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.
Nrf2: A Guardian of Healthspan and Gatekeeper of Species LongevityLifeVantage
This document summarizes a symposium discussing the role of the transcription factor Nrf2 in aging and longevity. Nrf2 regulates over 200 genes involved in cytoprotection, metabolism of toxins, oxidative stress response, and protein stability/degradation. Constitutively high expression and activation of Nrf2 and its downstream targets are observed in long-lived species and models of extended lifespan, suggesting Nrf2 plays a critical role in determining species longevity and regulating the aging process by protecting against age-related diseases and stressors. The document hypothesizes that Nrf2 is a "master regulator" of aging.
The document discusses epigenetics and its role in environmental diseases. It defines epigenetics as mechanisms that regulate gene expression without changing DNA sequence. Environmental factors can cause epigenetic changes through pathways like DNA methylation and histone modification. Abnormal epigenetic changes have been implicated in diseases like cancer, aging, and neurodevelopmental disorders. Certain environmental exposures are also linked to epigenetic alterations, though causal relationships are difficult to establish.
1) The document describes the design and synthesis of new (bis)ureidopropyl and (bis)thioureidopropyl diamine compounds as inhibitors of the histone demethylase LSD1.
2) Key compounds featured 3-5-3 and 3-6-3 carbon backbone architectures. Several compounds displayed single-digit micromolar IC50 values against recombinant LSD1 in vitro.
3) Compound 6d showed low micromolar cell viability IC50 values against lung and breast cancer cell lines. It also increased mRNA expression of silenced tumor suppressor genes in lung cancer cells.
This document summarizes rheumatoid arthritis (RA), including its risks and management. RA is a chronic inflammatory autoimmune disease characterized by inflamed joints, which can lead to articular tissue damage. Genetic, environmental, and epigenetic factors are associated with RA risk. The major mediator of disease progression is inflammation of the synovium. Current treatments suppress RA symptoms but have adverse effects, so further therapeutic advancement is needed to better manage the disease.
This document summarizes research analyzing relationships between lysine ubiquitylation and acetylation sites in Arabidopsis thaliana proteins from proteomic datasets. The researchers compared ubiquitylation and acetylation sites identified in previous publications and found that 9 proteins experienced both post-translational modifications. Most of these proteins were located in energy-associated organelles, supporting the hypothesis that ubiquitylation and acetylation interactions could influence metabolic pathways. Future work is needed to identify more acetylation and ubiquitylation sites to better understand potential direct or indirect influences between the two modifications.
This document summarizes the effects of environmental chemical exposures on epigenetics and disease. It finds that environmental pollutants like air pollution and chemicals like arsenic and aluminum can cause epigenetic changes including DNA methylation, histone modifications, and microRNA expression. These epigenetic alterations have been linked to various diseases. The document provides a table listing specific epigenetic changes found for different environmental chemicals and the diseases studied. It concludes that while many reports link environmental exposures to epigenetic changes, most have not been directly associated with disease outcomes, and more research is still needed.
This document summarizes research analyzing relationships between lysine ubiquitylation and acetylation sites from proteomic datasets in Arabidopsis thaliana proteins. The research compared ubiquitylation and acetylation sites identified in previous publications and found that 9 non-nuclear proteins experienced both post-translational modifications. Most of these proteins were located in energy-associated organelles. While this provides support for potential cross-talk between ubiquitylation and acetylation, more acetylation and ubiquitylation data is still needed to further investigate these relationships and whether acetylation directly or indirectly influences ubiquitylation.
Nrf2: A Guardian of Healthspan and Gatekeeper of Species LongevityLifeVantage
This document summarizes a symposium discussing the role of the transcription factor Nrf2 in aging and longevity. Nrf2 regulates over 200 genes involved in cytoprotection, metabolism of toxins, oxidative stress response, and protein stability/degradation. Constitutively high expression and activation of Nrf2 and its downstream targets are observed in long-lived species and models of extended lifespan, suggesting Nrf2 plays a critical role in determining species longevity and regulating the aging process by protecting against age-related diseases and stressors. The document hypothesizes that Nrf2 is a "master regulator" of aging.
The document discusses epigenetics and its role in environmental diseases. It defines epigenetics as mechanisms that regulate gene expression without changing DNA sequence. Environmental factors can cause epigenetic changes through pathways like DNA methylation and histone modification. Abnormal epigenetic changes have been implicated in diseases like cancer, aging, and neurodevelopmental disorders. Certain environmental exposures are also linked to epigenetic alterations, though causal relationships are difficult to establish.
1) The document describes the design and synthesis of new (bis)ureidopropyl and (bis)thioureidopropyl diamine compounds as inhibitors of the histone demethylase LSD1.
2) Key compounds featured 3-5-3 and 3-6-3 carbon backbone architectures. Several compounds displayed single-digit micromolar IC50 values against recombinant LSD1 in vitro.
3) Compound 6d showed low micromolar cell viability IC50 values against lung and breast cancer cell lines. It also increased mRNA expression of silenced tumor suppressor genes in lung cancer cells.
This document summarizes rheumatoid arthritis (RA), including its risks and management. RA is a chronic inflammatory autoimmune disease characterized by inflamed joints, which can lead to articular tissue damage. Genetic, environmental, and epigenetic factors are associated with RA risk. The major mediator of disease progression is inflammation of the synovium. Current treatments suppress RA symptoms but have adverse effects, so further therapeutic advancement is needed to better manage the disease.
This document summarizes research analyzing relationships between lysine ubiquitylation and acetylation sites in Arabidopsis thaliana proteins from proteomic datasets. The researchers compared ubiquitylation and acetylation sites identified in previous publications and found that 9 proteins experienced both post-translational modifications. Most of these proteins were located in energy-associated organelles, supporting the hypothesis that ubiquitylation and acetylation interactions could influence metabolic pathways. Future work is needed to identify more acetylation and ubiquitylation sites to better understand potential direct or indirect influences between the two modifications.
This document summarizes the effects of environmental chemical exposures on epigenetics and disease. It finds that environmental pollutants like air pollution and chemicals like arsenic and aluminum can cause epigenetic changes including DNA methylation, histone modifications, and microRNA expression. These epigenetic alterations have been linked to various diseases. The document provides a table listing specific epigenetic changes found for different environmental chemicals and the diseases studied. It concludes that while many reports link environmental exposures to epigenetic changes, most have not been directly associated with disease outcomes, and more research is still needed.
This document summarizes research analyzing relationships between lysine ubiquitylation and acetylation sites from proteomic datasets in Arabidopsis thaliana proteins. The research compared ubiquitylation and acetylation sites identified in previous publications and found that 9 non-nuclear proteins experienced both post-translational modifications. Most of these proteins were located in energy-associated organelles. While this provides support for potential cross-talk between ubiquitylation and acetylation, more acetylation and ubiquitylation data is still needed to further investigate these relationships and whether acetylation directly or indirectly influences ubiquitylation.
This study examined the effects of arsenic exposure on eavesdropping behavior in female Betta splendens (Siamese fighting fish). Female fish were exposed to 0 ppb, 10 ppb, or 100 ppb of arsenic. During eavesdropping trials, all females spent more time near interacting males, indicating they were eavesdropping. Males exposed to higher arsenic levels displayed less aggression. During subsequent mate choice trials, exposed females did not spend more time near the winner of male interactions, suggesting arsenic may impair their ability to retain information gained from eavesdropping. The study aims to understand how environmental pollutants like arsenic impact important fish behaviors.
Presentation format4 posttranslational modification in cell adhesion and migr...Birgit Kastberger
Posttranslational modifications (PTMs), such as phosphorylation, acetylation, and methylation regulate cell adhesion and migration. PTMs modify integrin function and the activity of proteins involved in cytoskeletal dynamics like cofilin, alpha-tubulin, cortactin, and EF1a1. Acetylation of cortactin and alpha-tubulin reduces cell migration by decreasing their association with actin and microtubule stability, respectively. Phosphorylation of cofilin regulates its actin binding activity and ability to sever actin filaments. Methylation of EF1a1 is required for neural crest cell migration. Drugs that alter the cell's acetylation state through inhibition of HDACs or modulation of acetyl-
Role of genetics in periodontal diseasesAnushri Gupta
Terminologies in Genetics
Genetic study design
genetic syndrome and disease associated with periodontal diseases, heretibility of periodontal disease, gene library, gene therapy
This study investigated whether supplementing glutathione (GSH) could attenuate lipopolysaccharide (LPS)-induced mitochondrial dysfunction and apoptosis in a mouse model of acute lung injury (ALI). The researchers found that LPS increased oxidative stress, mitochondrial dysfunction, and apoptosis in mouse lung tissue. However, pre-treating the mice with GSH-ethyl ester prevented the LPS-induced increases in oxidative stress, preserved mitochondrial function, and reduced apoptosis. Thus, GSH supplementation may protect against LPS-induced mitochondrial damage and cellular apoptosis in ALI.
EngenuitySC's Science Cafe - March with Dr. Patrick WosterEngenuitySC
This document discusses epigenetic modulation through inhibition of histone demethylases like LSD1. It summarizes that:
1) Polyamino(bis)guanidines and polyaminobiguanides can inhibit the histone demethylase LSD1 in vitro and in human colon cancer cells.
2) These inhibitors are non-competitive inhibitors of LSD1 and promote increased histone H3 lysine 4 dimethylation.
3) One inhibitor, verlindamycin (compound 2d), re-expresses tumor suppressor genes silenced in cancer cells and reduces tumor growth in mouse models of human colon cancer, especially in combination with 5-azacytidine.
Nutrigenomics is the study of how nutrients and other food components influence gene expression. It seeks to understand how nutrition impacts homeostasis at the cellular and genetic levels. The main concepts are that specific diets can modulate health and disease by affecting gene expression, an individual's genetic makeup influences their response to diet and disease risk, and personalized diets based on genetics may lower risk. Nutrigenomics examines how nutrients directly or indirectly regulate genes and how genetic variations impact nutrient metabolism and disease. It studies relationships between diet, genes and chronic diseases like obesity, diabetes, cancer and cardiovascular disease.
Genes related to aging, obesity and myocardial infarctionThet Su Win
This document discusses genes related to aging, obesity, and myocardial infarction. It summarizes several genes that influence aging in model organisms like yeast, worms, and flies. Genes discussed in relation to obesity include OB, leptin, genes in the melanocortin pathway, and FTO. The document also reviews causes of myocardial infarction and genes involved like PCSK9, LDLR, ApoE, and ApoB100. An update article discusses findings from a study that identified single nucleotide polymorphisms (SNPs) in genes like GHRL, AGRP, CPE, NPY1R, and others that were significantly associated with increased body mass index.
One of the most worrying applications of molecular
technology in sport is the gene doping, which is an outgrowth
of gene therapy. In gene therapy, the missing or out- functioned
gene or gene fragment is replaced with the functioning one, by
the help of transfectionable devices such as viruses. In gene
doping, the interested region is mostly the genetic material
for enhancing athletic capacity.
This document summarizes research aiming to determine the role of post-translational modifications like ubiquitination on the lipid binding affinity of the Angiomotin 130 protein. Site-directed mutagenesis was used to induce mutations mimicking or preventing ubiquitination at specific lysine residues in the ACCH domain. Preliminary spot blot assays suggest mutating lysine 87 to glutamate does not decrease lipid binding affinity, but more work is needed to characterize the effects of ubiquitinating specific residues. Future work will confirm mutations, perform lipid binding assays to quantify affinity changes, and identify residues for further study using full-length protein constructs.
1) The document discusses the need for large-scale studies of environmental exposures, known as environment-wide association studies (EWAS), to discover environmental factors associated with disease and address issues with past fragmented studies of single exposures.
2) EWAS can systematically analyze multiple personal exposures simultaneously and adjust for multiple testing to identify strongest associations, which can then be validated in independent data sets.
3) However, establishing causal inferences from observational EWAS data remains challenging due to complex correlations between many environmental factors.
This document summarizes a study investigating the role of Set1-mediated histone H3 lysine 4 (H3K4) methylation in Saccharomyces cerevisiae survival under histidine starvation conditions. The study found that mono-methylation of H3K4 by Set1 is advantageous for optimal growth under these stressful conditions. New Set1 mutant strains, including ones capable of only mono-methylation or hyper methylation, were constructed to further examine the role of H3K4 methylation levels.
The document summarizes information about the apolipoprotein E (APOE) gene, including its location, alleles, and association with various health conditions. It discusses how the APOE gene codes for a protein involved in lipid transport, with different alleles (e2, e3, e4) producing slightly different proteins and phenotypes. Studies have shown the e4 allele increases Alzheimer's risk and earlier onset, while the rarer e2 allele may protect against or delay Alzheimer's. The e4 and e2 alleles also impact cardiovascular disease risk. The document aims to study the distribution of APOE alleles in the population of Hotchkiss School and any differences among demographics.
This document provides an overview of the contents of an A2 Biology unit on control systems. The unit covers topics including the human nervous system, nerve cells, the nerve impulse, synapses, receptors, muscle, animal and plant responses, control of heart rate, the hormone system, homeostasis, and molecular genetics. The genetics section will discuss topics such as the genetic code, protein synthesis, gene mutations, stem cells, control of gene expression, and biotechnology techniques including DNA sequencing and genetically modified organisms.
Dr Una L Fairbrother
Telomere length: a 21st century biomarker" discusses DNA structure and the nature of telomeres. This talk explains the importance of telomere length and the impact of this feature on human health. The talk finishes describing the exciting work being carried out in London Metropolitan University to help develop this measure as a 21st century biomarker.
This study examines how high levels of vitamin D receptor (VDR) in genetic hypercalciuric stone-forming (GHS) rat tissues may cause excess calcium excretion in urine. The researchers suggest that over-expression of VDR is due to altered post-translational modification by ubiquitin and SUMO proteins, which regulate degradation of nuclear proteins like VDR. Experiments will overexpress ubiquitin and SUMO in rat tissues to analyze effects on VDR signaling and mimic the GHS phenotype. Knocking down ubiquitination enzymes will measure VDR level changes. Finally, GHS cell proteins will be isolated to check for high ubiquitin levels compared to controls. Evidence that ubiquitin/SUMO alterations affect V
This document discusses genetics and periodontics. It provides an introduction to genetics concepts like genes, genomes, alleles and genetic testing. It discusses the human genome project and evidence that genetics plays a role in periodontal diseases. Certain genes like IL-1, TNF-α and PGE2 are candidates for influencing periodontal diseases based on their roles in immune-inflammatory processes and bone metabolism. Genetic variations involved in periodontal diseases can be determined through studies of candidate genes, genomic scans and proteomics.
Rose S, Frye RE, Slattery J, Wynne R, Tippett M, et al. (2014) Oxidative Stress Induces Mitochondrial Dysfunction in a Subset of Autism Lymphoblastoid Cell Lines in a Well-Matched Case Control Cohort. PLoS ONE 9(1):e85436.doi:10.1371/journal.pone.0085436.
This document describes research into developing new treatments for amyotrophic lateral sclerosis (ALS) by inhibiting mutant superoxide dismutase 1 (SOD1)-dependent protein aggregation. Researchers identified pyrimidine-2,4,6-trione (PYT) derivatives as promising compounds through high-throughput screening. Structure-activity relationship studies led to the optimization of PYT analogs, with certain modifications showing improved potency and properties. The most potent analogs contained electron-withdrawing groups and an aromatic ring at the R3 position. X-ray crystallography revealed the PYT core adopts a flat, benzene-like conformation that may contribute to biological activity. Overall, PYTs represent
This document describes a label-free quantitative proteomics method using liquid chromatography coupled to mass spectrometry (LC/MS). The method relies on comparing changes in peptide signal responses and retention times (accurate mass retention time or AMRT components) between control and experimental samples to determine relative protein abundance changes. The method was tested by spiking increasing amounts of standard proteins into human serum samples and observing a linear relationship between signal response and protein concentration. The quantitative proteomics strategy provides a simple LC/MS-based method for comparing protein profiles between samples without using stable isotope labeling.
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.
The document provides an overview of bioinformatics and examples of how it is used at different biological scales and levels of complexity, from genomics to proteomics to biological networks and systems biology. It discusses how bioinformatics integrates biological data from different sources and scales to offer new biological insights. Examples are given of how bioinformatics is applied to analyze genomic, metagenomic, and proteomic data as well as protein structures and interactions.
This study examined the effects of arsenic exposure on eavesdropping behavior in female Betta splendens (Siamese fighting fish). Female fish were exposed to 0 ppb, 10 ppb, or 100 ppb of arsenic. During eavesdropping trials, all females spent more time near interacting males, indicating they were eavesdropping. Males exposed to higher arsenic levels displayed less aggression. During subsequent mate choice trials, exposed females did not spend more time near the winner of male interactions, suggesting arsenic may impair their ability to retain information gained from eavesdropping. The study aims to understand how environmental pollutants like arsenic impact important fish behaviors.
Presentation format4 posttranslational modification in cell adhesion and migr...Birgit Kastberger
Posttranslational modifications (PTMs), such as phosphorylation, acetylation, and methylation regulate cell adhesion and migration. PTMs modify integrin function and the activity of proteins involved in cytoskeletal dynamics like cofilin, alpha-tubulin, cortactin, and EF1a1. Acetylation of cortactin and alpha-tubulin reduces cell migration by decreasing their association with actin and microtubule stability, respectively. Phosphorylation of cofilin regulates its actin binding activity and ability to sever actin filaments. Methylation of EF1a1 is required for neural crest cell migration. Drugs that alter the cell's acetylation state through inhibition of HDACs or modulation of acetyl-
Role of genetics in periodontal diseasesAnushri Gupta
Terminologies in Genetics
Genetic study design
genetic syndrome and disease associated with periodontal diseases, heretibility of periodontal disease, gene library, gene therapy
This study investigated whether supplementing glutathione (GSH) could attenuate lipopolysaccharide (LPS)-induced mitochondrial dysfunction and apoptosis in a mouse model of acute lung injury (ALI). The researchers found that LPS increased oxidative stress, mitochondrial dysfunction, and apoptosis in mouse lung tissue. However, pre-treating the mice with GSH-ethyl ester prevented the LPS-induced increases in oxidative stress, preserved mitochondrial function, and reduced apoptosis. Thus, GSH supplementation may protect against LPS-induced mitochondrial damage and cellular apoptosis in ALI.
EngenuitySC's Science Cafe - March with Dr. Patrick WosterEngenuitySC
This document discusses epigenetic modulation through inhibition of histone demethylases like LSD1. It summarizes that:
1) Polyamino(bis)guanidines and polyaminobiguanides can inhibit the histone demethylase LSD1 in vitro and in human colon cancer cells.
2) These inhibitors are non-competitive inhibitors of LSD1 and promote increased histone H3 lysine 4 dimethylation.
3) One inhibitor, verlindamycin (compound 2d), re-expresses tumor suppressor genes silenced in cancer cells and reduces tumor growth in mouse models of human colon cancer, especially in combination with 5-azacytidine.
Nutrigenomics is the study of how nutrients and other food components influence gene expression. It seeks to understand how nutrition impacts homeostasis at the cellular and genetic levels. The main concepts are that specific diets can modulate health and disease by affecting gene expression, an individual's genetic makeup influences their response to diet and disease risk, and personalized diets based on genetics may lower risk. Nutrigenomics examines how nutrients directly or indirectly regulate genes and how genetic variations impact nutrient metabolism and disease. It studies relationships between diet, genes and chronic diseases like obesity, diabetes, cancer and cardiovascular disease.
Genes related to aging, obesity and myocardial infarctionThet Su Win
This document discusses genes related to aging, obesity, and myocardial infarction. It summarizes several genes that influence aging in model organisms like yeast, worms, and flies. Genes discussed in relation to obesity include OB, leptin, genes in the melanocortin pathway, and FTO. The document also reviews causes of myocardial infarction and genes involved like PCSK9, LDLR, ApoE, and ApoB100. An update article discusses findings from a study that identified single nucleotide polymorphisms (SNPs) in genes like GHRL, AGRP, CPE, NPY1R, and others that were significantly associated with increased body mass index.
One of the most worrying applications of molecular
technology in sport is the gene doping, which is an outgrowth
of gene therapy. In gene therapy, the missing or out- functioned
gene or gene fragment is replaced with the functioning one, by
the help of transfectionable devices such as viruses. In gene
doping, the interested region is mostly the genetic material
for enhancing athletic capacity.
This document summarizes research aiming to determine the role of post-translational modifications like ubiquitination on the lipid binding affinity of the Angiomotin 130 protein. Site-directed mutagenesis was used to induce mutations mimicking or preventing ubiquitination at specific lysine residues in the ACCH domain. Preliminary spot blot assays suggest mutating lysine 87 to glutamate does not decrease lipid binding affinity, but more work is needed to characterize the effects of ubiquitinating specific residues. Future work will confirm mutations, perform lipid binding assays to quantify affinity changes, and identify residues for further study using full-length protein constructs.
1) The document discusses the need for large-scale studies of environmental exposures, known as environment-wide association studies (EWAS), to discover environmental factors associated with disease and address issues with past fragmented studies of single exposures.
2) EWAS can systematically analyze multiple personal exposures simultaneously and adjust for multiple testing to identify strongest associations, which can then be validated in independent data sets.
3) However, establishing causal inferences from observational EWAS data remains challenging due to complex correlations between many environmental factors.
This document summarizes a study investigating the role of Set1-mediated histone H3 lysine 4 (H3K4) methylation in Saccharomyces cerevisiae survival under histidine starvation conditions. The study found that mono-methylation of H3K4 by Set1 is advantageous for optimal growth under these stressful conditions. New Set1 mutant strains, including ones capable of only mono-methylation or hyper methylation, were constructed to further examine the role of H3K4 methylation levels.
The document summarizes information about the apolipoprotein E (APOE) gene, including its location, alleles, and association with various health conditions. It discusses how the APOE gene codes for a protein involved in lipid transport, with different alleles (e2, e3, e4) producing slightly different proteins and phenotypes. Studies have shown the e4 allele increases Alzheimer's risk and earlier onset, while the rarer e2 allele may protect against or delay Alzheimer's. The e4 and e2 alleles also impact cardiovascular disease risk. The document aims to study the distribution of APOE alleles in the population of Hotchkiss School and any differences among demographics.
This document provides an overview of the contents of an A2 Biology unit on control systems. The unit covers topics including the human nervous system, nerve cells, the nerve impulse, synapses, receptors, muscle, animal and plant responses, control of heart rate, the hormone system, homeostasis, and molecular genetics. The genetics section will discuss topics such as the genetic code, protein synthesis, gene mutations, stem cells, control of gene expression, and biotechnology techniques including DNA sequencing and genetically modified organisms.
Dr Una L Fairbrother
Telomere length: a 21st century biomarker" discusses DNA structure and the nature of telomeres. This talk explains the importance of telomere length and the impact of this feature on human health. The talk finishes describing the exciting work being carried out in London Metropolitan University to help develop this measure as a 21st century biomarker.
This study examines how high levels of vitamin D receptor (VDR) in genetic hypercalciuric stone-forming (GHS) rat tissues may cause excess calcium excretion in urine. The researchers suggest that over-expression of VDR is due to altered post-translational modification by ubiquitin and SUMO proteins, which regulate degradation of nuclear proteins like VDR. Experiments will overexpress ubiquitin and SUMO in rat tissues to analyze effects on VDR signaling and mimic the GHS phenotype. Knocking down ubiquitination enzymes will measure VDR level changes. Finally, GHS cell proteins will be isolated to check for high ubiquitin levels compared to controls. Evidence that ubiquitin/SUMO alterations affect V
This document discusses genetics and periodontics. It provides an introduction to genetics concepts like genes, genomes, alleles and genetic testing. It discusses the human genome project and evidence that genetics plays a role in periodontal diseases. Certain genes like IL-1, TNF-α and PGE2 are candidates for influencing periodontal diseases based on their roles in immune-inflammatory processes and bone metabolism. Genetic variations involved in periodontal diseases can be determined through studies of candidate genes, genomic scans and proteomics.
Rose S, Frye RE, Slattery J, Wynne R, Tippett M, et al. (2014) Oxidative Stress Induces Mitochondrial Dysfunction in a Subset of Autism Lymphoblastoid Cell Lines in a Well-Matched Case Control Cohort. PLoS ONE 9(1):e85436.doi:10.1371/journal.pone.0085436.
This document describes research into developing new treatments for amyotrophic lateral sclerosis (ALS) by inhibiting mutant superoxide dismutase 1 (SOD1)-dependent protein aggregation. Researchers identified pyrimidine-2,4,6-trione (PYT) derivatives as promising compounds through high-throughput screening. Structure-activity relationship studies led to the optimization of PYT analogs, with certain modifications showing improved potency and properties. The most potent analogs contained electron-withdrawing groups and an aromatic ring at the R3 position. X-ray crystallography revealed the PYT core adopts a flat, benzene-like conformation that may contribute to biological activity. Overall, PYTs represent
This document describes a label-free quantitative proteomics method using liquid chromatography coupled to mass spectrometry (LC/MS). The method relies on comparing changes in peptide signal responses and retention times (accurate mass retention time or AMRT components) between control and experimental samples to determine relative protein abundance changes. The method was tested by spiking increasing amounts of standard proteins into human serum samples and observing a linear relationship between signal response and protein concentration. The quantitative proteomics strategy provides a simple LC/MS-based method for comparing protein profiles between samples without using stable isotope labeling.
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.
The document provides an overview of bioinformatics and examples of how it is used at different biological scales and levels of complexity, from genomics to proteomics to biological networks and systems biology. It discusses how bioinformatics integrates biological data from different sources and scales to offer new biological insights. Examples are given of how bioinformatics is applied to analyze genomic, metagenomic, and proteomic data as well as protein structures and interactions.
The document discusses using targeted next-generation sequencing (NGS) panels to identify genetic causes of nephrotic diseases in individuals with unknown etiology. The study found rare pathogenic/likely pathogenic mutations in nephrotic disease-related genes in 12% of patients using the ACMG-AMP variant classification standards. NGS can improve diagnosis accuracy for nephrotic diseases by discovering uncommon pathogenic variations compatible with clinical diagnoses in 65% of samples. The document provides background on genetics, genetic variations, inheritance patterns, and NGS techniques for detecting mutations.
1. P4 medicine requires vast amounts of data because biology and disease are incredibly complex, arising from random and chaotic evolutionary processes that build upon existing structures in complex ways, like Rube Goldberg machines.
2. Viewing biology through an informational lens helps organize vast amounts of data by recognizing two types of biological information - genetic and environmental - that are integrated across molecular, cellular, tissue, and systemic levels in hierarchical networks and machines.
3. Studying the progression of prion disease in mice revealed over 7,000 differentially expressed genes across disease progression, which was reduced to around 300 genes most associated with neurodegeneration through various subtractions. These 300 genes mapped to four major biological networks involved in
Noise in multiple sclerosis: unwanted and necessaryMutiple Sclerosis
Isabella Bordi, Vito A G Ricigliano, Renato Umeton, Giovanni Ristori, Francesca Grassi, Andrea Crisanti, Alfonso Sutera, and Marco Salvetti
As our knowledge about the etiology of multiple sclerosis (MS) increases, deterministic paradigms appear insufficient to describe the pathogenesis of the disease, and the impression is that stochastic phenomena (i.e. random events not necessarily resulting in disease in all individuals) may contribute to the development of MS. However, sources and mechanisms of stochastic behavior have not been investigated and there is no proposed framework to incorporate nondeterministic processes into disease biology. In this report, we will first describe analogies between physics of nonlinear systems and cell biology, showing how small-scale random perturbations can impact on large-scale phenomena, including cell function. We will then review growing and solid evidence showing that stochastic gene expression (or gene expression "noise") can be a driver of phenotypic variation. Moreover, we will describe new methods that open unprecedented opportunities for the study of such phenomena in patients and the impact of this information on our understanding of MS course and therapy.
Bioinformatics is an interdisciplinary field that uses computational tools and techniques to analyze and interpret biological data. It plays a key role in areas like agriculture and healthcare. Some major areas of bioinformatics research include gene finding, protein structure prediction, and drug design. All organisms possess genetic material DNA that controls cell functioning and is the basis for inheritance. Understanding genomes, genes, and how genetic information is expressed presents many challenges. Comparative genomics through genome projects of different organisms can provide insights into evolution and aid in drug development.
This research article describes the generation of new mouse models of partial trisomy and monosomy of human chromosome 21. The models contain extra or missing copies of a 9.4 Mb region on mouse chromosome 16 that is syntenic to a region on human chromosome 21. Mice with an extra copy (trisomy model) showed impaired locomotion and increased muscle strength/mass, while mice missing a copy (monosomy model) showed the opposite phenotype. Gene expression analysis revealed that genes related to muscle energy metabolism and mitochondria were downregulated in the trisomy mice and upregulated in the monosomy mice. This correlated with changes in mitochondrial proliferation and function in skeletal muscle, providing insight into the muscle and locom
1. All classes of biomolecules can be affected in diseases, either primarily through defects or secondarily by alterations in other molecules.
2. Biochemical alterations can occur rapidly, such as from cyanide poisoning, or slowly over years, like some cases of Niemann-Pick disease.
3. Diseases can be caused by deficiencies or excesses of biomolecules, such as rickets from vitamin D deficiency or hypercalcemia from excess.
Interpreting Health Status Of Indian Population Using Phase Angle As Health P...IJRES Journal
Bio Electrical Impedance Analyser is a simple Non-Invasive tool that is used for the Human body composition Analysis. It has been found that the basic principle of Human Body composition Analysis is the measurement of fat vs lean muscle tissue. And it is well known fact that biological tissues the path of least resistance. While Analysing the body composition through Bio Electrical Impedance Analyser body resistance and body reactance are taken into account. Phase Angle is directly calculated from resistance and reactance and Phase Angle is an important indicator of cellular health and integrity. This paper aims at discussing the significance of Phase Angle in Analysis of Human Body Composition and developing and validating prediction equation of Phase Angle at different frequencies.
Genetic terms used in psychiatry include:
- Concordance and heritability which measure the genetic influences on traits and diseases
- Mendelian and complex diseases which can be caused by single genes or interactions between genes and environment
- Alleles, haplotypes, loci, genetic maps, and linkage disequilibrium which describe genetic variations and inheritance patterns
Genome-wide association studies typically assay common genetic variants like single nucleotide polymorphisms to identify associations with traits and diseases.
A Mechanistic, Stochastic Model Helps Understand Multiple Sclerosis Course an...Mutiple Sclerosis
Isabella Bordi, Renato Umeton, Vito A. G. Ricigliano, Viviana Annibali, Rosella Mechelli, Giovanni Ristori, Francesca Grassi, Marco Salvetti, and Alfonso Sutera
Heritable and nonheritable factors play a role in multiple sclerosis, but their effect size appears too small, explaining relatively little about disease etiology. Assuming that the factors that trigger the onset of the disease are, to some extent, also those that generate its remissions and relapses, we attempted to model the erratic behaviour of the disease course as observed on a dataset containing the time series of relapses and remissions of 70 patients free of disease-modifying therapies. We show that relapses and remissions follow exponential decaying distributions, excluding periodic recurrences and confirming that relapses manifest randomly in time. It is found that a mechanistic model with a random forcing describes in a satisfactory manner the occurrence of relapses and remissions, and the differences in the length of time spent in each one of the two states. This model may describe how interactions between "soft" etiologic factors occasionally reach the disease threshold thanks to comparably small external random perturbations. The model offers a new context to rethink key problems such as "missing heritability" and "hidden environmental structure" in the etiology of complex traits.
Recent advances in Kriya Sharir (Ayurveda Physiology)Kishor Patwardhan
Recent advances in systems biology, neuroendocrinology, immunology, tissue engineering, chronobiology, gut microbiota, pharmacogenomics, and the study of extra-gustatory taste receptors provide opportunities to better understand Ayurveda using a systems approach. A systems approach views the human body as a complex system of interconnected parts and aims to understand how biological processes function as a whole, rather than focusing only on reductionist studies of individual molecules.
The transformational role of polymerase chain reaction (pcr) in environmental...Alexander Decker
This document discusses the transformational role of polymerase chain reaction (PCR) in environmental health research. PCR allows for exponential amplification of target DNA sequences, which has enabled rapid and sensitive detection of pathogens in environmental samples as an alternative to traditional culture methods. While PCR is widely used in developed countries, its benefits have yet to be fully realized in developing countries like Nigeria. The document provides background on DNA replication and the basics of how PCR works to exponentially amplify DNA. It argues that PCR could greatly aid environmental health monitoring and disease diagnosis in Nigeria.
Repurposing large datasets for exposomic discovery in diseaseChirag Patel
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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.
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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4. The Anthropocene
Social challenge: Understand patters of
causes and consequences of regime shifts
!
How common they are?
What possible interactions?
Where are they likely to occur?
Who will be most affected?
What can we do to avoid them?
5. Regime Shifts
Regime shifts are abrupt reorganization of a
system’s structure and function. A regime
correspond to characteristic behavior of the
system maintained by mutually reinforcing
processes or feedbacks. The shift occurs when
the strength of such feedbacks change, usually
driven by cumulative change in slow variables,
external disturbances or shocks.
low
low
high
Vegetation
collapse
Vegetation
recovery
high
low
Precipitation
Irreversible
Pre
cip
ita
tio
n
Pre
cip
ita
tio
n
high
Vegetation
Vegetation
low
Precipitation
low
high
Vegetation
Equilibria
high
Vegetation
high
Vegetation
high
low
collapse
high
Vegetation
Hystersis
Pre
cip
it
low
Pre
cip
ita
t
ion
Stability
Landscape
Threshold
ati
on
Gradual
low
Precipitation
Precipitation
(Gordon et al 2008)
6. Regime Shifts
Regime shifts are abrupt reorganization of a
system’s structure and function. A regime
correspond to characteristic behavior of the
system maintained by mutually reinforcing
processes or feedbacks. The shift occurs when
the strength of such feedbacks change, usually
driven by cumulative change in slow variables,
external disturbances or shocks.
J.S. Collie et al. / Progress in Oceanography 60 (2004) 281–302
287
(Collie 2004)
Fig. 3. Catastrophe manifold illustrating that the three types of regime shifts are special cases along a continuum of internal ecosystem
structure. Adapted from Jones and Walters (1976).
7. Regime Shifts
Regime shifts are abrupt reorganization of a
system’s structure and function. A regime
correspond to characteristic behavior of the
system maintained by mutually reinforcing
processes or feedbacks. The shift occurs when
the strength of such feedbacks change, usually
driven by cumulative change in slow variables,
external disturbances or shocks.
Science challenge: understand multicausal phenomena where experimentation
is rarely an option and time for action a
constraint
8. 1. A comparative framework: The database
2. Global drivers of Regime Shifts
3. Future developments
10. Regime Shifts DataBase
The shift substantially affect the
set of ecosystem services
provided by a social-ecological
system
Established or proposed
feedback mechanisms exist
that maintain the different
regimes.
!
The shift persists on time scale
that impacts on people and
society
11.
12.
13.
14.
15.
16.
17.
18. Existence
Well
established
Dryland degradation
Forest to savanna
Steppe to tundra
Mangroves collapse
Encroachment
Fisheries collapse
Marine Eutrophication
Proposed
Contested
Soil structure
Contested
Marine foodwebs
Monsoon weakening
Termohaline circulation
Proposed
Mechanism
Bivalves collapse
Coral transitions
Lake Eutrophication
Hypoxia
Kelps transitions
Sea grass
Peatlands
River channel change
Salt marshes
Soil salinization
Floating plants
Greenland
Arctic sea ice
West Antarctica Ice Sheet
Tundra to forest
Well established
19. Ecosystem Services
Biodiversity
Primary production
Nutrient cycling
Water cycling
Soil Formation
Fisheries
Wild animals and plants food
Freshwater
Foodcrops
Livestock
Timber
Woodfuel
Other crops
Hydropower
Water purification
Climate regulation
Regulation of soil erosion
Pest and disease regulation
Natural hazard regulation
Air quality regulation
Pollination
Recreation
Aesthetic values
Knowledge and educational values
Spiritual and religious
Livelihoods and economic activity
Food and nutrition
Cultural, aesthetic and recreational values
Security of housing and infrastructure
Health
Social confict
No direct impact
Regime Shifts DataBase
Ecosystem services
!
Drivers ...
Supporting
Provisioning
Regulating
Cultural
Human well being
0
8
15
23
30
25. Methods
•Bipartite network and
one-mode projections:
20 Regime shifts + 55
Drivers
4
•10 random bipartite
graphs to explore
significance of couplings:
mean degree, cooccurrence & clustering
coefficient statistics on
one-mode projections.
Drivers
Regime shifts
26. Methods
•Bipartite network and
one-mode projections:
20 Regime shifts + 55
Drivers
4
•10 random bipartite
graphs to explore
significance of couplings:
mean degree, cooccurrence & clustering
coefficient statistics on
one-mode projections.
Drivers
Regime shifts
27. Simulation results for 25 Regime Shifts across the
globe
Demand
Drivers Network
Co−occurrence Index
6
5
Global warming
5
7
9
12 14 16 19
0.4
Density
0.2
Sewage
2.0
2.2
2.4
2.6
Agriculture
Sediments
Rainfall variability
Floods
Sea level rise
Landscape fragmentation
Upwellings
0.0
1.8
Fishing
Human population
Urbanization
Temperature
Sea surface temperature
1
0
3
Erosion
Nutrients inputs
22
23
24
25
26
27
28
Degree
s−squared
Regime Shifts Network
Co−occurrence Index
29
Mean Degree
Clustering Coefficient
Average Degree in simulated
Regime Shifts Networks
0.6
0.8
River channel change
Eutrophication
0.2
0.4
Mangroves collapse
Forest to savannas
Hypoxia
0.2
Density
0.6
Bivalves collapse
0.4
Density
30
20
5 10
Soil structure
Soil salinization
Dry land degradation
Peatlands
Marine Eutrophication
Floating plants
0.20
0.25
0.30
0.35
Clustering coefficient
0.40
Kelps transitions
0.0
0.0
Coral transitions
0
Density
Deforestation
4
3
2
Density
15
10
5
0
1
Atmospheric CO2
Droughts
0.6
30
20
25
Degree distribution
Average Degree in simulated
Drivers Networks
10
11
12
13
s−squared
14
15
16
Monsoon weakening
18
19
20
21
22
23
Encroachment
Sea grass
Mean Degree
Fisheries collapse
Thermohaline circulation
Greenland
Salt marshes
Arctic sea ice
Marine foodwebs
Tundra to Forest
Western Antarctic IceSheet Collapse
28. Global drivers of Regime Shifts
Fishing
Urbanization
Nutrients inputs
Demand
Global warming
Deforestation
Human population
Agriculture
Atmospheric CO2
Droughts
Food production & climate
change drive the most
frequent drivers of regime
shifts
Few frequent drivers: Only 5
out of 55 drivers influence
more than 1/2 of the regime
shifts analyzed.
More shared drivers: 11
drivers interact with >50% of
other drivers when causing
regime shifts.
29. Count
0 15 30
Global drivers of Regime Shifts
2
4 6
Value
8
Biophysical
Biogeochemical Cycle
Land Cover Change
Biodiversity Loss
Water
Climate
Human Indirect Activities
Encroachment
Monsoon weakening
Soil salinization
Dry land degradation
Forest to savannas
Fisheries collapse
Marine foodwebs
Floating plants
Peatlands
Salt marshes
Soil structure
River channel change
Tundra to Forest
Greenland
Thermohaline circulation
Coral transitions
Bivalves collapse
Kelps transitions
Eutrophication
Hypoxia
0
Food production & climate
change drive the most
frequent drivers of regime
shifts
Few frequent drivers: Only 5
out of 55 drivers influence
more than 1/2 of the regime
shifts analyzed.
More shared drivers: 11
drivers interact with >50% of
other drivers when causing
regime shifts.
30. How drivers tend to interact?
Tundra to Forest
Thermohaline circulation
Greenland
Fisheries collapse
Marine foodwebs
Salt marshes
Monsoon weakening
Dry land degradation
Coral transitions
Encroachment
Kelps transitions
Floating plants
Eutrophication
Forest to savannas
Bivalves collapse
Peatlands
Hypoxia
Soil structure
Soil salinization
River channel change
Marine regime shifts
share significantly more
drivers and have more
similar feedback
mechanisms, suggesting
they may synchronize in
space and time.
Terrestrial regime shifts
share fewer drivers.
Higher diversity of drivers
makes management
more context
dependent.
32. Impacts of Regime Shifts
on Ecosystem Services
Encroachment
Bivalves collapse
Dry land degradation
Sea Grass
Eutrophication
Greenland
Peatlands
Hypoxia
Kelps transitions
Marine foodwebs
Mangroves collapse
Termohaline circulation
Western Antarctic IceSheet Collapse
Forest to savannas
Soil salinization
Arctic sea ice
Tundra to Forest
Floating plants
Monsoon weakening
River channel change
Marine eutrophication
Fisheries collapse
Soil structure
Coral transitions
Salt marshes
33. Impacts of Regime Shifts
on Ecosystem Services
Air quality regulation
Encroachment
Bivalves collapse
Dry land degradation
Sea Grass
Eutrophication
Greenland
Timber
Primary production
Water regulation
Peatlands
Hypoxia
Kelps transitions
Marine foodwebs
Biodiversity
Mangroves collapse
Termohaline circulation
Western Antarctic IceSheet Collapse
Forest to savannas
Soil salinization
Arctic sea ice
Knowledge and educational values
Wild animal and plant foods
Regulation of soil erosion
Freshwater
Water cycling
Floating plants
River channel change
Marine eutrophication
Water purification
Fisheries
Feed, fuel & fiber crops
Soil formation
Nutrient cycling
Pest and disease regulation
Fisheries collapse
Natural hazard regulation
Soil structure
Coral transitions
Salt marshes
Climate regulation
Livestock
Tundra to Forest
Monsoon weakening
Wood fuel
Foodcrops
Pollination
Recreation
Aesthetic values
Spiritual and religious
34. Impacts of Regime Shifts
on Ecosystem Services
Air quality regulation
Encroachment
Bivalves collapse
Dry land degradation
Sea Grass
Eutrophication
Greenland
Timber
Primary production
Water regulation
Peatlands
Hypoxia
Kelps transitions
Marine foodwebs
Biodiversity
Mangroves collapse
Termohaline circulation
Western Antarctic IceSheet Collapse
Forest to savannas
Soil salinization
Arctic sea ice
Knowledge and educational values
Wild animal and plant foods
Regulation of soil erosion
Freshwater
Water cycling
Floating plants
River channel change
Marine eutrophication
Fisheries
Soil formation
Nutrient cycling
Pest and disease regulation
Fisheries collapse
Natural hazard regulation
Pollination
Recreation
Spiritual and religious
Aesthetic values
Color Key
and Histogram
0
5
Value
10
15
Feed, fuel & fiber crops
Freshwater
Pest and disease regulation
Regulation of soil erosion
Soil formation
Natural hazard regulation
Wood fuel
Timber
Water regulation
Livestock
Foodcrops
Spiritual and religious
Knowledge and educational values
Pollination
Air quality regulation
Climate regulation
Water cycling
Wild animal and plant foods
Aesthetic values
Fisheries
Water purification
Nutrient cycling
Primary production
Recreation
Biodiversity
Green house gases
Sea surface temperature
Fire frequency
Low tides
Thermal anomalies in summer
Invasive species
Aquaculture
Irrigation infrastructure
Tides
Surface melting ponds
Surface melt water
Stratospheric ozone
Ocean temperature (deep water)
Ice surface melting
Glaciers growth
Climate variability (SAM)
Glaciers
Drainage
Water infrastructure
Aquifers
Water availability
Food supply
Water stratification
Tragedy of the commons
Access to markets
Subsidies
Development policies
Immigration
Logging
Ranching (livestock)
Production intensification
Food prices
Labor availability
Hurricanes
Ocean acidification
Pollutants
Disease
Turbidity
Flushing
Urban storm water runoff
Fishing technology
Impoundments
Fertilizers use
Precipitation
ENSO like events
Upwellings
Infrastructure development
Sea level rise
Sediments
Irrigation
Erosion
Landscape fragmentation
Rainfall variability
Atmospheric CO2
Temperature
Nutrients inputs
Floods
Sewage
Fishing
Urbanization
Global warming
Agriculture
Deforestation
Droughts
Demand
Human population
0 300 700
Count
Water purification
Feed, fuel & fiber crops
Soil structure
Coral transitions
Salt marshes
Climate regulation
Livestock
Tundra to Forest
Monsoon weakening
Wood fuel
Foodcrops
35. Impacts of Regime Shifts
on Ecosystem Services
Air quality regulation
Encroachment
Bivalves collapse
Dry land degradation
Sea Grass
Eutrophication
Greenland
Timber
Primary production
Water regulation
Peatlands
Hypoxia
Kelps transitions
Marine foodwebs
Biodiversity
Mangroves collapse
Termohaline circulation
Western Antarctic IceSheet Collapse
Forest to savannas
Soil salinization
Arctic sea ice
Knowledge and educational values
Wild animal and plant foods
Regulation of soil erosion
Freshwater
Water cycling
Floating plants
River channel change
Marine eutrophication
Fisheries
Soil formation
Nutrient cycling
Pest and disease regulation
Fisheries collapse
Natural hazard regulation
Pollination
Recreation
Spiritual and religious
Aesthetic values
Color Key
and Histogram
0
5
Value
10
15
Feed, fuel & fiber crops
Freshwater
Pest and disease regulation
Regulation of soil erosion
Soil formation
Natural hazard regulation
Wood fuel
Timber
Water regulation
Livestock
Foodcrops
Spiritual and religious
Knowledge and educational values
Pollination
Air quality regulation
Climate regulation
Water cycling
Wild animal and plant foods
Aesthetic values
Fisheries
Water purification
Nutrient cycling
Primary production
Recreation
Biodiversity
Green house gases
Sea surface temperature
Fire frequency
Low tides
Thermal anomalies in summer
Invasive species
Aquaculture
Irrigation infrastructure
Tides
Surface melting ponds
Surface melt water
Stratospheric ozone
Ocean temperature (deep water)
Ice surface melting
Glaciers growth
Climate variability (SAM)
Glaciers
Drainage
Water infrastructure
Aquifers
Water availability
Food supply
Water stratification
Tragedy of the commons
Access to markets
Subsidies
Development policies
Immigration
Logging
Ranching (livestock)
Production intensification
Food prices
Labor availability
Hurricanes
Ocean acidification
Pollutants
Disease
Turbidity
Flushing
Urban storm water runoff
Fishing technology
Impoundments
Fertilizers use
Precipitation
ENSO like events
Upwellings
Infrastructure development
Sea level rise
Sediments
Irrigation
Erosion
Landscape fragmentation
Rainfall variability
Atmospheric CO2
Temperature
Nutrients inputs
Floods
Sewage
Fishing
Urbanization
Global warming
Agriculture
Deforestation
Droughts
Demand
Human population
0 300 700
Count
Water purification
Feed, fuel & fiber crops
Soil structure
Coral transitions
Salt marshes
Climate regulation
Livestock
Tundra to Forest
Monsoon weakening
Wood fuel
Foodcrops
• Ecosystem services
frequently affected by
regime shifts are:
biodiversity, food production
(fisheries, primary
production, nutrient cycling),
recreation and aesthetic
values.
36. Managing regime shift drivers
Drivers by Management Type
Tundra to Forest
River channel change
Thermohaline circulation
Greenland
Marine foodwebs
Peatlands
Monsoon weakening
Kelps transitions
Dry land degradation
Forest to savannas
Soil structure
Soil salinization
Salt marshes
Encroachment
Hypoxia
Coral transitions
Fisheries collapse
Eutrophication
Bivalves collapse
Floating plants
International cooperation
to manage most drivers
of 75% of regime shifts.
Local
National
International
Regulating single drivers,
such as Climate change,
won’t prevent regime
shifts.
Regulating local drivers
can build resilience to
global drivers
0.0
0.2
0.4
0.6
Proportion of RS Drivers
0.8
1.0
Avoiding regime shifts
requires poly-centric
institutions.
37. Conclusions
Regime shifts are tightly connected both when sharing drivers and their
underlying feedback dynamics. The management of immediate causes or
well studied variables might not be enough to avoid such catastrophes.
Food production and climate change are the main causes of regime shifts
globally.
Marine regime shifts share more drivers, while terrestrial regime shifts are
more context dependent.
Management of regime shifts requires multi-level governance:
coordinating efforts across multiple scales of action.
Network analysis is an useful approach to study regime shifts couplings
when knowledge about system dynamics or time series of key variables
are limited.
39. Methods
•Bipartite network and onemode projections: 20
Regime shifts + 55 Drivers
Drivers
Regime shifts
4
•10 random bipartite graphs
to explore significance of
couplings: mean degree and
co-occurrence statistics on
one-mode projections.
•ERGM models using Jaccard
similarity index on the RSDB
as edge covariates
Regime Shift Database
A
1
0
1
1
0
0
0
0
1
1
1
1
0
1
0
1
B
1
0
0
0
1
1
0
0
1
1
1
0
0
1
0
1
C
Ecosystem services
Spatial scale
Ecosystem processes
Temporal scale
Ecosystem type
Reversibility
Impact on human well being
Evidence
Land use
...
40. Work in Progress
Causal Networks: Cascading effects and regime shifts controllability
Causal-loop diagrams is a
technique to map out the
feedback structure of a system
(Sterman 2000)
41. Topological features of Causal Networks
Degree centrality
Betweenness centrality
Eigenvector centrality
42. Marine Regime Shifts
Global centrality
10
0.10
0.12
Local centrality
Nutrients input
Phytoplankton
Nutrients input
Bivalves abundance
Zooplankton
Space
Top predators
Planktivore fish
GlobalUrban Macrophytes Phytoplankton
warminggrowth
Turbidity
SST Erosion
Biodiversity
Coral abundance
Unpalatability
Water vapor
AtmosphericDemand
CO2 Plankton and Macroalgae abundance
Human population
Upwellings
Precipitation
Flushing
ConsumptionFertilizers use runoff filamentous algae
preferences
Urban Sewage
Herbivores
Landscape fragmentation/conversion
Localstorm water
water movements
Deforestation Sediments
Global warming
Bivalves abundance
Dissolved oxygen
SST
0.04
ENSO−like Water temperature
events frequency
Canopy−forming algae algae
Turf−forming
Greenhouse gasesand meso−predators
Disease outbreak Urchin barren
Lobsters Nekton
Noxious gases
0.06
Betweenness
5
Floods
Algae
Fishing
Coral abundance
Disease outbreak
Water frequency
Invasive
Droughts
Impoundments densityLeakage
Thermal annomalies species
Tragedy of thecolumn acidification
Perverse incentives mixing
Low tides commons
Wind release
OceanIrrigation contrast
Sulfide stress
TechnologyWater Zooxanthellae
Stratification relative cooling structural complexity
Mortality rate
Daily competitors
Habitat
Hurricanescontrast in the water
Noxious gases
Other
SubsidiesPollutants low pressurecolumn
Density Thermal Fishmatter
Organic
Trade
Phosphorous in water
0.02
Water vapor
0
Biodiversity
Space
Upwellings
Turbidity
0.00
Outdegree
Agriculture
0.08
Fishing
Dissolved oxygenMid−predators
0
5
10
Indegree
15
Nekton
Zooplankton
Mid−predators
Algae
Water gases
Floods
Greenhousetemperature
Thermal low pressureErosion Macrophytes
Turf−forming algae
Macroalgae abundance
Flushing
Wind stress
Water column density contrast
Lobsters and meso−predatorsTop predators
Urchin barren
Herbivores
Canopy−forming algae
Habitat structural complexity
Urban
Leakage Plankton
Phosphorous in growth
Droughts
Density contrast inOrganic matter and filamentous algae
Unpalatability frequency
Agriculture
Mortality the
rate
ENSO−like events water column
Zooxanthellae mixing water
Planktivore fish
Landscape coolingwater incentives
fragmentation/conversion
OceanHumanPerverseDemand
acidification theuse
DailyInvasiveLocalSewage runoff
relativePrecipitationTrade
Low PollutantsFish Subsidies
tidesUrban Stratificationcommons
Irrigation
frequency
Tragedy
Impoundments
species
Other competitors Sediments
AtmosphericWater Technology preferences
Consumption
population
HurricanesCO2 release
Thermal annomalies of water
Sulfide
storm
Fertilizers
Deforestation movements
0.00
0.02
0.04
0.06
Eigenvector
0.08
0.10
0.12
43. Terrestrial Regime Shifts
Global centrality
8
0.08
Local centrality
Precipitation
Precipitation
Woody plants dominance
4
Agriculture
Rainfall variability
Irrigation
Albedo
Droughts
Land−Ocean temperature
Rainfall deficit
Savanna
Demand
Native vegetation
gradient
Agriculture
Fire frequency
Deforestation
0.04
Grass dominance
Deforestation
Forest
Betweenness
6
Global warming
Cropland−Grassland area
Albedo
Irrigation
Soil productivity
Woody plants dominance
0.02
2
Atmospheric temperature
Floodsdemand
Water
SST
Grazing Water infrastructure Evapotranspiration
Erosion
Atmospheric CO2
Vegetation Space
Water availability
Human population Palatability
Soil moisture productivity
Soil
Soil impermeability Solar radiation
WindTree release
maturity
Infrastructure developmentstress
Aquifers
LatentSoil quality
heatevents
Monsoon circulation
Biomass
ENSO−likeDust frequency
Vapor Soil salinity
Logging industryShadow_rooting level
ImmigrationWater consumption
Land−Ocean pressure gradient concentration
Lifting Ranching
condensation Advection
FertilizersAbsorption of solar radiation
use Moisture Carbon storage
Aerosol
Illegal logging
Brown clouds
Sea tides
Roughness
Temperature
Land conversion
Grazers
Productivity
Ground water table
0.00
4
Indegree
Global warming
Brown radiation
Rainfall deficit
Solar clouds
Land conversion
Absorption of solar radiation
Rainfall
Evapotranspiration variability
Cropland−Grassland
Aquifers
Droughts
Native vegetation
2
Savanna
Vegetation
Water infrastructure
Water availability
Advection
Carbon storage
Soil salinity
Aerosol concentration
Soil moisture
Vapor
0
Grass dominance
Forest
Demand
Productivity
Atmospheric temperature
Land−Ocean temperature gradient
Erosion
0
Outdegree
0.06
Fire frequency
6
8
area
ENSO−like events
SSTMonsoon
Ground Waterstress frequencyGrazers
Land−Ocean water table
pressure gradient circulation
Wind demand
Shadow_rooting Moisture
Dust LiftingRoughnessTree maturity
Soil quality
WaterTemperature
consumptioncondensation level
Palatability
RanchingFloods
Grazing
Immigration
Soil impermeabilityBiomass population
Infrastructure
Atmospheric CO2
Fertilizers Illegal development
use
Human
Sea tides releaseindustry
Latent heat Logginglogging
0.00
0.02
Space
0.04
Eigenvector
0.06
0.08
44. Cascading effects
D1
RS1
RS2
RS3
Floating plants
Kelp transitions
Arctic salt marsh
Eutrophication
Fisheries collapse
River channel change
Bivalves collapse
Foodwebs
Soil structure
Hypoxia
Forks: when sharing a driver
synchronize two regime shifts
Coral bleaching
Coral transitions
Encroachment
Forest to savanna
!RS1
...
D1
RS2
Causal chains: the domino effect
Soil salinization
!
Desertification
Forest to cropland
Monsoon
RS1
!
Peatlands
Thermohaline
Tundra to forest
Greenland icesheet collapse
Arctic Icesheet collapse
!
D2
D1
RS2
Inconvenient feedbacks: when two
shifts reinforce or dampen each
other
45. Are regime shifts controllable?
To what extent can we manage them?
• Critics to Liu et al.:
• Topology is not enough
• Internal dynamics
• Unmatched nodes change if
the periphery of the causal
networks change - The limits of
the system blur
• Unmatched nodes change
when joining causal networks
to understand cascading
effects.
ARTICLE
doi:10.1038/nature10011
Controllability of complex networks
´ ´
´
Yang-Yu Liu1,2, Jean-Jacques Slotine3,4 & Albert-Laszlo Barabasi1,2,5
The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them.
Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a
framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the
controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent
control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the
number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse
inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that
dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in
both model and real systems the driver nodes tend to avoid the high-degree nodes.
According to control theory, a dynamical system is controllable if, with a
suitable choice of inputs, it can be driven from any initial state to any
desired final state within finite time1–3. This definition agrees with our
intuitive notion of control, capturing an ability to guide a system’s
behaviour towards a desired state through the appropriate manipulation
of a few input variables, like a driver prompting a car to move with the
desired speed and in the desired direction by manipulating the pedals
and the steering wheel. Although control theory is a mathematically
highly developed branch of engineering with applications to electric
circuits, manufacturing processes, communication systems4–6, aircraft,
spacecraft and robots2,3, fundamental questions pertaining to the controllability of complex systems emerging in nature and engineering have
resisted advances. The difficulty is rooted in the fact that two independent factors contribute to controllability, each with its own layer of
unknown: (1) the system’s architecture, represented by the network
encapsulating which components interact with each other; and (2) the
dynamical rules that capture the time-dependent interactions between
the components. Thus, progress has been possible only in systems where
both layers are well mapped, such as the control of synchronized networks7–10, small biological circuits11 and rate control for communication networks4–6. Recent advances towards quantifying the topological
characteristics of complex networks12–16 have shed light on factor (1),
prompting us to wonder whether some networks are easier to control
than others and how network topology affects a system’s controllability.
Despite some pioneering conceptual work17–23 (Supplementary
Information, section II), we continue to lack general answers to these
questions for large weighted and directed networks, which most commonly emerge in complex systems.
Network controllability
of traffic that passes through a node i in a communication network24
or transcription factor concentration in a gene regulatory network25.
The N 3 N matrix A describes the system’s wiring diagram and the
interaction strength between the components, for example the traffic
on individual communication links or the strength of a regulatory
interaction. Finally, B is the N 3 M input matrix (M # N) that identifies the nodes controlled by an outside controller. The system is
controlled using the time-dependent input vector u(t) 5 (u1(t), …,
uM(t))T imposed by the controller (Fig. 1a), where in general the same
signal ui(t) can drive multiple nodes. If we wish to control a system, we
first need to identify the set of nodes that, if driven by different signals,
can offer full control over the network. We will call these ‘driver
nodes’. We are particularly interested in identifying the minimum
number of driver nodes, denoted by ND, whose control is sufficient
to fully control the system’s dynamics.
The system described by equation (1) is said to be controllable if it
can be driven from any initial state to any desired final state in finite
time, which is possible if and only if the N 3 NM controllability matrix
C~(B, AB, A2 B, . . . , AN{1 B)
has full rank, that is
rank(C)~N
ð2Þ
ð3Þ
This represents the mathematical condition for controllability, and is
called Kalman’s controllability rank condition1,2 (Fig. 1a). In practical
terms, controllability can be also posed as follows. Identify the minimum
number of driver nodes such that equation (3) is satisfied. For example,
equation (3) predicts that controlling node x1 in Fig. 1b with the input
signal u1 offers full control over the system, as the states of nodes x1, x2, x3
and x4 are uniquely determined by the signal u1(t) (Fig. 1c). In contrast,
46. Are regime shifts controllable?
To what extent can we manage them?
• Critics to Liu et al.:
• Topology is not enough
• Internal dynamics
• Unmatched nodes change if
the periphery of the causal
networks change - The limits of
the system blur
• Unmatched nodes change
when joining causal networks
to understand cascading
effects.
ARTICLE
doi:10.1038/nature10011
Controllability of complex networks
´ ´
´
Yang-Yu Liu1,2, Jean-Jacques Slotine3,4 & Albert-Laszlo Barabasi1,2,5
The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them.
Although control theory offers mathematical tools for steering engineered and natural systems towards a desired state, a
framework to control complex self-organized systems is lacking. Here we develop analytical tools to study the
controllability of an arbitrary complex directed network, identifying the set of driver nodes with time-dependent
control that can guide the system’s entire dynamics. We apply these tools to several real networks, finding that the
number of driver nodes is determined mainly by the network’s degree distribution. We show that sparse
inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control, but that
dense and homogeneous networks can be controlled using a few driver nodes. Counterintuitively, we find that in
both model and real systems the driver nodes tend to avoid the high-degree nodes.
According to control theory, a dynamical system is controllable if, with a
suitable choice of inputs, it can be driven from any initial state to any
desired final state within finite time1–3. This definition agrees with our
intuitive notion of control, capturing an ability to guide a system’s
behaviour towards a desired state through the appropriate manipulation
of a few input variables, like a driver prompting a car to move with the
desired speed and in the desired direction by manipulating the pedals
and the steering wheel. Although control theory is a mathematically
highly developed branch of engineering with applications to electric
circuits, manufacturing processes, communication systems4–6, aircraft,
spacecraft and robots2,3, fundamental questions pertaining to the controllability of complex systems emerging in nature and engineering have
resisted advances. The difficulty is rooted in the fact that two independent factors contribute to controllability, each with its own layer of
unknown: (1) the system’s architecture, represented by the network
encapsulating which components interact with each other; and (2) the
dynamical rules that capture the time-dependent interactions between
the components. Thus, progress has been possible only in systems where
both layers are well mapped, such as the control of synchronized networks7–10, small biological circuits11 and rate control for communication networks4–6. Recent advances towards quantifying the topological
characteristics of complex networks12–16 have shed light on factor (1),
prompting us to wonder whether some networks are easier to control
than others and how network topology affects a system’s controllability.
Despite some pioneering conceptual work17–23 (Supplementary
Information, section II), we continue to lack general answers to these
questions for large weighted and directed networks, which most commonly emerge in complex systems.
Network controllability
of traffic that passes through a node i in a communication network24
or transcription factor concentration in a gene regulatory network25.
The N 3 N matrix A describes the system’s wiring diagram and the
interaction strength between the components, for example the traffic
on individual communication links or the strength of a regulatory
interaction. Finally, B is the N 3 M input matrix (M # N) that identifies the nodes controlled by an outside controller. The system is
controlled using the time-dependent input vector u(t) 5 (u1(t), …,
uM(t))T imposed by the controller (Fig. 1a), where in general the same
signal ui(t) can drive multiple nodes. If we wish to control a system, we
first need to identify the set of nodes that, if driven by different signals,
can offer full control over the network. We will call these ‘driver
nodes’. We are particularly interested in identifying the minimum
number of driver nodes, denoted by ND, whose control is sufficient
to fully control the system’s dynamics.
The system described by equation (1) is said to be controllable if it
can be driven from any initial state to any desired final state in finite
time, which is possible if and only if the N 3 NM controllability matrix
C~(B, AB, A2 B, . . . , AN{1 B)
has full rank, that is
rank(C)~N
ð2Þ
ð3Þ
This represents the mathematical condition for controllability, and is
called Kalman’s controllability rank condition1,2 (Fig. 1a). In practical
terms, controllability can be also posed as follows. Identify the minimum
number of driver nodes such that equation (3) is satisfied. For example,
equation (3) predicts that controlling node x1 in Fig. 1b with the input
signal u1 offers full control over the system, as the states of nodes x1, x2, x3
and x4 are uniquely determined by the signal u1(t) (Fig. 1c). In contrast,
47. Trade Networks
• Test empirically cascading
effects by using trade networks
• Which countries are driving the
resource collapse of others
• Where trade matters?
1. Fisheries collapse
2. Land transitions
48. Using language to detect potential change in
ecosystem services in the light of ecological
surprises
Juan Carlos Rocha & Robin Wikström
49. Foley et al. 2005. Science
Ecosystem services are the benefits humans receive from nature (MEA 2005)
50. Foley et al. 2005. Science
Ecosystem services are the benefits humans receive from nature (MEA 2005)