the EQ-5D index was 1.Covariates included sex, age, sociodemographic factors, health behavior, somatic health, and mental health, all of which were adjusted for in stages. For assessment of associations between TMD prevalence and quality of life, sex, age, sociodemographic factors, and health behavior were adjusted. Somatic and mental health were regarded as confounding factors to be considered in TMD, and were not included in adjustments for associations with quality of life.th revision, and then divided into employed/unemployed. Health behavior covered BMI, smoking status, alcohol consumption, and moderate physical activity. BMI was categorized into <18.5, <25, and ≥25 kg/m2 groups in accordance with the WHO Asia-Pacific perspective. Current smokers who had smoked 5 packs of cigarettes or more over their lifetimes were considered to be ‘smokers’, and current smokers who had smoked less than 5 packs, ex- and non-smokers as ‘non-smokers’. ‘Non-drinkers,’ were defined as those who did not consume alcohol or drank to levels where daily activities were unaffected by drinking, and ‘drinkers’ as those whose daily activities were affected by alcohol consumption at least once a month. Any of the following was considered to be moderate physical activity, or adherence to regular exercise: (a) at least 3 days of 20 minute-sessions of intense, physically demanding exercise involving heavy breathing  during the past week; (b) at least 5 days of 30-minute moderate physical exercise sessions involving slightly labored breathing  during the past week; or (c) at least 5 days of 30 minute walks during the past week.Participants were categorized into male/female, and age was assessed as a continuous value. Sociodemographic factors included education, income level, occupation, urban-rural gradient, and marital status. Education level was categorized as middle school graduation or lower, high school graduation or higher, and college graduation or higher. Income level was recategorized as binary values  from quartiles by equalized average monthly household income (average monthly household income divided by household member number). Employment was assessed in accordance with the Korean Standard Occupation Classification 6Somatic health was assessed with presence of any chronic hypertension, diabetes, asthma, and osteoarthritis of 3 months or more over the past year. Mental health was determined by presence of stress, depressive symptoms, thoughts of suicide, and sleep duration. Stress was categorized into high-stress, and low- or almost no-stress groups, and depressive symptoms were defined as 2 or more consecutive weeks of depressive symptoms. Thoughts of suicide were defined as thoughts of suicide over the past year, and sleep duration was assessed as a continuous variable.KNHANES is a national-level sample survey that applies stratified cluster sampling and weighted values. We thus analyzed data using stratified, cluster, and weighted variables for complex sampling design factors in complex sampling design analysis. All analyses were performed using statistical package SAS Ver. 9.3 , and P < 0.05 was considered to be statistically significant. Continuous variables are given as mean and standard error, and categorical variables as frequency and percentage (%). Characteristics differences by chronic TMD were assessed by Rao-Scott chi-square test or t-test. Complex sampling design regression analysis adjusted for covariates was used to calculate regression coefficient and 95% confidence intervals (CIs) and assess the association between chronic TMD and quality of life. Age, sex and other covariates were included in models as separate covariates, covariate blocks, and all covariates in the full model to calculate the regression coefficient for chronic TMD.The surveyor was not provided any information on a participant prior to the survey, and all participants provided written consent to participate in the health survey and examination. The study was approved by the Institutional Review Boards (IRBs) of Jaseng Hospital of Korean Medicine in Seoul, Korea .Prevalence of ever-TMD was 3.2% , and that of chronic TMD was 1.6%  of the 17,198 respondents.Univariate analysis showed that age, sex, education level, marital status, chronic hypertension, chronic diabetes, and stress level were associated with chronic TMD prevalence .Factors that were negatively associated with EQ-5D were higher chronicity, old age, female gender, low household income, low education level, unemployment, unmarried status, rural area residence, smoking, alcohol consumption, obesity, and not exercising regularly.In assessing gender differences in association with quality of life, men appeared to be more affected by TMD than women. Also, while smoking status, alcohol consumption, and obesity were not significantly associated with quality of life in men, they were in women .After adjusting for age and sex, influence of the covariate blocks  on the association between TMD and EQ-5D was assessed .First, regarding EQ-5D scores, the nonstandardized regression coefficient was -0.031, which decreased after adjusting for age and sex. This implies that chronic TMD negatively impacts EQ-5D scores. The nonstandardized regression coefficient was used as standard for comparison with other variables. Therefore, after including adjustments for somatic health, mental health, health behavior or sociodemographic factors, the absolute value of the difference between the two coefficients will reflect the difference in the effect sizes of each factor.The absolute difference between -0.031 and the regression coefficient of each factor reflects its respective influence size, and the most influential factor was somatic health which decreased the association with TMD by 27%. Other influential factors included mental health, sociodemographic factors, and health behavior, which weakened the relationship with TMD by 23%, 12%, and 5%, respectively. Of the variable blocks, somatic health was most influential with a 23% effect mainly due to inclusion of osteoarthritis. Other chronic diseases were not as influential. Of individual factors, stress and depressive symptoms  were the second most prominent factors with a 14% association. In investigations on gender differences, TMD influenced women more than it did men, which had similar distributions with the whole data set. Of variable blocks, somatic health weakened associations by 31%, and mental health by 29% in women. Of individual variables, osteoarthritis decreased associations by 26%, stress by 17%, depressive symptoms by 16%, and thoughts of suicide by 15% in women. Sociodemographic factors weakened relations by 15% in men, followed by somatic health (14%), mental health (10%), and health behavior (1%). Of individual variables, occupation, marital status, stress, and osteoarthritis impacted relationships by 21%, 16%, 12% and 11%, respectively.Upon full adjustment, association between TMD and quality of life decreased by 27% in men, and by 67% in women, showing that quality of life in men was impacted more heavily by TMD.These results show that chronic TMD negatively influenced quality of life even after adjusting for sex, age, sociodemographic factors, and health behavior. Low sociodemographic factors and health behavior also generally had a negative impact on quality of life. In the association between chronic TMD and quality of life, somatic health and mental health were most influential. The prevalence of chronic and ever-TMD was higher in women, and women were more heavily influenced by chronic TMD. In relationship with quality of life, women displayed stronger associations with osteoarthritis and general mental health , while men were influenced most by employment status.The fact that osteoarthritis, a somatic health variable, and not mental health, was most powerful in weakening associations with TMD, was highly surprising. This may be because TMD and osteoarthritis are musculoskeletal pain disorders that share similar pathophysiological pathways, and individuals with TMD are susceptible to other musculoskeletal disorders, consequently affecting quality of life. Of the 5 EQ-5D dimensions, mobility, self care, usual activities, and pain/discomfort are likely to be easily influenced by musculoskeletal pain disorders.Plesh et al. studied the association between TMD pain, comorbid pains and muscle disorder-type pains in a national US sample, and discovered that TMD pain does not occur separately, usually accompanied by 2 or more comorbid pains, and that this tendency is especially strong in women .These results are in line with previous reports on TMD pain concurring with other common pains such as migraine/headache –25, neckExisting literature suggest that psychological factors are important and related with TMD, and a relationship between the onset, symptoms, prognosis and treatment of TMD and psychosocial disorders has previously been demonstrated –37. StreWomen appear to be more strongly influenced by mental health and psychological factors in the association with TMD. Rollman et al. broke down the gender disparity in TMD as follows: First, women sought treatment 7 times more than men for TMD , which rThere are also numerous studies pointing to a relationship between lifestyle and health-related behavior and TMD, such as a study on nonspecific physical symptoms  in the younger population by Rodrigues et al. . In addiSome major strengths of this study are that the health examination and surveys were systematically conducted by trained surveyors in a national-level sample of South Koreans. This is the first study to our knowledge to comprehensively assess the impact of TMD on general quality of life as opposed to oral HRQoL in a national-level study. Factors that may act as confounding variables in the association between chronic TMD and quality of life were adjusted to investigate the extent to which these factors impact TMD and quality of life.A limitation of this study is that only associations, not causal relationships, can be drawn from the results owing to its cross-sectional design, where investigations are restricted to a certain point within a short window of time. Another limitation is that data on TMD patients was based on self-assessment and self-report in questionnaire form as opposed to physician diagnosis or diagnostic examination. Only 273 participants were found to have chronic TMD in the current study, which is 1.6% of the participant Korean population. This number is lower than the corresponding U.S. temporomandibular joint and muscle disorder prevalence of 4.6%. While TMD studies based in the U.S. employ questions widely used in other studies and examined for validity to define TMD such as “facial ache or pain in the jaw muscles or the joint in front of the ear” ,57, KNHASeveral studies have been conducted to determine the principle and mechanism through which TMD physically and psychologically affects patients. Christidis et al. discovered that 5-HT3A(serotonin)-receptor, an important pain transmitter, exists in high levels in the masseter and tibialis muscles, and women with TMD possess more nerve fibers releasing 5-HT3A-receptors . Also, OIn conclusion, while TMD itself is negatively associated with general quality of life, comorbidities such as osteoarthritis and mental health issues strengthen this negative relationship. The association was stronger in women, which may reflect susceptibility to chronic pain and mental health disorders. Further items that may be of interest include association of quality of life with severity or prevalence period of TMD symptoms, and gender analysis of the various pathological states that may accompany TMD.