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The objective of the Bienestar Health Program is to provide children with 50 sessions of health programming distributed throughout 7 months (October 1, 2001, to April 26, 2002). The health sessions were used to transmit to children 3 health behavior messages shown to be associated with diabetes mellitus control (decrease dietary saturated fat intake, increase dietary fiber intake, and increase physical activity in children). These behaviors were taught and reinforced through classroom, home, school cafeteria, and after-school care educational activities ( Sneakernews Sale Online Cheap Footlocker Aliceolivia Woman Jane Midrise Skinny Jeans Ecru Size 26 Alice amp; Olivia Outlet Visit New hBYdreA9UQ
). Physical education teachers, parents, school cafeteria staff, and after-school caretakers were asked to encourage less dietary saturated fat intake, more dietary fiber intake, and more physical activity; to have less dietary saturated fat, more dietary fiber, and more physical activity available; and to be role models for the children. Children were asked to set goals aimed at accomplishing the targeted behaviors and to keep records of their accomplishments. Children were also asked to encourage their peers and adult caretakers to practice the 3 health behaviors. The 4 components of the Bienestar Health Program cohesively addressed the individual, relevant social groups, culture, and a health promotion environment that supports behaviors consistent with diabetes mellitus prevention.

Descriptions and Schedules of the Bienestar Health Program

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Parents and students who practiced the targeted behaviors were rewarded with “Bienestar coupons” denominated in dollar amounts as an incentive. At the end of a semester, a “tiendita” or little store was held at each school. Participants could purchase merchandise (donated clothes, household appliances, school supplies, toys, sporting goods, and gift certificates) with their Bienestar coupons.

OUTCOME MEASURES

The primary end point was FCG concentration, and the secondary end points were percentage of body fat, physical fitness level, and dietary fiber and dietary saturated fat intake. Outcome measures were collected at the beginning and end of the school year (September 4, 2001, and April 29, 2002). Temporary staff, separate from programs and masked to the intervention, were hired and trained to collect the data. Students were asked to fast overnight so that school district nurses and Bienestar medical assistants could collect FCG samples in the morning. Levels of FCG were measured by collecting a blood drop from a student’s fingerstick. The blood drop was placed on a reagent strip and inserted into a blood glucose monitoring system (Glucometer Elite XL; Bayer Corp, Mishawaka, Ind). Students with an FCG level less than 110 mg/dL (<6.1 mmol/L) were given a written notice explaining to the parents that the student’s diabetes mellitus test result was normal. Students with an FCG level of 110 mg/dL or greater (≥6.1 mmol/L) (positive screening results) were referred to a physician for further testing. This study was conducted before the new American Diabetes Association guidelines for diagnosing diabetes mellitus were published.

Dietary fiber intake and percentage of energy intake from saturated fat were assessed using a 24-hour dietary recall protocol. Studies have found dietary recalls to be reliable and valid in children in this age group. Three 24-hour dietary recalls, including 2 weekdays and a Sunday or holiday, were collected and recorded by trained staff. Because most children in this study participated in the federal school lunch program, 2 of 3 meals a day are provided by school cafeterias during weekdays, and all meals are provided by parents during weekends.

Bienestar staff training focused on interviewing and measuring techniques. The interviewing technique consisted of a script for dialogue, prompting methods, and recording methods. The measuring technique consisted of food models and measuring utensils used to increase portion size accuracy. Dietary intake data were entered for analysis using nutrition calculation software (, version 4.02; Nutrition Coordinating Center, University of Minnesota, Minneapolis).

Physical fitness rather than physical activity was assessed because recall questionnaires in children younger than 10 years are less reliable and less valid than those in older children and because short-term recall questionnaires may reveal seasonal instead of usual physical activity. In the present study, physical fitness was measured using a modified Harvard step test. The step test consists of connecting a heart rate monitor (Polar Vantage XL; Polar Electric Co, Port Washington, NY) transmitter to the lower part of the child’s chest and connecting a monitor to the wrist. A baseline heart rate is recorded, then the child is asked to start stepping onto and off (both feet) a stool 30 cm high, 42 cm wide, and 38 cm deep for 5 minutes. The student is paced at 30 cycles per minute. Heart rates were recorded 0, 1, and 2 minutes after the child either completely finished the exercise or stopped the exercise prematurely. A physical fitness score was calculated from the total time of exercise (in seconds) multiplied by 100 and divided by the sum of 3 heart rate values measured 0, 1, and 2 minutes after exercise.

Body fat was measured using bioelectric impedance analysis (Tanita Corporation of America Inc, Arlington Heights, Ill) and body mass index. Bioelectric impedance analysis was used for body fat measurement because body fatness has been shown to relate closely to atherogenic and diabetogenic risk factors in children and because body mass index may not represent true body fatness in prepubertal children. The children, in indoor clothing, were asked to remove their shoes and socks and step on the metal box. Within 30 seconds, the instrument prints out percentage of body fat and weight. Students, in indoor clothing and barefooted, also had their height measured using a wall stop measuring tape (stadiometer) (Seca Bodymeter 206; Seca Corp, Hanover, Md). Body mass index was calculated as weight in kilograms divided by the square of height in meters using the Quetelet Index measure.

MEASURES OF STUDENT AND SCHOOL CHARACTERISTICS

Information on student age, sex, and ethnic background and the percentage of students classified as economically disadvantaged (eligible for free lunch) were collected from student self-report and cross-checked with information provided by the school. Parents or guardians of students were also asked to complete a questionnaire at baseline. Information collected from parents or guardians included family health history, parental education, family income, and self-reported health status. Finally, information was collected to assess differences between schools. Each school’s level of performance on the Texas Academic Assessment Systems was obtained from the Texas Education Agency Web site. The levels of performance were categorized as “exemplary,” “recognized,” “acceptable,” and “low.” Information was also gathered on the percentage of students who identified themselves as Mexican American or Hispanic and the percentage of students classified as being economically disadvantaged based on free school lunch program eligibility.

STATISTICAL ANALYSIS

Because randomization and implementation of the health intervention occurred at the school level, we used mixed models to adjust the clustering effect controlling for the dependency at the student level. This approach effectively models outcomes at the individual level in terms of student- and school-level covariates while concurrently estimating and adjusting for the intraclass correlation present in the data. We used hierarchical linear and nonlinear modeling software (; Scientific Software International, Lincolnwood, Ill) to conduct the mixed-models analyses.

All regression analyses were conducted using the intent-to-treat method, with students analyzed in their originally assigned treatment condition regardless of midyear transferring and attendance at Bienestar program activities. The change in the 5 outcome variables from baseline to follow-up was the dependent variable, and sex, ethnicity, and age were the student-level covariates. Height change from baseline to follow-up was included in all models to control for differential growth effect. The baseline value of the outcome variable was also included in all models. For dietary intake outcomes, total daily caloric intake (mean) at baseline was added to the regression as a covariate in addition to other covariates. All student-level covariates were originally allowed to vary randomly. In most cases, only the baseline value of the outcome variable varied randomly across schools.

Three school-level contextual variables (percentage disadvantaged, percentage Mexican American, and Texas Academic Assessment Systems level) that provided an indication of the social and cultural characteristics of the schools involved were included as covariates. In the original analyses, they were all entered into the regression models as school-level covariates to control for their potential effects on intervention outcomes. School-level contextual variables were then eliminated from the model if they did not contribute significantly to variations in the differences in the outcome variables. We estimated the differences in changes in the outcome variables and their 95% confidence intervals (95% CIs), adjusting for student- and school-level covariates. We included only statistically significant fixed and random variables in the final models.

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PARTICIPATION LEVEL

Of 1993 fourth-grade students available from 27 elementary schools, 1419 (71%) returned signed parent consent and student assent forms. The response rate in the intervention schools (74%) was 4.6% (95% CI, 0.07%–8.0%) higher than that in control schools (69%) ( Cassie PaperbagWaisted Romper Adelyn Rae Cheap Sale Recommend lG2z1Bq4tD
). Because there was no evidence that students and parents were aware of their assignments, it was unclear why the response rate was different.

Baseline and follow-up FCG levels were collected from 1419 (713 intervention and 706 control) and 1221 (619 intervention and 602 control) fourth-grade children, respectively ( Figure ). Sample sizes for the other outcome measures varied slightly. The 198 students missing were either lost to follow-up (119 students) or excluded from analysis because of missing values, extreme values, or exclusion criteria (79 students).

At baseline, there were no statistically significant differences in student characteristics between control and intervention schools except for intervention schools tending to have a higher percentage of Mexican American students ( = .02). At baseline, there were no statistically significant differences in outcome measures between intervention and control schools except for physical fitness measures (fitness score, 65.93 for control schools and 63.90 for intervention schools; = .001 after adjusting for sex, ethnicity, and age). Potential bias that could be introduced by differences in attrition rates between control and intervention schools was examined. No difference in attrition due to student sex, ethnicity, or treatment condition was found.

Household demographic questionnaires were completed and returned by 956 of 1434 families who had signed parent consent forms. The annual household income averaged $11000 for intervention schools and $12000 for control schools. The average number of persons living in a household was 5 for intervention and control schools. Twenty-one percent of intervention schools and 18% of control schools had households with single parents. Mother’s level of education was similar for both groups. For each group, 82% of mothers had a high school education or less and 18% had some college education or more. Fifty-five percent of questionnaire respondents from intervention schools and 60% from control schools reported having a fair to poor health status. Family members responding to the questionnaire reported that 55% of children from intervention schools and 60% from control schools had a first- or second-degree relative with diabetes mellitus. See Table 2 for student characteristics.

If just type T is given and no variable v, then the catch clause is still executed.

It is an error if any CatchParameter type T1 hides a subsequent Catch with type T2, i.e. it is an error if T1 is the same type as or a base class of T2.

The FinallyStatement is always executed, whether the try ScopeStatement exits with a goto, break, continue, return, exception, or fall-through.

If an exception is raised in the FinallyStatement and is not caught before the original exception is caught, it is chained to the previous exception via the next member of Throwable . Note that, in contrast to most other programming languages, the new exception does not replace the original exception. Instead, later exceptions are regarded as 'collateral damage' caused by the first exception. The original exception must be caught, and this results in the capture of the entire chain.

Thrown objects derived from Error are treated differently. They bypass the normal chaining mechanism, such that the chain can only be caught by catching the first Error . In addition to the list of subsequent exceptions, Error also contains a pointer that points to the original exception (the head of the chain) if a bypass occurred, so that the entire exception history is retained.

A FinallyStatement may not exit with a goto, break, continue, or return; nor may it be entered with a goto.

A FinallyStatement may not contain any Catches . This restriction may be relaxed in future versions.

Throws an exception.

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at the close of the current scope, rather than at the point where the ScopeGuardStatement appears. scope(exit) executes NonEmptyOrScopeBlockStatement when the scope exits normally or when it exits due to exception unwinding. scope(failure) executes NonEmptyOrScopeBlockStatement when the scope exits due to exception unwinding. scope(success) executes NonEmptyOrScopeBlockStatement when the scope exits normally.

If there are multiple ScopeGuardStatement s in a scope, they will be executed in the reverse lexical order in which they appear. If any scope instances are to be destroyed upon the close of the scope, their destructions will be interleaved with the ScopeGuardStatement s in the reverse lexical order in which they appear.

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unas nuevas medidas máximas y mínimas grosor 11,66 centímetros erecto y 9,31 en reposo.

Para establecer las medidas se basaron en una escala mínima de 4,8 centímetros para el más pequeño y 21,2 para el más grande, siempre en estado erecto. Si no tienes una regla o cinta métrica a mano, cuenta con que en un billete de 5 euros mide 12 centímetros de largo y uno de 50 unos 14 centímetros. Por si se da el caso de sobrepasar la media, y con cuidado de no cortarnos con el filo de la hoja, te resultará también útil saber que un folio A4 mide 29,7 centímetros de largo y 21 centímetros de ancho.

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El doctor Veale se muestra convencido de la labor social que su estudio puede aportar a millones de hombres en todo el mundo que sufren algún tipo de complejo en cuanto al tamaño de su miembro viril: “Creemos que estos gráficos ayudarán a los médicos a tranquilizar a la gran mayoría de los hombres para que sean conscientes de que el tamaño de su pene está dentro del rango normal”.

médicos dentro del rango normal”.

De las casi 15.000 operaciones de alargamientos de pene, una quinta parte del total son de Alemania, seguida de Venezuela y España

Saber si las proporciones de su pene son o no normales obsesiona a muchos hombres. Pero más allá de comentarios o comparativas simpáticas con amigos y conocidos, algunos pueden acabar padeciendo lo que se conoce como trastorno dismórfico corporal : una preocupación obsesiva por el tamaño de su pene que genera mucha angustia y complejos que deben ser tratados con la ayuda de profesionales.

trastorno dismórfico corporal

La preocupación por el tamaño incluso anima a muchos hombres a someterse a tratamientos de alargamiento de pene . Sin ir más lejos, según el último informe de la Sociedad Internacional de Cirugía Estética (ISAPS, por sus siglas en inglés), de las casi 15.000 operaciones registradas en 2013 en todo el mundo, los alemanes protagonizaron una quinta parte del total seguidos de Venezuela y España, en segundo y tercer lugar, que abarcaron juntas el 6,3%.

15.000 operaciones

Los investigadores del Instituto de Psiquiatría han desarrollado este estudio con el objetivo de establecer la media de lo que se considera “normal” en cuanto a la longitud y grosor del pene, pero el estudio no saca conclusiones sobre las diferencias en los tamaños según las diferentes razas. De hecho, hasta ahora no se han realizado revisiones sistemáticas formales de mediciones del tamaño del pene ni ha habido ningún intento para crear un diagrama gráfico que muestre la distribución mundial del tamaño del pene flácido o erecto según la raza o edad.

The above studies provide very relevant findings for our goal of using ERP to examine the initial stages of L3/Ln acquisition. The logic is as follows: if L2ers show ERP signatures akin to native speakers for a given grammatical property, then we can assume that, in principle, the property at stake is available as a source of transfer. If so, we might expect that advanced L1 English L2 Spanish bilinguals learning Portuguese as an L3 might show a positivity in the P600 time window for both number and gender violations in Portuguese. Showing this for gender would make them different from the English-speaking learners of Spanish reported in Gabriele et al. (2013b) , who only showed this positivity for number. Such findings would be consistent with the TPM and the CEM (for different reasons), but crucially not with the L2 Status Factor. Recall that, under the current formulation of the L2 Status Factor, the L2 and L3 are hypothesized to be stored in declarative memory. As stated in Ullman (2001 , 2005 ), learners’ greater reliance on declarative memory is predicted to yield N400 effects for grammatical violations where native speakers show qualitatively different components (e.g., a biphasic LAN-P600 pattern according to Peter Pilotto Printed Pencil Skirt Grey Outlet Store Online Wholesale Quality Quality Free Shipping Low Price Cheap Sale Best Sale biV1jR
). Therefore, if the L2 Status Factor is on the right track, novice learners of L3 Portuguese whose L1 and L2 are English and Spanish, respectively, should show, at most, N400 effects for gender agreement violations in L3 Portuguese. This is one example of how the ERP methodology (i.e., the fact that the N400 and the P600 have been argued to be associated with different aspects of processing and different memory systems) can be used to adjudicate between the above models in a way that behavioral methodologies cannot. With respect to the CEM and the TPM, since transfer by either facilitation (CEM) or by typological proximity (TPM) would always favor Spanish transfer, there is no way to tease apart these models with the present domain of grammar. In Section “Sample ERP Methodology,” we will provide a sample methodology that is able to tease apart all three initial stages models.

In order to test the above models of L3 acquisition, we detail a novel methodology that is part of our in progress work, which relies on the use of artificial languages (AL) as L3s and which combines behavioral and processing measures (i.e., grammaticality judgment task and ERP data). The use of ALs offers two crucial advantages. First, we can test truly ab initio learners, allowing us to better contrast the predictions of the above models, all of which are initial stages models. Second, by using ALs we can systematically manipulate the similarity between the L3 and the L1/L2 in terms of (1) the presence/absence of a given feature and (2) the levels of the cue hierarchy which, according to Rothman (2013 , 2015 ), will determine the parser’s selection of a transfer source. In addition, the use of ERP will shed light on the qualitative nature of processing at L3 initial stages. This is especially relevant, given the current articulations of the L3 models under review. For example, the L2 Status Factor ( Bardel and Falk, 2012 ) argues that L3 acquisition relies mainly on declarative memory and, therefore, L3 beginners are predicted to show N400 responses for morphosyntactic properties associated with qualitatively different components in native speakers (e.g., P600 or a biphasic LAN-P600; e.g., Ullman, 2001 ; Morgan-Short et al., 2012 ). In contrast, the TPM assumes that the initial state of L3 acquisition is the entire L1 or L2 and, therefore, this model predicts that “transferable” morphosyntactic properties should be associated with ERP signatures that are qualitatively native-like from the start (e.g., P600; Rothman, 2015 ).

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