Academia biologyPub Date : 2025-01-01Epub Date: 2025-02-26DOI: 10.20935/acadbiol7544
Dan Stoicov, Carolina A Bonin, Andre J van Wijnen, Eric A Lewallen
{"title":"Seasonal patterns of mercury bioaccumulation in lobsters (<i>Homarus americanus</i>) from Maine.","authors":"Dan Stoicov, Carolina A Bonin, Andre J van Wijnen, Eric A Lewallen","doi":"10.20935/acadbiol7544","DOIUrl":"https://doi.org/10.20935/acadbiol7544","url":null,"abstract":"<p><p>Mercury (Hg) pollutes marine ecosystems and accumulates in benthic species. This ecological case study investigated the temporal accumulation of Hg in American lobster (<i>Homarus americanus; H. Milne Edwards, 1837</i>) from coastal Maine (Casco Bay, ME, USA). We analyzed total Hg levels in legal-sized lobsters (carapace length: 8.255-12.5 cm; n = 34) collected during the early (May-July 1) or late (July 15-October) recreational harvest seasons. Morphometric data show that body size correlates with body weight (R<sup>2</sup> = 0.76; p < 0.001), and average body sizes were similar in early and late seasons. The average chelipod size was ~7% larger in male lobsters (p < 0.02), reflecting sexual dimorphism. Hg levels in select tissues from boiled lobsters were analyzed using atomic absorption spectroscopy. Hg in ambient water was undetectable, indicating that Hg in tissues reflects bioaccumulation. Hg content correlated with the lengths (cm) and weights (g) of cephalothorax, carapace, chelipod, and hepatopancreas in both male and female lobsters. Total Hg levels in most tissues were within safe and acceptable limits for human consumption (<0.2 ppm). Compared to late-season lobsters, early-season lobsters had significantly higher Hg levels in tail (~55% increase; 0.130 ppm vs. 0.084 ppm; p < 0.05) and hepatopancreas tissues (~29% increase; 0.099 ppm vs. 0.077 ppm; p < 0.05), suggesting that seasonal factors influence Hg content (e.g., spring river runoff, lobster migration, inert biological cycles). Observed seasonal fluctuations in lobster Hg levels may inform future strategies for mitigating pollution in coastal marine ecosystems.</p>","PeriodicalId":519944,"journal":{"name":"Academia biology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11999571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Surekha, Asha Rani Sheoran, Nita Lakra, Prof. (Dr.) Baljeet Singh Saharan, Annu Luhach, Neelam Kumari Mandal, Chandra Shekhar Seth, Deepansh Sharma, A. Santal, P. K. Sadh, Vishnu D. Rajput, J. S. Duhan
{"title":"Enduring drought: effects and strategies for Brassica crop resilience","authors":"Surekha, Asha Rani Sheoran, Nita Lakra, Prof. (Dr.) Baljeet Singh Saharan, Annu Luhach, Neelam Kumari Mandal, Chandra Shekhar Seth, Deepansh Sharma, A. Santal, P. K. Sadh, Vishnu D. Rajput, J. S. Duhan","doi":"10.20935/acadbiol6265","DOIUrl":"https://doi.org/10.20935/acadbiol6265","url":null,"abstract":"","PeriodicalId":519944,"journal":{"name":"Academia biology","volume":"103 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The crucial role of 5hmC in neuroprotection and repair after cerebrovascular injury","authors":"Y. Tsenkina","doi":"10.20935/acadbiol7285","DOIUrl":"https://doi.org/10.20935/acadbiol7285","url":null,"abstract":"","PeriodicalId":519944,"journal":{"name":"Academia biology","volume":" 1278","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Academia biologyPub Date : 2024-01-01Epub Date: 2024-05-27DOI: 10.20935/AcadBiol6245
Andre J van Wijnen, Eric A Lewallen
{"title":"Natural selection and evolution: evolving concepts.","authors":"Andre J van Wijnen, Eric A Lewallen","doi":"10.20935/AcadBiol6245","DOIUrl":"10.20935/AcadBiol6245","url":null,"abstract":"<p><p>Many recent studies in evolutionary biology have expanded and refined definitions of biological evolution and natural selection. Current evolutionary models incorporate different adaptive and non-adaptive processes based on molecular genetic changes and how DNA is modified over time in unicellular species, or in germline versus somatic cells in metazoan species. Cogent arguments can be raised for the view that natural selection should be considered a biological law, consistent with quantitative mathematical equations that describe the fitness of individuals, as well as variations within and among populations. Evolution is an overarching framework that incorporates the laws of natural selection and clarifies why phenotypic variation can increase in prevalence and result in species adaptations. The conceptual framework for biological evolution incorporates many cohesive principles that collectively have a predictive value. This framework will continue to evolve with improvements in high-resolution technologies that enable us to examine both adaptive and non-adaptive changes that drive biological phenotypes.</p>","PeriodicalId":519944,"journal":{"name":"Academia biology","volume":"2 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11175172/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Academia biologyPub Date : 2024-01-01Epub Date: 2024-11-27DOI: 10.20935/acadbiol7428
Ravindra Kumar, Rajrani Ruhel, Andre J van Wijnen
{"title":"Unlocking biological complexity: the role of machine learning in integrative multi-omics.","authors":"Ravindra Kumar, Rajrani Ruhel, Andre J van Wijnen","doi":"10.20935/acadbiol7428","DOIUrl":"https://doi.org/10.20935/acadbiol7428","url":null,"abstract":"<p><p>The increasing complexity of biological systems demands advanced analytical approaches to decode the underlying mechanisms of health and disease. Integrative multi-omics approaches use multi-layered datasets such as genomic, transcriptomic, proteomic, and metabolomic data to understand biological processes much more comprehensively compared to the single-omics analysis and to provide a comprehensive view of cellular and molecular processes. However, these integrative approaches have their own computational and analytical challenges due to the large volume and nature of multi-omics data. Machine learning has emerged as a powerful tool to help and resolve these challenges. It offers sophisticated algorithms that can identify and discover hidden patterns and provide insights into complex biological networks. By integrating machine learning in multi-omics, we can enhance our understanding of drug discovery, disease, pathway, and network analysis. Machine learning and ensemble methods allow researchers to model nonlinear relationships and manage high-dimensional data, improving the precision of predictions. This approach paves the way for personalized medicine by identifying unique molecular signatures for individual patients, which can provide valuable insights into treatment planning and support more effective treatment. As machine learning continues to evolve, its role in multi-omics analysis will be pivotal in advancing our ability to interpret biological complexity and translate findings into clinical applications.</p>","PeriodicalId":519944,"journal":{"name":"Academia biology","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11741185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143019619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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