Emerging Topics in Life Sciences最新文献_第10页

Crown of thorns starfish life-history traits contribute to outbreaks, a continuing concern for coral reefs 棘冠海星的生活史特征有助于爆发，这是珊瑚礁持续关注的问题

IF 3.8

Emerging Topics in Life Sciences Pub Date : 2022-02-28 DOI: 10.1042/ETLS20210239

D. Deaker, M. Byrne

{"title":"Crown of thorns starfish life-history traits contribute to outbreaks, a continuing concern for coral reefs","authors":"D. Deaker, M. Byrne","doi":"10.1042/ETLS20210239","DOIUrl":"https://doi.org/10.1042/ETLS20210239","url":null,"abstract":"Crown of thorns starfish (COTS, Acanthaster sp.) are notorious for their destructive consumption of coral that decimates tropical reefs, an attribute unique among tropical marine invertebrates. Their populations can rapidly increase from 0–1 COTS ha−1 to more than 10–1000 COTS ha−1 in short order causing a drastic change to benthic communities and reducing the functional and species diversity of coral reef ecosystems. Population outbreaks were first identified to be a significant threat to coral reefs in the 1960s. Since then, they have become one of the leading causes of coral loss along with coral bleaching. Decades of research and significant investment in Australia and elsewhere, particularly Japan, have been directed towards identifying, understanding, and managing the potential causes of outbreaks and designing population control methods. Despite this, the drivers of outbreaks remain elusive. What is becoming increasingly clear is that the success of COTS is tied to their inherent biological traits, especially in early life. Survival of larval and juvenile COTS is likely to be enhanced by their dietary flexibility and resilience to variable food conditions as well as their phenotypically plastic growth dynamics, all magnified by the extreme reproductive potential of COTS. These traits enable COTS to capitalise on anthropogenic disturbances to reef systems as well as endure less favourable conditions.","PeriodicalId":46394,"journal":{"name":"Emerging Topics in Life Sciences","volume":"6 1","pages":"67 - 79"},"PeriodicalIF":3.8,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44156743","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}

引用次数: 7

Contingency planning for coral reefs in the Anthropocene; The potential of reef safe havens. 人类世珊瑚礁应急规划；珊瑚礁安全避难所的潜力。

IF 3.8

Emerging Topics in Life Sciences Pub Date : 2022-02-28 DOI: 10.1042/ETLS20210232

E. Camp

{"title":"Contingency planning for coral reefs in the Anthropocene; The potential of reef safe havens.","authors":"E. Camp","doi":"10.1042/ETLS20210232","DOIUrl":"https://doi.org/10.1042/ETLS20210232","url":null,"abstract":"Reducing the global reliance on fossil fuels is essential to ensure the long-term survival of coral reefs, but until this happens, alternative tools are required to safeguard their future. One emerging tool is to locate areas where corals are surviving well despite the changing climate. Such locations include refuges, refugia, hotspots of resilience, bright spots, contemporary near-pristine reefs, and hope spots that are collectively named reef 'safe havens' in this mini-review. Safe havens have intrinsic value for reefs through services such as environmental buffering, maintaining near-pristine reef conditions, or housing corals naturally adapted to future environmental conditions. Spatial and temporal variance in physicochemical conditions and exposure to stress however preclude certainty over the ubiquitous long-term capacity of reef safe havens to maintain protective service provision. To effectively integrate reef safe havens into proactive reef management and contingency planning for climate change scenarios, thus requires an understanding of their differences, potential values, and predispositions to stress. To this purpose, I provide a high-level review on the defining characteristics of different coral reef safe havens, how they are being utilised in proactive reef management and what risk and susceptibilities they inherently have. The mini-review concludes with an outline of the potential for reef safe haven habitats to support contingency planning of coral reefs under an uncertain future from intensifying climate change.","PeriodicalId":46394,"journal":{"name":"Emerging Topics in Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46222874","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}

引用次数: 3

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data. 机器学习在阿尔茨海默病研究中的应用：组学、成像和临床数据。

IF 3.8

Emerging Topics in Life Sciences Pub Date : 2021-12-21 DOI: 10.1042/ETLS20210249

Ziyi Li, Xiaoqian Jiang, Yizhuo Wang, Yejin Kim

{"title":"Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data.","authors":"Ziyi Li, Xiaoqian Jiang, Yizhuo Wang, Yejin Kim","doi":"10.1042/ETLS20210249","DOIUrl":"10.1042/ETLS20210249","url":null,"abstract":"Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.","PeriodicalId":46394,"journal":{"name":"Emerging Topics in Life Sciences","volume":"5 6","pages":"765-777"},"PeriodicalIF":3.8,"publicationDate":"2021-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10605919","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}

引用次数: 15

Graph representation learning for structural proteomics. 结构蛋白质组学的图表示学习。

IF 3.8

Emerging Topics in Life Sciences Pub Date : 2021-12-21 DOI: 10.1042/ETLS20210225

Romanos Fasoulis, Georgios Paliouras, Lydia E Kavraki

引用次数: 7

It takes guts to learn: machine learning techniques for disease detection from the gut microbiome. 学习需要勇气：从肠道微生物组检测疾病的机器学习技术。

IF 3.8

Emerging Topics in Life Sciences Pub Date : 2021-12-21 DOI: 10.1042/ETLS20210213

Kristen D Curry, Michael G Nute, Todd J Treangen

引用次数: 0

Molecular-based precision oncology clinical decision making augmented by artificial intelligence. 人工智能增强的基于分子的精准肿瘤临床决策。

IF 3.8

Emerging Topics in Life Sciences Pub Date : 2021-12-21 DOI: 10.1042/ETLS20210220

Jia Zeng, Md Abu Shufean

引用次数: 4

Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. 用于临床结果预测的人工智能、机器学习和深度学习。

IF 3.8

Emerging Topics in Life Sciences Pub Date : 2021-12-20 DOI: 10.1042/ETLS20210246

Rowland W Pettit, Robert Fullem, Chao Cheng, Christopher I Amos

{"title":"Artificial intelligence, machine learning, and deep learning for clinical outcome prediction.","authors":"Rowland W Pettit, Robert Fullem, Chao Cheng, Christopher I Amos","doi":"10.1042/ETLS20210246","DOIUrl":"10.1042/ETLS20210246","url":null,"abstract":"AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.","PeriodicalId":46394,"journal":{"name":"Emerging Topics in Life Sciences","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39740906","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}

引用次数: 0

Bioinformatics approach to spatially resolved transcriptomics. 空间解析转录组学的生物信息学方法。

IF 3.8

Emerging Topics in Life Sciences Pub Date : 2021-11-12 DOI: 10.1042/ETLS20210131

Ivan Krešimir Lukić

{"title":"Bioinformatics approach to spatially resolved transcriptomics.","authors":"Ivan Krešimir Lukić","doi":"10.1042/ETLS20210131","DOIUrl":"https://doi.org/10.1042/ETLS20210131","url":null,"abstract":"Spatially resolved transcriptomics encompasses a growing number of methods developed to enable gene expression profiling of individual cells within a tissue. Different technologies are available and they vary with respect to: the method used to define regions of interest, the method used to assess gene expression, and resolution. Since techniques based on next-generation sequencing are the most prevalent, and provide single-cell resolution, many bioinformatics tools for spatially resolved data are shared with single-cell RNA-seq. The analysis pipelines diverge at the level of quantification matrix, downstream of which spatial techniques require specific tools to answer key biological questions. Those questions include: (i) cell type classification; (ii) detection of genes with specific spatial distribution; (iii) identification of novel tissue regions based on gene expression patterns; (iv) cell-cell interactions. On the other hand, analysis of spatially resolved data is burdened by several specific challenges. Defining regions of interest, e.g. neoplastic tissue, often calls for manual annotation of images, which then poses a bottleneck in the pipeline. Another specific issue is the third spatial dimension and the need to expand the analysis beyond a single slice. Despite the problems, it can be predicted that the popularity of spatial techniques will keep growing until they replace single-cell assays (which will remain limited to specific cases, like blood). As soon as the computational protocol reach the maturity (e.g. bulk RNA-seq), one can foresee the expansion of spatial techniques beyond basic or translational research, even into routine medical diagnostics.","PeriodicalId":46394,"journal":{"name":"Emerging Topics in Life Sciences","volume":"5 5","pages":"669-674"},"PeriodicalIF":3.8,"publicationDate":"2021-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39293317","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}

引用次数: 0

Transthyretin-mediated protein and peptide oligomerization for enhanced target clustering. 转甲状腺视黄醛介导的蛋白和肽寡聚增强靶聚类。

IF 3.8

Emerging Topics in Life Sciences Pub Date : 2021-11-12 DOI: 10.1042/ETLS20210028

Daniel Yoo, Kenneth W Walker

引用次数: 1

Artificial nucleic acid backbones and their applications in therapeutics, synthetic biology and biotechnology. 人工核酸骨架及其在治疗学、合成生物学和生物技术中的应用。

IF 3.8

Emerging Topics in Life Sciences Pub Date : 2021-11-12 DOI: 10.1042/ETLS20210169

Sven Epple, Afaf H El-Sagheer, Tom Brown

引用次数: 0