Previous genome-level genetic interaction screens with the single essential actin gene of yeast identified 238 nonessential genes that upon deletion result in deleterious, digenic complex haploinsufficiences with an actin null allele. Deletion alleles of these 238 genes were tested for complex heterozygous interactions with 32 actin alanine scan alleles, which target clusters of residues on the surface of actin. A total of 891 deleterious digenic combinations were identified with 203 of the 238 genes. Two-dimensional hierarchical cluster analysis of the interactions identified nine distinct groups, and the alleles within clusters tended to affect localized regions on the surface of actin. The mutants in one cluster all affect electrostatic interactions between stacked subunits in the long pitch helix of the actin filament. A second cluster that contains the most highly interactive alleles may disrupt the tropomyosin/myosin system, as one of the mutants in that cluster cannot support Type V myosin-dependent movement of secretory vesicles in haploids and causes processivity defects in heterozygous diploids. These examples suggest the clusters represent mutations with shared protein−protein interaction defects. These results show that complex heterozygous interaction screens have benefit for detecting actin-related genes and suggest that having actin filaments of mixed composition, containing both mutant and wild-type subunits, presents unique challenges to the cell. The genomes of eukaryotic organisms are rich with polymorphisms . SpecifiGenetic interactions also can be examined systematically by diploid genetics. In particular, we are interested in the analysis of complex haploinsufficiency (CHI) or the genetic circumstance associated with diploid cells that are heterozygous for null alleles in two (or more) different genes. We suspect that CHI interactions are highly relevant to human genetics, specifically disorders with multigenic influences. For example, simple haploinsufficiency in 32 different transcription factors causes a diverse array of human genetic disorders , haploinDrosophila (better known as dominant enhancers) (ACT1) for CHI interactions with the set of ~4800 deletion alleles of nonessential genes and identified 208 deleterious interactions. The resulting network was highly enriched for actin-related genes as well as others not previously connected to cytoskeletal functions . More recently we completed CHI screens with an actin-null allele against deletion alleles for the ~1000 essential genes of yeast and identified an additional 60 CHI interactions. This network was also highly functionally enriched. For example, we found highly significant functional enrichment among the interacting partners for the GO terms “cytoskeleton” , “TFIID complex” , and “proteasome”  (CHI interactions have been used to study gene function in ACT1) displays a rich set of CHI interactions, it may not be representative for all genes. Actin is one of the most functionally diverse proteins in eukaryotic cells; it is directly involved in cell growth, division, polarity, morphogenesis, motility, cytokinesis, endocytosis, exocytosis, and a number of nuclear functions as well  gene because this gives a very tight selection. Therefore, we undertook reconstructing the actin alanine scan alleles in a more suitable background. During this process, we discovered that seven of the original actin alanine scan mutants had additional mutations. We corrected all seven but found that two mutants previously reported to be recessive lethal alleles were in fact likely dominant lethal alleles and therefore could not be used in our analysis. The correction of these seven alleles and their phenotypic analysis is described in a letter to Genetics  to BY4741; an act1-159:NatR segregant was backcrossed to BY4741 and a haploid act1-159:NatR segregant from this diploid was transformed with pKFW29 and used in the complex heterozygosity screens. Unlike the other act1 strains, this strain carries the act1-linked tub2-201 mutation.The R-marked mutant act1 alleles and also containing ACT1 on a URA3CEN  plasmid (pKFW29) were mated to strains deleted (by the kanR/G418R marker) for genes previously shown to display complex haploinsufficiency with act1∆  using an Orca ER camera (Hamamatsu Photonics). Images were processed with Zeiss AxioVision software and Adobe Photoshop.The Sec4-GFP expressing plasmid pRC556 (gift of Anthony Bretscher) . We constructed strains carrying 31 different alanine scan alleles  against 9 genes  in quadruplicate. The 26 interactions found across two or more screens were defined as our standard. We then evaluated precision and recall using an independent screen against that standard. Eighteen of the 20 interactions called were found in the standard, yielding a precision of 90%. Eighteen of the 26 interactions in the standard were identified in the independent screen, resulting in a recall of 69%.To evaluate precision and recall of our CHI data, we repeated CHI analysis for six alleles . We also examined the distributions of interactions among those CHI genes that interacted with one or more of the alleles tested (see P = 0.02). We also found that the number of interactions was positively correlated with chemical-genetic degree , the number of chemicals which induced single-mutant specific sensitivity in a large screen (P = 4 × 10−5), which reflects variability in actin-based morphological phenotypes in the single mutants . Note that for this analysis, act1-159 and act1-136 were not included because act1-159 alters residues known to be inside the ATP cleft and therefore is not on the surface of the molecule and act1-136 alters residues that have not been resolved in crystal structures. In this analysis, the left front of actin appears to be the most active region . This finding suggests that the smaller the distance between mutations in the stacked dimer, the more similar the genetic interaction profiles tend to be. Backed and monomer distances did not show significant correlations. This may imply that the mutations in general are more detrimental to how actin functions as a filament than as the monomer, and that between stacking and backing, stacking has more influence over the filament stability. In addition, it may suggest that filament-binding proteins are more likely to contact multiple subunits within a filament strand than between filament strands.When we compared the interaction profile similarities of the alleles to the distance of the mutated residues on a stacked dimer, we found a significant negative correlation .The data suggest that clustered alleles may lead to related structural perturbations. Interestingly act1-131 (shown in orange on the red subunit), forms a predicted salt bridge with Asp288 mutated in allele act-107 (shown in cyan on the blue subunit) and Arg290 mutated in allele act1-106 (shown green on the blue subunit) forms a salt-bridge with Asp244 mutated in allele act1-128 (shown in yellow on the red subunit). In addition, Glu241 mutated in allele act1-128 (shown in yellow on the red subunit) is predicted to contact Thr324 . It’s possible that the alleles in cluster 3 group together because they similarly destabilize the long pitch helix of the actin filament which sensitizes the cell to the further loss, through deletion, of a set of functionally related genes.In the case of cluster 3 (shown in yellow), which appears to affect residues distant on the surface of the actin monomer, the mutated residues map to an important region for inter-subunit contacts within the actin filament . There is one interesting exception; the act1-112 allele had 51 CHI interactions but only 4 significant negative SGA interactions with the genes that are CHI with actin. We hypothesize that this allele can in part illuminate the different challenges posed by having uniformly mutant actin filaments vs. filaments of a mixed subunit composition. In particular, the myosin system for moving secretory vesicles and organelles into the bud may be sensitive to mixed subunit filaments, which could perturb the binding or processivity of these motors. Focusing specifically on the type V myosin, Myo2p and its directed movement of secretory vesicles into the bud  but in the act1-112 heterozygous diploids we could observe bursts of bud-directed movement frequently followed by backward movement  is predicted by the model to make an electrostatic contact with myosin. However, none of the residues mutated in act1-112 (rendered in red) are predicted to contact myosin in this model. More recently, an 8 Å cryo-electron microscopy-derived model for the actin-tropomyosin-myosin rigor complex was published  that appears to be more deleterious in the heterozygous diploid vs. haploid context. This suggested to us that these mutants could be affecting the critically important myosin/tropomyosin systems. In yeast there are 5 myosins: 1) Myo1p is a type II myosin that is involved in contraction of the cytokinetic ring; 2) Myo2p is a type V myosin that moves secretory vesicles, vacuoles, golgi, peroxisomes, and the mitotic spindle into the bud; 3) Myo3 and Myo5 are redundant type I myosins that regulate actin assembly in the actin cortical patches; and 4) Myo4p is a second type V myosin that moves mRNA into the bud (Tpm1p and Tpm2p. Tpm1p is the major tropomyosin and it is required for stability of the actin cables which are the tracks used by both type V myosins to move cargo into the bud (Myo2p (act1-112 strains by using the secretory-vesicle associated tag GFP-Sec4p (act1-112 haploids and staccato forward movement followed by backward directed movement in act1-112 heterozygous diploids. This result is consistent with Myo2p being unable to bind to homogeneous act1-112 filaments and being able to bind but having processivity defects in the heterozygous diploid. Note that there is a rearward flux of actin subunits in actin cables (act1-112 affecting tropomyosin binding (see act1-112 haploids (data not shown) suggesting that the defects may lie in Myo2p binding; models indicate that the myosin binding site lies very close to the residues affected in act1-112 (see One cluster of particular interest is cluster 1 red in  as this  the bud ,b. In adding see ; however-112 see . In sum,Another hypothesis was that the interacting genes would be clustered by their interactions with actin alleles into groups with shared functions; however, we have not been able to find statistically significant functional enrichment in the gene clusters. One explanation for this could be in the experimental design. The alanine scan alleles were only screened against the genes that are CHI with an actin null allele and so we began with a relatively small set of genes that was already highly enriched for actin-related functions, which could have led to a lack in statistical power to find functional enrichment in the gene clusters. Furthermore, we would have missed genes that would only show allele specific CHI interactions. As a test of this possibility, we screened two alanine scan alleles against the entire nonessential deletion collection and identified several more genes beyond those identified within the subset of 238 genes that are CHI with the null allele. In some cases we found that the new genes were also CHI with the null allele so we can conclude that there are many allele-specific CHI interactors but also that our original CHI screen with the null allele was subsaturating. Both of these issues could have led to a loss in the necessary statistical power to find functional enrichment in the CHI gene clusters. A second possibility that may contribute to the lack in apparent enrichment in the gene cluster might be explained by the multifunctionality of actin. For any particular process or function, actin may influence that process in multiple ways such that the CHI interactions with the actin-null allele reflect multiple inputs into that process. The alanine scan alleles may show specificity for subsets of those inputs as do the interacting genes, resulting in genes with shared functions becoming split by the alleles in the clustergram.In conclusion, complex heterozygosity screening has clear utility for identifying genes that are relevant to the systems-level functions of the query gene. Our analysis shows that genetic interaction analysis can be used to define functionally related regions on the surface of a protein and that heterozygosity, particularly for polymer systems, can present the cell with unique challenges.


Podcasts are popular with medical students, but the impact of podcast use on learning outcomes in undergraduate medical education has not been studied in detail.Our aim was to assess the impact of podcasts accompanied by quiz questions and lecture attendance on short- and medium-term knowledge retention.Students enrolled for a cardio-respiratory teaching module were asked to prepare for 10 specific lectures by watching podcasts and submitting answers to related quiz questions before attending live lectures. Performance on the same questions was assessed in a surprise test and a retention test.Watching podcasts and submitting answers to quiz questions (versus no podcast/quiz use) was associated with significantly better test performance in all items in the surprise test and 7 items in the retention test. Lecture attendance (versus no attendance) was associated with higher test performance in 3 items and 1 item, respectively. In a linear regression analysis adjusted for age, gender, and overall performance levels, both podcast/quiz use and lecture attendance were significant predictors of student performance. However, the variance explained by podcast/quiz use was greater than the variance explained by lecture attendance in the surprise test (38.7% vs 2.2%) and retention test (19.1% vs 4.0%).When used in conjunction with quiz questions, podcasts have the potential to foster knowledge acquisition and retention over and above the effect of live lectures. In recent years, there has been increasing interest in the use of podcasts as means of knowledge transmission. In broad terms, podcasts can generally be described as audio and/or video files that can be played back on various electronic devices including tablets and smartphones. In fact, the word “podcast,” first used in 2004, is a portmanteau created from the name of one particular device (the iPod) and the word “broadcast.” There is no uniform consensus as to what format or content is required for an electronic source to be called a “podcast.” As a consequence, anything from straight-forward recordings of lectures or conference presentations to complex animated films can be referred to as podcasts. However, some authors have used the term “vodcast” to describe online material containing videos  and “enhWithin 10 years of their invention, technologies to capture lectures and make them available to students have been embraced by medical teachers involved in both undergraduate and continuing medical education. At the same time, both massive open online courses  and schoA PubMed search combining “medical education” with the terms “podcasts,” “lecture video,” “online lecture,” or “streaming lecture”  yielded 357 unique citations, and 6 additional articles were identified from reference lists and by contacting experts in the field. Only 78 out of these 363 papers had a specific focus on podcasts. Only 55 of these presented original data, and about half of these (n=27) were related to undergraduate medical education. While half of these (n=13) just reported usage patterns and student satisfaction with podcasts, only 14 original articles assessed the association between podcast use and learning outcome . Notably, none of these studies addressed podcast use for preparatory purposes. Instead, podcasts were used to either completely replace or supplement live lectures. In summary, there is currently no scientific data on the effectiveness of using podcasts to stimulate student learning prior to attending a lecture.The aims of this study were to assess the impact of preparatory podcast use in conjunction with quiz questions versus lecture attendance on short-term and medium-term knowledge retention, and identify significant predictors of short-term and medium-term knowledge retention.It was hypothesized that students engaging with the material presented in podcasts and submitting answers to quiz questions would retain significantly more knowledge than students not using podcasts. With regard to the second study aim, it was hypothesized that podcast/quiz use would be at least as effective in promoting short-term and medium-term knowledge retention as lecture attendance.This study did not address any specific psychological framework underlying a potential effect of podcast/quiz use. Instead, it focused on effects elicited by one particular teaching intervention  in a “real-world” educational setting.This study was conducted at Göttingen Medical School. Like most German medical schools, it offers a 6-year undergraduate curriculum comprising 2 preclinical years and 3 clinical years, followed by a practice year. This prospective trial included a cohort of fourth-year medical students who were enrolled in a 6-week cardio-respiratory module in winter term 2013/14. In the preceding summer term, all 37 lectures held during the 6-week module had been recorded using Camtasia Studio 7 (TechSmith). The resulting videos featured the presentation slides used and the lecturer’s voice . Following the summer term 2013, the material was reviewed, and the best 10 lecture recordings with regard to sound and image quality were selected to be used in this study. Lecturers were asked to identify key aspects with particular relevance for general internal medicine and to draft free-text questions addressing that content .In winter term 2013/14, students enrolled in our module were provided with online access to the 10 selected videos for a period of 7 days before the respective live lectures. A free-text quiz question was linked to each podcast, and students were invited to submit their answers via email until the night before the live lecture. On the day of the lecture, the principal investigator (TR) revealed the correct answer to the entire class and also projected a 1-minute clip from the podcast containing the answer. He then raffled a book voucher (€20) among all students who had submitted a correct answer.As part of an e-learning session in the final week of the module, students were invited to complete the same 10 quiz questions that had been provided with the podcasts (surprise test). In this session, students were also asked to indicate which lectures they had attended. In order to assess long-term retention, students were invited to answer the same 10 questions again 2 months later in an unannounced retention test. The study outline is summarized in Both the surprise test and the retention test were timed to coincide with scheduled e-learning activity in our institution’s computer facilities. Both tests were unannounced in order to avoid confounding by specific preparation, and a time limit of 15 minutes was set for the completion of the 10 quiz questions in both tests. At the beginning of each e-learning session, the study rationale was explained and students were asked to provide written consent to have their data analyzed for study purposes. A total of 10 book vouchers (€20) were raffled among all participants at both the surprise test and the retention test, regardless of test performance.Questionnaires were created with EvaSys (Electric Paper). In the surprise test, students were asked to provide their age and gender and to indicate whether they had attended each of the 10 lectures and which podcasts they had watched. The number of quiz answers submitted during the module was derived from the emails sent to the module’s administrative staff. The potential impact of recall bias and/or podcast use without answering quiz questions was assessed by comparing the number of students indicating they had watched a particular podcast with the number of students who had submitted an answer to the corresponding quiz question. In order to adjust the analyses for student performance levels, the percent score achieved by each student in the summative end-of-module examination was also obtained . This exThe marking procedure was identical for all three time points . After agreeing on corrects answers, 2 raters (TR and CG) independently marked all answers as correct (1) or incorrect (0). Inconsistencies were resolved by discussion. In addition to marking each single question, a sum score (0-10) was calculated, reflecting student attainment in quizzes throughout the module, in the surprise test, and in the retention test.Unique student identifier codes were used to merge data collected in the surprise and retention tests as well as examination results and data on podcast/quiz use. Data analysis was performed using SPSS Statistics 21 (IBM Corporation). Inter-rater agreement of the marking procedure was assessed by calculating kappa and internal consistency of both tests was assessed by calculating Cronbach alpha.In order to assess the impact of podcast/quiz use and lecture attendance on short-term and medium-term knowledge retention, the percentage of students providing a correct answer to each question in the surprise and retention tests was calculated. Proportions of students who had/had not submitted a correct quiz answer during the module and those who had/had not attended the corresponding lecture were compared by chi-square tests. Multivariate logistic regression models were run for each of the 10 items with the answer in the surprise/retention test as the dependent variable and controlling for age, gender, and percent score in the end-of-module examination. The comparison between podcast/quiz use and no podcast/quiz use was also adjusted for lecture attendance. Likewise, podcast/quiz use was adjusted for when assessing the impact of lecture attendance on test performance.A multivariate linear regression analysis was run to identify significant predictors of short-term and medium-term knowledge retention. The dependent variable was the sum score in the surprise/retention test. Age, gender, and percent score in the end-of-module examination as well as the number of submitted correct quiz answers and the number of lectures attended during the module were entered as independent variables.t tests (continuous variables). Results of descriptive analyses are presented as percentages and mean with standard deviation (SD), as appropriate. Results of linear regression analyses are reported as unadjusted and adjusted beta values  and as the amount of variance explained. Significance levels were set to .05.Group comparisons were performed using chi-square tests (dichotomous variables) and The local Institutional Review Board (application number 13/12/13) waived ethical approval as the study protocol was not deemed to represent biomedical or epidemiological research. Study participation was voluntary, and all participants signed an informed consent form before entering the study.Of 130 students enrolled in the module, 126 gave written consent to have their data analyzed for this study. Only students with complete data in both the surprise and the retention tests and the end-of-module examination were included in the final analysis. A total of 101 students attended both tests, but 3 of these did not take the end-of-module examination and another 31 failed to provide complete information on lecture attendance. Thus, complete data of 67 students  were available. Of these, 34 had submitted at least one correct answer during the module (mean 3.5 [SD 2.6]). On average, students had attended 7.8 (SD 2.3) live lectures. The percentage of students who recalled watching the podcast among those who had submitted a quiz answer was over 80% for all items, suggesting podcast use was not hugely underreported. On the contrary, the proportion of students who had not submitted a quiz answer among those who recalled watching the podcast ranged from 20% to 50%.Inter-rater agreement for quiz questions and the surprise and retention tests were acceptable . Cronbach alpha was .68 and .65 in the surprise and retention tests, respectively. The mean number of correct answers in these tests were 2.9 (SD 2.3) and 2.6 (SD 2.0), respectively. As shown in In the surprise test, podcast use was associated with significantly better knowledge on all test items while such associations with lecture attendance were observed for only 3 items. Similarly, podcast use enhanced knowledge in the retention test for 7 items while there was no such effect of lecture attendance for 9 of 10 items. Adjusting for age, gender, and examination performance attenuated the associations, but the pattern of results was unchanged.In a sensitivity analysis, percentages of correct answers in the surprise and the retention tests were calculated separately for students who (Group 1) had submitted a quiz answer and recalled having watched the corresponding podcast and students who (Group 2) recalled having watched the podcast but had not submitted a quiz answer. With one exception (Item 2), test performance in the second group was similar to the performance of students who had neither submitted a correct answer nor watched the podcast. The proportion of correct answers in Group 2 was less than half of that observed in Group 1 and was 0% for 3 items in the surprise test and 4 items in the retention test (data not shown).Results of the linear regression analyses are reported in This is the first study to examine the impact of podcast use in conjunction with quizzes prior to lecture attendance on knowledge acquisition and retention in undergraduate medical students. Students who engaged with the material before the lecture displayed improved short-term and medium-term retention, regardless of whether they also attended the lecture. The impact of lecture attendance on knowledge retention was considerably weaker despite the correct answers and the decisive part of the podcast being presented to all students in the lecture hall. The most likely explanation for our finding is that—just like interaction during a live lecture —the quesThere has been some debate about the usefulness of podcasts in medical education. While some authors regard them as “toys”  and haveThe 14 published reports on the impact of podcast use on learning outcomes in undergraduate medical students vary considerably with regard to study design and outcome measure used. One retrospective analysis detected a small effect of podcast availability on national licensing examination scores that coincided with a national trend for better examination scores . While 2how this technology might be used to improve learning outcome  vs 25.4 [SD 2.9]; An alternative approach to addressing our research question would have been to conduct a randomized controlled trial. Although this would have yielded higher internal validity, we doubt that we would have been able to restrict podcast use to a specific student group. The aim of this trial was not to test learning processes induced by the availability of podcasts and quiz questions. Instead, this study assessed the effect of one particular teaching intervention in the “real world” of undergraduate medical education. With regard to generalizability, our findings will need to be replicated in other settings. At the same time, there is no reason to believe that using podcasts supplemented with quiz questions as tools to stimulate student learning would be completely ineffective if implemented in a different medical school.When used in conjunction with quiz questions, lecture podcasts have the potential to foster knowledge acquisition and retention over and above the effect of live lectures. Our findings might help pave the way to move knowledge acquisition from the lecture hall to the preparatory phase, thereby freeing up valuable lecture time for more effective learner-teacher interactions.


The dopamine hypothesis suggests that excessive dopamine release results in the symptoms of schizophrenia. The purpose of this study was to elucidate the dopaminergic and noradrenergic neurons using 3-T neuromelanin magnetic resonance imaging (MRI) in patients with schizophrenia and healthy control subjects.SN) and LC (CRLC), which were compared between patients and healthy controls using unpaired t-tests.We prospectively examined 52 patients with schizophrenia  and age- and sex-matched healthy controls. Using a 3T MRI unit, we obtained oblique T1-weighted axial images perpendicular to the brainstem. We measured the signal intensity and area for the substantia nigra (SNc), midbrain tegmentum, locus ceruleus (LC), and pons. We then calculated the contrast ratios (CR) for the SNc . We observed no difference in the CRLC values between the patients and healthy controls . Furthermore, there was no difference in area of the SNc and LC between schizophrenia patients and controls.The SNc and LC were readily identified in both patients and healthy controls as areas with high signal intensities in the posterior part of the cerebral peduncle and in the upper pontine tegmentum. The CRNeuromelanin MRI might reveal increased signal intensity in the SNc of patients with schizophrenia. Our results indicate the presence of excessive dopamine products in the SNc of these patients. In vivo positron emission tomography (PET) studies in patients with schizophrenia have indicated an increased baseline occupancy of D2 receptors by dopamine 11C radiopharmaceuticals restricts their use to only institutions having a cyclotron on-site.Dopamine dysfunction plays an important role in the pathogenesis of schizophrenia Neuromelanin is a byproduct of the synthesis of monoamine neurotransmitters, such as noradrenalin and dopamine, and is mainly distributed within neurons of the substantia nigra (SNc) or locus ceruleus (LC) The purpose of this study was to use 3T neuromelanin MRI for examining dopaminergic and noradrenergic nuclei in patients with schizophrenia and healthy controls.th EditionDiagnostic and Statistical Manual of Mental Disorders, 4, (DSM-IV) diagnostic criteria using 3T-MRI. Eleven patients were excluded because of motion artifacts (6 patients) and equipment failure (5 patients). Therefore, 52 patients  were included in the analysis. In addition, we obtained MRI data from age- and sex-matched healthy controls. Controls were recruited from the community through local advertisements at Osaka University. An institutional review board approved this study, and written informed consent was obtained from all subjects before their participation. We used the Japanese version of the Positive and Negative Symptom Scale (PANSS) From April to November 2012, we prospectively examined 63 consecutive patients with schizophrenia who met the This study was performed in accordance with the World Medical Association’s Declaration of Helsinki and approved by the local institutional review board . Written informed consent was obtained by all subjects. If the subjects were under 20 years old, written informed consent was obtained from both minors and guardians. If the patients with schizophrenia were difficult condition to accept consent by theirself, these patients were not included in this study.Using a 3T MRI unit , we obtained oblique T1-weighted oblique axial images perpendicular to the brainstem. The T1-weighted sequence was acquired with a 3D-spoiled GRASS sequence with magnetization transfer contrast: TR/TE = 38.4/2.4 ms, FA = 20 degrees, matrix size 480×320 in axial plane, FOV = 220 mm, and acquisition time = 3 min 25 s. A 40-mm slab thickness was used and images were reconstructed 40 slices with a slice thickness of 2 mm with in-slice zero-fill interpolation (ZIP2). We also obtained axial T2-weighted images of the whole brain to exclude coexisting disorders and any abnormal findings that might influence the signals for the SNc or LC. The T2-weighted image parameters are as follows: TR/TE = 4500/88 ms, FOV = 220 mm, Matrix = 512×256, 24 slices with slice thickness 5 mm, and 6 mm slice interval.SN) and LC (CRLC) using the following equations: CRSN = (SSN−STM)/STM, CRLC = (SLC−SP)/SP. In these equations, SSN and STM are the signal intensities for the SNc and midbrain tegmentum, respectively, and SLC and SP are the signal intensities of the LC and pons, respectively.We measured the signal intensity of the SNc, midbrain tegmentum, LC, and pons. The region of interest (ROI) for the SNc was traced manually around the high signal area on two consecutive axial slices and ellipse ROI was set at midbrain tegmentum in the same slice . An elliSN and CRLC, we selected a subset of subjects who were under 30 years of age  and compared their ROI values.To reduce the effects of age-related changes in the CRt-tests to determine the differences between patients with schizophrenia and healthy controls.Statistical analyses were performed using unpaired SN or CRLC for patients with schizophrenia and healthy controls. To elucidate the medication effects for the contrast ratio, the correlation between the chlorpromazine (CPZ) equivalents and CRSN or CRLC was analyzed.We calculated the correlation between age and CRSN values and MaxSR SNc were significantly higher in patients with schizophrenia than in healthy controls  for healthy controls and  for schizophrenia. There is no correlation between age and CRLC  for healthy controls and  for schizophrenia. The CPZ equivalent and CRSN showed weak correlation  and there is no correlation between CPZ equivalent and CRLC  for patients with schizophrenia.There is weak correlation between age and CROur results demonstrate the excessive levels of dopamine products in the SNc of living patients with schizophrenia and this supports the dopamine hypothesis for schizophrenia. Recently, Howers et al It has been suggested that dopamine dysfunction plays an important role in the pathogenesis of schizophrenia. This hypothesis is supported by evidence provided by numerous observations and studies. For example, the stimulants amphetamine and cocaine, which increase dopamine levels in the brain, can cause symptoms resembling those for psychosis SN values were higher in patients with schizophrenia ) than in those with depression , as well as controls . However, a post hoc Tukey’s test indicated no significant difference among schizophrenia and controls.It has been reported that T1-weighted MRI with 3T can indicate T1-shortening tissues containing neuromelanin at SNc and LC SN values were higher in patients with schizophrenia  than those with depression  and controls . A post hoc test confirmed a significant difference between patients with schizophrenia and those with depression, but not between patients with schizophrenia and controls.Sasaki et al SN in patients with schizophrenia is higher than that in controls, but this was not statistically significant due to a small sample size and large variations. We performed a similar comparison in our study using a lager patient group (52 patients) and observed a significant difference. The mean CRSN was significantly higher in patients with schizophrenia than in healthy controls, but the CRSN values showed large variations with overlap between patients and healthy controls , which is the origin of the dopaminergic cell bodies of the mesolimbic-cortical dopaminergic system, because of the difficulty in detection the border of the VTA by neuromelanin-MRI. Third, the years of education and IQ were significantly higher in healthy controls than in patients with schizophrenia. Schizophrenia is characterized by general intellectual deficits There are several limitations to our study. First, we included all patients examined with neuromelanin MRI from April to November 2012. Many of these patients were under treatment with antipsychotic drugs. Our results showed that there is a weak correlation between CPZ equivalent and CRIn conclusion, neuromelanin MRI revealed increased signal intensity in the SNc of patients with schizophrenia. This finding indicates the presence of an excessive level of dopamine products in the SNc of these patients. Therefore, neuromelanin imaging has the potential to be useful for accurate diagnosis of schizophrenia and to serve as a surrogate marker for medication.


Poor diet quality in early childhood is inconsistently linked to obesity risk. Understanding may be limited by the use of cross-sectional data and the use of body mass index (BMI) to define adiposity in childhood.The objective of this study is to examine the effects of continued exposure to diets of varying quality across early childhood in relation to adiposity at 6 years.One thousand and eighteen children from a prospective UK birth cohort were studied. Diet was assessed using food frequency questionnaires when the children were aged 6 and 12 months, and 3 and 6 years; diet quality was determined according to scores for a principal component analysis-defined dietary pattern at each age . At each age, children were allocated a value of 0/1/2 according to third of the distribution (bottom/middle/top) their diet quality score was in; values were summed to calculate an overall diet quality index (DQI) for early childhood (range 0–8). Obesity outcomes considered at 6 years were dual-energy X-ray absorptiometry-assessed fat mass and BMI.z-score at 6 years that was robust to adjustment for confounders : −0.09, −0.01), P=0.01). In comparison with children who had the highest diet quality (DQI=7–8), this amounted to a difference in fat mass of 14%  at 6 years for children with the poorest diets (DQI=0). In contrast, no independent associations were observed between DQI and BMI.One hundred and seven (11%) children had a DQI=0, indicating a consistently low diet quality, 339 (33%) had a DQI=1–3, 378 (37%) had a DQI=4–6 and 194 (19%) had a DQI=7–8. There was a strong association between lower DQI and higher fat mass Continued exposure to diets of low quality across early childhood is linked to adiposity at the age of 6 years. Another issue may be the limitation of using body mass index (BMI) as an indicator of adiposity in children.13There is a growing interest in the role of diet quality in early childhood as a determinant of obesity risk.In this study we describe the relations of diet quality at four ages from infancy to early childhood, with body composition and obesity risk at 6 years, among 1018 children who participated in a prospective birth cohort, the Southampton Women's Survey (SWS). We consider the associations with a diet quality index (DQI), that describes overall exposure to diets of varying quality across early childhood, and diet quality at each age, in relation to dual-energy X-ray absorptiometry (DXA)-assessed measures of fat mass and BMI.14 Assessments of lifestyle, diet and anthropometry were performed at study entry (April 1998–December 2002). Women in the SWS study, who subsequently became pregnant, were followed up at 11, 19 and 34 weeks of gestation; the offsprings have been studied in infancy and childhood.The SWS study is an ongoing, prospective cohort study of 12 583, initially non-pregnant, women aged 20–34 years, living in the city of Southampton, UK.The SWS study was conducted according to the guidelines laid down in the Declaration of Helsinki and was approved by the Southampton and South West Hampshire Local Research Ethics Committee (06/Q1702/104). Written informed consent was obtained from all participating women and by a parent or guardian with parental responsibility on behalf of their children.17 At 6 months of age, food intake over the previous 7 days was assessed using a 34-item food frequency questionnaire (FFQ).15 At 12 months of age, food intake over the previous 4 weeks was assessed using a 78-item FFQ.16 At 3 and 6 years of age, diet over the preceding 3 months was assessed using an 80-item FFQ.17 The average frequency of consumption of the listed foods was recorded. At all interviews, prompt cards were used to show the foods included in each food group, to ensure standardized responses to the FFQ. At the end of each FFQ, an open section in the same format was included, to record consumption frequencies of any foods that were not listed on the FFQ, if they were consumed once per week or more. Daily volumes and types of milks consumed and amount of sugar added to foods were recorded.Diet was assessed when the children were aged 6 and 12 months, and 3 and 6 years, using age-specific validated questionnaires that were administered to the child's parent or guardian by trained research nurses.18 The first component at each age, which described the greatest variance in the dietary data, was a ‘healthy' dietary pattern that complied with dietary recommendations, characterized by frequent consumption of fruit, vegetables and fish.18 We named this an ‘infant guidelines' pattern at 6 and 12 months and a ‘prudent' dietary pattern at 3 and 6 years; the coefficients for the prudent component in the PCA analysis at 6 years are shown in The dietary patterns of children in the SWS study were identified at each age using principal component analysis (PCA). Before the PCA analysis, the foods listed on the FFQ were grouped on the basis of similarity of type of food and nutrient composition; a total of 46 groups at 6 months, 56 groups at 12 months and 51 groups at 3 and 6 years were entered into the PCA analysis. More detailed descriptions of dietary patterns in infancy and childhood have been published elsewhere.17 There was no separate validation study at 6 years. Energy-adjusted Spearman's correlation coefficients for macro- and micronutrient intake ranged between 0.55 and 0.89 at 6 months (median 0.74),15 0.24–0.75 (median 0.5) at 12 months16 and 0.41–0.59 (median 0.51) at 3 years.17 PCA analysis was carried out using a 24-h recall data at 6 months and food diary data at 3 years; Spearman's correlation coefficients for the first component (infant guidelines at 6 months and prudent diet at 3 years) scores were 0.81ref.  and 0.72,17 respectively.The relative validity of the estimates of intake from the FFQs was established in comparison with intakes assessed using prospective food diaries in validation studies carried out at 6 months, 12 months and 3 years.19 When the children were aged 4 years, physical activity level was determined according to the child's average number of hours spent ‘on the move' and screen time (TV or computer) each day, as reported by their parent.20 Average time for sleeping per night was assessed using information provided by the parents about the child's usual bedtime, an estimate of how long they were awake for during the night and usual time of rising. In addition, parents were asked about the duration of any naps during the day. At a home visit when the children were aged 6 years, height (cm) was measured with the use of a portable stadiometer ; weight (kg) was measured using calibrated digital scales (Seca). These data were used to calculate children's BMI. The children who completed the home visit survey (n=1103) were subsequently invited to clinic for an assessment of body composition by DXA. Total and proportionate fat mass were derived from the whole-body scan through the use of pediatric software. The total X-ray dose for the whole-body scans were ~10.5 microsieverts (pediatric scan mode), which is equivalent to ~1- to 2-day background radiation. All scan results were checked independently by two trained operators. The coefficient of variation for body composition analysis using the DXA instrument was 1.4–1.9%.At birth, the baby was weighed on calibrated digital scales . Duration of breastfeeding was defined according to the date of the last breastfeed recorded at the 6- and 12-month visits.21 and parity were obtained during the pre-pregnant interviews. Height and weight were measured at this visit and these measurements were used to calculate prepregnancy BMI. Among women who became pregnant, smoking status in pregnancy was ascertained at the 11- and 34-week interviews. At 34 weeks of gestation, the research nurses weighed the women again. In previous analyses of body composition data in this cohort,23 we have described associations between maternal vitamin D status and plasma fatty acid concentrations with offspring adiposity at 6 years. We therefore considered these variables as potential determinants of adiposity and included them in the statistical models. A venous blood sample was taken into heparinized tubes at 34 weeks of gestation and an aliquot of maternal serum was frozen at −80 °C. Serum 25-hydroxyvitamin D concentration was analyzed by radioimmunoassay . This assay measures both 25-hydroxyvitamin D2 and D3. The assay met the requirements of the UK National Vitamin D External Quality Assurance Scheme; intra-assay and inter-assay coefficients of variation were <10%. Fatty acid composition of maternal plasma phosphatidylcholine was also measured as described previously.24Details of maternal educational attainment A total of 1852 women became pregnant and delivered healthy, term (after 37 weeks' gestation) singleton infant, up to the end of 2003. Of these mother–child pairs, 834 were not included in the analyses, either because they did not have body composition data at 6 years or dietary data for at least two ages in early childhood. Data for 1018 children were presented; 725 (71%) children had complete dietary data at all four ages.Descriptive data are presented as mean (s.d.) or median (interquartile range) for continuous variables and percentages of subjects for categorical variables. To investigate whether there was evidence of a cumulative effect of continued exposure to diets of varying quality across early childhood and a single effect of diet quality at each of the four ages, an index of overall diet quality was derived. The distributions of diet quality scores at each of the four ages were categorized into thirds. Each child was assigned a value of 0, 1 or 2 according to where their diet quality score was in the distribution at each age: 0 (lowest), 1 (middle) and 2 (highest). The values were summed to yield a DQI across early childhood, ranging from 0  to 8 . For children who did not have four dietary assessments (42 children had 2 and 251 had 3), the average value from their assessments was substituted for the missing value. The DQI across early childhood was used both as a continuous score and also as a categorical  variable. Spearman's correlation was used to describe the associations between diet quality scores at each of the four ages and the relations with the DQI across early childhood. Differences in selected characteristics according to the DQI were examined using a linear model.25 smoking status in pregnancy, vitamin D status (serum 25-hydroxyvitamin D) at 34 weeks of gestation22 and plasma n-6 polyunsaturated fatty acid status at 34 weeks of gestation;23 offspring factors were duration of breastfeeding, average time ‘on the move' at 4 years (h per day) and time spent watching TV or on the computer at 4 years (h per day). In addition, in order to provide measures of effect of diet quality score on fat mass, we back transformed the effect sizes calculated using the logged fat mass data (by exponentiating the estimates) to describe differences in fat mass as measured (in kilograms); these are expressed as percentage differences. Further analyses with binary outcomes for risk of overweight/obesity used Poisson regression with robust variance to calculate relative risks.26 Where dietary data were categorized into thirds of diet quality scores at each age and four groups of the DQI, the highest category was used as a reference group.Univariate and multiple linear regression analyses were performed to explore the associations of diet quality scores at each of the four ages and the DQI across early childhood with BMI and body fat mass at 6 years. Children's BMI at 6 years was adjusted for sex and age at measurement using a regression model; children's fat mass at 6 years was adjusted for sex, age and height using a regression model, the adjustment for height being necessary to ensure that any associations were independent of children's stature. These adjusted outcomes were used throughout the unadjusted (Model 1) and adjusted (Model 2) analyses. All outcomes were positively skewed and thus were log-transformed and internally standardized  for ease of interpretation. In the regression analyses (Model 2), we also considered associations between diet quality and body composition outcomes after taking account of a number of potential confounding factors; maternal factors were prepregnancy BMI, Institute of Medicine weight-gain categories,All statistical analyses were performed using Stata version 13.1 .n=834), those included in the analyses  were slightly older at delivery and taller, and they tended to have a higher level of educational attainment, to be primiparous, and to be less likely to smoke in pregnancy . There were no differences in gestational age, child's birthweight, maternal prepregnancy BMI, maternal serum 25-hydroxyvitamin D concentration, plasma n-6 polyunsaturated fatty acid concentration in late pregnancy or Institute of Medicine weight-gain categories between mother–child pairs studied and the remaining pairs.The children included in the analyses were more likely to have been breastfed for longer than other SWS children . Comparer=0.32–0.69). Although the strongest correlations were observed in assessments carried out at the closest ages, diet quality at 6 months of age was associated with diet quality at 6 years (r=0.32), indicating tracking in diet from early infancy.The correlation matrix for the diet quality scores at each age is shown in When the children were classified into four groups according to the DQI across early childhood, 107 (10.5%) of the children had the lowest diet quality across early childhood , 339 (33.3%) had a DQI=1–3, 378 (37.1%) had a DQI=4–6 and 194 (19.1%) consistently had a high diet quality across early childhood (DQI=7–8). Mean diet quality scores assessed at each age according to DQI group are shown in P<0.01) and to spend less time watching television at 4 years, although unexpectedly they were also less likely to ‘be on the move' each day. Higher DQI was associated with older mothers who had greater levels of education and lower prepregnancy BMI, and who were less likely to have smoked and who had higher vitamin D status in pregnancy than other mothers.Across the distribution of DQI, there were differences in mother–child characteristics, although there were no differences in the proportion of boys and girls across the groups, or in height and weight of the children at 6 years . ChildreP<0.001; P=0.01). In contrast, diet quality score at 6 and 12 months, and 3 years were inversely associated with BMI at 6 years, but these associations were no longer apparent after adjustment for potential confounding factors. There was no cross-sectional association between diet quality score at 6 years and BMI.Inverse associations were observed between diet quality scores at all four ages in relation to fat mass at 6 years  were defined as overweight or obese at 6 years. There was no association between the DQI and the risk of being overweight/obese based on BMI , P for trend=0.55; see There was a strong inverse association between DQI across early childhood and fat mass at 6 years, which was evident before and after adjustment for confounding factors. This is illustrated in n=725, 39% of all live births). We found similar results, observing inverse associations between DQI and fat mass , P for trend=0.01), but no association was found with BMI , P for trend=0.81). Second, we calculated an alternative DQI based on summed values allocated to quarters (rather than thirds) of the distributions of diet quality scores at each age. Using the alternative DQI, similar results were observed to the main analysis, showing inverse associations with fat mass , P for trend=0.002) and no association with BMI , P for trend=0.92).We checked the robustness of our findings in two final analyses. First, we ran the models using only the data from the subgroup of children who had complete FFQ data at all four ages , which suggests a stability in eating habits persisting from infancy to early childhood. As diet quality has been inconsistently linked to differences in body composition in cross-sectional analyses, we created a DQI in this study to represent overall diet quality across early childhood. As observed in The tracking of dietary patterns from infancy to childhood has been described in several studies.30 Consistent with some previous studies,31 the present study showed clear relations between poor diet quality at 6 months and at 3 and 6 years of age, with a higher fat mass at 6 years, although an independent association was not found at 12 months. By creating an index to represent overall diet quality between 6 months and 6 years, our study extended these findings to examine cumulative effects of continued exposure to diets of varying quality during early childhood. Although our findings require replication in other studies, they highlight the importance of diet quality from very early life and the potential for a protective role of diets of better quality in terms of risk of later obesity.Some observational studies have shown associations between diet quality assessed at multi-time points in childhood and measures of adiposity, although the evidence is inconsistent. For example, in the Raine study, better diet quality at 1 year (but not at 2 or 3 years) was weakly associated with a lower BMI at 5, 8 and 10 years of age, but not at 3 years of age or in adolescence (at 14 or 17 years of age).12 and may be explained by energy-dense, high-fat foods undermining innate appetite control, leading to greater energy consumption. In comparison, higher dietary fiber intakes increase satiety levels and may delay or decrease subsequent energy intake.32 However, although more prospective studies considering cumulative effects of the quality of children's diet are needed to further clarify the role of diet in the etiology of obesity, some insights from the present study are relevant for future interventions. First, the tracking of dietary patterns from 6 months onwards indicates that public health initiatives to improve diet quality of young children need to start very early in life. Second, the evident differences in social background and other health behaviors that are associated with poor diet quality (The mechanisms linking variations in the diet quality across early childhood and lower fat mass at 6 years are unknown. One potential mechanism underlying these associations might be that a diet of lower quality is characterized by energy-dense, high-fat foods and a lack of fruits and vegetables, as we observed in the present study. This would be consistent with a recent study showing that a diet high in energy-dense, high-fat and low-fiber foods in childhood or adolescence was associated with later obesity risk, quality  mean tha31which suggests that BMI may be of limited use as a measure of adiposity in early childhood. As BMI is based on only weight and height, both of which greatly change during growth and development, a high BMI can reflect increase in not only fat mass but also fat-free mass,33 indicating that adiposity defined by BMI may be less sensitive than a direct assessment of fat mass, such as DXA, in childhood.In the present study, there were graded associations of dietary patterns and the DQI with body fat mass assessed by DXA measurement, whereas we observed no relations with BMI, used as a surrogate measure of adiposity. The lack of association with BMI is consistent with findings of previous studies,17 misreporting of food intake is a source of measurement error. However, this error would be expected to attenuate associations and we do not think that misreporting on the FFQs could explain the associations we describe. Importantly, FFQs have been shown to identify similar patterns of diet to other dietary assessment methods and dietary pattern scores determined using different methods are highly correlated.18 Finally, higher diet-quality scores may act as markers for more healthy behaviors and less obesogenic family influences, which could potentially confound associations with adiposity. Although we controlled for a large number of antenatal and postnatal potential confounding factors and other influences on child adiposity previously described in this cohort,25 we cannot rule out unmeasured or residual confounding in an observational study.Strengths of the present study include a prospective design, studying a general population sample of mother–child pairs from a wide range of sociodemographic backgrounds, with comprehensive assessments at multiple time points in pregnancy and childhood, and the use of DXA to provide direct measures of fat mass. However, there are a number of limitations. First, children's body composition measurements were not available for the whole SWS cohort. In comparison with mothers without available children's DXA measurement, the subset of mothers tended to be better educated, less likely to smoke in pregnancy, older and to have breastfed for longer. However, we adjusted for each of these factors in our statistical models, and unless the associations between diet quality scores at each of four ages or the DQI and child's obesity outcomes are different in the remainder of the cohort, it is unlikely that selection bias could explain our findings. Second, although diet at each age was assessed using validated FFQs that were administered by trained research nurses,In conclusion, an index of overall diet quality from infancy to early childhood was associated with adiposity at the age of 6 years, with the greatest adiposity observed in children who had consistently poorer diets over this period. Our data, together with growing evidence showing that patterns of food intake established in infancy persist to later childhood, suggest that promotion of healthy dietary habits in early life could have an important role as part of public health strategies to prevent childhood obesity and other related chronic diseases in later life.


To explore the co-evolution of friendship tie choice and alcohol use behavior among 1,284 adolescents from 12 small schools and 976 adolescents from one big school sampled in the National Longitudinal Study of Adolescent to Adult Health , we apply a Stochastic Actor-Based (SAB) approach implemented in the R-based Simulation Investigation for Empirical Network Analysis (RSiena) package. Our results indicate the salience of both peer selection and peer influence effects for friendship tie choice and adolescent drinking behavior. Concurrently, the main effect models indicate that parental monitoring and the parental home drinking environment affected adolescent alcohol use in the small school sample, and that parental home drinking environment affected adolescent drinking in the large school sample. In the small school sample, we detect an interaction between the parental home drinking environment and choosing friends that drink as they multiplicatively affect friendship tie choice. Our findings suggest that future research should investigate the synergistic effects of both peer and parental influences for adolescent friendship tie choices and drinking behavior. And given the tendency of adolescents to form ties with their friends' friends, and the evidence of local hierarchy in these networks, popular youth who do not drink may be uniquely positioned and uniquely salient as the highest rank of the hierarchy to cause anti-drinking peer influences to diffuse down the social hierarchy to less popular youth. As such, future interventions should harness prosocial peer influences simultaneously with strategies to increase parental support and monitoring among parents to promote affiliation with prosocial peers. In the United States, the prevalence of adolescent drinking has declined since the late 1990s—however the problem is far from being solved. Recent reports find that 71% of 9th- to 12th-grade students indicate having had at least one drink of alcohol (other than a few sips) during their lifetime, with about one fifth of them starting to drink by age 13 ,6, drug Ecological models ,11 suggePeer networks are a salient reference group for youth, with peers playing a particularly important and influential role in helping shape adolescents' evolving social worlds . VariousIn addition to shaping adolescents' friendship tie choices, peer networks also shape adolescent drinking behavior. Two important socialization processes operating in peer networks are peer influence and peer selection. Peer influence is the process wherein youth alter their behavior such that it aligns with that of their peers. Peer selection is the process by which youth select friends who are similar to themselves, on various dimensions. Adolescent alcohol use can be conceptualized as a result of both peer influence and selection processes, with friends influencing one another's drinking behavior and adolescents selecting friends who engage in similar levels of drinking –20. And Stochastic Actor-Based (SAB) models ,26 have Parents are also influential referents in adolescents' social worlds, affecting both adolescent friendship tie choices and their drinking behavior. Parents exert influences on the socialization of adolescent youth through multiple processes, including the provision of social support and parental monitoring. Indeed, parental influences have a critical impact on adolescent development, particularly in the area of building youths' social competence regarding friendship formation. One study indicated that familial factors are associated with social competence, peer acceptance, and the ability to form and maintain close friendships among youth . MoreovePast studies also demonstrate that parental influences affect adolescent drinking behavior directly. Previous research indicates that parental support prevents the early initiation of alcohol use and reduces the frequency of alcohol use among adolescents ,41,42. MIt is possible that peer and parental influences may function together in impacting adolescent friendship tie choices and drinking behavior, given insights from ecological models of development suggesting that influences from these two contexts may act synergistically . In a stFinally, we also examine the effect of depressive symptoms among adolescents on their alcohol use. Adolescent drinking has been positively related to depression in previous studies ,49–53. DThe current study builds upon extant literature examining the importance of peer influence and selection processes related to adolescent friendship tie choice and alcohol use. In addition, we also focus on the role of key parental influences on adolescent friendship tie choice and alcohol use behavior. With data from the AddHealth study, we explore the co-evolution of adolescents' friendship ties and their drinking behavior over two waves. We hypothesize that we will observe both peer influence and peer selection effects, as adolescents will select friends with similar alcohol use levels, and adjust their drinking behavior based on the drinking behavior of their friends. We also expect that parental monitoring, parental support, and the parental home drinking environment, will influence the friendship tie choice and adolescent drinking, both directly and synergistically.http://www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.html. Written informed consent was given by participants (or next of kin/caregiver) for their answers to be used in this study.The data utilized in this study come from early waves of AddHealth. The respondent record/information was anonymized and de-identified prior to analysis. This study has been approved by the Institutional Review Board of the University of California, Irvine (2013). This study does not employ human subjects directly, as our analyses utilize secondary data which are de-identified. AddHealth participants provided written informed consent for participation in all aspects of AddHealth in accordance with the University of North Carolina School of Public Health Institutional Review Board guidelines that are based on the Code of Federal Regulations on the Protection of Human Subjects 45CFR46: AddHealth is a longitudinal study of a stratified sample of US schools from 7th to 12th grades . The AddAddHealth contains a saturated sample of 16 schools out of the total 132 participant schools . Among tDuring the administration of the AddHealth survey, all students in 14 schools were invited to take the survey over three waves. Information on the social and demographic characteristics of the respondents was collected, as well as their risk behaviors including alcohol use. Adolescent sociometric networks were constructed from a network elicitation item asking respondents to name up to five male and five female best friends from a name list of students in his/her school, and thus researchers are able to attain longitudinal complete sociometric networks in these schools. The measures of youths' parental contexts came from a parent survey between April and December of 1995, the same time when the respondents also took a wave 1 In-Home Survey. However, due to another administrative error, about 37% respondents in the 12 small schools and about 5% respondents in "Jefferson High" were recorded to nominate only one (instead of five) female and male best friends. To overcome this limited nomination difficulty, in this study we utilize the information retrieved from the first (In-School Survey during 1994 and 1995) and third (wave 2 In-Home Survey between April and December of 1996) time points for our dynamic network analysis, skipping that from the second time point (wave 1 In-Home Survey).The dependent (behavior) variable measures drinking frequency in the past 12 months. At wave 1, the survey question was "During the past twelve months, how often did you drink beer, wine, or liquor?" with response categories of "0—never", "1–1 once or twice", "2–2 once a month or less", "3–2 or 3 days a month", "4—once or twice a week", "5–3 to 5 days a week", and "6—nearly every day." In waves 2 and 3, the survey question was "During the past 12 months, on how many days did you drink alcohol?" with response categories of "1—every day or almost every day", "2–3 to 5 days a week", "3–1 or 2 days a week", "4–2 or 3 days a month", "5—once a month or less (3–12 times in the past 12 months)", "6–1 or 2 days in the past 12 months", and "7—never". We recoded these such that response categories specify non-drinkers (0 = never), casual-drinkers (1 = 1 or 2 days), light-drinkers (2 = once a month or less or 3–12 times in the past 12 months), medium-drinkers (3 = 2 or 3 days a month), and heavy-drinkers (4 = more than 1 or 2 days a week). gender , grade (7~12), depressive symptoms, parental support, parental monitoring, and the parental home drinking environment. Depressive symptom status is generated as a factor score of 19 ordinal items modified from the Center for Epidemiologic Studies Depression Scale  )The alcohol use and network descriptive statistics of the 12 small schools and "Jefferson High" are summarized in Among the 1,284 respondents in the 12 small schools, the number of out-going ties decreased from 6,671 during the In-School Survey to 2,704 during the wave 2 In-Home Survey due to graduation, moving, dropping out, and attrition/non-response/missing network data. A similar pattern was also observed in "Jefferson High". The proportional change of reciprocal ties was more variable in the small schools (from 0.45 to 0.33). The transitivity index, which captures the tendency for individuals to experience triadic closure, was found to be relatively stable over time, although stronger in small schools (34%) compared to "Jefferson High" (18~20%). As indicated by the Jaccard index, there was a high turnover of friendship ties in both samples, with only 21~22% of ties persisting over the two waves.The descriptive statistics of covariates are reported in b = .33, p <. 01) than in "Jefferson High" . These provide evidence of a peer selection effect, as students were more likely to select as friends others with similar levels of alcohol use.As shown in the network equation of Model 1 in b = .28, p <. 05) compared to "Jefferson High" . Adolescents having higher drinking levels were inclined to nominate fewer best friends that those with lower drinking levels, but this effect is only significant in "Jefferson High" .In both samples we also find that those drinking more frequently were more popular than those drinking less frequently, although they seemed to be even appealing in small schools  and the significantly positive reciprocity parameters  suggest that adolescents in both samples tended to form fewer ties, but when they did such ties were more likely to be reciprocal ones. The adolescents also preferred to be friends with their friends' friends (triadic closure), as indicated by the positive transitive triplets effect . The significantly negative three-cycle effect implies a tendency toward local hierarchy . Adolescents were more likely to be named as a tie if they already had many in-coming ties (high in-degree popularity) . And the negative parameter for in-in degree assortativity (square root) again suggests the presence of preferential attachment as adolescents with high in-degree were more likely to be nominated as best friends by those with low in-degree, but this effect is only significant in the small school sample .In terms of structural network effects, the significantly negative out-degree  and in the same grade . The homophily effects of parental educational levels are insignificant in both samples, after controlling for the other measures in the model.We find that homophily preferences in gender and grade drove friendship tie formation: adolescents were more likely to send friendship nominations to other adolescents with the same gender . This effect for parental support was insignificant in "Jefferson High". And we find that more parental monitoring and a home drinking environment did not affect tie choice.As for how the parental influences affected friendship tie choice, we find that adolescents who received more parental support nominate more friends in the 12 small schools : whereas higher-level drinkers were more popular in general, those with high levels of drinking in their home environment were particularly likely to form ties with those who drink more, see When focusing on the moderating effect of family contexts on friendship tie choice in Models 2 to 4 of Tables b = .22, p <. 05 in the small schools and b = .36, p <. 05 in "Jefferson High"), implying that adolescents tended to match the drinking behavior of their friends .As shown in the behavior equation of Model 1 in b = -1.78, p <. 001 in small schools and b = -1.36, p <. 001 in "Jefferson High") and the significantly positive quadratic shape effect  suggest that adolescents tended to drink less over time, but there was also a self-reinforcement effect of alcohol use: there appeared to be a tendency towards polarization on both ends of drinking behavior, as adolescents were inclined to either become or remain a non-drinker or escalate to heavy use. Popular adolescents  were not found to drink more frequently in either case. Additional significant effects were found for grade in small schools  and gender in "Jefferson High" : higher-grade adolescents in the 12 small schools and males in "Jefferson High" were generally found to increase their drinking frequency over time more than their counterparts.The significantly negative linear shape effect . Adolescents with higher levels of parental monitoring engaged in less drinking behavior over time, but this effect is only significant in small school sample . The parental support respondents received had a negative effect on the frequency of adolescent drinking, although not significant in either case.Regarding the parental influences, we find that adolescents who experienced more drinking at home were more likely to increase their own drinking frequency Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.