Brycon dentex and its variations within this basin, although its plasticity has been proposed as an indicator of the maintenance of biodiversity. The goal of this study was to analyze the effects of anthropogenic activity and habitat modification on the morphological variation of Brycon dentex and to determine the usefulness of discriminant analysis in the morphometric differentiation of three populations of Brycon dentex in Ecuador. The Brycon dentex morphometric model could be used as a framework in conservation and, thus, an indicator of habitat status by quickly detecting changes in fish shape.The conservation status of a native fish species is often a key indicator of the state of habitat alteration, which supports strong anthropogenic disturbance. Ecuador contains the Guayas basin, the largest basin in the Pacific Ocean, which is a biodiversity reserve. However, there is little information regarding the morphometric characterization of Brycon dentex in the Guayas basin rivers and their connections to fishing management and environmental conditions. A total of 200 mature fish were captured, and 26 morphometric parameters were measured. The fishing policies (Hypothesis 1) and environmental conditions (Hypothesis 2) were considered fixed factors and were validated by t-tests. The morphological variation among the three populations (Hypothesis 3) was validated through a discriminant analysis. Fishing policies and resource management were found to generate morphological differences associated with body development. In addition, the environmental conditions were found to influence the size and structure of Brycon dentex populations. The analyzed populations were discriminated by the generated morphometric models, which differentiated Cluster 1 (Quevedo and Mocache rivers) with high fishing pressure from Cluster 2 (Pintado river) with medium–low fishing pressure. Morphometric differentiation by discriminant analysis is a direct and economic methodology that can be applied as an indicator of diversity maintenance.The Guayas, located in Ecuador, is the largest basin in the Pacific Ocean and has an inventory of 123 native freshwater species. Most of these are endemic species that are threatened or at-risk due to anthropogenic activity and the modification, fragmentation, and destruction of habitats. The aim of this study was to determine the morphometric variation in three wild populations of  The ecological theory of diversification  and stud2 , from the Bryconidae family, is a native species that is widely distributed in western Ecuador in different rivers of the CHG [Brycon dentex [ the CHG ,32,33, i the CHG , and in  the CHG . In a prn dentex . Morpholn dentex . Samplinn dentex . Its conBrycon dentex, is widely spread across a broad geographical range of ecological conditions accompanied by equally diverse morphological variations in Ecuador. It is included on the IUCN Red List as LC, although there are insufficient data for characterization. Brycon dentex has an omnivorous mode of feeding and a strong ability to rapidly adapt to different environmental conditions. In addition to characterizing Brycon dentex, it is of great interest to relate its morphological variability to biodiversity maintenance. The variation among the stocks of river populations could be a consequence of phenotypic plasticity in response to unusual hydrological conditions [The subject of our study, nditions ,17.Brycon dentex and the variation in the traits of different rivers in the Guayas basin. In this context, this study aimed to contribute to the attainment of an adequate management ecosystem equilibrium and the characterization of animal genetic resources.Ferrito et al.  and Mir Brycon dentex in the CHG have undergone significant morphological diversification and whether these differences can be related to fishing management and environmental conditions. Knowledge of the phenotypic variations in relation to environmental modifications could be used to identify key factors for policy makers in terms of the development of both diversity conservation programs and sustainable fishing practices.Therefore, we investigated whether three wild populations of Brycon dentex. Subsequently, three hypotheses were formulated, as displayed below:The general hypothesis of this study was that fishing policies, resource management, and environmental conditions influence the differentiation of populations due to the phenotypic plasticity of Hypothesis 1.0) was that there would be an equality of means between the populations of Brycon dentex in the Pintado and Mocache rivers (H0: μ1 = μ3). In contrast, the alternative hypothesis was that significant differences would exist due to fishing management practices (H1: μ1 ≠ μ3).Fishing and resource management practices  influence the morphological variation of Brycon dentex in the CHG. The null hypothesis (HHypothesis 2.0) was that there would be an equality of means between the populations of Brycon dentex in the Quevedo and Mocache rivers (H0: μ2 = μ3). In contrast, the alternative hypothesis was that significant differences would exist due to a change in environmental conditions (H1: μ2 ≠ μ3).The physical environmental conditions influence the morphological variation of Brycon dentex. The null hypothesis  with a medium fishing pressure and a low flow velocity, Population 2 (Quevedo river) with a high fishing pressure and white water or water containing a high concentration of oxygen, and Population 3 (Mocache river) with a high fishing pressure and a low flow velocity.Brycon dentex—from the Pintado , Quevedo , and Mocache  rivers—was chosen. The non-representative specimens of Brycon dentex with small size, fish of other species, mutilated, and young individuals were immediately returned to the river. These areas are in the CHG , chemical indicators , and biological indicators (phytoplankton) of the water in the three rivers show values within the recommended ranges for freshwater life ,7,31. TaThe most commonly used types of fishing gear are hand-held lines with hooks (10% of catches); cloth or tape mode nets (20%) with a length of 100–150 m, a height of 4–5 m, and a mesh light diameter of ¾–1 inch; trammels (30%) with a mesh size of between 2 and 3½ inches; casts (35%) with a mesh size of between ½ and 1 inch; and others such as harpoon hooks (5%). Manual riverbank throw nets, trammel nets, and fishing spears are the most frequently used fishing methods in the Pintado river. By contrast, trawl lines, riverbank manual throw nets, trammel nets, throw nets, and fishhooks are the most commonly used methods in the Quevedo and Mocache rivers.Balsas and canoes are ancestrally used by fishermen, while bongos  = 0.98632 horsepower (hp)) boats 25–50 cv), and lanchas (30–180 cv) use motors from low to high strengths and are autonomous. Previous research by Pacheco-Bedoya  cv, and , MAGAP , and OchBrycondentex specimens  were caught by fishermen between January and March 2019. The capture, transport, and stunning of specimens were conducted following the recommendations of Gonzalez-Martinez et al. [z et al. . A veterMorphometric trait data were collected using an ichthyometer with graduated digital calipers and tape with a precision of 0.01 mm. To avoid errors, the same researcher measured all fish starting from the left side, except for the widths and perimeters, following the conventional method described by Diodatti et al. . A total3), where BW is the total weight (g) and SL is the standard length (cm).The Fulton condition coefficient (K) is a widely used indicator of animal welfare in fisheries and general fish biology studies to quantify variations in fish populations . It was p < 0.001), thus indicating that the analysis was suitable [t-tests (hypotheses 1 and 2), whilst meristic counts were compared with the Kruskal–Wallis test. Sex was not considered as a fixed effect, since no significant differences between males and females have been found previously. To contrast Hypothesis 3, the DISCRIM procedure was used to perform a canonical discriminant analysis of size-adjusted geometric morphometric data using the three populations as the grouping variable. The probabilities of entering and staying in the model were both set at p < 0.05. The selection of the most discriminant variables was conducted by applying the F-Snedecor, Wilks’ lambda, and 1-Tolerance methods. The correct assignment percentage was considered, and the Mahalanobis distances are represented graphically as clusters. Statistica 12.0 for Windows software was used to perform the statistical analyses .The morphometric characters were standardized in accordance with the work of Elliot et al. . The effsuitable ,14. The Brycon dentex population were shown to have an average body weight of 154.47 g; standard and total lengths of 15.65 and 21.46 cm, respectively; and an average head length of 5.65 cm (p < 0.05) (data not presented). The highest weight and K condition factor were obtained in the Pintado river , and the lowest were obtained in the Quevedo river  (The fish in the  5.65 cm . The morctively) .p > 0.05). Eleven of the 25 morphometric measures showed significant differences (p < 0.05) for the populations from the Pintado and Mocache rivers . Regarding Hypothesis 2, Brycon dentex. Significant differences between the Quevedo and Mocache rivers were found for 10 morphometric variables: TL, SL, HL, Pre-OL, Pre-DL, Pre-PcL, Pre-PvL, Pre-AL, AFL, and UJL.In relation to Hypothesis 1, the results of the morphometric comparisons between two wild populations from the Pintado and Mocache rivers with different fishing management practices are shown in p > 0.05) affect the meristic traits, with close values shown for the three populations.Meristic traits based on the number of dorsal, pectoral, pelvic, anal, and caudal fin rays showed mean values of 9.98, 10.41, 7.81, 29.39, and 19.01, respectively (data not presented). Coefficients of variation were low. The river did not significantly (p < 0.05). According to the results of the F-Snedecor, Wilks’ lambda, and 1-Toler methods, there were six major discriminant variables in the model (25%): the anal fin length (AFL), body perimeter 3 (P3), body depth 3 (AC3), total length (TL), upper jaw length (UJL), and pre-pelvic fin length (Pre-PvL)  variables were accepted in the model and 10 measurements (41.67%) were significant (Pre-PvL) . The relistances . There wBrycondentex individuals caught in our study were smaller than those recorded by Revelo [With a total average length of 21.46 cm, the y Revelo  , overfishing through several fishing methods , unsuitable fishing policies, land use, agricultural practices, and the destruction of Ecuador river habitats [Baetis spp., and larvae from Astyanax spp. [Polymyxus coronalis has been related to eutrophic waters.The results of this study showed significant differences between rivers; however, a low standard error and low variability within each river were found. This indicates the presence of homogeneity due to a potentially shared morphological plasticity and parallel adaptation to similar habitat types . The avahabitats ,45,61,62Brycon dentex populations between the Pintado and Mocache rivers, was accepted for 11 of the 25 variables. For the traits linked to the body development of both populations , significant differences were found. The morphological differences in BW, K, TL, and SL between the Pintado and Mocache rivers were not significant. Contrary to expectations, there were no differences in the size and structure of the fish between these rivers (Hypothesis 1), most likely because the local fishing regimes are closer than initially considered [Brycon dentex populations in the Pintado river were found to have greater body depths, and specimens from the Mocache river had greater perimeters and widths. In the Pintado river, the fishing pressure was low, as this area acts as a reservoir of native freshwater species and encourages the use of environmentally friendly agricultural practices.Hypothesis 1, that fishing policies and resource management influence the morphological differentiation of nsidered ,47,59. BBrycon dentex, was accepted in 12 out of 25 variables. Body weight and the condition factor (K), which indicates the nutritional status of fish, were higher in the Mocache river population . In contrast, the total length, standard length, and eye diameter were higher in specimens in the Quevedo river. Specimens of greater size and with a greater hydrodynamic structure were found in the Quevedo river. These factors enhance their ability to survive and give them speed during flight from predators [The differences between specimens in the rivers were influenced by the high fishing pressure in some areas, the overfishing of native species in the Mocache river, and the introduction of non-native species ,46,59,63redators , such asredators , seasonaredators , and theredators . Thereforedators . Phytoplredators . Food avredators .Brycon dentex with factors associated with the fragmentation and deterioration of the ecosystem and a high fishing pressure existing in areas with the highest vulnerability level. According to Ochoa Ubilla et al. [The three rivers showed differences in the morphology of a et al. , the nata et al. ,59. Our a et al.  and Escaa et al. , who rela et al.  related Brycon dentex populations were discriminated by the generated morphometric models. Melvin et al. [Hypothesis 3 was partially accepted. Though the discriminant model was accepted, Mahalanobis distances only differentiated two groups. Six morphologic variables with a high discriminant power were selected. The three analyzed n et al.  and Ujjan et al.  pointed n et al. . SurprisBrycon dentex specimens in the Quevedo river (fast and highly oxygenated waters) are larger and narrower than those in the other rivers. On the contrary, fish in warm, slow, and less oxygenated waters (Mocache and Pintado rivers) respond by modifying their body development (width and deep). These results agree with those of Cavalcanti et al. [Oreochromis spp.); overfishing; environmental contamination ; the development of large-scale intensive forestry practices; a loss of modification of the natural hydrological regime, including basin river connectivity; and, finally, climate change [AFL, UJL, and Pre-PvL variables were common in the differentiation of both factors. The AC3 and P3 traits were found to be associated with body development and the fishing management factor. The TL variable was linked with size, structure, and the water characteristics in each river. Native species tend to maintain a constant body mass and adaptively respond to anthropogenic and environmental conditions. i et al.  and Olayi et al. . The CHGe change ,30,75,76e change ,33,37,59e change . The adee change ,60. These change ,37,78.Brycon dentex could be used as an indicator of habitat conservation and endogeneity degree [Native freshwater populations are very sensitive to environmental changes and quickly adapt to morphological changes . Therefoy degree ,15,75. Ty degree . It alsoy degree .Brycon dentex lacked a genetic analysis of the species.The interactions between factors were not considered in this research, nor were the action mechanisms existing for each factor. Additionally, the characterization of Brycon dentex in the CHG showed morphological differences due to fishing management and environmental conditions. Both factors are considered to be direct drivers of diversity maintenance.The three populations of The different fishing policies and resource management practices present in the studied rivers have generated morphological differences that are associated with body development. Under the different environmental conditions present in the rivers, differences in traits related to the size and structure of fish have primarily arisen.The analyzed populations could be discriminated using the generated morphometric model, showing that discriminant analysis is a useful way to differentiate populations. Six morphometric measurements were found to have the greatest discriminant power and were deemed to be appropriate for population discrimination. These variables were the anal fin length, body perimeter 3, body depth 3, total length, upper jaw length, and pre-pelvic fin length.Brycon dentex morphometric model could be a useful framework for the conservation of the species, so it could indicate habitat status by quickly detecting changes in fish shape. A very small number of easily obtained morphometric traits in each population could be used to determine the biodiversity status, and this information could also be used in the implementation of fishing management policies in Ecuador’s rivers. This model could be extended for use in other rivers in Latin America.The


This paper reports results from an impact study of Brain Games (BGs), a classroom-based intervention designed to build preschool and school-aged children’s executive functions (EFs) and related self-regulation skills. The study employed a classroom-randomized, experimental design with 626 students in 36 pre-K through fourth grade classrooms in charter schools in a mid-sized urban district. In one set of models with child covariates, children in intervention classrooms showed marginal positive impacts on regulation-related behaviors, attention control and impulsivity, and negative effects on global EF and marginal increases in discipline problems. A second set of models with a smaller sample and both child and classroom covariates included indicate positive impacts of BGs on global EFs, prosocial behavior, and attention control and impulsivity. There were no significant impacts on the teacher–student relationship as reported by the teacher or on direct assessments of inhibitory control, short term and working memory, or another measure of global EF in either set of models. These promising findings offer a signal that implementation of targeted, easy to implement intervention approaches in classroom contexts can influence children’s regulation-related and prosocial outcomes, but this signal should be investigated further with larger and more tightly controlled designs. Over the past several decades, a robust body of literature has emerged documenting the foundational role of social and emotional skills for learning, behavior, and health . Long-teChildren’s development across social, emotional, behavioral, and cognitive domains is posited to build on a set of core executive functions and self-regulation skills  task, for children who were initially rated by parents as high in hyperactivity/inattention . A potential hypothesis is that the intervention was not long enough for teachers to see meaningful changes in student behavior in the classroom. Additionally, it is possible that teachers are more likely to see changes in EF behaviors when they implement the interventions. In this case, trained facilitators implemented the games, not teachers, and three to five research assistants also participated during each playgroup to support student engagement. Implementation therefore did not reflect a traditional classroom environment. In a more recent study of RLPL, Head Start teachers were assigned to one of three conditions: RLPL intervention, a revised RLPL intervention with added academic content, and a business-as-usual control group. Students’ SR as measured by the HTKS improved (ES=0.31), but the difference between either intervention group and the control group was not statistically significant, perhaps due to insufficient power, given the small sample size . In a large pilot study of the effects of SECURe, six schools with 4,000 students in pre-K through third grade classrooms were randomized to either receive the SECURe intervention or to be in the control group (three schools per condition). Results indicated that the program increased students’ attention skills, reduced their impulsive behavior, and had positive effects on literacy skills, especially among the lowest-achieving students in the sample . Over 75n=1,248 students). Teachers from two schools were trained in the fall of 2015 and teachers in the third school in the winter  through first grade and three schools were elementary schools serving children in grades two through four. Each school had two classrooms per grade for a total of six classrooms per school and 36 classrooms total. With our small number of schools overall, in an effort to minimize the impact of differences between them, randomization occurred at the classroom level. Using a random number generator, one classroom in each grade was assigned to the intervention group and one classroom to the business-as-usual control group for a total of 18 classrooms in each condition. Teachers in the intervention group participated in BG training and implemented the intervention (described below). Teachers in the control condition continued with business-as-usual classroom activities.n=322) or to a business-as-usual control group (n=304). Following randomization, independent sample t tests indicated that the children did not differ significantly across study conditions on baseline demographic characteristics, suggesting that the randomization process was successful. The sample also included 65 teachers . Approximately 63% of teachers identified as White, 3% as Black or African American, 12.5% as Hispanic or Latino, less than 1% as Asian, and 20.3% as Other. On average, teachers had worked in the profession for 6.34years, SD=3.55 and in the current school for 3.53, SD=2.34. Independent sample t tests indicated that there were no statistically significant differences between treatment and control teachers on the number of years teachers had worked in the profession, years in the current school, or age.Participants included 626 children (50.64% boys) in pre-K through fourth grade. Children were 95.43% Latinx, 3.11% White, and <1% Black/African-American and Other. As noted above, classrooms were randomly assigned within each school to the BG intervention group ] range from 60.3 to 64.8%, however these numbers reflect an average from all students in grades pre-K through eight; data are not available only for the grades in our sample.t tests were used to compare the stable sample to all children in the fall and then to all children in the spring for the treatment and control groups separately for all outcomes as well as for the child-level covariates. The t tests were not statistically significant for any variables except two outcomes. In the control group, the mean of the stable sample for teacher-reported prosocial behavior was statistically significantly higher in spring than the mean of the full sample. In both treatment and control groups, teacher-reported teacher-student relationship quality in spring were statistically significantly lower in the stable sample than the full sample.Challenges with data collection led to variability in sample sizes between waves. All analyses were conducted using only the stable sample, meaning children for whom we have data at both time points. See Data were collected during the fall and spring of the 2017–2018 school year. In the beginning of the school year, consent forms in English and Spanish were sent home to all families informing them of the study and seeking consent for their child to participate in data collection. The overall consent rate was 80.65% across grades (ranging from 65.65% in the first grade to 91.34% in the fourth grade) and consent rates did not differ significantly between the intervention (82.94%) and control (78.35%) groups.Information on student skills was collected through direct assessments conducted by trained assessors and teacher reports. Assessors participated in an in-depth 1-day training with members of the research team to learn about and practice administering the assessments. The assessors, who were blind to treatment conditions, then visited each school to administer the one-on-one direct assessments. In two schools (one early childhood center and one lower school), nearly all students with consent participated in the direct assessment. Due to substantial constraints on time for direct assessments, a randomly selected group of six consented children  from each classroom were chosen to participate in the direct assessment in four of the six schools, representing approximately 30% of the students in each classroom. The sampling of children within classrooms to address school limits on data collection time follows established practice for RCTs e.g., . Prior tAt each timepoint, teachers completed reports about each student in their classroom as well as the characteristics of their classroom. In pre-K through second grade classrooms, there were two teachers in each classroom. In the third and fourth grade, there was one teacher per classroom and one teacher who floated between the two classrooms at each grade level. In the younger grades, surveys were distributed to both teachers in the classroom. Floater teachers received a survey to complete for a randomly selected subgroup of students from all of the classrooms with which they work. The paper-based surveys were distributed in-person to each school and were collected approximately 1–2weeks later. The same data collection procedures were conducted in fall and spring of the 2017–2018 school year.Brain Games is a classroom-based intervention designed to build preschool and elementary-aged children’s EFs and related self-regulation skills. The intervention consists of a small box of 31 games, each designed to target one of three core EF skills, called Brain Powers: attention control (Focus Power), working memory (Remember Power), and inhibitory control (Stop and Think Power). For example, in the “Remember Power” game titled “On My Pizza, I Like” students are asked to add pizza toppings to a pretend pizza and to remember the increasing list of pizza toppings – targeting working memory by requiring students to remember and manipulate information in their minds. The card provides prompts to make the games both simpler or more challenging . After each game, teachers are encouraged to lead discussions to help students recognize the skills they are building, identify successful strategies, and connect skills to other times of the day when they need to focus, remember, or use self-control. In “On My Pizza, I Like,” the post-game talk might include a discussion of how students use their Remember Brain Power to keep track of the toppings in the right order  and how those strategies can apply to other times of the day when Remember Power is needed . Brain Games are not implemented during a designated period (as most SEL interventions are), rather teachers are encouraged to integrate the games into their daily routines and transitions across classroom activities and various settings . The box also includes concrete strategies and support for teachers; key findings about brain development, learning, and behavior; and a set of three classroom posters to help reinforce the use of skills throughout the day. Prior to the beginning of the school year, intervention teachers participated in a 90-min BG training that covered the science behind the development of EFs and self-regulation, introduced the games, and provided teachers with scaffolded opportunities to practice. In May, teachers in the intervention group received four virtual booster training modules focused on various implementation topics .The BG Theory of Change (ToC) is closely aligned to the BG intervention and the three “Brain Powers.” The expected near-transfer outcomes include (1) the core EF skills of attention control, working memory, and inhibition and (2) classroom tone and relationships (teacher–student and peer relationships), which the games foster through fun and inclusive game play as well as rich post-game talk. Medium-transfer outcomes include prosocial behavior, impulse control, and broader behavioral regulation and positive behavior, which build on the foundational EFs and SR skills, and fewer disciplinary events. Importantly, the ToC guided the selection of measures used in the study, which are closely related to the key components and goals of the BG. This paper focuses only on child-level outcomes.The primary constructs and measures are presented below by measurement type, followed by the child and classroom baseline covariates.Our battery of EF direct assessments was designed to align with the BG ToC and to capture component EF skills as well as more global EFs. Two assessments were used to assess EFs with pre-K and kindergarten children, Pencil Tap and DCCS. The DCCS, Corsi Blocks (Forward and Backward Digit Spans which capture short-term and working memory), and Trail-Making-Task were used with first grade through fourth grade children. After each assessment, the trained assessor completed the Preschool Self-Regulation Assessment Assessor Report (PSRA-AR).Inhibitory control was assessed in pre-K and kindergarten children using the Pencil Tap. In this task, the assessor tapped a pencil either once or twice and the child was instructed to tap the opposite number of times . Because children in the early years  vary so much in motor capacity, the total time to complete all 16 trials was recorded and then controlled in all analyses . Scores were created by calculating the mean number of correct responses (if less than 80% were missing) and dividing by the total number of trials. If more than 80% of responses were missing for a child, a total score was not calculated for the Pencil Tap. Children who were correct more often received higher scores.Global EF was assessed using (1) the DCCS for all children in the sample and (2) the Trail Making Task (TMT) for first through fourth graders. The DCCS card-sorting task  was usedThis task has been described by researchers as a measure of attention shifting, working memory, cognitive flexibility, and as an index of self-control/complex EF e.g., . Based oThe TMT is primarily used as a measure of attention control, attention shifting, and global EF. The TMT has three parts. In the first part, children are asked to draw a continuous line connecting numbers in sequential order. In the second part, children draw a line connecting letters. In the third part, children connect numbers and letters in alternating order . Before each part, each child completed a short trial to ensure they understood the directions. The number of parts completed and total completion time were recorded. Children received zero points if they did not complete any of the parts, one point if they completed either Part 1 (numbers) or Part 2 (letters), two points if they completed both 1 and 2, and three points if they completed Part 1, Part 2, and Part 3 (mixed numbers and letters). Children who complete more parts have higher scores. As with the Pencil Tap, due to developmental variation in motor skills, the total completion time is used as a covariate in all analyses.Short-term memory and working memory were assessed using the Forward Digit Span (FDS) and Backward Digit Span (BDS), respectively, of The Corsi Blocks. In the first phase of this task, the experimenter tapped a series of blocks and asked the child to tap the blocks in the same order (FDS). In the second phase, the experimenter tapped a series of blocks and asked the child to tap the blocks in the reverse order (BDS). The task starts with only two blocks, and another block is added each time the child taps a pattern correctly. The process continues up to a maximum of nine blocks for forward digit span and six blocks for the backward digit span. The child completed two test trials (each beginning with two blocks) for each phase (forward and backward). The highest number of blocks tapped correctly was recorded and averaged across both trials in each phase  to generate the final scores. Children who correctly recalled a greater number of blocks received higher scores.Attention and impulsivity was measured using the Attention and Impulsivity subscale of the Preschool Self-Regulation Assessment – Assessor Report PSRA-AR; . The assIn fall, 37% of the teacher surveys across intervention and control groups were completed by two teachers. In spring, 60% of the surveys were completed by two teachers. Surveys were highly correlated between fall and spring and between the two teachers  and were therefore combined to create one teacher rating per timepoint  per child. All scale scores were computed as the mean across the items for each construct. Scores were only calculated if fewer than 80% of items were missing for each scale.never=0; all of the time=4). Cronbach alphas for this scale for fall and spring are 0.95–0.97, respectively. Higher ratings on the CEFS indicate higher levels of EFs and related self-regulation skills.Regulation-related behavior was measured using teacher reports on the Classroom Executive Function Survey CEFS; , a measuProsocial behavior was measured using items adapted from the prosocial behavior scale of the Teacher Observation of Classroom Adaption-Revised TOCA-R; . The surdefinitely does not apply=0; definitely applies=4). Items were combined across scales to create an average relationship score. Cronbach alphas for fall and spring are 0.89 and 0.90, respectively. Higher scores on the TSRS indicate a closer, less conflictual teacher–student relationship.The teacher–student relationship was measured using an adapted version of the Teacher Student Relationship Scale – Short Form TSRS-SF; . The surnone=0; two or more times=2). Items were averaged to create a final discipline score, with higher scores indicating more frequent discipline. Cronbach’s alpha was not calculated for discipline events because it is a count.Discipline events were measured using a set of four items that ask the teacher how often the student had been sent to the principal’s office, to an in-school suspension, home or removed from school, or to an alternative classroom in the preceding 3weeks. Items are rated on a three-point scale , and special education status (SPED) were compiled from administrative records and included as baseline covariates.We began by conducting a set of descriptive analyses including an examination of attrition  and baseline equivalence. We examined overall attrition in two ways. First by comparing the number of students who consented to participate in the study to those who remain and for whom we have spring outcome data and second by comparing the number of students for whom we have fall data to those who remain and for whom we have spring outcome data. We also examined differential attrition, or attrition by intervention status, using both approaches. Differences between children who attritted vs. those who did not were calculated by regressing a dummy variable for attrition (1=attritted) on child demographic characteristics with school fixed effects and classroom random intercepts included. A significant coefficient would indicate a significant difference between children who attritted and those who did not on that variable.g  and each dichotomous variable  using Cox’s Index.To assess baseline equivalence, following What Works Clearinghouse Guidelines (WWC), a standardized mean difference or effect size was calculated for each continuous variable using Hedge’s p<0.05 and also note marginal significance at p<0.10. Effect sizes are also provided for all statistically significant outcomes which were calculated by dividing the relevant treatment estimate by the SD of the control group  approach. As described in the Materials and Methods section, randomization occurred at the classroom level such that children were nested in classrooms and classrooms were nested in schools. To account for the nesting of students in classrooms, we employed multilevel models with random intercepts for each classroom. All models also included school fixed effects and controlled the relevant outcome measures at baseline. Each outcome was included in a separate model. For outcomes with both time and accuracy scores (Pencil Tap and TMT), time was included as a covariate. To determine the significance of effects, we adhered to traditional significance levels of In the following section, we present results from a taxonomy of four models for each of our primary outcomes with sets of covariates added across models: (1) no covariates, (2) only child-level covariates including gender, ELL status, and SPED status, (3) only classroom covariates including teacher reported number of children (i) in the classroom, (ii) from homes in which English is not the primary language, (iii) developmentally behind most of the other children, (iv) with learning disabilities, (v) who have poor attendance, (vi) who have behavior problems, and (vii) who are performing below grade level, and (4) combined child and classroom covariates. We interpret Model 2, with child-level covariates, and Model 4, which includes both child- and classroom-level covariates due to (1) the association between classroom covariates and implementation (described on pp. 31 and 32 below), (2) associations between classroom covariates and child outcomes see , and (3)SD between treatment conditions, and the stable sample of 550 is sufficient to detect minimum mean differences of ~0.37 SD between treatment conditions. Our ability to detect effects are further reduced for the direct assessments, which include both fewer students and, in many cases, fewer classrooms .Overall, our study is underpowered, which influences our interpretation of null findings as we note in the Discussion. Specifically, assuming an alpha of 0.05 and power of 0.80, our total possible sample size of 775 is sufficient to detect minimum mean differences of ~0.31 To examine attrition, we first compared students who consented to participate in the study (626 students) with those who have data for at least one outcome at the end of the study (602 students). Approximately 3.83 percent of the children who consented to participate in the study at baseline did not participate in data collection at the end of the study. Rates of attrition for the treatment and control groups were similar at 4.04 and 3.62%, respectively. With overall levels of attrition at 3.83 percent and differential attrition at less than 1%, the study falls into acceptable rates of attrition according to WWC under the conservative attrition standards . We noteResults of analyses examining baseline equivalence across intervention and control conditions in child demographic characteristics and baseline outcomes for the sample used in Model 2 are shown in practical significance in that they can influence implementation of BG and therefore their impacts on outcomes, as well as overall classroom functioning. For example, in larger classrooms, gaining and maintaining student attention and engagement may be more challenging than in classrooms with fewer students. In addition, as is the case with this study, in larger classrooms with more students who are considered to be developmentally behind their peers, the teacher may need to either provide various adaptations for students or provide additional support to students who are struggling. We also note that the F-test for overall balance revealed no statistically significant differences between classrooms assigned to intervention vs. control groups F =0.65, p=0.78.Examination of baseline classroom characteristics reveal standardized mean differences between intervention and control groups requiring covariate adjustment see . SpecifiSD=1.26) for an average of 14 total minutes per week (SD=6.74). Pre-K classrooms tended to play the most games, approximately four per week (SD=0.16) and second grade classrooms spent the most time playing BG each week, playing for an average of 21min weekly (SD=7.09). We also note that Brain Game play was high in the older grades, with third and fourth grade classrooms playing an average of 3.73 (SD=2.17) and 1.79 (SD=0.27) games per week, respectively.Implementation of Brain Games was systematically tracked during the 40-week implementation period . Teachers completed weekly log-books in which they tracked how many games they played and the number of minutes per game. Teachers were asked to play one game per day, or five games per week. On average, teachers played three games per week (SD=10.69) to 157 games in pre-K (SD=6.43). Notably, only one third grade classroom played the intended number of games per week, suggesting that more work is needed to understand the feasibility of daily implementation and thresholds for implementation effectiveness .If BGs were implemented fully as intended, children would have played 200 total games over the course of the 40-week implementation period (five games per week or one per day). Average implementation at the grade level ranged from approximately 72 in fourth grade (r=0.36), students coming from homes in which English is not the primary language (r=0.27), students with learning (r=0.14) or physical disabilities (r=0.63), and students who have poor attendance (r=0.27). Teachers were significantly less likely to play BGs in classrooms with higher numbers of children performing below grade level (r=−0.27). These associations suggest an important link between how teacher view their classrooms and their interaction with and use of the intervention.Correlational analysis examining associations between implementation and baseline classroom covariates indicate that teachers were significantly more likely to implement BGs in classrooms rated as having greater numbers of students in them , fit well as students arrive or during morning meeting, and could help students refocus, calm down, and transition from one activity to another. In addition, teachers integrate BGs into academic content , blending BGs with core academic content. Unlike interventions that replace a part of the day such as RLPL which took place over 16 structured playgroups ; however, we note that this effect was not robust to alternative approaches to scoring the DCCS. A continuous scoring approach . There was a marginally significant effect on the attention/impulsivity subscale of the Assessor Report . In Model 4, in which both child and classroom covariates were included, we did not observe statistically significant impacts of BG on the direct assessments of inhibitory control (Pencil Tap), short-term or working memory (Corsi Blocks), or global EF measured via the DCCS. There was, however, a statistically significant effect on global EF measured by the Trail Making Task, b=0.23, S.E. =0.11, p<0.05, with an effect size of nearly 1/2 a SD (ES=0.41). In addition, we observed impacts on attention/impulsivity measured using the PSRA-Assessor Report, b=0.11, S.E. =0.05, p<0.05, with an effect size of nearly 1/3 of a SD (ES=0.27).Models 1 and 2 of ch e.g.,  where thb=0.13, S.E. =0.08, p <0.10) and a negative effect of the BGs on teacher-reported discipline , but we note that there were relatively few instances of disciplinary infractions making this estimate likely quite imprecise. As shown in the bottom panel of b=0.16, S.E. =0.07, p<0.05, ES=0.24.As shown in the bottom panel of Despite the immense interest and investment in EFs and related self-regulation skills in school contexts, findings thus far are equivocal, resulting in the need for additional compelling evidence from rigorous studies of school-based interventions. This paper contributes to the relatively sparse body of literature on non-curricular, strategy-based approaches designed to cultivate EFs and related self-regulation skills in classroom contexts, providing additional evidence for the feasibility and potential efficacy of these interventions e.g., . The goaEvidence presented in this article from Model 2 (with child covariates) indicate marginal improvements in teacher-reported regulation-related skills and observer-reported attention and impulsivity, though all marginal effects should be interpreted with caution. Model 2 also shows decreases in global EF as measured by the DCCS direct assessment  and increases in disciplinary incidents. Model 4are not demonstrating these skills, perhaps leading to increased disciplinary action. However, with the inclusion of classroom covariates, there were no remaining statistically significant differences between the intervention and control groups on these measures, suggesting that disciplinary incidents may be more closely linked to the composition of children in the classroom rather than a byproduct or impact of the intervention. The lack of effects on the regulation-related skills survey could be explained by the highly controlled nature of the classroom environment in these charter schools, which provide few opportunities for students to practice regulating their own behavior . At the same time, they may also be more likely to notice when students Similarly, in the first two models (no covariates and child covariates only) there was a significant, negative impact of the intervention on children’s global EF as measured by the DCCS. As above, with the inclusion of classroom covariates, these findings were no longer statistically significant. This is puzzling as there was a significant and positive impact of BG on the other measure of global EF, the TMT, in the final two models (classroom covariates only and combined child and classroom covariates). Though both measures assess global EF, they differ in what they ask children to do. The DCCS is a sorting activity with a high-level of involvement from the assessor who presents each card for the child to sort. On the other hand, the TMT involves more independence for the child as they work on their own to link numbers, letters, and a combination of the two, with the assessor present in a supervisory capacity. The TMT may more closely mirror the independent work of the classroom and the ways in which children practice deploying their EFs in context .Though we leave it to the reader to make their judgement, we find Model 4 compelling for two key reasons. First, the decisions teachers make about intervention implementation  influence children’s outcomes e.g.,  and are An important consideration for interpreting these findings is the association between bilingualism and executive function, particularly in Latinx populations. Foundational and interconnected executive function and language skills develop bidirectionally  beginning very early in life . StudiesWhile this study has several strengths, a number of limitations should be noted. First, although classroom-level randomization allowed us to account for between-school differences, there is potential for spillover effects between classrooms. This is especially possible in the older grades, where the floater teacher spent time in both the intervention and control classrooms. Though we have no evidence that spillover effects occurred, it is possible that impacts were attenuated if teachers in the control group received any materials or information from their peers. Second, although the BG promote autonomy and are easy to integrate into daily activities and routines, the charter network followed a strict schedule, with very little room for integration. In contrast with previous studies of BG where teachers could easily implement the games into any part of their daily routine e.g., , it may In summary, our findings contribute to the existing literature in three key ways. First, our results provide support for the feasibility of low-cost, easy to implement intervention approaches to foster social and emotional skills in school settings see . The minstephanie_m_jones@gse.harvard.edu.The datasets presented in this article are not readily available because data may be shared in certain situations, with PI approval. Requests to access the datasets should be directed to The studies involving human participants were reviewed and approved by Harvard’s Committee on the Use of Human Subjects (CUHS). Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.SB, RB, and SJ contributed substantively to key elements of this work from original conceptualization. RB and SJ contributed to the design of the BGs. SJ, SB, and RB contributed to funding. SB and SJ contributed to study design and execution, data management, analysis, and write-up. All authors contributed to the article and approved the submitted version.This project was funded by the Dean’s Venture Fund, Harvard Graduate School of Education of Education, Harvard University.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Results: The predictors of smoking in OSY were analyzed using demographic, intrapersonal, interpersonal, and community factors. Period after discontinuation of school, self-control, parental attachment, and friends’ smoking behavior were significant predictors. Smoking behavior was found to decrease when the period after discontinuation of school was over than one year, when the parents attachment increased and when self-control increased. Conversely, smoking increased when friends’ smoking increased. Conclusions: When administering smoking prevention programs for OSY, parental attachment and psychological traits, such as self-control, should be taken into consideration. Therefore, for effective results, such programs need to strengthen self-control, stress the importance of parental attachment through parent education, and enroll adolescents along with their friends who smoke. Implications and Contribution: Based on an ecological model, this descriptive survey, conducted to investigate the association between smoking behavior among out-of-school youths and individual, interpersonal, and organizational factors, proposes that smoking prevention programs should consider parental attachment and psychological traits, including self-control, for optimal effectiveness.Purpose: To investigate the association between smoking behavior in out-of-school youths (OSY) and individual, interpersonal, and organizational factors through an ecological model. Methods: Participants were 297 OSY aged 13–18 years, who visited J area’s counseling center. The independent variables were self-control , parental attachment and social network , and exposure to no-smoking policy (community factor). The dependent variable was smoking. Descriptive statistics, χ The number of Korean students in elementary, middle, and high schools who drop out has been rising every year: 47,663 in 2016  to 52,539 in 2018  [As OSY are not within school boundaries, their families are the ones who can likely control their smoking . HoweverPrevious studies have reported that the smoking rate is higher among OSY than in-school adolescents ,2,3,4,5.Smoking initiation at an early age intensifies nicotine dependence, which increases the likelihood of becoming a lifelong smoker and substantially shortens lifespan . It is aSmoking behaviors in adolescents are associated with genetic, social, and environmental factors. Individuals’ health behaviors are determined by their relationships with family or colleagues, physical environment, and personal characteristics . TherefoMost studies on smoking trends have been conducted with adolescents in school; they invariably examined intrapersonal and interpersonal factors ,12. Few We aimed to identify factors associated with smoking behaviors in OSY based on the ecological model proposed by McLeroy et al. . Intrape1. Examine the demographic characteristics and intrapersonal, interpersonal, and community factors in OSY.2. Compare the differences in smoking behaviors according to demographic characteristics and intrapersonal, interpersonal, and community factors in OSY.3. Identify the demographic characteristics and intrapersonal, interpersonal, and community factors associated with smoking behaviors in OSY. A descriptive survey was used in the study.The study population was the OSY of an adolescent counseling and welfare center in J Province in 2020. In adolescent welfare and counselling centers, out-of-school youth are offered a preparation guide for the College Scholastic Ability Test, career development programs and healthy behavior program. The sample size was determined using G*Power 3.1 software. We conducted a logistic regression analysis with a medium effect size , significance of 0.05, power of 0.90, and a two-tailed test . The minWe identified existing instruments appropriate for the construct through a literature review and focus-group interviews. The instruments were modified for use in this study. The content validity of 17 items on demographic characteristics, 20 items on the Self-control Scale , 9 items on the Important People Instrument (version for smoking), 25 items on the Inventory of Parent attachment , and 7 items on no-smoking policy exposure (community factor) were evaluated by a panel comprising four nursing professors and two OSY experts over two rounds of assessment. A pilot study was conducted with 30 OSY from 15 January to 30 January 2020, to ensure the survey was comprehensible and to determine how much time would be required to complete it. The questionnaire consisted of 78 items and took approximately 20 minutes to complete. The questionnaire was easy to understand, without any awkward phrasing.Adolescents’ self-control was measured using the Self-control Scale developed by Gottfredson and Hirschi  and modinever true) to 5 . Ten items  were reverse scored. Total scores range from 25 to 125, with higher scores indicating stronger parental attachment. Cronbach’s αs were 0.94 for paternal attachment in a previous study [Parental attachment was measured using the modified Korean version of the Inventory of Parent and Peer Attachment developed by Yoo and colleagues , by tranus study  and 0.95never) to 5 . This tool consists of social network traits, social support, and involvement of smokers in the social network. The formula for computing the factors of these subscales is shown below:Social networks related to adolescent smoking were measured using the Important People Instrument (version for smoking)  after moSocial network traits consist of the size of the social network, the proportion of friends, and average contact frequency. Social network size refers to the number of important people in the social network, scored 1–10. The proportion of friends is the percentage of friends in the social network based on the total number of people in the network. The average contact frequency was calculated by measuring the contact frequency with each member of the social network (times/day) and dividing this by the total number of people in the social network.strongly disagree) to 5 (strongly agree). A higher frequency of support indicates greater social support from an important person. Social support consists of average general social support, social support frequency, smoking support, no-smoking support, and quality of ties. The average general support was calculated by dividing the frequency of support by the total number of people in the social network. The range of average social support was between 1 and 5. The frequency of social support was calculated by multiplying the contact frequency with each member (times/day) and social support and adding the values for all members. Social support was rated from 1 (strongly disagree) to 5 (strongly agree). A higher score for smoking support indicates greater smoking support by people in the social network. Similarly, a higher score for no-smoking support indicates greater no-smoking support. The quality of ties is the sum of the contact frequency (times/day) with each member of the social network multiplied by the importance of the member. A higher score indicates a better quality of ties. Support related to smoking is the sum of smoking support and no-smoking support provided by each member of the social network . Smoking support and no-smoking support were rated from 1  with each member of the social network multiplied by their smoking intensity (number of cigarettes per day).strongly disagree) to 5 (strongly agree). The no-smoking policy exposure was measured using seven items. The total score ranged from 7 to 35, with a higher score indicating greater no-smoking policy exposure. Cronbach’s α was 0.81 in this study.The public policies of the smoking section in the 14th Korea Youth Risk Behavior Web-based survey  were useThe study was approved by E University Institutional Review Boards . The participants were informed of the study’s purpose, duration, procedure, voluntary participation, matters pertinent to protection of personal information and data disposal, and the freedom to withdraw consent without any disadvantages. Completed questionnaires were inserted in a sealed envelope and were collected in a box placed inside the institution. The questionnaire data does not contain personally identifiable information such as name and contact information, and they were assigned personal ID numbers and were stored as encrypted electronic data. The questionnaires and electronic data will be retained for 3 years following the completion of the study, after which they will be permanently destroyed and discarded.Parent consent was obtained for underage adolescents as per the guidelines of the institutional review board. If adolescents wished to participate, but could not obtain their parents’ consent, they gained it from their counselors at the welfare centers. Data were collected from 15 February to 31 March 2020, from adolescents at the adolescent counseling and welfare center. The study recruitment advertisement was posted on a bulletin board at the welfare center. For the pilot study, the questionnaires, caregiver and participant consent forms were sent to the homes of the consenting adolescents in a sealed envelope by the counselors at the adolescent counseling and welfare center. Counselors at the center sent out a packet consisting of the questionnaire, caregiver and participant consent form to those who consented to participate. The completed forms were sealed in the envelope and dropped into a designated box at the center, which were then collected for analysis.t-tests. The correlations among participants’ self-control, parental attachment, social network, and no-smoking policy exposure were analyzed using Pearson’s correlation coefficients. The effects of participants’ demographics, parental attachment, social network, and no-smoking policy exposure on smoking behaviors were analyzed using a logistic regression analysis and presented as ORs and 95% confidence intervals (CIs). A four-stage logistic regression model was used. Model 1 contained demographic characteristics; while intrapersonal, interpersonal, and community factors were additionally entered in Models 2, 3, and 4, respectively.Collected data were analyzed with IBM SPSS statistics 25.0 . Significance was set at 5%. The differences in smoking behaviors according to demographic characteristics, self-control, parental attachment, social network, and no-smoking policy exposure were analyzed using χ2- and p < 0.001). A greater percentage of OSY dropped out of middle school in the smoker group than in the non-smoker group (p = 0.003). The percentage of OSY with less than 1 years of schooling was higher in the smoker group than in the non-smoker group (p < 0.001). The percentage of OSY who dropped out of school owing to family matters was higher in the smoker group than in the non-smoker group (p = 0.001). The percentage of OSY who dropped out of school to hang out with friends outside the school was higher in the smoker group than in the non-smoker group (p < 0.001). The percentage of OSY who dropped out of school owing to conflict with parents was higher in the smoker group than in the non-smoker group (p < 0.001). The percentage of OSY who used to have poor grades was higher in the smoker group than in the non-smoker group (p < 0.001).The differences in smoking behaviors based on participants’ demographic characteristics are presented in p < 0.001). Concerning interpersonal factors, the parental attachment score was higher in the non-smoker group than in the smoker group (p < 0.001). Concerning social network, the smoking group had a higher proportion of friends (p < 0.001), average contact frequency (p = 0.002), social support frequency (p = 0.013), smoking support (p < 0.001), quality of ties (p = 0.002), smokers in the social network (p < 0.001), friends’ smoking (p < 0.001), and involvement of smokers in the social network (p < 0.001) compared with the non-smoker group. Conversely, the non-smoker group had higher general social support (p = 0.009) and no smoking support (p < 0.001). Concerning community factors, no-smoking policy exposure was higher in the smoker group than in the non-smoker group (p = 0.007).Smoking behaviors according to intrapersonal, interpersonal, and community factors are shown in Self-control, parental attachment, proportion of friends, average contact frequency, average general social support, social support frequency, smoking support, no-smoking support, quality of ties, social network smokers, friend smokers, the social network smoker’s involvement, and no-smoking policy exposure were statistically significant in smoking behaviors.The correlations among independent variables are presented in To identify the predictors of smoking behaviors, a four-stage logistic regression analysis was performed by including significant variables. Although proportion of friends and non-smoking support was significant, we excluded them as they are constructs that include friends and are relatively less associated with smoking. No-smoking support and smoking support were mutually complementary constructs; therefore, we excluded no-smoking support owing to our objective. The results are presented in Model 1 contained significant demographic characteristics: age, period after discontinuation of school, and school achievement. The odds for smoking behaviors decreased by 46% with increasing age and by 85% with one year or longer off school compared to less than one year off school. The odds for smoking increased by approximately 275% with 1 unit decreased in school achievement.The intrapersonal factor (self-control) was added to Model 2. The odds for smoking decreased by 23% with 1 unit increase in self-control. Interpersonal factors  were added to Model 3. The odds for smoking decreased by 8% with 1 unit increase in parental attachment and increased by 4% with 1 unit increase in friends’ smoking.The final model (Model 4) containing demographic characteristics, intrapersonal factors, interpersonal factors, and community factors (no-smoking policy exposure) indicated that the odds for smoking were 85% lower when away from school for one year or longer as compared to less than one year. The odds for smoking decreased by 15% with 1 unit increase in self-control, by 7% with 1 unit increase in parental attachment, and it increased by 4% with 1 unit increase in friends’ smoking.Using an ecological approach as the conceptual basis, we established four models to identify factors associated with smoking behaviors in OSY. We examined key relationships by establishing Model 1 with demographic characteristics and adding intrapersonal, interpersonal, and community factors in Models 2, 3, and 4, respectively. The final model (Model 4) showed that period after discontinuation of school, self-control, parental attachment, and friends’ smoking were associated with smoking behaviors. OSY with one year or longer off school were less likely to smoke than their counterparts who had been off school for less than one year. Soon after dropping out, adolescents experience a spike in their stress levels as they adjust to the new environment and impulsively express their emotions through smoking; however, over time, they may build coping strategies against stress and adapt to their situation. Once addicted to smoking, education or promotion on quitting smoking is much less effective; hence, it is extremely important to provide education on smoking cessation to adolescents before addiction occurs . TherefoSmoking rate increases with decreasing self-control, a finding similar to that from previous studies ,23. PeopParental attachment, an interpersonal factor, was also found to be associated with smoking ,27. PareThe odds of smoking increased with an increase in the number of friends who smoked within the social network. Influences one’s family members exert on them decrease as they age; thus, their peers become their bigger influencers at puberty . Peer grThe community factor (no-smoking policy exposure) was added to Model 4, but its association with smoking in OSY was non-significant. A previous study found that acceptance of a no-smoking policy increases with increasing exposure to the policy ,34. The This study has several implications. First, we identified multidimensional factors associated with smoking in OSY using an ecological model. Second, we presented theoretical evidence to focus on interpersonal factors when developing smoking prevention programs for OSY. These findings will be useful as foundational data for developing smoking prevention programs for OSY.Nonetheless, this study has limited generalizability because the sample was restricted to a single region. Subsequent studies should include participants from diverse regions to analyze the predictors of smoking. Additionally, based on an ecological model, we confirmed that period after discontinuation of school, self-control, parental attachment, and friends’ smoking are associated with smoking in OSY. Subsequent studies should develop smoking prevention interventions for OSY and assess the effectiveness of these programs. Furthermore, future studies should utilize objective indices , as opposed to self-report questionnaires, for a more accurate measure of the degree of smoking.Fresh dropouts (within one year off school), self-control, parental attachment, and friends’ smoking should be considered when developing smoking prevention and smoking cessation programs for OSY.When implementing smoking prevention programs for OSY, it is important to note that OSY showed poorer parental attachment and personal psychological characteristics such as self-control compared to their in-school counterparts. Thus, programs that boost self-control, parent education programs stressing the importance of parental attachment, and smoking cessation education programs that enroll smokers along with their friends are needed for OSY.


Subgroups were weighed and then terminated on day 5, 33, or 54 post-operatively; blood and brains were collected. ELISA kits were used to measure receptors for ghrelin, insulin, and leptin in the DVC, ARC, and PVN, as well as plasma ghrelin, insulin, and leptin. As expected, body weight increased rapidly after ovariectomy. However, ghrelin receptors did not change in any of the areas for either group, nor did circulating ghrelin. Thus, the receptor:hormone ratio indicated comparable ghrelin signaling in these CNS areas for both groups. Insulin receptors in the DVC and PVN decreased in the OVX group over time, increased in the PVN of the Sham group, and were unchanged in the ARC. These changes were accompanied by elevated circulating insulin in the OVX group. Thus, the receptor:hormone ratio indicated reduced insulin signaling in the DVC and PVN of OVX rats. Leptin receptors were unchanged in the DVC and ARC, but increased over time in the PVN of the Sham group. These changes were accompanied by elevated circulating leptin in both groups that was more pronounced in the OVX group. Thus, the receptor:hormone ratio indicated reduced leptin signaling in the DVC and PVN of both groups, but only in the OVX group for the ARC. Together, these data suggest that weight gain that occurs after removal of ovarian hormones by ovariectomy is associated with selective changes in metabolic hormone signaling in the CNS. While these changes may reflect behavioral or physiological alterations, it remains to be determined whether they cause post-ovariectomy weight gain or result from it.Although increasing research focuses on the phenomenon of body weight gain in women after menopause, the complexity of body weight regulation and the array of models used to investigate it has proven to be challenging. Here, we used ovariectomized (OVX) rats, which rapidly gain weight, to determine if receptors for ghrelin, insulin, or leptin in the dorsal vagal complex (DVC), arcuate nucleus (ARC), or paraventricular nucleus (PVN) change during post-ovariectomy weight gain. Female Sprague-Dawley rats with  As of 2019, 62% of women in the United States are considered to be either overweight or obese , and weiNotably, behavioral and physiological mechanisms that control feeding and body weight involve well-defined sex differences, as has been reported in studies of laboratory rodents . AcyclicIncreasingly, studies have focused on estrogen interactions with ghrelin, leptin, and insulin in food intake and body weight. Estrogen has been shown to decrease sensitivity to the orexigenic effects of ghrelin . Furtherad libitum access to standard laboratory chow and water throughout the experiment. All procedures were approved by the Oklahoma State University—Center for Health Sciences Animal Care and Use Committee and were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.Female Sprague-Dawley rats  were housed individually in plexiglass cages on a 12:12 dark:light cycle (lights on at 07:00) in a temperature controlled room (21–25°C) at Oklahoma State University-Center for Health Sciences and given n = 24) or sham OVX (n = 24). Ovariectomy was performed as previously described , was terminated on day 5 post-operatively. The remaining subgroups of rats were weighed weekly thereafter and terminated at day 33  or day 54  post-operatively. These time points were selected based on our previous publication , ARC (–2.64 to –3.24 from bregma), and PVN (–1.32 to –1.92 from bregma)  . As desc bregma) , DVC, ARg at 4°C for 20 min. Supernatants were then collected and stored at −80°C.Bilateral punches were taken from each area using a 1.0 mm Uni-Core punch (Ted Pella), collected into pre-weighed Eppendorf vials, and weighed to determine total tissue weight. Tissue was sonicated in 750 ml of cold PBS , and then centrifuged at 20,000 × Total protein concentration of each supernatant was determined using a BCA protein assay as previously described . Levels To characterize the relationship between levels of receptors and circulating hormones, receptor: plasma hormone ratios were calculated for each rat by dividing the concentration of receptor for each hormone in each area of the brain by the plasma concentration of the corresponding hormone.Data are presented as mean ± standard error of the mean (SEM). Data points that were more than 2 standard deviations from the mean were considered outliers and removed.p < 0.05.Data were analyzed by two-way ANOVA with treatment  and day  as factors. Pairwise comparisons of significant main effects or interactions were made using Fisher LSD tests. Significance was set at Body weights of all groups over time are shown in the F = 102.8, p < 0.0001] and treatment  and a significant interaction between day and treatment  for the change in body weights. Pairwise comparisons of the interaction indicated that weight gain in the OVX group was significantly greater on both day 33 and 54 than on day 5, as well as on day 54 than day 33 . While the Sham group also gained significantly more weight on day 33 and 54 than on day 5 , and significantly more weight on day 54 than on day 33 (p = 0.0071), weight gain in the OVX group was significantly greater than that in the Sham group on both day 33 and day 54 .F = 8.230, p = 0.0010] and treatment  and a significant interaction between day and treatment  for uterine weights. Pairwise comparisons of the interaction showed significant differences for the OVX group. Specifically, day 5 was significantly greater than both day 33 and day 54 (both ps < 0.0001). There were also differences between the OVX and Sham groups at each day, with uterine weights in the OVX group significantly less at day 5 (p = 0.0007), day 33 (p < 0.0001) and day 54 (p < 0.0001). There were no differences in uterine weight over time for the Sham group.In Levels of circulating ghrelin are shown in DVC: A two-way ANOVA revealed no main effects of day or treatment and no interaction between day and treatment for ghrelin receptors in the DVC. Similarly, there were no differences in the ratio of ghrelin receptors to plasma ghrelin levels.ARC: A two-way ANOVA revealed no main effects of day or treatment and no interaction between day and treatment for ghrelin receptors in the ARC, nor were there differences in the ratio of ghrelin receptors to plasma ghrelin levels.PVN: A two-way ANOVA revealed no main effects of day or treatment and no interaction between day and treatment for ghrelin receptors in the PVN, or in the ratio of ghrelin receptors to plasma ghrelin levels.F = 16.86, p = 0.0002], with circulating insulin levels greater in the OVX group, overall. There was no main effect of day and no interaction between day and treatment for plasma insulin.Levels of circulating insulin are shown in DVC: The OVX group displayed a downward trend in insulin receptor levels over time. A two-way ANOVA revealed no main effects of day or treatment for insulin receptors in the DVC, but a significant interaction between day and treatment . Pairwise comparisons of the interaction showed that insulin receptors in the DVC of the OVX group were significantly greater on day 5 than on day 54 (p = 0.0027). Moreover, at day 54, insulin receptor levels in the OVX group were significantly less than those in the Sham group (p = 0.0059). There were no differences in insulin receptors in the DVC of the Sham group at any of the days examined.ARC: A two-way ANOVA revealed no main effects of day or treatment and no interaction between day and treatment for insulin receptors in the ARC.PVN: Similar to DVC insulin receptor levels, the OVX group exhibited a downward trend in insulin receptor levels in the PVN over day. A two-way ANOVA found no main effect of day, but a significant main effect of treatment  for insulin receptors in the PVN. There also was a significant interaction between day and treatment  and pairwise comparisons of the interaction showed that insulin receptor levels in the OVX group receptor levels on day 5 were significantly greater than those on day 54 (p = 0.0096).p < 0.0001). At the same time, insulin receptors in the Sham group were significantly greater on day 54 than on day 33 (p = 0.005).In addition, insulin receptor levels in the OVX group were significantly less than those in the Sham group on day 54  = 7.690, p = 0.0086] on the ratio of insulin receptors to insulin plasma levels in the DVC. There also was a significant interaction between day and treatment  and pairwise comparisons of the interaction showed that the ratio was significantly less in the OVX group than in the Sham group on both day 33 (p = 0.003) and day 54 (p = 0.0232).p = 0.0163).In addition, the ratio was significantly greater on day 33 than on day 5 in the Sham group  = 16.17, p = 0.0003) on the ratio of insulin receptors to plasma insulin levels in the PVN, but no effect of day, and no interaction between day and treatment.F = 18.34, p < 0.0001] and treatment  on plasma leptin levels, and a significant interaction between the two . Pairwise comparisons of the interaction found an increase in circulating leptin levels over time in the OVX group, with levels on both day 33 and day 54 significantly greater than on day 5 , and levels on day 54 significantly greater than on day 33 (p = 0.0066). Leptin levels also increased over time in the Sham group, with levels on both day 33 and day 54 significantly greater than on day 5 . However, the increase in circulating leptin was more pronounced in the OVX group, with levels on day 54 significantly greater than those in the Sham group (p < 0.0001).Levels of circulating leptin are shown in DVC: A two-way ANOVA revealed no main effects of day or treatment and no interaction between day and treatment for leptin receptors in the DVC.ARC: A two-way ANOVA revealed no main effects of day or treatment and no interaction between day and treatment for leptin receptors in the ARC.PVN: A two-way ANOVA revealed significant main effects of day  and treatment  on leptin receptors in the PVN, as well as a significant interaction between the two . Pairwise comparisons of the interaction showed leptin receptor levels in the OVX group were significantly greater on day 33 than on day 5 (p = 0.0212). In the Sham group, leptin receptor were significantly greater on day 54 than those on day 5 (p = 0.0008) and day 33 (p = 0.0044). Finally, leptin receptors in the OVX group on day 54 were significantly less than those in the Sham group (p = 0.0005).DVC: Both sham and OVX groups displayed a decrease in the leptin receptor:plasma leptin ratio over time. A two-way ANOVA revealed significant main effects of both day  and treatment  on the ratio of leptin receptors to plasma leptin levels. A pairwise comparison of the main effect of day showed that, independent of treatment, the ratio was significantly greater on day 5 than on both day 33 and day 54 . There was no interaction between day and treatment.ARC: The leptin receptor:plasma leptin ratio in the ARC also displayed a decrease over time in both groups. A two-way ANOVA revealed a significant main effect of day  on the leptin receptor:plasma leptin ratio, but no main effect of treatment. In addition, there was a significant interaction between day and treatment  and pairwise comparisons of the interaction showed that, in the OVX group, the ratio on day 5 was significantly greater than those on both day 33 (p = 0.0002) and day 54 (p < 0.0001). There were no differences in the ratio in the Sham group over time, nor did the two groups differ on any day.PVN: The OVX group, but not the Sham group, displayed a downward trend in leptin receptor:plasma leptin ratio over time. A two-way ANOVA revealed significant main effects of both day  and treatment . A pairwise comparison of the main effect of day showed that, independent of treatment, the ratio was significantly greater on day 5 than on day 33 (p = 0.0013) and on day 54 (p = 0.0027). There was no interaction between day and treatment.It is well-known that ovarian hormones such as estrogen are involved in the control of food intake and body weight, and that this effect is, in part, attributable to the modification of central responses to metabolic hormones . It alsoLevels of insulin, ghrelin, and leptin in circulation depend upon various aspects of metabolic state and inform about fuel availability and/or stores, while serving as signals that affect body weight, feeding behaviors, and other metabolic processes . The speAs expected, OVX rats gained considerable body weight . In factCirculating levels of ghrelin did not change in either group, nor were ghrelin receptor levels altered in any of the areas examined. Thus, the ratio between ghrelin receptor to plasma ghrelin was unaffected in OVX rats. These findings are somewhat surprising, given the role of ghrelin to increase feeding and body weight , and theCirculating levels of insulin were greater in the OVX group throughout the study, which may reflect greater food intake and/or more frequent meals . NotwithCirculating levels of leptin increased in both groups, though this increase was more pronounced in OVX rats. Leptin levels increased in tandem with body weight  and wereIn our previous study , we repoArcuate nucleus, PVN, and DVC have been identified as areas in which receptors for estrogen have been localized. Thus, reduction of circulating estrogen levels after OVX may alter activity within these areas—and thereby affect feeding and body weight regulation. Interestingly, there are region specific differences in the subtypes. For example, the PVN contains primarily the ERβ subtype , while tRegardless of the subtype, however, the presence of estrogen receptors in these areas suggests a mechanism by which the presence or absence of estrogen may change the levels of receptors for ghrelin, insulin, or leptin. This effect may be direct, via the genomic effects of estrogen binding to its nuclear receptor in neurons that also contain receptors for ghrelin, insulin, or leptin. Alternatively, changes in metabolic hormone receptors in the ARC, PVN, or DVC may occur as the result of estrogen receptor-mediated actions at up-stream sites. An important caveat to these possibilities is that changes in the levels of receptors do not inform about receptor kinetics—and this difference may account for the discrepancy between the present observations of reduced insulin signaling in OVX rats and findings of reduced insulin sensitivity after estrogen treatment . FinallyAn important goal in this study was to distinguish between changes in central receptors that occurred during the phase of rapid weight gain after ovariectomy from those that occurred in the face of established weight gain. In doing so, we selected specific time points after the surgeries and were able to detect differences in receptors for specific hormones as early as day 33  in specific areas. However, there was no temporal pattern of the observed changes that could provide information about the sequences in which those areas communicate. It is possible that a more exhaustive time course, in conjunction with evaluation of receptors for ghrelin, insulin, and leptin in other central areas  would haThe weight gain that occurs after ovariectomy was associated with region-specific differences in receptors for metabolic hormones and with changes in circulating levels of those hormones. Although our procedures did not reveal changes in ghrelin signaling, insulin signaling was decreased in the DVC and PVN of OVX rats and leptin signaling was decreased in ARC, PVN, and DVC, suggesting that reduced anorexigenic hormone responses contribute to the post-ovariectomy weight gain. Taken together, these findings further illustrate that the regulation of feeding and body weight involves a complex interplay of metabolic hormones and their receptors in interconnected CNS areas. In the absence of ovarian hormones, this multifaceted process may be disrupted at multiple levels—and with profound impact.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The animal study was reviewed and approved by Oklahoma State University Center for Health Sciences Institutional Animal Care and Use Committee.KB, KC, and RD conceived the study and designed the experiments. KB performed all the experiments with the assistance of KM, DB, RD, and KC. KB, KC, and DS analyzed the data with assistance from DB. KB, DS, and KC wrote the manuscript with input from DB, KM, and RD. All authors approved the submission of the final version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Evaluation of the feasibility for osteopontin (OPN) to serve as a biomarker in the prognosis and clinical-pathological features of prostate cancer (PCA) patients.Methods: The original publications related to OPN and PCA were comprehensively searched in the online databases, including PubMed, Embase, Cochrane Library, Web of Science, Medline, Wanfang and China National Knowledge Infrastructure up to August 2019. Results were analyzed by Revman 5.3 and Stata 12.0.Results: A total of 21 studies were included in the analysis and the result showed that the positive OPN expression group had a lower overall survival than the negative expression group (univariate: hazards ratio (HR) = 2.32, 95% confidence interval (95% CI) , multivariate: HR = 2.41, 95% CI ) and a lower biochemical relapse-free survival than the negative group . In addition, there was a higher expression level of OPN in PCA tissues than in normal prostate tissues  and benign prostatic hyperplasia (BPH) tissues . Moreover, OPN positive expression was also related to high Gleason score , high TNM stage , high Whitmore–Jewett stage , high lymph node , and distant metastasis . There was no difference observed in the differentiation of PCA .Conclusion: OPN could be recognized as a promising diagnostic and prognostic biomarker for PCA patients. Prostate cancer (PCA) is the second most common malignancy in males, with an increasing risk in aged population ,2. CurreOsteopontin (OPN), as one of the major bone matrix proteins, is widely distributed across organs and tissues, and plays an important role in modulating inflammation and tumor responses . As a cyAlthough OPN has the potential to serve as a novel biomarker for the diagnosis of PCA and an indicator of the level of malignancy, unfortunately, it still lacks large-scale clinical trials and a systematic analysis. Therefore, we conduct this systemic review and meta-analysis to evaluate the possibility of OPN as a biomarker for the diagnosis, malignancy, and prognosis of PCA patients.All the studies were case-controlled or randomized-controlled trials.None of the patients or healthy donors ever received any radiotherapy, chemotherapy, or hormone therapy before sampling.All the original clinical pathological data can be obtained directly.The detection of OPN expression were based on the pathological examination to determine the presence of PCA tissue, rather than speculating the presence of PCA by PSA, MRI, CT, or Doppler ultrasound.The best data quality were selected in duplicated studies.Animal or cell studies.Case report, review, meta-analysis.Insufficient data from original studies.Homo sapiens, but not by time, race, data source, language, age, or publication status. The following MeSH searching terms were used: (osteopontin) or  or (Secreted Phosphoprotein 1) or  or  or  or  or (Uropontin) AND (Prostate Neoplasms) or  or  or (Prostate Neoplasm) or  or (Prostate Cancer) or (Prostate Cancers). Our search strategy adjusted according to different databases. Meta-analysis was performed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) Statement.The literatures were comprehensively searched in the online databases, including PubMed, Embase, Cochrane Library, Web of Science, Medline, Wanfang database, and China National Knowledge Infrastructure up to August 2019. The Clinicaltrial.gov was also searched. All the studies or clinical trials were restricted to We used the Newcastle–Ottawa quality assessment scale of case control studies (NOS) as the method to assess the quality of included studies. All the studies have a score range from 6 to 9, which proved that they are suitable for the inclusion.All the included articles were full-text reviewed and the data were extracted by two reviewers independently. The methodological differences of classification were solved after discussion by the whole authors. The extracted data were dichotomous and analyzed by software Revman 5.3 and Stata 12.0. The results of the analyses were shown by the odds ratio (OR) and 95% confidence interval (95% CI).P<0.1 or I2> 50%). Finally, all the included articles were tested for publication bias through Begg’s test and risk of bias summary.Sensitivity analysis was performed to rule out the impact of a specific study when the heterogeneity of the results was large (n=18), case report, review, meta-analysis (n=7), and insufficient data from original studies (n=8)  . As a reP=0.981, 2I = 0%), so a fixed-effects model was used and showed that the OPN positive group had a lower overall survival than the negative group (hazards ratio (HR) = 2.32, 95% CI , P<0.05). Besides, the multivariate studies also had no heterogeneity , so the fixed-effects model was used and presented the same result   A. The daP=0.010, 2I = 78.5%), so the random-effects model was used and showed that the OPN positive group had a lower biochemical relapse-free survival than the negative group . Sensitivity analysis showed that the result was stable. In addition, the multivariate studies had no heterogeneity , so the fixed-effects model was used and the same result  was observed , so the fixed-effects model is adopted and showed that the expression of OPN in PCA tissues was significantly higher than that in normal prostate cancer tissues   A. Then, 0.00001) D. In sumP<0.00001, 2I = 81%), so the random-effects model was adopted. To further determine the stability and reliability of the results, we performed a sensitivity analysis of the included literatures to determine whether individual studies would have an impact on the results. And we found that the heterogeneity decreased after excluding the study of Tozawa et al. [P<0.0001). The result was stable and acceptable , and the fixed-effects model was adopted (P=0.0009).Gleason score is a widely used histological scoring system in PCA, which is associated with the prognosis and biological behavior. A total of nine studies reported the relationship between OPN expression and the Gleason score of PCA tissues, including 148 high Gleason score samples (>8) and 282 low Gleason score samples (≤7). The result had no heterogeneity , and the fixed-effects model was adopted (P=0.0009).A total of four studies included the relationship between OPN and the clinical TNM stage of prostate cancer, among which 93 T III–IV stage patients and 155 T I–II stage patients were reported. The results had no heterogeneity , and the fixed-effects model was used (P=0.04).Whitmore–Jewett stage is the most common staging strategery in clinical practice. This staging method divides PCA into four stages  according to the degrees of infiltration. A total of four studies reported the relationship between OPN and the Whitmore–Jewett stage of PCA, including 89 stage C+D patients and 130 stage A+B patients. The results had a low heterogeneity , so the random-effects model was adopted and showed that the expression of OPN had no difference between high and low differentiation of PCA tissues   D. Then w P=0.46) I. In conP=0.16, 2I = 42%), so the fixed-effects model was adopted, and showed that the expression of OPN was positively correlated with lymph node metastasis of PCA   E. Sensit=0.0001) J.P=0.44, 2I = 0%), so the fixed-effects model was adopted, and showed that the expression of OPN was positively correlated with distant metastasis of PCA   F. Sensit P=0.01) K.After analyzing all the results, we examined whether there was publication bias in the included literatures by using Begg’s test, and the conclusions showed that there was no significant publication bias in each analysis , so the PCA is an epithelial malignancy with the second highest incidence rate in aged men, and the patients are usually treated with castration therapy or surgery ,19–24. HSPP1 between 52 normal prostate samples and 498 PCA samples in The Cancer Genome Atlas (TCGA) database. No significant difference in SPP1 expression was detected between normal tissues and PCA tissues  . This meta-analysis and TCGA database displayed the contradictory results. This discrepancy may arise from the different detection methods. The pooled analysis is compromise of four studies based on IHC method and one study based on western blot method, while the samples included in the TCGA database are based on RNA-seq data. Therefore, we need more researches and evidences to further solve this problem.OPN binds to its receptor integrin and regulates the migration, adhesion, and mobilization of tumor cells, which is related to the invasion and proliferation ability of multiple cancers . FirstlyIn addition, our meta-analysis compared the expression of OPN in relation to the clinical pathological parameters of PCA such as Gleason score, the clinical TNM stage, the Whitmore–Jewett stage, the differentiation, the lymph node and distant metastasis. Overall, the expression of OPN was positively correlated with the malignant degree and metastatic ability of PCA. However, comparisons showed that there was no obvious correlation between the expression of OPN and the degree of PCA differentiation. We reason that it is due to the subjectivity of different researchers for them to define the degree of differentiation, because some researchers categorized the poorly and moderately differentiated PCAs together, while others categorized the moderately and highly differentiated PCAs together. Therefore, the correlation between the positive expression rate of OPN and the differentiation of PCA was still uncertain, and more researches are needed to achieve a conclusion.In addition to the detection of OPN expression in prostate tissue by IHC, some scholars have also detected OPN expression in blood and urine by enzyme-linked immunosorbent assay (ELISA) ,33,34. TOPN expression has been indicated to correlate with cancer prognosis and survival. Lindahl et al.’s  study haThe results of our meta-analysis indicate that OPN positive expression is not conducive to overall survival and biochemical relapse-free survival in PCA patients. OPN has a higher positive rate in PCA than in BPH tissues and normal tissue. In addition, the positive rate of OPN expression was positively correlated with Gleason score, TNM stage, Whitmore–Jewett stage, lymph node metastasis, and bone metastasis.Click here for additional data file.