ANALYSIS OF NEW PRIMER PAIR CANDIDATES OF rbcL GENE FOR IDENTIFICATION OF MICROALGAE SCENEDESMACEAE

Scenedesmaceae is one of the microalgae groups that has been widely studied as promising biodiesel feedstock. Its morphological identification is often confused by environmental changes, so it requires molecular identification as well. The current study aimed to obtain primer pair candidates that identify the Scenedesmaceae based on the rbcL gene. The research used bioinformatics tools, which harvested rbcL protein sequence data, performed multiple sequence alignments, and designed primers based on conserved and less-conserved regions. The best left and right primers selected based on sequence length, melting temperature, 3' end stability, number of hairpins, and self-dimers, were paired, and three candidates were obtained. The three pairs were examined based on melting temperature difference, number of hetero-dimers, length of amplified nucleotide product, number of hits, and number of genera captured from the GenBank. Sce-16 (F, 5'-TGGTCGTGCTGTTTATGAATGT-3' and 1_RL, 5'-TGCCAAACATGAATACCACCA-3'), which is back-translated according to Hariotina sp. (AOY36008.1), is the most preferred candidate compared to the other two pairs after discussing their advantages and disadvantages. In the future, the proposed primer candidate needs to be validated through in vitro amplification with some optimizations to eliminate potential weaknesses

Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae

Optimisation of Phaeodactylum tricornutum as a microalgal expression host for industrial biotechnology

Phaeodactylum tricornutum is a polymorphic marine diatom and can undergo morphological conversions between cell morphotypes, mainly fusiform, triradiate and oval. However, limited information is available about the conditions that can be used for controlling cell morphology and maintaining a specific cell morphotype with high growth rate and biomass productivity. In this study, the effects of culture medium and culture age on morphological changes in P. tricornutum were first investigated. Mann and Myers’ medium was identified as eliciting significant morphotype conversion from fusiform to oval in P. tricornutum. Liquid cultures containing more than 90% oval cells were obtained and well-maintained in this medium under the constant shaking condition, allowing high dry biomass concentration (0.73 g L-1) to be achieved. The subsequent biochemical composition analyses of different cell morphotypes revealed that pigments, particularly fucoxanthin and chlorophyll a, were markedly accumulated and higher protein content (% dry weight) was obtained in oval cell cultures maintained in M & M medium compared to fusiform cell cultures maintained in f/2 medium, where lipid and carbohydrate were significantly accumulated over 21 days cultivation. The further investigation of downstream processing of different cell morphotypes using ultra scale-down approaches predicted that a high cell recovery efficiency (>93%) without evident cell damage could be obtained for both morphotypes when using either a hermetically or a non-hermetically sealed disc-stack centrifuge. Additionally, cell disruption analysis by focused acoustics demonstrated that oval cells were much more robust against mechanical forces, requiring a longer treatment time for complete cell rupture than fusiform cells. This study offered an effective and practical way to achieve high biomass production of oval cells in liquid cultures and provided significant implications for upstream cultivation strategies and downstream bioprocessing to optimise the manufacture of different classes of products in different morphotypes of P. tricornutum

Molecular support for temporal dynamics of induced anti-herbivory defenses in the brown seaweed Fucus Vesiculosus

Grazing by the isopod Idotea baltica induces chemical defenses in the brown seaweed Fucus vesiculosus. A combination of a 33 day induction experiment, feeding choice assays and functional genomic analyses was used to investigate temporal defense patterns and to correlate changes in palatability to changes in gene expression. Despite permanent grazing, seaweed palatability varied over time. Controls were significantly more consumed than grazed pieces only after 18 and 27 days of grazing. Relative to controls, 562/402 genes were up-/down-regulated in seaweed pieces that were grazed for 18 days, i.e. when defense induction was detected. Reprogramming of the regulative expression orchestra (translation, transcription), up-regulation of genes involved in lipid and carbohydrate metabolism, intracellular trafficking, defense and stress response, as well as downregulation of photosynthesis was found in grazed seaweed. These findings indicate short-term temporal variation in defenses and that modified gene expression patterns arise at the same time when grazed seaweed pieces show reduced palatability. Several genes with putative defensive functions and cellular processes potentially involved in defence, such as reallocation of resources from primary to secondary metabolism, were reveale

Successful invaders are better defended: The example of Gracilaria vermiculophylla

To evaluate the importance of anti-herbivore resistance for algal invasion success we compared resistance traits among specimens of the red macroalga Gracilaria vermiculophylla from six native populations in Korea and China and eight invasive populations in Europe and Mexico that were maintained under identical conditions in the laboratory. Herbivorous snails both from the native range (Littorina brevicula) and from the invaded range (Littorina littorea) consumed significantly less of seaweed specimens originating from non-native populations. Metabolome profiling revealed that this preference was correlated with an increased woundactivated production of deterring prostaglandins and hydroxyeicosatetraenoic acids. Thus, invasive populations of G. vermiculophylla are more strongly defended against challenge by herbivores and other biological enemies that cause local tissue or cell disruption and activate oxylipin production. Anthropogenic distribution of genotypes adapted to resist elevated feeding pressure probably contributed to the invasion success of this species

Algae

Algae - Organisms for Imminent Biotechnology will be useful source of information on basic and applied aspects of algae for post graduate students, researchers, scientists, agriculturists, and decision makers. The book comprises a total of 12 chapters covering various aspects of algae particularly on microalgal biotechnology, bloom dynamics, photobioreactor design and operation of microalgal mass cultivation, algae used as indicator of water quality, microalgal biosensors for ecological monitoring in aquatic environment, carbon capture and storage by microalgae to enhancing CO2 removal, synthesis and biotechnological potentials of algal nanoparticles, biofilms, silica-based nanovectors, challenges and opportunities in marine algae, and genetic identification and mass propagation of economically important seaweeds and seaweeds as source of new bioactive prototypes

Microplastics Degradation and Characterization

In the last decade, issues related to pollution from microplastics in all environmental compartments and the associated health and environmental risks have been the focus of intense social, media, and political attention worldwide. The assessment, quantification, and study of the degradation processes of plastic debris in the ecosystem and its interaction with biota have been and are still the focus of intense multidisciplinary research. Plastic particles in the range from 1 to 5 mm and those in the sub-micrometer range are commonly denoted as microplastics and nanoplastics, respectively. Microplastics (MPs) are being recognized as nearly ubiquitous pollutants in water bodies, but their actual concentration, distribution, and effects on natural waters, sediments, and biota are still largely unknown. Contamination by microplastics of agricultural soil and other environmental areas is also becoming a matter of concern. Sampling, separation, detection, characterization and evaluating the degradation pathways of micro- and nano-plastic pollutants dispersed in the environment is a challenging and critical goal to understand their distribution, fate, and the related hazards for ecosystems. Given the interest in this topic, this Special Issue, entitled “Microplastics Degradation and Characterization”, is concerned with the latest developments in the study of microplastics

Microalgae downstream processing and economical approaches of biodiesel producton processes

Microalgae oil has been identified as a reliable resource for biodiesel production due to its high lipid productivity and potential cultivation in non-fertile locations. However, high scale production of microalgae based biodiesel depends on the optimization of the entire process to be economically feasible. The present work combine the optimization of microalgae downstream processes with computational tools for the modeling of different scenarios of the harvesting, oil extraction and transesterification with data obtained at pilot scale. This approach detects process bottlenecks that could have led to an overestimation of the potentiality of the microalgae lipids as a resource for the biodiesel production. Reduction of the cultivation step is mandatory to consider the microalgae-based biodiesel competitive with respect to the fossil diesels. The required biodiesel market price for the best alternative assessed is $5700 per MT where only the biomass production (cultivation/harvesting) is responsible of the 65Según caso-base es necesaria la reducción de inversión inicial del proceso de producción de biomasa, (cultivo y concentrado) responsables del 65$/MT) de producción de biodiesel a partir de microalgas para que el proceso sea económicamente viable

Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries

S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.Zlepšení průmyslových procesů, Model založený na datech, Optimalizace procesu, Strojové učení, Průmyslové systémy, Energeticky náročná průmyslová odvětví, Umělá inteligence.