Algal Research最新文献

{"title":"Withdrawn: Co-hydropyrolysis of sewage sludge and algae biomass: A systematic study on thermal decomposition behaviors and kinetic characteristics","authors":"Ke Song, Bolun Hao, Guozhang Chang, Haoran Liu, Yao Gao, Jie Li, Jian Zhang","doi":"10.1016/j.algal.2024.103559","DOIUrl":"https://doi.org/10.1016/j.algal.2024.103559","url":null,"abstract":"This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause.","PeriodicalId":501543,"journal":{"name":"Algal Research","volume":"48 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141512126","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":"Inducing heritable genomic deletions in APT gene of Chlorella sorokiniana using CRISPR/Cas9","authors":"Joung Sug Kim, Sangmuk Lee, Seungchan Cho, Yuchul Jung","doi":"10.1016/j.algal.2024.103435","DOIUrl":"https://doi.org/10.1016/j.algal.2024.103435","url":null,"abstract":"","PeriodicalId":501543,"journal":{"name":"Algal Research","volume":"241 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139876981","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":"Artificial intelligence-driven microalgae autotrophic batch cultivation: A comparative study of machine and deep learning-based image classification models","authors":"J. Chong, K. Khoo, K. Chew, H. Ting, Iwamoto Koji, Roger Ruan, Zengling Ma, P. Show","doi":"10.1016/j.algal.2024.103400","DOIUrl":"https://doi.org/10.1016/j.algal.2024.103400","url":null,"abstract":"","PeriodicalId":501543,"journal":{"name":"Algal Research","volume":"113 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139825914","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":"Impact of cold atmospheric pressure plasma (CAPP) treatments on the prebiotic potential of Arthrospira platensis (Spirulina)","authors":"M. C. Pina-Pérez, Neus Ricós-Muñoz, Ella Karina López-Suárez, Consuelo Esteve, Sergi Maicas, Michael Beyrer","doi":"10.1016/j.algal.2024.103432","DOIUrl":"https://doi.org/10.1016/j.algal.2024.103432","url":null,"abstract":"","PeriodicalId":501543,"journal":{"name":"Algal Research","volume":"21 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139830518","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 telomere-to-telomere, gapless, phased diploid genome and methylome of the green alga Scenedesmus obliquus UTEX 3031 reveals significant heterozygosity and genetic divergence of the haplotypes","authors":"Thomas C. Biondi, Colin P.S. Kruse, Samuel I. Koehler, Taehyung Kwon, Aubrey K. Davis, Wyatt Eng, Yuliya Kunde, Cheryl D. Gleasner, Kayley You Mak, Juergen Polle, B. Hovde, Erik Hanschen, S. Starkenburg","doi":"10.1016/j.algal.2024.103431","DOIUrl":"https://doi.org/10.1016/j.algal.2024.103431","url":null,"abstract":"","PeriodicalId":501543,"journal":{"name":"Algal Research","volume":"196 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139828267","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":"Biomass losses of Sodalinema alkaliphilum in alkaline, high pH, open raceway ponds","authors":"Marianne Haines, William Daniel Loty Richardson, Angela Kouris, Marc Strous","doi":"10.1016/j.algal.2023.103373","DOIUrl":"https://doi.org/10.1016/j.algal.2023.103373","url":null,"abstract":"<p>Growing cyanobacteria in high pH (10+), high carbonate alkalinity medium (0.5 M) increases the driving force for CO₂ capture and helps exclude competitors and predators. But in these conditions, cyanobacteria might expend more energy to maintain osmotic gradients across their membrane. Thus, these extremophiles may respire more fixed carbon, increasing biomass losses compared to growth in milder conditions. In this work, a microbial consortium primarily composed of <em>Sodalinema alkaliphilum</em> (formerly <em>Phormidium alkaliphilum</em>) from alkaline soda lakes was grown in an outdoor open raceway pond. Night-time biomass losses were ca. 5 % by mass. Stable isotope probing (SIP) found respiration accounted for 0–2 % of daily biomass losses with no detectable difference in respiration rates between day and night. Comparisons of SIP and mass density measurements indicated respiration was not always the primary driver of biomass loss and that DOC release may contribute, even during stable operation. Proteomics and 16S rRNA DNA sequencing showed the abundance of bacterial heterotrophs was low with <em>Cyclonatronum</em> spp. representing the largest fraction (&lt;1 %). The relative abundance of proteins within the <em>S. alkaliphilum</em> proteome was stable but the rate of protein synthesis varied. Overall rates of protein synthesis were highest in the afternoon (when photosynthesis was most active), but quality control proteins were preferentially made in the morning, likely in preparation for the work ahead. Understanding when and how biomass is lost in cultivation systems is crucial in informing efforts to improve biomass models and enhance biomass yield.</p>","PeriodicalId":501543,"journal":{"name":"Algal Research","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029418","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":"An overview of potential algae-derived bioactive compounds against urease-positive microorganisms","authors":"Najmeh Oliyaei, Mohammad Hashem Hashempur, Kamiar Zomorodian, Nader Tanideh, Milad Noori, Ali Abbasi, Mohammad Mahdavi, Aida Iraji","doi":"10.1016/j.algal.2023.103372","DOIUrl":"https://doi.org/10.1016/j.algal.2023.103372","url":null,"abstract":"<p>Urease (EC 3.5.1.5) is a virulence factor found in various microorganisms, including <em>Helicobacter pylori</em>, <em>Proteus mirabilis</em>, and <em>Klebsiella pneumoniae</em>. As a nickel-containing enzyme, urease plays the most significant role in the colonization and maintenance of microorganisms in host organisms, making it a potential target for the treatment of resistant urease-positive infections. Notably, urease positive pathogens pose significant public health concerns due to their association with gastric ulcers, gastritis, kidney stones, urinary tract infections, and other diseases. This review focuses on exploring ureases' structure and catalytic properties, with a special emphasis on <em>H. pylori</em>. Moreover, the other virulence factors of <em>H. pylori</em>, including flagella, outer membrane proteins, accessory proteins, cytotoxin-associated gene A (CagA), and vacuolating cytotoxin A (VacA), are discussed. Also, the adaptive mechanism of <em>H. pylori</em>, relying on urease to survive the acidic gastric environment, is explored, along with the detrimental effects of ammonium on gastric epithelial cells produced by urease. Urease inhibitors have garnered interest as potential therapeutic agents to reduce the complications of urease-positive microorganisms. In this context, natural bioactive compounds gain interest in developing alternative therapeutic strategies. Algae are rich in several biochemical compounds with various biological attributes, particularly antibacterial and anti-inflammatory properties. Numerous studies have explored to identify the novel perspective anti-<em>H. pylori</em> agents from different algae species. This review also provides insights into the potential of algal bioactive compounds as effective agents against urease-positive infections. The significance of exploring new therapeutic strategies to address <em>H. pylori</em>-induced diseases is highlighted, considering the emergence of antibiotic resistance and the need for improved therapeutic approaches.</p>","PeriodicalId":501543,"journal":{"name":"Algal Research","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029414","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":"A novel system for assessing paraquat toxicity using Desmodesmus maximus as a potential bio-indicator and deep learning-based approach","authors":"Phon-ubon Suanoi, Nitiphong Kaewman, Jeeraporn Pekkoh, Phasit Charoenkwan, Chayakorn Pumas","doi":"10.1016/j.algal.2023.103370","DOIUrl":"https://doi.org/10.1016/j.algal.2023.103370","url":null,"abstract":"<p>Microalgae play a crucial role as environmental indicators, offering valuable insights into ecosystem health and aiding in the assessment of water source contamination by toxins. In recent times, the presence of paraquat in water sources has become a grave concern due to its high toxicity and persistence. The detection of paraquat in natural water is of paramount importance for safeguarding water quality and public safety. Microalgae are invaluable bio-indicators for pollutant detection, but using small-sized microalgae according to OECD guidelines may not suit toxicity field combined with deep learning due to limitations. In this regard, this study proposes the use of <em>Desmodesmus maximus</em> as a potential bio-indicator, known for its larger cell size, surpassing <em>Desmodesmus subspicatus</em>, the reference strain specified by OECD guidelines. Exposure of <em>D. maximus</em> to 2 mg/L paraquat resulted in significant internal damage and loss of chlorophyll content, with a determined 72 h-EC₅₀ of 0.25 mg/L. Accurate microalgae recognition typically requires time-consuming and inaccessible expert analysis. Therefore, this study explores deep learning techniques to enhance the efficiency and accuracy of microalgae toxicity testing. Deep convolutional neural networks (D-CNNs), including RetinaNet, YOLOv5, EfficientDet, and Faster R-CNN models, are compared for microalgae detection and differentiation. The analysis demonstrates the superiority of the Faster R-CNN model, achieving a remarkable mAP@0.5 value of 0.98 in multiclass conditions for identifying normal, empty, and toxified colonies. These findings underscore the considerable potential of deep learning techniques in advancing microalgae toxicity testing, thereby facilitating enhanced accessibility and cost-effectiveness in monitoring the environmental impact on water resources.</p>","PeriodicalId":501543,"journal":{"name":"Algal Research","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029228","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}