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Using macroinvertebrate-based biotic indices and diversity indices to assess water quality: A case study on the Karasu Stream (Kastamonu, Türkiye)

İbrahim Küçükbasmacı, Abdulhamed M Omar Etriieki

Biomonitoring is an approach that uses indicators or sentinel species to assess the health or pollution of an environment, combining diversity based on specific taxonomic groups with the taxa’s indicator of pollution into a single index or score. Benthic macroinvertebrates are among the most preferred and valuable indicators of the biotic index. Our study aims to evaluate water quality and ecological status using biotic and diversity indices based on benthic macroinvertebrates. The macroinvertebrate samples used in this study were collected seasonally from 16 stations determined in Karasu Stream. These samples consist of 14747 specimens belonging to 9 orders and 40 families of Insecta, Malacostraca, Hirudinea and Oligochaeta. In addition, some physicochemical parameters were measured at the stations. The data obtained were analyzed using the Turkey-Biological Monitoring Working Group (TR-BMWP), Average Score per Taxon (ASPT), Family Biotic Index (FBI), Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxon richness index, and Shannon-Wiener (SWDI), Simpson (SDI) and Hill’s diversity indices. Our results showed that the last six stations of Karasu Stream were under pressure due to various anthropogenic effects. According to biotic indexes, the ecological quality status of the water in the stations was determined to be High or Good in the first ten stations and Average, Poor, or Bad in the last six stations. The results of our study showed that TR-BMWP, FBI, and EPT indices are more reliable in determining water quality than the ASPT index and reflect the environmental situation better. Since the TR-BMWP index is adapted to macroinvertebrate taxa distributed in Türkiye, it can be used as a suitable tool for evaluating other rivers’ water quality in Türkiye.

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Using macroinvertebrate‐based biotic indices and diversity indices to assess water quality: A case study on the Karasu Stream (Kastamonu, Türkiye)

Abdulhamed M Omar Etriieki, İbrahim Küçükbasmacı

Ecohydrology, e2627, 2024

Biomonitoring is an approach that uses indicators or sentinel species to assess the health or pollution of an environment, combining diversity based on specific taxonomic groups with the taxa's indicator of pollution into a single index or score. Benthic macroinvertebrates are among the most preferred and valuable indicators of the biotic index. Our study aims to evaluate water quality and ecological status using biotic and diversity indices based on benthic macroinvertebrates. The macroinvertebrate samples used in this study were collected seasonally from 16 stations determined in Karasu Stream. These samples consist of 14,747 specimens belonging to 9 orders and 40 families of Insecta, Malacostraca, Hirudinea and Oligochaeta. In addition, some physicochemical parameters were measured at the stations. The data obtained were analysed using the Turkey‐Biological Monitoring Working Group (TR‐BMWP), Average Score per Taxon (ASPT), Family Biotic Index (FBI), Ephemeroptera, Plecoptera and Trichoptera (EPT) taxon richness index, and Shannon–Wiener, Simpson and Hill's diversity indices. Our results showed that the last six stations of Karasu Stream were under pressure due to various anthropogenic effects. According to biotic indexes, the ecological quality status of the water in the stations was determined to be high or good in the first 10 stations and average, poor or bad in the last 6 stations. The results of our study showed that TR‐BMWP, FBI and EPT indices are more reliable in determining water quality than the ASPT index and reflect the environmental situation better. Since the TR‐BMWP index is adapted to macroinvertebrate taxa distributed in Türkiye, it can be used as a suitable tool for evaluating other rivers' water quality in Türkiye.

This review aimed to investigate the effect of pH on morphology, and size of gold nanoparticles (AuNPs) biosynthesized using extracts of various plant parts and agricultural waste. By reviewing previous studies, it was found that the formation of AuNPs was more rapid in neutral and basic mediums than in acidic mediums. Furthermore, most of those studies indicated that the smallest sizes of biosynthesized AuNPs, produced by plant extracts, were in neutral and alkaline mediums. Regarding the morphology, it was found that multiple shapes of the AuNPs were obtained in an acidic medium of the reaction, while there was typically a dominant shape of the AuNPs in the basic medium. In conclusion, significant quantities of AuNPs with appropriate sizes and morphology could be obtained by controlling the medium of the reaction when AuNPs are biosynthesized using extracts of plant parts and agricultural waste.

Background and aims

In recent years, the education sector in Libya have been affected by wars and political fluctuations, impacting the quality and availability of education. There have been challenges in providing the necessary educational resources, as well as in training and qualifying teachers. E-learning, it can be an effective tool in improving education under difficult circumstances. Online education can provide opportunities for students to access educational resources easily, and can contribute to enhancing communication between teachers and students. E-learning can also play a role in developing students’ technology skills, which is important in our modern era. In 2019, the Faculty of Science at the University of Gharyan partially implemented online learning as a response to COVID-19 pandemic, and this program continued for two academic terms. Therefore, the paper aims to highlights both the advantages and limitations of E-learning within the environment of university education in Libya

This study delves into the evaluation of daily profits within the network of Sephora cosmetics branches, employing distributed systems, the K-means algorithm, and WEKA visualization techniques. The objective is to analyze the profitability patterns across Sephora branches and uncover underlying clusters to optimize strategic decision-making. The research methods encompassed the utilization of distributed systems to collect and integrate daily profit data from multiple Sephora branches. The K-means clustering algorithm was applied to segment the dataset, discerning distinct clusters based on profit margins and performance metrics. The WEKA visualization tool was instrumental in depicting these clusters in a comprehensive and interpretable manner, offering insights into profit distributions and potential areas for enhancement. This research revealed intricate profitability structures within the Sephora branch network. The application of the K-means algorithm, distinct clusters of branches with varying profitability profiles were identified. WEKA visualization facilitated the graphical representation of these clusters, elucidating the relationships between different branches based on daily profits. Such insights can aid decision-makers in strategizing resource allocation, marketing initiatives, and operational improvements tailored to each cluster's profitability dynamics. The amalgamation of distributed systems, K-means algorithm, and WEKA visualization offered a robust framework for assessing and optimizing the financial performance of  Sephora branches. This research contributes valuable insights into the evaluation of daily profits across  Sephora cosmetics branches, showcasing the potential of distributed systems and advanced analytical tools in understanding profitability patterns. The findings provide a foundation for strategic interventions aimed at enhancing profitability and operational efficiencies within the Sephora branch network.

Clostridioides difficile is an important human pathogen causing antibiotic-associated diarrhoea worldwide. Besides using antibiotics for treatment, the interest in bacteriophages as an alternative therapeutic option has increased. Prophage abundance and genetic diversity are well-documented in clinical strains, but the carriage of prophages in environmental strains of C. difficile has not yet been explored. Thus, the prevalence and genetic diversity of integrated prophages in the genomes of 166 environmental C. difficile isolates were identified. In addition, the clustered regularly interspaced short palindromic repeats (CRISPR)-Cas systems were determined in the genomes of prophage regions. Predicted prophages and CRISPR-Cas systems were identified by using the PHASTER web server and CRISPRCasFinder, respectively. Phylogenetic relationships among predicated prophages were also constructed based on phage-related genes, terminase large (TerL) subunits and LysM. Among 372 intact prophages, the predominant prophages were phiCDHM1, phiCDHM19, phiMMP01, phiCD506, phiCD27, phiCD211, phiMMP03, and phiC2, followed by phiMMP02, phiCDKM9, phiCD6356, phiCDKM15, and phiCD505. Two newly discovered siphoviruses, phiSM101- and phivB_CpeS-CP51-like Clostridium phages, were identified in two C. difficile genomes. Most prophages were found in sequence types (STs) ST11, ST3, ST8, ST109, and ST2, followed by ST6, ST17, ST4, ST5, ST44, and ST58. An obvious correlation was found between prophage types and STs/ribotypes. Most predicated prophages carry CRISPR arrays. Some prophages carry several gene products, such as accessory gene regulator (Agr), putative spore protease, and abortive infection (Abi) systems. This study shows that prophage carriage, along with genetic diversity and their CRISPR arrays, may play a role in the biology, lifestyle, and fitness of their host strains.

Description

Background and aims

The physical parameters and the occurrence and abundance of zooplankton in Ain-Zayanah lagoon, Benghazi, Libya, were studied from August, 2005–July, 2006, as a part of detailed investigation on the feasibility of converting the lagoon into an aquaculture farm for captive breeding and culturing of different economically important edible fish, fish fry, molluscan and crustacean animals, and for culturing the zooplankton which form the food of these organisms.

Methods

Five different sites located near to the shore were selected in the lagoon for water and zooplankton monthly sampling and analyses. The physical parameters of the water included air and water temperatures, transparency, electrical conductivity, total dissolved solids, pH, dissolved oxygen and total hardness; they were analysed using standard methods. Various differences were revealed in the relationships of these parameters in and between sites, months and seasons.

Results

Seven major groups of zooplankton inhabited Ain-Zayanah lagoon; rotifers formed the dominant one (40.50%), followed by rhabtida (21.61%), crustacean larva (14.55%), foraminifera (13.23%), ostracoda (5.97%), copepoda (4.13%) and cladocera (0.01%). Air and water temperatures, pH, DO, electrical conductivity, TDS and hardness had different relationships with the seven zooplankton groups in Ain-Zayanah lagoon.

Conclusion

The results obtained were discussed extensively in the light of available literature in the fields, and suitable conclusions, suggestions and precautions to be taken were given in connection with the establishment of Aquaculture farm in Ain-Zayanah lagoon

Abstract:

In this research study, we investigate the effectiveness of a proposed automated framework that leverages AI algorithms to improve the accuracy, scalability, and efficiency of data analysis. We compare the performance of the proposed framework to traditional data analysis methods in terms of accuracy, time and effort required for data analysis, and scalability. We conducted a questionnaire-based survey to collect data on the application of the proposed framework and traditional data analysis methods in real-world scenarios. We used statistical analyses, including t-tests and regression analyses, to analyze the data and draw conclusions. Our findings indicate that the proposed automated framework outperforms traditional data analysis methods in terms of accuracy, reduces the time and effort required for data analysis, and has better scalability for processing large datasets in real-time. Our results suggest that the proposed framework offers significant benefits for organizations seeking to improve their data analysis processes. However, we also note the importance of considering the costs and benefits of implementing the framework, as well as any ethical or legal implications. Further research is recommended to confirm the findings and to investigate the effectiveness of the proposed framework in different contexts and with different datasets.