Stly dark (a few veins may be unpigmented). Antenna length/body length: antenna about as long as body (head to apex of metasoma); if slightly shorter, at least extending beyond anterior 0.7 metasoma length. Body in lateral view: not distinctly flattened dorso entrally. Body length (head to apex of metasoma): 3.5?.6 mm or 3.7?.8 mm. Fore wing length: 3.5?.6 mm or 3.7?.8 mm. Ocular cellar line/posterior ocellus diameter: 1.7?.9. Interocellar distance/posterior ocellus diameter: 1.7?.9. Antennal flagellomerus 2 length/width: 2.3?.5. Antennal flagellomerus 14 length/width: 1.4?.6. Length of flagellomerus 2/length of flagellomerus 14: 2.0?.2. Tarsal claws: simple (?). Metafemur length/width: 3.0?.1. Metatibia inner spur length/metabasitarsus length: 0.6?.7. Anteromesoscutum: Sinensetin custom synthesis mostly with deep, dense punctures (separated by less than 2.0 ?its maximum diameter). Mesoscutellar disc: mostly smooth. Number of pits in scutoscutellar sulcus: 11 or 12. Maximum NVP-BEZ235 chemical information height of mesoscutellum lunules/maximum height of lateral face of mesoscutellum: 0.4?.5. Propodeum areola: completely defined by carinae, but only partial or absent transverse carina (?). Propodeum background sculpture: mostly sculptured. Mediotergite 1 length/width at posterior margin: 2.6?.8. Mediotergite 1 shape: mostly parallel ided for 0.5?.7 of its length, then narrowing posteriorly so mediotergite anterior width >1.1 ?posterior width. Mediotergite 1 sculpture: mostly sculptured, excavated area centrally with transverse striation inside and/or a polished knob centrally on posterior margin of mediotergite. Mediotergite 2 width at posterior margin/length: 1.6?.9. Mediotergite 2 sculpture: mostly smooth. Outer margin of hypopygium: with a wide, medially folded, transparent, semi esclerotized area; usually with 4 or more pleats. Ovipositor thickness: about same width throughout its length. Ovipositor sheaths length/ metatibial length: 1.8?.9. Length of fore wing veins r/2RS: 2.3 or more. Length of fore wing veins 2RS/2M: 1.1?.3. Length of fore wing veins 2M/(RS+M)b: 0.5?.6. Pterostigma length/width: 2.6?.0. Point of insertion of vein r in pterostigma: about half way point length of pterostigma. Angle of vein r with fore wing anterior margin: more or less perpendicular to fore wing margin. Shape of junction of veins r and 2RS in fore wing: distinctly but not strongly angled. Male. Unknown. Molecular data. Sequences in BOLD: 26, barcode compliant sequences: 25. Biology/ecology. Solitary (Fig. 239). Host: Elachistidae, six species of Antaeotricha, Stenoma Janzen58. Distribution. Costa Rica, ACG. Etymology. We dedicate this species to Juan Carlos Carrillo in recognition of his diligent efforts for the ACG Programa de Ecoturismo.Jose L. Fernandez-Triana et al. / ZooKeys 383: 1?65 (2014)Apanteles juangazoi Fern dez-Triana, sp. n. http://zoobank.org/C130A607-00B2-4A2A-A965-A0C83D842D0F http://species-id.net/wiki/Apanteles_juangazoi Fig. 131 Type locality. COSTA RICA, Alajuela, ACG, Sector San Cristobal, Rio Blanco Abajo, 500m, 10.90037, -85.37254. Holotype. in CNC. Specimen labels: 1. DHJPAR0027225. 2. San Gerardo, Rio Blanco Abajo, 17-23 April 2008. Description. Female. Body color: body mostly dark except for some sternites which may be pale. Antenna color: scape, pedicel, and flagellum dark. Coxae color (pro-, meso-, metacoxa): dark, dark, dark. Femora color (pro-, meso-, metafemur): anteriorly dark/posteriorly pale, dark, dark. Tibiae color (pro-, meso-, metatibia): pale, anteriorly pale/posteri.Stly dark (a few veins may be unpigmented). Antenna length/body length: antenna about as long as body (head to apex of metasoma); if slightly shorter, at least extending beyond anterior 0.7 metasoma length. Body in lateral view: not distinctly flattened dorso entrally. Body length (head to apex of metasoma): 3.5?.6 mm or 3.7?.8 mm. Fore wing length: 3.5?.6 mm or 3.7?.8 mm. Ocular cellar line/posterior ocellus diameter: 1.7?.9. Interocellar distance/posterior ocellus diameter: 1.7?.9. Antennal flagellomerus 2 length/width: 2.3?.5. Antennal flagellomerus 14 length/width: 1.4?.6. Length of flagellomerus 2/length of flagellomerus 14: 2.0?.2. Tarsal claws: simple (?). Metafemur length/width: 3.0?.1. Metatibia inner spur length/metabasitarsus length: 0.6?.7. Anteromesoscutum: mostly with deep, dense punctures (separated by less than 2.0 ?its maximum diameter). Mesoscutellar disc: mostly smooth. Number of pits in scutoscutellar sulcus: 11 or 12. Maximum height of mesoscutellum lunules/maximum height of lateral face of mesoscutellum: 0.4?.5. Propodeum areola: completely defined by carinae, but only partial or absent transverse carina (?). Propodeum background sculpture: mostly sculptured. Mediotergite 1 length/width at posterior margin: 2.6?.8. Mediotergite 1 shape: mostly parallel ided for 0.5?.7 of its length, then narrowing posteriorly so mediotergite anterior width >1.1 ?posterior width. Mediotergite 1 sculpture: mostly sculptured, excavated area centrally with transverse striation inside and/or a polished knob centrally on posterior margin of mediotergite. Mediotergite 2 width at posterior margin/length: 1.6?.9. Mediotergite 2 sculpture: mostly smooth. Outer margin of hypopygium: with a wide, medially folded, transparent, semi esclerotized area; usually with 4 or more pleats. Ovipositor thickness: about same width throughout its length. Ovipositor sheaths length/ metatibial length: 1.8?.9. Length of fore wing veins r/2RS: 2.3 or more. Length of fore wing veins 2RS/2M: 1.1?.3. Length of fore wing veins 2M/(RS+M)b: 0.5?.6. Pterostigma length/width: 2.6?.0. Point of insertion of vein r in pterostigma: about half way point length of pterostigma. Angle of vein r with fore wing anterior margin: more or less perpendicular to fore wing margin. Shape of junction of veins r and 2RS in fore wing: distinctly but not strongly angled. Male. Unknown. Molecular data. Sequences in BOLD: 26, barcode compliant sequences: 25. Biology/ecology. Solitary (Fig. 239). Host: Elachistidae, six species of Antaeotricha, Stenoma Janzen58. Distribution. Costa Rica, ACG. Etymology. We dedicate this species to Juan Carlos Carrillo in recognition of his diligent efforts for the ACG Programa de Ecoturismo.Jose L. Fernandez-Triana et al. / ZooKeys 383: 1?65 (2014)Apanteles juangazoi Fern dez-Triana, sp. n. http://zoobank.org/C130A607-00B2-4A2A-A965-A0C83D842D0F http://species-id.net/wiki/Apanteles_juangazoi Fig. 131 Type locality. COSTA RICA, Alajuela, ACG, Sector San Cristobal, Rio Blanco Abajo, 500m, 10.90037, -85.37254. Holotype. in CNC. Specimen labels: 1. DHJPAR0027225. 2. San Gerardo, Rio Blanco Abajo, 17-23 April 2008. Description. Female. Body color: body mostly dark except for some sternites which may be pale. Antenna color: scape, pedicel, and flagellum dark. Coxae color (pro-, meso-, metacoxa): dark, dark, dark. Femora color (pro-, meso-, metafemur): anteriorly dark/posteriorly pale, dark, dark. Tibiae color (pro-, meso-, metatibia): pale, anteriorly pale/posteri.

Ron-specific; ECF sigma factorcluster 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3Table 1. Nineteen genes form the specific phylogroup A MPEC core genome. These genes cluster into three loci, including two adjacent genes ymdE and ycdU, ten genes from the phenylacetic acid degradation operon (feaR, feaB, paaFGHIJKXY) and the seven genes of the ferric citrate uptake system (fecIRABCDE).Figure 5. The carriage of the genes surrounding ymdE and ycdU. These data shows that several genes between pgaD and ycdU are more abundant in MPEC genomes from phylogroup A than they are in the wider phylogroup A population. These genes comprise what appears to be a genome island, flanked by the core genes efeB/phoH, and ghrA/TAPI-2 chemical information ycdXYZ. Although pgaB and pgaA, along with ymdE and ycdU, are present in less than 446 of all phylogroup A (blue open circles), only ymdE and ycdU are also present in at least 65/66 MPEC genomes (red filled circles), qualifying these as MPEC-specific core.A (100 of MPEC), pgaA is found only in approximately 80 of all phylogroup A (90 in MPEC). This suggests that the pga locus is relatively unstable and prone to attrition in different genomes of both MPEC and other phylogroup A strains. The pga genes are responsible for the biosynthesis of biofilm polysaccharides31, and so these data suggest against a consistent role for biofilms in mastitis. Unlike this attrition of the pga genes, in MPEC, ymdE and ycdU are robustly co-maintained. The adjacent gene ycdT could also be considered a core MPEC gene, since it is present in 65/66 strains, however its abundance in other phylogroup A strains is sufficiently high that it can be captured in the core genome of sixty-six randomly sampled stains more than 0.015 of the time, so although we have not identified it as part of the specific MPEC core, both its distribution and proximity to ymdE and ycdU suggest that this gene could also contribute to the MPEC lifestyle. The ycdT gene is annotated in EPZ004777 web MG1655 as a membrane-anchored diguanylate cyclase. This type of gene regulates the turnover of the second messenger cyclic-di-GMP, which is known to affect behaviourScientific RepoRts | 6:30115 | DOI: 10.1038/srepwww.nature.com/scientificreports/Figure 6. Carriage of the paa region in MPEC compared with phylogroup A E. coli. The core MPEC genes are coloured green, whilst a region of the paa locus which has been deleted in several MPEC is coloured yellow. The ybdA gene, which in MG1655 is a pseudogene, is outlined in magenta – the carriage for this gene is for the full length composite sequence from the MG1655 genome rather than for each half separately.such as motility, virulence, and biofilm production in a wide range of bacterial species32, and its homologue in Yersinia species (hmsT) regulates the pga genes (termed hmsHFRS) in that genus33. However, evidence suggests that although ycdT is co-regulated with the pga genes, the function of this gene is not linked to the expression of the pga operon34,35. For the phenylacetic acid degradation locus which, in MG1655, consists of a seventeen gene locus encoded by feaRB-tynA-paaZABCDEFGHIJKXY36, we detected only ten of these (feaR, feaB, paaFGHIJKXY) to be components of the MPEC-specific core genome. We include the feaR, feaB, and tynA in the paa locus since these genes are involved in the metabolism of phenylethylamine to phenylacetic acid37,38, which provides substrate for the pathway encoded by the paa genes. We then analysed the distribution of the paa locus and s.Ron-specific; ECF sigma factorcluster 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3Table 1. Nineteen genes form the specific phylogroup A MPEC core genome. These genes cluster into three loci, including two adjacent genes ymdE and ycdU, ten genes from the phenylacetic acid degradation operon (feaR, feaB, paaFGHIJKXY) and the seven genes of the ferric citrate uptake system (fecIRABCDE).Figure 5. The carriage of the genes surrounding ymdE and ycdU. These data shows that several genes between pgaD and ycdU are more abundant in MPEC genomes from phylogroup A than they are in the wider phylogroup A population. These genes comprise what appears to be a genome island, flanked by the core genes efeB/phoH, and ghrA/ycdXYZ. Although pgaB and pgaA, along with ymdE and ycdU, are present in less than 446 of all phylogroup A (blue open circles), only ymdE and ycdU are also present in at least 65/66 MPEC genomes (red filled circles), qualifying these as MPEC-specific core.A (100 of MPEC), pgaA is found only in approximately 80 of all phylogroup A (90 in MPEC). This suggests that the pga locus is relatively unstable and prone to attrition in different genomes of both MPEC and other phylogroup A strains. The pga genes are responsible for the biosynthesis of biofilm polysaccharides31, and so these data suggest against a consistent role for biofilms in mastitis. Unlike this attrition of the pga genes, in MPEC, ymdE and ycdU are robustly co-maintained. The adjacent gene ycdT could also be considered a core MPEC gene, since it is present in 65/66 strains, however its abundance in other phylogroup A strains is sufficiently high that it can be captured in the core genome of sixty-six randomly sampled stains more than 0.015 of the time, so although we have not identified it as part of the specific MPEC core, both its distribution and proximity to ymdE and ycdU suggest that this gene could also contribute to the MPEC lifestyle. The ycdT gene is annotated in MG1655 as a membrane-anchored diguanylate cyclase. This type of gene regulates the turnover of the second messenger cyclic-di-GMP, which is known to affect behaviourScientific RepoRts | 6:30115 | DOI: 10.1038/srepwww.nature.com/scientificreports/Figure 6. Carriage of the paa region in MPEC compared with phylogroup A E. coli. The core MPEC genes are coloured green, whilst a region of the paa locus which has been deleted in several MPEC is coloured yellow. The ybdA gene, which in MG1655 is a pseudogene, is outlined in magenta – the carriage for this gene is for the full length composite sequence from the MG1655 genome rather than for each half separately.such as motility, virulence, and biofilm production in a wide range of bacterial species32, and its homologue in Yersinia species (hmsT) regulates the pga genes (termed hmsHFRS) in that genus33. However, evidence suggests that although ycdT is co-regulated with the pga genes, the function of this gene is not linked to the expression of the pga operon34,35. For the phenylacetic acid degradation locus which, in MG1655, consists of a seventeen gene locus encoded by feaRB-tynA-paaZABCDEFGHIJKXY36, we detected only ten of these (feaR, feaB, paaFGHIJKXY) to be components of the MPEC-specific core genome. We include the feaR, feaB, and tynA in the paa locus since these genes are involved in the metabolism of phenylethylamine to phenylacetic acid37,38, which provides substrate for the pathway encoded by the paa genes. We then analysed the distribution of the paa locus and s.

Tal set-up that creates potential conflicts between dynamical and static information. We use simulation studies to determine which of our models were better at explaining both the observed fine-scale movement dynamics and the large-scale distributions of fish movements between the two coral patches. We also determine whether some individuals were more Sulfatinib clinical trials likely to NS-018 web initiate and lead crossings and whether hierarchical leader ollower relationships existed when groups crossed between patches.2. Results2.1. Distribution of fish and their movement between coral patchesFish spent significantly more time on the coral patches than in any other region of the arena, indicating strong bias to associate with either coral patch (group sizes of three: binomial test, N ?16, n ?15, p , 0.001; group sizes of four: binomial test, N ?16, n ?14, p , 0.01; group sizes of five: binomial test, N ?11, n ?11, p , 0.001, group sizes of six: binomial test, N ?14, n ?11, p ?0.029). Crossings to the left of the tank were as frequent as crosses to the right side of the tank indicating no side preference in the arena (N ?4433, n ?2207, two-sided sign test: p . 0.78 in all trials). The distribution of the proportion of time different numbers of fish were on the left-hand side of the arena generally followed an n-shaped distribution (figure 2), where all individuals were generally not found together on one side of the arena. However, it was clear that individuals in the arena generally tended to cross in groups (figure 3). Indeed, the number of fish in the crossing group was often equal to the total number of fish(a) no. fish on left side of tank 3 2 1 R to L L to Rrsif.royalsocietypublishing.org0 (b) no. fish on left side of tank 4 3 2 1 0 (c) no. fish on left side of tank 5 4 3 2 1 0 (d) 6 5 4 3 2 1 0 200 time (s) 400 600 no. fish on left side of tankJ. R. Soc. Interface 11:Figure 3. Examples of recorded crossings in experiments of different group sizes. Each panel shows the number of fish on the right-hand side of the tank over the duration of the experiment for group sizes of (a) three, (b) four, (c) six and (d ) six fish. Black marks indicate times where a fish crossed from the left-hand side to the right-hand side, white marks where a fish crossed from the right-hand side to the left-hand side.(a) (b)static modeldynamic modelFigure 4. An illustration of the difference between static and dynamic models. (a) In the static model, the fish on the right are individually more likely to be the next movers (red), because they are in the smaller group and are attracted to the larger group. (b) In the dynamic model the fish on the left are individually more likely to move despite being in the larger group, because they would be following the last mover (shown by a triangle).necessary condition of any model that it can reproduce the large-scale patterns in the data, because we aim to understand how these emerge from the interactions between individuals (see [45]). Therefore, using the rules of interaction specified by these models, we simulated crossing events and investigated whether each model was adequate in reproducing the larger-scale dynamics of the system. In particular, we asked whether these models reproduced the observation that the crossing group size tended to equal the number of fish that could have potentially moved from that side of the tank (shown in figure 5b). We found that only the dynamic models, where individuals only pay attention to local changes, reprodu.Tal set-up that creates potential conflicts between dynamical and static information. We use simulation studies to determine which of our models were better at explaining both the observed fine-scale movement dynamics and the large-scale distributions of fish movements between the two coral patches. We also determine whether some individuals were more likely to initiate and lead crossings and whether hierarchical leader ollower relationships existed when groups crossed between patches.2. Results2.1. Distribution of fish and their movement between coral patchesFish spent significantly more time on the coral patches than in any other region of the arena, indicating strong bias to associate with either coral patch (group sizes of three: binomial test, N ?16, n ?15, p , 0.001; group sizes of four: binomial test, N ?16, n ?14, p , 0.01; group sizes of five: binomial test, N ?11, n ?11, p , 0.001, group sizes of six: binomial test, N ?14, n ?11, p ?0.029). Crossings to the left of the tank were as frequent as crosses to the right side of the tank indicating no side preference in the arena (N ?4433, n ?2207, two-sided sign test: p . 0.78 in all trials). The distribution of the proportion of time different numbers of fish were on the left-hand side of the arena generally followed an n-shaped distribution (figure 2), where all individuals were generally not found together on one side of the arena. However, it was clear that individuals in the arena generally tended to cross in groups (figure 3). Indeed, the number of fish in the crossing group was often equal to the total number of fish(a) no. fish on left side of tank 3 2 1 R to L L to Rrsif.royalsocietypublishing.org0 (b) no. fish on left side of tank 4 3 2 1 0 (c) no. fish on left side of tank 5 4 3 2 1 0 (d) 6 5 4 3 2 1 0 200 time (s) 400 600 no. fish on left side of tankJ. R. Soc. Interface 11:Figure 3. Examples of recorded crossings in experiments of different group sizes. Each panel shows the number of fish on the right-hand side of the tank over the duration of the experiment for group sizes of (a) three, (b) four, (c) six and (d ) six fish. Black marks indicate times where a fish crossed from the left-hand side to the right-hand side, white marks where a fish crossed from the right-hand side to the left-hand side.(a) (b)static modeldynamic modelFigure 4. An illustration of the difference between static and dynamic models. (a) In the static model, the fish on the right are individually more likely to be the next movers (red), because they are in the smaller group and are attracted to the larger group. (b) In the dynamic model the fish on the left are individually more likely to move despite being in the larger group, because they would be following the last mover (shown by a triangle).necessary condition of any model that it can reproduce the large-scale patterns in the data, because we aim to understand how these emerge from the interactions between individuals (see [45]). Therefore, using the rules of interaction specified by these models, we simulated crossing events and investigated whether each model was adequate in reproducing the larger-scale dynamics of the system. In particular, we asked whether these models reproduced the observation that the crossing group size tended to equal the number of fish that could have potentially moved from that side of the tank (shown in figure 5b). We found that only the dynamic models, where individuals only pay attention to local changes, reprodu.

W each other, interpersonal skills of nurses, and age/generational issues. Nurses reported that time could positively or6 programs that could improve nurses’ interpersonal skills. An educational program that focuses on the development of “social intelligence” would be beneficial. Social intelligence (SI) according to Albrecht [31] is the ability to effectively interact or get along well with X-396 custom synthesis others and to manage social relationships in a variety of contexts. Albrecht describes SI as “people skills” that includes an awareness of social situations and a knowledge of interaction styles and strategies that can help an individual interact with others. From the perspective of interpersonal skills, Albrecht classifies behaviour toward others as on a spectrum between “toxic effect and nourishing effect.” Toxic behaviour makes individuals feel devalued, angry, and inadequate. Nourishing behaviour makes individuals feel valued, respected, and competent. The nurses in our study reported experiencing negative comments and toxic behaviours from other nurses, and this reduced their interest in socially and professionally interacting with those nurses. Fortunately, social intelligence can be learned, first by understanding that SI encompasses a combination of skills expressed through learned behaviour and then by assessing the impact of one’s own behaviour on others [31]. While it is not an easy task to be undertaken, nursing leadership needs to address the attitudes and behaviours of nurses, as these interpersonal skills are needed for both social interaction and collaboration. This could be accomplished by role modeling collaborative behaviours, having policies and/or programs in place that support a collaborative practice model, providing education on the basic concepts of SI and collaborative teamwork, and lastly facilitating the application of these concepts during social and professional interaction activities.Nursing Research and Practice social interaction among the nurses. Nursing leadership attention to these organizational and individual Oroxylin A web factors may strengthen nurse-nurse collaborative practice and promote healthy workplaces.Conflict of InterestsThe authors declare that there is no conflict of interests regarding the publication of this paper.AcknowledgmentsThe authors wish to thank the fourteen oncology nurses who actively participated in the study. The research was supported by the University Advancement Fund, the employer of the first and second authors.
doi:10.1093/scan/nsqSCAN (2011) 6, 507^Physical temperature effects on trust behavior: the role of insulaYoona Kang,1 Lawrence E. Williams,2 Margaret S. Clark,1 Jeremy R. Gray,1 and John A. BarghPsychology Department, Yale University, and 2Leeds School of Business, University of Colorado at BoulderTrust lies at the heart of person perception and interpersonal decision making. In two studies, we investigated physical temperature as one factor that can influence human trust behavior, and the insula as a possible neural substrate. Participants briefly touched either a cold or warm pack, and then played an economic trust game. Those primed with cold invested less with an anonymous partner, revealing lesser interpersonal trust, as compared to those who touched a warm pack. In Study 2, we examined neural activity during trust-related processes after a temperature manipulation using functional magnetic resonance imaging. The left-anterior insular region activated more strongly than baseline only.W each other, interpersonal skills of nurses, and age/generational issues. Nurses reported that time could positively or6 programs that could improve nurses’ interpersonal skills. An educational program that focuses on the development of “social intelligence” would be beneficial. Social intelligence (SI) according to Albrecht [31] is the ability to effectively interact or get along well with others and to manage social relationships in a variety of contexts. Albrecht describes SI as “people skills” that includes an awareness of social situations and a knowledge of interaction styles and strategies that can help an individual interact with others. From the perspective of interpersonal skills, Albrecht classifies behaviour toward others as on a spectrum between “toxic effect and nourishing effect.” Toxic behaviour makes individuals feel devalued, angry, and inadequate. Nourishing behaviour makes individuals feel valued, respected, and competent. The nurses in our study reported experiencing negative comments and toxic behaviours from other nurses, and this reduced their interest in socially and professionally interacting with those nurses. Fortunately, social intelligence can be learned, first by understanding that SI encompasses a combination of skills expressed through learned behaviour and then by assessing the impact of one’s own behaviour on others [31]. While it is not an easy task to be undertaken, nursing leadership needs to address the attitudes and behaviours of nurses, as these interpersonal skills are needed for both social interaction and collaboration. This could be accomplished by role modeling collaborative behaviours, having policies and/or programs in place that support a collaborative practice model, providing education on the basic concepts of SI and collaborative teamwork, and lastly facilitating the application of these concepts during social and professional interaction activities.Nursing Research and Practice social interaction among the nurses. Nursing leadership attention to these organizational and individual factors may strengthen nurse-nurse collaborative practice and promote healthy workplaces.Conflict of InterestsThe authors declare that there is no conflict of interests regarding the publication of this paper.AcknowledgmentsThe authors wish to thank the fourteen oncology nurses who actively participated in the study. The research was supported by the University Advancement Fund, the employer of the first and second authors.
doi:10.1093/scan/nsqSCAN (2011) 6, 507^Physical temperature effects on trust behavior: the role of insulaYoona Kang,1 Lawrence E. Williams,2 Margaret S. Clark,1 Jeremy R. Gray,1 and John A. BarghPsychology Department, Yale University, and 2Leeds School of Business, University of Colorado at BoulderTrust lies at the heart of person perception and interpersonal decision making. In two studies, we investigated physical temperature as one factor that can influence human trust behavior, and the insula as a possible neural substrate. Participants briefly touched either a cold or warm pack, and then played an economic trust game. Those primed with cold invested less with an anonymous partner, revealing lesser interpersonal trust, as compared to those who touched a warm pack. In Study 2, we examined neural activity during trust-related processes after a temperature manipulation using functional magnetic resonance imaging. The left-anterior insular region activated more strongly than baseline only.

Rat murine chimeric TNF-alpha antibody of IgG2ak isotype (Centocor, Malvern, PA, USA) was administered once a week 10 mg/kg intraperitoneally for four weeks. The development of joint manifestations was monitored as described above. The mice were killed at 15 weeks of infection. WP1066 web Tissue samples from ear, bladder and hind tibiotarsal joint were collected for culture and PCR analyses. Blood was collected for serology, and one tibiotarsal joint for histology. In experiment III, eight dbpAB/dbpAB (group 14), eight dbpAB (group 15) infected animals, and four uninfected control (group 13) animals were killed at two weeks of infection. Samples from ear, bladder and hind tibiotarsal joint were collected for culture. One hind tibiotarsal joint was collected for PCR analysis of B. burgdorferi tissue load, and blood was collected for serology. In experiment IV, eight animals we infected with dbpAB/dbpAB (groups 17 and 19) and eight animals with dbpAB (groups 18 and 20). Four uninfected animals (group 16) were negative controls. Eight animals (groups 19 and 20) were treated with ceftriaxone at six weeks. The development of joint manifestations was monitored as ACY 241 msds explained above. The mice were killed at 15 weeks of infection. Tissue samples from ear, bladder and hind tibiotarsal joint were collected for culture and PCR analyses. Blood was collected for serology.PLOS ONE | DOI:10.1371/journal.pone.0121512 March 27,3 /DbpA and B Promote Arthritis and Post-Treatment Persistence in MiceFig 1. Design of the mouse experiments. In Experiment I, four dbpAB/dbpAB (group 2), eight dbpAB/ dbpA (group 3), eight dbpAB/dbpB (group 4), two dbpAB (group 5) infected animals and two uninfected control animals (group 1) were killed at seven weeks of infection. In Experiment II, 16 infected animals (groups 4 and 5) were treated with ceftriaxone and 16 (groups 6 and 7) with ceftriaxone and anti-TNF-alpha. The ceftriaxone treatment was started at two weeks (25 mg/kg twice a day for 5 days) and the anti-TNF-alpha treatment at seven weeks of infection (10 mg/kg once a week for 4 weeks). Ear biopsy samples were collected at 6 and 9 weeks of infection to monitor the dissemination of the infection. In Experiment III, mice were killed at two weeks to study infection kinetics and bacterial load in joints. In Experiment IV, eight infected animals were treated with ceftriaxone at six weeks of infection (groups 14 and 15). doi:10.1371/journal.pone.0121512.gPreparation and B. burgdorferi culture of tissue samplesIn experiments II, the infection status of the mice was assessed by culturing ear biopsy samples at 6 and 9 weeks of infection. Ear, bladder and hind tibiotarsal joint samples were collected at seven weeks (experiments I), at 15 weeks (experiments II and IV), or at 2 weeks (experiment III) of the infection. All instruments were disinfected in ethanol between the dissections of the different samples. The tissue samples were grown in BSK II medium supplemented withPLOS ONE | DOI:10.1371/journal.pone.0121512 March 27,4 /DbpA and B Promote Arthritis and Post-Treatment Persistence in Micephosphomycin (50 g/ml; Sigma-Aldrich) and rifampin (100 g/ml; Sigma-Aldrich) at 33 for a maximum of 6 weeks.DNA extraction and PCR analysisEar, bladder and joint tissue samples were stored at -20 before the DNA extraction. Tissue samples were incubated with proteinase-K (275 g/ml, Promega, Madison, WI, USA) at 56 for overnight before the DNA was extracted using NucliSENS easyMAG kit (Biom ieux, M.Rat murine chimeric TNF-alpha antibody of IgG2ak isotype (Centocor, Malvern, PA, USA) was administered once a week 10 mg/kg intraperitoneally for four weeks. The development of joint manifestations was monitored as described above. The mice were killed at 15 weeks of infection. Tissue samples from ear, bladder and hind tibiotarsal joint were collected for culture and PCR analyses. Blood was collected for serology, and one tibiotarsal joint for histology. In experiment III, eight dbpAB/dbpAB (group 14), eight dbpAB (group 15) infected animals, and four uninfected control (group 13) animals were killed at two weeks of infection. Samples from ear, bladder and hind tibiotarsal joint were collected for culture. One hind tibiotarsal joint was collected for PCR analysis of B. burgdorferi tissue load, and blood was collected for serology. In experiment IV, eight animals we infected with dbpAB/dbpAB (groups 17 and 19) and eight animals with dbpAB (groups 18 and 20). Four uninfected animals (group 16) were negative controls. Eight animals (groups 19 and 20) were treated with ceftriaxone at six weeks. The development of joint manifestations was monitored as explained above. The mice were killed at 15 weeks of infection. Tissue samples from ear, bladder and hind tibiotarsal joint were collected for culture and PCR analyses. Blood was collected for serology.PLOS ONE | DOI:10.1371/journal.pone.0121512 March 27,3 /DbpA and B Promote Arthritis and Post-Treatment Persistence in MiceFig 1. Design of the mouse experiments. In Experiment I, four dbpAB/dbpAB (group 2), eight dbpAB/ dbpA (group 3), eight dbpAB/dbpB (group 4), two dbpAB (group 5) infected animals and two uninfected control animals (group 1) were killed at seven weeks of infection. In Experiment II, 16 infected animals (groups 4 and 5) were treated with ceftriaxone and 16 (groups 6 and 7) with ceftriaxone and anti-TNF-alpha. The ceftriaxone treatment was started at two weeks (25 mg/kg twice a day for 5 days) and the anti-TNF-alpha treatment at seven weeks of infection (10 mg/kg once a week for 4 weeks). Ear biopsy samples were collected at 6 and 9 weeks of infection to monitor the dissemination of the infection. In Experiment III, mice were killed at two weeks to study infection kinetics and bacterial load in joints. In Experiment IV, eight infected animals were treated with ceftriaxone at six weeks of infection (groups 14 and 15). doi:10.1371/journal.pone.0121512.gPreparation and B. burgdorferi culture of tissue samplesIn experiments II, the infection status of the mice was assessed by culturing ear biopsy samples at 6 and 9 weeks of infection. Ear, bladder and hind tibiotarsal joint samples were collected at seven weeks (experiments I), at 15 weeks (experiments II and IV), or at 2 weeks (experiment III) of the infection. All instruments were disinfected in ethanol between the dissections of the different samples. The tissue samples were grown in BSK II medium supplemented withPLOS ONE | DOI:10.1371/journal.pone.0121512 March 27,4 /DbpA and B Promote Arthritis and Post-Treatment Persistence in Micephosphomycin (50 g/ml; Sigma-Aldrich) and rifampin (100 g/ml; Sigma-Aldrich) at 33 for a maximum of 6 weeks.DNA extraction and PCR analysisEar, bladder and joint tissue samples were stored at -20 before the DNA extraction. Tissue samples were incubated with proteinase-K (275 g/ml, Promega, Madison, WI, USA) at 56 for overnight before the DNA was extracted using NucliSENS easyMAG kit (Biom ieux, M.

M pM p 0 ?h p 0 ?i JM M ;??PLOS ONE | DOI:10.1371/journal.pone.0157088 June 15,4 /Benchmarking for Bayesian Reinforcement Learningwhere p 0 ?is the algorithm trained offline on p0 . In our Bayesian RL setting, we want to M M find the algorithm ?which maximises JpMM for the hp0 ; pM i experiment: M p?2 arg maxp p 0 ?p 0 ?JpMM :??In addition to the performance criterion, we also measure the empirical computation time. In practice, all problems are subject to time constraints. Hence, it is important to take this parameter into account when comparing different algorithms.3.2 The experimental protocolIn practice, we can only sample a finite number of trajectories, and must rely on estimators to compare algorithms. In this section our experimental protocol is described, which is based on our comparison U0126-EtOH site criterion for BRL and provides a detailed computation time analysis. An experiment is defined by (i) a prior distribution p0 and (ii) a test distribution pM . Given M these, an agent is evaluated as follows: 1. Train offline on p0 . M 2. Sample N MDPs from the test distribution pM .p ? p ?3. For each sampled MDP M, compute estimate J M M of JM M .0p ?4. Use these values to compute an estimate J pM M . To estimate JMp 0 ?M, the expected return of agent trained offline on p0 , one trajectory is Mp 0 ?p 0 ?sampled on the MDP M, and the cumulated return is computed Mi M ?RM M 0 ? J To estimate this return, each trajectory is truncated after T steps. Therefore, given an MDPp ? p ?M and its initial state x0, we AZD1722 web observe R M M 0 ? an approximation of RM M 0 ?0p ?R M M 0 ??T X t?gt rt :If Rmax denotes the maximal instantaneous reward an agent can receive when interacting with an MDP drawn from pM , then choosing T as guarantees the approximation error is bounded by > 0: 7 6 6 log ?? ?7 6 Rmax 7 5: T? log g = 0.01 is set for all experiments, as a compromise between measurement accuracy and computation time. Finally, to estimate our comparison criterion JpMM , the empirical average of the algorithm performance is computed over N different MDPs, sampled from pM : 0 1 X p 0 ?1 X p 0 ? p ?J Mi M ???R M 0 ?J pMM ?N 0 i 0, we want to identify the best algorithms.M pM p 0 ?h p 0 ?i JM M ;??PLOS ONE | DOI:10.1371/journal.pone.0157088 June 15,4 /Benchmarking for Bayesian Reinforcement Learningwhere p 0 ?is the algorithm trained offline on p0 . In our Bayesian RL setting, we want to M M find the algorithm ?which maximises JpMM for the hp0 ; pM i experiment: M p?2 arg maxp p 0 ?p 0 ?JpMM :??In addition to the performance criterion, we also measure the empirical computation time. In practice, all problems are subject to time constraints. Hence, it is important to take this parameter into account when comparing different algorithms.3.2 The experimental protocolIn practice, we can only sample a finite number of trajectories, and must rely on estimators to compare algorithms. In this section our experimental protocol is described, which is based on our comparison criterion for BRL and provides a detailed computation time analysis. An experiment is defined by (i) a prior distribution p0 and (ii) a test distribution pM . Given M these, an agent is evaluated as follows: 1. Train offline on p0 . M 2. Sample N MDPs from the test distribution pM .p ? p ?3. For each sampled MDP M, compute estimate J M M of JM M .0p ?4. Use these values to compute an estimate J pM M . To estimate JMp 0 ?M, the expected return of agent trained offline on p0 , one trajectory is Mp 0 ?p 0 ?sampled on the MDP M, and the cumulated return is computed Mi M ?RM M 0 ? J To estimate this return, each trajectory is truncated after T steps. Therefore, given an MDPp ? p ?M and its initial state x0, we observe R M M 0 ? an approximation of RM M 0 ?0p ?R M M 0 ??T X t?gt rt :If Rmax denotes the maximal instantaneous reward an agent can receive when interacting with an MDP drawn from pM , then choosing T as guarantees the approximation error is bounded by > 0: 7 6 6 log ?? ?7 6 Rmax 7 5: T? log g = 0.01 is set for all experiments, as a compromise between measurement accuracy and computation time. Finally, to estimate our comparison criterion JpMM , the empirical average of the algorithm performance is computed over N different MDPs, sampled from pM : 0 1 X p 0 ?1 X p 0 ? p ?J Mi M ???R M 0 ?J pMM ?N 0 i 0, we want to identify the best algorithms.

TraliaHigh skin temperatures also affect thermal sensation and comfort. Very few studies in the present PM01183 supplier reviewApart from the normal thermoregulatory and subjective responses, heat stress may also impact worker health in terms of heat exhaustion and occasionally heat stroke. While not captured in the present review as physiological markers of heat strain (core temperature) were not measured in the workplace, Donoghue, Sinclair and Bates investigated the thermal conditions and personal risk factors and the clinical characteristics associated with 106 cases of heat exhaustion in the deep mines at Mt Isa, QLD.64 The overall incidence of heat exhaustion was 43.0 cases / million man-hours of underground work with a peak incidence rate in February at 147 cases / million-man hours. Specific to this review the workplace thermal conditions were recorded in 74 (70 ) cases. Air temperature and humidity were very close to those shown in Table 2 but air PD150606MedChemExpress PD150606 velocity was lower averaging 0.5 ?0.6 m�s? (range 0.0?.0 m�s?). The incidence of heat exhaustion increased steeply when air temperature >34 C,TEMPERATUREwet bulb temperature >25 C and air velocity <1.56 m�s?. These observations highlight the critical importance of air movement in promoting sweat evaporation in conditions of high humidity.12,23,65 The occurrence of heat exhaustion in these conditions contrasts with the apparent rarity of heat casualties in sheep shearers who seem to work at higher Hprod (?50?00 W)14 compared to the highest value measured in mines (?80 Wm?; 360 W for a 2.0 m2 worker; personal communication ?Graham Bates), and in similar ambient air temperatures and air velocity but much lower humidity. Symptoms of heat exhaustion also caused soldiers to drop out from forced marches.66 Self-pacing presumably maintains tolerable levels of strain but implies that increasing environmental heat stress would affect work performance and productivity. Shearers' tallies declined by about 2 sheep per hour from averages of about 17 sheep per hour when Ta exceeded 42 C; shearing ceased on a day when Ta reached 46 C.14 Bush firefighters spent less time in active work in warmer weather. Although their active work intensity was not affected their overall energy expenditure was slightly reduced.32 In the Defense Force marches not all soldiers, particularly females, were able to complete the tasks in the allotted times, with failure rates being most common in warmer conditions.5 The lower physiological responses of non-heat acclimatised search and rescue personnel operating in the Northern Territory compared to acclimatised personnel likely reflected a behavioral response to avoid excessive stress and strain.Current gaps in knowledge and considerationsOnly three studies were identified that examined in situ occupational heat stress in the Australian construction industry. Since workers in this industry, which is one of the largest sectors in Australia, typically experience the greatest amount of outdoor environmental heat exposure, this is a clear knowledge gap that needs addressing. There also seems to be a paucity of information for the agriculture/horticulture sector, particularly for manual labor jobs such as fruit picking and grape harvesting, which are usually performed in hot weather, often by foreign workers on temporary work visas. No occupational heat stress studies were captured for the Australian Capital Territory (ACT) orTasmania. The climate within the ACT is similar to New South Wales and Vi.TraliaHigh skin temperatures also affect thermal sensation and comfort. Very few studies in the present reviewApart from the normal thermoregulatory and subjective responses, heat stress may also impact worker health in terms of heat exhaustion and occasionally heat stroke. While not captured in the present review as physiological markers of heat strain (core temperature) were not measured in the workplace, Donoghue, Sinclair and Bates investigated the thermal conditions and personal risk factors and the clinical characteristics associated with 106 cases of heat exhaustion in the deep mines at Mt Isa, QLD.64 The overall incidence of heat exhaustion was 43.0 cases / million man-hours of underground work with a peak incidence rate in February at 147 cases / million-man hours. Specific to this review the workplace thermal conditions were recorded in 74 (70 ) cases. Air temperature and humidity were very close to those shown in Table 2 but air velocity was lower averaging 0.5 ?0.6 m�s? (range 0.0?.0 m�s?). The incidence of heat exhaustion increased steeply when air temperature >34 C,TEMPERATUREwet bulb temperature >25 C and air velocity <1.56 m�s?. These observations highlight the critical importance of air movement in promoting sweat evaporation in conditions of high humidity.12,23,65 The occurrence of heat exhaustion in these conditions contrasts with the apparent rarity of heat casualties in sheep shearers who seem to work at higher Hprod (?50?00 W)14 compared to the highest value measured in mines (?80 Wm?; 360 W for a 2.0 m2 worker; personal communication ?Graham Bates), and in similar ambient air temperatures and air velocity but much lower humidity. Symptoms of heat exhaustion also caused soldiers to drop out from forced marches.66 Self-pacing presumably maintains tolerable levels of strain but implies that increasing environmental heat stress would affect work performance and productivity. Shearers' tallies declined by about 2 sheep per hour from averages of about 17 sheep per hour when Ta exceeded 42 C; shearing ceased on a day when Ta reached 46 C.14 Bush firefighters spent less time in active work in warmer weather. Although their active work intensity was not affected their overall energy expenditure was slightly reduced.32 In the Defense Force marches not all soldiers, particularly females, were able to complete the tasks in the allotted times, with failure rates being most common in warmer conditions.5 The lower physiological responses of non-heat acclimatised search and rescue personnel operating in the Northern Territory compared to acclimatised personnel likely reflected a behavioral response to avoid excessive stress and strain.Current gaps in knowledge and considerationsOnly three studies were identified that examined in situ occupational heat stress in the Australian construction industry. Since workers in this industry, which is one of the largest sectors in Australia, typically experience the greatest amount of outdoor environmental heat exposure, this is a clear knowledge gap that needs addressing. There also seems to be a paucity of information for the agriculture/horticulture sector, particularly for manual labor jobs such as fruit picking and grape harvesting, which are usually performed in hot weather, often by foreign workers on temporary work visas. No occupational heat stress studies were captured for the Australian Capital Territory (ACT) orTasmania. The climate within the ACT is similar to New South Wales and Vi.

Interviews, chart review, and clinician report) caused ambiguity–Two capability determinations were ambiguous due to discrepancies between information collected from participant interviews, chart review, and clinician report. In both examples, the participants described themselves as more capable than was indicated in data from patient charts or from treating clinicians.Author Manuscript Author Manuscript Author Manuscript Author ManuscriptDiscussionDetermining financial capability is complicated. One reason capability is difficult to judge is that managing a limited income, with or without a disabling illness, is very difficult. The challenges disabled people face–poverty, substance use (21), gambling (22), crime, financial dysfunction, psychiatric symptomatology (23) and financial predation (6) — contribute to their financial difficulties. Most beneficiaries and, in fact, most people do not spend all of their funds on basic needs. A Bureau of Labor Statistics report found that Americans in the lowest, middle, and highest income quintiles spend 7?0 of their income on nonessential items and that those in the lowest quintile spend a greater percentage of their money than those in the highest quintile on basic necessities such as housing, food, utilities, fuels and public services, healthcare, and medications (24, 25).Emerging literature suggests that because of the stresses of poverty, it is particularly difficult for purchase LY-2523355 someone who is poor to exert the planning, self-control and attention needed to JC-1MedChemExpress JC-1 resist unnecessary purchases (26). Second, determinations of the amount of nonessential or harmful spending and the circumstances around such spending that would merit payee assignment is a subjective judgment with few guidelines. The Social Security Administration guidelines about how representative payees must use a beneficiary’s monthly benefits allow for some nonessential purchases (i.e. clothing and recreation), but only after food and shelter are provided for (27). This paper highlights areas requiring special deliberation. Clinicians assessing financial capability need to consider the extent of the harm spending patterns have on the individual being assessed (i.e. misspending that results in a few missed meals might cause minor discomfort but not measureable harm, whereas misspending that results in an inability to pay for rent may be very harmful). When looking at harmful spending, clinicians should discern whether the beneficiary has a financial problem or an addiction problem. If improved financial skills or payee assignment would not impact the acquisition of drugs of abuse, then the beneficiaries’ substance use probably does not reflect financial incapability. Another important issue that clinicians face when making determinations about beneficiaries’ ability to manage funds is attempting to predict future functioning, which is inherently uncertain. There is evidence that clinicians have difficulty predicting behaviors such as future medication adherence (28, 29), so some uncertainty in predicting financialPsychiatr Serv. Author manuscript; available in PMC 2016 March 01.Lazar et al.Pagecapability is to be expected. Frequent reevaluations of financial capability might help with complicated determinations. Extensive and serial evaluations of capability to manage one’s funds are probably beyond the mandate and the resources of the Social Security Administration, but re-evaluating the capability of beneficiaries who are admitted to.Interviews, chart review, and clinician report) caused ambiguity–Two capability determinations were ambiguous due to discrepancies between information collected from participant interviews, chart review, and clinician report. In both examples, the participants described themselves as more capable than was indicated in data from patient charts or from treating clinicians.Author Manuscript Author Manuscript Author Manuscript Author ManuscriptDiscussionDetermining financial capability is complicated. One reason capability is difficult to judge is that managing a limited income, with or without a disabling illness, is very difficult. The challenges disabled people face–poverty, substance use (21), gambling (22), crime, financial dysfunction, psychiatric symptomatology (23) and financial predation (6) — contribute to their financial difficulties. Most beneficiaries and, in fact, most people do not spend all of their funds on basic needs. A Bureau of Labor Statistics report found that Americans in the lowest, middle, and highest income quintiles spend 7?0 of their income on nonessential items and that those in the lowest quintile spend a greater percentage of their money than those in the highest quintile on basic necessities such as housing, food, utilities, fuels and public services, healthcare, and medications (24, 25).Emerging literature suggests that because of the stresses of poverty, it is particularly difficult for someone who is poor to exert the planning, self-control and attention needed to resist unnecessary purchases (26). Second, determinations of the amount of nonessential or harmful spending and the circumstances around such spending that would merit payee assignment is a subjective judgment with few guidelines. The Social Security Administration guidelines about how representative payees must use a beneficiary’s monthly benefits allow for some nonessential purchases (i.e. clothing and recreation), but only after food and shelter are provided for (27). This paper highlights areas requiring special deliberation. Clinicians assessing financial capability need to consider the extent of the harm spending patterns have on the individual being assessed (i.e. misspending that results in a few missed meals might cause minor discomfort but not measureable harm, whereas misspending that results in an inability to pay for rent may be very harmful). When looking at harmful spending, clinicians should discern whether the beneficiary has a financial problem or an addiction problem. If improved financial skills or payee assignment would not impact the acquisition of drugs of abuse, then the beneficiaries’ substance use probably does not reflect financial incapability. Another important issue that clinicians face when making determinations about beneficiaries’ ability to manage funds is attempting to predict future functioning, which is inherently uncertain. There is evidence that clinicians have difficulty predicting behaviors such as future medication adherence (28, 29), so some uncertainty in predicting financialPsychiatr Serv. Author manuscript; available in PMC 2016 March 01.Lazar et al.Pagecapability is to be expected. Frequent reevaluations of financial capability might help with complicated determinations. Extensive and serial evaluations of capability to manage one’s funds are probably beyond the mandate and the resources of the Social Security Administration, but re-evaluating the capability of beneficiaries who are admitted to.

As the population mean (Loeve, 1977). Stuttered and non-stuttered disfluencies–Our second finding that preschool-age CWS produce significantly more stuttered and non-stuttered disfluencies than CWNS corroborates findings from previous studies (Ambrose Yairi, 1999; BIM-22493 chemical information Johnson et al., 1959; Yairi Ambrose, 2005). Whereas the frequency of stuttered disfluencies has been commonly used as a talker-group classification criterion, our data suggest that non-stuttered disfluencies could also be employed to augment decisions about talker group classification based on stuttered disfluencies. The finding that preschool-age CWS produce significantlyNIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript7Present authors recognize that syllable-level measures of stuttering can be converted to word-level measures of stuttering and vice versa (Yaruss, 2001). purchase R848 However, this issue goes beyond the purpose and scope of the present study. J Commun Disord. Author manuscript; available in PMC 2015 May 01.Tumanova et al.Pagemore non-stuttered disfluencies than CWNS and that the number of non-stuttered disfluencies was a significant predictor for talker group classification provides empirical support for the notion that total number of disfluencies may be another augmentative measure useful for distinguishing between children who do and do not stutter (Adams, 1977). One seemingly apparent assumption, whether children are classified according to parental report (e.g., Boey et al., 2007; Johnson et al., 1959) or objective criteria (e.g., Pellowski Conture, 2002), is that the speech disfluencies exhibited by CWS versus those of CWNS are more dimensional (i.e., continuous) than categorical (i.e., non-continuous) in nature. Our data suggests that both talker groups produce instances of stuttered disfluencies as well as speech disfluencies not classified as stuttering. Thus, the disfluency distributions for the two talker groups overlap to some degree (something earlier discussed and/or recognized by Johnson et al., 1963). This, of course, does not mean that the two groups are identical. Neither does this overlook the fact that some individuals close to the between-group classification criterion will be challenging to classify. However, clinicians and researchers alike must make decisions about who does and who does not stutter when attempting to empirically study or clinically treat such children. One attempt to inform this decision-making process or minimize behavioral overlap between the two talker groups is the establishment of a priori criteria for talker group classification (taking into consideration empirical evidence, as well as parental, caregiver and/or professional perceptions). The present finding that the number of non-stuttered disfluencies significantly predicted talker group classification support the use of that variable as an adjunct to (but certainly not replacement for) the 3 stuttered disfluencies criterion for talker group classification. It should be noted, however, that while minimizing one type of error (e.g., false negatives) this practice may increase the chances of false positives (see Conture, 2001, Fig. 1.1, for further discussion of the issue of false positives and false negatives when classifying children as CWS vs. CWNS). At present, it seems safe to say that there are no absolute, error-free demarcations that perfectly (i.e., 100 of the time) separate the two talker groups. However, as movement toward a more da.As the population mean (Loeve, 1977). Stuttered and non-stuttered disfluencies–Our second finding that preschool-age CWS produce significantly more stuttered and non-stuttered disfluencies than CWNS corroborates findings from previous studies (Ambrose Yairi, 1999; Johnson et al., 1959; Yairi Ambrose, 2005). Whereas the frequency of stuttered disfluencies has been commonly used as a talker-group classification criterion, our data suggest that non-stuttered disfluencies could also be employed to augment decisions about talker group classification based on stuttered disfluencies. The finding that preschool-age CWS produce significantlyNIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript7Present authors recognize that syllable-level measures of stuttering can be converted to word-level measures of stuttering and vice versa (Yaruss, 2001). However, this issue goes beyond the purpose and scope of the present study. J Commun Disord. Author manuscript; available in PMC 2015 May 01.Tumanova et al.Pagemore non-stuttered disfluencies than CWNS and that the number of non-stuttered disfluencies was a significant predictor for talker group classification provides empirical support for the notion that total number of disfluencies may be another augmentative measure useful for distinguishing between children who do and do not stutter (Adams, 1977). One seemingly apparent assumption, whether children are classified according to parental report (e.g., Boey et al., 2007; Johnson et al., 1959) or objective criteria (e.g., Pellowski Conture, 2002), is that the speech disfluencies exhibited by CWS versus those of CWNS are more dimensional (i.e., continuous) than categorical (i.e., non-continuous) in nature. Our data suggests that both talker groups produce instances of stuttered disfluencies as well as speech disfluencies not classified as stuttering. Thus, the disfluency distributions for the two talker groups overlap to some degree (something earlier discussed and/or recognized by Johnson et al., 1963). This, of course, does not mean that the two groups are identical. Neither does this overlook the fact that some individuals close to the between-group classification criterion will be challenging to classify. However, clinicians and researchers alike must make decisions about who does and who does not stutter when attempting to empirically study or clinically treat such children. One attempt to inform this decision-making process or minimize behavioral overlap between the two talker groups is the establishment of a priori criteria for talker group classification (taking into consideration empirical evidence, as well as parental, caregiver and/or professional perceptions). The present finding that the number of non-stuttered disfluencies significantly predicted talker group classification support the use of that variable as an adjunct to (but certainly not replacement for) the 3 stuttered disfluencies criterion for talker group classification. It should be noted, however, that while minimizing one type of error (e.g., false negatives) this practice may increase the chances of false positives (see Conture, 2001, Fig. 1.1, for further discussion of the issue of false positives and false negatives when classifying children as CWS vs. CWNS). At present, it seems safe to say that there are no absolute, error-free demarcations that perfectly (i.e., 100 of the time) separate the two talker groups. However, as movement toward a more da.

Intimacy to develop incrementally and to disclose as trust builds is eliminated or at least burdened with the possibility of felony charges. Structural interventions can also compromise autonomy by imposing the interventionists’ priorities and values. In most cases, interventionists operate under the assumption that health takes precedence over any priorities that the intervention efforts replace (e.g., pleasure, relationship development, economic T0901317 solubility security). When these assumptions serve as a basis for structural interventions, the affect of which may be virtually unavoidable for those in the intervention area, the intervention effectively imposes this priority on others. Micro finance interventions are based on the assumption that individuals should welcome the opportunity to become entrepreneurs. However, many of these endeavors produced mixed results, in part because entrepreneurship is not universally desirable.97,98 Efforts to routinely test all U.S. adults can serve as another example. While concentrating on the important goal of testing individuals for HIV infection, practitioners may persuade individuals to be tested at a time when an HIV-positive diagnosis could topple an already unstable housing or employment situation or end a primary relationship. Structural interventions can also incur risk for persons who do not consent to test. Routine HIV testing increases the likelihood that some persons will be diagnosed with HIV or another condition when they do not have health insurance. The intervention then creates a documented preexisting condition and may preclude an individual from receiving health benefits in theAIDS Behav. Author manuscript; available in PMC 2011 December 1.Latkin et al.Pagecontext of current insurance coverage standards. Increasing risk for individuals who have not consented to this new risk is especially of concern if the individual who is put at risk by the intervention does not receive benefit from the intervention. This occurs, for example, with criminal HIV disclosure laws, which increase the risk of unwanted secondary disclosure of HIV-positive persons’ serostatus by requiring disclosure if they want to engage in sex. Because structural interventions make system wide changes, there is the risk that intervening factors may produce unanticipated and potentially deleterious outcomes. These outcomes may not only be difficult to anticipate, they may be difficult to neutralize or to control. Public trust, once called into question, especially by persons who occupy marginal positions in society, may be exceedingly difficult to regain. The collective memory of a community is a significant structure in itself. Methods to Study Structural Factors The broad scope and complex nature of structural factors and structural interventions create myriad challenges for research. Studies of structural factors affecting HIV-related behavior have fallen into three general categories. The first approach is to Z-DEVD-FMK biological activity assess the impact of structural interventions at the macro, meso, and micro levels that were not initially designed to change HIV-related behaviors directly. The second is to assess structural factors that shape the context and processes of the epidemic and its eradication. A third approach includes experimental tests of the effects of structural interventions specifically designed to reduce the transmission and impact of HIV. One example of the first approach is to assess the impact of district-wide interventions to redu.Intimacy to develop incrementally and to disclose as trust builds is eliminated or at least burdened with the possibility of felony charges. Structural interventions can also compromise autonomy by imposing the interventionists’ priorities and values. In most cases, interventionists operate under the assumption that health takes precedence over any priorities that the intervention efforts replace (e.g., pleasure, relationship development, economic security). When these assumptions serve as a basis for structural interventions, the affect of which may be virtually unavoidable for those in the intervention area, the intervention effectively imposes this priority on others. Micro finance interventions are based on the assumption that individuals should welcome the opportunity to become entrepreneurs. However, many of these endeavors produced mixed results, in part because entrepreneurship is not universally desirable.97,98 Efforts to routinely test all U.S. adults can serve as another example. While concentrating on the important goal of testing individuals for HIV infection, practitioners may persuade individuals to be tested at a time when an HIV-positive diagnosis could topple an already unstable housing or employment situation or end a primary relationship. Structural interventions can also incur risk for persons who do not consent to test. Routine HIV testing increases the likelihood that some persons will be diagnosed with HIV or another condition when they do not have health insurance. The intervention then creates a documented preexisting condition and may preclude an individual from receiving health benefits in theAIDS Behav. Author manuscript; available in PMC 2011 December 1.Latkin et al.Pagecontext of current insurance coverage standards. Increasing risk for individuals who have not consented to this new risk is especially of concern if the individual who is put at risk by the intervention does not receive benefit from the intervention. This occurs, for example, with criminal HIV disclosure laws, which increase the risk of unwanted secondary disclosure of HIV-positive persons’ serostatus by requiring disclosure if they want to engage in sex. Because structural interventions make system wide changes, there is the risk that intervening factors may produce unanticipated and potentially deleterious outcomes. These outcomes may not only be difficult to anticipate, they may be difficult to neutralize or to control. Public trust, once called into question, especially by persons who occupy marginal positions in society, may be exceedingly difficult to regain. The collective memory of a community is a significant structure in itself. Methods to Study Structural Factors The broad scope and complex nature of structural factors and structural interventions create myriad challenges for research. Studies of structural factors affecting HIV-related behavior have fallen into three general categories. The first approach is to assess the impact of structural interventions at the macro, meso, and micro levels that were not initially designed to change HIV-related behaviors directly. The second is to assess structural factors that shape the context and processes of the epidemic and its eradication. A third approach includes experimental tests of the effects of structural interventions specifically designed to reduce the transmission and impact of HIV. One example of the first approach is to assess the impact of district-wide interventions to redu.