ALBERT

Description: Despite the large evolutionary distances between metazoan species, they can show remarkable commonalities in their biology, and this has helped to establish fly and worm as model organisms for human biology. Although studies of individual elements and factors have explored similarities in gene regulation, a large-scale comparative analysis of basic principles of transcriptional regulatory features is lacking. Here we map the genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors, generating a total of 1,019 data sets from diverse cell types, developmental stages, or conditions in the three species, of which 498 (48.9%) are presented here for the first time. We find that structural properties of regulatory networks are remarkably conserved and that orthologous regulatory factor families recognize similar binding motifs in vivo and show some similar co-associations. Our results suggest that gene-regulatory properties previously observed for individual factors are general principles of metazoan regulation that are remarkably well-preserved despite extensive functional divergence of individual network connections. The comparative maps of regulatory circuitry provided here will drive an improved understanding of the regulatory underpinnings of model organism biology and how these relate to human biology, development and disease.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336544/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4336544/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Boyle, Alan P -- Araya, Carlos L -- Brdlik, Cathleen -- Cayting, Philip -- Cheng, Chao -- Cheng, Yong -- Gardner, Kathryn -- Hillier, LaDeana W -- Janette, Judith -- Jiang, Lixia -- Kasper, Dionna -- Kawli, Trupti -- Kheradpour, Pouya -- Kundaje, Anshul -- Li, Jingyi Jessica -- Ma, Lijia -- Niu, Wei -- Rehm, E Jay -- Rozowsky, Joel -- Slattery, Matthew -- Spokony, Rebecca -- Terrell, Robert -- Vafeados, Dionne -- Wang, Daifeng -- Weisdepp, Peter -- Wu, Yi-Chieh -- Xie, Dan -- Yan, Koon-Kiu -- Feingold, Elise A -- Good, Peter J -- Pazin, Michael J -- Huang, Haiyan -- Bickel, Peter J -- Brenner, Steven E -- Reinke, Valerie -- Waterston, Robert H -- Gerstein, Mark -- White, Kevin P -- Kellis, Manolis -- Snyder, Michael -- F32GM101778/GM/NIGMS NIH HHS/ -- P50GM081892/GM/NIGMS NIH HHS/ -- R01 HG004037/HG/NHGRI NIH HHS/ -- RC2HG005679/HG/NHGRI NIH HHS/ -- U01 HG004267/HG/NHGRI NIH HHS/ -- U01HG004264/HG/NHGRI NIH HHS/ -- U01HG004267/HG/NHGRI NIH HHS/ -- U54 HG004558/HG/NHGRI NIH HHS/ -- U54 HG006996/HG/NHGRI NIH HHS/ -- U54HG004558/HG/NHGRI NIH HHS/ -- U54HG006996/HG/NHGRI NIH HHS/ -- UL1 TR000430/TR/NCATS NIH HHS/ -- England -- Nature. 2014 Aug 28;512(7515):453-6. doi: 10.1038/nature13668.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA [2]. ; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA. ; Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 06520, USA. ; Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520, USA. ; Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA. ; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. ; 1] Department of Computer Science, Stanford University, Stanford, California 94305, USA [2] Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. ; 1] Department of Statistics, University of California, Berkeley, California 94720, USA [2] Department of Statistics, University of California, Los Angeles, California 90095, USA. ; Institute for Genomics and Systems Biology, University of Chicago, Chicago, Ilinois 60637, USA. ; National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892, USA. ; Department of Statistics, University of California, Berkeley, California 94720, USA. ; 1] Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA [2] Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25164757" target="_blank"〉PubMed〈/a〉

Description: T regulatory cells that express the transcription factor Foxp3 (Foxp3(+) T(regs)) promote tissue homeostasis in several settings. We now report that symbiotic members of the human gut microbiota induce a distinct T(reg) population in the mouse colon, which constrains immuno-inflammatory responses. This induction-which we find to map to a broad, but specific, array of individual bacterial species-requires the transcription factor Rorgamma, paradoxically, in that Rorgamma is thought to antagonize FoxP3 and to promote T helper 17 (T(H)17) cell differentiation. Rorgamma's transcriptional footprint differs in colonic T(regs) and T(H)17 cells and controls important effector molecules. Rorgamma, and the T(regs) that express it, contribute substantially to regulating colonic T(H)1/T(H)17 inflammation. Thus, the marked context-specificity of Rorgamma results in very different outcomes even in closely related cell types.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4700932/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4700932/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Sefik, Esen -- Geva-Zatorsky, Naama -- Oh, Sungwhan -- Konnikova, Liza -- Zemmour, David -- McGuire, Abigail Manson -- Burzyn, Dalia -- Ortiz-Lopez, Adriana -- Lobera, Mercedes -- Yang, Jianfei -- Ghosh, Shomir -- Earl, Ashlee -- Snapper, Scott B -- Jupp, Ray -- Kasper, Dennis -- Mathis, Diane -- Benoist, Christophe -- R01 AI110630/AI/NIAID NIH HHS/ -- R01-AI51530/AI/NIAID NIH HHS/ -- R37 AI051530/AI/NIAID NIH HHS/ -- R56 AI110630/AI/NIAID NIH HHS/ -- R56-AI110630/AI/NIAID NIH HHS/ -- New York, N.Y. -- Science. 2015 Aug 28;349(6251):993-7. doi: 10.1126/science.aaa9420. Epub 2015 Aug 13.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston 02115, MA, USA. ; Division of Gastroenterology and Hepatology, Brigham and Women's Hospital, Boston, MA 02115, USA, and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. ; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. ; Tempero Pharmaceuticals, a GSK Company, Cambridge, MA 02115, USA. ; UCB Pharma, Slough, Berkshire, UK. ; Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston 02115, MA, USA. Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA. cbdm@hms.harvard.edu.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/26272906" target="_blank"〉PubMed〈/a〉

Description: The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4155737/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4155737/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Gerstein, Mark B -- Rozowsky, Joel -- Yan, Koon-Kiu -- Wang, Daifeng -- Cheng, Chao -- Brown, James B -- Davis, Carrie A -- Hillier, LaDeana -- Sisu, Cristina -- Li, Jingyi Jessica -- Pei, Baikang -- Harmanci, Arif O -- Duff, Michael O -- Djebali, Sarah -- Alexander, Roger P -- Alver, Burak H -- Auerbach, Raymond -- Bell, Kimberly -- Bickel, Peter J -- Boeck, Max E -- Boley, Nathan P -- Booth, Benjamin W -- Cherbas, Lucy -- Cherbas, Peter -- Di, Chao -- Dobin, Alex -- Drenkow, Jorg -- Ewing, Brent -- Fang, Gang -- Fastuca, Megan -- Feingold, Elise A -- Frankish, Adam -- Gao, Guanjun -- Good, Peter J -- Guigo, Roderic -- Hammonds, Ann -- Harrow, Jen -- Hoskins, Roger A -- Howald, Cedric -- Hu, Long -- Huang, Haiyan -- Hubbard, Tim J P -- Huynh, Chau -- Jha, Sonali -- Kasper, Dionna -- Kato, Masaomi -- Kaufman, Thomas C -- Kitchen, Robert R -- Ladewig, Erik -- Lagarde, Julien -- Lai, Eric -- Leng, Jing -- Lu, Zhi -- MacCoss, Michael -- May, Gemma -- McWhirter, Rebecca -- Merrihew, Gennifer -- Miller, David M -- Mortazavi, Ali -- Murad, Rabi -- Oliver, Brian -- Olson, Sara -- Park, Peter J -- Pazin, Michael J -- Perrimon, Norbert -- Pervouchine, Dmitri -- Reinke, Valerie -- Reymond, Alexandre -- Robinson, Garrett -- Samsonova, Anastasia -- Saunders, Gary I -- Schlesinger, Felix -- Sethi, Anurag -- Slack, Frank J -- Spencer, William C -- Stoiber, Marcus H -- Strasbourger, Pnina -- Tanzer, Andrea -- Thompson, Owen A -- Wan, Kenneth H -- Wang, Guilin -- Wang, Huaien -- Watkins, Kathie L -- Wen, Jiayu -- Wen, Kejia -- Xue, Chenghai -- Yang, Li -- Yip, Kevin -- Zaleski, Chris -- Zhang, Yan -- Zheng, Henry -- Brenner, Steven E -- Graveley, Brenton R -- Celniker, Susan E -- Gingeras, Thomas R -- Waterston, Robert -- 1U01HG007031-01/HG/NHGRI NIH HHS/ -- 5U01HG004695-04/HG/NHGRI NIH HHS/ -- 5U54HG004555/HG/NHGRI NIH HHS/ -- HG007000/HG/NHGRI NIH HHS/ -- HG007355/HG/NHGRI NIH HHS/ -- K99 HG006698/HG/NHGRI NIH HHS/ -- P30 CA045508/CA/NCI NIH HHS/ -- R01 GM076655/GM/NIGMS NIH HHS/ -- RC2-HG005639/HG/NHGRI NIH HHS/ -- T15 LM007056/LM/NLM NIH HHS/ -- T32 HD060555/HD/NICHD NIH HHS/ -- U01 HG 004263/HG/NHGRI NIH HHS/ -- U01 HG004261/HG/NHGRI NIH HHS/ -- U01 HG004271/HG/NHGRI NIH HHS/ -- U01 HG007031/HG/NHGRI NIH HHS/ -- U01-HG004261/HG/NHGRI NIH HHS/ -- U01HG004258/HG/NHGRI NIH HHS/ -- U41 HG007000/HG/NHGRI NIH HHS/ -- U41 HG007234/HG/NHGRI NIH HHS/ -- U41 HG007355/HG/NHGRI NIH HHS/ -- U54 HG004555/HG/NHGRI NIH HHS/ -- U54 HG006944/HG/NHGRI NIH HHS/ -- U54 HG006994/HG/NHGRI NIH HHS/ -- U54 HG007004/HG/NHGRI NIH HHS/ -- U54 HG007005/HG/NHGRI NIH HHS/ -- U54HG007005/HG/NHGRI NIH HHS/ -- WT098051/Wellcome Trust/United Kingdom -- ZIA DK015600-18/Intramural NIH HHS/ -- Howard Hughes Medical Institute/ -- England -- Nature. 2014 Aug 28;512(7515):445-8. doi: 10.1038/nature13424.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3] Department of Computer Science, Yale University, 51 Prospect Street, New Haven, Connecticut 06511, USA [4] [5]. ; 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [3]. ; 1] Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA [2] Institute for Quantitative Biomedical Sciences, Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire 03766, USA [3]. ; 1] Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2] Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA [3]. ; 1] Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA [2]. ; 1] Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA [2]. ; 1] Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA [2] Department of Statistics, University of California, Los Angeles, California 90095-1554, USA [3] Department of Human Genetics, University of California, Los Angeles, California 90095-7088, USA [4]. ; 1] Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA [2]. ; 1] Centre for Genomic Regulation, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain [2] Departament de Ciencies Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain [3]. ; 1] Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA [2] Department of Molecular Biophysics and Biochemistry, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA. ; Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, Massachusetts 02115, USA. ; Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA. ; Department of Statistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA. ; Department of Genome Sciences and University of Washington School of Medicine, William H. Foege Building S350D, 1705 Northeast Pacific Street, Box 355065 Seattle, Washington 98195-5065, USA. ; 1] Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2] Department of Biostatistics, University of California, Berkeley, 367 Evans Hall, Berkeley, California 94720-3860, USA. ; Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA. ; 1] Department of Biology, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA [2] Center for Genomics and Bioinformatics, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA. ; MOE Key Lab of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China. ; National Human Genome Research Institute, National Institutes of Health, 5635 Fishers Lane, Bethesda, Maryland 20892-9307, USA. ; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK. ; 1] Centre for Genomic Regulation, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain [2] Departament de Ciencies Experimentals i de la Salut, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain. ; 1] Center for Integrative Genomics, University of Lausanne, Genopode building, Lausanne 1015, Switzerland [2] Swiss Institute of Bioinformatics, Genopode building, Lausanne 1015, Switzerland. ; 1] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK [2] Medical and Molecular Genetics, King's College London, London WC2R 2LS, UK. ; Department of Genetics, Yale University School of Medicine, New Haven, Connecticut 06520-8005, USA. ; Department of Molecular, Cellular and Developmental Biology, PO Box 208103, Yale University, New Haven, Connecticut 06520, USA. ; Department of Biology, Indiana University, 1001 East 3rd Street, Bloomington, Indiana 47405-7005, USA. ; Sloan-Kettering Institute, 1275 York Avenue, Box 252, New York, New York 10065, USA. ; 1] Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA [2] Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 USA. ; Department of Cell and Developmental Biology, Vanderbilt University, 465 21st Avenue South, Nashville, Tennessee 37232-8240, USA. ; 1] Developmental and Cell Biology, University of California, Irvine, California 92697, USA [2] Center for Complex Biological Systems, University of California, Irvine, California 92697, USA. ; Section of Developmental Genomics, Laboratory of Cellular and Developmental Biology, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892, USA. ; Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA. ; 1] Department of Genetics and Drosophila RNAi Screening Center, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA [2] Howard Hughes Medical Institute, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA. ; Center for Integrative Genomics, University of Lausanne, Genopode building, Lausanne 1015, Switzerland. ; 1] Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SA, UK [2] European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK. ; 1] Bioinformatics and Genomics Programme, Center for Genomic Regulation, Universitat Pompeu Fabra (CRG-UPF), 08003 Barcelona, Catalonia, Spain [2] Institute for Theoretical Chemistry, Theoretical Biochemistry Group (TBI), University of Vienna, Wahringerstrasse 17/3/303, A-1090 Vienna, Austria. ; 1] Department of Genetics and Developmental Biology, Institute for Systems Genomics, University of Connecticut Health Center, 400 Farmington Avenue, Farmington, Connecticut 06030, USA [2] Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China. ; 1] Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong [2] 5 CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong. ; 1] Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA [2] Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA [3]. ; 1] Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA [2].〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/25164755" target="_blank"〉PubMed〈/a〉

Description: One of the advantages of 3D woven composites is the ability to create unique fiber architectures in which tows can be individually arranged to optimize performance to meet specific structural requirements. This flexibility may lead to a design with unintended mismatch of thermal properties in areas of the composite, creating the potential for manufacturing issues such as process-induced microcracking. This paper presents results of a case study in which various architectures were analyzed, including some that led to intra-tow microcracks in the molded composite and others that did not. The objective was to develop an approach using meso-scale finite element modeling at the unit cell level to evaluate the propensity of these architectures to microcrack. Analysis results showed that the transverse principal stresses which developed in tow elements during cooling from cure to room temperature were significantly different in the various architectures. Comparison to the micro-compute...

Description: FloodRisk is an interdisciplinary project focusing on the effects of mine water level rise in bandoned coal mine regions in Germany. Such effects are heterogeneous ground uplift, stress changes due to the change in pore pressure and the reactivation of potential faults. One of the most directly measurable effects is certainly the induced micro seismicity. It is known from previous studies that the flooding of old mines can lead to a renewed increase level in induced micro seismicity in these regions. In this study the relationship between mine water rise, fluid-induced stress changes and induced seismicity in the Haus Aden dewatering area in the eastern Ruhr area (Germany) will be investigated in more detail. For this purpose, we operate a network of currently 21 short period seismic stations in the region of the former "Bergwerk Ost" colliery, which had the highest seismicity rate in the Ruhr area during active underground coal mining. This network is still to be expanded to cover the entire water drainage area, about 30 Raspberry Shake sensors are waiting for the possibility of installation. Nevertheless, the existing network registered almost 1000 induced micro seismic events in a magnitude range from -0.7 up to 2.6 MLv. Many of these events are spatially clustered and some show quite high waveform similarity. This allows relative localisation and can increase the accuracy of the location. The depth location of the earthquakes, within the limits of localisation accuracy, agrees very well with the distribution of seismicity at the time of active mining. The spatial distribution so far seems to be limited by a large inactive transverse fault in the west. It needs to be clarified what influence this fault has on the propagation of mine water in the underground. The measured temporal trend of the mine water level, after pumps were shut down in mid-2019, shows a strong correlation with the temporal evolution of the observed micro seismicity. In the first months after the pumps are switched off, the water levels at the observation points rise only slowly and isolated microseismic events occur again. In November 2019, the rise in water levels doubled and at the same time, the strongest induced event in the measurement period was recorded with a magnitude of 2.6 MLv . In the following months, the seismicity rate ranged from 8 to 34 events above 0.5 MLv per month, some of which were felt.

Description: Bundesministerium für Bildung und Forschung

Description: poster

Description: The Floodrisk project takes a muti- and interdisciplinary look on the effects of the rise in mine water level in abandoned coal mine regions in Germany. Such effects are heterogeneous ground uplift, stress changes due to the pore pressure changes and the reactivation potential of faults. One of the most directly measurable effects is the induced seismicity. It is known from previous studies that the flooding of old mines can lead to a renewed increase in induced microseismicity in these regions. We focused on the observation of the eastern Ruhr area and investigate in detail the relationship between mine water rise and induced seismicity in the Haus Aden dewatering area. For this purpose, we operate a network of up to 30 short period seismic stations in the region of the former "Bergwerk Ost" colliery, which had the highest seismicity rate in the Ruhr area during active mining. Continuous monitoring of seismicity and mine water levels is available for this region from the active mining phase, through the post-mining phase to flooding. Since the beginning of the flooding, more than 20000 onsets were picked and over 1700 induced events were localised in a magnitude range from -0.7 up to 2.6 MLv. For some larger events, focal mechanisms could be determined. The spatial distribution of hypocentres is divided into two areas, with few events in the central study area and over 95% of earthquakes in its eastern part. Many of these events are spatially clustered and some show quite high waveform similarity. This allows relative localisation to increase the accuracy of the location. Comparing the old galleries,which today serve as the main underground waterways, with the localisations from the relative localisation, strong correlations can be seen. The measured temporal trend of the mine water level, after pumps were shut down in mid-2019, shows a strong correlation with the temporal evolution of the observed micro seismicity. In the first months after the pumps are switched off, the water levels at the observation points rise only slowly and isolated microseismic events occur again. In November 2019, the rise in water levels doubled and at the same time, the strongest induced event in the measurement period was recorded with a magnitude of 2.6 MLv. In the years 2020, 2021 66 and 58 events 〉= MLv 1 were observed, respectively. In contrast to this number only 2- 9 events 〉= MLv 1 per year were observed in the post-minig phase before flooding.

Description: Bundesministerium für Bildung und Forschung

Description: poster

Description: Induced seismicity during mine flooding is the focus of the FloodRisk project. One of the study areas is the Ruhr area, which is characterised by centuries of intensive coal mining. After the closure of the last mines, controlled flooding began. Within the FloodRisk project, we investigate ground uplift, stress changes due to pore pressure changes and the reactivation potential of faults to explain induced seismicity. We concentrate on the seismicity monitoring and geomechanics of the Haus Aden catchment, for which we investigate the relationship between water rise, tectonic stress and induced seismicity. The monitoring of seismicity is based on a network of up to 30 short-period seismic stations installed by the Ruhr University in the area of the former "Bergwerk Ost", which exhibited the highest seismicity in the Ruhr area during active mining. The stations cover an area of about 160 km 2 and are spaced between 0.5 and 3.5 km apart. They allow continuous monitoring of seismicity. Since 2019, more than 2200 induced events have been localised. A prerequisite for the interpretation of seismicity is a detailed localisation of the events. The relative localisation of the induced earthquakes has significantly reduced the location uncertainty and allowed the spatial and temporal evolution of earthquake clusters due to the rise in mine water levels to be studied. The resulting pattern of seismicity was compared with known underground structures. This comparison indicates that most of the events occur approximately 300 m below the main pillars between the longwall panels in the already flooded deepest level of the mine. A generic FE numerical model was developed for a section of the Heinrich Robert mine based on the geometry of the pillars, shafts and longwall panels. The stress data for model calibration are based on a compilation of the regional stress state in the eastern Ruhr area. For this purpose, hydraulic fracture tests carried out in the mines to minimise rock bursts were re-evaluated and compared with stress orientations derived from independent sources such as borehole fractures and earthquake source mechanisms. Using this 3D numerical approach, we conclude that there is increased vertical stress within and below the pillars as a result of stress arching. As the horizontal stress changes below the mine levels are small, this results in increasing differential stresses that can lead to the observed events below the mine level when the mine water level rises.

Description: Bundesministerium für Bildung und Forschung

Description: poster