Penerapan Pembelajaran Kooperatif Tipe Jigsaw Dalam Meningkatkan Motivasi Dan Hasil Belajar IPA Pada Siswa Kelas VII Semester II SMP Negeri 2 Pulokulon

Permasalahan pokok yang akan dipecahkan lewat Penelitian Tindakan Kelas ini adalah: apakah penerapan model pembelajaran kooperatif tipe jigsaw dapat meningkatkan hasil belajar IPA. Tujuannya untuk meningkatkan motivasi dan hasil belajar siswa dalam mata pelajaran IPA..Penelitian ini merupakan tindakan guru untuk memperbaiki hasil belajar siswa kelas VII SMP Negeri 2 Pulokulon Semester 2 Tahun Pelajaran 2013/2004, dan pelakunya adalah guru IPA. Penelitian dilakukan dalam 2 siklus dan meliputi 4 tahapan, yaitu perencanaan, tindakan,pengamatan dan refleksi.Hasil penelitian menunjukkan bahwa dari keseluruhan siklus yang telah dilakukan motivasi dan perolehan nilai siswa kelas VII SMP Negeri 2 Pulokulon Semester 2 Tahun Pelajaran 2013/2004 mengalami peningkatan dari satu siklus ke siklus berikutnya. Jadi secara keseluruhan siklus yang telah dilakukan, penerapan model pembelajaran kooperatif tipe jigsaw dapat meningkatkan motivasi dan hasil belajar siswa dalam mata pelajaran IPA

Powerful extreme phenotype sampling designs and score tests for genetic association studies

We consider cross‐sectional genetic association studies (common and rare variants) where non‐genetic information is available or feasible to obtain for N individuals, but where it is infeasible to genotype all N individuals. We consider continuously measurable Gaussian traits (phenotypes). Genotyping n < N extreme phenotype individuals can yield better power to detect phenotype‐genotype associations, as compared to randomly selecting n individuals. We define a person as having an extreme phenotype if the observed phenotype is above a specified threshold or below a specified threshold. We consider a model where these thresholds can be tailored to each individual. The classical extreme sampling design is to set equal thresholds for all individuals. We introduce a design (z‐extreme sampling) where personalized thresholds are defined based on the residuals of a regression model including only non‐genetic (fully available) information. We derive score tests for the situation where only n extremes are analyzed (complete case analysis) and for the situation where the non‐genetic information on N − n non‐extremes is included in the analysis (all case analysis). For the classical design, all case analysis is generally more powerful than complete case analysis. For the z‐extreme sample, we show that all case and complete case tests are equally powerful. Simulations and data analysis also show that z‐extreme sampling is at least as powerful as the classical extreme sampling design and the classical design is shown to be at times less powerful than random sampling. The method of dichotomizing extreme phenotypes is also discussed

The eGenVar data management system-cataloguing and sharing sensitive data and metadata for the life sciences

Systematic data management and controlled data sharing aim at increasing reproducibility, reducing redundancy in work, and providing a way to efficiently locate complementing or contradicting information. One method of achieving this is collecting data in a central repository or in a location that is part of a federated system and providing interfaces to the data. However, certain data, such as data from biobanks or clinical studies, may, for legal and privacy reasons, often not be stored in public repositories. Instead, we describe a metadata cataloguing system and a software suite for reporting the presence of data from the life sciences domain. The system stores three types of metadata: file information, file provenance and data lineage, and content descriptions. Our software suite includes both graphical and command line interfaces that allow users to report and tag files with these different metadata types. Importantly, the files remain in their original locations with their existing access-control mechanisms in place, while our system provides descriptions of their contents and relationships. Our system and software suite thereby provide a common framework for cataloguing and sharing both public and private data

Computationally efficient familywise error rate control in genome‐wide association studies using score tests for generalized linear models

In genetic association studies, detecting phenotype–genotype association is a primary goal. We assume that the relationship between the data—phenotype, genetic markers and environmental covariates—can be modeled by a generalized linear model. The number of markers is allowed to be far greater than the number of individuals of the study. A multivariate score statistic is used to test each marker for association with a phenotype. We assume that the test statistics asymptotically follow a multivariate normal distribution under the complete null hypothesis of no phenotype–genotype association. We present the familywise error rate order k approximation method to find a local significance level (alternatively, an adjusted p‐value) for each test such that the familywise error rate is controlled. The special case k=1 gives the Šidák method. As a by‐product, an effective number of independent tests can be defined. Furthermore, if environmental covariates and genetic markers are uncorrelated, or no environmental covariates are present, we show that covariances between score statistics depend on genetic markers alone. This not only leads to more efficient calculations but also to a local significance level that is determined only by the collection of markers used, independent of the phenotypes and environmental covariates of the experiment at hand

Computationally efficient familywise error rate control in genome-wide association studies using score tests for generalized linear models

In genetic association studies, detecting phenotype–genotype association is a primary goal. We assume that the relationship between the data—phenotype, genetic markers and environmental covariates—can be modeled by a generalized linear model. The number of markers is allowed to be far greater than the number of individuals of the study. A multivariate score statistic is used to test each marker for association with a phenotype. We assume that the test statistics asymptotically follow a multivariate normal distribution under the complete null hypothesis of no phenotype–genotype association. We present the familywise error rate order k approximation method to find a local significance level (alternatively, an adjusted p‐value) for each test such that the familywise error rate is controlled. The special case k=1 gives the Šidák method. As a by‐product, an effective number of independent tests can be defined. Furthermore, if environmental covariates and genetic markers are uncorrelated, or no environmental covariates are present, we show that covariances between score statistics depend on genetic markers alone. This not only leads to more efficient calculations but also to a local significance level that is determined only by the collection of markers used, independent of the phenotypes and environmental covariates of the experiment at hand

Identification of novel genetic variants associated with cardiorespiratory fitness

Introduction Low maximal oxygen uptake (VO2max) is a strong and independent risk factor for all-cause and cardiovascular disease (CVD) mortality. For other CVD risk factors, numerous genetic association studies have been performed, revealing promising risk markers and new therapeutic targets. However, large genomic association studies on VO2max are still lacking, despite the fact that VO2max has a large genetic component. Methods We performed a genetic association study on 123.545 single-nucleotide polymorphisms (SNPs) and directly measured VO2max in 3470 individuals (exploration cohort). Candidate SNPs from the exploration cohort were analyzed in a validation cohort of 718 individuals, in addition to 7 wild-card SNPs. Sub-analyses were performed for each gender. Validated SNPs were used to create a genetic score for VO2max. In silico analyses and genotype-phenotype databases were used to predict physiological function of the SNPs. Results In the exploration cohort, 41 SNPs were associated with VO2max (p < 5.0 ∗ 10−4). Six of the candidate SNPs were associated with VO2max also in the validation cohort, in addition to three wild-card SNPs (p < 0.05, in men, women or both). The cumulative number of high-VO2max-SNPs correlated negatively with CVD risk factors, e.g. waist-circumference, visceral fat, fat %, cholesterol levels and BMI. In silico analysis indicated that several of the VO2max-SNPs influence gene expression in adipose tissue, skeletal muscle and heart. Conclusion We discovered and validated new SNPs associated with VO2max and proposed possible links between VO2max and CVD. Studies combining several large cohorts with directly measured VO2max are needed to identify more SNPs associated with this phenotype

Computationally efficient familywise error rate control in genome‐wide association studies using score tests for generalized linear models

In genetic association studies, detecting phenotype–genotype association is a primary goal. We assume that the relationship between the data—phenotype, genetic markers and environmental covariates—can be modeled by a generalized linear model. The number of markers is allowed to be far greater than the number of individuals of the study. A multivariate score statistic is used to test each marker for association with a phenotype. We assume that the test statistics asymptotically follow a multivariate normal distribution under the complete null hypothesis of no phenotype–genotype association. We present the familywise error rate order k approximation method to find a local significance level (alternatively, an adjusted p‐value) for each test such that the familywise error rate is controlled. The special case k=1 gives the Šidák method. As a by‐product, an effective number of independent tests can be defined. Furthermore, if environmental covariates and genetic markers are uncorrelated, or no environmental covariates are present, we show that covariances between score statistics depend on genetic markers alone. This not only leads to more efficient calculations but also to a local significance level that is determined only by the collection of markers used, independent of the phenotypes and environmental covariates of the experiment at hand

Identification of novel genetic variants associated with cardiorespiratory fitness

Introduction: Low maximal oxygen uptake (VO) is a strong and independent risk factor for all-cause and cardiovascular disease (CVD) mortality. For other CVD risk factors, numerous genetic association studies have been performed, revealing promising risk markers and new therapeutic targets. However, large genomic association studies on VO are still lacking, despite the fact that VO has a large genetic component. Methods: We performed a genetic association study on 123.545 single-nucleotide polymorphisms (SNPs) and directly measured VO in 3470 individuals (exploration cohort). Candidate SNPs from the exploration cohort were analyzed in a validation cohort of 718 individuals, in addition to 7 wild-card SNPs. Sub-analyses were performed for each gender. Validated SNPs were used to create a genetic score for VO. In silico analyses and genotype-phenotype databases were used to predict physiological function of the SNPs. Results: In the exploration cohort, 41 SNPs were associated with VO (p < 5.0 ∗ 10). Six of the candidate SNPs were associated with VO also in the validation cohort, in addition to three wild-card SNPs (p < 0.05, in men, women or both). The cumulative number of high-VO SNPs correlated negatively with CVD risk factors, e.g. waist-circumference, visceral fat, fat %, cholesterol levels and BMI. In silico analysis indicated that several of the VO-SNPs influence gene expression in adipose tissue, skeletal muscle and heart. Conclusion: We discovered and validated new SNPs associated with VO and proposed possible links between VO and CVD. Studies combining several large cohorts with directly measured VO are needed to identify more SNPs associated with this phenotype

The PBMC transcriptome profile after intakeof oxidized versus high-quality fish oil: anexplorative study in healthy subjects

Background: Marine long-chain polyunsaturated fatty acids are susceptible to oxidation, generating a range of different oxidation products with suggested negative health effects. The aim of the present study was to utilize sensitive high-throughput transcriptome analyses to investigate potential unfavorable effects of oxidized fish oil (PV: 18 meq/kg; AV: 9) compared to high-quality fish oil (PV: 4 meq/kg; AV: 3). Methods: In a double-blinded randomized controlled study for seven weeks, 35 healthy subjects were assigned to 8 g of either oxidized fish oil or high quality fish oil. The daily dose of EPA+DHA was 1.6 g. Peripheral blood mononuclear cells were isolated at baseline and after 7 weeks and transcriptome analyses were performed with the illuminaHT-12 v4 Expression BeadChip. Results: No gene transcripts, biological processes, pathway or network were significantly changed in the oxidized fish oil group compared to the fish oil group. Furthermore, gene sets related to oxidative stress and cardiovascular disease were not differently regulated between the groups. Within group analyses revealed a more prominent effect after intake of high quality fish oil as 11 gene transcripts were significantly (FDR < 0.1) changed from baseline versus three within the oxidized fish oil group. Conclusion: The suggested concern linking lipid oxidation products to short-term unfavorable health effects may therefore not be evident at a molecular level in this explorative study. Trial registration: ClinicalTrials.gov, NCT0103442