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14 results for “Constructive Influences”
Experimental Data on Pragmatic, Constructive and Reconstructive Memory Influences on the Hindsight Bias
<p>After knowing how events turned out, we are quick to say ‘we knew it all along.’ Decades of research on hindsight bias have shown that outcome information biases what we later present as our original judgments. This experiment combined established between- and within-participant designs in a longitudinal study.</p>
Fig. 2 in Influence of the microhabitat on the trap construction of Myrmeleon brasiliensis (Neuroptera: Myrmeleontidae) larvae
Fig. 2. Average trap size (±SD) of Myrmelon brasiliensis (Návas, 1914) (Neuroptera: Myrmeleontidae) larvae registered in the period from Agust 2016 to July 2017 in exposed and protected microhabitats, Aquidauana, MS, Brazil.
Fig. 1 in Influence of the microhabitat on the trap construction of Myrmeleon brasiliensis (Neuroptera: Myrmeleontidae) larvae
Fig. 1. Abundance of Myrmelon brasiliensis (Návas, 1914) (Neuroptera: Myrmeleontidae) larvae and rainfall (mm) registered in the period from August 2016 to July 2017 in exposed and protected microhabitats, Aquidauana, MS, Brazil.
Influence of RNA-Seq library construction, sampling methods, and tissue harvesting time on gene expression estimation
<p>RNA sequencing (RNA-Seq) is popular for measuring gene expression in non-model organisms, including wild populations. While RNA-Seq can detect gene expression variation among wild-caught individuals and yield important insights into biological function, sampling methods may influence gene expression estimates. We examined the influence of multiple technical variables on estimated gene expression in a non-model fish, the westslope cutthroat trout (<em>Oncorhynchus clarkii lewisi</em>), using two RNA-Seq library types: 3' RNA-Seq (QuantSeq) and whole mRNA-Seq (NEB). We evaluated effects of dip netting versus electrofishing, and of harvesting tissue immediately versus 5 minutes after euthanasia on estimated gene expression in blood, gill, and muscle. We found no significant differences in gene expression between sampling methods or tissue collection times with either library type. When library types were compared using the same blood samples, 58% of genes detected by both NEB and QuantSeq showed significantly different expression between library types, and NEB detected 31% more genes than QuantSeq. Although QuantSeq and NEB recovered different numbers of genes and expression levels, there were no differences in gene expression between sampling methods and tissue harvesting time for either library type. Our study suggests that researchers can safely rely on different fish sampling strategies in the field. In addition, while QuantSeq is more cost-effective, NEB detects more expressed genes. Therefore, when it is crucial to detect as many genes as possible (especially low expressed genes), when alternative splicing is of interest, or when working with an organism lacking good genomic resources, whole mRNA-Seq is more powerful.</p>
Influence of RNA-Seq library construction, sampling methods, and tissue harvesting time on gene expression estimation
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Supplemental Material of Relationships between Economic, Legal, Social, and Global Risks with the Success in International Construction Projects at Caribbean Islands with British Influence
<p>Suplemental Material consisting on: 1. Instrument for collecting data and, 2. Survey results of the study.</p>
Data from: Genetic variation in social environment construction influences the development of aggressive behavior in Drosophila melanogaster
Individuals are not merely subject to their social environments; they choose and create them, through a process called social environment (or social niche) construction. When genotypes differ in social environment-constructing behaviors, different genotypes are expected to experience different social environments. As social experience often affects behavioral development, quantitative genetics and psychology theories predict that genetic variation in social environment construction should have an important role in determining phenotypic variation; however, this hypothesis has not been tested directly. I identify multiple mechanisms of social environment construction that differ among natural genotypes of Drosophila melanogaster and investigate their consequences for the development of aggressive behavior. Male genotypes differed in the group sizes that they preferred and in their aggressive behavior; both of these behaviors influenced social experience, demonstrating that these behaviors function as social environment-constructing traits. Further, the effects of social experience—as determined in part by social environment construction—carried over to affect focal male aggression at a later time and with a new opponent. These results provide manipulative experimental support for longstanding hypotheses in psychology, that genetic variation in social environment construction has a causal role in behavioral development. More broadly, these results imply that studies of the genetic basis of complex traits should be expanded to include mechanisms by which genetic variation shapes the environments that individuals experience.
Data from: Do constructional constraints influence cyprinid (Cyprinidae: Leuciscinae) craniofacial coevolution?
Constraints on form may determine how organisms diversify. As a result of competition for the limited space within the body, investment in adjacent structures could represent an evolutionary compromise. For example, evolutionary trade-offs resulting from limited space in the head could have influenced how the sizes of the jaw muscle, as well as the eyes, evolved in North American cyprinid fishes. To test the evolutionary independence of the size of these structures, we measured the mass of the three major adductor mandibulae muscles and determined the eye volume in 36 cyprinid species. Using a novel phylogeny, we tested the hypotheses that the sizes of these four structures were negatively correlated with each other during cyprinid evolution. We found that evolutionary change in the adductor mandibulae muscles was generally positively and/or not correlated, suggesting that competition for space among cyprinid jaw muscles has not influenced their evolution. However, there was a negative relationship between mass of adductor mandibulae 1 and eye volume, indicating that change in these physically adjacent structures is consistent with an evolutionary constructional constraint.
Influence of Project Planning on Performance of Housing Construction Projects in Rwanda: case of Silver Back Mall and Makuza Building Projects in Kigali City
<p><span>The study investigated the </span><span>influence of project planning on performance of Housing Construction Projects in Rwanda. </span><span>The study has the specific objectives of</span><span> determining the influence of project scope planning on performance of housing construction project in Kigali City;</span><span> t</span><span>o establish the influence of project resource planning on performances of housing construction project;</span><span> and t</span><span>o analyze the influence of project risk planning on</span><span> performance of </span><span>housing construction projects performances of housing construction project. <span>The descriptive research design and </span></span><span>correlative approach were used. </span><span>Target population was 161 staff in the housing construction and </span><span>sample size was 161 respondents selected using census survey sampling. Data collection methods were questionnaire and documentary analysis.<span> </span>Methods of analysis were <span>descriptive statistic method; correlation coefficient and </span></span><span>multiple linear regression analysis models.</span><span><span> </span>The findings revealed that correlations between project planning factors and the performance of housing construction projects are highly significant (Sig. = 0.000) and strongly positive. In this model, the R-value is 0.948, indicating a strong positive linear relationship between the predictors and the dependent variable. This means that the independent variables collectively explain 94.8% of the variation in the dependent variable. In this model, the R-Square value is 0.899, which means that approximately 89.9% of the variation in the Performance of housing construction projects can be explained by the predictors included in the model. According to the findings on <span>Ha1 stated that “there is a significant influence of project scope planning on the performance of housing construction projects in Kigali City”. In the ANOVA table, the "Regression" row provides this information where the "F" statistic for the "Regression" component is 467.870, and the associated "Sig." (p-value) is very close to zero (0.000). This indicates that the regression model (which includes </span>project scope planning<span>) is statistically significant. The Ha2 stated that “</span>t<span>here is a significant influence of project resource planning on the performance of housing construction projects in Kigali City”. Similarly, we look at the significance of the predictor "</span>project resource planning<span>" in the regression model. The "F" statistic for the "Regression" component is 467.870, and the associated "Sig." (p-value) is very close to zero (0.000). The Ha3 said that “there is a significant influence of project risk planning on the performance of housing construction projects in Kigali City”. Again, we examine the significance of the predictor "</span>project risk planning<span>" in the regression model. The "F" statistic for the "Regression" component is 467.870, and the associated "Sig." (p-value) is very close to zero (0.000). Therefore, based on the analysis, </span>project risk planning <span>also has a significant influence on the performance of housing construction projects in Kigali City.</span> <span> </span></span></p> <p><span> </span></p> <p><strong><span>Key words: </span></strong><em><span>project planning; performance of Housing Construction Projects; </span></em><em><span>project scope planning; project resource planning; project risk planning</span></em></p>
Analysis of Influencing Factors and Construction of Prediction Models of Artificial Joint Replacement in China
ClinicalTrials.gov study NCT05170321. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Do constructional constraints influence cyprinid (Cyprinidae: Leuciscinae) craniofacial coevolution?
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Data from: Genetic variation in social environment construction influences the development of aggressive behavior in Drosophila melanogaster
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Cohort Construction for Preterm Infants With Growth Retardation and Its Influencing Factors
ClinicalTrials.gov study NCT04817878. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A geospatial big data cloud computing-based method for constructing spatiotemporal dataset of dengue influencing factors in Brazil
<p>This datasets includes 12 dengue-associated factors of 418 epi weeks from 2013 to 2020 in microregion-level in Brazil.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.