Academic literature on the topic 'Biological interactions data'
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Journal articles on the topic "Biological interactions data"
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Du, Yinhao, Kun Fan, Xi Lu, and Cen Wu. "Integrating Multi–Omics Data for Gene-Environment Interactions." BioTech 10, no. 1 (2021): 3. http://dx.doi.org/10.3390/biotech10010003.
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Gene-environment (G×E) interaction is critical for understanding the genetic basis of complex disease beyond genetic and environment main effects. In addition to existing tools for interaction studies, penalized variable selection emerges as a promising alternative for dissecting G×E interactions. Despite the success, variable selection is limited in terms of accounting for multidimensional measurements. Published variable selection methods cannot accommodate structured sparsity in the framework of integrating multiomics data for disease outcomes. In this paper, we have developed a novel variable selection method in order to integrate multi-omics measurements in G×E interaction studies. Extensive studies have already revealed that analyzing omics data across multi-platforms is not only sensible biologically, but also resulting in improved identification and prediction performance. Our integrative model can efficiently pinpoint important regulators of gene expressions through sparse dimensionality reduction, and link the disease outcomes to multiple effects in the integrative G×E studies through accommodating a sparse bi-level structure. The simulation studies show the integrative model leads to better identification of G×E interactions and regulators than alternative methods. In two G×E lung cancer studies with high dimensional multi-omics data, the integrative model leads to an improved prediction and findings with important biological implications.
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Salim, José Augusto, Antonio Saraiva, Kayna Agostini, et al. "Brazilian Network on Plant-Pollinator Interactions: an update on the initiative of a standard for plant-pollinator interactions data." Biodiversity Information Science and Standards 2 (May 21, 2018): e25343. https://doi.org/10.3897/biss.2.25343.
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The Brazilian Plant-Pollinator Interactions Network*1 (REBIPP) aims to develop scientific and teaching activities in plant-pollinator interaction. The main goals of the network are to: generate a diagnosis of plant-pollinator interactions in Brazil; integrate knowledge in pollination of natural, agricultural, urban and restored areas; identify knowledge gaps; support public policy guidelines aimed at the conservation of biodiversity and ecosystem services for pollination and food production; and encourage collaborative studies among REBIPP participants. To achieve these goals the group has resumed and built on previous works in data standard definition done under the auspices of the IABIN-PTN (Etienne Américo et al. 2007) and FAO (Saraiva et al. 2010) projects (Saraiva et al. 2017). The ultimate goal is to standardize the ways data on plant-pollinator interactions are digitized, to facilitate data sharing and aggregation. A database will be built with standardized data from Brazilian researchers members of the network to be used by the national community, and to allow sharing data with data aggregators. To achieve those goals three task groups of specialists with similar interests and background (e.g botanists, zoologists, pollination biologists) have been created. Each group is working on the definition of the terms to describe plants, pollinators and their interactions. The glossary created explains their meaning, trying to map the suggested terms into Darwin Core (DwC) terms, and following the TDWG Standards Documentation Standard*2 in definition. Reaching a consensus on terms and their meaning among members of each group is challenging, since researchers have different views and concerns about which data are important to be included into a standard. That reflects the variety of research questions that underlie different projects and the data they collect. Thus, we ended up having a long list of terms, many of them useful only in very specialized research protocols and experiments, sometimes rarely collected or measured. Nevertheless we opted to maintain a very comprehensive set of terms, so that a large number of researchers feel that the standard meets their needs and that the databases based on it are a suitable place to store their data, thus encouraging the adoption of the data standard. An update of the work will soon be available at REBIPP website and will be open for comments and contributions. This proposal of a data standard is also being discussed within the TDWG Biological Interaction Data Interest Group*3 in order to propose an international standard for species interaction data. The importance of interaction data for guiding conservation practices and ecosystem services provision management has led to the proposal of defining Essential Biodiversity Variables (EBVs) related to biological interactions. Essential Biodiversity Variables (Pereira et al. 2013) were developed to identify key measurements that are required to monitoring biodiversity change. EBVs act as intermediate abstract layer between primary observations (raw data) and indicators (Niemeijer 2002). Five EBV classes have been defined in an initial stage: genetic composition, species populations, species traits, community composition, ecosystem function and ecosystem structure. Each EBV class defines a list of candidate EBVs for biodiversity change monitoring (Fig. 1). Consequently, digitalization of such data and making them available online are essential. Differences in sampling protocols may affect data scalability across space and time, hence imposing barriers to the full use of primary data and EBVs calculation (Henry et al. 2008). Thus, common protocols and methods should be adopted as the most straightforward approach to promote integration of collected data and to allow calculation of EBVs (Jürgens et al. 2011). Recently a Workshop was held by GLOBIS B*4 (GLOBal Infrastructures for Supporting Biodiversity research) to discuss Species Interactions EBVs (February, 26-28, Bari, Italy). Plant-pollinator interactions deserved a lot of attention and REBIPP's work was presented there. As an outcome we expect to define specific EBVs for interactions, and use plant-pollinators as an example, considering pairwise interactions as well as interaction network related variables. The terms in the plant-pollinator data standard under discussion at REBIPP will provide information not only on EBV related with interactions, but also on other four EBV classes: species populations, species traits, community composition, ecosystem function and ecosystem structure. As we said, some EBVs for specific ecosystem functions (e.g. pollination) lay beyond interactions network structures. The EBV 'Species interactions' (EBV class 'Community composition') should incorporate other aspects such as frequency (Vázquez et al. 2005), duration and empirical estimates of interaction strengths (Berlow et al. 2004). Overall, we think the proposed plant-pollinator interaction data standard which is currently being developed by REBIPP will contribute to data aggregation, filling many data gaps and can also provide indicators for long-term monitoring, being an essential source of data for EBVs.
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Allen, S., S. M. Rigby-Singleton, H. Harris, M. C. Davies, and P. O'Shea. "Measuring and visualizing single molecular interactions in biology." Biochemical Society Transactions 31, no. 5 (2003): 1052–57. http://dx.doi.org/10.1042/bst0311052.
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In recent years, considerable attention has focused upon the biological applications of the atomic force microscope (AFM), and in particular in its ability to explore biomolecular interaction events at the single molecule level. Such measurements can provide considerable advantages, as they remove the data averaging inherent in other biophysical/biochemical approaches that record measurements over large ensembles of molecules. To this end AFM has been used for both the high-resolution imaging of a range of individual biological molecules and their complexes, and to record interaction forces between single interacting molecules. In a recently initiated project we have begun to utilize these approaches to explore the interactions of a range of biologically important peptides with model and cell membrane surfaces. In this review, the potential value of AFM for the investigation of a range of biomolecular interaction events will be discussed, but highlighting in particular its potential for the study of interactions of peptides/proteins with biological membranes.
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Pinheiro, Rafael, Leonardo Jorge, and Thomas Lewinsohn. "Classification of Biological Interactions: Challenges in the field and in analysis." Biodiversity Information Science and Standards 5 (September 14, 2021): e74375. https://doi.org/10.3897/biss.5.74375.
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Within biological communities, species interact in a wide variety of ways. Species interactions have always been noted and classified by naturalists in describing living organisms and their ways. Moreover, they are essential to characterize ecological communities as functioning entities.Biodiversity databases, as a rule, are comprised of species records in certain localities and times. Many, if not most, originated as databases of museum specimens and/or published records. As such, they provide data on species occurrences and distribution, with little functional information. Currently, online databases for species interaction data are being formed or proposed. Usually, these databases set out to compile data from actual field studies, and their design reflects the singularities of particular studies that seed their development. In two online databases: the Web of Life (2021) and the Interaction Web DataBase (2020) (IWDB), the categories of interactions are quite heterogeneous (Table 1). For instance, they may refer explicitly to certain taxonomic groups (e.g., anemone-fish), or do so implicitly (host-parasitoid; parasitoids are all holometabolous insects with arthropod hosts); conversely, they may encompass almost any taxon (food webs). In another example, the Global Biotic Interactions database (Poelen et al. 2014) (GloBI) offers a choice of relational attributes when entering data, ranging from undefined to quite restricted (Table 2).Here we intend to contribute to the development of interaction databases, from two different points of view. First, what categories can be effectively applied to field observations of biotic interactions? Second, what theoretical and applied questions do we expect to address with interaction databases? These should be equally applicable to comparisons of studies of the same kind or mode of interaction, and to contrasts between interactions in multimodal studies.
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Milano, Marianna, Giuseppe Agapito, and Mario Cannataro. "Challenges and Limitations of Biological Network Analysis." BioTech 11, no. 3 (2022): 24. http://dx.doi.org/10.3390/biotech11030024.
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High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms’ properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein–Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein–protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment.
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Salim, José Augusto, Katja Seltmann, Jorrit Poelen, and Antonio Saraiva. "Indexing Biotic Interactions in GBIF data." Biodiversity Information Science and Standards 6 (August 23, 2022): e93565. https://doi.org/10.3897/biss.6.93565.
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The Global Biodiversity Information Facility (GBIF 2022a) has indexed more than 2 billion occurrence records from 70,147 datasets. These datasets often include "hidden" biotic interaction data because biodiversity communities use the Darwin Core standard (DwC, Wieczorek et al. 2012) in different ways to document biotic interactions. In this study, we extracted biotic interactions from GBIF data using an approach similar to that employed in the Global Biotic Interactions (GloBI; Poelen et al. 2014) and summarized the results. Here we aim to present an estimation of the interaction data available in GBIF, showing that biotic interaction claims can be automatically found and extracted from GBIF. Our results suggest that much can be gained by an increased focus on development of tools that help to index and curate biotic interaction data in existing datasets. Combined with data standardization and best practices for sharing biotic interactions, such as the initiative on plant-pollinators interaction (Salim 2022), this approach can rapidly contribute to and meet open data principles (Wilkinson 2016).We used Preston (Elliott et al. 2020), open-source software that versions biodiversity datasets, to copy all GBIF-indexed datasets. The biodiversity data graph version (Poelen 2020) of the GBIF-indexed datasets used during this study contains 58,504 datasets in Darwin Core Archive (DwC-A) format, totaling 574,715,196 records. After retrieval and verification, the datasets were processed using Elton. Elton extracts biotic interaction data and supports 20+ existing file formats, including various types of data elements in DwC records. Elton also helps align interaction claims (e.g., host of, parasite of, associated with) to the Relations Ontology (RO, Mungall 2022), making it easier to discover datasets across a heterogeneous collection of datasets. Using specific mapping between interaction claims found in the DwC records to the terms in RO*1, Elton found 30,167,984 <em>potential records</em> (with non-empty values for the scanned DwC terms) and 15,248,478 records with recognized interaction types.Taxonomic name validation was performed using Nomer, which maps input names to names found in a variety of taxonomic catalogs. We only considered an interaction record valid where the interaction type could be mapped to a term in RO and where Nomer found a valid name for <em>source </em>and <em>target </em>taxa. Based on the workflow described in Fig. 1, we found 7,947,822 interaction records (52% of the <em>potential </em>interactions). Most of them were generic interactions (<em>interacts_</em><em>with</em>, 87.5%), but the remaining 12.5% (993,477 records) included host-parasite and plant-animal interactions. The majority of the interactions records found involved plants (78%), animals (14%) and fungi (6%).In conclusion, there are many biotic interactions embedded in existing datasets registered in large biodiversity data indexers and aggregators like iDigBio, GBIF, and BioCASE. We exposed these biotic interaction claims using the combined functionality of biodiversity data tools Elton (for interaction data extraction), Preston (for reliable dataset tracking) and Nomer (for taxonomic name alignment). Nonetheless, the development of new vocabularies, standards and best practice guides would facilitate aggregation of interaction data, including the diversification of the GBIF data model (GBIF 2022b) for sharing biodiversity data beyond occurrences data. That is the aim of the TDWG Interest Group on Biological Interactions Data (TDWG 2022).
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Markovic, Svetomir, Allan Sprau, Virginia McElroy, Wendy Kay Nevala, Alexey Leontovich, and Harold Frisch. "Cancer Immune Control Dynamics (CICD): A clinical data driven model of systemic immunity." Journal of Clinical Oncology 37, no. 15_suppl (2019): e21058-e21058. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e21058.
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e21058 Background: Recent clinical advances in cancer immuno-therapeutics underscore the need for improved understanding of the complex relationship between cancer and the multiple, multi-functional, inter-dependent, cellular and humoral mediators/regulators of the human immune system. This will lead to improvements in the efficacy of existing immune therapies (patient selection; drug combinations) and insight into new therapeutic targets that may significantly reduce the time to new therapeutics discoveries capable of meaningful clinical impacts. Methods: A systems analysis approach (Cancer Immune Control Dynamics, CICD) attempts to identify the differences between systemic immune homeostasis of healthy volunteers versus patients with metastatic malignant melanoma based on serial measurements of a set of conventional peripheral blood biomarkers of immune function (15 cell subsets and 35 cytokines) assessed daily over 7-10 days. The modeling strategy takes aerospace engineering control theory and system identification algorithms to analyze the state of an individual’s immune system based on the biomarkers’ dynamic non-linear oscillatory behaviors. The analysis framework builds on the classical predator-prey equations commonly used to model biological system dynamics. Tens of thousands of biologically possible biomarker interactions are modeled by a set of matrix equations that creates a system interaction model. CICD quantifies the system equations with biomarker values and solves it to measure the interactions. Results: Quantification of thousands of unique biomarker relationships per participant enables classification based on their immune profile rather than their clinical diagnosis and a basis for a visualization of a person’s immunity state. Conclusions: Results provide initial evidence that a model-based analysis of serially collected biomarker data can lead to biological insights into human immunity, of potential benefit in clinical decisions. It is anticipated that CICD-based capabilities will provide tools to specifically address cancer and treatment modulated (immune checkpoint inhibitors) parameters of human immunity, revealing clinically relevant biological interactions.
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Rogers, Mark F., Colin Campbell, and Yiming Ying. "Probabilistic Inference of Biological Networks via Data Integration." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/707453.
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There is significant interest in inferring the structure of subcellular networks of interaction. Here we consider supervised interactive network inference in which a reference set of known network links and nonlinks is used to train a classifier for predicting new links. Many types of data are relevant to inferring functional links between genes, motivating the use of data integration. We use pairwise kernels to predict novel links, along with multiple kernel learning to integrate distinct sources of data into a decision function. We evaluate various pairwise kernels to establish which are most informative and compare individual kernel accuracies with accuracies for weighted combinations. By associating a probability measure with classifier predictions, we enable cautious classification, which can increase accuracy by restricting predictions to high-confidence instances, and data cleaning that can mitigate the influence of mislabeled training instances. Although one pairwise kernel (the tensor product pairwise kernel) appears to work best, different kernels may contribute complimentary information about interactions: experiments inS. cerevisiae(yeast) reveal that a weighted combination of pairwise kernels applied to different types of data yields the highest predictive accuracy. Combined with cautious classification and data cleaning, we can achieve predictive accuracies of up to 99.6%.
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Gill, Jaskaran Kaur, Madhu Chetty, Suryani Lim, and Jennifer Hallinan. "Large language model based framework for automated extraction of genetic interactions from unstructured data." PLOS ONE 19, no. 5 (2024): e0303231. http://dx.doi.org/10.1371/journal.pone.0303231.
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Extracting biological interactions from published literature helps us understand complex biological systems, accelerate research, and support decision-making in drug or treatment development. Despite efforts to automate the extraction of biological relations using text mining tools and machine learning pipelines, manual curation continues to serve as the gold standard. However, the rapidly increasing volume of literature pertaining to biological relations poses challenges in its manual curation and refinement. These challenges are further compounded because only a small fraction of the published literature is relevant to biological relation extraction, and the embedded sentences of relevant sections have complex structures, which can lead to incorrect inference of relationships. To overcome these challenges, we propose GIX, an automated and robust Gene Interaction Extraction framework, based on pre-trained Large Language models fine-tuned through extensive evaluations on various gene/protein interaction corpora including LLL and RegulonDB. GIX identifies relevant publications with minimal keywords, optimises sentence selection to reduce computational overhead, simplifies sentence structure while preserving meaning, and provides a confidence factor indicating the reliability of extracted relations. GIX’s Stage-2 relation extraction method performed well on benchmark protein/gene interaction datasets, assessed using 10-fold cross-validation, surpassing state-of-the-art approaches. We demonstrated that the proposed method, although fully automated, performs as well as manual relation extraction, with enhanced robustness. We also observed GIX’s capability to augment existing datasets with new sentences, incorporating newly discovered biological terms and processes. Further, we demonstrated GIX’s real-world applicability in inferring E. coli gene circuits.
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Trivellone, Valeria, Christopher H. Dietrich, Dmitry Dmitriev, and Matthew Yoder. "TaxonWorks: A Use Case in Documenting Complex Biological Relationships." Biodiversity Information Science and Standards 2 (May 22, 2018): e25723. http://dx.doi.org/10.3897/biss.2.25723.
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Compilation and retrieval of reliable data on biological interactions is one of the critical bottlenecks affecting efficiency and statistical power in testing ecological theories. TaxonWorks, a web-based workbench, can facilitate such research by enabling the digitization of complex biological interactions involving multiple species, individuals, and trophic levels. These data can be further organized into spatial and temporal axes, and annotated at the level of individual or grouped interactions (e.g. singularly citing the combined elements of a tritrophic interaction). The simple, customizable nature of tools ultimately reduces the time-consuming steps of data gathering, cleaning, and formatting of datasets for subsequent exploration and analysis while also improving the asserted semantics. An example use case is provided with a dataset of associations among plants, pathogens and insect vectors. The curated data are accessed through the JSON serving TaxonWorks API (Application Programming Interface) by an R package. Analysis and visualization of the network graphs persisted in TaxonWorks is demonstrated using core R functionality and the igraph package (Csardi and Nepusz 2006). TaxonWorks is open-source, collaboratively built software available at http://taxonworks.org.
Dissertations / Theses on the topic "Biological interactions data"
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Bar-Joseph, Ziv 1971. "Inferring interactions, expression programs and regulatory networks from high throughput biological data." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/28289.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (leaves 171-180).<br>(cont.) For the networks level I present an algorithm that efficiently combines complementary large-scale expression and protein-DNA binding data to discover co-regulated modules of genes. This algorithm is extended so that it can infer sub-networks for specific systems in the cell. Finally, I present an algorithm which combines some of the above methods to automatically infer a dynamic sub-network for the cell cycle system.<br>In this thesis I present algorithms for analyzing high throughput biological datasets. These algorithms work on a number of different analysis levels to infer interactions between genes, determine gene expression programs and model complex biological networks. Recent advances in high-throughput experimental methods in molecular biology hold great promise. DNA microarray technologies enable researchers to measure the expression levels of thousands of genes simultaneously. Time series expression data offers particularly rich opportunities for understanding the dynamics of biological processes. In addition to measuring expression data, microarrays have been recently exploited to measure genome-wide protein-DNA binding events. While these types of data are revolutionizing biology, they also present many computational challenges. Principled computational methods are required in order to make full use of each of these datasets, and to combine them to infer interactions and discover networks for modeling different systems in the cell. The algorithms presented in this thesis address three different analysis levels of high throughput biological data: Recovering individual gene values, pattern recognition and networks. For time series expression data, I present algorithms that permit the principled estimation of unobserved time-points, alignment and the identification of differentially expressed genes. For pattern recognition, I present algorithms for clustering continuous data, and for ordering the leaves of a clustering tree to infer expression programs.<br>by Ziv Bar-Joseph.<br>Ph.D.
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Lasher, Christopher Donald. "Discovering contextual connections between biological processes using high-throughput data." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/77217.
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Hearkening to calls from life scientists for aid in interpreting rapidly-growing repositories of data, the fields of bioinformatics and computational systems biology continue to bear increasingly sophisticated methods capable of summarizing and distilling pertinent phenomena captured by high-throughput experiments. Techniques in analysis of genome-wide gene expression (e.g., microarray) data, for example, have moved beyond simply detecting individual genes perturbed in treatment-control experiments to reporting the collective perturbation of biologically-related collections of genes, or "processes". Recent expression analysis methods have focused on improving comprehensibility of results by reporting concise, non-redundant sets of processes by leveraging statistical modeling techniques such as Bayesian networks.
Simultaneously, integrating gene expression measurements with gene interaction networks has led to computation of response networks--subgraphs of interaction networks in which genes exhibit strong collective perturbation or co-expression. Methods that integrate process annotations of genes with interaction networks identify high-level connections between biological processes, themselves. To identify context-specific changes in these inter-process connections, however, techniques beyond process-based expression analysis, which reports only perturbed processes and not their relationships, response networks, composed of interactions between genes rather than processes, and existing techniques in process connection detection, which do not incorporate specific biological context, proved necessary.
We present two novel methods which take inspiration from the latest techniques in process-based gene expression analysis, computation of response networks, and computation of inter-process connections. We motivate the need for detecting inter-process connections by identifying a collection of processes exhibiting significant differences in collective expression in two liver tissue culture systems widely used in toxicological and pharmaceutical assays. Next, we identify perturbed connections between these processes via a novel method that integrates gene expression, interaction, and annotation data. Finally, we present another novel method that computes non-redundant sets of perturbed inter-process connections, and apply it to several additional liver-related data sets. These applications demonstrate the ability of our methods to capture and report biologically relevant high-level trends.<br>Ph. D.
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Sachse, René. "Influence of climate change on lake ecosystems - disentangling physical, chemical and biological interactions." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-167312.
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Climate change affects lake ecosystems in many ways by direct and indirect temperature impacts on hydrophysical structure, hydrology, chemical cycles and on biological interactions. The identification of these climate signals can be complicated and superimposed by other environmental changes, such as land use changes. The aim of this thesis was to seperate temperature effects from effects of management and other environmental influences and to understand the underlying complex processes causing significant changes of ecosystem states. This understanding is important for decisions of lake and reservoir managers to counteract unfavorable consequences of climate change. For the recent study, long-term data of meteorological, hydrophysical and biological variables (phytoplankton, crustacean zooplankton, fish) of the German drinking water reservoir Saidenbach were analysed. Based on this long-term data set, strong indicators for climate induced changes have been identified. In particular, increasing water temperatures since 1975, earlier break up of ice covers and an altered hydrophysical structure could be detected. Thereby, stratification stability increased and turbulent mixing decreased in summer. The water of the reservoir warmed more than the tributaries resulting in a trend to deeper entrainment of the inflows. As further indicators of climate change, an increased annual biomass of phytoplankton and species shifts in the phytoplankton community in spring and summer had been observed. During the spring mass development, the diatom Aulacoseira subarctica became dominant in recent years with warm winters and early ice-out. Its unusual spatial pattern with occurrences in aphotic depths could be explained by easier resuspension compared to other diatoms. By being resuspended first and establishing a high inoculum, A. subarctica profits from an earlier ice-out and earlier full circulation. In spite of a reduced nutrient loading to the epilimnion, in summer, the diatom Fragilaria crotonensis was displaced increasingly by cyanobacteria. This species shift could be explained well by the hydrophysical regime shift. Although, the annual total phytoplankton biomass increased since 1990, the crustacean zooplankton in Saidenbach reservoir did not seem to profit from improved food resources. To the contrary, Daphnia abundances reduced tremendously. We could show that the influcence of fish stocks were underestimated. The stocked silvercarp may have contributed up to 70 % of the total zooplanktivorous fish biomass which had a temperature and density dependent effect. The faster growth of Daphnia at higher temperatures could not compensate for the more actively grazing fish when stock of zooplankitvorous fish was too high. Still, temperature was identified as the most important factor that explained 29 % of the zooplankton phenology, while the second most important predictors were zooplanktivorous fish biomass and nutrient loading, explaining 18 % of the variance. The importance of submerged macrophytes in shallow lakes is well investigated. To increase also the understanding of their impact and their role during climate change on water quality in deep lakes, a model for stratified lakes that includes submerged macrophytes was developed. The simulations showed that macrophyte effects were mainly positive for water quality and macrophytes in deep lakes were able to potentially reduce summer phytoplankton, especially cyanobacteria by 50 % in 11 m deep and still by 15 % in 100 m deep oligotrophic lakes. Nutrient competition with phytoplankton contributed most to this macrophyte effect. In conclusion, for deep lake restoration the re-establishment of submerged macrophytes might be as important as for shallow lakes. The full lake model includes hydrophysical and ecological submodules and thus will allow further comprehensive climate simulations and the evaluation of the effectivity of adaptive strategies and scenarios for deep lakes and reservoirs<br>Der Klimawandel beeinflusst Seeökosysteme vielfältig durch direkte und indirekte Temperatureffekte auf die hydrophysikalische Struktur, die Hydrologie, chemische Kreisläufe und biologische Interaktionen. Die Identifikation von Klimasignalen kann durch Landnutzungs- und weitere Umweltveränderungen überlagert werden. Ziel dieser Arbeit war es, Temperatureffekte von Effekten zu unterscheiden, die durch Gewässermanagement oder anderen Umweltveränderungen verursacht werden. Weiterhin sollten komplexe Prozesse verstanden werden, die zu signifikanten Veränderungen in Seeökosystemen führen. Dieses Verständnis ist für Talsperren- und Gewässermanager von besonderer Bedeutung, um ungewünschten Folgen des Klimawandels entgegenwirken zu können. Für die Studie wurden Langzeitdaten für meteorologische, hydrophysikalische und biologische Variablen (Phytoplankton, Crustaceen-Zooplankton, Fischbesatz) der Talsperre Saidenbach ausgewertet. Mehrere Indikatoren für die Auswirkungen des Klimawandels konnten basierend auf diesen Daten identifiziert werden. Diese gehören insbesondere eine Erhöhung der Wassertemperatur seit 1975, zeitigere Eisaufbrüche und eine veränderte Schichtungsstruktur. Dabei wurde im Sommer die Schichtungsstabilität höher und die Intensität der Durchmischung geringer. Das Wasser in der Talsperre erwärmte sich stärker als die Zuflüsse, wodurch diese sich zunehmend tiefer und unterhalb der Thermokline einschichten. Eine zunehmende jährliche Phytoplanktonbiomasse und Artenwechsel innerhalb der Planktongemeinschaft sind weitere Indikatoren für Klimafolgen. Die Kieselalge Aulacoseira subarctica erlangte in Jahren mit warmen Wintern und zeitigen Eisaufbrüchen eine Dominanz während der Frühjahrsentwicklung. Das ungewöhnliche Auftreten dieser Art in aphotischen Tiefen konnte durch eine leichtere Resuspension im Vergleich zu anderen Kieselalgen erklärt werden. Durch zeitige Resuspension gleich nach Eisaufbruch kann A. subarctica ein hohes Inokulum etablieren. Trotz reduzierter Nährstoffzufuhr ins Epilimnion wurde im Sommer die Kieselalge Fragilaria crotonensis vermehrt durch Cyanobakterien verdrängt, was jedoch gut mit den Änderungen der hydrophysikalischen Struktur erklärt werden kann. Obwohl seit 1990 insgesamt mehr Phytoplankton zur Verfügung steht konnte das Crustaceen- Zooplankton in der Talsperre Saidenbach nicht von diesen verbesserten Nahrungsbedingungen profitieren. Die Daphnia-Abundanzen waren seit 1990 stark verringert. Es konnte gezeigt werden, dass der Einfluss des Fischbesatzes unterschätzt wurde und die künstlich ins System eingebrachten Silberkarpfen zeitweise bis zu 70% der zooplanktivoren Fischbiomasse ausgemacht haben. Zoo- planktivore Fische haben einen temperatur- und dichteabhängigen Effekt auf das Zooplankton. Das schnellere Populationswachstum von Daphnia kann bei hohem Besatz mit zooplanktivoren Fischen nicht die erhöhte Fraßaktivität der Fische ausgleichen. Dennoch konnte Temperatur als der wichtigste Einflussfaktor auf die Phänologie des Zooplanktons identifiziert werden, gefolgt von zooplanktivorem Fisch und Nährstoffzufuhr. Die Wichtigkeit von submersen Makrophyten in Flachseen ist gut untersucht. Um den Einfluss von Makropyhten auf die Wasserqualität während des Klimawandels auch in tiefen Seen abschätzen zu können, wurde ein komplexes Seemodell entwickelt. Die Simulationen zeigen, dass sich Makrophyten hauptsächlich positiv auf die Wassergüte auswirken und zur Reduktion von Phytoplankton, insbesondere von Cyanobakterien im Sommer, beitragen. In einem 11 m tiefen See betrug die Reduktion 50 %, in 100 m tiefen oligotrophen Seen immer noch 15 %. Die Konkurrenz um Nährstoffe mit dem Phytoplankton war dabei die ursächliche Makrophyten-Plankton-Interaktion. Submerse Makrophyten könnten für die Restaurierung von tiefen Seen folglich genauso wichtig sein, wie für Flachseen. Das komplette Seemodell enthält hydrophysikalische und ökologische Module und ermöglicht damit weitere umfassende Simulationen zur Untersuchung der Auswirkungen des Klimawandels und zur Evaluation von Adaptionsstrategien für Seen und Talsperren
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López-Blanco, Efrén. "Ecosystem-atmosphere interactions in the Arctic : using data-model approaches to understand carbon cycle feedbacks." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/33101.
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The terrestrial CO2 exchange in the Arctic plays an important role in the global carbon (C) cycle. The Arctic ecosystems, containing a large amount of organic carbon (C), are experiencing ongoing warming in recent decades, which is affecting the C cycling and the feedback interactions between its different components. To improve our understanding of the atmosphere-ecosystem interactions, the Greenland Ecosystem Monitoring (GEM) program measures ecosystem CO2 exchange and links it to biogeochemical processes. However, this task remains challenging in northern latitudes due to an insufficient number of measurement sites, particularly covering full annual cycles, but also the frequent gaps in data affected by extreme conditions and remoteness. Combining ecosystem models and field observations we are able to study the underlying processes of Arctic CO2 exchange in changing environments. The overall aim of the research is to use data-model approaches to analyse the patterns of C exchange and their links to biological processes in Arctic ecosystems, studied in detail both from a measurement and a modelling perspective, but also from a local to a pan-arctic scale. In Paper I we found a compensatory response of photosynthesis (GPP) and ecosystem respiration (Reco), both highly sensitive to the meteorological drivers (i.e. temperatures and radiation) in Kobbefjord, West Greenland tundra. This tight relationship led to a relatively insensitive net ecosystem exchange (NEE) to the meteorology, despite the large variability in temperature and precipitations across growing seasons. This tundra ecosystem acted as a consistent sink of C (-30 g C m-2), except in 2011 (41 g C m-2), which was associated with a major pest outbreak. In Paper II we estimated this decrease of C sink strength of 118-144 g C m-2 in the anomalous year (2011), corresponding to 1210-1470 tonnes C at the Kobbefjord catchment scale. We concluded that the meteorological sensitivity of photosynthesis and respiration were similar, and hence compensatory, but we could not explain the causes. Therefore, in Paper III we used a calibrated and validated version of the Soil-Plant-Atmosphere model to explore full annual C cycles and detail the coupling between GPP and Reco. From this study we found two key results. First, similar metrological buffering to growing season reduced the full annual C sink strength by 60%. Second, plant traits control the compensatory effect observed (and estimated) between gross primary production and ecosystem respiration. Because a site-specific location is not representative of the entire Arctic, we further evaluated the pan-Arctic terrestrial C cycling using the CARDAMOM data assimilation system in Paper IV. Our estimates of C fluxes, pools and transit times are in good agreement with different sources of assimilated and independent data, both at pan-Arctic and local scale. Our benchmarking analysis with extensively used Global Vegetation Models (GVM) highlights that GVM modellers need to focus on the vegetation C dynamics, but also the respiratory losses, to improve our understanding of internal C cycle dynamics in the Arctic. Data-model approaches generate novel outputs, allowing us to explore C cycling mechanisms and controls that otherwise would not have been possible to address individually. Also, discrepancies between data and models can provide information about knowledge gaps and ecological indicators not previously detected from field observations, emphasizing the unique synergy that models and data are capable of bringing together.
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bickel, thomas. "Interactions polymères-membranes: une approche locale." Phd thesis, Université Louis Pasteur - Strasbourg I, 2001. http://tel.archives-ouvertes.fr/tel-00002391.
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L'insertion d'objets polymériques dans les solutions de molécules tensioactives conduit à des changements importants des propriétés macroscopiques: modification des constantes élastiques, séparations de phases, augmentation ou diminution de la viscosité. En particulier, le problème de polymères greffés sur des membranes fluides est l'objet d'intenses recherches du fait de ces nombreuses applications industrielles. L'objectif de cette thèse est de mieux comprendre les couplages entre conformations de la surface et des macromolécules: quand un polymère est greffé sur une paroi repulsive, certaines configurations autorisées en volume ne sont plus réalisables. La reduction d'entropie du polymère engendre alors une pression non homogène sur la surface. Les calculs peuvent être menés analytiquement pour une chaîne Gaussienne et montrent que le polymère exerce une force sur une région de taille comparable à son rayon de giration. Le problème important du volume exclu est introduit en notant que la pression est proportionnelle à la concentration en monomères au voisinage de la paroi. Cet argument de loi d'échelle est confirmé par des simulations Monte-Carlo, l'effet du volume exclu étant uniquement d'augmenter la portée de la pression. La surface de greffage étant déformable, celle-ci adopte un profil determiné par l'équilibre entre la contrainte appliquée par le polymère et l'énergie de courbure de la membrane. La déformation est d'abord conique au voisinage du point d'ancrage, puis relaxe avec une forme qui dépend des conditions aux limites. Dans le cas où plusieurs chaînes sont greffées, le champ de courbure de la membrane induit alors un potentiel d'interaction entre polymères. Ce potentiel est attractif (resp. répulsif) pour deux polymères ancrés du même côté (resp. du côté opposé) de la membrane. La dernière partie de la thèse est consacrée à l'étude du spectre de fluctuations d'une bicouche décorée. Contrairement à ce qu'indiquent les approches de champ moyen, l'integration explicite de tous les degrés de liberté des macromolécules montre que le module de courbure effectif dépend du vecteur d'onde considéré. Finalement, ce résultat est appliqué au calcul de l'exposant de Caillé d'une phase lamellaire décorée.
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Tiwari, Purushottam Babu. "Multimode Analysis of Nanoscale Biomolecular Interactions." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/1923.
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Biomolecular interactions, including protein-protein, protein-DNA, and protein-ligand interactions, are of special importance in all biological systems. These interactions may occer during the loading of biomolecules to interfaces, the translocation of biomolecules through transmembrane protein pores, and the movement of biomolecules in a crowded intracellular environment. The molecular interaction of a protein with its binding partners is crucial in fundamental biological processes such as electron transfer, intracellular signal transmission and regulation, neuroprotective mechanisms, and regulation of DNA topology. In this dissertation, a customized surface plasmon resonance (SPR) has been optimized and new theoretical and label free experimental methods with related analytical calculations have been developed for the analysis of biomolecular interactions.
Human neuroglobin (hNgb) and cytochrome c from equine heart (Cyt c) proteins have been used to optimize the customized SPR instrument. The obtained Kd value (~13 µM), from SPR results, for Cyt c-hNgb molecular interactions is in general agreement with a previously published result. The SPR results also confirmed no significant impact of the internal disulfide bridge between Cys 46 and Cys 55 on hNgb binding to Cyt c. Using SPR, E. coli topoisomerase I enzyme turnover during plasmid DNA relaxation was found to be enhanced in the presence of Mg2+. In addition, a new theoretical approach of analyzing biphasic SPR data has been introduced based on analytical solutions of the biphasic rate equations.
In order to develop a new label free method to quantitatively study protein-protein interactions, quartz nanopipettes were chemically modified. The derived Kd (~20 µM) value for the Cyt c-hNgb complex formations matched very well with SPR measurements (Kd ~16 µM). The finite element numerical simulation results were similar to the nanopipette experimental results. These results demonstrate that nanopipettes can potentially be used as a new class of a label-free analytical method to quantitatively characterize protein-protein interactions in attoliter sensing volumes, based on a charge sensing mechanism.
Moreover, the molecule-based selective nature of hydrophobic and nanometer sized carbon nanotube (CNT) pores was observed. This result might be helpful to understand the selective nature of cellular transport through transmembrane protein pores.
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Dessinges, Marie Noelle. "Proprietes elastiques d'une molécule d'ADN simple brin, et interactions ADN hélicases à l'échelle de la molécule unique." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2002. http://tel.archives-ouvertes.fr/tel-00001765.
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Dans la première partie de cette thèse, nous avons étudié l'élasticité d'une molécule d'ADN simple brin à l'aide de pinces magnétiques. Les modèles de polymères idéaux ne rendent pas bien compte de l'élasticité de cette molécule, alors qu'ils décrivent très bien l'ADN double brin. Nous avons donc étudié expérimentalement l'élasticité d'une molécule simple brin dans différentes conditions salines, i.e. d'écrantage de charges, et dans des conditions modulant les interactions internes du polymère. En utilisant des conditions dénaturantes, nous avons pu séparer les effets des différentes interactions intramoléculaires et ainsi mieux caractériser leur importance respective pour décrire l'élasticité de la molécule. Il est ressorti de ces mesures qu'une description complète de l?ADN simple brin doit prendre en compte d'une part la formation de structures secondaires à basses forces, et d'autre part les effets de volume exclu: contrairement au double brin, le simple brin est tellement flexible que les répulsions électrostatiques dues aux groupements phosphates le long de la chaîne ne peuvent plus être négligées. Des simulations numériques prenant en compte ces deux effets sont en excellent accord avec nos observations expérimentales. Dans la seconde partie de cette thèse, nous avons étudié l'activité de deux hélicases à l'échelle de la molécule unique. Les hélicases sont des moteurs moléculaires qui catalysent la séparation des deux brins de la double hélice d?ADN, permettant ainsi l'accès aux bases individuelles formant le code génétique. Dans notre expérience, une unique molécule d'ADN double brin était immobilisée par pinces magnétiques et son extension mesurée avec une précision de 10 nm. Ceci nous a permis de suivre l'activité des hélicases, puisque celles-ci provoquent un changement de l'extension de la molécule lorsque le double brin est converti en simple brin. Cette méthode présente l'avantage de permettre l'observation en temps réel de l'activité d'une seule protéine. L'analyse statistique des données nous a permis d'accéder aux distributions complètes de vitesse et de processivité des enzymes. Nous avons ainsi caractérisé le comportement dynamique d?hélicases uniques (leur vitesse, leur processivité, leur coopérativité et leur pas élémentaire sur l'ADN). Le suivi en temps réel de l'activité des hélicases nous a permis d'observer une instabilité dans leur mode d'ouverture, et ainsi de rendre compte de mesures de vitesse obtenues en volume.
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Chen, Li. "Searching for significant feature interaction from biological data." Diss., Online access via UMI:, 2007.
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LEVY, Raphael. "Interactions intra et inter moléculaires, conformation des polymères adsorbés, transition de phase sous étirement : que peut-on apprendre des mesures de force." Phd thesis, Université Louis Pasteur - Strasbourg I, 2002. http://tel.archives-ouvertes.fr/tel-00001565.
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Le microscope à force atomique (AFM) est un outil privilégié pour sonder la matière à l'échelle nanométrique. Dans cette thèse, nous l'utilisons comme instrument de mesure de force. Nous montrons que la mesure des fluctuations thermiques des ressorts permet de déterminer précisément leur raideur à condition d'utiliser un modèle qui prend en compte la forme des modes et la méthode de détection. A l'aide de nouvelle méthodes d'analyse des courbes de rétraction, nous étudions la conformation de polymères et de copolymères adsorbés, ainsi que l'interaction spécifique entre un complexe du nickel (Ni-NTA) et l'acide aminé histidine. Nous mettons en évidence des comportements inattendus en présence de liens multiples et nous en proposons une interprétation basée sur un équilibre entre formations et ruptures des liaisons. Nous présentons des premiers résultats expérimentaux concernant la transition conformationnelle de la polylysine sous étirement.
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Rodriguez, Palacios Miguel Andres. "Reversed Voodoo Dolls: An exploration of physical visualizations of biological data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-175796.
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Physical visualizations are artifacts that materialize abstract data. They take advantage of human natural abilities to interact with information in the physical world. These visualizations present an opportunity to be applied on new application domains. With the objective of discovering if physical visualizations can support remote monitoring of biological data, a technology probe is presented in the form of a reversed voodoo doll. This probe uses the natural affordance of an anthropomorphic figure to represent a person and reverses the concept of voodoo dolls in a playful way. The scenario of safety is selected for testing physical visualizations of bio-data. Two measurements from the human body, heart rate and motion are chosen as a light way to monitor remotely over a person’s conditions. During the study, a group of six participants were exposed to the technology probe and their interactions with it were observed. The study reports on the users’ interpretations of the data and uses given to the alternative modalities of the probe. The results suggest that the data mapping to the object’s body parts was effective for conveying meaning. Additionally, the results confirm that the use of multiple modalities in physical visualizations offers an opportunity to present information in situated contexts in the real world. The degree of physicality achieved by the reversed voodoo doll and the effects of the selected metaphors are discussed. In conclusion, it is argued that the responses and interpretations from the users indicate that the reversed voodoo doll served as a means in its own right to transmit information for monitoring of bio-data.<br>Fysiska visualiseringar är artefakter som materialiserar abstrakt data. Genom att använda sig av mänskliga naturliga förmågor interagerar de med information i den fysiska världen. Dessa visualiseringar skapar möjligheter för appliceringar inom nya tillämpningsområden. För att undersöka om fysiska visualiseringar kan stödja fjärrövervakning av biologisk data introducerades en sond i form av en omvänd voodoodocka. Med en människolik figur representerar denna sond en verklig person. På så sätt utnyttjar den naturliga associationer till mänskliga egenskaper och omvänder konceptet vodoodockor på ett lekfullt sätt. De fysiska visualiseringarna av biologisk data testas ur ett säkerhetsperspektiv. Två värden, hjärtfrekvens och rörelse, mäts från en människokropp för att göra det möjligt att övervaka en persons tillstånd på distans. Under studien observeras sex användare då de interagerar med sonden. Studien visar hur användarna tolkar sondens data och hur användningen varierar med avseende på sondens olika modaliteter. Resultaten från denna studie tyder på att datamappningen till sondens kroppsdelar effektivt ökade förståelsen. Dessutom bekräftar resultaten att användning av flera modaliteter i fysiska visualiseringar gör det möjligt att presentera information, anpassat till olika situationer i den verkliga världen. Till vilken grad voodoodockan ger en känsla av kroppslighet samt konsekvenser av de valda metaforerna diskuteras. I slutsatsen hävdas att användarnas svar och tolkningar tyder på att den omvända voodoodockan fungerade som ett medel för att övervaka biologisk data.
Books on the topic "Biological interactions data"
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1969-, Li Xiao-Li, and Ng See-Kiong, eds. Biological data mining in protein interaction networks. Medical Information Science Reference, 2009.
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Vasyukova, Anna. Functional nutrition. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2178360.
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The textbook material is based on current issues related to the range and functional properties of food, biologically active substances and their complex interaction. Special attention is paid to the regulatory requirements for the functional nutrition of various population groups. The role of public catering in the organization of production and sale of functional products is also considered. The theoretical material, presented in an accessible form and supported by numerous illustrations, will allow future specialists in functional nutrition technology to organize and conduct technological processes in accordance with instructions, regulations, standards, sanitary and epidemiological regulations and rules, as well as the requirements of other documents. Modern data on the functional nutrition of athletes, schoolchildren, students and other categories of the population are presented. It offers control questions that can be used as individual assignments for students' independent work, and a list of references. Meets the requirements of the latest generation of federal state educational standards for higher education. For students of higher educational institutions studying in the field of Product Technology and catering organization.
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Grunert, Marcel, Andreas Perrot, and Silke Rickert-Sperling. Complex network interactions: cardiovascular systems biology. Edited by José Maria Pérez-Pomares, Robert G. Kelly, Maurice van den Hoff, et al. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198757269.003.0033.
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A large quantity of molecular information on heart development, function, and disease has been generated over recent decades. However, most recent studies have been dominated by reductionistic approaches, and thus many aspects remain unclear, particularly regarding the primary causes of complex cardiovascular diseases such as congenital heart malformations. With the advent of high-throughput technologies, systems-based approaches have developed rapidly in biology and medicine. In the biology of cardiovascular systems complex data within or across different molecular levels of biological systems or pathways can be integrated and combined to identify the causes underlying cardiac diseases, which might not be possible otherwise. This is in agreement with data suggesting that biological molecules in individual regulatory layers, such as transcripts, proteins, and metabolites, act within networksrather than independently of each other. Thus systems biology provides a promising approach to fully addressing the complexities of congenital heart disease.
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Li, Xiao-Li. Biological Data Mining in Protein Interaction Networks. IGI Global, 2009.
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Averdijk, Margit. Victim Selection. Edited by Wim Bernasco, Jean-Louis van Gelder, and Henk Elffers. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199338801.013.14.
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This chapter reviews what is known about victim selection—that is, the question of why offenders select some people, but not others, to be victims of crime. It first addresses theoretical perspectives on victim selection, namely the structural–choice model of victim selection, social interactionism, and target congruence. It then describes three data sources that have been used in prior research to study victim selection: police reports, victimization surveys, and offender interviews. Subsequently, empirical findings on victim selection are reviewed and organized into nine subsections: victims’ demographic characteristics, psychological characteristics, physical characteristics, behavior, biological characteristics, prior victimization, relationship to the offender, behavior during the offense, and the role of randomness. The final section discusses research gaps and potential future directions in the field, including an emphasis on theoretical explanations and mediators, cross-cultural studies, methodological innovation and diversity, interactions between victim and offender characteristics, and generality across crime types and subpopulations.
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Epstein, Irving R., and John A. Pojman. An Introduction to Nonlinear Chemical Dynamics. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195096705.001.0001.
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Just a few decades ago, chemical oscillations were thought to be exotic reactions of only theoretical interest. Now known to govern an array of physical and biological processes, including the regulation of the heart, these oscillations are being studied by a diverse group across the sciences. This book is the first introduction to nonlinear chemical dynamics written specifically for chemists. It covers oscillating reactions, chaos, and chemical pattern formation, and includes numerous practical suggestions on reactor design, data analysis, and computer simulations. Assuming only an undergraduate knowledge of chemistry, the book is an ideal starting point for research in the field. The book begins with a brief history of nonlinear chemical dynamics and a review of the basic mathematics and chemistry. The authors then provide an extensive overview of nonlinear dynamics, starting with the flow reactor and moving on to a detailed discussion of chemical oscillators. Throughout the authors emphasize the chemical mechanistic basis for self-organization. The overview is followed by a series of chapters on more advanced topics, including complex oscillations, biological systems, polymers, interactions between fields and waves, and Turing patterns. Underscoring the hands-on nature of the material, the book concludes with a series of classroom-tested demonstrations and experiments appropriate for an undergraduate laboratory.
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Lambin, Xavier. The population dynamics of bite-sized predators: prey dependence, territoriality, and mobility. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198759805.003.0004.
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The dependency of mustelid demographic rates on prey abundance has the potential to cause a strong coupling between predator-prey populations. Data on mustelid dynamics show that such strong reciprocal interactions only materialise in some restricted conditions. Bite-size mustelid predators searching for scarce, depleted prey expose themselves to increased risk of predation by larger predators of small mammal that are themselves searching for similar prey species. As voles or muskrats become scarcer, weasels and mink searching for prey over larger areas become increasingly exposed to intra-guild predation, unless they operate in a habitat refuge such as the sub-nivean space. Where larger predators are sufficiently abundant or exert year-round predation pressure on small mustelids, their impact on mustelids may impose biological barrier to dispersal that are sufficient to weaken the coupling between small mustelids and their rodent prey, and thus impose a degree of top down limitation on mustelids.
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strach,, Bayla, Shir Lerman,, and Merrill Singer. Stigma Syndemics. The Rowman & Littlefield Publishing Group, 2017. https://doi.org/10.5040/9781978731097.
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Central to this volume, and critical to its unique creative significance and contribution, is the conceptual unification of syndemics and stigma. Syndemics theory is increasingly recognized in social science and medicine as a crucial framework for examining and addressing pathways of interaction between biological and social aspects of chronic and acute suffering in populations. While much research to date addresses known syndemics such as those involving HIV, diabetes, and mental illness, this book explores new directions just beginning to emerge in syndemics research – revealing what syndemics theory can illuminate about, for example the health consequences of socially pathologized pregnancy or infertility, when stigmatization of reproductive options or experiences affect women’s health. In other chapters, newly identified syndemics affecting incarcerated or detained individuals are highlighted, demonstrating the physical, psychological, structural, and political-economic effects of stigmatizing legal frameworks on human health, through a syndemic lens. Elsewhere in the volume, scholars examine the stigma of poverty and how it affects both nutritional and oral health. The common thread across all chapters is linkages of social stigmatization, structural conditions, and how these societal forces drive biological and disease interactions affecting human health, in areas not previously explored through these lenses.
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Kuykendall, Sally. Bullying. ABC-CLIO, LLC, 2012. http://dx.doi.org/10.5040/9798400622298.
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This important text presents bullying as a health issue and proposes effective strategies for diagnosis, treatment, and prevention based on current scientific research of aggressive behaviors. Bullying goes far beyond typical treatments of the topic by presenting an overview of the research concerning the causes, symptoms, and prevalence of bullying to illustrate how it is not simply a social issue but both a genuine medical and health issue. The author draws upon both clinical data and her own extensive experience observing children's interactions on school playgrounds and from interviewing parents, teachers, administrators, and children themselves to reach conclusions about evidence-based prevention and treatment. The work provides a deeper understanding of bullying by presenting biological and psychological theories of aggression, describing why bystanders who witness bullying react in the way that they do, offering novel ways to deal with the problem, and presenting proven methods that concerned bystanders of all ages can employ to break bullying behaviors—without increasing their own risk. It provides information of great relevance to students, parents, counselors, educators, teaching assistants, and administrators.
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Shamoo, Adil E., and David B. Resnik. Responsible Conduct of Research. 4th ed. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780197547090.001.0001.
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Responsible Conduct of Research provides an overview of ethical, legal, and social issues in scientific research for science students, trainees, and professional scientists. Written by two leading scholars in the field of research ethics, one with a background in natural science and the other with a background in philosophy and law, the book incorporates insights from these diverse disciplines throughout the text. The book provides in-depth analyses of a wide array of topics, including ethical theory and decision-making, misconduct, questionable research practices, research record-keeping, data sharing, data auditing, reproducibility, authorship, publication, peer review, intellectual property, conflict of interest, mentoring, safe research environment, animal experimentation, research with human subjects, and social responsibility. The book also includes interesting case studies and provocative questions at the end of each chapter that can serve as a basis for further analysis and discussion. The concluding chapter of the book describes some steps that researchers, institutional officials, government agencies, and scientific organizations can take to promote ethical conduct in scientific research. The 4th edition of Responsible Conduct of Research includes updated references and discussions of new and evolving topics, such as digital image manipulation, text recycling (sometimes called self-plagiarism), retractions, publication on pre-print servers, sexual and other forms of harassment, research with human biological samples, revisions to the Common Rule for research with human subjects, dual use research, the COVID-19 pandemic, providing science advice, and interactions with the media.
Book chapters on the topic "Biological interactions data"
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Kokošar, Jaka, Martin Špendl, and Blaž Zupan. "Gene Interactions in Survival Data Analysis: A Data-Driven Approach Using Restricted Mean Survival Time and Literature Mining." In Discovery Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_20.
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AbstractUnveiling gene interactions is crucial for comprehending biological processes, particularly their combined impact on phenotypes. Computational methodologies for gene interaction discovery have been extensively studied, but their application to censored data has yet to be thoroughly explored. Our work introduces a data-driven approach to identifying gene interactions that profoundly influence survival rates through the use of survival analysis. Our approach calculates the restricted mean survival time (RMST) for gene pairs and compares it against their individual expressions. If the interaction’s RMST exceeds that of the individual gene expressions, it suggests a potential functional association. We focused on L1000 landmark genes using TCGA na METABRIC data sets. Our findings demonstrate numerous additive and competing interactions and a scarcity of XOR-type interactions. We substantiated our results by cross-referencing with existing interactions in STRING and BioGRID databases and using large language models to summarize complex biological data. Although many potential gene interactions were hypothesized, only a fraction have been experimentally explored. This novel approach enables biologists to initiate a further investigation based on our ranked gene pairs and the generated literature summaries, thus offering a comprehensive, data-driven approach to understanding gene interactions affecting survival rates.
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Cho, Young-Rae. "Filtering Protein-protein Interactions By Integration of Ontology Data." In Biological Knowledge Discovery Handbook. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118617151.ch04.
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Andrews, Larry S., Stephen L. Longacre, and Robert Snyder. "Application of in Vivo Data on Chemical—Biological Interactions." In Toxic Substances and Human Risk. Springer US, 1987. http://dx.doi.org/10.1007/978-1-4684-5290-7_13.
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Dieckmann, Gerhard S., Hajo S. Eicken, Christian S. Haas, et al. "A compilation of data on sea ice algal standing crop from the Belingshausen, Amundsen and Weddell Seas from 1983 to 1994." In Antarctic Sea Ice: Biological Processes, Interactions and Variability. American Geophysical Union, 1998. http://dx.doi.org/10.1029/ar073p0085.
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Boucetta, Sabrine, Farid Derbal, Zitouni Boutiba, and M. Hichem Kara. "First Biological Data on the Marine Snails Osilinus turbinatus (Gastropoda, Trochidae) of Eastern Coasts of Algeria." In Global Change: Mankind-Marine Environment Interactions. Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8630-3_57.
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Chourpa, I., H. Morjani, and M. Manfait. "Specific Molecular Interactions of Antitumor Drug m-AMSA with Topoisomerase II: Micro-Sers Data from Living Cancer Cells Correlate with the Model Complexes." In Spectroscopy of Biological Molecules. Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0371-8_216.
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le Roux, Johannes J. "Molecular ecology of plant-microbial interactions during invasions: progress and challenges." In Plant invasions: the role of biotic interactions. CABI, 2020. http://dx.doi.org/10.1079/9781789242171.0340.
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Abstract Microbes are omnipresent, yet their interactions with invasive plants remain understudied. This is surprising, given the importance of microbes in plant community ecology and their influence on plant performance in new environments. Recent advances in molecular genetic approaches have opened the door to studying this unseen majority in great detail and to understand how they fit into ecological interaction networks. Molecular approaches allow rapid assessments of microbial diversity at reasonable cost while providing both taxonomic and evolutionary information. Here I discuss how these approaches have contributed to a better understanding of plant-microbial interactions in the context of biological invasions. By drawing insights from various case studies, I illustrate how next-generation sequencing (DNA barcoding) has revolutionized the way we understand such interactions. Tight-knit and coevolved mutualist (e.g. mycorrhizal) and antagonist (e.g. pathogen) interactions appear particularly promising to understand the structure and function of invasive plant-microbial interaction networks, the impacts of invasive plants on native networks and the vulnerability of native networks to infiltration by non-native species. I also discuss novel ways in which molecular data can aid the study of invasive plant-microbial interactions, such as incorporating phylogenetic data into network analyses to better understand the role of evolutionary history in network dynamics and how such dynamics respond to plant invasions. DNA barcoding of microbes also presents unique challenges to the study of network ecology, such as uncertainty in the legitimacy and efficiency of interactions. Future research should incorporate overall plant-associated microbial communities (microbiomes) into interaction networks to better understand the role microbes play during plant invasions.
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Wang, Hua, Heng Huang, Chris Ding, and Feiping Nie. "Predicting Protein-Protein Interactions from Multimodal Biological Data Sources via Nonnegative Matrix Tri-Factorization." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29627-7_33.
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Lorenzi, Marco, Marie Deprez, Irene Balelli, Ana L. Aguila, and Andre Altmann. "Integration of Multimodal Data." In Machine Learning for Brain Disorders. Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_19.
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AbstractThis chapter focuses on the joint modeling of heterogeneous information, such as imaging, clinical, and biological data. This kind of problem requires to generalize classical uni- and multivariate association models to account for complex data structure and interactions, as well as high data dimensionality.Typical approaches are essentially based on the identification of latent modes of maximal statistical association between different sets of features and ultimately allow to identify joint patterns of variations between different data modalities, as well as to predict a target modality conditioned on the available ones. This rationale can be extended to account for several data modalities jointly, to define multi-view, or multi-channel, representation of multiple modalities. This chapter covers both classical approaches such as partial least squares (PLS) and canonical correlation analysis (CCA), along with most recent advances based on multi-channel variational autoencoders. Specific attention is here devoted to the problem of interpretability and generalization of such high-dimensional models. These methods are illustrated in different medical imaging applications, and in the joint analysis of imaging and non-imaging information, such as -omics or clinical data.
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Mørch, Alexander M., and Falk Schneider. "Investigating Diffusion Dynamics and Interactions with Scanning Fluorescence Correlation Spectroscopy (sFCS)." In The Immune Synapse. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3135-5_5.
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AbstractActivation of immune cells and formation of immunological synapses (IS) rely critically on the reorganization of the plasma membrane. These highly orchestrated processes are driven by diffusion and oligomerization dynamics, as well as by single molecule interactions. While slow macro- and meso-scale changes in organization can be observed with conventional imaging, fast nano-scale dynamics are often missed with traditional approaches, but resolving them is, nonetheless, essential to understand the underlying biological mechanisms at play. Here, we describe the use of scanning fluorescence correlation spectroscopy (sFCS) and scanning fluorescence cross-correlation spectroscopy (sFCCS) to study reorganization and changes in molecular diffusion dynamics and interactions during IS formation and in other biological settings. We focus on the practical aspects of the measurements including calibration and alignment of the optical setup, present a comprehensive protocol to perform the measurements, and provide data analysis pipelines and strategies. Finally, we show an exemplary application of the technology to studying Lck diffusion during T-cell signaling.
Conference papers on the topic "Biological interactions data"
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Permeh, Samanbar, Mayren Echeverria Boan, Berrin Tansel, Kingsley Lau, and Matthew Duncan. "Effect of Fouling on Cathodic Protection Current on Steel Submerged in Natural Waters." In CORROSION 2020. NACE International, 2020. https://doi.org/10.5006/c2020-14831.
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Abstract Civil infrastructure immersed in natural waters is subjected to physical, chemical and biological interactions with the environment that can result in material degradation. Corrosion is of particular concern due to its adverse effects on the mechanical integrity of steel structural elements, but other natural interactions of steel in the marine environment such as marine fouling can intersect, creating complex systems exacerbating material degradation. Cathodic protection (CP) can be applied, but complications arise in the marine environment. Shielding of current due separation of the steel surface from the bulk solution can develop with the presence of uniform fouling encrustations. The objective of this research here was to provide field data on the total currents provided by a galvanic CP system to steel immersed in bridge environments that support macro- and micro- fouling. Field testing incorporated steel arrays submerged in two Florida natural water bodies that could sustain typical fouling. Bulk zinc anodes provided beneficial cathodic polarization of the steel array (&lt;-1000 mVSCE and &gt;10mA) that significantly decreased the overall corrosion rate, but localized corrosion still developed below fouling encrustations. Smaller local CP currents were measured where uniform and compact fouling and calcareous deposits formed. Testing indicating that the barrier provided by the fouling layers and surface deposits do not provide overall corrosion protection but fouling layers can reduce the available surface area to support cathodic reactions.
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Ballarini, F. "Modeling the Action of Protons and Heavier Ions in Biological Targets: Nuclear Interactions in Hadrontherapy and Space Radiation Protection." In INTERNATIONAL CONFERENCE ON NUCLEAR DATA FOR SCIENCE AND TECHNOLOGY. AIP, 2005. http://dx.doi.org/10.1063/1.1945314.
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Gao, Jianbo, Jing Hu, and Wen-wen Tung. "Multiscale Analysis of Biological Signals." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-6084.
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Complex systems often generate highly nonstationary and multiscale signals, due to nonlinear and stochastic interactions among their component systems and hierarchical regulations imposed by the operating environments. The further advances in the fields of life sciences, systems biology, nano-sciences, information systems, and physical sciences, have made it increasingly important to develop complexity measures that incorporate the concept of scale explicitly, so that different behaviors of the signals on varying scales can be simultaneously characterized by the same scale-dependent measure. Here, we propose such a measure, the scale-dependent Lyapunov exponent (SDLE), and develop a unified theory of multiscale analysis of complex data. We show that the SDLE can readily characterize low-dimensional chaos and random 1/fα processes, as well as accurately detect epileptic seizures from EEG data and distinguish healthy subjects from patients with congestive heart failure from heart rate variability (HRV) data. More importantly, our analyses of EEG and HRV data illustrate that commonly used complexity measures from information theory, chaos theory, and random fractal theory can be related to the values of the SDLE at specific scales, and useful information on the structured components of the data is also embodied by the SDLE.
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Florentino, Bruno Rafael, Natan Henrique Sanches, Robson Parmezan Bonidia, and André C. P. L. F. de Carvalho. "BioPrediction: Democratizing Machine Learning in the Study of Molecular Interactions." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234271.
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Given the increasing number of biological sequences stored in databases, there is a large source of information that can benefit several sectors such as agriculture and health. Machine Learning (ML) algorithms can extract useful and new information from these data, increasing social and economic benefits, in addition to productivity. However, the categorical and unstructured nature of biological sequences makes this process difficult, requiring ML expertise. In this paper, we propose and experimentally evaluate an end-to-end automated ML-based framework, named BioPrediction, able to identify implicit interactions between sequences, e.g., long non-coding RNA and protein pairs, without the need for end-to-end ML expertise. Our experimental results show that the proposed framework can induce ML models with high predictive accuracy, between 77% and 91%, which are competitive with state-of-the-art tools.
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Dimitrakopoulos, Georgios Ν., Konstantinos Lazaros, Aristidis G. Vrahatis, et al. "A Machine Learning approach combining omics data for Alzheimer’s Disease analysis." In 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.342d.
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Alzheimer’s disease (AD) is a complex neurological disorder whose underlying mechanisms remain elusive to this day. Molecular biology methodologies, especially techniques like single-cell RNA sequencing (scRNA-seq), offer unparalleled granularity in deciphering the disease’s cellular intricacies. However, despite the potential of scRNA-seq, comprehensive machine-learning analyses are yet to be fully harnessed. Emphasizing the multi-omics machine-learning-based approaches, which integrate diverse single-cell omics datasets, could highlight novel therapeutic targets and deepen our understanding of AD’s intricate nature. In this work, we propose a machine-learning-based method to embed gene expression into a protein interaction graph. Specifically, we model each interaction with a regression model on the participating genes and we use the R2 score as edge weight. Our aim is to detect parts of the PPI graph that differentiate between control and disease conditions. Application on a scRNA-seq AD dataset managed to identify interactions forming small subgraphs, which consisted of genes involved with biological processes related to neurons.
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Gromov, Andrey, and Alexey Kazarnitsky. "Types of artificial cranial deformations and biological affinities of the medieval Sogdian population." In Antiquities of East Europe, South Asia and South Siberia in the context of connections and interactions within the Eurasian cultural space (new data and concepts). Institute for the History of Material Culture Russian Academy of Sciences, 2019. http://dx.doi.org/10.31600/978-5-907053-34-2-196-197.
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Aleixo, Frederico, Matthias Knorr, and João Leite. "Revising Boolean Logical Models of Biological Regulatory Networks." In 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/2.
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Boolean regulatory networks are used to represent complex biological processes, modelling the interactions of biological compounds, such as proteins or genes, with each other and with other substances in a cell. Creating and maintaining computational models of these networks is crucial for comprehending corresponding cellular processes, as they allow reproducing known behaviours and testing new hypotheses and predictions in silico. In this context, model revision focuses on validating and (if necessary) repairing existing models based on new experimental data. However, model revision is commonly performed manually, which is inefficient and prone to error, and the few existing automated solutions either only apply to simpler networks or are limited in their revision process, since they may not be able to produce a solution within a reasonable time frame or miss the optimal solution. In this paper, we develop a solution for revising logical models of Boolean regulatory networks, able to find repairs that are consistent with provided, possibly incomplete experimental data, and minimal w.r.t. the differences to the original network. We show that our solution can be used to revise different real-world Boolean logical models very efficiently, surpassing a previous solution in terms of solved instances and with a considerable margin w.r.t. processing time.
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Hall, Benjamin, and Asheesh Lanba. "Three-dimensional analysis of biological systems via a novel laser ablation technique." In ICALEO 2018: 37th International Congress on Applications of Lasers & Electro-Optics. Laser Institute of AmericaLIA, 2018. https://doi.org/10.2351/7.0004088.
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Current technology and methods are not feasible for the rapid three-dimensional visualization of complex biological systems. MRI and x-ray tomography are slow, costly, with significant trade-offs between resolution, speed and volume. Additionally, contrasting agents are required to differentiate features. Here, we present a novel 3D imaging and analysis technique, Laser Ablation Tomography (LATscan™). An ultrafast, ultraviolet pulsed laser is utilized to continuously ablate thin surface layers of samples, and these surfaces are simultaneously imaged as the sample is fed into the laser ablation plane. The resulting data is processed using machine learning techniques to identify structural and compositional features at micron resolution. The images are then stacked, further processed and reconstructed into high-resolution volume renderings that can be analyzed, quantified, segmented and virtually dissected. The 3D renderings obtained are in full, natural color that results from the interaction of the materials with the ultraviolet laser during ablation. The application of LATscan to both biological and non-biological samples has shown excellent results in the fields of botany, entomology, life sciences, material science, and pharmacology, among many others. The most successful roll-out of the technology has been in the agrochemical industry, specifically in studies involving high-throughput phenotyping, root-soil and plant-pest interactions, biomimetics, pesticide efficacy. The technology has also offered exciting results in the life sciences, and the combination of the visualization with 3D chemical mapping could be a fundamental research and diagnostic tool.
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"SOCIAL COMPETENCE, A PREDICTOR OF ACADEMIC ACHIEVEMENT; A COMPARATIVE STUDY OF PRE-CLINICAL STUDENTS IN STATE OWNED AND PRIVATE MEDICAL SCHOOLS." In 4th International Conference on Biological & Health Sciences (CIC-BIOHS’2022). Cihan University, 2022. http://dx.doi.org/10.24086/biohs2022/paper.720.
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Objective: To investigate differences on Social Competence and Academic Achievement between medical students from state owned and private medical institutes. To examine social competence as a predictor of academic achievement.Materials & Methods: A sample of 400 pre-clinical medical students (n= 200 males and n=200 females) with age range 17-20 years participated in the study. The data was collected from the medical schools of Islamabad & Rawalpindi. The study variables were measured via demographic data sheet, Student grades, and Youth Social Competence Scale. The results were analyzed through descriptive, t-test and regression analysis in SPSS 21.Results: In a sample of 400 medical students from state owned and private medical schools 50% respondents were males and 50% were female students. The respondents represented different years of medical school, 46% from first year, 38.5% from second year and 15.5% from third year. Results through t-test revealed that students from state owned medical schools had higher social competence and academic achievement as compared to students from private medical institutes. Regression analysis indicated that social competence is a significant predictor of academic achievement.Conclusion: Social competence can facilitate performance outcomes in medical students. Therefore, encouraging positive social interactions and skills with the peers and teachers can be an incentive for academic achievement.
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BOSKOVIC, JELENA, JELENA MLADENOVIC, VERA POPOVIC, ALEKSANDAR STEVANOVIC, and LJUBBICA Sarcevic-Todosijevic. "APPLICATION AND EFFECT OF ARTIFICIAL INTELLIGENCE FOR THE FUNCTIONING OF METABOLIC BIOENGINEERING IN SYSTEMS BIOLOGY." In IRASA International Scientific Conference. IRASA – International Research Academy of Science and Art, 2024. https://doi.org/10.62982/seti06.boje.13.
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Abstract Emerging synthetic biology capabilities have the potential to dramatically improve our ability to engineer biological systems. However, a fundamental obstacle to realizing this potential is the inability to accurately predict biological behavior following modification of the corresponding genotype. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones. Metabolic bioengineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi- omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, given the great potential of transcriptomics, proteomics, and metabolomics, integrating the data to gain a more comprehensive understanding is not a trivial task. Key words: Artificial intelligence, systems biology, machine learning, metabolic bioengineering.Abstract Emerging synthetic biology capabilities have the potential to dramatically improve our ability to engineer biological systems. However, a fundamental obstacle to realizing this potential is the inability to accurately predict biological behavior following modification of the corresponding genotype. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones. Metabolic bioengineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi- omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, given the great potential of transcriptomics, proteomics, and metabolomics, integrating the data to gain a more comprehensive understanding is not a trivial task. Key words: Artificial intelligence, systems biology, machine learning, metabolic bioengineering.
Reports on the topic "Biological interactions data"
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Hawkins, Brian T., and Sonia Grego. A Better, Faster Road From Biological Data to Human Health: A Systems Biology Approach for Engineered Cell Cultures. RTI Press, 2017. http://dx.doi.org/10.3768/rtipress.2017.rb.0015.1706.
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Traditionally, the interactions of drugs and toxicants with human tissue have been investigated in a reductionist way—for example, by focusing on specific molecular targets and using single-cell-type cultures before testing compounds in whole organisms. More recently, “systems biology” approaches attempt to enhance the predictive value of in vitro biological data by adopting a comprehensive description of biological systems and using computational tools that are sophisticated enough to handle the complexity of these systems. However, the utility of computational models resulting from these efforts completely relies on the quality of the data used to construct them. Here, we propose that recent advances in the development of bioengineered, three-dimensional, multicellular constructs provide in vitro data of sufficient complexity and physiological relevance to be used in predictive systems biology models of human responses. Such predictive models are essential to maximally leveraging these emerging bioengineering technologies to improve both therapeutic development and toxicity risk assessment. This brief outlines the opportunities presented by emerging technologies and approaches for the acceleration of drug development and toxicity testing, as well as the challenges lying ahead for the field.
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Wilson, Charles, and Edo Chalutz. Biological Control of Postharvest Diseases of Citrus and Deciduous Fruit. United States Department of Agriculture, 1991. http://dx.doi.org/10.32747/1991.7603518.bard.
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The objectives of this research were to develop control measures of postharvest diseases of citrus and deciduous fruits by using naturally-occurring, non-antibiotic-producing antagonists; study the mode of action of effective antagonists and optimize their application methods. Several antagonists were found against a variety of diseases of fruits and vegetables. One particularly effective yeast antagonist (US-7) was chosen for more in-depth studies. This antagonist outcompetes rot pathogens at the wound site for nutrients and space; it is better adapted than the pathogen to extreme environmental conditions such as temperature, humidity and osmotic changes, and is relatively resistant to common postharvest fungicides. Our data suggests that other modes of action may also be involved. These are induction of host resistance by the antagonists or its products, and direct interaction between the antagonists and the pathogen with the possible involvement of an extracellular material and/or cell wall degrading enzymes produced by the antagonist. However, these interactions were not fully elucidated. The antagonistic activity of US-7 and other biocontrol agents isolated, was enhanced by calcium salts. While the mode of action is not known, the addition of these salts had a significant effect both in laboratory experiments and in large-scale tests. Compatibility of the yeast antagonist with present packinghouse treatments and procedures was determined. An integrated control procedure was developed, utilizing the antagonists together with ultra-low dosages of fungicides and activity-enhancing additives. This cooperative research resulted in numerous publications describing the antagonistic agents. their mode of action and possible commercial application. Patents were developed from this research and a commercial company is pursuing the licensing of these patents and the testing of the procedure on a commercial scale. Our research findings have expanded the potential for using non-antibiotic-producing antagonistic microorganisms in the control of postharvest diseases of fruits and vegetables; thus meeting a critical need to find alternatives to the use of synthetic fungicides on food products.
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Rodriguez Muxica, Natalia. Open configuration options Bioinformatics for Researchers in Life Sciences: Tools and Learning Resources. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0003982.
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The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.
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Matthew, Gray. Data from "Winter is Coming – Temperature Affects Immune Defenses and Susceptibility to Batrachochytrium salamandrivorans". University of Tennessee, Knoxville Libraries, 2021. http://dx.doi.org/10.7290/t7sallfxxe.
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Environmental temperature is a key factor driving various biological processes, including immune defenses and host-pathogen interactions. Here, we evaluated the effects of environmental temperature on the pathogenicity of the emerging fungus, Batrachochytrium salamandrivorans (Bsal), using controlled laboratory experiments, and measured components of host immune defense to identify regulating mechanisms. We found that adult and juvenile Notophthalmus viridescens died faster due to Bsal chytridiomycosis at 14 ºC than at 6 and 22 ºC. Pathogen replication rates, total available proteins on the skin, and microbiome composition likely drove these relationships. Temperature-dependent skin microbiome composition in our laboratory experiments matched seasonal trends in wild N. viridescens, adding validity to these results. We also found that hydrophobic peptide production after two months post-exposure to Bsal was reduced in infected animals compared to controls, perhaps due to peptide release earlier in infection or impaired granular gland function in diseased animals. Using our temperature-dependent infection results, we performed a geographic analysis that suggested that N. viridescens populations in the northeastern United States and southeastern Canada are at greatest risk for Bsal invasion. Our results indicate that environmental temperature will play a key role in the epidemiology of Bsal and provide evidence that temperature manipulations may be a viable Bsal management strategy.
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Gurevitz, Michael, Michael E. Adams, Boaz Shaanan, et al. Interacting Domains of Anti-Insect Scorpion Toxins and their Sodium Channel Binding Sites: Structure, Cooperative Interactions with Agrochemicals, and Application. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7585190.bard.
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Integrated pest management in modern crop protection may combine chemical and biological insecticides, particularly due to the risks to the environment and livestock arising from the massive use of non-selective chemicals. Thus, there is a need for safer alternatives, which target insects more specifically. Scorpions produce anti-insect selective polypeptide toxins that are biodegradable and non-toxic to warm-blooded animals. Therefore, integration of these substances into insect pest control strategies is of major importance. Moreover, clarification of the molecular basis of this selectivity may provide valuable information pertinent to their receptor sites and to the future design of peptidomimetic anti-insect specific substances. These toxins may also be important for reducing the current overuse of chemical insecticides if they produce a synergistic effect with conventional pesticides. Based on these considerations, our major objectives were: 1) To elucidate the three-dimensional structure and toxic-site of scorpion excitatory, "depressant, and anti-insect alpha toxins. 2) To obtain an initial view to the sodium channel recognition sites of the above toxins by generating peptide decoys through a phage display system. 3) To investigate the synergism between toxins and chemical insecticides. Our approach was to develop a suitable expression system for toxin production in a recombinant form and for elucidation of toxin bioactive sites via mutagenesis. In parallel, the mode of action and synergistic effects of scorpion insecticidal toxins with pyrethroids were studied at the sodium channel level using electrophysiological methods. Objective 1 was achieved for the alpha toxin, LqhaIT Zilberberg et al., 1996, 1997; Tugarinov et al., 1997; Froy et al., 2002), and the excitatory toxin, Bj-xtrIT (Oren et al., 1998; Froy et al., 1999; unpublished data). The bioactive surface of the depressant toxin, LqhIT2, has been clarified and a crystal of the toxin is now being analyzed (unpublished). Objective 2 was not successful thus far as no phages that recognize the toxins were obtained. We therefore initiated recently an alternative approach, which is introduction of mutations into recombinant channels and creation of channel chimeras. Objective 3 was undertaken at Riverside and the results demonstrated synergism between LqhaIT or AaIT and pyrethroids (Lee et al., 2002). Furthermore, negative cross-resistance between pyrethroids and scorpion toxins (LqhaIT and AaIT) was demonstrated at the molecular level. Although our study did not yield a product, it paves the way for future design of selective pesticides by capitalizing on the natural competence of scorpion toxins to distinguish between sodium channels of insects and vertebrates. We also show that future application of anti-insect toxins may enable to decrease the amounts of chemical pesticides due to their synergism.
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Ghanim, Murad, Joe Cicero, Judith K. Brown, and Henryk Czosnek. Dissection of Whitefly-geminivirus Interactions at the Transcriptomic, Proteomic and Cellular Levels. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7592654.bard.
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Our project focuses on gene expression and proteomics of the whitefly Bemisia tabaci (Gennadius) species complex in relation to the internal anatomy and localization of expressed genes and virions in the whitefly vector, which poses a major constraint to vegetable and fiber production in Israel and the USA. While many biological parameters are known for begomovirus transmission, nothing is known about vector proteins involved in the specific interactions between begomoviruses and their whitefly vectors. Identifying such proteins is expected to lead to the design of novel control methods that interfere with whitefly-mediated begomovirus transmission. The project objectives were to: 1) Perform gene expression analyses using microarrays to study the response of whiteflies (B, Q and A biotypes) to the acquisition of begomoviruses (Tomato yellow leaf curl (TYLCV) and Squash leaf curl (SLCV). 2) Construct a whitefly proteome from whole whiteflies and dissected organs after begomovirus acquisition. 3) Validate gene expression by q-RTPCR and sub-cellular localization of candidate ESTs identified in microarray and proteomic analyses. 4) Verify functionality of candidate ESTs using an RNAi approach, and to link these datasets to overall functional whitefly anatomical studies. During the first and second years biological experiments with TYLCV and SLCV acquisition and transmission were completed to verify the suitable parameters for sample collection for microarray experiments. The parameters were generally found to be similar to previously published results by our groups and others. Samples from whole whiteflies and midguts of the B, A and Q biotypes that acquired TYLCV and SLCV were collected in both the US and Israel and hybridized to B. tabaci microarray. The data we analyzed, candidate genes that respond to both viruses in the three tested biotypes were identified and their expression that included quantitative real-time PCR and co-localization was verified for HSP70 by the Israeli group. In addition, experiments were undertaken to employ in situ hybridization to localize several candidate genes (in progress) using an oligonucleotide probe to the primary endosymbiont as a positive control. A proteome and corresponding transcriptome to enable more effective protein identification of adult whiteflies was constructed by the US group. Further validation of the transmission route of begomoviruses, mainly SLCV and the involvement of the digestive and salivary systems was investigated (Cicero and Brown). Due to time and budget constraints the RNAi-mediated silencing objective to verify gene function was not accomplished as anticipated. HSP70, a strong candidate protein that showed over-expression after TYLCV and SLCV acquisition and retention by B. tabaci, and co-localization with TYLCV in the midgut, was further studies. Besides this protein, our joint research resulted in the identification of many intriguing candidate genes and proteins that will be followed up by additional experiments during our future research. To identify these proteins it was necessary to increase the number and breadth of whitefly ESTs substantially and so whitefly cDNAs from various libraries made during the project were sequenced (Sanger, 454). As a result, the proteome annotation (ID) was far more successful than in the initial attempt to identify proteins using Uniprot or translated insect ESTs from public databases. The extent of homology shared by insects in different orders was surprisingly low, underscoring the imperative need for genome and transcriptome sequencing of homopteran insects. Having increased the number of EST from the original usable 5500 generated several years ago to >600,000 (this project+NCBI data mining), we have identified about one fifth of the whitefly proteome using these new resources. Also we have created a database that links all identified whitefly proteins to the PAVEdb-ESTs in the database, resulting in a useful dataset to which additional ESTS will be added. We are optimistic about the prospect of linking the proteome ID results to the transcriptome database to enable our own and other labs the opportunity to functionally annotate not only genes and proteins involved in our area of interest (whitefly mediated transmission) but for the plethora of other functionalities that will emerge from mining and functionally annotating other key genes and gene families in whitefly metabolism, development, among others. This joint grant has resulted in the identification of numerous candidate proteins involved in begomovirus transmission by B. tabaci. A next major step will be to capitalize on validated genes/proteins to develop approaches to interfere with the virus transmission.
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Ortega de Farias, Mª Clara, and Francisco José Valverde Albacete. Dynamic Modeling of C. elegans Locomotion: Integrating Synaptic, Electrical, and Peptidergic Pathways. Fundación Avanza, 2025. https://doi.org/10.60096/fundacionavanza/4002025.
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This paper presents a functional neural model of C. elegans locomotion, integrating synaptic, gap junction, and neuropeptidergic data to simulate brain-body-environment interactions in a biologically grounded, closed-loop framework.
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Houck, Marilyn, Uri Gerson, and Robert Luck. Two Predator Model Systems for the Biological Control of Diaspidid Scale Insects. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7570554.bard.
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Hemisarcoptes (Acari: Hamisarcoptidae) is a parasite of scale insects (Diaspididae), tenacious pests of vascular plants. Hemisarcoptes also has a stenoxenic phoretic (dispersal) relationship with Chilocorus (Coleoptera: Coccinellidae). Chilocorus feeds on diaspidids, transports mites as they feed, and has been applied to the control of scales, with limited success. U.S.-Israeli cooperation focused on this mite-beetle interaction so that a two-component system could be applied to the control of scale insects effectively. Life history patterns of Hemisarcoptes were investigated in response to host plant type and physical parameters. Field and lab data indicated that mites attack all host stages of scales tested, but preferred adult females. Scale species and host plant species influenced the bionomics of Hemisarcoptes. Beetle diet also influenced survival of phoretic mites. Mites use a ventral sucker plate to extract material from Chilocorus, that is essential for development. Seven alkaloids were found in the hemolymph of Chilocorus and three were characterized. Examination of the subelytral surface of Chilocorus indicated that microsetae play a role in the number and distribution of mites a beetle transports. While Hemisarcoptes can be innoculatd into agroecosystems using various indigenous or imported Chilocorus species, the following are preferred: C. bipustulatus, C. cacti, C. distigma, C. fraternus, C. orbus, and C. tristis.
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Nachtrieb, Julie. Field site analysis of giant salvinia nitrogen content and salvinia weevil density. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42060.
Full textAbstract:
In 2012, a giant salvinia (Salvinia molesta Mitchell) biological control project was initiated in Louisiana. Although similar quantities of salvinia weevils (Cyrtobagous salviniae Calder and Sands) were released at all sites, weevil densities were highly variable among sites. Additionally, signs of plant nitrogen depletion (yellowing plants) were observed at some sites. Because it is well known that plant nutrition can affect the success of a biocontrol agent because of slowed development and/or reduced fecundity, the correlation between giant salvinia nitrogen content and Salvinia weevil density was investigated during the growing seasons of the second and fourth years. During 2013, weevils were reintroduced to sites, and the magnitude of adult weevil density increase varied by site. Giant salvinia nitrogen content varied among sites and sampling dates. Upper Big Break plants had greater nitrogen than all other sites during 75% of sampling dates. Additionally, adult and larval densities were significantly correlated to plant nitrogen content. During 2015, trends were less distinct and weevil densities and nitrogen content varied based on the interaction between sampling date and site, but a significant correlation was not detected. Results from 1-yr of a 2-yr study confirmed published reports of the importance of plant nitrogen content to salvinia weevil productivity. Additional studies are warranted to evaluate and understand the role of nitrogen at giant salvinia biocontrol field sites.
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Grumet, Rebecca, and Benjamin Raccah. Identification of Potyviral Domains Controlling Systemic Infection, Host Range and Aphid Transmission. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7695842.bard.
Full textAbstract:
Potyviruses form one of the largest and most economically important groups of plant viruses. Individual potyviruses and their isolates vary in symptom expression, host range, and ability to overcome host resistance genes. Understanding factors influencing these biological characteristics is of agricultural importance for epidemiology and deployment of resistance strategies. Cucurbit crops are subject to severe losses by several potyviruses including the highly aggressive and variable zucchini yellow mosaic virus (ZYMV). In this project we sought to investigate protein domains in ZYMV that influence systemic infection and host range. Particular emphasis was on coat protein (CP), because of known functions in both cell to cell and long distance movement, and helper component-protease (HC-Pro), which has been implicated to play a role in symptom development and long distance movement. These two genes are also essential for aphid mediated transmission, and domains that influence disease development may also influence transmissibility. The objectives of the approved BARD project were to test roles of specific domains in the CP and HC-Pro by making sequence alterations or switches between different isolates and viruses, and testing for infectivity, host range, and aphid transmissibility. These objectives were largely achieved as described below. Finally, we also initiated new research to identify host factors interacting with potyviral proteins and demonstrated interaction between the ZYMV RNA dependent RNA polymerase and host poly-(A)-binding protein (Wang et al., in press). The focus of the CP studies (MSU) was to investigate the role of the highly variable amino terminus (NT) in host range determination and systemic infection. Hybrid ZYMV infectious clones were produced by substituting the CP-NT of ZYMV with either the CP-NT from watermelon mosaic virus (overlapping, but broader host range) or tobacco etch virus (TEV) (non- overlapping host range) (Grumet et al., 2000; Ullah ct al., in prep). Although both hybrid viruses initially established systemic infection, indicating that even the non-cucurbit adapted TEV CP-NT could facilitate long distance transport in cucurbits, after approximately 4-6, the plants inoculated with the TEV-CPNT hybrid exhibited a distinct recovery of reduced symptoms, virus titer, and virus specific protection against secondary infection. These results suggest that the plant recognizes the presence of the TEV CP-NT, which has not been adapted to infection of cucurbits, and initiates defense responses. The CP-NT also appears to play a role in naturally occurring resistance conferred by the zym locus in the cucumber line 'Dina-1'. Patterns of virus accumulation indicated that expression of resistance is developmentally controlled and is due to a block in virus movement. Switches between the core and NT domains of ZYMV-NAA (does not cause veinal chlorosis on 'Dina-1'), and ZYMV-Ct (causes veinal chlorosis), indicated that the resistance response likely involves interaction with the CP-NT (Ullah and Grumet, submitted). At the Volcani Center the main thrust was to identify domains in the HC-Pro that affect symptom expression or aphid transmissibility. From the data reported in the first and second year report and in the attached publications (Peng et al. 1998; Kadouri et al. 1998; Raccah et al. 2000: it was shown that: 1. The mutation from PTK to PAK resulted in milder symptoms of the virus on squash, 2. Two mutations, PAK and ATK, resulted in total loss of helper activity, 3. It was established for the first time that the PTK domain is involved in binding of the HC-Pro to the potyvirus particle, and 4. Some of these experiments required greater amount of HC-Pro, therefore a simpler and more efficient purification method was developed based on Ni2+ resin.