The document describes the construction of networks to represent relationships between human genetic disorders and disease genes. Specifically, it details:
1) The creation of a "diseasome" bipartite network connecting 1,284 disorders and 1,777 disease genes based on known associations between genetic mutations and phenotypes.
2) The projection of this network into a "human disease network" where nodes represent disorders connected if they share disease genes, and a "disease gene network" where nodes represent genes connected if associated with the same disorder.
3) Analysis of the properties of these networks, finding most disorders are linked to only a few other disorders and disease genes, though some relate to dozens, and the networks display many connections
The document discusses two networks constructed from genetic disorder and disease gene data: the human disease network (HDN) and the disease gene network (DGN). The HDN connects disorders that share disease genes, revealing many connections between disorders. The DGN connects disease genes associated with the same disorders. Most disorders relate to a few genes, while a few disorders like cancers relate to dozens of genes. The networks provide an integrated framework to explore relationships between genetic disorders and disease genes.
This document discusses harnessing the power of teams and networks to build better models of disease in real time. It notes that new technologies now allow the generation of massive amounts of human omics data and emerging network modeling approaches for diseases. Cloud computing infrastructure allows a generative open approach to biomedical problem solving. A nascent movement aims to give patients more control over their sensitive health information to facilitate sharing. Open social media also allows experts and citizens to collaborate to solve biomedical problems. The overall opportunity is to conduct more open, collaborative biomedical research involving diverse teams.
Stephen Friend Koo Foundation / Sun Yat-Sen Cancer Center 2012-03-12Sage Base
The document discusses moving beyond linear investigations in science and how we work by integrating layers of omics data models and building computational spaces capable of enabling models to be evolved by teams. It was presented by Stephen Friend of Sage Bionetworks on March 12, 2012 to the Koo Foundation and Sun Yat-Sen Cancer Center.
Stephen Friend Genetic Alliance 25th Anniversary 2011-06-24Sage Base
This document discusses using data-intensive science to build better models of human disease. It argues that advances in data generation, computing power, and open information systems now make it possible to comprehensively monitor disease and molecular traits in populations. This could allow evolving disease models in a shared compute space to develop better understanding of complex biological pathways and their relationship to diseases. The document outlines several initiatives, including the Clinical Trial Comparator Arm Partnership and Sage Bionetworks, that aim to facilitate open sharing of genomic and clinical trial data to generate more powerful models and accelerate progress against human diseases.
This document summarizes Kristina Hettne's PhD thesis defense on applying next-generation text mining to toxicogenomics data analysis. The thesis investigated improving information coverage in biomedical and chemical thesauri used for text mining by developing new chemical concept identification methods. A next-generation text mining approach was developed to statistically relate chemical information to gene expression data, allowing identification of toxicity effects at an earlier stage than manual curation alone. The approach was shown to complement and sometimes outperform existing databases, with potential to reduce animal testing through early prediction of drug toxicity.
Talk given to the Emory Cancer Control and Population Science Program 2/17/2011 Describing Biomedical Informatics, Integrative Cancer Research, caBIG and CTSA
The document summarizes a thesis that aimed to identify gene expression modules in colorectal cancer using three different methods. The results conclusively identified functional gene expression modules and mapped them to known pathways. Some modules predicted tumor relapse in colorectal cancer patients and survival in breast cancer patients. The conclusions state that gene expression modules regularly occurring in colorectal cancer were identified and their functional significance was found.
The document discusses two networks constructed from genetic disorder and disease gene data: the human disease network (HDN) and the disease gene network (DGN). The HDN connects disorders that share disease genes, revealing many connections between disorders. The DGN connects disease genes associated with the same disorders. Most disorders relate to a few genes, while a few disorders like cancers relate to dozens of genes. The networks provide an integrated framework to explore relationships between genetic disorders and disease genes.
This document discusses harnessing the power of teams and networks to build better models of disease in real time. It notes that new technologies now allow the generation of massive amounts of human omics data and emerging network modeling approaches for diseases. Cloud computing infrastructure allows a generative open approach to biomedical problem solving. A nascent movement aims to give patients more control over their sensitive health information to facilitate sharing. Open social media also allows experts and citizens to collaborate to solve biomedical problems. The overall opportunity is to conduct more open, collaborative biomedical research involving diverse teams.
Stephen Friend Koo Foundation / Sun Yat-Sen Cancer Center 2012-03-12Sage Base
The document discusses moving beyond linear investigations in science and how we work by integrating layers of omics data models and building computational spaces capable of enabling models to be evolved by teams. It was presented by Stephen Friend of Sage Bionetworks on March 12, 2012 to the Koo Foundation and Sun Yat-Sen Cancer Center.
Stephen Friend Genetic Alliance 25th Anniversary 2011-06-24Sage Base
This document discusses using data-intensive science to build better models of human disease. It argues that advances in data generation, computing power, and open information systems now make it possible to comprehensively monitor disease and molecular traits in populations. This could allow evolving disease models in a shared compute space to develop better understanding of complex biological pathways and their relationship to diseases. The document outlines several initiatives, including the Clinical Trial Comparator Arm Partnership and Sage Bionetworks, that aim to facilitate open sharing of genomic and clinical trial data to generate more powerful models and accelerate progress against human diseases.
This document summarizes Kristina Hettne's PhD thesis defense on applying next-generation text mining to toxicogenomics data analysis. The thesis investigated improving information coverage in biomedical and chemical thesauri used for text mining by developing new chemical concept identification methods. A next-generation text mining approach was developed to statistically relate chemical information to gene expression data, allowing identification of toxicity effects at an earlier stage than manual curation alone. The approach was shown to complement and sometimes outperform existing databases, with potential to reduce animal testing through early prediction of drug toxicity.
Talk given to the Emory Cancer Control and Population Science Program 2/17/2011 Describing Biomedical Informatics, Integrative Cancer Research, caBIG and CTSA
The document summarizes a thesis that aimed to identify gene expression modules in colorectal cancer using three different methods. The results conclusively identified functional gene expression modules and mapped them to known pathways. Some modules predicted tumor relapse in colorectal cancer patients and survival in breast cancer patients. The conclusions state that gene expression modules regularly occurring in colorectal cancer were identified and their functional significance was found.
The document discusses integrating genomics data and evidence-based medicine into electronic health records (EHRs) for precision healthcare. It notes the gap between what is known and what is done in healthcare. Integrating genomics could help do the right thing for each patient through pharmacogenomics. However, challenges include representing huge volumes of molecular data in a usable way in EHRs. A three step approach is proposed: 1) get genomic data into EHRs in a structured format, 2) use that data for clinical decision support, 3) evaluate outcomes and continually improve the system.
DNA microarrays, also known as DNA chips or biochips, allow researchers to measure gene expression levels or genotype multiple genomic regions simultaneously. They work by hybridizing sample DNA to probes attached to a solid surface based on complementary base pairing. Researchers can now run thousands of samples at once under identical conditions using microarray technology. It has accelerated genetic research by enabling many tests to be done in parallel. Microarray data analysis involves image analysis, data processing, and statistical classification methods to organize and interpret the large datasets generated.
Bioinformatics uses techniques from applied mathematics, computer science, and statistics to understand and organize biological information on a large scale, especially regarding molecules like DNA, RNA, and proteins. Functional genomics uses high-throughput methods and bioinformatics to describe gene and protein functions and interactions at a genome-wide level. Key tools for functional genomics include sequence-based tools, microarray-based tools, and Gene Ontology for organizing gene function information. A systems biology approach integrates vast amounts of correlative genomic and proteomic data to help understand complex human diseases.
Michelangelo Ceci – Tecniche di data-mining per la caratterizzazione di entit...eventi-ITBbari
The document discusses various techniques for characterizing biological entities using data mining. It describes discovering frequent syntactic structures in biomedical literature that link entities like mutations and proteins. It also covers extracting bio-molecular events and entities from text, discovering miRNA-mRNA interaction networks through biclustering approaches, and using protein-protein interaction networks to predict gene functions in a hierarchical multi-label classification setting. Finally, it outlines the IS-BioBank project which aims to enable interoperability between biological data sources to support analysis of cancer microenvironments.
INBIOMEDvision Workshop at MIE 2011. Victoria LópezINBIOMEDvision
1) Personalized medicine currently faces challenges in processing large-scale genomic data, interpreting the functional effects of genomic variations, integrating systems-level data, and translating discoveries into medical practice.
2) Bioinformatics can help address these challenges through algorithms for mapping and aligning sequencing data, predicting functional effects, prioritizing genes, integrating multi-omics data into networks, and disseminating discoveries through databases to inform medical practice.
3) Fully realizing personalized medicine will require overcoming limitations of current approaches, validating computational predictions, and updating medical practice and education to routinely incorporate genomic information.
This document discusses various techniques for clustering microarray data, including one-way, two-way, co-clustering, and biclustering techniques. It provides examples of methods such as gene shaving, COSA, coupled two-way clustering, spectral bi-clustering, and SAMBA. While many novel clustering methods have been developed, the document notes that few are widely used in practice and there has been little work evaluating the performance of different techniques. Development of clustering methods for microarray data continues.
The document summarizes a lecture on viral structure and classification. It outlines the structure of viruses including the core, which contains the viral DNA or RNA genome, capsids made of protein subunits, and some viruses also have an outer envelope. It also discusses different viral morphologies like helical and icosahedral shapes. The lecture covers the relationship between viral structure and function.
The document discusses reproducible bioscience data. It describes Susanna-Assunta Sansone as a principal investigator and team leader at the University of Oxford e-Research Centre who gives a presentation on policies, communities, and standards around reproducible bioscience data. The presentation covers topics like preserving institutional memory, utilizing public data, and addressing reproducibility and reuse of public data through community standards and structured data annotation.
Journal of Ethnobotany | Applications of DNA barcoding and future directions ...Innspub Net
DNA barcoding help to recognize the plant based on short, gene sequences in a rapid, accurate, and cost effective manner. Current focus is on the investigation of phytomedicinals and herbal product integrity and authenticity through DNA barcoding with the goal of protecting consumers from potential health risks associated with product substitution and contamination. Recent reports reveal that DNA barcoding can be used for the assignment of unknown specimens to a taxonomic group, authentic identification of phytomedicinals, and in plant biodiversity conservation. Research indicates that there is no single universal barcode candidate for identification of all plant groups. Hence, comparative analysis of plant barcode loci is essential for choosing a best candidate for authenticating particular medicinal plant genus/families. Currently, both chloroplast/nuclear regions are used as universal barcodes for the authentication of phytomedicinals. A recent advance in genomics has further enhanced the progress in DNA barcoding of plants by the introduction of high-throughput techniques like next generation sequencing, which has paved the way for complete plastome sequencing that is now termed as super-barcodes. Hence, current focus is on the investigation of phytomedicines and herbal product integrity and authenticity through DNA barcoding with the goal of protecting consumers from potential health risks associated with product substitution and contamination. These approaches could improve the traditional ethnobotanical and scientific knowledge of phytomedicines and their safe use.
The document provides an overview of biobanking from the perspective of a user. It discusses three examples of biobanking: 1) Using postmortem brain samples from the NIH NeuroBioBank to validate findings related to Sturge-Weber syndrome. 2) Establishing a biobank for Sturge-Weber syndrome. 3) Discovering mosaic mutations in autism samples by analyzing genomic data and then validating findings using samples from existing biobanks. It also outlines several issues, lessons, and principles for biobanking including usefulness, existing biobanks, importance of identifiers, role of data science, use of standards, informed consent considerations, and ongoing needs and opportunities.
ArrayGen Technologies Pvt Ltd is a Genomics service provider company with the wide array of expertise in Genomics algorithm development, next-generation sequencing(NGS), microarray and Bioinformatics services. Also, involved in various services in both industry and academia.
This document discusses data management and curation in bioinformatics. It describes Susanna-Assunta Sansone as the principal investigator and team leader at the University of Oxford e-Research Centre, where her team works on data management, biocuration, software development, databases, and community standards and ontologies for various domains including toxicology, health, and agriculture. The document promotes the importance of data standards to enable data sharing and reproducibility in bioscience research.
Microarray analysis allows scientists to determine which genes are active or inactive in a cell. It involves isolating genetic material from a cell and identifying which genes have messenger RNA present, indicating they are turned on. DNA microarrays contain thousands of unique DNA sequences spotted onto a solid surface that fluorescently-labeled genetic material from a sample can bind to. This allows scientists to measure gene expression levels across the entire genome simultaneously and understand molecular mechanisms of toxicity. Protein microarrays similarly contain arrays of capture proteins that can be used to analyze protein-protein interactions and activities on a large scale.
This document provides an overview of association mapping as a tool for dissecting phenotypic variation and mapping quantitative trait loci (QTLs). It discusses the differences between traditional QTL mapping using biparental mapping populations versus association mapping using natural populations. Association mapping offers higher mapping resolution, uses more diverse germplasm, and is less time-consuming and costly than traditional QTL mapping. It then describes linkage disequilibrium-based association mapping and factors that influence linkage disequilibrium. The document also discusses different approaches to association mapping, including candidate gene association studies, genome-wide association studies, and localized association studies.
Association mapping approaches for tagging quality traits in maizeSenthil Natesan
Association mapping has been widely used to study the genetic basis of complex traits in human and animal systems and is a very efficient and effective method for confirming candidate genes or for identifying new genes (Altshuler et al., 2008). Association mapping is now being increasingly used in a wide range of plants (Rafalski, 2010), where it appears to be more powerful than in humans or animals (Zhu et al., 2008). Unlike linkage mapping, association mapping can explore all the recombination events and mutations in a given population and with a higher resolution (Yu and Buckler, 2006). However, association mapping has a lower power to detect rare alleles in a population, even those with large effects, than linkage mapping (Hill et al., 2008). Yan et al., (2010) demonstrated that the gene encoding β-carotene hydroxylase 1 (crtRB1) underlies a principal quantitative trait locus associated with β-carotene concentration and conversion in maize kernels has been identified through candidate gene strategy of association mapping.
This document summarizes a study that integrated pathway and gene expression data from over 13,000 samples across 17 platforms to perform multi-label classification of 48 diseases. Pathway activity scores were calculated for each sample and used as features for classification, along with sample labels determined through manual dataset analysis. Classification was performed using multiple algorithms and validated through cross-validation and comparison to previous studies. Performance was improved over previous work, as shown by increased recall and precision. Relationships between diseases and pathways were also modeled in a network graph.
Presentation about how much bioinformatics involved in the medical field. This was presented at the University of Colombo in 2007 for an undergraduate seminar
Personalized medicine via molecular interrogation, data mining and systems bi...Gerald Lushington
One of the major problems in our medical system is the prescription of medicines that, although well validated over a general group of clinical trial patients for specific ailments, may produce unhelpful or even harmful results in some individuals. A major emerging goal in the pharmaceutical and biomedical industries is the ability to tailor medicines to the individual. This can be achieved, but in practice still requires careful analysis of an extensive array of data and thus has not yet entered the mainstream medical practice.
The document is a script for a randomized exam webpage that will display 4 images randomly selected from a pool of 713 images.
The script uses JavaScript to generate a random number between 1-713 to select the image source for each image displayed. It also includes some random text between elements.
The purpose is to randomly display a set of images on an exam webpage to prevent students from sharing answers.
Misperception of feedbacks: another source of vulnerability in social-ecologi...Juan C. Rocha
Here I describe my previous work analyzing lobster fishery in the Caribbean as a social-ecological systems. Main findings show that dynamic failures lead the system to undesirable states: less and shorter lobsters, less profits and less fishermen. These failures typically fall within subsystems interactions: e.g. losses of lobster reproductive potential, perception of non-resource exhaustion, a poverty trap of fishing effort, and a socially based reinforcing feedback for legitimate norms breaking.
presentation by Derek Skaletsky, Chief Opportunity Officer, Traackr (@traackr) at #RLTM Realtime Marketing Lab, October 14, 2013 at The Altman Building, NYC.
The document discusses integrating genomics data and evidence-based medicine into electronic health records (EHRs) for precision healthcare. It notes the gap between what is known and what is done in healthcare. Integrating genomics could help do the right thing for each patient through pharmacogenomics. However, challenges include representing huge volumes of molecular data in a usable way in EHRs. A three step approach is proposed: 1) get genomic data into EHRs in a structured format, 2) use that data for clinical decision support, 3) evaluate outcomes and continually improve the system.
DNA microarrays, also known as DNA chips or biochips, allow researchers to measure gene expression levels or genotype multiple genomic regions simultaneously. They work by hybridizing sample DNA to probes attached to a solid surface based on complementary base pairing. Researchers can now run thousands of samples at once under identical conditions using microarray technology. It has accelerated genetic research by enabling many tests to be done in parallel. Microarray data analysis involves image analysis, data processing, and statistical classification methods to organize and interpret the large datasets generated.
Bioinformatics uses techniques from applied mathematics, computer science, and statistics to understand and organize biological information on a large scale, especially regarding molecules like DNA, RNA, and proteins. Functional genomics uses high-throughput methods and bioinformatics to describe gene and protein functions and interactions at a genome-wide level. Key tools for functional genomics include sequence-based tools, microarray-based tools, and Gene Ontology for organizing gene function information. A systems biology approach integrates vast amounts of correlative genomic and proteomic data to help understand complex human diseases.
Michelangelo Ceci – Tecniche di data-mining per la caratterizzazione di entit...eventi-ITBbari
The document discusses various techniques for characterizing biological entities using data mining. It describes discovering frequent syntactic structures in biomedical literature that link entities like mutations and proteins. It also covers extracting bio-molecular events and entities from text, discovering miRNA-mRNA interaction networks through biclustering approaches, and using protein-protein interaction networks to predict gene functions in a hierarchical multi-label classification setting. Finally, it outlines the IS-BioBank project which aims to enable interoperability between biological data sources to support analysis of cancer microenvironments.
INBIOMEDvision Workshop at MIE 2011. Victoria LópezINBIOMEDvision
1) Personalized medicine currently faces challenges in processing large-scale genomic data, interpreting the functional effects of genomic variations, integrating systems-level data, and translating discoveries into medical practice.
2) Bioinformatics can help address these challenges through algorithms for mapping and aligning sequencing data, predicting functional effects, prioritizing genes, integrating multi-omics data into networks, and disseminating discoveries through databases to inform medical practice.
3) Fully realizing personalized medicine will require overcoming limitations of current approaches, validating computational predictions, and updating medical practice and education to routinely incorporate genomic information.
This document discusses various techniques for clustering microarray data, including one-way, two-way, co-clustering, and biclustering techniques. It provides examples of methods such as gene shaving, COSA, coupled two-way clustering, spectral bi-clustering, and SAMBA. While many novel clustering methods have been developed, the document notes that few are widely used in practice and there has been little work evaluating the performance of different techniques. Development of clustering methods for microarray data continues.
The document summarizes a lecture on viral structure and classification. It outlines the structure of viruses including the core, which contains the viral DNA or RNA genome, capsids made of protein subunits, and some viruses also have an outer envelope. It also discusses different viral morphologies like helical and icosahedral shapes. The lecture covers the relationship between viral structure and function.
The document discusses reproducible bioscience data. It describes Susanna-Assunta Sansone as a principal investigator and team leader at the University of Oxford e-Research Centre who gives a presentation on policies, communities, and standards around reproducible bioscience data. The presentation covers topics like preserving institutional memory, utilizing public data, and addressing reproducibility and reuse of public data through community standards and structured data annotation.
Journal of Ethnobotany | Applications of DNA barcoding and future directions ...Innspub Net
DNA barcoding help to recognize the plant based on short, gene sequences in a rapid, accurate, and cost effective manner. Current focus is on the investigation of phytomedicinals and herbal product integrity and authenticity through DNA barcoding with the goal of protecting consumers from potential health risks associated with product substitution and contamination. Recent reports reveal that DNA barcoding can be used for the assignment of unknown specimens to a taxonomic group, authentic identification of phytomedicinals, and in plant biodiversity conservation. Research indicates that there is no single universal barcode candidate for identification of all plant groups. Hence, comparative analysis of plant barcode loci is essential for choosing a best candidate for authenticating particular medicinal plant genus/families. Currently, both chloroplast/nuclear regions are used as universal barcodes for the authentication of phytomedicinals. A recent advance in genomics has further enhanced the progress in DNA barcoding of plants by the introduction of high-throughput techniques like next generation sequencing, which has paved the way for complete plastome sequencing that is now termed as super-barcodes. Hence, current focus is on the investigation of phytomedicines and herbal product integrity and authenticity through DNA barcoding with the goal of protecting consumers from potential health risks associated with product substitution and contamination. These approaches could improve the traditional ethnobotanical and scientific knowledge of phytomedicines and their safe use.
The document provides an overview of biobanking from the perspective of a user. It discusses three examples of biobanking: 1) Using postmortem brain samples from the NIH NeuroBioBank to validate findings related to Sturge-Weber syndrome. 2) Establishing a biobank for Sturge-Weber syndrome. 3) Discovering mosaic mutations in autism samples by analyzing genomic data and then validating findings using samples from existing biobanks. It also outlines several issues, lessons, and principles for biobanking including usefulness, existing biobanks, importance of identifiers, role of data science, use of standards, informed consent considerations, and ongoing needs and opportunities.
ArrayGen Technologies Pvt Ltd is a Genomics service provider company with the wide array of expertise in Genomics algorithm development, next-generation sequencing(NGS), microarray and Bioinformatics services. Also, involved in various services in both industry and academia.
This document discusses data management and curation in bioinformatics. It describes Susanna-Assunta Sansone as the principal investigator and team leader at the University of Oxford e-Research Centre, where her team works on data management, biocuration, software development, databases, and community standards and ontologies for various domains including toxicology, health, and agriculture. The document promotes the importance of data standards to enable data sharing and reproducibility in bioscience research.
Microarray analysis allows scientists to determine which genes are active or inactive in a cell. It involves isolating genetic material from a cell and identifying which genes have messenger RNA present, indicating they are turned on. DNA microarrays contain thousands of unique DNA sequences spotted onto a solid surface that fluorescently-labeled genetic material from a sample can bind to. This allows scientists to measure gene expression levels across the entire genome simultaneously and understand molecular mechanisms of toxicity. Protein microarrays similarly contain arrays of capture proteins that can be used to analyze protein-protein interactions and activities on a large scale.
This document provides an overview of association mapping as a tool for dissecting phenotypic variation and mapping quantitative trait loci (QTLs). It discusses the differences between traditional QTL mapping using biparental mapping populations versus association mapping using natural populations. Association mapping offers higher mapping resolution, uses more diverse germplasm, and is less time-consuming and costly than traditional QTL mapping. It then describes linkage disequilibrium-based association mapping and factors that influence linkage disequilibrium. The document also discusses different approaches to association mapping, including candidate gene association studies, genome-wide association studies, and localized association studies.
Association mapping approaches for tagging quality traits in maizeSenthil Natesan
Association mapping has been widely used to study the genetic basis of complex traits in human and animal systems and is a very efficient and effective method for confirming candidate genes or for identifying new genes (Altshuler et al., 2008). Association mapping is now being increasingly used in a wide range of plants (Rafalski, 2010), where it appears to be more powerful than in humans or animals (Zhu et al., 2008). Unlike linkage mapping, association mapping can explore all the recombination events and mutations in a given population and with a higher resolution (Yu and Buckler, 2006). However, association mapping has a lower power to detect rare alleles in a population, even those with large effects, than linkage mapping (Hill et al., 2008). Yan et al., (2010) demonstrated that the gene encoding β-carotene hydroxylase 1 (crtRB1) underlies a principal quantitative trait locus associated with β-carotene concentration and conversion in maize kernels has been identified through candidate gene strategy of association mapping.
This document summarizes a study that integrated pathway and gene expression data from over 13,000 samples across 17 platforms to perform multi-label classification of 48 diseases. Pathway activity scores were calculated for each sample and used as features for classification, along with sample labels determined through manual dataset analysis. Classification was performed using multiple algorithms and validated through cross-validation and comparison to previous studies. Performance was improved over previous work, as shown by increased recall and precision. Relationships between diseases and pathways were also modeled in a network graph.
Presentation about how much bioinformatics involved in the medical field. This was presented at the University of Colombo in 2007 for an undergraduate seminar
Personalized medicine via molecular interrogation, data mining and systems bi...Gerald Lushington
One of the major problems in our medical system is the prescription of medicines that, although well validated over a general group of clinical trial patients for specific ailments, may produce unhelpful or even harmful results in some individuals. A major emerging goal in the pharmaceutical and biomedical industries is the ability to tailor medicines to the individual. This can be achieved, but in practice still requires careful analysis of an extensive array of data and thus has not yet entered the mainstream medical practice.
The document is a script for a randomized exam webpage that will display 4 images randomly selected from a pool of 713 images.
The script uses JavaScript to generate a random number between 1-713 to select the image source for each image displayed. It also includes some random text between elements.
The purpose is to randomly display a set of images on an exam webpage to prevent students from sharing answers.
Misperception of feedbacks: another source of vulnerability in social-ecologi...Juan C. Rocha
Here I describe my previous work analyzing lobster fishery in the Caribbean as a social-ecological systems. Main findings show that dynamic failures lead the system to undesirable states: less and shorter lobsters, less profits and less fishermen. These failures typically fall within subsystems interactions: e.g. losses of lobster reproductive potential, perception of non-resource exhaustion, a poverty trap of fishing effort, and a socially based reinforcing feedback for legitimate norms breaking.
presentation by Derek Skaletsky, Chief Opportunity Officer, Traackr (@traackr) at #RLTM Realtime Marketing Lab, October 14, 2013 at The Altman Building, NYC.
The document discusses three ways to make a school more eco-friendly: 1) Reduce usage of polystyrene plates and cups by having students and teachers bring their own reusable containers, 2) Encourage the use of binder notebooks to reduce paper waste, and 3) Organize an exhibition to raise awareness about being eco-friendly and protecting the environment.
The document discusses three ways to make a school more eco-friendly: 1) Reduce usage of polystyrene plates and cups by having students and teachers bring their own reusable containers, 2) Encourage the use of binder notebooks to reduce paper waste, and 3) Organize an exhibition to raise awareness about being eco-friendly and protecting the environment.
The document discusses ways for a school to become more environmentally friendly. It suggests bringing reusable water bottles to reduce waste, organizing a monthly "green day" to separate reusable, recyclable, and non-recyclable items, and planting more trees to reduce carbon dioxide levels and create a healthier environment. The overall message is that small individual actions can collectively make a significant positive impact on the environment.
Super Wi-Fi - What to do with White Spaces in SAHenk Kleynhans
The document discusses the potential for using unused TV white space spectrum to enable "Super Wi-Fi" networks in South Africa. It notes that existing spectrum management approaches have led to underutilization and scarcity issues. The document advocates adopting a model similar to the FCC's, which made TV white space spectrum available on an unlicensed but managed basis. This would allow providers to access spectrum through an online database, avoiding interference while enabling widespread, affordable connectivity across the country.
This document discusses using data-intensive science to build better disease maps by integrating layers of omics data and computational models. It proposes that Sage Bionetworks, a non-profit organization, could act as a commons to facilitate collaborative work on disease network models using data repositories and discovery platforms. The document outlines Sage's mission and vision, as well as its collaborators from pharmaceutical companies, foundations, government agencies, and academics, to accelerate the elimination of human disease through evolving integrative biological network representations.
Stephen Friend Cytoscape Retreat 2011-05-20Sage Base
Use of Bionetworks to Build Maps of Disease
Stephen Friend proposes using "data intensive science", also known as the "fourth scientific paradigm", to build better maps of human disease. This approach utilizes massive amounts of biological and clinical data from populations, along with computational modeling, to construct networks that model disease. Integrating diverse data types can provide insights into disease mechanisms and causal relationships that enable more accurate predictions. Bionetworks approaches have the potential to advance the understanding and treatment of complex diseases.
1. The document discusses using heterogeneous biological data to advance scientific discovery by overcoming complexity.
2. It describes how new technologies now allow generation of massive human "omics" data and emerging network modeling approaches for diseases.
3. Integrating this data through cloud computing infrastructure can enable a generative open approach to solving biomedical problems.
Stephen Friend NIH PPP Coordinating Committee Meeting 2012-02-16Sage Base
The document discusses using networked team approaches and integrating omics data to build better disease maps through public-private partnerships like CTCAP and Arch2POCM. It proposes sharing clinical and genomic data from comparator arms of trials to create models and de-risking novel drug targets through developing test compounds in a precompetitive space to accelerate new therapies.
Stephen Friend Food & Drug Administration 2011-07-18Sage Base
The document discusses potential opportunities for participating in clinical trial projects that study network perturbations in clinical specimens to better understand how to select effective drug targets for different diseases and patients. It describes four potential projects: 1) a clinical trial comparator arm project, 2) a project to decode biology using drug compounds, 3) an oncology non-responders project, and 4) a project to free up failed drug compounds. It asks what actions the FDA or other executive/legislative bodies might take regarding these projects.
Stratified Medicine - Applications and Case StudiesSpace IDEAS Hub
Stratified medicine opportunities for businesses were discussed at a conference. The agenda included talks on systems biology in cancer, single molecule imaging technology, and knowledge engineering for biomedical research. The document also provided details on various speakers and their presentations. It summarized the goals and tools used in computational systems biology of cancer at Institut Curie, including building maps of cancer signaling networks. Examples were given of how these maps could be used to analyze data, find alternative pathways, and model cell fate decisions.
The document discusses using "data intensive science" and integrated network maps to build better models of human disease. It proposes using massive amounts of data from various omics technologies, along with open sharing of data, tools and models, to generate comprehensive maps of diseases like cancer on a molecular level. The goal is to move beyond treating symptoms to modifying disease pathways by developing more personalized treatments and predictive markers through integrated analysis of multi-omics datasets.
Stephen Friend Institute for Cancer Research 2011-11-01Sage Base
This document discusses building models of disease using data intensive science. It describes integrating omics data and computational models in a compute space. The challenges of the current drug discovery process are outlined, noting a need to better understand disease biology before testing compounds. Network models are proposed to capture disease complexity beyond single components. Examples are given of building gene co-expression networks from large datasets and using them to identify disease modules and key drivers. The potential for predictive models of genotype-specific drug responses is also mentioned.
This document proposes using data intensive science to build better models of disease. It notes that current disease models make simplistic assumptions and that personalized medicine requires better representations of overlapping pathways. It advocates adopting the "fourth paradigm" of data intensive science to generate massive datasets, ensure interoperability, create open information systems, and host evolving computational models. Six pilot projects are described that involve collaborative data sharing between industry, academia, and non-profits to build disease maps and models. These include initiatives like CTCAP to share clinical trial data, Arch2POCM to de-risk drug targets, and forming a federation to enhance interoperability. The document argues this approach could help address issues like a lack of standard
This document discusses the evolution of systems biology and its various approaches over time, such as genomic and proteomic profiling, molecular interaction networks, disease models, and drug trials. It notes the movement from studying individual components to constructing network models of entire biological systems and pathways. It also addresses challenges like overgeneralizing findings and the need for more collaborative and open research.
This document provides an introduction to biological network inference using Gaussian graphical models. It discusses motivations for network inference based on the central dogma of molecular biology and common questions in functional genomics. The challenges of modeling high-dimensional omics data are described, including what network nodes and edges represent statistically and biologically. Gaussian graphical models are proposed as a tool for modeling dependencies between biological variables in genomic data, with the goal of reconstructing biological networks from large-scale omics experiments.
Stephen Friend WIN Symposium 2011 2011-07-06Sage Base
Searching for opportunities to use data-intensive science to build better disease maps through open innovation and collaboration across silos. The key advantages discussed are generating more clinically validated targets through data sharing, helping deliver more new drugs for patients, and improving health outcomes. Issues around current drug discovery redundancy and high failure rates are also addressed.
Biotechnology Industry has changed a lot during last decade , which means moving ahead from traditional ways to more advanced technological developments
2012 Biotechnology Industry is not the same as it was in 2001
This document discusses using virtual physiological modeling and simulation to enable personalized medicine approaches. It describes the Virtual Physiological Human initiative, which aims to enable collaborative investigation of the human body across all relevant scales through multiscale modeling. As a case study, it discusses using VPH simulation to model HIV protease drug binding at an atomic level to predict patient-specific drug efficacy and rank available drugs for treatment. Automating such simulations through high-performance computing resources could help clinicians interpret genetic information and select optimal drug therapies on an individual basis.
Albert Laszlo Barabasi - Innovation inspired positive change in health careponencias_mihealth2012
This document summarizes network medicine and its applications. It discusses how human diseases can be modeled and studied as complex networks. Disease genes are found to cluster together in protein interaction networks, forming disease modules. Mapping disease genes onto interactome networks can help identify new candidate genes and delineate disease modules. Validation using various biological data shows the predicted disease genes are statistically associated with the disease. Mapping asthma genes in this way identified a statistically significant disease module within the first 200 prioritized genes. Network medicine approaches provide a framework for understanding the molecular basis of diseases.
Interactomics, Integromics to Systems Biology: Next Animal Biotechnology Fron...Varij Nayan
“Organisms function in an integrated manner-our senses, our muscles, our metabolism and our minds work together seamlessly. But biologists have historically studied organisms part by part and celebrated the modern ability to study them molecule by molecule, gene by gene. Systems biology is critical science of future that seeks to understand the integration of the pieces to form biological
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Behavioural Economics in Social-Ecological Systems with ThresholdsJuan C. Rocha
1) The study examines how people in fishing communities in Colombia behave when facing uncertainty related to climate thresholds through a behavioral experiment.
2) 256 participants across 4 communities played a dynamic common pool resource game in groups, with some groups facing information about a past climate event or potential future events with known or unknown probabilities.
3) Groups that were told about past climate thresholds or potential future events with known probabilities achieved higher average resource stock sizes, exhibiting more conservative exploitation behaviors when facing uncertainty.
This document discusses regime shifts in social-ecological systems. It addresses the social challenge of understanding when and where regime shifts are likely to occur, who they will affect, and how to avoid them. The science challenge is to study these phenomena using limited data and time for action. Potential interactions between regime shifts are examined through comparative frameworks analyzing shared drivers and feedback dynamics. This could help elucidate cascading effects between apparently disconnected systems and identify management strategies to build resilience.
The document discusses regime shifts in the Anthropocene. It defines regime shifts as abrupt reorganizations of a system's structure and function. Understanding regime shifts is a social and scientific challenge. The document presents a framework to compare regime shifts across scales and drivers. It analyzes a database of 25 regime shifts and their associations with 57 drivers. Many regime shifts are connected to drivers like climate change, fishing, and coastal development. Managing local and regional drivers can help build resilience to global drivers and delays in regime shifts.
This document discusses using network controllability analysis to assess how manageable regime shifts are. It notes that heterogeneous networks are difficult to control due to many driving nodes, while homogeneous networks are more controllable with fewer driving nodes. However, critics argue that a system's internal dynamics and observability are more important than just structure. The document also discusses how sparse heterogeneous networks may actually have more controllable edge dynamics. It explores using network science and resilience science to determine when to build resilience versus enable transformational change.
This document summarizes a network analysis of global environmental regime shifts. It finds that food production and climate change are the most frequent drivers of regime shifts globally. Marine regime shifts share more drivers than terrestrial ones. Managing regime shifts requires multi-level governance across local, national, and international scales as regulating single drivers may not prevent shifts. The analysis finds network approaches useful for studying interconnected social-ecological systems when data availability is limited.
Using natural language processing, the document describes an attempt to automatically identify topics within a corpus of 812 papers related to ecosystem services. Latent Dirichlet Allocation was used to extract 80 topics from the text. The top words for each topic were analyzed and many closely matched the categories of ecosystem services established by the Millennium Ecosystem Assessment. However, some topics did not cleanly fit into the MEA framework, highlighting challenges in applying computerized topic modeling across disciplines.
This document discusses regime shifts, which are abrupt reorganizations of a system's structure and function that occur when feedbacks maintaining the system's behavior change. Disease outbreaks can act as disturbances that trigger regime shifts. Specifically, outbreaks can become part of feedback processes that lead to chaotic dynamics and strange cycles. The frequency of disease outbreaks is partially determined by a system's history and memory. Increasing speed, connectivity, and scale of modern systems may impact their resilience to outbreaks. Further research is needed on developing theory and case studies to better understand these complex dynamics.
Arctic resilience assessment: exploring methods for scaling upJuan C. Rocha
This document summarizes work on assessing resilience in the Arctic region. It discusses developing a conceptual model to understand system thresholds and factors influencing resilience. It also outlines combining a regime shift database with case studies to better understand sources of adaptive capacity. Specific regime shifts related to the Arctic are identified. Networks of drivers impacting ecosystem services and human well-being are mapped. Finally, potential case studies for understanding resilience and adaptive capacity in Arctic communities are listed.
This document discusses regime shifts, which are abrupt reorganizations of a system's structure and function. A regime corresponds to characteristic behavior maintained by mutually reinforcing feedback processes. Regime shifts occur when these feedbacks change due to changes in slow variables, external disturbances, or shocks. The document presents examples of regime shifts including vegetation shifts driven by changes in precipitation. It notes that regime shifts are common in the Anthropocene due to human impacts and discusses the need to better understand their patterns, interactions, likelihood, impacts, and how to avoid them. The rest of the document outlines a framework for comparing regime shifts using a database and examines global drivers of regime shifts like climate change, deforestation and fishing.
Licentiate: Regime shifts in the AnthropoceneJuan C. Rocha
This document discusses regime shifts in ecosystems driven by human impacts in the Anthropocene. It provides background on regime shifts, which are abrupt reorganizations of an ecosystem's structure and function. A database is being developed to compare regime shifts across different systems. The database will classify regime shifts based on their drivers, impacts on ecosystem services, and proposed feedback mechanisms. Challenges include developing consistent methods and assessing uncertainties given complex social and ecological interactions. The goal is to better understand multi-causal regime shifts in order to inform management and policy responses.
Juan-Carlos Rocha is a PhD student studying patterns of global regime shifts. He aims to map vulnerability to climate change-driven regime shifts and use network science and data mining to anticipate impacts on ecosystem services. His research analyzes over 20 regime shifts across marine, terrestrial and polar ecosystems to identify the main drivers. Food production, global warming, agriculture and human population are among the most important drivers. Marine regime shifts tend to share more drivers and feedbacks, indicating potential for synchronization. Avoiding regime shifts requires managing key international drivers. Rocha is collaborating on related projects applying text mining, experimental economics and resource networks to further understand regime shifts.
The domino effect: A network analysis of regime shifts drivers and causal pat...Juan C. Rocha
We present an exploratory analysis of the causal interactions among global change drivers of regime shifts, based on information collated in the Regime Shifts Database*. We reviewed the documented evidence of over 20 policy-relevant regime shifts in ecosystems. Information on the dynamics of each regime shift was synthesized using causal-loop diagrams, a generic structure map of the system. We then identified the main drivers of change, the key impacts on ecosystem services, as well as possible cross-scale interactions among regime shifts drivers using network analysis.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
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How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
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বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
Rocha comple net2012-melbourne
1. The Network of Driving Forces of
Global Environmental Change
Juan-Carlos Rocha, Oonsie Biggs & Garry Peterson
Stockholm Resilience Centre
Stockholm University
2.
3. The challenge
Frequency and intensity of
regime shifts are likely to
increase.
ES’s may be substantially
affected.
Where?
Vulnerable areas?
Possible synergistic effects?
Cross-scale interactions?
Rockström et al., 2009
4. Regime shifts that matter to people
Regime shifts: Large, abrupt, persistent change in the structure and function of a
system.
Policy relevant = Substantial change in Ecosystem Services
5. Research agenda on RS: Early warnings!!
Bayesian
Web crawlers &
networks -
local knowledge
models
Knowledge of the
Models &
Jacobians
system
Statistics:
Autocorrelation
and variance
Data quality
(time series)
6. Research agenda on RS: Early warnings!!
Bayesian
Web crawlers &
networks -
local knowledge
models
Knowledge of the
Models &
Jacobians
system
? Statistics:
Autocorrelation
and variance
Data quality
(time series)
12. Regime shift database
Description of the alternative
regimes and reinforcing
feedbacks
The drivers that precipitate the
regime shift
Impacts on ecosystem services
and human well-being
Management options
www.regimeshifts.org
13. N Policy relevant regime shifts Mechanism Reversibility
1 Bivalves collapse Established H
2 Coral transitions Established H
3 Desertification Contested H, I
4 Encroachment Established H
5 Eutrophication Established H, I, R
6 Fisheries collapse Contested U
7 Marine foodwebs collapse Contested U
8 Forest - Savanna Established I
9 Hypoxia Established H, R
10 Kelp transitions Established H, R
11 Soil salinization Established H, I
12 Steppe - Tundra Established I
13 Tundra - Forest Established I
14 Monsoon circulation Established I
15 Thermohaline circulation collapse Established I
16 Greenland ice sheet collapse Established I
17 Arctic salt marshes Established I
18 Peatlands Established I
19 River channel position Established I
20 Soil structure Established H, I
Reversibility: H = Hysteretic; I = Irreversible; R= Reversible; U = Unknown
Current data: 20 Regime Shifts in Social-Ecological Systems
14. Hurricanes tides
Thermal anomalies in summerLow
Ocean acidification
Sea level rise
Disease
Fishing technology
Pollutants Wind stress
25
Thermal low pressure
Upwellings
Water column density contrast
Invasive species Sediments Tragedy of the commons
Urban storm water runoff
Fishing
Water vapor
Turbidity Urbanization Sea surface temperature
Sewage
20
Daily Relative cooling
Coral.transitions Logging
Salt.marshes Marine.foodwebs
Nutrients inputs
Fisheries.collapse house consumption preferences
Green Fish gases
Water stratification Kelps.transitions
Precipitation Bivalves.collapse
Number of vertex
15
River.channel.change Hypoxia
Floating.plants
Flushing Fertilizers use ENSO like events
Erosion Food supply
Eutrophication Subsidies
Floods Demand
Global warming
Impoundments Human population
Agriculture Access to markets
10
Deforestation
Leaking Termohaline.circulation
Forest.to.savannas
Rainfall variability
Landscape fragmentation Immigration
Greenland
Peatlands Monsoon.weakening
Soil.salinization
Irrigation
5
Encroachment Tundra.to.Forest
Dry.land.degradation Infrastructure development
Droughts
Migration
Aquifers
Drainage
Fire frequency Temperature Dry−spells
0
Atmospheric CO2
Irrigation infrastructure Soil.structure Managerial practices diversity
1 2 3 4 5 6 7 9 10 11 12 13 14 15 16 18 19 20 22 23 26
Ranching (livestock)
Water infrastructure
Degree
Water availability Development policies
Production intensification cycles
Length of production
Labor availability
Food prices
Regime Shifts - Drivers
Bipartite Network
16. 500
400
Number of links
300
200
100
0
1 2 3 4 5 6 7 8 9 10 11
Number of Regime Shifts jointly caused
Drivers Network
17. 500
400
Number of links
300
200
100
0
1 2 3 4 5 6 7 8 9 10 11
Number of Regime Shifts jointly caused
Drivers Network
18. Green house gases
500
Global warming
400
Turbidity
Number of links
Fishing Food supply
300
Nutrients inputs Irrigation
200
Fertilizers use Agriculture
Human population
Demand
100
Sewage
Deforestation
Floods
0
1 2 3 4 5 6 7 8 9 10 11
Urbanization
Number of Regime Shifts jointly caused Erosion
Droughts
Drivers Network
19. How our results differ from random?
Average Degree in simulated DN Co−occurrence Index
2500
3000
2500
2000
2000
1500
Frequency
Frequency
1500
1000
1000
500
500
0
0
29 30 31 32 33 34 35 36 −1776.6 −1776.4 −1776.2 −1776.0 −1775.8
Mean Degree s−squared
20. Causal-loop diagrams is a
N Policy relevant Regime Shifts Mechanism Reversibility
technique to map out the
1 Bivalves collapse Established H feedback structure of a system
2 Coral transitions Established H (Sterman 2000)
3 Coral bleaching Established H
4 Desertification Contested H, I
5 Encroachment Established H
6 Eutrophication Established H, I, R
7 Fisheries collapse Contested U
8 Marine foodwebs collapse Contested U
9 Forest - Savanna Established I
10 Hypoxia Established H, R
11 Kelp transitions Established H, R
12 Soil salinization Established H, I
13 Steppe - Tundra Established I
14 Tundra - Forest Established I
15 Monsoon circulation Established I
16 Thermohaline circulation collapse Established I
17 Greenland ice sheet collapse Established I
18 Arctic salt marshes Established I
19 Arctic ice collapse Established I
Reversibility: H = Hysteretic; I = Irreversible; R= Reversible; U = Unknown
Current data: 19 Regime Shifts descriptions + CLD.
21. Topological features of Causal Network
Centrality Definition
Degree The number edges a vertex is connected to
(Newman 2010): In-degree and Out-degree
Betweenness The extent to which a vertex lies on paths
between other vertices (Newman 2010)
Eigenvector A vertex is important if it is directly or Degree centrality
indirectly connected to other vertices that are
in turn important (Allesina and Pascual 2009),
like Google PageRank
22. Topological features of Causal Network
Centrality Definition
Degree The number edges a vertex is connected to
(Newman 2010): In-degree and Out-degree
Betweenness The extent to which a vertex lies on paths
between other vertices (Newman 2010)
Eigenvector A vertex is important if it is directly or Betweenness centrality
indirectly connected to other vertices that are
in turn important (Allesina and Pascual 2009),
like Google PageRank
23. Topological features of Causal Network
Centrality Definition
Degree The number edges a vertex is connected to
(Newman 2010): In-degree and Out-degree
Betweenness The extent to which a vertex lies on paths
between other vertices (Newman 2010)
Eigenvector A vertex is important if it is directly or Eigenvector centrality
indirectly connected to other vertices that are
in turn important (Allesina and Pascual 2009),
like Google PageRank
24. D1
1. What are the major global change
drivers of regime shifts? RS1 RS2 RS3
80
60
Numbervertex vertex
Number vertexvertex
50
60
40
of
Number of of
Number of
40
30
20
20
10
0
0
1 2 3 4 5 6 7 8 9 11 12 14 15 17 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 19 22
Outgoing links
Outdegree
Incoming links
Indegree
Few nodes have a lot of links!
25. D1
Marine Regime Shifts RS1 RS2 RS3
Local centrality Global centrality
0.12
0.10
Nutrients input
10
Phytoplankton
Nutrients input
Fishing
0.08
Dissolved oxygenMid−predators
Noxious gases
Global warming
Betweenness
Algae Bivalves abundance
Outdegree
Agriculture Bivalves abundance
0.06
Floods Zooplankton
5
Top predators Space
GlobalUrban Macrophytes Phytoplankton
Planktivore fish
warminggrowth Dissolved oxygen
Turbidity
SST Erosion SST
ENSO−like Water temperature
events frequency
Canopy−forming algae algae
Turf−forming Biodiversity
Fishing
0.04
Greenhouse gasesand meso−predators
Disease outbreak Urchin barren
Lobsters Nekton Coral abundance
Unpalatability
AtmosphericDemand
Water vapor
CO2 Plankton and Macroalgae abundance
Human population Upwellings
ConsumptionFertilizers use runoff filamentous algae
Precipitation Flushing Coral abundance
Urban Sewage
Deforestation Sediments
preferences
Localstorm water Herbivores
Landscape fragmentation/conversion
water movements
Disease outbreak
Tragedy of thecolumn acidification
Impoundments densityLeakage
Water frequency
OceanIrrigation contrast
Thermal annomalies species
Invasive
Droughts
Perverse incentives mixing
TechnologyWater Zooxanthellae
Low tides commons
Sulfide stress
Wind release
Stratification relative cooling structural complexity
Mortality rate
Habitat
Density Thermal Fishmatter
Daily competitors
SubsidiesPollutants low pressurecolumn
Hurricanescontrast in the water
Noxious gases
Trade Other Organic Phosphorous in water Water vapor
0.02 Biodiversity Zooplankton
Nekton
Space Upwellings
0
Mid−predators
Turbidity Algae
Water temperature
Greenhouse gases Floods
Thermal low pressureErosion Macrophytes
Turf−forming algae
Macroalgae abundance Flushing
Lobsters and meso−predatorsTop predators
Wind stress
Water column density contrast
Urchin barren
Herbivores
Canopy−forming algae
Habitat structural complexity
Phosphorous in growth
Urban
Density contrast inOrganic matter and filamentous algae
Leakage Plankton
0.00
Zooxanthellae mixing water
ENSO−like events water column
Mortality the
Unpalatability frequency
Droughts
OceanHumanPerverseDemand
rate Agriculture Planktivore fish
AtmosphericWater Technology preferences
Landscape coolingwater incentives
fragmentation/conversion
acidification theuse
Other competitors Sediments
DailyInvasiveLocalSewage runoff
Low PollutantsFish Subsidies
population
HurricanesCO2 release
Consumption
relativePrecipitationTrade
Deforestation movements
Thermal annomalies of water
tidesUrban Stratificationcommons
storm
Fertilizers
Irrigation
frequency
Tragedy
Impoundments
species
Sulfide
0.00 0.02 0.04 0.06 0.08 0.10 0.12
0 5 10 15
Eigenvector
Indegree
26. D1
Terrestrial Regime Shifts RS1 RS2 RS3
Local centrality Global centrality
0.08
8
Fire frequency Precipitation
0.06
Global warming Precipitation Agriculture
Woody plants dominance
6
Fire frequency
Forest Grass dominance Deforestation
Cropland−Grassland area Deforestation
Betweenness
Outdegree
Agriculture Irrigation Albedo
0.04
Albedo Grass dominance
4
Irrigation
Rainfall variability
Soil productivity Forest
Droughts
DemandLand−Ocean temperature
Rainfall deficit
Savanna Native vegetation gradient
Woody plants dominance
Demand
Productivity
Land−Ocean temperature gradient
Atmospheric temperature
Erosion
Savanna
SST Atmospheric temperature
Floodsdemand
Grazing Water infrastructure Evapotranspiration
Water Erosion
Vegetation Space
Water availability
2
Atmospheric CO2
0.02
Human population Palatability
Soil moisture productivity
Soil Vegetation
Water infrastructure
Water availability
Advection
Carbon storage Global warming
Soil impermeability Solar radiation
Infrastructure developmentstress
WindTree release
maturity
Aquifers
LatentSoil quality
heatevents
Monsoon circulation
ENSO−likeDust frequency Vapor Soil salinity Soil salinity
Biomass
Logging industryShadow_rooting level
ImmigrationWater consumption
Land−Ocean pressure gradient concentration Productivity Aerosol concentration Soil moisture Rainfall deficit
use Moisture Carbon storage
Lifting Ranching
condensation Advection
FertilizersAbsorption of solar radiation
Aerosol Brown radiation
Solar clouds
Illegal logging
Sea tides Brown clouds Roughness
Temperature
Land conversion Ground water table
Grazers Absorption of solar radiation
Aquifers Evapotranspiration variability
Land conversion Rainfall Cropland−Grassland area
Vapor Droughts
Native vegetation
Ground Waterstress frequencyGrazers
ENSO−like events
SSTMonsoon
Land−Ocean water table
pressure gradient circulation
Wind demand
WaterTemperature
Shadow_rooting Moisture
Dust LiftingRoughnessTree maturity
Soil quality
consumptioncondensation level
Palatability
0
0.00
RanchingFloods
Grazing Space
Soil impermeabilityBiomass population
Human
Latent heat Logginglogging Atmospheric CO2
Fertilizers Illegal development
Immigration
Sea tides releaseindustry
Infrastructure
use
0 2 4 6 8 0.00 0.02 0.04 0.06 0.08
Indegree Eigenvector
27. Interaction of regime
shifts drivers?
Regime shifts are tightly connected. The
management of immediate causes or well
studied variables might not be enough to
avoid such catastrophes.
Agricultural processes and global warming
are the main causes of regime shifts.
Network analysis might be a useful
approach to address causality relationships
28. Thanks!
Drs. Oonsie Biggs & Garry
Peterson for their supervision
RSDB folks for inspiring
discussion and writing
examples
SRC for an inspiring research
space and funding!
Questions??
e-mail: juan.rocha@stockholmresilience.su.se
Twitter: @juanrocha
Blog: http://criticaltransitions.wordpress.com/
What is a regime shift?
Science pub May 2009 - SRC
Editor's Notes
\n
human population has grown six-fold, the world’s economy 50-fold and energy consumption 40-fold (Steffen et al. 2007)\n\n
\n
\n
\n
\n
methods from physics and social sciences applied to medicine to figure out multicausality patterns.\n
\n
\n
\n
\n
20RS - 67 Drivers, 239 links, density 6.3%\n
82% density\nMarine RS are tightly connected: water as a transport media for disturbances: turbidity, SST, pollutants, sediments, etc.\n
Outdegree: Variables which have a lot of causal links to other variables.\nIndegree: Variables hard to manage because they receive a lot of causal connections\n
Few nodes have a lot of links!\nMost connections are positive.\n
Few nodes have a lot of links!\nMost connections are positive.\n
MANAGEMENT CHALLENGES\n1.the increasing forcing on global change drivers should slow down enough to allow species adaptation and keep food webs stable.\n2. New methods to close the nutrient cycle on farms are needed.\nseparate on 3 slides for each question\n