Moreover, we present an overview of the central aspects of small RNA as regulators of gene expression connecting system networks and the potential application into plant biotechnology.Plants, as biological systems, are organized and regulated by a complex network of interactions from the genetic to the morphological level and suffer substantial influence from the environment. Reductionist approaches have been widely used in plant biology but have failed to reveal the mechanisms by which plants can growth under adverse conditions. It seems likely, therefore, that to understand the complexity of plant metabolic responses it is necessary to adopt non-reductionist approaches such as those from systems biology. Although such approaches seem methodologically complex to perform and difficult to interpret, they have been successfully applied in both metabolic and gene expression networks in a wide range of microorganisms and more recently in plants. Given the advance of techniques that allow complex analysis of plant cells, high quantities of data are currently generated and are available for in silico analysis and mathematical modeling. It is increasingly recognized, therefore, that the use of different methods such as graph analysis and dynamic network modeling are needed to better understand this abundance of information. However, before these practical advances, one of the main challenges currently in plant biology is to change the paradigm from the classical reductionism to the systemic level, which requires not only scientific but also educational changes.Eukaryotic DNA exist in the nuclei in the form of a complex with proteins called chromatin. Access to the information encoded in the DNA requires the opening of the chromatin. Modulation of the chromatin structure is therefore an important layer of regulation for DNA-templated processes. The basic unit of the chromatin is the nucleosome, which contains DNA wrapped around an octamer of histones, H2A, H2B, H3, and H4. Because histones are a structural part of the nucleosome, its modification can lead to changes in chromatin structure. Amino acid residues in histones could be modified with at least 20 different types of functional groups leading to a vast number of modified residues. Here, an overview of the histone modifications found in plants is provided. We focus mainly in proteomic-based studies either aimed to identify PTMs on purified histones or proteome-wide analysis of particular modifications. The strategies used for cataloging modifications in plants are also described. Profiling of histone modifications is important to begin to understand their functions as mediators of gene regulation in plant biological systems.Biological networks can be defined as a set of molecules and all the interactions among them. Their study can be useful to predict gene function, phenotypes, and regulate molecular patterns. Probabilistic graphical models (PGMs) are being widely used to integrate different data sources with modeled biological networks. The inference of these models applied to large-scale experiments of molecular biology allows us to predict influences of the experimental treatments in the behavior/phenotype of organisms. Here, we introduce the main types of PGMs and their applications in a biological networks context.Any part of the Central Dogma of Molecular Biology (DNA replication, transcription, and translation) is based on an intricate protein-protein interaction. On this chapter, we will navigate over the techniques that enable us to construct or fulfill the gaps on an interactome study, directly using assessment of the biochemical and/or molecular machinery that allow two proteins to interact with each other; or rely on computational biology techniques to gather information on PPI from public available databases and evaluate this interaction.Metabolomics is a valuable approach used to acquire comprehensive information about the set of metabolites in a cell or tissue, enabling a functional screen of the cellular activities in biological systems. Although metabolomics provides a more immediate and dynamic picture of phenotypes in comparison to the other omics, it is also the most complicated to measure because no single analytical technology can capture the extraordinary complexity of metabolite diversity in terms of structure and physical properties. Metabolomics has been extensively employed for a wide range of applications in plant science, which will be described in detail in this chapter. Among them, metabolomics is used for discriminating patterns of plant responses to genetic and environmental perturbations, as diagnostics and prediction tool to elucidate the function of genes for important and complex agronomic traits in crop species, and flux measurements are used to dissect the structure and regulatory properties of metabolic networks.In eukaryotic organisms, subcellular protein location is critical in defining protein function and understanding sub-functionalization of gene families. Some proteins have defined locations, whereas others have low specificity targeting and complex accumulation patterns. There is no single approach that can be considered entirely adequate for defining the in vivo location of all proteins. By combining evidence from different approaches, the strengths and weaknesses of different technologies can be estimated, and a location consensus can be built. The Subcellular Location of Proteins in Arabidopsis database ( http//suba.live/ ) combines experimental data sets that have been reported in the literature and is analyzing these data to provide useful tools for biologists to interpret their own data. Foremost among these tools is a consensus classifier (SUBAcon) that computes a proposed location for all proteins based on balancing the experimental evidence and predictions. Further tools analyze sets of proteins to dlocation layer, inform models of compartmented cell function and protein-protein interaction network, guide future molecular crop breeding strategies, or simply answer a specific question-where is my protein of interest inside the cell?Proteome analysis of model and non-model plants is a genuine scientific field in expansion. Several technological advances have contributed to the implementation of different proteomics approaches for qualitative and quantitative analysis of the dynamics of cellular responses at the protein level. The design of time-resolved experiments and the emergent use of multiplexed proteome analysis using chemical or isotopic and isobaric labeling strategies as well as label-free approaches are generating a vast amount of proteomics data that is going to be essential for analysis of protein posttranslational modifications and implementation of systems biology approaches. Through the target proteomics analysis, especially the ones that combine the untargeted methods, we should expect an improvement in the completeness of the identification of proteome and reveal nuances of regulatory cellular mechanisms related to plant development and responses to environmental stresses. Both genomic sequencing and proteomic advancements in the last decades coupled to integrative data analysis are enriching biological information that was once confined to model plants. Therewith, predictions of a changing environment places proteomics as an especially useful tool for crops performance.The collection of all transcripts in a cell, a tissue, or an organism is called the transcriptome, or meta-transcriptome when dealing with the transcripts of a community of different organisms. Nowadays, we have a vast array of technologies that allow us to assess the (meta-)transcriptome regarding its composition (which transcripts are produced) and the abundance of its components (what are the expression levels of each transcript), and we can do this across several samples, conditions, and time-points, at costs that are decreasing year after year, allowing experimental designs with ever-increasing complexity. Here we will present the current state of the art regarding the technologies that can be applied to the study of plant transcriptomes and their applications, including differential gene expression and coexpression analyses, identification of sequence polymorphisms, the application of machine learning for the identification of alternative splicing and ncRNAs, and the ranking of candidate genes for downstream studies. We continue with a collection of examples of these approaches in a diverse array of plant species to generate gene/transcript catalogs/atlases, population mapping, identification of genes related to stress phenotypes, and phylogenomics. We finalize the chapter with some of our ideas about the future of this dynamic field in plant physiology.How the complexity of biological systems can be understood is currently limited by the amount of biological information we have available to be incorporate in the vastitude of possibilities that could represent how a biological organism function. This point of view is, of course, alive under the paradigm that describes a living thing as a whole that could never be interpreted as to the sole understanding of its separated parts.If we are going to achieve the knowledge to understand all the complex relations between the molecules, pathways, organelles, cells, organs, phenotypes, and environments is unknown. However, that is exactly what moves us toward digging the most profound nature of relationships present in the living organisms.During the last 20 years, a big workforce was dedicated to the development of techniques, instruments, and scientific approaches that guided a whole new generation of scientists into the universe of omics approaches. The implementation of technological advances in several omics apply available and the ones to come. This unification may represent the necessary breakthrough to the achievement of the understanding of complex phenomena. To do so, the inclusion of systems biology thinking into the training of undergraduate and graduate students of plant sciences and related areas seems to be also a contribution that is necessary to be organized and implemented in a worldwide scale.Portal vein aneurysms are rare abnormal dilations of the portal vein and represent less than 3% of all visceral aneurysms. They may be congenital or acquired, symptomatic or asymptomatic, complicated or uncomplicated. Portal vein aneurysms may be fusiform or saccular and this last one has a low prevalence. Due to the small number of cases reported in the medical literature and the lack of specific guidelines, the management and treatment of this condition is still undefined. In this review, we report a case of saccular portal vein aneurysm in a 73-year old man with liver cirrhosis and discuss all cases of portal vein aneurysms reported in literature.
To increase the number of physician-scientists in research, the Drug Abuse Research Training (DART) program at the Medical University of South Carolina offers a 2-year research track for psychiatry residents and a 10-week summer fellowship for students. The goal of this study was to examine program outcomes and alumni diversity levels over DART's 15-year history.
To date, 215 trainees (44 residents, 171 summer fellows) have completed the program. Sodium oxamate in vivo An anonymous online survey was sent to the 143 program alumni with valid contact information. Survey data included demographic characteristics, post-program research involvement, and self-reported barriers to continued research engagement.
Overall survey completion response was 83.5% (N = 122). The alumni included 59.0% women, and 36.1% of respondents identified as a member of a minority racial/ethnic group. Following program completion, 77.0% of the alumni reported continued research involvement. More than half of the alumni reported scientific publications (57.Sodium oxamate in vivo
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