Of counting. It may be the case that CS relationships are established only between individuals so close that individual optimization does not occur, such that these relationships may not need to rest on equal contributions overall. For its part, the RMT definition of AR is based on the presence of a linear hierarchy and states that superiors generally get more and better things, but have the obligation to act generously according to the principle “noblesse oblige” [1] (pp. 42-43). There is a deep principle of asymmetry and inequality, expressed for instance in [25] (pp. 343-344): “When people transferPLOS ONE | DOI:10.1371/journal.pone.0120882 March 31,13 /A Generic Model of Dyadic Social Relationshipsthings from person to person in an AR mode, higher-ranking people get more and better things, and get them sooner, than their subordinates. Higher-ranking people may preempt rare or valuable items, so that inferior people get none at all.” In that quote, only one side of the relationship is looked at, namely what the higher-ranking people get from subordinates. Yet AR relationships entail an exchange of protection (or management, etc.) in return for obedience, loyalty, tax payments, and so on. In our representation, the equality would be between the protection offered by the leader and the obedience of the subordinates, whereby the leader may well get “more and better things,” but matching in value the safety she offers to her subjects. Nevertheless, it may be that respective contributions match only in idealized AR relationships, because in practice it is difficult for subordinates to monitor and enforce equality in an buy ACY-241 essentially asymmetrical relationship. Another point concerning AR is that, according to Fiske [1] (p. 209), the distance between ranks is not socially meaningful; only the linear ordering of ranks is (i.e. which rank is higher, without specifying how much higher). Yet, a value function would allow to measure the distance between ranks. We point out that just because a value function is introduced does not mean that the use of AR requires any computation from the agents. Just as we adapt our every move to the law of gravity without solving mentally at each instant the corresponding equations, or as dogs catch frisbees using simple heuristics [34, 35], it is perfectly conceivable that we are able to recognize and CCX282-B web interact with individuals of different ranks without using or having defined any measure of ranks differences or action values. In the case of humans, these heuristics are facilitated by evolved language and culture, which permit the existence of predefined roles (for instance “chief” or “servitor”) offering an idea of what is expected from each party. An agent-based model would be a convenient approach to observe and test the evolution of a system toward value equalities. Naturally, it would also be of high interest to examine real social relationships in the making. This, however, raises practical difficulties such as the fact that even new relationships develop within a cultural context that largely predefines how RMs should be implemented, making transient forms unlikely to occur or last long enough to be observed.ConclusionWe introduced a model of social interactions between a pair of individuals A and B, each of ! whom can perform a social action X, Y or nothing, symbolized by A B. We demonstrated X=Y=;X=Y=;that from this setting arise six exhaustive and disjoint categories of relationships, four of which mat.Of counting. It may be the case that CS relationships are established only between individuals so close that individual optimization does not occur, such that these relationships may not need to rest on equal contributions overall. For its part, the RMT definition of AR is based on the presence of a linear hierarchy and states that superiors generally get more and better things, but have the obligation to act generously according to the principle “noblesse oblige” [1] (pp. 42-43). There is a deep principle of asymmetry and inequality, expressed for instance in [25] (pp. 343-344): “When people transferPLOS ONE | DOI:10.1371/journal.pone.0120882 March 31,13 /A Generic Model of Dyadic Social Relationshipsthings from person to person in an AR mode, higher-ranking people get more and better things, and get them sooner, than their subordinates. Higher-ranking people may preempt rare or valuable items, so that inferior people get none at all.” In that quote, only one side of the relationship is looked at, namely what the higher-ranking people get from subordinates. Yet AR relationships entail an exchange of protection (or management, etc.) in return for obedience, loyalty, tax payments, and so on. In our representation, the equality would be between the protection offered by the leader and the obedience of the subordinates, whereby the leader may well get “more and better things,” but matching in value the safety she offers to her subjects. Nevertheless, it may be that respective contributions match only in idealized AR relationships, because in practice it is difficult for subordinates to monitor and enforce equality in an essentially asymmetrical relationship. Another point concerning AR is that, according to Fiske [1] (p. 209), the distance between ranks is not socially meaningful; only the linear ordering of ranks is (i.e. which rank is higher, without specifying how much higher). Yet, a value function would allow to measure the distance between ranks. We point out that just because a value function is introduced does not mean that the use of AR requires any computation from the agents. Just as we adapt our every move to the law of gravity without solving mentally at each instant the corresponding equations, or as dogs catch frisbees using simple heuristics [34, 35], it is perfectly conceivable that we are able to recognize and interact with individuals of different ranks without using or having defined any measure of ranks differences or action values. In the case of humans, these heuristics are facilitated by evolved language and culture, which permit the existence of predefined roles (for instance “chief” or “servitor”) offering an idea of what is expected from each party. An agent-based model would be a convenient approach to observe and test the evolution of a system toward value equalities. Naturally, it would also be of high interest to examine real social relationships in the making. This, however, raises practical difficulties such as the fact that even new relationships develop within a cultural context that largely predefines how RMs should be implemented, making transient forms unlikely to occur or last long enough to be observed.ConclusionWe introduced a model of social interactions between a pair of individuals A and B, each of ! whom can perform a social action X, Y or nothing, symbolized by A B. We demonstrated X=Y=;X=Y=;that from this setting arise six exhaustive and disjoint categories of relationships, four of which mat.

Rt for a link between temperature and trust processing, as revealed in brain activity rather than in behavior. In particular, the insula showed greater response to cold temperature, and this differential activation was re-observed during decision phases of trust game, suggesting a plausible neural basis for a relationship between experienced temperature and interpersonal trust. GENERAL DISCUSSION Physical coldness led to decreased trust behavior, compared to warmth. Furthermore, trust-related decisions recruited regions that also activated differentially to cold temperatures. Specifically, insula was more active during cold temperature perception, and also active in trust decisions after havingSCAN (2011)experienced cold. This differential brain activation during trust decisions as a function of prior experiences of different temperatures may explain how physical experiences with temperature can alter LT-253 site psychological states related to trust, as observed in several Tyrphostin AG 490 web previous studies. Based on our data as well as those previous findings, our interpretation is that physical temperature experiences primed the insula, leading both to differences in behavioral responding (Study 1) and in patterns of neural activation (Study 2). A deeper understanding of the mechanisms by which cold temperatures obstruct trusting behaviors can inform both cognitive science and practice. The present work represents an important step towards further elucidating the mechanisms by which physical environmental cues can influence people’s judgments and decisions, by examining the neuropsychological consequences of exposure to cold vs warm temperatures. Furthermore, these studies provide initial evidence for the process by which conceptual scaffolding occurs (Williams et al., 2009), by highlighting how an evolutionarily significant physical concept (temperature) is functionally linked on a neural level to the metaphorically related higher order psychosocial concept (trust). Similar to the way in which the processing of physical and psychological pain overlaps in specific areas of the brain (ACC; Eisenberger et al., 2003), so too it appears that there is functional overlap in the processing of information related to physical and psychological warmth. Considering practical implications, given the present findings and previous demonstrations of the effects of physical temperatures on psychological states (Zhong and Leonardelli, 2008; Ijzerman and Semin, 2009), it may be prudent to take physical temperature into account for cognitive and behavioral therapies treating psychopathological conditions, such as borderline personality disorder in which difficulties in expressing trust contribute to dysfunction (King-Casas et al., 2008). For example, it may be possible that physical experience with cold temperatures can lead patients to be less receptive to attempts at behavioral change designed to increase their capacity for trusting others (perhaps via increasing insula activity normally associated with cold temperatures and the expectation of risk; Knutson and Bossaerts, 2007). Risk perception literature provides possible explanations for the differential insula activity following temperature priming. Mounting evidence supports the association of insula and expected risk (Knutson and Bossaerts, 2007). Activation in insula increased proportionally to increasing risk (Dreher et al., 2006; Preuschoff et al., 2006, 2008), as well as in response to uncertainty in other financial and.Rt for a link between temperature and trust processing, as revealed in brain activity rather than in behavior. In particular, the insula showed greater response to cold temperature, and this differential activation was re-observed during decision phases of trust game, suggesting a plausible neural basis for a relationship between experienced temperature and interpersonal trust. GENERAL DISCUSSION Physical coldness led to decreased trust behavior, compared to warmth. Furthermore, trust-related decisions recruited regions that also activated differentially to cold temperatures. Specifically, insula was more active during cold temperature perception, and also active in trust decisions after havingSCAN (2011)experienced cold. This differential brain activation during trust decisions as a function of prior experiences of different temperatures may explain how physical experiences with temperature can alter psychological states related to trust, as observed in several previous studies. Based on our data as well as those previous findings, our interpretation is that physical temperature experiences primed the insula, leading both to differences in behavioral responding (Study 1) and in patterns of neural activation (Study 2). A deeper understanding of the mechanisms by which cold temperatures obstruct trusting behaviors can inform both cognitive science and practice. The present work represents an important step towards further elucidating the mechanisms by which physical environmental cues can influence people’s judgments and decisions, by examining the neuropsychological consequences of exposure to cold vs warm temperatures. Furthermore, these studies provide initial evidence for the process by which conceptual scaffolding occurs (Williams et al., 2009), by highlighting how an evolutionarily significant physical concept (temperature) is functionally linked on a neural level to the metaphorically related higher order psychosocial concept (trust). Similar to the way in which the processing of physical and psychological pain overlaps in specific areas of the brain (ACC; Eisenberger et al., 2003), so too it appears that there is functional overlap in the processing of information related to physical and psychological warmth. Considering practical implications, given the present findings and previous demonstrations of the effects of physical temperatures on psychological states (Zhong and Leonardelli, 2008; Ijzerman and Semin, 2009), it may be prudent to take physical temperature into account for cognitive and behavioral therapies treating psychopathological conditions, such as borderline personality disorder in which difficulties in expressing trust contribute to dysfunction (King-Casas et al., 2008). For example, it may be possible that physical experience with cold temperatures can lead patients to be less receptive to attempts at behavioral change designed to increase their capacity for trusting others (perhaps via increasing insula activity normally associated with cold temperatures and the expectation of risk; Knutson and Bossaerts, 2007). Risk perception literature provides possible explanations for the differential insula activity following temperature priming. Mounting evidence supports the association of insula and expected risk (Knutson and Bossaerts, 2007). Activation in insula increased proportionally to increasing risk (Dreher et al., 2006; Preuschoff et al., 2006, 2008), as well as in response to uncertainty in other financial and.

Eated groups.doi: 10.1371/journal.pone.0073376.ggene acquisition events [80?2]. In contrast to S. aureus, it has been shown that biofilm formation and 3-MA web dispersal by a number of S. epidermidis strains is not sensitive to Proteinase K or other proteases [76,77]. Similar to these results, we found biofilm formation by S. epidermidis strains 1457 and NJ9709 to be insensitive to Proteinase K inhibition and Proteinase K caused little to no detachment in mature biofilms of these strains as well. Extracellular DNA (eDNA) is another component of the biofilm matrix and the structural role of eDNA in promoting biofilm stability is highly variable and dependent on the bacterial species, growth conditions, and age of the biofilm [61,83?6]. We found DNaseI treatment to have a varying effect on both biofilm inhibition and dispersal. Specifically, when DNaseI was added at the time of inoculation, all of the strains tested displayed a range of sensitivity, from little to no effect to strong, nearly complete inhibition of biofilm formation. DNaseI was observed to have varying effects on the dispersal as well, with some strains showing a much higher degree ofsensitivity to this enzyme than others. Both inhibition and dispersal by DNaseI seem to vary among S. aureus strains and MLST types indicating that eDNA may be a more significant component in some MLST types of S. aureus than in others. The ST398 strains in particular were the most sensitive to both inhibition of biofilm formation and dispersal of pre-formed biofilms by DNaseI, with a greater reduction in biofilm biomass than other non-ST398 strains, including other swine-origin isolates. The polysaccharide PNAG has been extensively studied as a biofilm matrix component and is a target for the enzyme DspB [52]. PNAG is the product of the icaADBC operon, which is highly conserved among Staphylococcus order 1,1-Dimethylbiguanide hydrochloride isolates [87]. Many studies have shown the importance of this polysaccharide in S. epidermidis biofilms, where it is proposed to be the major component of the biofilm matrix, as DspB can inhibit biofilm formation and disperse pre-formed biofilms [59,76,77,88]. However, the role of PNAG in S. aureus biofilms is less clear, as studies have shown that some strains of S. aureus producePLOS ONE | www.plosone.orgSwine MRSA Isolates form Robust BiofilmsFigure 5. Dispersal of established biofilms by Proteinase K. Strains tested are shown along the x-axis and grouped based on methicillin-sensitivity and isolation source. The indicated strains were grown statically for 24 hours to allow biofilm formation. Wells were washed and treated with buffer alone (- Prot. K) or 100 /ml Proteinase K (+ Prot. K) for 2 hours. Biofilm formation was then quantified by standard microtiter assays and measuring the absorbance at 538 nm, plotted along the y-axis. Bars represent the average absorbance obtained from at least 3 independent plates representing biological replicates; error bars represent the SEM. Asterisks (*) denote a p-value less than 0.05 between the treated and untreated groups.doi: 10.1371/journal.pone.0073376.ghigh levels of PNAG, while others produce little to no PNAG [60]. Additionally, some strains have been shown to be sensitive to biofilm dispersal by DspB whereas other S. aureus strains are unaffected by this enzyme [59] or the compound sodium metaperiodate, which breaks down PNAG via an oxidation reaction [60,89]. Our results show that DspB has little effect on both biofilm formation and dispersal in the S. aur.Eated groups.doi: 10.1371/journal.pone.0073376.ggene acquisition events [80?2]. In contrast to S. aureus, it has been shown that biofilm formation and dispersal by a number of S. epidermidis strains is not sensitive to Proteinase K or other proteases [76,77]. Similar to these results, we found biofilm formation by S. epidermidis strains 1457 and NJ9709 to be insensitive to Proteinase K inhibition and Proteinase K caused little to no detachment in mature biofilms of these strains as well. Extracellular DNA (eDNA) is another component of the biofilm matrix and the structural role of eDNA in promoting biofilm stability is highly variable and dependent on the bacterial species, growth conditions, and age of the biofilm [61,83?6]. We found DNaseI treatment to have a varying effect on both biofilm inhibition and dispersal. Specifically, when DNaseI was added at the time of inoculation, all of the strains tested displayed a range of sensitivity, from little to no effect to strong, nearly complete inhibition of biofilm formation. DNaseI was observed to have varying effects on the dispersal as well, with some strains showing a much higher degree ofsensitivity to this enzyme than others. Both inhibition and dispersal by DNaseI seem to vary among S. aureus strains and MLST types indicating that eDNA may be a more significant component in some MLST types of S. aureus than in others. The ST398 strains in particular were the most sensitive to both inhibition of biofilm formation and dispersal of pre-formed biofilms by DNaseI, with a greater reduction in biofilm biomass than other non-ST398 strains, including other swine-origin isolates. The polysaccharide PNAG has been extensively studied as a biofilm matrix component and is a target for the enzyme DspB [52]. PNAG is the product of the icaADBC operon, which is highly conserved among Staphylococcus isolates [87]. Many studies have shown the importance of this polysaccharide in S. epidermidis biofilms, where it is proposed to be the major component of the biofilm matrix, as DspB can inhibit biofilm formation and disperse pre-formed biofilms [59,76,77,88]. However, the role of PNAG in S. aureus biofilms is less clear, as studies have shown that some strains of S. aureus producePLOS ONE | www.plosone.orgSwine MRSA Isolates form Robust BiofilmsFigure 5. Dispersal of established biofilms by Proteinase K. Strains tested are shown along the x-axis and grouped based on methicillin-sensitivity and isolation source. The indicated strains were grown statically for 24 hours to allow biofilm formation. Wells were washed and treated with buffer alone (- Prot. K) or 100 /ml Proteinase K (+ Prot. K) for 2 hours. Biofilm formation was then quantified by standard microtiter assays and measuring the absorbance at 538 nm, plotted along the y-axis. Bars represent the average absorbance obtained from at least 3 independent plates representing biological replicates; error bars represent the SEM. Asterisks (*) denote a p-value less than 0.05 between the treated and untreated groups.doi: 10.1371/journal.pone.0073376.ghigh levels of PNAG, while others produce little to no PNAG [60]. Additionally, some strains have been shown to be sensitive to biofilm dispersal by DspB whereas other S. aureus strains are unaffected by this enzyme [59] or the compound sodium metaperiodate, which breaks down PNAG via an oxidation reaction [60,89]. Our results show that DspB has little effect on both biofilm formation and dispersal in the S. aur.

Ay to assemble interactomes relevant to vascular inflammation and thrombosis in order to characterize further the pathogenesis of relevant cardiovascular diseases, particularly myocardial infarction (MI). The National Institutes of Health-sponsored consortium MAPGen (www.mapgenprogram.org), for example, consists of five university centers with access to large human sample repositories and clinical data from international, multi-centered cardiovascular trials that are anticipated to generate broad and unbiased inflammasome and thrombosome networks. These large-scale individual networks and sub-networks created by overlap between them are currently being analyzed to define unrecognized protein-protein interactions pertinent to stroke, MI, and venous thromboemoblic disease. The selection of specific protein(s) or protein product(s) from this data set or other networks of similar scale for validation experimentally is likely to hinge on the strength of association, location of targets within the network, their proximity to other important protein/products, and/or data linking naturally-occurring loss- or gain-of-function mutations of the putative target to relevant clinical disorders, among other factors. While systematic analysis of data from the MAPGen project is forthcoming, other reports from smaller cardiovascular disease datasets have emerged. For example, proteomic analysis of circulating microvesicles harvested from patients with acute ST-segment elevation myocardial infarction or stable coronary artery disease was performed by mass spectrometry 67. Using this approach, investigators were able to identify 117 proteins that varied by at least 2-fold between groups, such as 2-macroglobulin isoforms and fibrinogen.Author Manuscript Author Manuscript Author Manuscript Author ManuscriptWiley Interdiscip Rev Syst Biol Med. Author manuscript; available in PMC 2016 July 01.Wang et al.PageProtein discovery was then subjected to Ingenuity?pathway analysis to generate a proteinprotein interaction network. Findings from this work suggest that a majority of microvesiclederived proteins are located within inflammatory and thrombosis networks, LixisenatideMedChemExpress Lixisenatide affirming the contemporary view that myocardial infarction is a consequence of these interrelated processes. Parenchymal lung disease Owing to the complex interplay between numerous cell types comprising the lungpulmonary vascular axis, a number of important pathophenotypes affecting these systems have evolved as attractive fields for systems biology investigations 68. Along these lines, chronic obstructive pulmonary disease (COPD), which comprises a heterogeneous range of parenchymal lung disorders, has been increasingly studied using network analyses to parse out differences and similarities among patients with respect to gene expression profiles and subpathophenotypes. Using the novel diVIsive Shuffling Approach (VIStA) designed to optimize identification of patient subgroups through gene expression differences, it was demonstrated that Procyanidin B1 solubility characterizing COPD subtypes according to many common clinical characteristics was inefficacious at grouping patients according to overlap in gene expression differences 69. Important exceptions to this observation were airflow obstruction and emphysema severity, which proved to be drivers of COPD patients’ gene expression clustering. Among the most noteworthy of the secondary characteristics (i.e., functional to inform the genetic signature of COPD) was walk distance, rai.Ay to assemble interactomes relevant to vascular inflammation and thrombosis in order to characterize further the pathogenesis of relevant cardiovascular diseases, particularly myocardial infarction (MI). The National Institutes of Health-sponsored consortium MAPGen (www.mapgenprogram.org), for example, consists of five university centers with access to large human sample repositories and clinical data from international, multi-centered cardiovascular trials that are anticipated to generate broad and unbiased inflammasome and thrombosome networks. These large-scale individual networks and sub-networks created by overlap between them are currently being analyzed to define unrecognized protein-protein interactions pertinent to stroke, MI, and venous thromboemoblic disease. The selection of specific protein(s) or protein product(s) from this data set or other networks of similar scale for validation experimentally is likely to hinge on the strength of association, location of targets within the network, their proximity to other important protein/products, and/or data linking naturally-occurring loss- or gain-of-function mutations of the putative target to relevant clinical disorders, among other factors. While systematic analysis of data from the MAPGen project is forthcoming, other reports from smaller cardiovascular disease datasets have emerged. For example, proteomic analysis of circulating microvesicles harvested from patients with acute ST-segment elevation myocardial infarction or stable coronary artery disease was performed by mass spectrometry 67. Using this approach, investigators were able to identify 117 proteins that varied by at least 2-fold between groups, such as 2-macroglobulin isoforms and fibrinogen.Author Manuscript Author Manuscript Author Manuscript Author ManuscriptWiley Interdiscip Rev Syst Biol Med. Author manuscript; available in PMC 2016 July 01.Wang et al.PageProtein discovery was then subjected to Ingenuity?pathway analysis to generate a proteinprotein interaction network. Findings from this work suggest that a majority of microvesiclederived proteins are located within inflammatory and thrombosis networks, affirming the contemporary view that myocardial infarction is a consequence of these interrelated processes. Parenchymal lung disease Owing to the complex interplay between numerous cell types comprising the lungpulmonary vascular axis, a number of important pathophenotypes affecting these systems have evolved as attractive fields for systems biology investigations 68. Along these lines, chronic obstructive pulmonary disease (COPD), which comprises a heterogeneous range of parenchymal lung disorders, has been increasingly studied using network analyses to parse out differences and similarities among patients with respect to gene expression profiles and subpathophenotypes. Using the novel diVIsive Shuffling Approach (VIStA) designed to optimize identification of patient subgroups through gene expression differences, it was demonstrated that characterizing COPD subtypes according to many common clinical characteristics was inefficacious at grouping patients according to overlap in gene expression differences 69. Important exceptions to this observation were airflow obstruction and emphysema severity, which proved to be drivers of COPD patients’ gene expression clustering. Among the most noteworthy of the secondary characteristics (i.e., functional to inform the genetic signature of COPD) was walk distance, rai.

He frequency of non-stuttered and total disfluencies in both talker groups. Fourth, parental PNPP web concern about children’s stuttering was significantly associated with frequency of children’s stuttered disfluencies. These findings will be discussed immediately below. Number of disfluencies is not normally distributed–Present findings that frequency distributions of speech disfluencies were non-normal are consistent with earlier observations ( Davis, 1939; Johnson et al., 1963; Jones et al., 2006). The distributions of total, stuttered and non-stuttered disfluencies found in the present study conformed best to a negative binomial distribution. This type of distribution can be characteristic of variables that represent count (i.e., discrete) data. This distribution is often used to model theJ Commun Disord. Author manuscript; available in PMC 2015 May 01.Tumanova et al.Pageoccurrence of relatively rare events, such as, in our case, the number of disfluencies children produce during a conversational sample. As applied to the present speech disfluency data set, negative binomial distribution of frequency of disfluencies signifies that there are more cases of mild stuttering among CWS and fewer cases of severe stuttering. From a data analytic standpoint, the fact that disfluency count data is not-normally distributed suggests that traditional inferential, parametric statistical methods such as ANOVA or ordinary least squares regression are inappropriate for these data. In such cases the mean and variance may not be good descriptors of the central tendency, leading to a potential increase of type 1 error. Going forward, when empirically studying the speech disfluenicies of children who do and do not stutter, it may be more appropriate to employ models that make assumptions that the data actually meet. Generalized linear models (GLM), as used in the present study, allow a choice among several distributions in which the response or dependent variable can have a non-normal distribution (Nelder Wedderburn, 1972). Table 10 presents frequency of disfluencies found in the present study and in previous studies of children who do and do not stutter. RM-493 side effects Although tempting, it is not possible to make absolute comparisons between the present dataset and other studies that also collected comparably large samples (e.g., Johnson et al., 1959; Yairi Ambrose, 2005; Yaruss, LaSalle, et al., 1998; Yaruss, Max, et al., 1998). This is due to the fact that some of these studies (e.g., Johnson et al., 1959) included children older than the age range of the present study and/or did not report a typically fluent comparison group (e.g., Yaruss, LaSalle, et al., 1998; Yaruss, Max, et al., 1998) and other studies employed a syllable-level measure of frequency (Ambrose Yairi, 1999; Yairi Ambrose, 2005).7 Thus, even though the present findings of mean values of 1.2 stuttered disfluencies per 100 words for CWNS and 9.2 for CWS is close to the mean values of 1.88 for CWNS and 11.5 for CWS reported by Johnson et al. (1959) and the mean value of 10.67 for CWS reported by Yaruss, LaSalle, et al. (1998) readers should be aware that differences in age range of participants and/or measurement methodology render absolute comparisons problematic. Likewise, there are challenges with making direct comparisons between the present relatively large dataset and other smaller datasets, since larger sample sizes generally lead to increased precision when estimating unknown parameters such.He frequency of non-stuttered and total disfluencies in both talker groups. Fourth, parental concern about children’s stuttering was significantly associated with frequency of children’s stuttered disfluencies. These findings will be discussed immediately below. Number of disfluencies is not normally distributed–Present findings that frequency distributions of speech disfluencies were non-normal are consistent with earlier observations ( Davis, 1939; Johnson et al., 1963; Jones et al., 2006). The distributions of total, stuttered and non-stuttered disfluencies found in the present study conformed best to a negative binomial distribution. This type of distribution can be characteristic of variables that represent count (i.e., discrete) data. This distribution is often used to model theJ Commun Disord. Author manuscript; available in PMC 2015 May 01.Tumanova et al.Pageoccurrence of relatively rare events, such as, in our case, the number of disfluencies children produce during a conversational sample. As applied to the present speech disfluency data set, negative binomial distribution of frequency of disfluencies signifies that there are more cases of mild stuttering among CWS and fewer cases of severe stuttering. From a data analytic standpoint, the fact that disfluency count data is not-normally distributed suggests that traditional inferential, parametric statistical methods such as ANOVA or ordinary least squares regression are inappropriate for these data. In such cases the mean and variance may not be good descriptors of the central tendency, leading to a potential increase of type 1 error. Going forward, when empirically studying the speech disfluenicies of children who do and do not stutter, it may be more appropriate to employ models that make assumptions that the data actually meet. Generalized linear models (GLM), as used in the present study, allow a choice among several distributions in which the response or dependent variable can have a non-normal distribution (Nelder Wedderburn, 1972). Table 10 presents frequency of disfluencies found in the present study and in previous studies of children who do and do not stutter. Although tempting, it is not possible to make absolute comparisons between the present dataset and other studies that also collected comparably large samples (e.g., Johnson et al., 1959; Yairi Ambrose, 2005; Yaruss, LaSalle, et al., 1998; Yaruss, Max, et al., 1998). This is due to the fact that some of these studies (e.g., Johnson et al., 1959) included children older than the age range of the present study and/or did not report a typically fluent comparison group (e.g., Yaruss, LaSalle, et al., 1998; Yaruss, Max, et al., 1998) and other studies employed a syllable-level measure of frequency (Ambrose Yairi, 1999; Yairi Ambrose, 2005).7 Thus, even though the present findings of mean values of 1.2 stuttered disfluencies per 100 words for CWNS and 9.2 for CWS is close to the mean values of 1.88 for CWNS and 11.5 for CWS reported by Johnson et al. (1959) and the mean value of 10.67 for CWS reported by Yaruss, LaSalle, et al. (1998) readers should be aware that differences in age range of participants and/or measurement methodology render absolute comparisons problematic. Likewise, there are challenges with making direct comparisons between the present relatively large dataset and other smaller datasets, since larger sample sizes generally lead to increased precision when estimating unknown parameters such.

Perceptions about HIV testing and their access to HIV tests. Formal social control can significantly affect HIV testing uptake. Most relevant are laws and policies that influence individuals’ decisions to be tested (e.g., anonymous testing, case reporting, partner notification) and laws and policies that address the consequences of an HIV-positive test result (e.g., anti-discrimination, access to treatment). HIV-related laws to protect individual privacy and prohibit discrimination against persons living with or affected by HIV addressed perceived barriers to testing such as fears about these repercussions.NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptAIDS Behav. Author manuscript; available in PMC 2011 December 1.Latkin et al.PageThese rights-protective laws encouraged persons at risk to seek testing voluntarily, which, by increasing testing rates, in turn required that resources be allocated for more HIV testing.NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptNew science and technologies, including the advent of effective treatment and rapid HIV testing technologies as well as research pointing to a disproportionate number of infections attributed to individuals unaware of their HIV positive status,75 lead public health CPI-455 cost leaders to reformulate the national approach to HIV testing. Relying on individuals to seek HIV testing services proved insufficient to increase the number of identified cases to significantly reduce HIV incidence.78 Consequently, the CDC began to recommend that most adults be routinely tested.94 Because this approach does not require individuals to initiate the testing process, motivational RR6 site interventions to increase HIV testing may play a lesser role in achieving national HIV testing objectives than increasing access to HIV tests (e.g., efforts to mitigate the effect of competing priorities on provider ability and willingness to offer patients HIV tests and to recruit and train additional testing personnel).79,94,95 From a structural systems perspective it is important to assess how national HIV testing guidelines may lead to unanticipated changes at the macro, meso, and micro levels. It is also important to examine how the reallocation of resources to support increased testing may impact other HIV prevention programs and organizations and to assess whether policy changes alter norms regarding pre- and post-test counseling. One potential unanticipated outcome may be the altering of social interconnectedness through greater serosorting behaviors. Ethical Issues with Structural-level HIV Interventions Although structural interventions make fewer demands on individual resources, the ethical implications of attempting to manipulate structural-level factors to affect individual behavior can be quite serious. As described above, structural forces are broad, external to the individual, and beyond individual control. Structural interventions may leave some individuals pursuing goals that they did not choose with methods that they cannot avoid. Such programs can compromise individual autonomy by burdening or eliminating behavioral options, thereby reducing individual choice. For example, criminal laws that require persons living with HIV to disclose their serostatus to prospective sexual partners effectively preclude infected individuals from legally exercising other options, such as practicing safer sex or engaging in alternatives to penetrative sex.96 The option to allow.Perceptions about HIV testing and their access to HIV tests. Formal social control can significantly affect HIV testing uptake. Most relevant are laws and policies that influence individuals’ decisions to be tested (e.g., anonymous testing, case reporting, partner notification) and laws and policies that address the consequences of an HIV-positive test result (e.g., anti-discrimination, access to treatment). HIV-related laws to protect individual privacy and prohibit discrimination against persons living with or affected by HIV addressed perceived barriers to testing such as fears about these repercussions.NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptAIDS Behav. Author manuscript; available in PMC 2011 December 1.Latkin et al.PageThese rights-protective laws encouraged persons at risk to seek testing voluntarily, which, by increasing testing rates, in turn required that resources be allocated for more HIV testing.NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptNew science and technologies, including the advent of effective treatment and rapid HIV testing technologies as well as research pointing to a disproportionate number of infections attributed to individuals unaware of their HIV positive status,75 lead public health leaders to reformulate the national approach to HIV testing. Relying on individuals to seek HIV testing services proved insufficient to increase the number of identified cases to significantly reduce HIV incidence.78 Consequently, the CDC began to recommend that most adults be routinely tested.94 Because this approach does not require individuals to initiate the testing process, motivational interventions to increase HIV testing may play a lesser role in achieving national HIV testing objectives than increasing access to HIV tests (e.g., efforts to mitigate the effect of competing priorities on provider ability and willingness to offer patients HIV tests and to recruit and train additional testing personnel).79,94,95 From a structural systems perspective it is important to assess how national HIV testing guidelines may lead to unanticipated changes at the macro, meso, and micro levels. It is also important to examine how the reallocation of resources to support increased testing may impact other HIV prevention programs and organizations and to assess whether policy changes alter norms regarding pre- and post-test counseling. One potential unanticipated outcome may be the altering of social interconnectedness through greater serosorting behaviors. Ethical Issues with Structural-level HIV Interventions Although structural interventions make fewer demands on individual resources, the ethical implications of attempting to manipulate structural-level factors to affect individual behavior can be quite serious. As described above, structural forces are broad, external to the individual, and beyond individual control. Structural interventions may leave some individuals pursuing goals that they did not choose with methods that they cannot avoid. Such programs can compromise individual autonomy by burdening or eliminating behavioral options, thereby reducing individual choice. For example, criminal laws that require persons living with HIV to disclose their serostatus to prospective sexual partners effectively preclude infected individuals from legally exercising other options, such as practicing safer sex or engaging in alternatives to penetrative sex.96 The option to allow.

Dhesion molecules [5, 51]. The role of resistin in insulin resistance and diabetes is controversial because a variety of studies have shown that resistin levels improve with improved central adiposity along with other studies have demonstrated a substantial lower in resistin levels in enhanced adiposity. PAI-1 is present in elevated levels in obesity along with the metabolic syndrome. It has been linked for the elevated occurrence of thrombosis in sufferers with these circumstances. Angiotensin II can also be present in adipose tissue and has an important impact on endothelial function. When angiotensin II binds the angiotensin II type 1 receptor on endothelial cells, it stimulates the production of ROS by way of NADPH oxidase, increases expression of ICAM-1 and increases ET1 release from the endothelium [52?4]. Angiotensin also activates JNK and MAPK pathways in endothelial cells, which results in elevated serine phosphorylation of IRS-1, impaired PI-3 kinase activity and ultimately endothelial dysfunction and almost certainly apoptosis. This can be among the list of explanations why an ACE inhibitor and angiotensin II variety 1 receptor6 blockers (ARBs) guard against cardiovascular comorbidity in patients with diabetes and vice versa [55]. Insulin receptor substrate 1 (IRS-1) can be a protein downstream with the insulin receptor, which can be vital for signaling to metabolic effects like glucose uptake in fat cells and NO-production in endothelial cells. IRS-1 in endothelial cells and fat cells could be downregulated by stressors like hyperglycemia and dyslipidemia, causing insulin resistance and endothelial dysfunction. A low adipocyte IRS-1 expression might thereby be a marker for insulin resistance [19, 56, 57]. five.four. Inflammation. Today atherosclerosis is regarded to become an inflammatory disease plus the reality that atherosclerosis and resulting cardiovascular disease is a lot more prevalent in individuals with chronic inflammatory illnesses like rheumatoid arthritis, systemic lupus erythematosus and ankylosing spondylitis than in the wholesome population supports this statement. Inflammation is regarded as an essential independent cardiovascular threat aspect and is related with endothelial dysfunction. Interestingly, a study performed by bij van Eijk et al. shows that individuals with active ankylosing spondylitis, an inflammatory disease, also have impaired microvascular endothelium-dependent vasodilatation and capillary recruitment in skin, which improves right after TNF-blocking therapy with etanercept [58]. The existence of chronic inflammation in diabetes is mainly determined by the elevated plasma concentrations of C-reactive protein (CRP), fibrinogen, interleukin-6 (IL6), interleukin-1 (IL-1), and TNF PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/20407268 [59?1]. Inflammatory cytokines raise vascular permeability, transform vasoregulatory responses, boost leukocyte adhesion to endothelium, and facilitate thrombus formation by inducing order AM152 procoagulant activity, inhibiting anticoagulant pathways and impairing fibrinolysis by way of stimulation of PAI-1. NF-B consists of a loved ones of transcription variables, which regulate the inflammatory response of vascular cells, by transcription of different cytokines which causes an improved adhesion of monocytes, neutrophils, and macrophages, resulting in cell harm. However, NF-B is also a regulator of genes that control cell proliferation and cell survival and protects against apoptosis, amongst others by activating the antioxidant enzyme superoxide dismutase (SOD) [62]. NFB is activated by TNF and IL-1 subsequent to hyper.

He site of sampling as random effect. Firstly, the cattle seroprevalence GLPG0187 msds dataset was split randomly into 10 parts. Then, the model was fitted to 90 of the data and used to predict the serological status of the remaining 10 individuals as validation step. The procedure was performed 10 times, each time with 1 of the 10 parts as validation step. [42]. Finally, parameter estimations derived from the best cattle model were used to predict and map cattle seroprevalence at the commune scale for the whole island. Data analyses were performed using R software version 3.0.1 [43?9].Results Environmental characterization of Malagasy communesFour MFA AMG9810 site Factors contributing to 60 of the total variance were selected. Table 1 shows the correlation between each quantitative covariate included in the MFA and each of these four factors: ?Factor 1 separated areas based on seasonality in primary productivity (photosynthetic activity measured by NDVI), vegetation, land use and temperature. Large positive values described ecosystems with high seasonal primary productivity dominated by herbaceous vegetation and with low surfaces of crops under dry and hot climatic conditions (Fig 2A inPLOS Neglected Tropical Diseases | DOI:10.1371/journal.pntd.July 14,6 /Rift Valley Fever Risk Factors in MadagascarTable 1. Correlation between each quantitative covariate included in the MFA and each factor (Factor 1, Factor 2, Factor 3 and Factor 4). Covariate Mean LST-day Mean LST-night Mean precipitation Seasonality of precipitation Mean NDVI NDVI seasonality Herbaceous Shrubs Wood rees Urbanization Crops Irrigated area Wetlands Water bodies Marshlands Factor 1 0.92 0.50 -0.70 0.17 -0.83 0.63 0.84 0.11 -0.33 / -0.62 / / / / Factor 2 -0.19 -0.66 / -0.15 -0.34 0.45 -0.12 0.40 0.56 0.14 -0.61 0.66 0.24 / 0.07 Factor 3 0.11 0.14 0.32 0.82 / 0.08 -0.24 0.30 0.37 -0.30 -0.24 -0.08 -0.39 0.07 0.18 Factor 4 / 0.26 0.31 0.09 / 0.08 0.11 -0.17 -0.19 0.27 0.10 0.37 0.46 0.22 0./: The correlation coefficients were not significantly different from zero and so not included in the results doi:10.1371/journal.pntd.0004827.tgreen). Large negative values described ecosystems with low seasonal primary productivity including crops under wet and less hot climatic conditions (Fig 2A in brown). The communes with the largest positive values for Factor1 are located in the south-western part of Madagascar (Fig 2A in green) while the communes with the largest negative values for Factor1 are located on the north-eastern part (Fig 2A in brown); ?Factor 2 separated areas based on seasonality in primary productivity, vegetation, land use and temperature. Large positive values described ecosystems with high seasonal primaryFig 2. Geographical representation of the MFA factor values and cattle density of the 1,578 Malagasy communes. (A) Factor 1, (B) Factor 2, (C) Factor 3, (D) Factor 4, (E) cattle density categories. For each factor, green colors represent positive values and brown negative values. The darkest colors represent the highest values. Cattle were sampled in communes surrounded in black and human were enrolled in communes surrounded in purple. doi:10.1371/journal.pntd.0004827.gPLOS Neglected Tropical Diseases | DOI:10.1371/journal.pntd.July 14,7 /Rift Valley Fever Risk Factors in Madagascarproductivity including ligneous vegetation and irrigated areas (rice fields) under climatic conditions characterized by low night temperatures (Fig 2B in green). Large negative values described ecosystems wit.He site of sampling as random effect. Firstly, the cattle seroprevalence dataset was split randomly into 10 parts. Then, the model was fitted to 90 of the data and used to predict the serological status of the remaining 10 individuals as validation step. The procedure was performed 10 times, each time with 1 of the 10 parts as validation step. [42]. Finally, parameter estimations derived from the best cattle model were used to predict and map cattle seroprevalence at the commune scale for the whole island. Data analyses were performed using R software version 3.0.1 [43?9].Results Environmental characterization of Malagasy communesFour MFA factors contributing to 60 of the total variance were selected. Table 1 shows the correlation between each quantitative covariate included in the MFA and each of these four factors: ?Factor 1 separated areas based on seasonality in primary productivity (photosynthetic activity measured by NDVI), vegetation, land use and temperature. Large positive values described ecosystems with high seasonal primary productivity dominated by herbaceous vegetation and with low surfaces of crops under dry and hot climatic conditions (Fig 2A inPLOS Neglected Tropical Diseases | DOI:10.1371/journal.pntd.July 14,6 /Rift Valley Fever Risk Factors in MadagascarTable 1. Correlation between each quantitative covariate included in the MFA and each factor (Factor 1, Factor 2, Factor 3 and Factor 4). Covariate Mean LST-day Mean LST-night Mean precipitation Seasonality of precipitation Mean NDVI NDVI seasonality Herbaceous Shrubs Wood rees Urbanization Crops Irrigated area Wetlands Water bodies Marshlands Factor 1 0.92 0.50 -0.70 0.17 -0.83 0.63 0.84 0.11 -0.33 / -0.62 / / / / Factor 2 -0.19 -0.66 / -0.15 -0.34 0.45 -0.12 0.40 0.56 0.14 -0.61 0.66 0.24 / 0.07 Factor 3 0.11 0.14 0.32 0.82 / 0.08 -0.24 0.30 0.37 -0.30 -0.24 -0.08 -0.39 0.07 0.18 Factor 4 / 0.26 0.31 0.09 / 0.08 0.11 -0.17 -0.19 0.27 0.10 0.37 0.46 0.22 0./: The correlation coefficients were not significantly different from zero and so not included in the results doi:10.1371/journal.pntd.0004827.tgreen). Large negative values described ecosystems with low seasonal primary productivity including crops under wet and less hot climatic conditions (Fig 2A in brown). The communes with the largest positive values for Factor1 are located in the south-western part of Madagascar (Fig 2A in green) while the communes with the largest negative values for Factor1 are located on the north-eastern part (Fig 2A in brown); ?Factor 2 separated areas based on seasonality in primary productivity, vegetation, land use and temperature. Large positive values described ecosystems with high seasonal primaryFig 2. Geographical representation of the MFA factor values and cattle density of the 1,578 Malagasy communes. (A) Factor 1, (B) Factor 2, (C) Factor 3, (D) Factor 4, (E) cattle density categories. For each factor, green colors represent positive values and brown negative values. The darkest colors represent the highest values. Cattle were sampled in communes surrounded in black and human were enrolled in communes surrounded in purple. doi:10.1371/journal.pntd.0004827.gPLOS Neglected Tropical Diseases | DOI:10.1371/journal.pntd.July 14,7 /Rift Valley Fever Risk Factors in Madagascarproductivity including ligneous vegetation and irrigated areas (rice fields) under climatic conditions characterized by low night temperatures (Fig 2B in green). Large negative values described ecosystems wit.

Dhesion molecules [5, 51]. The role of resistin in insulin resistance and diabetes is controversial considering that a variety of studies have shown that resistin levels improve with increased central adiposity along with other research have demonstrated a considerable reduce in resistin levels in improved adiposity. PAI-1 is present in elevated levels in obesity as well as the metabolic syndrome. It has been linked towards the enhanced occurrence of thrombosis in individuals with these circumstances. Angiotensin II can also be present in adipose tissue and has a crucial impact on endothelial function. When angiotensin II binds the angiotensin II sort 1 receptor on endothelial cells, it stimulates the production of ROS by means of NADPH oxidase, increases expression of ICAM-1 and increases ET1 release in the endothelium [52?4]. Angiotensin also activates JNK and MAPK pathways in endothelial cells, which leads to increased serine phosphorylation of IRS-1, impaired PI-3 kinase activity and ultimately endothelial dysfunction and almost certainly apoptosis. That is among the list of explanations why an ACE inhibitor and angiotensin II sort 1 receptor6 blockers (ARBs) defend against cardiovascular comorbidity in sufferers with diabetes and vice versa [55]. Insulin receptor substrate 1 (IRS-1) can be a protein RG3039 downstream on the insulin receptor, that is crucial for signaling to metabolic effects like glucose uptake in fat cells and NO-production in endothelial cells. IRS-1 in endothelial cells and fat cells is usually downregulated by stressors like hyperglycemia and dyslipidemia, causing insulin resistance and endothelial dysfunction. A low adipocyte IRS-1 expression may thereby be a marker for insulin resistance [19, 56, 57]. 5.four. Inflammation. These days atherosclerosis is thought of to be an inflammatory illness plus the truth that atherosclerosis and resulting cardiovascular illness is far more prevalent in sufferers with chronic inflammatory ailments like rheumatoid arthritis, systemic lupus erythematosus and ankylosing spondylitis than in the healthier population supports this statement. Inflammation is regarded as an important independent cardiovascular risk factor and is related with endothelial dysfunction. Interestingly, a study performed by bij van Eijk et al. shows that sufferers with active ankylosing spondylitis, an inflammatory illness, also have impaired microvascular endothelium-dependent vasodilatation and capillary recruitment in skin, which improves after TNF-blocking therapy with etanercept [58]. The existence of chronic inflammation in diabetes is mainly according to the enhanced plasma concentrations of C-reactive protein (CRP), fibrinogen, interleukin-6 (IL6), interleukin-1 (IL-1), and TNF PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/20407268 [59?1]. Inflammatory cytokines boost vascular permeability, change vasoregulatory responses, improve leukocyte adhesion to endothelium, and facilitate thrombus formation by inducing procoagulant activity, inhibiting anticoagulant pathways and impairing fibrinolysis by means of stimulation of PAI-1. NF-B consists of a family of transcription aspects, which regulate the inflammatory response of vascular cells, by transcription of a variety of cytokines which causes an elevated adhesion of monocytes, neutrophils, and macrophages, resulting in cell damage. Alternatively, NF-B can also be a regulator of genes that handle cell proliferation and cell survival and protects against apoptosis, amongst other folks by activating the antioxidant enzyme superoxide dismutase (SOD) [62]. NFB is activated by TNF and IL-1 subsequent to hyper.

Mic selection, nor HLA restriction, but rather is a result of recombinatorial usage bias, or ranking of various segments. Figure 4 demonstrates this phenomenon, and it is also reflected in the power law distribution of the final T-cell clonal distribution observed. The relationship between TCR locus organization and segment selection in this rearrangement process and its impact on the T-cell repertoire generation has been a focus of intensive study in the recent years. Recently, a biophysical model describing yeast chromosome conformation has been applied to the murine TCR b-D and -J segment and the derived model based on `genomic distance’ between these segments has partially recapitulated the observed bias in J segment usage [36]. This supports the notion that chromatin conformation, and TCR spatial organization has a formative role in the T-cell repertoire generation. Regardless of the mechanism of recombination, it has become obvious that the T-cell repertoire that emerges has a `biased’ VDJ segment usage, with certain segments being used more frequently than others. This suggests that these segments may be more efficiently rearranged resulting in their over representation in the repertoire and vice versa. The effect of spatial organization of TCR gene segments on recombination frequency is also evident when modelling the rearrangement likelihood in the murine TRA taking into account the relative positioning of V and J segments [37]. Assuming sequential availability of V and J segments to recombine with each other in a time-dependent process, it was demonstrated that the proximal, central and distal J segments had a greater likelihood of recombining with the correspondingly positioned V segments. The model output demonstrates a `wavefront’ of recombination probability propagating through each of the regions when individual J segments were analysed for their ability to recombine with the V segments and vice versa. A similar model examined the recombination probabilities as a function of the size of the `window’ of the TRA-V and -J regions available, putting forth the notion that sequential availability of individual gene segments determines the recombination frequencies [38]. These models reinforce the deterministic aspect of the TCR locus recombination and highlight the importance of the scaling observations we report in this paper. Given the emergence of the constant p in the equations describing the fractal PD168393 side effects nature of the T-cell repertoire in normal stem cell donors and the periodic nature of TCR gene segments on the TCR locus, their relative positions were examined using trigonometric Olmutinib site functions to account for the helical nature of DNA. Similarity was observed in the relative location of the V, D and J segments across the TRA and TRB loci when they were examined using logarithmic scaling, with increasingly complex waveforms observed as higher-order harmonics were evaluated (data not shown). There are several important implications of this observation. First, analogous to the phenomenon of superposition (constructive or destructive interference) observed in the mechanical and electromagnetic waves, one may consider that relative position of a particular segment, reflected by the coordinates on the DNA helix (estimated by the sine and cosine functions, and angular distancersif.royalsocietypublishing.org J. R. Soc. Interface 13:V 1 2 2 3Jrsif.royalsocietypublishing.org1.0 0.5 5?0 3?J. R. Soc. Interface 13:3?5?Figure 5. A model depicti.Mic selection, nor HLA restriction, but rather is a result of recombinatorial usage bias, or ranking of various segments. Figure 4 demonstrates this phenomenon, and it is also reflected in the power law distribution of the final T-cell clonal distribution observed. The relationship between TCR locus organization and segment selection in this rearrangement process and its impact on the T-cell repertoire generation has been a focus of intensive study in the recent years. Recently, a biophysical model describing yeast chromosome conformation has been applied to the murine TCR b-D and -J segment and the derived model based on `genomic distance’ between these segments has partially recapitulated the observed bias in J segment usage [36]. This supports the notion that chromatin conformation, and TCR spatial organization has a formative role in the T-cell repertoire generation. Regardless of the mechanism of recombination, it has become obvious that the T-cell repertoire that emerges has a `biased’ VDJ segment usage, with certain segments being used more frequently than others. This suggests that these segments may be more efficiently rearranged resulting in their over representation in the repertoire and vice versa. The effect of spatial organization of TCR gene segments on recombination frequency is also evident when modelling the rearrangement likelihood in the murine TRA taking into account the relative positioning of V and J segments [37]. Assuming sequential availability of V and J segments to recombine with each other in a time-dependent process, it was demonstrated that the proximal, central and distal J segments had a greater likelihood of recombining with the correspondingly positioned V segments. The model output demonstrates a `wavefront’ of recombination probability propagating through each of the regions when individual J segments were analysed for their ability to recombine with the V segments and vice versa. A similar model examined the recombination probabilities as a function of the size of the `window’ of the TRA-V and -J regions available, putting forth the notion that sequential availability of individual gene segments determines the recombination frequencies [38]. These models reinforce the deterministic aspect of the TCR locus recombination and highlight the importance of the scaling observations we report in this paper. Given the emergence of the constant p in the equations describing the fractal nature of the T-cell repertoire in normal stem cell donors and the periodic nature of TCR gene segments on the TCR locus, their relative positions were examined using trigonometric functions to account for the helical nature of DNA. Similarity was observed in the relative location of the V, D and J segments across the TRA and TRB loci when they were examined using logarithmic scaling, with increasingly complex waveforms observed as higher-order harmonics were evaluated (data not shown). There are several important implications of this observation. First, analogous to the phenomenon of superposition (constructive or destructive interference) observed in the mechanical and electromagnetic waves, one may consider that relative position of a particular segment, reflected by the coordinates on the DNA helix (estimated by the sine and cosine functions, and angular distancersif.royalsocietypublishing.org J. R. Soc. Interface 13:V 1 2 2 3Jrsif.royalsocietypublishing.org1.0 0.5 5?0 3?J. R. Soc. Interface 13:3?5?Figure 5. A model depicti.