Search Results (10)

This paper presents a prototype of an autonomous hydrochemical monitoring system developed for large freshwater aquaculture facilities, directly addressing the need for smart monitoring in Agriculture 4.0. The proposed solution employs low-power sensor nodes based on commercially available components and long-range LoRaWAN communication to achieve continuous, scalable, and energy-efficient water quality monitoring. Each sensor module performs on-board signal preprocessing, including anomaly detection and short-term forecasting of key hydrochemical parameters. An ecological pond dynamics model incorporating an Extended Kalman Filter is used to fuse heterogeneous sensor data with predictive estimates, thus increasing measurement reliability. High-level data analysis, long-term storage, and cross-site comparison are performed on the server side. This integration enables adaptive tracking of environmental variations, supports early detection of hazardous trends associated with fish mortality risks, and allows one to explain and justify the reasoning behind every recommended corrective action. The performance of the forecasting and filtering algorithms is evaluated, and key system characteristics—including measurement accuracy, power consumption, and scalability—are discussed. Preliminary tests of the system prototype have shown that it can predict the dissolved oxygen level with $RMSE$ = 0.104 mg/L even with a minimum set of sensors. The results demonstrate that the proposed conceptual design of the system can be used as a base for real-time monitoring and predictive assessment of hydrochemical conditions in aquaculture environments. Full article

Climate change and anthropogenic alterations in biogeochemical cycles are intensifying the frequency, duration, and potential toxicity of harmful algal blooms (HABs) in marine ecosystems. However, these effects are highly variable and depend on species identity, strain-specific traits, and local environmental conditions. Key drivers include rising sea surface temperatures, changes in salinity resulting from altered precipitation patterns and runoff, and elevated CO₂ levels leading to ocean acidification. Heterosigma akashiwo, a euryhaline raphidophyte responsible for the widespread killing of fish, is particularly responsive to these changes. This study investigated the combined effects of temperature, salinity, and CO₂ concentration on the growth, yield, and cell membrane permeability of H. akashiwo using a Design of Experiment (DOE) approach. DOE facilitates a detailed and systematic analysis of multifactorial interactions, enabling a deeper understanding of complex relationships while maximizing efficiency and minimizing the use of experimental resources. The results revealed that growth and yield were maximized at higher temperatures and salinities, whereas cell permeability increased under cooler, less saline, and lower CO₂ conditions. These findings suggest that projected future ocean conditions may enhance biomass production while potentially reducing cellular permeability and, by extension, toxicity. This study highlights the value of the DOE framework in identifying key interactions among environmental drivers of HABs, offering a practical foundation for future predictive modeling under climate change scenarios. Full article

Photobacterium damselae subsp. Piscicida (PDP), a marine bacterium, has been reported to infect a variety of economically important marine species worldwide. Understanding the occurrence and pathogenicity of PDP is crucial for effective disease control and ensuring the success of aquaculture operations. In late August 2023, an epidemic outbreak of P. damselae subsp. piscicida DQ-SS1, accompanied by significant mortality, was recorded in cage-cultured black rockfish (Sebastes schlegelii) located on Daqin Island for the first time. Genomic analysis revealed that DQ-SS1 possesses 2 chromosomes, with a total size of 4,510,445 bp and 3923 predicted CDSs. Pathogenic genes analysis identified 573 and 314 genes related to pathogen–host interactions and virulence, respectively. Additionally, DQ-SS1 displayed susceptibility to 15 antimicrobials, was resistant to 11 antimicrobials, and was intermediately sensitive to four antibiotics. Meanwhile, the in vitro assay revealed that the extracellular products (ECP) of DQ-SS1 were lethal to macrophages and exhibited hemolysin, lipase, and amylase activities. Moreover, DQ-SS1 also demonstrated the ability to survive in fish serum and resist complement-mediated killing. The in vivo assay showed that the infected fish exhibited severe histopathological alterations, such as the infiltration of inflammatory cells, cellular degeneration and necrosis, and loose cell aggregation. Lastly, the in vivo infection assays revealed the LD₅₀ of DQ-SS1 was 1.7 × 10³ CFU/g. This is the first study to elucidate the pathogenicity and genomic characteristics of multidrug-resistant PDP in cage-cultured S. schlegelii, which contributes to the advancement of diagnostic and preventative strategies for this disease in marine-cultured fishes and provides information for an in-depth study of the pathogenic mechanism of PDP. Full article

Until recently, the only methods for finding out if a particular strain or species of bacteria could be a host for a particular bacteriophage was to see if the bacteriophage could infect that bacterium and kill it, releasing progeny phages. Establishing the host range of a bacteriophage thus meant infecting many different bacteria and seeing if the phage could kill each one. Detection of bacterial killing can be achieved on solid media (plaques, spots) or broth (culture clearing). More recently, additional methods to link phages and hosts have been developed. These include methods to show phage genome entry into host cells (e.g., PhageFISH); proximity of phage and host genomes (e.g., proximity ligation, polonies, viral tagging); and analysis of genomes and metagenomes (e.g., CRISPR spacer analysis, metagenomic co-occurrence). These methods have advantages and disadvantages. They also are not measuring the same interactions. Host range can be divided into multiple host ranges, each defined by how far the phage can progress in the infection cycle. For example, the ability to effect genome entry (penetrative host range) is different than the ability to produce progeny (productive host range). These different host ranges reflect bacterial defense mechanisms that block phage growth and development at various stages in the infection cycle. Here, I present a comparison of the various methods used to identify bacteriophage-host relationships with a focus on what type of host range is being measured or predicted. Full article

Circular RNA (circRNA), a type of non-coding RNA, has received a great deal of attention with regard to the initiation and progression of tumors. However, the molecular mechanism and function of circRNAs in breast cancer (BC) remain unclear. In the current study, we discovered that hsa_circ_0028899 (also called circRNF10) was significantly reduced in BC tissues, and a higher level of circRNF10 was markedly related to a favorable prognosis. The results of CCK8, colony formation, Transwell, ELISA, and NK cell-mediated cytotoxicity assays indicated that increased circRNF10 expression could significantly repress the proliferation, invasion, and migration of BC cells and enhance the killing efficiency of NK cells against BC cells. According to these biological functions, the possible role and molecular mechanism of circRNF10 in BC cells were further investigated. We used bioinformatics prediction tools to predict circRNF10-bound miRNAs, which were verified by many experimental studies, including FISH, luciferase reporter assays, RIP, and Western blots. These data suggest that circRNF10 serves as a molecular sponge for miR-934 to further regulate PTEN expression and PI3k/Akt/MICA signaling in vitro and tumor growth in vivo. Altogether, these findings reveal that circRNF10 functions as a novel anti-oncogene in BC via sponging miR-934 and suppressing the PI3K/Akt/MICA pathway. Full article

An ontogenetic dietary shift is crucial for the survival and growth of piscivorous largemouth bass (LB). However, there is much to learn about the predator-prey interaction during the switching process. We carried out a series of indoor experiments to examine the predation capacity, predation preference, and growth performance of exotic juvenile LB feeding on candidate prey species in the Pearl River Delta. The widely distributed oriental river prawn (Macrobranchium nipponense), barcheek goby (Ctenogobius giurinus), western mosquitofish (Gambusia affinis), silver carp (Hypophthalmichthys molitrix), and mud carp (Cirrhinus molitorella), with relatively similar total lengths, were selected as potential prey based on their availability and habitat use. Our results show that predation capacity and preference varied quantitatively and qualitatively among prey species. The number of oriental river prawns killed was significantly less than that of fish species, comparing the 1st hour with the 24th hour (p < 0.01). The feeding rhythm of LB varied significantly from crayfish to fish. Numerically, Jacobs’ selection index reinforced LB’s special preference for predating G. affinis. Although there were obvious variations in predation capacity and feed selection, no statistically significant growth differences were detected among LB groups feeding on live M. nipponense, G. affinis, H. molitrix, and C. molitorella (p < 0.05). These findings suggest that the successful ontogenetic dietary shift of juvenile LB may depend on the availability and vulnerability of local fish species. Further study on the reproductive phenology of potential fish prey may help to predict LB’s establishment. Full article

Harmful algal blooms (HABs), events that kill fish, impact human health in multiple ways, and contaminate water supplies, have increased in frequency, magnitude, and impacts in numerous marine and freshwaters around the world. Blooms of the toxic dinoflagellate Karenia brevis have resulted in thousands of tons of dead fish, deaths to many other marine organisms, numerous respiratory-related hospitalizations, and tens to hundreds of millions of dollars in economic damage along the West Florida coast in recent years. Four types of machine learning algorithms, Support Vector Machine (SVM), Relevance Vector Machine (RVM), Naïve Bayes classifier (NB), and Artificial Neural Network (ANN), were developed and compared in their ability to predict these blooms. Comparing the 21 year monitoring dataset of K. brevis abundance, RVM and NB were found to have better skills in bloom prediction than the other two approaches. The importance of upwelling-favorable northerly winds in increasing K. brevis probability, and of onshore westerly winds in preventing blooms from dispersing offshore, were quantified using RVM, and all models were used to explore the importance of large river flows and the nutrients they supply in regulating blooms. These models provide new tools for management of these devastating algal blooms. Full article

Harmful algal bloom (HAB) events have alarmed authorities of human health that have caused severe illness and fatalities, death of marine organisms, and massive fish killings. This work aimed to perform the long short-term memory (LSTM) method and convolution neural network (CNN) method to predict the HAB events in the West Coast of Sabah. The results showed that this method could be used to predict satellite time series data in which previous studies only used vector data. This paper also could identify and predict whether there is HAB occurrence in the region. A chlorophyll a concentration (Chl-a; mg/L) variable was used as an HAB indicator, where the data were obtained from MODIS and GEBCO bathymetry. The eight-day dataset interval was from January 2003 to December 2018. The results obtained showed that the LSTM model outperformed the CNN model in terms of accuracy using RMSE and the correlation coefficient r as the statistical criteria. Full article

Because of the rapid development of multidrug resistance, conventional antibiotics cannot kill pathogenic bacteria efficiently. New antibiotic treatments such as antimicrobial peptides (AMPs) can provide a possible solution to the antibiotic-resistance crisis. However, the identification of AMPs using experimental methods is expensive and time-consuming. Meanwhile, few studies use amino acid compositions (AACs) and physicochemical properties with different sequence lengths against different organisms to predict AMPs. Therefore, the major purpose of this study is to identify AMPs on seven categories of organisms, including amphibians, humans, fish, insects, plants, bacteria, and mammals. According to the one-rule attribute evaluation, the selected features were used to construct the predictive models based on the random forest algorithm. Compared to the accuracies of iAMP-2L (a web-server for identifying AMPs and their functional types), ADAM (a database of AMP), and MLAMP (a multi-label AMP classifier), the proposed method yielded higher than 92% in predicting AMPs on each category. Additionally, the sensitivities of the proposed models in the prediction of AMPs of seven organisms were higher than that of all other tools. Furthermore, several physicochemical properties (charge, hydrophobicity, polarity, polarizability, secondary structure, normalized van der Waals volume, and solvent accessibility) of AMPs were investigated according to their sequence lengths. As a result, the proposed method is a practical means to complement the existing tools in the characterization and identification of AMPs in different organisms. Full article

Slimy sculpin (Cottus cognatus) are increasingly being used as indicator species. This has primarily entailed measuring their condition, the assumption being that condition can be used as a surrogate for lipid content. While there is evidence to suggest this assumption is applicable to some fish, it has yet to be validated for C. cognatus. Further, there are several means by which one may calculate condition, the most commonly employed of which are indirect measurements of lipid content (namely, Fulton’s K, somatic K (Ks), and Le Cren’s relative condition factor (Kn)). We compared the ability of each of these morphometric indices to predict whole-body lipid content in C. cognatus. There was a moderate degree of evidence that Fulton’s K, Ks, and Kn are reliable predictors (Ks and Kn in particular). Of the latter we recommend Kn be used because, unlike Ks, it does not require that fish be killed. And while Fulton’s K did not perform quite as well, we consider it a sufficient substitute if the data necessary to calculate Kn are unavailable. Full article