Espace des ressources électronique en libre accès Bibliothèque de la Faculté SNVST de L'UAMOB
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Ouvrages de la bibliothèque en indexation 576 (3)
Faire une suggestion Affiner la recherche Interroger des sources externesGenetic diversity and ecological differentiation in the endangered fen orchid (Liparis loeselii) / Yohan Pillon
Titre : Genetic diversity and ecological differentiation in the endangered fen orchid (Liparis loeselii) Type de document : document électronique Auteurs : Yohan Pillon, Auteur ; Faridah Qamaruz-Zaman, Auteur ; Michael Fay, Auteur Editeur : Springer Verlag Année de publication : 2006 Importance : 8 (1), pp.177-184. Format : Langues : Anglais (eng) Catégories : 576 Évolution, génétique Tags : 'Liparis loeselii, conservation genetics, AFLP, clonality, autogamy génétique'. Index. décimale : 576 Résumé : Liparis loeselii is a rare and endangered orchid occurring in Europe and north-east America. Genetic diversity and structure of this species in north-west France and the United Kingdom were investigated using amplified fragment length polymorphisms (AFLPs). Although clonality and autogamy are common in L. loeselii, we found moderate to important variability within populations. We observed a significant genetic differentiation between populations occurring in dune slacks and fens. This may be correlated with leaf shape as dune slack individuals are sometimes treated as the distinct
variety L. loeselii var. ovata. Genetic differentiation between populations was generally low suggesting that gene flow can occur over long distances and possibly across the English Channel. These results show that populations from dune slacks and fens should be managed separately and that geographically distant populations may be equivalent.En ligne : https://hal.science/ird-03651885v1 Format de la ressource électronique : Genetic diversity and ecological differentiation in the endangered fen orchid (Liparis loeselii) [document électronique] / Yohan Pillon, Auteur ; Faridah Qamaruz-Zaman, Auteur ; Michael Fay, Auteur . - Springer Verlag, 2006 . - 8 (1), pp.177-184. ; PDF.
Langues : Anglais (eng)
Catégories : 576 Évolution, génétique Tags : 'Liparis loeselii, conservation genetics, AFLP, clonality, autogamy génétique'. Index. décimale : 576 Résumé : Liparis loeselii is a rare and endangered orchid occurring in Europe and north-east America. Genetic diversity and structure of this species in north-west France and the United Kingdom were investigated using amplified fragment length polymorphisms (AFLPs). Although clonality and autogamy are common in L. loeselii, we found moderate to important variability within populations. We observed a significant genetic differentiation between populations occurring in dune slacks and fens. This may be correlated with leaf shape as dune slack individuals are sometimes treated as the distinct
variety L. loeselii var. ovata. Genetic differentiation between populations was generally low suggesting that gene flow can occur over long distances and possibly across the English Channel. These results show that populations from dune slacks and fens should be managed separately and that geographically distant populations may be equivalent.En ligne : https://hal.science/ird-03651885v1 Format de la ressource électronique : 
Titre : Genomic Analysis and Artificial Intelligence: Predicting Viral Mutations and Future Pandemics Type de document : document électronique Auteurs : Fadhil G. Al-Amran, Auteur ; Abbas M. Hezam, Auteur ; Salman Rawaf, Auteur ; Maitham G. Yousif, Auteur Editeur : medical advances and innovations journal Année de publication : 2023 Note générale : Volume 1; Issue 3 Langues : Anglais (eng) Catégories : 576 Évolution, génétique Tags : 'Genomic analysis artificial intelligence , viral mutations , future pandemics predictive modeling génétique '. Index. décimale : 576 Résumé : This study presents a novel approach at the intersection of genomic analysis and artificial intelligence (AI) to predict viral mutations and assess the risks of future pandemics. Through comprehensive genomic analysis, genetic markers associated with increased virulence and transmissibility are identified. Advanced machine learning algorithms are employed to analyze genetic data and forecast viral mutations, taking into account factors such as replication rates, host-pathogen interactions, and environmental influences. The research also evaluates the risk of future pandemics by examining zoonotic reservoirs, human-animal interfaces, and climate change impacts. AI-powered risk assessment models provide insights into potential outbreak hotspots, facilitating targeted surveillance and preventive measures. This research offers a proactive approach to pandemic preparedness, enabling early intervention and the development of effective containment strategies and vaccines. The fusion of genomic analysis and AI enhances our ability to mitigate the impact of infectious diseases on a global scale, emphasizing the importance of proactive measures in safeguarding public health. En ligne : https://arxiv.org/abs/2309.15936 Format de la ressource électronique : Genomic Analysis and Artificial Intelligence: Predicting Viral Mutations and Future Pandemics [document électronique] / Fadhil G. Al-Amran, Auteur ; Abbas M. Hezam, Auteur ; Salman Rawaf, Auteur ; Maitham G. Yousif, Auteur . - medical advances and innovations journal, 2023.
Volume 1; Issue 3
Langues : Anglais (eng)
Catégories : 576 Évolution, génétique Tags : 'Genomic analysis artificial intelligence , viral mutations , future pandemics predictive modeling génétique '. Index. décimale : 576 Résumé : This study presents a novel approach at the intersection of genomic analysis and artificial intelligence (AI) to predict viral mutations and assess the risks of future pandemics. Through comprehensive genomic analysis, genetic markers associated with increased virulence and transmissibility are identified. Advanced machine learning algorithms are employed to analyze genetic data and forecast viral mutations, taking into account factors such as replication rates, host-pathogen interactions, and environmental influences. The research also evaluates the risk of future pandemics by examining zoonotic reservoirs, human-animal interfaces, and climate change impacts. AI-powered risk assessment models provide insights into potential outbreak hotspots, facilitating targeted surveillance and preventive measures. This research offers a proactive approach to pandemic preparedness, enabling early intervention and the development of effective containment strategies and vaccines. The fusion of genomic analysis and AI enhances our ability to mitigate the impact of infectious diseases on a global scale, emphasizing the importance of proactive measures in safeguarding public health. En ligne : https://arxiv.org/abs/2309.15936 Format de la ressource électronique :
Titre : Tackling the dimensions in imaging genetics with CLUB-PLS Type de document : document électronique Auteurs : Andre Altmann, Auteur ; Ana C Lawry Aguila, Auteur ; Neda Jahanshad, Auteur Editeur : arXiv Année de publication : 2023 Importance : 12 pages, 4 Figures, 2 Tables Format : Langues : Anglais (eng) Catégories : 576 Évolution, génétique Tags : 'Genomics (q-bio.GN) Machine Learning (cs.LG) Image and Video Processing (eess.IV) Quantitative Methods (q-bio.QM) génétique génome'. Index. décimale : 576 Résumé : A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brain imaging data. The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes. That entails executing one genome-wide association study (GWAS) for each pre-defined imaging measure. Although this approach has been tremendously successful, one shortcoming is that phenotypes must be pre-defined. Consequently, effects that are not confined to pre-selected regions of interest or that reflect larger brain-wide patterns can easily be missed. In this work we introduce a Partial Least Squares (PLS)-based framework, which we term Cluster-Bootstrap PLS (CLUB-PLS), that can work with large input dimensions in both domains as well as with large sample sizes. One key factor of the framework is to use cluster bootstrap to provide robust statistics for single input features in both domains. We applied CLUB-PLS to investigating the genetic basis of surface area and cortical thickness in a sample of 33,000 subjects from the UK Biobank. We found 107 genome-wide significant locus-phenotype pairs that are linked to 386 different genes. We found that a vast majority of these loci could be technically validated at a high rate: using classic GWAS or Genome-Wide Inferred Statistics (GWIS) we found that 85 locus-phenotype pairs exceeded the genome-wide suggestive (P<1e-05) threshold. En ligne : https://arxiv.org/abs/2309.07352 Format de la ressource électronique : Tackling the dimensions in imaging genetics with CLUB-PLS [document électronique] / Andre Altmann, Auteur ; Ana C Lawry Aguila, Auteur ; Neda Jahanshad, Auteur . - arXiv, 2023 . - 12 pages, 4 Figures, 2 Tables ; PDF.
Langues : Anglais (eng)
Catégories : 576 Évolution, génétique Tags : 'Genomics (q-bio.GN) Machine Learning (cs.LG) Image and Video Processing (eess.IV) Quantitative Methods (q-bio.QM) génétique génome'. Index. décimale : 576 Résumé : A major challenge in imaging genetics and similar fields is to link high-dimensional data in one domain, e.g., genetic data, to high dimensional data in a second domain, e.g., brain imaging data. The standard approach in the area are mass univariate analyses across genetic factors and imaging phenotypes. That entails executing one genome-wide association study (GWAS) for each pre-defined imaging measure. Although this approach has been tremendously successful, one shortcoming is that phenotypes must be pre-defined. Consequently, effects that are not confined to pre-selected regions of interest or that reflect larger brain-wide patterns can easily be missed. In this work we introduce a Partial Least Squares (PLS)-based framework, which we term Cluster-Bootstrap PLS (CLUB-PLS), that can work with large input dimensions in both domains as well as with large sample sizes. One key factor of the framework is to use cluster bootstrap to provide robust statistics for single input features in both domains. We applied CLUB-PLS to investigating the genetic basis of surface area and cortical thickness in a sample of 33,000 subjects from the UK Biobank. We found 107 genome-wide significant locus-phenotype pairs that are linked to 386 different genes. We found that a vast majority of these loci could be technically validated at a high rate: using classic GWAS or Genome-Wide Inferred Statistics (GWIS) we found that 85 locus-phenotype pairs exceeded the genome-wide suggestive (P<1e-05) threshold. En ligne : https://arxiv.org/abs/2309.07352 Format de la ressource électronique :
