Genomic prediction exploits historical genotypic and phenotypic data to predict performance on selection candidates based only on their genotypes. It achieves this by a process known as training that derives the value...
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Genomic prediction exploits historical genotypic and phenotypic data to predict performance on selection candidates based only on their genotypes. It achieves this by a process known as training that derives the values of all the chromosome fragments that can be characterized by regressing the historical phenotypes on some or all of the genotyped loci. A genome-wide association study (GWAS) involves a genome-wide search for chromosome fragments with significant association with phenotype. One Bayesian approach to GWAS makes inferences using samples from the posterior distribution of genotypic effects obtained in the training phase of genomic prediction. Here we describe how to do this from commonly used Bayesian methods for genomic prediction, and we comment on how to interpret the results. less
Genome-wide association studies (GWAS) have benefited from the advances of sequencing methods for the generation of high-density genomic data. By bridging genotype to phenotype, several genes have been associated with...
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Genome-wide association studies (GWAS) have benefited from the advances of sequencing methods for the generation of high-density genomic data. By bridging genotype to phenotype, several genes have been associated with traits of agricultural interest. Despite this, there is still a gap between genotyping and phenotyping due to the large difference in throughput between the two disciplines. Although cutting-edge phenomics technologies are available to the community, their costs are still prohibitive at the small lab level. Semiautomated methods of investigation provide a valid alternative to generate large-scale phenotyping data able to deeply investigate the characteristics of different plant organs. Beyond automation, phenomics data management is another major constraint to consider; while bioinformatics pipelines are well-trained forreleasing high-quality genomic data, fewer efforts have been done for phenotyping information. This chapter provides a guide for generating large-scale data related to the size and shape of fruits, leaves, seeds, and roots and for downstream analysis for curation and preparation of clean datasets, through removal of outliers and performing primary statistical analysis. Different steps to be carried out in the r environment will be shown for gathering the appropriate input information to use in GWAS avoiding any possible bias. less
In silico analysis of Big Data is a useful tool to identify putative kinase targets as well as nodes of signaling cascades that are difficult to discover by traditional single molecule experimentation. System approach...
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In silico analysis of Big Data is a useful tool to identify putative kinase targets as well as nodes of signaling cascades that are difficult to discover by traditional single molecule experimentation. System approaches that use a multi-tiered investigational methodology have been instrumental in advancing our understanding of cellular mechanisms that result in phenotypic changes. Here, we present a bioinformatics approach to identify AMP-activated protein kinase (AMPK) target proteins on a proteome-wide scale and an in vitro method for preliminary validation of these targets. This approach offers an initial screening for the identification of AMPK targets that can be further validated using mutagenesis and molecular biology techniques. less
Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on ...
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Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on users' engagement, namely their willingness to answer, comment or post technical questions. Still, many of these questions (including cybersecurity-related ones) remain unanswered, putting the site's relevance and reputation into jeopardy. Hence, it is important to understand users' participation in privacy and security discussions to promote engagement and foster the exchange of such expertise. Objective: Based on prior findings on online social networks, this work elaborates on the interplay between users' engagement and their privacy practices in SO. Particularly, it analyses developers' self-disclosure behaviourregarding profile visibility and their involvement in discussions related to privacy and security. Method: We followed a mixed-methods approach by (i) analysing SO data from 1239 cybersecurity-tagged questions along with 7048 user profiles, and (ii) conducting an anonymous online survey (N=64). results: About 33% of the questions we retrieved had no answer, whereas more than 50% had no accepted answer. We observed that proactive users tend to disclose significantly less information in their profiles than reactive and unengaged ones. However, no correlations were found between these engagement categories and privacy-related constructs such as perceived control or general privacy concerns. Implications: These findings contribute to (i) a better understanding of developers' engagement towards privacy and security topics, and (ii) to shape strategies promoting the exchange of cybersecurity expertise in SO.
Introduction: There are two main schools of thought about statistical inference: frequentist and Bayesian. The frequentist approach relies solely on available data for predictions, while the Bayesian approach incorpor...
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Introduction: There are two main schools of thought about statistical inference: frequentist and Bayesian. The frequentist approach relies solely on available data for predictions, while the Bayesian approach incorporates both data and prior knowledge about the event of interest. Bayesian methods were developed hundreds of years ago;however, they were rarely used due to computational challenges and conflicts between the two schools of thought. recent advances in computational capabilities and a shift toward leveraging prior knowledge for inferences have led to increased use of Bayesian ***: Many biostatisticians with expertise in frequentist approaches lack the skills to apply Bayesian techniques. To address this gap, four faculty experts in Bayesian modeling at the University of Michigan developed a practical, customized workshop series. The training, tailored to accommodate the schedules of full-time staff, focused on immersive, project-based learning rather than traditional lecture-based methods. Surveys were conducted to assess the impact of the ***: All 20 participants completed the program and when surveyed reported an increased understanding of Bayesian theory and greater confidence in using these techniques. Capstone projects demonstrated participants' ability to apply Bayesian methodology. The workshop not only enhanced the participants' skills but also positioned them to readily apply Bayesian techniques in their ***: Accommodating the schedules of full-time biostatistical staff enabled full participation. The immersive project-based learning approach resulted in building skills and increasing confidence among staff statisticians who were unfamiliar with Bayesian methods and their practical applications.
Effective data visualisation is vital for data exploration, analysis and communication in research. In ecology and evolutionary biology, data are often associated with various taxonomic entities. Graphics of organisms...
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Climate change poses the most significant threat to humanity today. This study examines the global warming trend by analyzing temperature changes over the past century, uncovering alarming results. Various models, inc...
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Estimating the average environmental impacts of a represen-tative crop in a specific region is a helpful starting point from which to propose improvements in the agricultural sector. However, data collection from offi...
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Estimating the average environmental impacts of a represen-tative crop in a specific region is a helpful starting point from which to propose improvements in the agricultural sector. However, data collection from official representative sources is complex, and often they require subsequent treatment to be transformed into meaningful inventory data. This article shows a comprehensive dataset for obtaining inventory data and developing an environmental life cycle impact assess-ment of representative agricultural production corresponding to reference holdings at a regional level (NUTS 2) in Spain. The dataset comprises Excel files with the data compiled from secondary sources to be used in the assessment and the r code scripts to transform the data into relevant inventory data to estimate the environmental impacts of the reference holdings. This dataset is a reliable tool forresearchers and other potential users to be used as a secondary information source for further studies. It can also be used to estimate the environmental impacts of the farming activity of agri-food products in otherregions or countries by collecting similar data for the specific region and adjusting the r code.
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