Understanding Freshwater Ecosystems and Human Health Implications in Recreational Water through Microbial Characterization, Source Tracking, and Sediment-Microbe Dynamics
Notice bibliographique
Résumé
Contamination of natural aquatic ecosystems is a serious global concern as populations increase and the environment is impacted by climate change. Nonpoint source (NPS) contamination of allochthonous materials, such as sediments, nutrients, and microorganisms, is commonly introduced to a body of water through runoff and wash-off which cumulates over a large area, and is subsequently transported to surface waters (e.g., rivers, streams, lakes) and shorelines. The principal form of microbial contamination of water resources is often from fecal pollution derived from humans, domesticated animals, or wildlife, and contains a variety of human pathogens. There are also numerous factors (with limited research) affecting pathogen survival, persistence, and growth in these environments, complicating research models and progress, and our overall understanding of the microbiology of natural waters. Thus, the potential for human health risk associated with recreational water use can be difficult to recognise and regulate without appropriate testing to identify and characterize the pathogenic profile in these environments. Traditional water quality assessments involve the use of an indicator organism (e.g., E. coli) as a proxy for fecal contamination in recreational waters. However, there are several limitations to these simplistic approaches which lead to unreliable water quality evaluations. These tests 1) are infrequent, time consuming, and nonrepresentative of in situ conditions; 2) target only one organism but omit other waterborne pathogens; 3) involve culture-based techniques or the use of environmental DNA, which cannot inform on microbial activity; 4) neglect to identify contamination origin or source; and perhaps the most significant shortcoming of these assessments is that they 5) overlook the sediment compartment, assuming pathogenic microbes only have planktonic lifestyles. The research presented though this dissertation aims to address the knowledge gap regarding the concern for human health implications involving microbial contamination associated with recreational water use. A spatiotemporal microbial biosignature was first established for freshwater bed sediment in Laurentian Great Lakes beaches. This baseline allowed for focused mRNA-based metatranscriptomic and rRNA-based targeted transcriptomic assessments of both bed and suspended sediment fractions of the nearshore swimming zone. Results indicated significant microbial activity (through diverse metabolic functions as well as pathogenic-related gene expression) associated with both sediment fractions, suggesting freshwater sediment acts as a reservoir and secondary source for microorganisms (including waterborne pathogens) through sediment dynamics (e.g., erosion, resuspension, transport, deposition). Microbial biomass and activity were typically upregulated at low-energy, fine-grained locations, such as Belle River and Kingsville, Ontario beaches. Microbial source tracking (MST) evaluations determined avian sources (i.e., gulls and geese) to be the largest NPS of fecal indicator bacteria (FIB) associated with the sediment compartment along these freshwater shorelines. MST targets provided superior results over general FIB targets and traditional water quality assessments by exposing contamination source details. The results obtained from this research significantly improve our understanding of freshwater ecosystems and human health implications in recreational water through microbial characterization (i.e., expansive community profiling and gene expression studies), MST, and sediment-microbe relationships.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».